From f3f1046477dbf54b94bd45e33c0a2f8ee1699c16 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 18:29:00 +0200 Subject: [PATCH 001/728] New translations README.md (Chinese Simplified) --- zh-CN/lang/zh/README.md | 165 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 zh-CN/lang/zh/README.md diff --git a/zh-CN/lang/zh/README.md b/zh-CN/lang/zh/README.md new file mode 100644 index 0000000000..3fcbb553de --- /dev/null +++ b/zh-CN/lang/zh/README.md @@ -0,0 +1,165 @@ +# graphkit-learn +[![Build Status](https://travis-ci.org/jajupmochi/graphkit-learn.svg?branch=master)](https://travis-ci.org/jajupmochi/graphkit-learn) [![Build status](https://ci.appveyor.com/api/projects/status/bdxsolk0t1uji9rd?svg=true)](https://ci.appveyor.com/project/jajupmochi/graphkit-learn) [![codecov](https://codecov.io/gh/jajupmochi/graphkit-learn/branch/master/graph/badge.svg)](https://codecov.io/gh/jajupmochi/graphkit-learn) [![Documentation Status](https://readthedocs.org/projects/graphkit-learn/badge/?version=master)](https://graphkit-learn.readthedocs.io/en/master/?badge=master) [![PyPI version](https://badge.fury.io/py/graphkit-learn.svg)](https://badge.fury.io/py/graphkit-learn) + +一个图核、图编辑距离和图预映射问题的 Python 库。 + +## Requirements + +* python>=3.6 +* numpy>=1.16.2 +* scipy>=1.1.0 +* matplotlib>=3.1.0 +* networkx>=2.2 +* scikit-learn>=0.20.0 +* tabulate>=0.8.2 +* tqdm>=4.26.0 +* control>=0.8.2 (for generalized random walk kernels only) +* slycot==0.3.3 (for generalized random walk kernels only, which requires a fortran compiler, gfortran for example) + +## 如何使用？ + +### 安装库 + +* Install stable version from PyPI (may not be up-to-date): +``` +$ pip install graphkit-learn +``` + +* Install latest version from GitHub: +``` +$ git clone https://github.com/jajupmochi/graphkit-learn.git +$ cd graphkit-learn/ +$ python setup.py install +``` + +### 运行测试 + +A series of [tests](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/tests) can be run to check if the library works correctly: +``` +$ pip install -U pip pytest codecov coverage pytest-cov +$ pytest -v --cov-config=.coveragerc --cov-report term --cov=gklearn gklearn/tests/ +``` + +### 查看示例 + +A series of demos of using the library can be found on [Google Colab](https://drive.google.com/drive/folders/1r2gtPuFzIys2_MZw1wXqE2w3oCoVoQUG?usp=sharing) and in the [`example`](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/examples) folder. + +### 其他示例 + +Check [`notebooks`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks) directory for more demos: +* [`notebooks`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks) directory includes test codes of graph kernels based on linear patterns; +* [`notebooks/tests`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks/tests) directory includes codes that test some libraries and functions; +* [`notebooks/utils`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks/utils) directory includes some useful tools, such as a Gram matrix checker and a function to get properties of datasets; +* [`notebooks/else`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks/else) directory includes other codes that we used for experiments. + +### 文档 + +The docs of the library can be found [here](https://graphkit-learn.readthedocs.io/en/master/?badge=master). + +## 主要内容 + +### 1 List of graph kernels + +* Based on walks + * [The common walk kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/common_walk.py) [1] + * Exponential + * Geometric + * [The marginalized kenrel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/marginalized.py) + * With tottering [2] + * Without tottering [7] + * [The generalized random walk kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/random_walk.py) [3] + * [Sylvester equation](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/sylvester_equation.py) + * Conjugate gradient + * Fixed-point iterations + * [Spectral decomposition](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/spectral_decomposition.py) +* Based on paths + * [The shortest path kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/shortest_path.py) [4] + * [The structural shortest path kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/structural_sp.py) [5] + * [The path kernel up to length h](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/path_up_to_h.py) [6] + * The Tanimoto kernel + * The MinMax kernel +* Non-linear kernels + * [The treelet kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/treelet.py) [10] + * [Weisfeiler-Lehman kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/weisfeiler_lehman.py) [11] + * [Subtree](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/weisfeiler_lehman.py#L479) + +A demo of computing graph kernels can be found on [Google Colab](https://colab.research.google.com/drive/17Q2QCl9CAtDweGF8LiWnWoN2laeJqT0u?usp=sharing) and in the [`examples`](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/examples/compute_graph_kernel.py) folder. + +### 2 Graph Edit Distances + +### 3 Graph preimage methods + +A demo of generating graph preimages can be found on [Google Colab](https://colab.research.google.com/drive/1PIDvHOcmiLEQ5Np3bgBDdu0kLOquOMQK?usp=sharing) and in the [`examples`](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/examples/median_preimege_generator.py) folder. + +### 4 Interface to `GEDLIB` + +[`GEDLIB`](https://github.com/dbblumenthal/gedlib) is an easily extensible C++ library for (suboptimally) computing the graph edit distance between attributed graphs. [A Python interface](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/gedlib) for `GEDLIB` is integrated in this library, based on [`gedlibpy`](https://github.com/Ryurin/gedlibpy) library. + +### 5 Computation optimization methods + +* Python’s `multiprocessing.Pool` module is applied to perform **parallelization** on the computations of all kernels as well as the model selection. +* **The Fast Computation of Shortest Path Kernel (FCSP) method** [8] is implemented in *the random walk kernel*, *the shortest path kernel*, as well as *the structural shortest path kernel* where FCSP is applied on both vertex and edge kernels. +* **The trie data structure** [9] is employed in *the path kernel up to length h* to store paths in graphs. + +## 问题 + +* This library uses `multiprocessing.Pool.imap_unordered` function to do the parallelization, which may not be able to run correctly under Windows system. For now, Windows users may need to comment the parallel codes and uncomment the codes below them which run serially. We will consider adding a parameter to control serial or parallel computations as needed. + +* Some modules (such as `Numpy`, `Scipy`, `sklearn`) apply [`OpenBLAS`](https://www.openblas.net/) to perform parallel computation by default, which causes conflicts with other parallelization modules such as `multiprossing.Pool`, highly increasing the computing time. By setting its thread to 1, `OpenBLAS` is forced to use a single thread/CPU, thus avoids the conflicts. For now, this procedure has to be done manually. Under Linux, type this command in terminal before running the code: +``` +$ export OPENBLAS_NUM_THREADS=1 +``` +Or add `export OPENBLAS_NUM_THREADS=1` at the end of your `~/.bashrc` file, then run +``` +$ source ~/.bashrc +``` +to make this effective permanently. + +## 结果 + +Check this paper for detailed description of graph kernels and experimental results: + +Linlin Jia, Benoit Gaüzère, and Paul Honeine. Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons. working paper or preprint, March 2019. URL https://hal-normandie-univ.archives-ouvertes.fr/hal-02053946. + +A comparison of performances of graph kernels on benchmark datasets can be found [here](https://graphkit-learn.readthedocs.io/en/master/experiments.html). + +## 如何贡献 + +Fork the library and open a pull request! Make your own contribute to the community! + +## 作者 + +* [Linlin Jia](https://jajupmochi.github.io/), LITIS, INSA Rouen Normandie +* [Benoit Gaüzère](http://pagesperso.litislab.fr/~bgauzere/#contact_en), LITIS, INSA Rouen Normandie +* [Paul Honeine](http://honeine.fr/paul/Welcome.html), LITIS, Université de Rouen Normandie + +## 引用 + +Still waiting... + +## 致谢 + +This research was supported by CSC (China Scholarship Council) and the French national research agency (ANR) under the grant APi (ANR-18-CE23-0014). The authors would like to thank the CRIANN (Le Centre Régional Informatique et d’Applications Numériques de Normandie) for providing computational resources. + +## 参考文献 +[1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003. + +[2] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In Proceedings of the 20th International Conference on Machine Learning, Washington, DC, United States, 2003. + +[3] Vishwanathan, S.V.N., Schraudolph, N.N., Kondor, R., Borgwardt, K.M., 2010. Graph kernels. Journal of Machine Learning Research 11, 1201–1242. + +[4] K. M. Borgwardt and H.-P. Kriegel. Shortest-path kernels on graphs. In Proceedings of the International Conference on Data Mining, pages 74-81, 2005. + +[5] Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre Baldi. Graph kernels for chemical informatics. Neural networks, 18(8):1093–1110, 2005. + +[6] Suard F, Rakotomamonjy A, Bensrhair A. Kernel on Bag of Paths For Measuring Similarity of Shapes. InESANN 2007 Apr 25 (pp. 355-360). + +[7] Mahé, P., Ueda, N., Akutsu, T., Perret, J.L., Vert, J.P., 2004. Extensions of marginalized graph kernels, in: Proc. the twenty-first international conference on Machine learning, ACM. p. 70. + +[8] Lifan Xu, Wei Wang, M Alvarez, John Cavazos, and Dongping Zhang. Parallelization of shortest path graph kernels on multi-core cpus and gpus. Proceedings of the Programmability Issues for Heterogeneous Multicores (MultiProg), Vienna, Austria, 2014. + +[9] Edward Fredkin. Trie memory. Communications of the ACM, 3(9):490–499, 1960. + +[10] Gaüzere, B., Brun, L., Villemin, D., 2012. Two new graphs kernels in chemoinformatics. Pattern Recognition Letters 33, 2038–2047. + +[11] Shervashidze, N., Schweitzer, P., Leeuwen, E.J.v., Mehlhorn, K., Borgwardt, K.M., 2011. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research 12, 2539–2561. From 342f664506b98ba1e55eda562dbf676b7306a3e9 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 18:29:01 +0200 Subject: [PATCH 002/728] New translations README.md (French) --- fr/lang/fr/README.md | 165 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 fr/lang/fr/README.md diff --git a/fr/lang/fr/README.md b/fr/lang/fr/README.md new file mode 100644 index 0000000000..484089f3c1 --- /dev/null +++ b/fr/lang/fr/README.md @@ -0,0 +1,165 @@ +# graphkit-learn +[![Build Status](https://travis-ci.org/jajupmochi/graphkit-learn.svg?branch=master)](https://travis-ci.org/jajupmochi/graphkit-learn) [![Build status](https://ci.appveyor.com/api/projects/status/bdxsolk0t1uji9rd?svg=true)](https://ci.appveyor.com/project/jajupmochi/graphkit-learn) [![codecov](https://codecov.io/gh/jajupmochi/graphkit-learn/branch/master/graph/badge.svg)](https://codecov.io/gh/jajupmochi/graphkit-learn) [![Documentation Status](https://readthedocs.org/projects/graphkit-learn/badge/?version=master)](https://graphkit-learn.readthedocs.io/en/master/?badge=master) [![PyPI version](https://badge.fury.io/py/graphkit-learn.svg)](https://badge.fury.io/py/graphkit-learn) + +A Python package for graph kernels, graph edit distances and graph pre-image problem. + +## Requirements + +* python>=3.6 +* numpy>=1.16.2 +* scipy>=1.1.0 +* matplotlib>=3.1.0 +* networkx>=2.2 +* scikit-learn>=0.20.0 +* tabulate>=0.8.2 +* tqdm>=4.26.0 +* control>=0.8.2 (for generalized random walk kernels only) +* slycot==0.3.3 (for generalized random walk kernels only, which requires a fortran compiler, gfortran for example) + +## How to use? + +### Install the library + +* Install stable version from PyPI (may not be up-to-date): +``` +$ pip install graphkit-learn +``` + +* Install latest version from GitHub: +``` +$ git clone https://github.com/jajupmochi/graphkit-learn.git +$ cd graphkit-learn/ +$ python setup.py install +``` + +### Run the test + +A series of [tests](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/tests) can be run to check if the library works correctly: +``` +$ pip install -U pip pytest codecov coverage pytest-cov +$ pytest -v --cov-config=.coveragerc --cov-report term --cov=gklearn gklearn/tests/ +``` + +### Check examples + +A series of demos of using the library can be found on [Google Colab](https://drive.google.com/drive/folders/1r2gtPuFzIys2_MZw1wXqE2w3oCoVoQUG?usp=sharing) and in the [`example`](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/examples) folder. + +### Other demos + +Check [`notebooks`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks) directory for more demos: +* [`notebooks`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks) directory includes test codes of graph kernels based on linear patterns; +* [`notebooks/tests`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks/tests) directory includes codes that test some libraries and functions; +* [`notebooks/utils`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks/utils) directory includes some useful tools, such as a Gram matrix checker and a function to get properties of datasets; +* [`notebooks/else`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks/else) directory includes other codes that we used for experiments. + +### Documentation + +The docs of the library can be found [here](https://graphkit-learn.readthedocs.io/en/master/?badge=master). + +## Main contents + +### 1 List of graph kernels + +* Based on walks + * [The common walk kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/common_walk.py) [1] + * Exponential + * Geometric + * [The marginalized kenrel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/marginalized.py) + * With tottering [2] + * Without tottering [7] + * [The generalized random walk kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/random_walk.py) [3] + * [Sylvester equation](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/sylvester_equation.py) + * Conjugate gradient + * Fixed-point iterations + * [Spectral decomposition](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/spectral_decomposition.py) +* Based on paths + * [The shortest path kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/shortest_path.py) [4] + * [The structural shortest path kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/structural_sp.py) [5] + * [The path kernel up to length h](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/path_up_to_h.py) [6] + * The Tanimoto kernel + * The MinMax kernel +* Non-linear kernels + * [The treelet kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/treelet.py) [10] + * [Weisfeiler-Lehman kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/weisfeiler_lehman.py) [11] + * [Subtree](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/weisfeiler_lehman.py#L479) + +A demo of computing graph kernels can be found on [Google Colab](https://colab.research.google.com/drive/17Q2QCl9CAtDweGF8LiWnWoN2laeJqT0u?usp=sharing) and in the [`examples`](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/examples/compute_graph_kernel.py) folder. + +### 2 Graph Edit Distances + +### 3 Graph preimage methods + +A demo of generating graph preimages can be found on [Google Colab](https://colab.research.google.com/drive/1PIDvHOcmiLEQ5Np3bgBDdu0kLOquOMQK?usp=sharing) and in the [`examples`](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/examples/median_preimege_generator.py) folder. + +### 4 Interface to `GEDLIB` + +[`GEDLIB`](https://github.com/dbblumenthal/gedlib) is an easily extensible C++ library for (suboptimally) computing the graph edit distance between attributed graphs. [A Python interface](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/gedlib) for `GEDLIB` is integrated in this library, based on [`gedlibpy`](https://github.com/Ryurin/gedlibpy) library. + +### 5 Computation optimization methods + +* Python’s `multiprocessing.Pool` module is applied to perform **parallelization** on the computations of all kernels as well as the model selection. +* **The Fast Computation of Shortest Path Kernel (FCSP) method** [8] is implemented in *the random walk kernel*, *the shortest path kernel*, as well as *the structural shortest path kernel* where FCSP is applied on both vertex and edge kernels. +* **The trie data structure** [9] is employed in *the path kernel up to length h* to store paths in graphs. + +## Issues + +* This library uses `multiprocessing.Pool.imap_unordered` function to do the parallelization, which may not be able to run correctly under Windows system. For now, Windows users may need to comment the parallel codes and uncomment the codes below them which run serially. We will consider adding a parameter to control serial or parallel computations as needed. + +* Some modules (such as `Numpy`, `Scipy`, `sklearn`) apply [`OpenBLAS`](https://www.openblas.net/) to perform parallel computation by default, which causes conflicts with other parallelization modules such as `multiprossing.Pool`, highly increasing the computing time. By setting its thread to 1, `OpenBLAS` is forced to use a single thread/CPU, thus avoids the conflicts. For now, this procedure has to be done manually. Under Linux, type this command in terminal before running the code: +``` +$ export OPENBLAS_NUM_THREADS=1 +``` +Or add `export OPENBLAS_NUM_THREADS=1` at the end of your `~/.bashrc` file, then run +``` +$ source ~/.bashrc +``` +to make this effective permanently. + +## Results + +Check this paper for detailed description of graph kernels and experimental results: + +Linlin Jia, Benoit Gaüzère, and Paul Honeine. Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons. working paper or preprint, March 2019. URL https://hal-normandie-univ.archives-ouvertes.fr/hal-02053946. + +A comparison of performances of graph kernels on benchmark datasets can be found [here](https://graphkit-learn.readthedocs.io/en/master/experiments.html). + +## How to contribute + +Fork the library and open a pull request! Make your own contribute to the community! + +## Authors + +* [Linlin Jia](https://jajupmochi.github.io/), LITIS, INSA Rouen Normandie +* [Benoit Gaüzère](http://pagesperso.litislab.fr/~bgauzere/#contact_en), LITIS, INSA Rouen Normandie +* [Paul Honeine](http://honeine.fr/paul/Welcome.html), LITIS, Université de Rouen Normandie + +## Citation + +Still waiting... + +## Acknowledgments + +This research was supported by CSC (China Scholarship Council) and the French national research agency (ANR) under the grant APi (ANR-18-CE23-0014). The authors would like to thank the CRIANN (Le Centre Régional Informatique et d’Applications Numériques de Normandie) for providing computational resources. + +## References +[1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003. + +[2] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In Proceedings of the 20th International Conference on Machine Learning, Washington, DC, United States, 2003. + +[3] Vishwanathan, S.V.N., Schraudolph, N.N., Kondor, R., Borgwardt, K.M., 2010. Graph kernels. Journal of Machine Learning Research 11, 1201–1242. + +[4] K. M. Borgwardt and H.-P. Kriegel. Shortest-path kernels on graphs. In Proceedings of the International Conference on Data Mining, pages 74-81, 2005. + +[5] Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre Baldi. Graph kernels for chemical informatics. Neural networks, 18(8):1093–1110, 2005. + +[6] Suard F, Rakotomamonjy A, Bensrhair A. Kernel on Bag of Paths For Measuring Similarity of Shapes. InESANN 2007 Apr 25 (pp. 355-360). + +[7] Mahé, P., Ueda, N., Akutsu, T., Perret, J.L., Vert, J.P., 2004. Extensions of marginalized graph kernels, in: Proc. the twenty-first international conference on Machine learning, ACM. p. 70. + +[8] Lifan Xu, Wei Wang, M Alvarez, John Cavazos, and Dongping Zhang. Parallelization of shortest path graph kernels on multi-core cpus and gpus. Proceedings of the Programmability Issues for Heterogeneous Multicores (MultiProg), Vienna, Austria, 2014. + +[9] Edward Fredkin. Trie memory. Communications of the ACM, 3(9):490–499, 1960. + +[10] Gaüzere, B., Brun, L., Villemin, D., 2012. Two new graphs kernels in chemoinformatics. Pattern Recognition Letters 33, 2038–2047. + +[11] Shervashidze, N., Schweitzer, P., Leeuwen, E.J.v., Mehlhorn, K., Borgwardt, K.M., 2011. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research 12, 2539–2561. From da92273f615f93c870408fb94c3d867c671ef981 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:39 +0200 Subject: [PATCH 003/728] New translations README.md (French) --- lang/fr/README.md | 165 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 lang/fr/README.md diff --git a/lang/fr/README.md b/lang/fr/README.md new file mode 100644 index 0000000000..484089f3c1 --- /dev/null +++ b/lang/fr/README.md @@ -0,0 +1,165 @@ +# graphkit-learn +[![Build Status](https://travis-ci.org/jajupmochi/graphkit-learn.svg?branch=master)](https://travis-ci.org/jajupmochi/graphkit-learn) [![Build status](https://ci.appveyor.com/api/projects/status/bdxsolk0t1uji9rd?svg=true)](https://ci.appveyor.com/project/jajupmochi/graphkit-learn) [![codecov](https://codecov.io/gh/jajupmochi/graphkit-learn/branch/master/graph/badge.svg)](https://codecov.io/gh/jajupmochi/graphkit-learn) [![Documentation Status](https://readthedocs.org/projects/graphkit-learn/badge/?version=master)](https://graphkit-learn.readthedocs.io/en/master/?badge=master) [![PyPI version](https://badge.fury.io/py/graphkit-learn.svg)](https://badge.fury.io/py/graphkit-learn) + +A Python package for graph kernels, graph edit distances and graph pre-image problem. + +## Requirements + +* python>=3.6 +* numpy>=1.16.2 +* scipy>=1.1.0 +* matplotlib>=3.1.0 +* networkx>=2.2 +* scikit-learn>=0.20.0 +* tabulate>=0.8.2 +* tqdm>=4.26.0 +* control>=0.8.2 (for generalized random walk kernels only) +* slycot==0.3.3 (for generalized random walk kernels only, which requires a fortran compiler, gfortran for example) + +## How to use? + +### Install the library + +* Install stable version from PyPI (may not be up-to-date): +``` +$ pip install graphkit-learn +``` + +* Install latest version from GitHub: +``` +$ git clone https://github.com/jajupmochi/graphkit-learn.git +$ cd graphkit-learn/ +$ python setup.py install +``` + +### Run the test + +A series of [tests](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/tests) can be run to check if the library works correctly: +``` +$ pip install -U pip pytest codecov coverage pytest-cov +$ pytest -v --cov-config=.coveragerc --cov-report term --cov=gklearn gklearn/tests/ +``` + +### Check examples + +A series of demos of using the library can be found on [Google Colab](https://drive.google.com/drive/folders/1r2gtPuFzIys2_MZw1wXqE2w3oCoVoQUG?usp=sharing) and in the [`example`](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/examples) folder. + +### Other demos + +Check [`notebooks`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks) directory for more demos: +* [`notebooks`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks) directory includes test codes of graph kernels based on linear patterns; +* [`notebooks/tests`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks/tests) directory includes codes that test some libraries and functions; +* [`notebooks/utils`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks/utils) directory includes some useful tools, such as a Gram matrix checker and a function to get properties of datasets; +* [`notebooks/else`](https://github.com/jajupmochi/graphkit-learn/tree/master/notebooks/else) directory includes other codes that we used for experiments. + +### Documentation + +The docs of the library can be found [here](https://graphkit-learn.readthedocs.io/en/master/?badge=master). + +## Main contents + +### 1 List of graph kernels + +* Based on walks + * [The common walk kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/common_walk.py) [1] + * Exponential + * Geometric + * [The marginalized kenrel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/marginalized.py) + * With tottering [2] + * Without tottering [7] + * [The generalized random walk kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/random_walk.py) [3] + * [Sylvester equation](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/sylvester_equation.py) + * Conjugate gradient + * Fixed-point iterations + * [Spectral decomposition](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/spectral_decomposition.py) +* Based on paths + * [The shortest path kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/shortest_path.py) [4] + * [The structural shortest path kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/structural_sp.py) [5] + * [The path kernel up to length h](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/path_up_to_h.py) [6] + * The Tanimoto kernel + * The MinMax kernel +* Non-linear kernels + * [The treelet kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/treelet.py) [10] + * [Weisfeiler-Lehman kernel](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/weisfeiler_lehman.py) [11] + * [Subtree](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/kernels/weisfeiler_lehman.py#L479) + +A demo of computing graph kernels can be found on [Google Colab](https://colab.research.google.com/drive/17Q2QCl9CAtDweGF8LiWnWoN2laeJqT0u?usp=sharing) and in the [`examples`](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/examples/compute_graph_kernel.py) folder. + +### 2 Graph Edit Distances + +### 3 Graph preimage methods + +A demo of generating graph preimages can be found on [Google Colab](https://colab.research.google.com/drive/1PIDvHOcmiLEQ5Np3bgBDdu0kLOquOMQK?usp=sharing) and in the [`examples`](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/examples/median_preimege_generator.py) folder. + +### 4 Interface to `GEDLIB` + +[`GEDLIB`](https://github.com/dbblumenthal/gedlib) is an easily extensible C++ library for (suboptimally) computing the graph edit distance between attributed graphs. [A Python interface](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/gedlib) for `GEDLIB` is integrated in this library, based on [`gedlibpy`](https://github.com/Ryurin/gedlibpy) library. + +### 5 Computation optimization methods + +* Python’s `multiprocessing.Pool` module is applied to perform **parallelization** on the computations of all kernels as well as the model selection. +* **The Fast Computation of Shortest Path Kernel (FCSP) method** [8] is implemented in *the random walk kernel*, *the shortest path kernel*, as well as *the structural shortest path kernel* where FCSP is applied on both vertex and edge kernels. +* **The trie data structure** [9] is employed in *the path kernel up to length h* to store paths in graphs. + +## Issues + +* This library uses `multiprocessing.Pool.imap_unordered` function to do the parallelization, which may not be able to run correctly under Windows system. For now, Windows users may need to comment the parallel codes and uncomment the codes below them which run serially. We will consider adding a parameter to control serial or parallel computations as needed. + +* Some modules (such as `Numpy`, `Scipy`, `sklearn`) apply [`OpenBLAS`](https://www.openblas.net/) to perform parallel computation by default, which causes conflicts with other parallelization modules such as `multiprossing.Pool`, highly increasing the computing time. By setting its thread to 1, `OpenBLAS` is forced to use a single thread/CPU, thus avoids the conflicts. For now, this procedure has to be done manually. Under Linux, type this command in terminal before running the code: +``` +$ export OPENBLAS_NUM_THREADS=1 +``` +Or add `export OPENBLAS_NUM_THREADS=1` at the end of your `~/.bashrc` file, then run +``` +$ source ~/.bashrc +``` +to make this effective permanently. + +## Results + +Check this paper for detailed description of graph kernels and experimental results: + +Linlin Jia, Benoit Gaüzère, and Paul Honeine. Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons. working paper or preprint, March 2019. URL https://hal-normandie-univ.archives-ouvertes.fr/hal-02053946. + +A comparison of performances of graph kernels on benchmark datasets can be found [here](https://graphkit-learn.readthedocs.io/en/master/experiments.html). + +## How to contribute + +Fork the library and open a pull request! Make your own contribute to the community! + +## Authors + +* [Linlin Jia](https://jajupmochi.github.io/), LITIS, INSA Rouen Normandie +* [Benoit Gaüzère](http://pagesperso.litislab.fr/~bgauzere/#contact_en), LITIS, INSA Rouen Normandie +* [Paul Honeine](http://honeine.fr/paul/Welcome.html), LITIS, Université de Rouen Normandie + +## Citation + +Still waiting... + +## Acknowledgments + +This research was supported by CSC (China Scholarship Council) and the French national research agency (ANR) under the grant APi (ANR-18-CE23-0014). The authors would like to thank the CRIANN (Le Centre Régional Informatique et d’Applications Numériques de Normandie) for providing computational resources. + +## References +[1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003. + +[2] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In Proceedings of the 20th International Conference on Machine Learning, Washington, DC, United States, 2003. + +[3] Vishwanathan, S.V.N., Schraudolph, N.N., Kondor, R., Borgwardt, K.M., 2010. Graph kernels. Journal of Machine Learning Research 11, 1201–1242. + +[4] K. M. Borgwardt and H.-P. Kriegel. Shortest-path kernels on graphs. In Proceedings of the International Conference on Data Mining, pages 74-81, 2005. + +[5] Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre Baldi. Graph kernels for chemical informatics. Neural networks, 18(8):1093–1110, 2005. + +[6] Suard F, Rakotomamonjy A, Bensrhair A. Kernel on Bag of Paths For Measuring Similarity of Shapes. InESANN 2007 Apr 25 (pp. 355-360). + +[7] Mahé, P., Ueda, N., Akutsu, T., Perret, J.L., Vert, J.P., 2004. Extensions of marginalized graph kernels, in: Proc. the twenty-first international conference on Machine learning, ACM. p. 70. + +[8] Lifan Xu, Wei Wang, M Alvarez, John Cavazos, and Dongping Zhang. Parallelization of shortest path graph kernels on multi-core cpus and gpus. Proceedings of the Programmability Issues for Heterogeneous Multicores (MultiProg), Vienna, Austria, 2014. + +[9] Edward Fredkin. Trie memory. Communications of the ACM, 3(9):490–499, 1960. + +[10] Gaüzere, B., Brun, L., Villemin, D., 2012. Two new graphs kernels in chemoinformatics. Pattern Recognition Letters 33, 2038–2047. + +[11] Shervashidze, N., Schweitzer, P., Leeuwen, E.J.v., Mehlhorn, K., Borgwardt, K.M., 2011. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research 12, 2539–2561. From a6c9fa29d4a7b19f80173ab9eb254d02d32dd193 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:41 +0200 Subject: [PATCH 004/728] New translations test_scikit_ksvm.ipynb (French) --- .../fr/notebooks/tests/test_scikit_ksvm.ipynb | 389 ++++++++++++++++++ 1 file changed, 389 insertions(+) create mode 100644 lang/fr/notebooks/tests/test_scikit_ksvm.ipynb diff --git a/lang/fr/notebooks/tests/test_scikit_ksvm.ipynb b/lang/fr/notebooks/tests/test_scikit_ksvm.ipynb new file mode 100644 index 0000000000..849e9d5202 --- /dev/null +++ b/lang/fr/notebooks/tests/test_scikit_ksvm.ipynb @@ -0,0 +1,389 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically created module for IPython interactive environment\n", + "# Tuning hyper-parameters for precision\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.986 (+/-0.016) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "0.959 (+/-0.029) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n", + "0.988 (+/-0.017) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "0.982 (+/-0.026) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n", + "0.988 (+/-0.017) for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "0.982 (+/-0.025) for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n", + "0.988 (+/-0.017) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "0.982 (+/-0.025) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n", + "0.975 (+/-0.014) for {'C': 1, 'kernel': 'linear'}\n", + "0.975 (+/-0.014) for {'C': 10, 'kernel': 'linear'}\n", + "0.975 (+/-0.014) for {'C': 100, 'kernel': 'linear'}\n", + "0.975 (+/-0.014) for {'C': 1000, 'kernel': 'linear'}\n", + "\n", + "Detailed classification report:\n", + "\n", + "The model is trained on the full development set.\n", + "The scores are computed on the full evaluation set.\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 89\n", + " 1 0.97 1.00 0.98 90\n", + " 2 0.99 0.98 0.98 92\n", + " 3 1.00 0.99 0.99 93\n", + " 4 1.00 1.00 1.00 76\n", + " 5 0.99 0.98 0.99 108\n", + " 6 0.99 1.00 0.99 89\n", + " 7 0.99 1.00 0.99 78\n", + " 8 1.00 0.98 0.99 92\n", + " 9 0.99 0.99 0.99 92\n", + "\n", + " micro avg 0.99 0.99 0.99 899\n", + " macro avg 0.99 0.99 0.99 899\n", + "weighted avg 0.99 0.99 0.99 899\n", + "\n", + "\n", + "# Tuning hyper-parameters for recall\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.986 (+/-0.019) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "0.957 (+/-0.029) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n", + "0.987 (+/-0.019) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "0.981 (+/-0.028) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n", + "0.987 (+/-0.019) for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "0.981 (+/-0.026) for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n", + "0.987 (+/-0.019) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n", + "0.981 (+/-0.026) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n", + "0.972 (+/-0.012) for {'C': 1, 'kernel': 'linear'}\n", + "0.972 (+/-0.012) for {'C': 10, 'kernel': 'linear'}\n", + "0.972 (+/-0.012) for {'C': 100, 'kernel': 'linear'}\n", + "0.972 (+/-0.012) for {'C': 1000, 'kernel': 'linear'}\n", + "\n", + "Detailed classification report:\n", + "\n", + "The model is trained on the full development set.\n", + "The scores are computed on the full evaluation set.\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 89\n", + " 1 0.97 1.00 0.98 90\n", + " 2 0.99 0.98 0.98 92\n", + " 3 1.00 0.99 0.99 93\n", + " 4 1.00 1.00 1.00 76\n", + " 5 0.99 0.98 0.99 108\n", + " 6 0.99 1.00 0.99 89\n", + " 7 0.99 1.00 0.99 78\n", + " 8 1.00 0.98 0.99 92\n", + " 9 0.99 0.99 0.99 92\n", + "\n", + " micro avg 0.99 0.99 0.99 899\n", + " macro avg 0.99 0.99 0.99 899\n", + "weighted avg 0.99 0.99 0.99 899\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Parameter estimation using grid search with cross-validation\n", + "from __future__ import print_function\n", + "\n", + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.svm import SVC\n", + "\n", + "print(__doc__)\n", + "\n", + "# Loading the Digits dataset\n", + "digits = datasets.load_digits()\n", + "\n", + "# To apply an classifier on this data, we need to flatten the image, to\n", + "# turn the data in a (samples, feature) matrix:\n", + "n_samples = len(digits.images)\n", + "X = digits.images.reshape((n_samples, -1))\n", + "y = digits.target\n", + "\n", + "# Split the dataset in two equal parts\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.5, random_state=0)\n", + "\n", + "# Set the parameters by cross-validation\n", + "tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n", + " 'C': [1, 10, 100, 1000]},\n", + " {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n", + "\n", + "scores = ['precision', 'recall']\n", + "\n", + "for score in scores:\n", + " print(\"# Tuning hyper-parameters for %s\" % score)\n", + " print()\n", + "\n", + " clf = GridSearchCV(SVC(), tuned_parameters, cv=5,\n", + " scoring='%s_macro' % score)\n", + " clf.fit(X_train, y_train)\n", + "\n", + " print(\"Best parameters set found on development set:\")\n", + " print()\n", + " print(clf.best_params_)\n", + " print()\n", + " print(\"Grid scores on development set:\")\n", + " print()\n", + " means = clf.cv_results_['mean_test_score']\n", + " stds = clf.cv_results_['std_test_score']\n", + " for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n", + " print(\"%0.3f (+/-%0.03f) for %r\"\n", + " % (mean, std * 2, params))\n", + " print()\n", + "\n", + " print(\"Detailed classification report:\")\n", + " print()\n", + " print(\"The model is trained on the full development set.\")\n", + " print(\"The scores are computed on the full evaluation set.\")\n", + " print()\n", + " y_true, y_pred = y_test, clf.predict(X_test)\n", + " print(classification_report(y_true, y_pred))\n", + " print()\n", + "\n", + "# Note the problem is too easy: the hyperparameter plateau is too flat and the\n", + "# output model is the same for precision and recall with ties in quality." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n", + " warnings.warn(CV_WARNING, FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "GridSearchCV(cv='warn', error_score='raise-deprecating',\n", + " estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False),\n", + " fit_params=None, iid='warn', n_jobs=None,\n", + " param_grid={'kernel': ('linear', 'rbf'), 'C': [1, 10]},\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring=None, verbose=0)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm, datasets\n", + "from sklearn.model_selection import GridSearchCV\n", + "iris = datasets.load_iris()\n", + "parameters = {'kernel': ('linear', 'rbf'), 'C': [1, 10]}\n", + "svc = svm.SVC()\n", + "clf = GridSearchCV(svc, parameters)\n", + "clf.fit(iris.data, iris.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['mean_fit_time',\n", + " 'mean_score_time',\n", + " 'mean_test_score',\n", + " 'mean_train_score',\n", + " 'param_C',\n", + " 'param_kernel',\n", + " 'params',\n", + " 'rank_test_score',\n", + " 'split0_test_score',\n", + " 'split0_train_score',\n", + " 'split1_test_score',\n", + " 'split1_train_score',\n", + " 'split2_test_score',\n", + " 'split2_train_score',\n", + " 'std_fit_time',\n", + " 'std_score_time',\n", + " 'std_test_score',\n", + " 'std_train_score']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(clf.cv_results_.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_values([array([0.00032242, 0.00050696, 0.00030732, 0.00045021]), array([5.32061151e-05, 8.45866051e-05, 1.21523332e-05, 2.86382520e-05]), array([0.0001688 , 0.00019749, 0.00016387, 0.0001924 ]), array([8.85899620e-06, 4.74580085e-06, 9.39730861e-06, 5.54376603e-06]), masked_array(data=[1, 1, 10, 10],\n", + " mask=[False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), masked_array(data=['linear', 'rbf', 'linear', 'rbf'],\n", + " mask=[False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), [{'C': 1, 'kernel': 'linear'}, {'C': 1, 'kernel': 'rbf'}, {'C': 10, 'kernel': 'linear'}, {'C': 10, 'kernel': 'rbf'}], array([1. , 0.98039216, 1. , 0.98039216]), array([0.96078431, 0.96078431, 0.92156863, 0.96078431]), array([0.97916667, 0.97916667, 1. , 1. ]), array([0.98 , 0.97333333, 0.97333333, 0.98 ]), array([0.01617914, 0.00902067, 0.03715363, 0.01592466]), array([1, 3, 3, 1], dtype=int32), array([0.97979798, 0.96969697, 0.95959596, 0.95959596]), array([1., 1., 1., 1.]), array([0.99019608, 0.98039216, 0.98039216, 0.98039216]), array([0.98999802, 0.98336304, 0.97999604, 0.97999604]), array([0.00824863, 0.01254825, 0.01649726, 0.01649726])])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.cv_results_.values()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically created module for IPython interactive environment\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(__doc__)\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import svm, datasets\n", + "\n", + "# import some data to play with\n", + "iris = datasets.load_iris()\n", + "X = iris.data[:, :2] # we only take the first two features. We could\n", + " # avoid this ugly slicing by using a two-dim dataset\n", + "Y = iris.target\n", + "\n", + "\n", + "def my_kernel(X, Y):\n", + " \"\"\"\n", + " We create a custom kernel:\n", + "\n", + " (2 0)\n", + " k(X, Y) = X ( ) Y.T\n", + " (0 1)\n", + " \"\"\"\n", + " M = np.array([[2, 0], [0, 1.0]])\n", + " return np.dot(np.dot(X, M), Y.T)\n", + "\n", + "\n", + "h = .02 # step size in the mesh\n", + "\n", + "# we create an instance of SVM and fit out data.\n", + "clf = svm.SVC(kernel=my_kernel)\n", + "clf.fit(X, Y)\n", + "\n", + "# Plot the decision boundary. For that, we will assign a color to each\n", + "# point in the mesh [x_min, x_max]x[y_min, y_max].\n", + "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", + "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", + "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", + "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", + "\n", + "# Put the result into a color plot\n", + "Z = Z.reshape(xx.shape)\n", + "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n", + "\n", + "# Plot also the training points\n", + "plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolors='k')\n", + "plt.title('3-Class classification using Support Vector Machine with custom'\n", + " ' kernel')\n", + "plt.axis('tight')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a9a8256ea79d6bf5bf55048aef2571493cdec138 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:42 +0200 Subject: [PATCH 005/728] New translations get_dataset_attributes.ipynb (French) --- .../utils/get_dataset_attributes.ipynb | 651 ++++++++++++++++++ 1 file changed, 651 insertions(+) create mode 100644 lang/fr/notebooks/utils/get_dataset_attributes.ipynb diff --git a/lang/fr/notebooks/utils/get_dataset_attributes.ipynb b/lang/fr/notebooks/utils/get_dataset_attributes.ipynb new file mode 100644 index 0000000000..7e0bdb0d61 --- /dev/null +++ b/lang/fr/notebooks/utils/get_dataset_attributes.ipynb @@ -0,0 +1,651 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Acyclic:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 183\n", + "ave_node_num : 8.153005464480874\n", + "min_node_num : 3\n", + "max_node_num : 11\n", + "ave_edge_num : 7.1530054644808745\n", + "min_edge_num : 2\n", + "max_edge_num : 10\n", + "ave_node_degree : 1.737561012151176\n", + "min_node_degree : 1.3333333333333333\n", + "max_node_degree : 1.8181818181818181\n", + "ave_fill_factor : 0.11241161841596808\n", + "min_fill_factor : 0.08264462809917356\n", + "max_fill_factor : 0.2222222222222222\n", + "node_label_num : 3\n", + "edge_label_num : 1\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 148\n", + "\n", + "\n", + "Alkane:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : False\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 150\n", + "ave_node_num : 8.873333333333333\n", + "min_node_num : 1\n", + "max_node_num : 10\n", + "ave_edge_num : 7.873333333333333\n", + "min_edge_num : 0\n", + "max_edge_num : 9\n", + "ave_node_degree : 1.7507830687830694\n", + "min_node_degree : 0.0\n", + "max_node_degree : 1.8\n", + "ave_fill_factor : 0.10199498404299989\n", + "min_fill_factor : 0.0\n", + "max_fill_factor : 0.25\n", + "node_label_num : 1\n", + "edge_label_num : 1\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 123\n", + "\n", + "\n", + "MAO:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : True\n", + "is_directed : False\n", + "dataset_size : 68\n", + "ave_node_num : 18.38235294117647\n", + "min_node_num : 11\n", + "max_node_num : 27\n", + "ave_edge_num : 19.63235294117647\n", + "min_edge_num : 12\n", + "max_edge_num : 29\n", + "ave_node_degree : 2.1347114940751464\n", + "min_node_degree : 2.090909090909091\n", + "max_node_degree : 2.2\n", + "ave_fill_factor : 0.060638921710159575\n", + "min_fill_factor : 0.039780521262002745\n", + "max_fill_factor : 0.09917355371900827\n", + "node_label_num : 3\n", + "edge_label_num : 4\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "PAH:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : False\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 94\n", + "ave_node_num : 20.70212765957447\n", + "min_node_num : 10\n", + "max_node_num : 28\n", + "ave_edge_num : 24.425531914893618\n", + "min_edge_num : 11\n", + "max_edge_num : 34\n", + "ave_node_degree : 2.3550919704450077\n", + "min_node_degree : 2.2\n", + "max_node_degree : 2.5\n", + "ave_fill_factor : 0.05799294134806485\n", + "min_fill_factor : 0.04336734693877551\n", + "max_fill_factor : 0.11\n", + "node_label_num : 1\n", + "edge_label_num : 1\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "MUTAG:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : True\n", + "is_directed : False\n", + "dataset_size : 188\n", + "ave_node_num : 17.930851063829788\n", + "min_node_num : 10\n", + "max_node_num : 28\n", + "ave_edge_num : 19.79255319148936\n", + "min_edge_num : 10\n", + "max_edge_num : 33\n", + "ave_node_degree : 2.1887720785524962\n", + "min_node_degree : 2.0\n", + "max_node_degree : 2.4444444444444446\n", + "ave_fill_factor : 0.06480462822996713\n", + "min_fill_factor : 0.039540816326530615\n", + "max_fill_factor : 0.1\n", + "node_label_num : 7\n", + "edge_label_num : 4\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "Letter-med:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : False\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 2250\n", + "ave_node_num : 4.674666666666667\n", + "min_node_num : 1\n", + "max_node_num : 9\n", + "ave_edge_num : 3.2057777777777776\n", + "min_edge_num : 0\n", + "max_edge_num : 7\n", + "ave_node_degree : 1.35270582010582\n", + "min_node_degree : 0.0\n", + "max_node_degree : 2.4\n", + "ave_fill_factor : 0.15517701625094482\n", + "min_fill_factor : 0.0\n", + "max_fill_factor : 0.3333333333333333\n", + "node_label_num : 0\n", + "edge_label_num : 0\n", + "node_attr_dim : 2\n", + "edge_attr_dim : 0\n", + "class_number : 15\n", + "\n", + "\n", + "ENZYMES:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 600\n", + "ave_node_num : 32.63333333333333\n", + "min_node_num : 2\n", + "max_node_num : 126\n", + "ave_edge_num : 62.13666666666666\n", + "min_edge_num : 1\n", + "max_edge_num : 149\n", + "ave_node_degree : 3.862625314410413\n", + "min_node_degree : 0.32\n", + "max_node_degree : 5.230769230769231\n", + "ave_fill_factor : 0.07509817146721588\n", + "min_fill_factor : 0.0016\n", + "max_fill_factor : 0.375\n", + "node_label_num : 3\n", + "edge_label_num : 0\n", + "node_attr_dim : 18\n", + "edge_attr_dim : 0\n", + "class_number : 6\n", + "\n", + "\n", + "D&D:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 1178\n", + "ave_node_num : 284.3166383701188\n", + "min_node_num : 30\n", + "max_node_num : 5748\n", + "ave_edge_num : 715.6587436332767\n", + "min_edge_num : 63\n", + "max_edge_num : 14267\n", + "ave_node_degree : 4.979061662020889\n", + "min_node_degree : 3.6116504854368934\n", + "max_node_degree : 8.933333333333334\n", + "ave_fill_factor : 0.013790239865199101\n", + "min_fill_factor : 0.0004318164098347239\n", + "max_fill_factor : 0.09666666666666666\n", + "node_label_num : 82\n", + "edge_label_num : 0\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "NCI1:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 4110\n", + "ave_node_num : 29.8654501216545\n", + "min_node_num : 3\n", + "max_node_num : 111\n", + "ave_edge_num : 32.3\n", + "min_edge_num : 2\n", + "max_edge_num : 119\n", + "ave_node_degree : 2.155013792267071\n", + "min_node_degree : 0.8\n", + "max_node_degree : 2.769230769230769\n", + "ave_fill_factor : 0.04239828192835043\n", + "min_fill_factor : 0.009522961908152367\n", + "max_fill_factor : 0.2222222222222222\n", + "node_label_num : 37\n", + "edge_label_num : 0\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "NCI109:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 4127\n", + "ave_node_num : 29.681124303368065\n", + "min_node_num : 4\n", + "max_node_num : 111\n", + "ave_edge_num : 32.13084565059365\n", + "min_edge_num : 3\n", + "max_edge_num : 119\n", + "ave_node_degree : 2.156446168619097\n", + "min_node_degree : 1.0909090909090908\n", + "max_node_degree : 2.769230769230769\n", + "ave_fill_factor : 0.04263668408405519\n", + "min_fill_factor : 0.009522961908152367\n", + "max_fill_factor : 0.1875\n", + "node_label_num : 38\n", + "edge_label_num : 0\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "AIDS:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : True\n", + "is_directed : False\n", + "dataset_size : 2000\n", + "ave_node_num : 15.6925\n", + "min_node_num : 2\n", + "max_node_num : 95\n", + "ave_edge_num : 16.195\n", + "min_edge_num : 1\n", + "max_edge_num : 103\n", + "ave_node_degree : 2.012865369646626\n", + "min_node_degree : 0.6\n", + "max_node_degree : 2.8333333333333335\n", + "ave_fill_factor : 0.08679744688995196\n", + "min_fill_factor : 0.011412742382271468\n", + "max_fill_factor : 0.25\n", + "node_label_num : 38\n", + "edge_label_num : 3\n", + "node_attr_dim : 4\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "Mutagenicity:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : True\n", + "is_directed : False\n", + "dataset_size : 4337\n", + "ave_node_num : 30.317731150564907\n", + "min_node_num : 4\n", + "max_node_num : 417\n", + "ave_edge_num : 30.76942587041734\n", + "min_edge_num : 3\n", + "max_edge_num : 112\n", + "ave_node_degree : 2.0379886162441148\n", + "min_node_degree : 0.47961630695443647\n", + "max_node_degree : 2.3703703703703702\n", + "ave_fill_factor : 0.0431047931997047\n", + "min_fill_factor : 0.0005750795047415305\n", + "max_fill_factor : 0.1875\n", + "node_label_num : 14\n", + "edge_label_num : 3\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "FIRSTMM_DB:\n", + "substructures : {'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 41\n", + "ave_node_num : 1377.2682926829268\n", + "min_node_num : 134\n", + "max_node_num : 5037\n", + "ave_edge_num : 3074.0975609756097\n", + "min_edge_num : 320\n", + "max_edge_num : 10888\n", + "ave_node_degree : 4.503061007447199\n", + "min_node_degree : 4.191919191919192\n", + "max_node_degree : 4.776119402985074\n", + "ave_fill_factor : 0.003689884678097613\n", + "min_fill_factor : 0.00042914515176536197\n", + "max_fill_factor : 0.017821341055914458\n", + "node_label_num : 5\n", + "edge_label_num : 0\n", + "node_attr_dim : 1\n", + "edge_attr_dim : 2\n", + "class_number : 11\n", + "\n", + "\n", + "MSRC9:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 221\n", + "ave_node_num : 40.57918552036199\n", + "min_node_num : 25\n", + "max_node_num : 55\n", + "ave_edge_num : 97.9366515837104\n", + "min_edge_num : 53\n", + "max_edge_num : 145\n", + "ave_node_degree : 4.8153400199203436\n", + "min_node_degree : 4.176470588235294\n", + "max_node_degree : 5.576923076923077\n", + "ave_fill_factor : 0.06021937645679636\n", + "min_fill_factor : 0.04521181915272339\n", + "max_fill_factor : 0.0848\n", + "node_label_num : 10\n", + "edge_label_num : 0\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 8\n", + "\n", + "\n", + "MSRC21:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 563\n", + "ave_node_num : 77.52042628774423\n", + "min_node_num : 51\n", + "max_node_num : 141\n", + "ave_edge_num : 198.32326820603907\n", + "min_edge_num : 121\n", + "max_edge_num : 405\n", + "ave_node_degree : 5.102391320310953\n", + "min_node_degree : 4.04\n", + "max_node_degree : 6.6\n", + "ave_fill_factor : 0.03357132864022473\n", + "min_fill_factor : 0.01873405612244898\n", + "max_fill_factor : 0.04652056901191849\n", + "node_label_num : 22\n", + "edge_label_num : 0\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 20\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SYNTHETIC:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 300\n", + "ave_node_num : 100.0\n", + "min_node_num : 100\n", + "max_node_num : 100\n", + "ave_edge_num : 196.0\n", + "min_edge_num : 196\n", + "max_edge_num : 196\n", + "ave_node_degree : 3.9200000000000017\n", + "min_node_degree : 3.92\n", + "max_node_degree : 3.92\n", + "ave_fill_factor : 0.019600000000000003\n", + "min_fill_factor : 0.0196\n", + "max_fill_factor : 0.0196\n", + "node_label_num : 8\n", + "edge_label_num : 0\n", + "node_attr_dim : 1\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "BZR:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 405\n", + "ave_node_num : 35.75061728395062\n", + "min_node_num : 13\n", + "max_node_num : 57\n", + "ave_edge_num : 38.358024691358025\n", + "min_edge_num : 13\n", + "max_edge_num : 60\n", + "ave_node_degree : 2.1466610247664697\n", + "min_node_degree : 2.0\n", + "max_node_degree : 2.2777777777777777\n", + "ave_fill_factor : 0.0314385616191916\n", + "min_fill_factor : 0.017851646660510926\n", + "max_fill_factor : 0.07692307692307693\n", + "node_label_num : 10\n", + "edge_label_num : 0\n", + "node_attr_dim : 3\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "COX2:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 467\n", + "ave_node_num : 41.224839400428266\n", + "min_node_num : 32\n", + "max_node_num : 56\n", + "ave_edge_num : 43.44539614561028\n", + "min_edge_num : 34\n", + "max_edge_num : 59\n", + "ave_node_degree : 2.1077350079995685\n", + "min_node_degree : 2.076923076923077\n", + "max_node_degree : 2.1739130434782608\n", + "ave_fill_factor : 0.025799177869507202\n", + "min_fill_factor : 0.01881377551020408\n", + "max_fill_factor : 0.033203125\n", + "node_label_num : 8\n", + "edge_label_num : 0\n", + "node_attr_dim : 3\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "DHFR:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 756\n", + "ave_node_num : 42.42724867724868\n", + "min_node_num : 20\n", + "max_node_num : 71\n", + "ave_edge_num : 44.544973544973544\n", + "min_edge_num : 21\n", + "max_edge_num : 73\n", + "ave_node_degree : 2.102359895640024\n", + "min_node_degree : 2.0338983050847457\n", + "max_node_degree : 2.2\n", + "ave_fill_factor : 0.026126638866896944\n", + "min_fill_factor : 0.0144812537195001\n", + "max_fill_factor : 0.0525\n", + "node_label_num : 9\n", + "edge_label_num : 0\n", + "node_attr_dim : 3\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "PROTEINS:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 1113\n", + "ave_node_num : 39.05750224618149\n", + "min_node_num : 4\n", + "max_node_num : 620\n", + "ave_edge_num : 72.8158131176999\n", + "min_edge_num : 5\n", + "max_edge_num : 1049\n", + "ave_node_degree : 3.734642171150555\n", + "min_node_degree : 1.7142857142857142\n", + "max_node_degree : 5.071428571428571\n", + "ave_fill_factor : 0.09599853508460923\n", + "min_fill_factor : 0.0027289281997918834\n", + "max_fill_factor : 0.375\n", + "node_label_num : 3\n", + "edge_label_num : 0\n", + "node_attr_dim : 1\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "\n", + "PROTEINS_full:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : False\n", + "is_directed : False\n", + "dataset_size : 1113\n", + "ave_node_num : 39.05750224618149\n", + "min_node_num : 4\n", + "max_node_num : 620\n", + "ave_edge_num : 72.8158131176999\n", + "min_edge_num : 5\n", + "max_edge_num : 1049\n", + "ave_node_degree : 3.734642171150555\n", + "min_node_degree : 1.7142857142857142\n", + "max_node_degree : 5.071428571428571\n", + "ave_fill_factor : 0.09599853508460923\n", + "min_fill_factor : 0.0027289281997918834\n", + "max_fill_factor : 0.375\n", + "node_label_num : 3\n", + "edge_label_num : 0\n", + "node_attr_dim : 29\n", + "edge_attr_dim : 0\n", + "class_number : 2\n", + "\n", + "load SDF: 100%|██████████| 4457424/4457424 [00:09<00:00, 489414.03it/s]\n", + "ajust data: 100%|██████████| 42687/42687 [00:09<00:00, 4562.13it/s] \n", + "\n", + "NCI-HIV:\n", + "substructures : {'linear', 'non linear'}\n", + "node_labeled : True\n", + "edge_labeled : True\n", + "is_directed : False\n", + "dataset_size : 42682\n", + "ave_node_num : 45.70945597675835\n", + "min_node_num : 2\n", + "max_node_num : 438\n", + "ave_edge_num : 47.7137903565906\n", + "min_edge_num : 1\n", + "max_edge_num : 441\n", + "ave_node_degree : 2.087755727203458\n", + "min_node_degree : 1.0\n", + "max_node_degree : 4.571428571428571\n", + "ave_fill_factor : 0.02739985514266206\n", + "min_fill_factor : 0.002298742728466879\n", + "max_fill_factor : 0.25\n", + "node_label_num : 63\n", + "edge_label_num : 3\n", + "node_attr_dim : 0\n", + "edge_attr_dim : 0\n", + "class_number : 3\n", + "\n" + ] + } + ], + "source": [ + "import sys\n", + "sys.path.insert(0, \"../../\")\n", + "from gklearn.utils.graphfiles import loadDataset\n", + "from gklearn.utils.graphdataset import get_dataset_attributes\n", + "\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../../datasets/acyclic/dataset_bps.ds',},\n", + " {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds',\n", + " 'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt',},\n", + " {'name': 'MAO', 'dataset': '../../datasets/MAO/dataset.ds',},\n", + " {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds',},\n", + " {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG_A.txt'},\n", + " {'name': 'Letter-med', 'dataset': '../../datasets/Letter-med/Letter-med_A.txt'},\n", + " {'name': 'ENZYMES', 'dataset': '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + " {'name': 'D&D', 'dataset': '../../datasets/DD/DD_A.txt'},\n", + " {'name': 'NCI1', 'dataset': '../../datasets/NCI1/NCI1_A.txt'},\n", + " {'name': 'NCI109', 'dataset': '../../datasets/NCI109/NCI109_A.txt'},\n", + " {'name': 'AIDS', 'dataset': '../../datasets/AIDS/AIDS_A.txt'},\n", + " \n", + " {'name': 'Mutagenicity', 'dataset': '../../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + " {'name': 'FIRSTMM_DB', 'dataset': '../../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'},\n", + " {'name': 'MSRC9', 'dataset': '../../datasets/MSRC_9_txt/MSRC_9_A.txt'},\n", + " {'name': 'MSRC21', 'dataset': '../../datasets/MSRC_21_txt/MSRC_21_A.txt'},\n", + " {'name': 'SYNTHETIC', 'dataset': '../../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'},\n", + " {'name': 'BZR', 'dataset': '../../datasets/BZR_txt/BZR_A_sparse.txt'},\n", + " {'name': 'COX2', 'dataset': '../../datasets/COX2_txt/COX2_A_sparse.txt'},\n", + " {'name': 'DHFR', 'dataset': '../../datasets/DHFR_txt/DHFR_A_sparse.txt'}, \n", + " {'name': 'PROTEINS', 'dataset': '../../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'},\n", + " {'name': 'PROTEINS_full', 'dataset': '../../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, \n", + " {'name': 'NCI-HIV', 'dataset': '../../datasets/NCI-HIV/AIDO99SD.sdf',\n", + " 'dataset_y': '../../datasets/NCI-HIV/aids_conc_may04.txt',},\n", + "\n", + "# # not working below\n", + "# {'name': 'PTC_FM', 'dataset': '../../datasets/PTC/Train/FM.ds',},\n", + "# {'name': 'PTC_FR', 'dataset': '../../datasets/PTC/Train/FR.ds',},\n", + "# {'name': 'PTC_MM', 'dataset': '../../datasets/PTC/Train/MM.ds',},\n", + "# {'name': 'PTC_MR', 'dataset': '../../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "\n", + "for ds in dslist:\n", + " dataset, y = loadDataset(\n", + " ds['dataset'],\n", + " filename_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n", + " attrs = get_dataset_attributes(\n", + " dataset, target=y, node_label='atom', edge_label='bond_type')\n", + " print()\n", + " print(ds['name'] + ':')\n", + " for atr in attrs:\n", + " print(atr, ':', attrs[atr])\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9294ffc9865f896d581c15c5009af749e4ebfd4a Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:44 +0200 Subject: [PATCH 006/728] New translations draw_running_time.py (French) --- lang/fr/notebooks/utils/draw_running_time.py | 86 ++++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 lang/fr/notebooks/utils/draw_running_time.py diff --git a/lang/fr/notebooks/utils/draw_running_time.py b/lang/fr/notebooks/utils/draw_running_time.py new file mode 100644 index 0000000000..4b18bce76b --- /dev/null +++ b/lang/fr/notebooks/utils/draw_running_time.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Draw running time. +Created on Mon Sep 24 17:37:26 2018 + +@author: ljia +""" + +import numpy as np +import matplotlib.pyplot as plt + +N = 7 +tgm1 = np.array([0.73, + 0.88, + 1.65, + 1.97, + 4.89, + 36.98, + 704.54]) +tgm2 = np.array([0.77, + 1.22, + 2.95, + 5.70, + 20.29, + 147.09, + 3477.65]) +tms1 = np.array([2.68, + 3.41, + 3.36, + 237.00, + 7.58, + 255.48, + 717.35]) +tms2 = np.array([3.93, + 4.96, + 5.84, + 833.06, + 26.62, + 807.84, + 3515.72]) + +fig, ax = plt.subplots(1, 1, figsize=(10.5, 4.2)) + +ind = np.arange(N) # the x locations for the groups +width = 0.23 # the width of the bars: can also be len(x) sequence + +p1 = ax.bar(ind - width * 0.03, tgm1, width, label='compute Gram matrix on $CRIANN$ ($t_1$)', zorder=3) +p2 = ax.bar(ind - width * 0.03, tms1 - tgm1, width, bottom=tgm1, label='model selection on $CRIANN$', zorder=3) +p3 = ax.bar(ind + width * 1.03, tgm2, width, label='compute Gram matrix on $laptop$ ($t_2$)', zorder=3) +p4 = ax.bar(ind + width * 1.03, tms2 - tgm2, width, bottom=tgm2, label='model selection on $laptop$', zorder=3) + +ax.set_yscale('log', nonposy='clip') +ax.set_xlabel('datasets') +ax.set_ylabel('runtime($s$)') +#ax.set_title('Runtime of the shortest path kernel on all datasets') +plt.xticks(ind + width / 2, ('Alkane', 'Acyclic', 'MAO', 'PAH', 'MUTAG', + 'Letter-med', 'ENZYMES')) +#ax.set_yticks(np.logspace(-16, -3, num=20, base=10)) +#ax.set_ylim(bottom=1e-15) +ax.grid(axis='y', zorder=0) +ax.spines['top'].set_visible(False) +ax.spines['bottom'].set_visible(False) +ax.spines['left'].set_visible(False) +ax.spines['right'].set_visible(False) +ax.xaxis.set_ticks_position('none') + +ax2 = ax.twinx() +p5 = ax2.plot(ind + width / 2, tgm2 / tgm1, 'bo-', + label='$t_2 / $ $t_1$') +ax2.set_ylabel('ratios') +ax2.spines['top'].set_visible(False) +ax2.spines['bottom'].set_visible(False) +ax2.spines['left'].set_visible(False) +ax2.spines['right'].set_visible(False) +ax2.xaxis.set_ticks_position('none') +ax2.yaxis.set_ticks_position('none' + ) +ax.yaxis.set_ticks_position('none') + +fig.subplots_adjust(right=0.63) +fig.legend(loc='right', ncol=1, frameon=False) # , ncol=5, labelspacing=0.1, handletextpad=0.4, columnspacing=0.6) + +plt.savefig('../check_gm/parallel_runtime_on_different_machines.eps', format='eps', dpi=300, + transparent=True, bbox_inches='tight') +plt.show() \ No newline at end of file From fcc3110e6152276355a19f3a0ea37c186d6e4a43 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:45 +0200 Subject: [PATCH 007/728] New translations draw_gm.py (French) --- lang/fr/notebooks/utils/draw_gm.py | 64 ++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 lang/fr/notebooks/utils/draw_gm.py diff --git a/lang/fr/notebooks/utils/draw_gm.py b/lang/fr/notebooks/utils/draw_gm.py new file mode 100644 index 0000000000..9daa057686 --- /dev/null +++ b/lang/fr/notebooks/utils/draw_gm.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Compare gram matrices +Created on Mon Sep 24 10:52:25 2018 + +@author: ljia +""" +import numpy as np +import matplotlib.pyplot as plt + +N = 7 +egmin = [-3.425001366427846e-15, + -5.513191435356332e-15, + -1.1563146193980238e-15, + -1.3999833987183273e-15, + -5.811474553224136e-15, + 2.196833029054622e-10, + 0.0001002381061317695] +egmin = np.abs(egmin) +egmin2 = [-9.433792343294819e-15, + np.NaN, + -7.502900269338164e-16, + -1.3999833987183273e-15, + -8.73626400337456e-15, + np.NaN, + -4.04460628433013e-14] +egmin2 = np.abs(egmin2) +egmax = [142.86649135778595, + 140.08307372708344, + 64.31844814063015, + 92.38382991977493, + 160.72585558445357, + 943.9175660197347, + 299.17895175532897] +egmax2 = [172.4203026547106, + np.NaN, + 65.53092059526354, + 92.38382991977493, + 180.374192331094, + np.NaN, + 529.3691973508182] + +fig, ax = plt.subplots() + +ind = np.arange(N) # the x locations for the groups +width = 0.20 # the width of the bars: can also be len(x) sequence + +p1 = ax.bar(ind, egmin, width) +p2 = ax.bar(ind, egmax, width, bottom=egmin) +p3 = ax.bar(ind + width, egmin2, width) +p4 = ax.bar(ind + width, egmax2, width, bottom=egmin2) + +ax.set_yscale('log', nonposy='clip') +ax.set_xlabel('datasets') +ax.set_ylabel('absolute eigen values') +ax.set_title('Absolute eigen values of gram matrices on all datasets') +plt.xticks(ind + width / 2, ('Acyclic', 'Alkane', 'MAO', 'PAH', 'MUTAG', 'Letter-med', 'ENZYMES')) +#ax.set_yticks(np.logspace(-16, -3, num=20, base=10)) +ax.set_ylim(bottom=1e-15) +ax.legend((p1[0], p2[0], p3[0], p4[0]), ('min1', 'max1', 'min2', 'max2'), loc='upper right') + +plt.savefig('../check_gm/compare_eigen_values.eps', format='eps', dpi=300) +plt.show() \ No newline at end of file From 7d6f1af635e95ab05eaab7299f8669891b4c50a2 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:47 +0200 Subject: [PATCH 008/728] New translations draw_datasize_vs_chunksize.py (French) --- .../utils/draw_datasize_vs_chunksize.py | 77 +++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100644 lang/fr/notebooks/utils/draw_datasize_vs_chunksize.py diff --git a/lang/fr/notebooks/utils/draw_datasize_vs_chunksize.py b/lang/fr/notebooks/utils/draw_datasize_vs_chunksize.py new file mode 100644 index 0000000000..79da4c7d53 --- /dev/null +++ b/lang/fr/notebooks/utils/draw_datasize_vs_chunksize.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Draw data size v.s. chunksize. +Created on Mon Oct 8 12:00:45 2018 + +@author: ljia +""" +import numpy as np +import matplotlib.pyplot as plt + +def loadmin(file): + result = np.load(file) + return np.argmin(result), np.min(result), result + +def idx2chunksize(idx): + if idx < 9: + return idx * 10 + 10 + elif idx < 18: + return (idx - 8) * 100 + elif idx < 27: + return (idx - 17) * 1000 + else: + return (idx - 26) * 10000 + +def idx2chunksize2(idx): + if idx < 5: + return idx * 20 + 10 + elif idx < 10: + return (idx - 5) * 200 + 100 + elif idx < 15: + return (idx - 10) * 2000 + 1000 + else: + return (idx - 15) * 20000 * 10000 + +idx, mrlt, rlt = loadmin('../test_parallel/myria/ENZYMES.npy') +csize = idx2chunksize2(idx) + +#dsize = np.array([183, 150, 68, 94, 188, 2250, 600]) +dsize = np.array([183, 150, 68, 94, 188, 2250]) +dsize = dsize * (dsize + 1) / 2 +#cs_sp_laptop = [900, 400, 70, 900, 2000, 8000, 300] +#cs_sp_myria = [900, 500, 500, 300, 400, 4000, 300] +cs_sp_laptop = [900, 400, 70, 900, 2000, 8000] +cs_sp_myria = [900, 500, 500, 300, 400, 4000] +cd_ssp_laptop = [500, 700, 500, 70, 3000, 3000] +cd_ssp_myria = [100, 90, 700, 30, 3000, 5000] + +cs_sp_laptop = [x for _, x in sorted(zip(dsize, cs_sp_laptop))] +cs_sp_myria = [x for _, x in sorted(zip(dsize, cs_sp_myria))] +cd_ssp_laptop = [x for _, x in sorted(zip(dsize[0:6], cd_ssp_laptop))] +cd_ssp_myria = [x for _, x in sorted(zip(dsize[0:6], cd_ssp_myria))] +dsize.sort() +cd_mean = np.mean([cs_sp_laptop[0:6], cs_sp_myria[0:6], cd_ssp_laptop, cd_ssp_myria], + axis=0) +#np.append(cd_mean, [6000]) + +fig, ax = plt.subplots() +##p1 = ax.plot(dsize, cs_sp_laptop, 'o-', label='sp laptop') +#p2 = ax.plot(dsize, cs_sp_myria, 'o-', label='sp CRIANN') +##p3 = ax.plot(dsize[0:6], cd_ssp_laptop, 'o-', label='ssp laptop') +#p4 = ax.plot(dsize[0:6], cd_ssp_myria, 'o-', label='ssp CRIANN') +#p5 = ax.plot(dsize[0:6], cd_mean, 'o-', label='mean') + +#p1 = ax.plot(dsize[0:5], cs_sp_laptop[0:5], 'o-', label='sp laptop') +p2 = ax.plot(dsize[0:5], cs_sp_myria[0:5], 'o-', label='sp CRIANN') +#p3 = ax.plot(dsize[0:5], cd_ssp_laptop[0:5], 'o-', label='ssp laptop') +p4 = ax.plot(dsize[0:5], cd_ssp_myria[0:5], 'o-', label='ssp CRIANN') +p5 = ax.plot(dsize[0:5], cd_mean[0:5], 'o-', label='mean') + + +#ax.set_xscale('log', nonposx='clip') +#ax.set_yscale('log', nonposy='clip') +ax.set_xlabel('data sizes') +ax.set_ylabel('chunksizes for the fastest computation') +ax.legend(loc='upper left') +plt.show() \ No newline at end of file From 460641c8f8122ade669caaf48c5144a993e3aaad Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:48 +0200 Subject: [PATCH 009/728] New translations check_gm_gstsp.py (French) --- lang/fr/notebooks/utils/check_gm_gstsp.py | 134 ++++++++++++++++++++++ 1 file changed, 134 insertions(+) create mode 100644 lang/fr/notebooks/utils/check_gm_gstsp.py diff --git a/lang/fr/notebooks/utils/check_gm_gstsp.py b/lang/fr/notebooks/utils/check_gm_gstsp.py new file mode 100644 index 0000000000..c6f0389e73 --- /dev/null +++ b/lang/fr/notebooks/utils/check_gm_gstsp.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +compute gm with load_data.py and test them. +Created on Wed Sep 19 16:12:13 2018 + +@author: ljia +""" + +"""Shortest-Path graph kernel. +Python implementation based on: "Shortest-path kernels on graphs", by +Borgwardt, K.M.; Kriegel, H.-P., in Data Mining, Fifth IEEE +International Conference on , vol., no., pp.8 pp.-, 27-30 Nov. 2005 +doi: 10.1109/ICDM.2005.132 +Author : Sandro Vega-Pons, Emanuele Olivetti +""" + +import sys +sys.path.insert(0, "../../") +import numpy as np +import networkx as nx +from gklearn.utils.graphfiles import loadDataset +import matplotlib.pyplot as plt +from numpy.linalg import eig + + +class GK_SP: + """ + Shorthest path graph kernel. + """ + + def compare(self, g_1, g_2, verbose=False): + """Compute the kernel value (similarity) between two graphs. + Parameters + ---------- + g1 : networkx.Graph + First graph. + g2 : networkx.Graph + Second graph. + Returns + ------- + k : The similarity value between g1 and g2. + """ + # Diagonal superior matrix of the floyd warshall shortest + # paths: + fwm1 = np.array(nx.floyd_warshall_numpy(g_1)) + fwm1 = np.where(fwm1 == np.inf, 0, fwm1) + fwm1 = np.where(fwm1 == np.nan, 0, fwm1) + fwm1 = np.triu(fwm1, k=1) + bc1 = np.bincount(fwm1.reshape(-1).astype(int)) + + fwm2 = np.array(nx.floyd_warshall_numpy(g_2)) + fwm2 = np.where(fwm2 == np.inf, 0, fwm2) + fwm2 = np.where(fwm2 == np.nan, 0, fwm2) + fwm2 = np.triu(fwm2, k=1) + bc2 = np.bincount(fwm2.reshape(-1).astype(int)) + + # Copy into arrays with the same length the non-zero shortests + # paths: + v1 = np.zeros(max(len(bc1), len(bc2)) - 1) + v1[range(0, len(bc1)-1)] = bc1[1:] + + v2 = np.zeros(max(len(bc1), len(bc2)) - 1) + v2[range(0, len(bc2)-1)] = bc2[1:] + + return np.sum(v1 * v2) + + def compare_normalized(self, g_1, g_2, verbose=False): + """Compute the normalized kernel value between two graphs. + A normalized version of the kernel is given by the equation: + k_norm(g1, g2) = k(g1, g2) / sqrt(k(g1,g1) * k(g2,g2)) + Parameters + ---------- + g1 : networkx.Graph + First graph. + g2 : networkx.Graph + Second graph. + Returns + ------- + k : The similarity value between g1 and g2. + """ + return self.compare(g_1, g_2) / (np.sqrt(self.compare(g_1, g_1) * + self.compare(g_2, g_2))) + + def compare_list(self, graph_list, verbose=False): + """Compute the all-pairs kernel values for a list of graphs. + This function can be used to directly compute the kernel + matrix for a list of graphs. The direct computation of the + kernel matrix is faster than the computation of all individual + pairwise kernel values. + Parameters + ---------- + graph_list: list + A list of graphs (list of networkx graphs) + Return + ------ + K: numpy.array, shape = (len(graph_list), len(graph_list)) + The similarity matrix of all graphs in graph_list. + """ + n = len(graph_list) + k = np.zeros((n, n)) + for i in range(n): + for j in range(i, n): + k[i, j] = self.compare(graph_list[i], graph_list[j]) + k[j, i] = k[i, j] + + k_norm = np.zeros(k.shape) + for i in range(k.shape[0]): + for j in range(k.shape[1]): + k_norm[i, j] = k[i, j] / np.sqrt(k[i, i] * k[j, j]) + + return k_norm + + +ds_name = 'PAH' +datafile = '../../datasets/PAH/dataset.ds' +dataset, y = loadDataset(datafile, filename_y=None, extra_params=None) +gk_sp = GK_SP() +x = gk_sp.compare_list(dataset) +np.savez('../check_gm/' + ds_name + '.gm.jstsp', gms=x) + +plt.imshow(x) +plt.colorbar() +plt.savefig('../check_gm/' + ds_name + '.gm.jstsp.eps', format='eps', dpi=300) +# print(np.transpose(x)) +print('if symmetric: ', np.array_equal(x, np.transpose(x))) + +print('diag: ', np.diag(x)) +print('sum diag < 0.1: ', np.sum(np.diag(x) < 0.1)) +print('min, max diag: ', min(np.diag(x)), max(np.diag(x))) +print('mean x: ', np.mean(np.mean(x))) + +[lamnda, v] = eig(x) +print('min, max lambda: ', min(lamnda), max(lamnda)) From 16fc35faa0e584df695a8bc9606d1eda4c66e6ae Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:50 +0200 Subject: [PATCH 010/728] New translations check_gm.py (French) --- lang/fr/notebooks/utils/check_gm.py | 48 +++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 lang/fr/notebooks/utils/check_gm.py diff --git a/lang/fr/notebooks/utils/check_gm.py b/lang/fr/notebooks/utils/check_gm.py new file mode 100644 index 0000000000..d0d08c45d6 --- /dev/null +++ b/lang/fr/notebooks/utils/check_gm.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Check basic properties of gram matrices. +Created on Wed Sep 19 15:32:29 2018 + +@author: ljia +""" + +import numpy as np +import matplotlib.pyplot as plt +from numpy.linalg import eig + +# read gram matrices from file. +results_dir = '../results/marginalizedkernel/myria' +ds_name = 'ENZYMES' +gmfile = np.load(results_dir + '/' + ds_name + '.gm.npz') +#print('gm time: ', gmfile['gmtime']) +# a list to store gram matrices for all param_grid_precomputed +gram_matrices = gmfile['gms'] +# param_list_pre_revised = gmfile['params'] # list to store param grids precomputed ignoring the useless ones +#y = gmfile['y'].tolist() +#x = gram_matrices[0] + +for idx, x in enumerate(gram_matrices): + print() + print(idx) + plt.imshow(x) + plt.colorbar() + plt.savefig('../check_gm/' + ds_name + '.gm.eps', format='eps', dpi=300) +# print(np.transpose(x)) + print('if symmetric: ', np.array_equal(x, np.transpose(x))) + + print('diag: ', np.diag(x)) + print('sum diag < 0.1: ', np.sum(np.diag(x) < 0.1)) + print('min, max diag: ', min(np.diag(x)), max(np.diag(x))) + print('min, max matrix: ', np.min(x), np.max(x)) + for i in range(len(x)): + for j in range(len(x)): + if x[i][j] > 1 + 1e-9: + print(i, j) + raise Exception('value bigger than 1 with index', i, j) + print('mean x: ', np.mean(np.mean(x))) + + [lamnda, v] = eig(x) + print('min, max lambda: ', min(lamnda), max(lamnda)) + if -1e-10 > min(lamnda): + raise Exception('wrong eigen values.') From 885a72c87d22007e53203458a4990e0b9636e6e5 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:51 +0200 Subject: [PATCH 011/728] New translations test_tqdm.py (French) --- lang/fr/notebooks/tests/test_tqdm.py | 33 ++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 lang/fr/notebooks/tests/test_tqdm.py diff --git a/lang/fr/notebooks/tests/test_tqdm.py b/lang/fr/notebooks/tests/test_tqdm.py new file mode 100644 index 0000000000..e408760775 --- /dev/null +++ b/lang/fr/notebooks/tests/test_tqdm.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Apr 3 10:38:59 2020 + +@author: ljia +""" + +from tqdm import tqdm +import sys + +print('start') + +for i in tqdm(range(10000000), file=sys.stdout): + x = i +# print(x) +# ============================================================================= +# summary +# terminal, IPython 7.0.1 (Spyder 4): Works. +# write to file: does not work. Progress bar splits as the progress goes. +# Jupyter: +# ============================================================================= + +# for i in tqdm(range(10000000)): +# x = i +# print(x) +# ============================================================================= +# summary +# terminal, IPython 7.0.1 (Spyder 4): does not work. When combines with other +# print, progress bar splits. +# write to file: does not work. Cannot write progress bar to file. +# Jupyter: +# ============================================================================= From 1d7b54a30a3eb30539f28ff537e89b53485a7043 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:53 +0200 Subject: [PATCH 012/728] New translations test_spkernel.ipynb (French) --- lang/fr/notebooks/tests/test_spkernel.ipynb | 779 ++++++++++++++++++++ 1 file changed, 779 insertions(+) create mode 100644 lang/fr/notebooks/tests/test_spkernel.ipynb diff --git a/lang/fr/notebooks/tests/test_spkernel.ipynb b/lang/fr/notebooks/tests/test_spkernel.ipynb new file mode 100644 index 0000000000..554bc9e9f8 --- /dev/null +++ b/lang/fr/notebooks/tests/test_spkernel.ipynb @@ -0,0 +1,779 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Elisabetta Ghisu\n", + "\n", + "\"\"\"\n", + "- Script containing functions for computing the shortest path kernel\n", + "- The Floyd Warshall algorithm is first implemented\n", + "- Then the SP is calculated\n", + "\"\"\"\n", + "\n", + "\n", + "#######################\n", + "# - IMPORT PACKAGES - #\n", + "#######################\n", + "\n", + "\n", + "\n", + "import numpy.matlib as matlib\n", + "import numpy as np\n", + "\n", + "\"\"\"\n", + "### FLOYD WARSHALL ALGORITHM\n", + "Input:\n", + "- Adjancency matrix A\n", + "Output:\n", + "- Shortest path matrix S\n", + "\"\"\"\n", + "\n", + "def floyd_warshall(A):\n", + "\n", + "\t# nuber of nodes\n", + "\tn = A.shape[0]\n", + "\n", + "\t# initialize shortes path matrix\n", + "\tS = np.zeros(shape = (n,n))\n", + "\n", + "\tfor i in range(n):\n", + "\t\tfor j in range(n):\n", + "\t\t\tif A[i,j] == 0 and i!=j:\n", + "\t\t\t\tS[i,j] = float(\"inf\")\n", + "\t\t\telse:\n", + "\t\t\t\tS[i,j] = A[i,j]\n", + "\n", + "\t# Compute the shortest path matrix\n", + "\tfor k in range(n):\n", + "\t\tfor i in range(n):\n", + "\t\t\tfor j in range(n):\n", + "\t\t\t\tif S[i,j] > S[i,k] + S[k,j]:\n", + "\t\t\t\t\tS[i,j] = S[i,k] + S[k,j]\n", + "\n", + "\treturn S\t\t\t\t\t\t\t\t\n", + "\n", + "\n", + "\n", + "\"\"\"\n", + "SHORTEST PATH KERNEL: This is a fast implementation of the shortest path\n", + "kernel algorithm\n", + "Inputs\n", + "- Adjancency matrix\n", + "- List of list of node labels for each graph\n", + "- Total number of node labels \n", + "Outputs\n", + "- Kernel matrix\n", + "- Feature matrix\n", + "\"\"\"\n", + "\n", + "def sp_kernel_fast(adj_mat, labels, L):\n", + "\n", + "\t# Number of graphs\n", + "\tn = len(adj_mat)\n", + "\tL = int(L)\n", + "\tS = []\n", + "\n", + "\t# shortest path matrices\n", + "\tfor i in xrange(n):\n", + "\t\tif i%1000 == 0 and i !=0:\n", + " \t\t\tprint('haha') #( \"%d\" % i)\n", + "\t\tS.append(floyd_warshall(adj_mat[i]))\n", + "\t\n", + "\t# maximum length of shortest paths in the dataset\n", + "\tmax_path = 0\n", + "\n", + "\t# for each graph in dataset\n", + "\tfor i in xrange(n):\n", + "\n", + "\t\tS_cur = np.copy(S[i])\n", + "\t\tS_cur[S_cur == np.inf] = 0\n", + "\t\tnew_max = np.max(S_cur)\n", + "\t\t\n", + "\t\tif new_max > max_path:\n", + "\t\t\tmax_path = new_max # get max short path in all Ss\n", + "\n", + "\t# maximum length of shortest paths\n", + "\tmax_path = int(max_path)\n", + "\n", + "\t# initialize feature matrix\n", + "\tsp = np.zeros(((max_path + 1) * L * (L+1) /2,n))\n", + "\n", + "\t# compute feature map for shortest path\n", + "\tfor i in xrange(n):\n", + "\n", + "\t\tif i % 1000 == 0:\n", + "\t\t\tprint('haha') #\"Processed %d graphs\" %i\n", + "\n", + "\t\tS_graph = S[i]\n", + "\t\tlabels_graph = np.asarray(labels[i].reshape((len(labels[i]),1)))\n", + "\t\tlabels_graph = labels_graph + 1\n", + "\t\t\n", + "\t\tlabels_aux = matlib.repmat(labels_graph, 1, len(labels_graph))\n", + "\t\t\n", + "\t\tmin_lab = np.minimum(labels_aux, labels_aux.T)\n", + "\t\t\n", + "\t\tmax_lab = np.maximum(labels_aux, labels_aux.T)\n", + "\t\tsub_path = np.triu(~(np.isinf(S_graph))).T\n", + "\n", + "\t\tmin_lab = min_lab[sub_path]\n", + "\t\tmax_lab = max_lab[sub_path]\n", + "\n", + "\n", + "\t\tind = S_graph[sub_path] * L * (L + 1) / 2 + (min_lab - 1) * (2*L + 2 - min_lab) / 2 + max_lab - min_lab\n", + "\t\tind = ind.astype(int)\n", + "\t\taccum = np.zeros((max_path + 1) * L * (L + 1) /2)\n", + "\t\taccum[:ind.max() + 1] += np.bincount(ind.astype(int))\n", + "\t\tsp[ind,i] = accum[ind]\n", + "\t\n", + "\tsum_cols = np.sum(sp, axis = 1)\n", + "\tind_true = sum_cols != 0\n", + "\tsp = sp[ind_true,:]\n", + "\t\n", + "\t# compute kernel matrix\n", + "\tK = np.dot(sp.T,sp)\n", + " \n", + "\treturn K, sp" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'igraph'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# iGraph imports to handle graphs and for graph I/O\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0migraph\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGraph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'igraph'" + ] + } + ], + "source": [ + "#Authors: Elisabetta Ghisu, Felipe Llinares Lopez\n", + "\n", + "\"\"\"\n", + "- This script includes a list of functions for analyzing \n", + "parsing and formatting graphs\n", + "- The graphs are given in graphml format\n", + "- It also contains functions for loading, processing the graphs\n", + "and extract graph statistics\n", + "\"\"\"\n", + "\n", + "\n", + "import numpy as np\n", + "from numpy import genfromtxt\n", + "\n", + "# iGraph imports to handle graphs and for graph I/O\n", + "from igraph import Graph\n", + "\n", + "\n", + "# ---------------------------------GRAPHML I/O FUNCTIONS------------------------------------ #\n", + "\n", + "# INPUT:\n", + "# filenames_graphs: list of GraphML files, where each file contains one graph in the dataset\n", + "# filename_labels: text file with labels corresponding to each graph in the dataset, in the same order as they are in\n", + "# filename_graphs\n", + "# OUTPUT:\n", + "# G: A list containing one iGraph object for each graph in the dataset\n", + "# Y: A Numpy array containing the labels corresponding to each graph, in the same order as G\n", + "def load_graphml(filenames_graphs, filename_labels):\n", + " G = []\n", + " for fname in filenames_graphs:\n", + " G.append(Graph.Read_GraphML(fname))\n", + " Y = genfromtxt(filename_labels)\n", + " return (G, Y)\n", + "\n", + "\n", + "# Loads a list of paths to GraphML files from filename_list\n", + "def load_file_list(filename_flist):\n", + " f = open(filename_flist, 'r')\n", + " f_graphs = []\n", + " for line in f:\n", + " f_graphs.append(line.strip())\n", + " f.close()\n", + " return f_graphs\n", + "\n", + "\n", + "# --------------------------------COMPUTE STATISTICS---------------------------------------- #\n", + "\n", + "\n", + "# Retrieve labels of all vertices belonging to any graph in the list of iGraph objects G and\n", + "# returns the entire list, and a list with the alphabet of the vertex labels\n", + "def get_all_vertex_labels(G, att_name='label'):\n", + " v_l = []\n", + " for g in G:\n", + " v_l += g.vs[att_name]\n", + " return (v_l, np.unique(v_l))\n", + "\n", + "\n", + "# Retrieve labels of all edges belonging to any graph in the list of iGraph objects G and\n", + "# returns the entire list, and a list with the alphabet of the edge labels\n", + "def get_all_edge_labels(G, att_name='label'):\n", + " e_l = []\n", + " for g in G:\n", + " e_l += g.es[att_name]\n", + " return (e_l, np.unique(e_l))\n", + "\n", + "\n", + "# Returns a list where each element is itself the adjacency list of the corresponding graph\n", + "# The adjacency lit of a graph has the following format:\n", + "# it is a list where each element is a list containing the id of adjacent nodes\n", + "def get_adj_list(G):\n", + " ad_l = []\n", + " for g in G:\n", + " ad_l.append(g.get_adjlist())\n", + " return ad_l\n", + "\n", + "# Returns a list where each element is the adjacency matrix of the graph \n", + "# The adjancency matrix is in iGraph format\n", + "def get_adj_mat(G):\n", + " ad_m = []\n", + " for g in G:\n", + " ad_m.append(g.get_adjacency())\n", + " return ad_m\n", + "\n", + "# Returns a list where each element contains the nodes label for a graph\n", + "def get_node_labels(G, att_name = 'label'):\n", + " node_l = []\n", + " for g in G:\n", + " node_l.append(g.vs[att_name])\n", + " return node_l\n", + "\n", + "\n", + "\n", + "# ----------------- LOAD AND PROCESS THE GRAPHS --------------- #\n", + "\n", + "\n", + "\"\"\"\n", + "Inputs:\n", + "- list of graphs file\n", + "- labels file\n", + "- path to the data folder\n", + "Outputs:\n", + "- List of node labels\n", + "- List of adjancency lists\n", + "- List of graphs in graphml format\n", + "- Targets\n", + "- number of classes\n", + "- sample size\n", + "\"\"\"\n", + "\n", + "\n", + "def load_and_process(filenames_graphs, filename_labels, path_to_dataset):\n", + "\n", + " # load a list of names to graphml files\n", + " f_graphs = load_file_list(filenames_graphs)\n", + " # sample size\n", + " n = len(f_graphs)\n", + "\n", + " # create a list of paths to the files\n", + " f_graphs_path =[]\n", + "\n", + " # for each graph in dataset\n", + " for i in range(n):\n", + "\n", + " # index the graph\n", + " graph_name = f_graphs[i]\n", + "\n", + " # path to the data folder\n", + " path = \"%s/%s\" % (path_to_dataset, graph_name)\n", + " f_graphs_path.append(path)\n", + "\n", + " # If the data is DD have to delete an element (corrupted file)\n", + " if graph_name == \"DD\":\n", + " del f_graphs_path[148]\n", + " n = n-1\n", + "\n", + " # Load the graphs in graphml format\n", + " # G is a llist of graphml graph\n", + " # Y is an array of targets\n", + " G,Y = load_graphml(f_graphs_path, filename_labels)\n", + "\n", + " # Delete corrupted file in DD\n", + " if graph_name == \"DD\": \n", + " Y = np.delete(Y, 148)\n", + "\n", + " # get adjacency list and matrix for all the graphs in G\n", + " ad_list = get_adj_list(G)\n", + " ad_mat = get_adj_mat(G)\n", + "\n", + " # get a list containing lists of node labels\n", + " node_label = get_node_labels(G)\n", + "\n", + " return node_label, ad_list, G, Y\n", + "\n", + "\n", + "\n", + "\"\"\"\n", + "RENAME NODES: function to rename nodes from 0,...,num_nodes\n", + "Input\n", + "- list of list of node labels in each graph\n", + "Output\n", + "- L: total number of different labels in the dataset\n", + "- node_label: new renamed labels\n", + "\"\"\"\n", + "\n", + "def rename_nodes(node_label): \n", + " \n", + " # number of graphs in the dataset\n", + " n = len(node_label)\n", + "\n", + " # labels will store the new labels\n", + " labels = [0] * n\n", + "\n", + " # disctionary containing the map from the old to the new labels\n", + " label_lookup = {}\n", + "\n", + " # counter of unique labels\n", + " label_counter = 0\n", + "\n", + " # for each graph in dataset\n", + " for i in range(n):\n", + "\n", + "\n", + " # number of nodes in graph[i]\n", + " num_nodes = len(node_label[i]) \n", + "\n", + " # will be used to store the new labels\n", + " labels[i] = np.zeros(num_nodes, dtype = np.uint64) # positive integers\n", + "\n", + " # for each node in the graph\n", + " for j in range(num_nodes):\n", + "\n", + " # the node label to a string\n", + " l_node_str = str(np.copy(node_label[i][j]))\n", + " \n", + " # if the string has not been observed yet\n", + " # the corresponding node is assigned a new label\n", + " # otherwise it will be named with the same label\n", + " # already assigned to an identical string\n", + "\n", + " if not label_lookup.has_key(l_node_str):\n", + " label_lookup[l_node_str] = label_counter\n", + " labels[i][j] = label_counter \n", + " label_counter += 1\n", + " else:\n", + " labels[i][j] = label_lookup[l_node_str]\n", + "\n", + " # total number of labels in the dataset\n", + " L = label_counter\n", + " print('haha') #'Number of original labels %d' % L \n", + "\n", + " return L, labels" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "usage: ipykernel_launcher.py [-h] --dataset DATASET\n", + "ipykernel_launcher.py: error: the following arguments are required: --dataset\n" + ] + }, + { + "ename": "SystemExit", + "evalue": "2", + "output_type": "error", + "traceback": [ + "An exception has occurred, use %tb to see the full traceback.\n", + "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:3273: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", + " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n" + ] + } + ], + "source": [ + "# Author: Elisabetta Ghisu\n", + "\n", + "\"\"\"\n", + "- Script for computing the kernel matrix and features map \n", + "using shortest path kernel\n", + "\"\"\"\n", + "\n", + "###########################\n", + "# --- IMPORT PACKAGES --- #\n", + "###########################\n", + "\n", + "import numpy as np\n", + "import argparse\n", + "import os\n", + "import pickle\n", + "\n", + "from numpy import genfromtxt\n", + "\n", + "# from sp_functions import *\n", + "# from parse_graphs import *\n", + "\n", + "\n", + "\n", + "##############################\n", + "### Command Line Arguments ###\n", + "##############################\n", + "\n", + "parser = argparse.ArgumentParser(description = \"Compute kernel and features matrices via shortest path kernel\")\n", + "parser.add_argument(\"--dataset\", required = True, help = \"Name of the dataset\")\n", + "args = parser.parse_args()\n", + "\n", + "\n", + "#####################\n", + "### LOAD THE DATA ###\n", + "#####################\n", + "\n", + "\"\"\"\n", + "- Here we load the data input and targets\n", + "- The data are assumed to be in graph formats\n", + "- They should be in graphml format \n", + "\"\"\"\n", + "\n", + "# path to the list of graphs and dataset\n", + "filenames_graphs = \"data/%s.list\" % (args.dataset)\n", + "path_to_dataset = \"data/%s\" % (args.dataset) \n", + "\n", + "# Load the targets\n", + "filename_labels = \"data/%s_label.txt\" % (args.dataset)\n", + "\n", + "# load and process graphs\n", + "node_label, ad_list, G, Y = load_and_process(filenames_graphs, filename_labels, path_to_dataset)\n", + "\n", + "# output directory\n", + "out_path = \"kernel_matrices/%s/sp\" % args.dataset\n", + "\n", + "# If the output directory does not exist, then create it\n", + "if not os.path.exists(out_path):\n", + " os.makedirs(out_path)\n", + "\n", + "\n", + "#########################\n", + "# --- SHORTEST PATH --- #\n", + "#########################\n", + "\n", + "\n", + "# assign labels starting from zero to the nodes\n", + "L, labels = rename_nodes(node_label)\n", + "\n", + "\n", + "# Compute adjancency matrix \n", + "adj_mat = get_adj_mat(G)\n", + "\n", + "# Compute kernel and feature maps using shortest path\n", + "K, phi = sp_kernel_fast(adj_mat, labels, L)\n", + "\n", + "# save kernel matrix\n", + "file_name = \"%s/%s_ker_mat\" % (out_path, args.dataset)\n", + "np.save(file_name, K)\n", + "\n", + "# save feature map\n", + "file_name = \"%s/%s_phi_map\" % (out_path, args.dataset)\n", + "np.save(file_name, phi)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + " \n", + "def loadCT(filename):\n", + " \"\"\"load data from .ct file.\n", + " \n", + " Notes\n", + " ------ \n", + " a typical example of data in .ct is like this:\n", + " \n", + " 3 2 <- number of nodes and edges\n", + " 0.0000 0.0000 0.0000 C <- each line describes a node, the last parameter in which is the label of the node, representing a chemical element @Q what are the first 3 numbers?\n", + " 0.0000 0.0000 0.0000 C\n", + " 0.0000 0.0000 0.0000 O\n", + " 1 3 1 1 <- each line describes an edge, the first two numbers represent two nodes of the edge, the last number represents the label. @Q what are the 3th numbers?\n", + " 2 3 1 1\n", + " \"\"\"\n", + " content = open(filename).read().splitlines()\n", + " G = nx.Graph(name=str(content[0])) # set name of the graph\n", + " tmp = content[1].split(\" \")\n", + " if tmp[0] == '':\n", + " nb_nodes = int(tmp[1]) # number of the nodes\n", + " nb_edges = int(tmp[2]) # number of the edges\n", + " else:\n", + " nb_nodes = int(tmp[0])\n", + " nb_edges = int(tmp[1])\n", + "\n", + " for i in range(0, nb_nodes):\n", + " tmp = content[i + 2].split(\" \")\n", + " tmp = [x for x in tmp if x != '']\n", + " G.add_node(i, label=tmp[3])\n", + "\n", + " for i in range(0, nb_edges):\n", + " tmp = content[i + G.number_of_nodes() + 2].split(\" \")\n", + " tmp = [x for x in tmp if x != '']\n", + " G.add_edge(int(tmp[0]) - 1, int(tmp[1]) - 1, label=int(tmp[3]))\n", + " return G\n", + "\n", + "\n", + "def loadGXL(filename):\n", + " import networkx as nx\n", + " import xml.etree.ElementTree as ET\n", + "\n", + " tree = ET.parse(filename)\n", + " root = tree.getroot()\n", + " index = 0\n", + " G = nx.Graph()\n", + " dic={}\n", + " for node in root.iter('node'):\n", + " label = node.find('attr')[0].text\n", + " dic[node.attrib['id']] = index\n", + " G.add_node(index, id=node.attrib['id'], label=label)\n", + " index += 1\n", + " \n", + " for edge in root.iter('edge'):\n", + " label = edge.find('attr')[0].text\n", + " G.add_edge(dic[edge.attrib['from']], dic[edge.attrib['to']], label=label)\n", + " return G\n", + " \n", + "def loadDataset(filename):\n", + " \"\"\"load file list of the dataset.\n", + " \"\"\"\n", + " from os.path import dirname, splitext\n", + "\n", + " dirname_dataset = dirname(filename)\n", + " extension = splitext(filename)[1][1:]\n", + " data = []\n", + " y = []\n", + " if(extension == \"ds\"):\n", + " content = open(filename).read().splitlines()\n", + " for i in range(0, len(content)):\n", + " tmp = content[i].split(' ')\n", + " data.append(loadCT(dirname_dataset + '/' + tmp[0].replace('#', '', 1))) # remove the '#'s in file names\n", + " y.append(float(tmp[1]))\n", + " elif(extension == \"cxl\"):\n", + " import xml.etree.ElementTree as ET\n", + "\n", + " tree = ET.parse(filename)\n", + " root = tree.getroot()\n", + " data = []\n", + " y = []\n", + " for graph in root.iter('print'):\n", + " mol_filename = graph.attrib['file']\n", + " mol_class = graph.attrib['class']\n", + " data.append(loadGXL(dirname_dataset + '/' + mol_filename))\n", + " y.append(mol_class)\n", + "\n", + " return data, y" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import pathlib\n", + "sys.path.insert(0, \"../../\")\n", + "\n", + "\n", + "import networkx as nx\n", + "import numpy as np\n", + "import time\n", + "\n", + "from gklearn.utils.utils import getSPGraph\n", + "\n", + "\n", + "def spkernel(Gn):\n", + " \"\"\"Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.\n", + " \n", + " Parameters\n", + " ----------\n", + " G : NetworkX graph\n", + " The graph to be tramsformed.\n", + " \n", + " Return\n", + " ------\n", + " S : NetworkX graph\n", + " The shortest-paths graph corresponding to G.\n", + " \n", + " References\n", + " ----------\n", + " [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.\n", + " \"\"\"\n", + " Kmatrix = np.zeros((len(Gn), len(Gn)))\n", + " \n", + " Sn = [] # get shortest path graphs of Gn\n", + " for i in range(0, len(Gn)):\n", + " Sn.append(getSPGraph(Gn[i]))\n", + " \n", + "# print(S1.nodes(data = True))\n", + "# print(S2.nodes(data = True))\n", + "# print(S1.edges(data = True))\n", + "# print(S2.edges(data = True))\n", + " \n", + " start_time = time.time()\n", + " for i in range(0, len(Gn)):\n", + " for j in range(i, len(Gn)):\n", + " for e1 in Sn[i].edges(data = True):\n", + " for e2 in Sn[j].edges(data = True): \n", + " if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])):\n", + " Kmatrix[i][j] += 1\n", + " Kmatrix[j][i] += (0 if i == j else 1)\n", + " \n", + " print(\"--- %s seconds ---\" % (time.time() - start_time))\n", + " \n", + " return Kmatrix" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. 2. 3. 1. 2.]]\n", + "{0: {0: [0], 3: [0, 3], 1: [0, 3, 1], 4: [0, 3, 4], 2: [0, 3, 4, 2]}, 1: {1: [1], 3: [1, 3], 0: [1, 3, 0], 4: [1, 3, 4], 2: [1, 3, 4, 2]}, 2: {2: [2], 4: [2, 4], 3: [2, 4, 3], 0: [2, 4, 3, 0], 1: [2, 4, 3, 1]}, 3: {3: [3], 0: [3, 0], 1: [3, 1], 4: [3, 4], 2: [3, 4, 2]}, 4: {4: [4], 2: [4, 2], 3: [4, 3], 0: [4, 3, 0], 1: [4, 3, 1]}}\n", + "[[0. 2. 3. 1. 2.]\n", + " [2. 0. 3. 1. 2.]\n", + " [3. 3. 0. 2. 1.]\n", + " [1. 1. 2. 0. 1.]\n", + " [2. 2. 1. 1. 0.]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dataset, y = loadDataset(\"../../datasets/acyclic/dataset_bps.ds\")\n", + "G1 = dataset[12]\n", + "\n", + "nx.draw_networkx(G1)\n", + "# print(list(dataset[12][4]))\n", + "\n", + "l = nx.shortest_path(G1)\n", + "\n", + "l2 = nx.floyd_warshall_numpy(G1)\n", + "print(np.array(l2[0]))\n", + "print(l)\n", + "print(l2)\n", + "from matplotlib import pyplot as plt\n", + "plt.show()\n", + "\n", + "S = getSPGraph(G1)\n", + "nx.draw_networkx(S)\n", + "pos = nx.spring_layout(S)\n", + "edge_labels = nx.get_edge_attributes(S,'cost')\n", + "print(edge_labels)\n", + "nx.draw_networkx_edge_labels(S, pos, edge_labels = edge_labels)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- 8.196406841278076 seconds ---\n", + "[[ 3. 1. 3. ... 1. 1. 1.]\n", + " [ 1. 6. 1. ... 0. 0. 3.]\n", + " [ 3. 1. 3. ... 1. 1. 1.]\n", + " ...\n", + " [ 1. 0. 1. ... 55. 21. 7.]\n", + " [ 1. 0. 1. ... 21. 55. 7.]\n", + " [ 1. 3. 1. ... 7. 7. 55.]]\n" + ] + } + ], + "source": [ + "dataset, y = loadDataset(\"../../datasets/acyclic/dataset_bps.ds\")\n", + "G1 = dataset[12]\n", + "G2 = dataset[20]\n", + "Kmatrix = spkernel(dataset)\n", + "\n", + "print(Kmatrix)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e1df1ae63e8759be4bfadc35904dce4a32b33aa0 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:54 +0200 Subject: [PATCH 013/728] New translations test_sp_methods.py (French) --- lang/fr/notebooks/tests/test_sp_methods.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 lang/fr/notebooks/tests/test_sp_methods.py diff --git a/lang/fr/notebooks/tests/test_sp_methods.py b/lang/fr/notebooks/tests/test_sp_methods.py new file mode 100644 index 0000000000..b37a3fd039 --- /dev/null +++ b/lang/fr/notebooks/tests/test_sp_methods.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Test networkx shortest paths methods. +Created on Tue Oct 9 14:49:09 2018 + +@author: ljia +""" + +import networkx as nx + +g = nx.Graph() +g.add_edge(1, 2) +g.add_edge(3, 2) +g.add_edge(1, 4) +g.add_edge(3, 4) +p1 = nx.shortest_path(g, 1, 3) +p1 = [p1] +p2 = list(nx.all_shortest_paths(g, 1, 3)) +p1 += p2 +pr = [sp[::-1] for sp in p1] +nx.draw(g) \ No newline at end of file From 5c77a75e6bf27f66074a25662b3a6f1674af281e Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:56 +0200 Subject: [PATCH 014/728] New translations test_parallel_chunksize_2.py (French) --- .../tests/test_parallel_chunksize_2.py | 690 ++++++++++++++++++ 1 file changed, 690 insertions(+) create mode 100644 lang/fr/notebooks/tests/test_parallel_chunksize_2.py diff --git a/lang/fr/notebooks/tests/test_parallel_chunksize_2.py b/lang/fr/notebooks/tests/test_parallel_chunksize_2.py new file mode 100644 index 0000000000..6f790b7595 --- /dev/null +++ b/lang/fr/notebooks/tests/test_parallel_chunksize_2.py @@ -0,0 +1,690 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Test of parallel, find the best parallel chunksize and iteration seperation scheme. +Created on Wed Sep 26 12:09:34 2018 + +@author: ljia +""" + +import sys +import time +from itertools import combinations_with_replacement, product, combinations +from functools import partial +from multiprocessing import Pool +from tqdm import tqdm +import networkx as nx +import numpy as np +import functools +#import multiprocessing +from matplotlib import pyplot as plt +from sklearn.model_selection import ParameterGrid + +sys.path.insert(0, "../") +sys.path.insert(0, "../../") +from libs import * +from gklearn.utils.utils import getSPGraph, direct_product +from gklearn.utils.graphdataset import get_dataset_attributes +from gklearn.utils.graphfiles import loadDataset +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + + +def spkernel(*args, + node_label='atom', + edge_weight=None, + node_kernels=None, + n_jobs=None, + chunksize=1): + """Calculate shortest-path kernels between graphs. + """ + # pre-process + Gn = args[0] if len(args) == 1 else [args[0], args[1]] + Gn = [g.copy() for g in Gn] + weight = None + if edge_weight is None: + print('\n None edge weight specified. Set all weight to 1.\n') + else: + try: + some_weight = list( + nx.get_edge_attributes(Gn[0], edge_weight).values())[0] + if isinstance(some_weight, (float, int)): + weight = edge_weight + else: + print( + '\n Edge weight with name %s is not float or integer. Set all weight to 1.\n' + % edge_weight) + except: + print( + '\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n' + % edge_weight) + ds_attrs = get_dataset_attributes( + Gn, + attr_names=['node_labeled', 'node_attr_dim', 'is_directed'], + node_label=node_label) + + # remove graphs with no edges, as no sp can be found in their structures, + # so the kernel between such a graph and itself will be zero. + len_gn = len(Gn) + Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_edges(G) != 0] + idx = [G[0] for G in Gn] + Gn = [G[1] for G in Gn] + if len(Gn) != len_gn: + print('\n %d graphs are removed as they don\'t contain edges.\n' % + (len_gn - len(Gn))) + + start_time = time.time() + + pool = Pool(n_jobs) + # get shortest path graphs of Gn + getsp_partial = partial(wrapper_getSPGraph, weight) + itr = zip(Gn, range(0, len(Gn))) + for i, g in tqdm( + pool.imap_unordered(getsp_partial, itr, chunksize), + desc='getting sp graphs', file=sys.stdout): + Gn[i] = g + pool.close() + pool.join() + + Kmatrix = np.zeros((len(Gn), len(Gn))) + + # ---- use pool.imap_unordered to parallel and track progress. ---- + def init_worker(gn_toshare): + global G_gn + G_gn = gn_toshare + do_partial = partial(wrapper_sp_do, ds_attrs, node_label, node_kernels) + itr = combinations_with_replacement(range(0, len(Gn)), 2) + with Pool(processes=n_jobs, initializer=init_worker, initargs=(Gn,)) as pool: + for i, j, kernel in tqdm(pool.imap_unordered(do_partial, itr, chunksize), + desc='calculating kernels', file=sys.stdout): + Kmatrix[i][j] = kernel + Kmatrix[j][i] = kernel + +# # ---- direct running, normally use single CPU core. ---- +# itr = combinations_with_replacement(range(0, len(Gn)), 2) +# for i, j in tqdm(itr, desc='calculating kernels', file=sys.stdout): +# kernel = spkernel_do(Gn[i], Gn[j], ds_attrs, node_label, node_kernels) +# Kmatrix[i][j] = kernel +# Kmatrix[j][i] = kernel + + run_time = time.time() - start_time + print( + "\n --- shortest path kernel matrix of size %d built in %s seconds ---" + % (len(Gn), run_time)) + + return Kmatrix, run_time, idx + + +def spkernel_do(g1, g2, ds_attrs, node_label, node_kernels): + + kernel = 0 + + # compute shortest path matrices first, method borrowed from FCSP. + vk_dict = {} # shortest path matrices dict + if ds_attrs['node_labeled']: + # node symb and non-synb labeled + if ds_attrs['node_attr_dim'] > 0: + kn = node_kernels['mix'] + for n1, n2 in product( + g1.nodes(data=True), g2.nodes(data=True)): + vk_dict[(n1[0], n2[0])] = kn( + n1[1][node_label], n2[1][node_label], + n1[1]['attributes'], n2[1]['attributes']) + # node symb labeled + else: + kn = node_kernels['symb'] + for n1 in g1.nodes(data=True): + for n2 in g2.nodes(data=True): + vk_dict[(n1[0], n2[0])] = kn(n1[1][node_label], + n2[1][node_label]) + else: + # node non-synb labeled + if ds_attrs['node_attr_dim'] > 0: + kn = node_kernels['nsymb'] + for n1 in g1.nodes(data=True): + for n2 in g2.nodes(data=True): + vk_dict[(n1[0], n2[0])] = kn(n1[1]['attributes'], + n2[1]['attributes']) + # node unlabeled + else: + for e1, e2 in product( + g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + kernel += 1 + return kernel + + # compute graph kernels + if ds_attrs['is_directed']: + for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + nk11, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[(e1[1], + e2[1])] + kn1 = nk11 * nk22 + kernel += kn1 + else: + for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + # each edge walk is counted twice, starting from both its extreme nodes. + nk11, nk12, nk21, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[( + e1[0], e2[1])], vk_dict[(e1[1], + e2[0])], vk_dict[(e1[1], + e2[1])] + kn1 = nk11 * nk22 + kn2 = nk12 * nk21 + kernel += kn1 + kn2 + + return kernel + + +def wrapper_sp_do(ds_attrs, node_label, node_kernels, itr): + i = itr[0] + j = itr[1] + return i, j, spkernel_do(G_gn[i], G_gn[j], ds_attrs, node_label, node_kernels) + + +def wrapper_getSPGraph(weight, itr_item): + g = itr_item[0] + i = itr_item[1] + return i, getSPGraph(g, edge_weight=weight) + + + +# +# +#def commonwalkkernel(*args, +# node_label='atom', +# edge_label='bond_type', +# n=None, +# weight=1, +# compute_method=None, +# n_jobs=None, +# chunksize=1): +# """Calculate common walk graph kernels between graphs. +# """ +# compute_method = compute_method.lower() +# # arrange all graphs in a list +# Gn = args[0] if len(args) == 1 else [args[0], args[1]] +# Kmatrix = np.zeros((len(Gn), len(Gn))) +# ds_attrs = get_dataset_attributes( +# Gn, +# attr_names=['node_labeled', 'edge_labeled', 'is_directed'], +# node_label=node_label, +# edge_label=edge_label) +# if not ds_attrs['node_labeled']: +# for G in Gn: +# nx.set_node_attributes(G, '0', 'atom') +# if not ds_attrs['edge_labeled']: +# for G in Gn: +# nx.set_edge_attributes(G, '0', 'bond_type') +# if not ds_attrs['is_directed']: # convert +# Gn = [G.to_directed() for G in Gn] +# +# start_time = time.time() +# +# # ---- use pool.imap_unordered to parallel and track progress. ---- +# pool = Pool(n_jobs) +# itr = combinations_with_replacement(range(0, len(Gn)), 2) +## len_itr = int(len(Gn) * (len(Gn) + 1) / 2) +## if len_itr < 100: +## chunksize, extra = divmod(len_itr, n_jobs * 4) +## if extra: +## chunksize += 1 +## else: +## chunksize = 100 +# +# # direct product graph method - exponential +# if compute_method == 'exp': +# do_partial = partial(_commonwalkkernel_exp, Gn, node_label, edge_label, +# weight) +# # direct product graph method - geometric +# elif compute_method == 'geo': +# do_partial = partial(_commonwalkkernel_geo, Gn, node_label, edge_label, +# weight) +# +# for i, j, kernel in tqdm( +# pool.imap_unordered(do_partial, itr, chunksize), +# desc='calculating kernels', +# file=sys.stdout): +# Kmatrix[i][j] = kernel +# Kmatrix[j][i] = kernel +# pool.close() +# pool.join() +# +# run_time = time.time() - start_time +# print( +# "\n --- kernel matrix of common walk kernel of size %d built in %s seconds ---" +# % (len(Gn), run_time)) +# +# return Kmatrix, run_time +# +# +#def _commonwalkkernel_exp(Gn, node_label, edge_label, beta, ij): +# """Calculate walk graph kernels up to n between 2 graphs using exponential +# series. +# """ +# i = ij[0] +# j = ij[1] +# g1 = Gn[i] +# g2 = Gn[j] +# +# # get tensor product / direct product +# gp = direct_product(g1, g2, node_label, edge_label) +# A = nx.adjacency_matrix(gp).todense() +# +# ew, ev = np.linalg.eig(A) +# D = np.zeros((len(ew), len(ew))) +# for i in range(len(ew)): +# D[i][i] = np.exp(beta * ew[i]) +# exp_D = ev * D * ev.T +# +# return i, j, exp_D.sum() +# +# +#def _commonwalkkernel_geo(Gn, node_label, edge_label, gamma, ij): +# """Calculate common walk graph kernels up to n between 2 graphs using +# geometric series. +# """ +# i = ij[0] +# j = ij[1] +# g1 = Gn[i] +# g2 = Gn[j] +# +# # get tensor product / direct product +# gp = direct_product(g1, g2, node_label, edge_label) +# A = nx.adjacency_matrix(gp).todense() +# mat = np.identity(len(A)) - gamma * A +# try: +# return i, j, mat.I.sum() +# except np.linalg.LinAlgError: +# return i, j, np.nan + + +#def structuralspkernel(*args, +# node_label='atom', +# edge_weight=None, +# edge_label='bond_type', +# node_kernels=None, +# edge_kernels=None, +# n_jobs=None, +# chunksize=1): +# """Calculate mean average structural shortest path kernels between graphs. +# """ +# # pre-process +# Gn = args[0] if len(args) == 1 else [args[0], args[1]] +# +# weight = None +# if edge_weight is None: +# print('\n None edge weight specified. Set all weight to 1.\n') +# else: +# try: +# some_weight = list( +# nx.get_edge_attributes(Gn[0], edge_weight).values())[0] +# if isinstance(some_weight, (float, int)): +# weight = edge_weight +# else: +# print( +# '\n Edge weight with name %s is not float or integer. Set all weight to 1.\n' +# % edge_weight) +# except: +# print( +# '\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n' +# % edge_weight) +# ds_attrs = get_dataset_attributes( +# Gn, +# attr_names=['node_labeled', 'node_attr_dim', 'edge_labeled', +# 'edge_attr_dim', 'is_directed'], +# node_label=node_label, edge_label=edge_label) +# +# start_time = time.time() +# +# # get shortest paths of each graph in Gn +# splist = [[] for _ in range(len(Gn))] +# pool = Pool(n_jobs) +# # get shortest path graphs of Gn +# getsp_partial = partial(wrap_getSP, Gn, weight, ds_attrs['is_directed']) +## if len(Gn) < 100: +## # use default chunksize as pool.map when iterable is less than 100 +## chunksize, extra = divmod(len(Gn), n_jobs * 4) +## if extra: +## chunksize += 1 +## else: +## chunksize = 100 +# # chunksize = 300 # int(len(list(itr)) / n_jobs) +# for i, sp in tqdm( +# pool.imap_unordered(getsp_partial, range(0, len(Gn)), chunksize), +# desc='getting shortest paths', +# file=sys.stdout): +# splist[i] = sp +# +# Kmatrix = np.zeros((len(Gn), len(Gn))) +# +# # ---- use pool.imap_unordered to parallel and track progress. ---- +# do_partial = partial(structuralspkernel_do, Gn, splist, ds_attrs, +# node_label, edge_label, node_kernels, edge_kernels) +# itr = combinations_with_replacement(range(0, len(Gn)), 2) +## len_itr = int(len(Gn) * (len(Gn) + 1) / 2) +## if len_itr < 100: +## chunksize, extra = divmod(len_itr, n_jobs * 4) +## if extra: +## chunksize += 1 +## else: +## chunksize = 100 +# for i, j, kernel in tqdm( +# pool.imap_unordered(do_partial, itr, chunksize), +# desc='calculating kernels', +# file=sys.stdout): +# Kmatrix[i][j] = kernel +# Kmatrix[j][i] = kernel +# pool.close() +# pool.join() +# +# run_time = time.time() - start_time +# print( +# "\n --- shortest path kernel matrix of size %d built in %s seconds ---" +# % (len(Gn), run_time)) +# +# return Kmatrix, run_time +# +# +#def structuralspkernel_do(Gn, splist, ds_attrs, node_label, edge_label, +# node_kernels, edge_kernels, ij): +# +# iglobal = ij[0] +# jglobal = ij[1] +# g1 = Gn[iglobal] +# g2 = Gn[jglobal] +# spl1 = splist[iglobal] +# spl2 = splist[jglobal] +# kernel = 0 +# +# try: +# # First, compute shortest path matrices, method borrowed from FCSP. +# if ds_attrs['node_labeled']: +# # node symb and non-synb labeled +# if ds_attrs['node_attr_dim'] > 0: +# kn = node_kernels['mix'] +# vk_dict = {} # shortest path matrices dict +# for n1, n2 in product( +# g1.nodes(data=True), g2.nodes(data=True)): +# vk_dict[(n1[0], n2[0])] = kn( +# n1[1][node_label], n2[1][node_label], +# [n1[1]['attributes']], [n2[1]['attributes']]) +# # node symb labeled +# else: +# kn = node_kernels['symb'] +# vk_dict = {} # shortest path matrices dict +# for n1 in g1.nodes(data=True): +# for n2 in g2.nodes(data=True): +# vk_dict[(n1[0], n2[0])] = kn(n1[1][node_label], +# n2[1][node_label]) +# else: +# # node non-synb labeled +# if ds_attrs['node_attr_dim'] > 0: +# kn = node_kernels['nsymb'] +# vk_dict = {} # shortest path matrices dict +# for n1 in g1.nodes(data=True): +# for n2 in g2.nodes(data=True): +# vk_dict[(n1[0], n2[0])] = kn([n1[1]['attributes']], +# [n2[1]['attributes']]) +# # node unlabeled +# else: +# vk_dict = {} +# +# # Then, compute kernels between all pairs of edges, which idea is an +# # extension of FCSP. It suits sparse graphs, which is the most case we +# # went though. For dense graphs, it would be slow. +# if ds_attrs['edge_labeled']: +# # edge symb and non-synb labeled +# if ds_attrs['edge_attr_dim'] > 0: +# ke = edge_kernels['mix'] +# ek_dict = {} # dict of edge kernels +# for e1, e2 in product( +# g1.edges(data=True), g2.edges(data=True)): +# ek_dict[((e1[0], e1[1]), (e2[0], e2[1]))] = ke( +# e1[2][edge_label], e2[2][edge_label], +# [e1[2]['attributes']], [e2[2]['attributes']]) +# # edge symb labeled +# else: +# ke = edge_kernels['symb'] +# ek_dict = {} +# for e1 in g1.edges(data=True): +# for e2 in g2.edges(data=True): +# ek_dict[((e1[0], e1[1]), (e2[0], e2[1]))] = ke( +# e1[2][edge_label], e2[2][edge_label]) +# else: +# # edge non-synb labeled +# if ds_attrs['edge_attr_dim'] > 0: +# ke = edge_kernels['nsymb'] +# ek_dict = {} +# for e1 in g1.edges(data=True): +# for e2 in g2.edges(data=True): +# ek_dict[((e1[0], e1[1]), (e2[0], e2[1]))] = kn( +# [e1[2]['attributes']], [e2[2]['attributes']]) +# # edge unlabeled +# else: +# ek_dict = {} +# +# # compute graph kernels +# if vk_dict: +# if ek_dict: +# for p1, p2 in product(spl1, spl2): +# if len(p1) == len(p2): +# kpath = vk_dict[(p1[0], p2[0])] +# if kpath: +# for idx in range(1, len(p1)): +# kpath *= vk_dict[(p1[idx], p2[idx])] * \ +# ek_dict[((p1[idx-1], p1[idx]), +# (p2[idx-1], p2[idx]))] +# if not kpath: +# break +# kernel += kpath # add up kernels of all paths +# else: +# for p1, p2 in product(spl1, spl2): +# if len(p1) == len(p2): +# kpath = vk_dict[(p1[0], p2[0])] +# if kpath: +# for idx in range(1, len(p1)): +# kpath *= vk_dict[(p1[idx], p2[idx])] +# if not kpath: +# break +# kernel += kpath # add up kernels of all paths +# else: +# if ek_dict: +# for p1, p2 in product(spl1, spl2): +# if len(p1) == len(p2): +# if len(p1) == 0: +# kernel += 1 +# else: +# kpath = 1 +# for idx in range(0, len(p1) - 1): +# kpath *= ek_dict[((p1[idx], p1[idx+1]), +# (p2[idx], p2[idx+1]))] +# if not kpath: +# break +# kernel += kpath # add up kernels of all paths +# else: +# for p1, p2 in product(spl1, spl2): +# if len(p1) == len(p2): +# kernel += 1 +# +# kernel = kernel / (len(spl1) * len(spl2)) # calculate mean average +# except KeyError: # missing labels or attributes +# pass +# +# return iglobal, jglobal, kernel +# +# +#def get_shortest_paths(G, weight, directed): +# """Get all shortest paths of a graph. +# """ +# sp = [] +# for n1, n2 in combinations(G.nodes(), 2): +# try: +# sptemp = nx.shortest_path(G, n1, n2, weight=weight) +# sp.append(sptemp) +# # each edge walk is counted twice, starting from both its extreme nodes. +# if not directed: +# sp.append(sptemp[::-1]) +# except nx.NetworkXNoPath: # nodes not connected +# # sp.append([]) +# pass +# # add single nodes as length 0 paths. +# sp += [[n] for n in G.nodes()] +# return sp +# +# +#def wrap_getSP(Gn, weight, directed, i): +# return i, get_shortest_paths(Gn[i], weight, directed) + + +def compute_gram_matrices(datafile, + estimator, + param_grid_precomputed, + datafile_y=None, + extra_params=None, + ds_name='ds-unknown', + n_jobs=1, + chunksize=1): + """ + + Parameters + ---------- + datafile : string + Path of dataset file. + estimator : function + kernel function used to estimate. This function needs to return a gram matrix. + param_grid_precomputed : dictionary + Dictionary with names (string) of parameters used to calculate gram matrices as keys and lists of parameter settings to try as values. This enables searching over any sequence of parameter settings. Params with length 1 will be omitted. + datafile_y : string + Path of file storing y data. This parameter is optional depending on the given dataset file. + """ + tqdm.monitor_interval = 0 + + # Load the dataset + dataset, y_all = loadDataset( + datafile, filename_y=datafile_y, extra_params=extra_params) + + # Grid of parameters with a discrete number of values for each. + param_list_precomputed = list(ParameterGrid(param_grid_precomputed)) + + gram_matrix_time = [ + ] # a list to store time to calculate gram matrices + + # calculate all gram matrices + for idx, params_out in enumerate(param_list_precomputed): + y = y_all[:] + params_out['n_jobs'] = n_jobs + params_out['chunksize'] = chunksize + rtn_data = estimator(dataset[:], **params_out) + Kmatrix = rtn_data[0] + current_run_time = rtn_data[1] + # for some kernels, some graphs in datasets may not meet the + # kernels' requirements for graph structure. These graphs are trimmed. + if len(rtn_data) == 3: + idx_trim = rtn_data[2] # the index of trimmed graph list + y = [y[idx] for idx in idx_trim] # trim y accordingly + + Kmatrix_diag = Kmatrix.diagonal().copy() + # remove graphs whose kernels with themselves are zeros + nb_g_ignore = 0 + for idx, diag in enumerate(Kmatrix_diag): + if diag == 0: + Kmatrix = np.delete(Kmatrix, (idx - nb_g_ignore), axis=0) + Kmatrix = np.delete(Kmatrix, (idx - nb_g_ignore), axis=1) + nb_g_ignore += 1 + # normalization + Kmatrix_diag = Kmatrix.diagonal().copy() + for i in range(len(Kmatrix)): + for j in range(i, len(Kmatrix)): + Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j]) + Kmatrix[j][i] = Kmatrix[i][j] + + gram_matrix_time.append(current_run_time) + + average_gram_matrix_time = np.mean(gram_matrix_time) + + return average_gram_matrix_time + + +dslist = [ + {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds', 'task': 'regression', + 'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt'}, + # contains single node graph, node symb + {'name': 'Acyclic', 'dataset': '../../datasets/acyclic/dataset_bps.ds', + 'task': 'regression'}, # node symb + {'name': 'MAO', 'dataset': '../../datasets/MAO/dataset.ds'}, # node/edge symb + {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds'}, # unlabeled + {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb + {'name': 'Letter-med', 'dataset': '../../datasets/Letter-med/Letter-med_A.txt'}, + # node nsymb + {'name': 'ENZYMES', 'dataset': '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, + # node symb/nsymb + {'name': 'AIDS', 'dataset': '../../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb +# {'name': 'Mutagenicity', 'dataset': '../../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# {'name': 'D&D', 'dataset': '../../datasets/D&D/DD.mat', +# 'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, # node symb +] + +fig, ax = plt.subplots() +ax.set_xscale('log', nonposx='clip') +ax.set_yscale('log', nonposy='clip') +ax.set_xlabel('parallel chunksize') +ax.set_ylabel('runtime($s$)') +ax.set_title('28 cpus') +ax.grid(axis='both') + +estimator = spkernel +if estimator.__name__ == 'spkernel': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'node_kernels': [ + {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]} + +elif estimator.__name__ == 'commonwalkkernel': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'compute_method': ['geo'], + 'weight': [1]} +elif estimator.__name__ == 'structuralspkernel': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'node_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}], + 'edge_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]} + +#list(range(10, 100, 20)) + +#chunklist = list(range(10, 100, 20)) + list(range(100, 1000, 200)) + \ +# list(range(1000, 10000, 2000)) + list(range(10000, 100000, 20000)) +# chunklist = list(range(300, 1000, 200)) + list(range(1000, 10000, 2000)) + list(range(10000, 100000, 20000)) +chunklist = list(range(10, 100, 10)) + list(range(100, 1000, 100)) + \ + list(range(1000, 10000, 1000)) + list(range(10000, 100000, 10000)) +#chunklist = list(range(1000, 10000, 1000)) +gmtmat = np.zeros((len(dslist), len(chunklist))) +cpus = 28 + +for idx1, ds in enumerate(dslist): + print() + print(ds['name']) + + for idx2, cs in enumerate(chunklist): + print(ds['name'], idx2, cs) + gmtmat[idx1][idx2] = compute_gram_matrices( + ds['dataset'], + estimator, + param_grid_precomputed, + + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] + if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=cpus, + chunksize=cs) + + print() + print(gmtmat[idx1, :]) + np.save('../test_parallel/' + estimator.__name__ + '.' + ds['name'] + '_' + + str(idx1), gmtmat[idx1, :]) + + p = ax.plot(chunklist, gmtmat[idx1, :], '.-', label=ds['name'], zorder=3) + ax.legend(loc='upper right', ncol=3, labelspacing=0.1, handletextpad=0.4, + columnspacing=0.6) + plt.savefig('../test_parallel/' + estimator.__name__ + str(idx1) + '_' + + str(cpus) + '.eps', format='eps', dpi=300) +# plt.show() \ No newline at end of file From 70d7cf1187fb3dbc42f80cb9ee524bbfc0f07c92 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:57 +0200 Subject: [PATCH 015/728] New translations .appveyor.yml (French) --- lang/fr/.appveyor.yml | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 lang/fr/.appveyor.yml diff --git a/lang/fr/.appveyor.yml b/lang/fr/.appveyor.yml new file mode 100644 index 0000000000..d63af3a00f --- /dev/null +++ b/lang/fr/.appveyor.yml @@ -0,0 +1,29 @@ +--- +environment: + matrix: + - + PYTHON: "C:\\Python36" + - + PYTHON: "C:\\Python36-x64" + - + PYTHON: "C:\\Python37" + - + PYTHON: "C:\\Python37-x64" + - + PYTHON: "C:\\Python38" + - + PYTHON: "C:\\Python38-x64" +#skip_commits: +#files: +#- "*.yml" +#- "*.rst" +#- "LICENSE" +install: + - "%PYTHON%\\python.exe -m pip install -U pip" + - "%PYTHON%\\python.exe -m pip install wheel" + - "%PYTHON%\\python.exe -m pip install -r requirements.txt" + - "%PYTHON%\\python.exe -m pip install -U pytest" +build: false +test_script: + - "%PYTHON%\\python.exe setup.py bdist_wheel" + - "%PYTHON%\\python.exe -m pytest -v gklearn/tests/ --ignore=gklearn/tests/test_median_preimage_generator.py" From d80a61a2b4e93d3e6e07634d3cb4cf2808f501a2 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:14:59 +0200 Subject: [PATCH 016/728] New translations test_parallel_chunksize.py (French) --- .../tests/test_parallel_chunksize.py | 689 ++++++++++++++++++ 1 file changed, 689 insertions(+) create mode 100644 lang/fr/notebooks/tests/test_parallel_chunksize.py diff --git a/lang/fr/notebooks/tests/test_parallel_chunksize.py b/lang/fr/notebooks/tests/test_parallel_chunksize.py new file mode 100644 index 0000000000..828d5c35ae --- /dev/null +++ b/lang/fr/notebooks/tests/test_parallel_chunksize.py @@ -0,0 +1,689 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Test of parallel, find the best parallel chunksize and iteration seperation scheme. +Created on Wed Sep 26 12:09:34 2018 + +@author: ljia +""" + +import sys +import time +from itertools import combinations_with_replacement, product, combinations +from functools import partial +from multiprocessing import Pool +from tqdm import tqdm +import networkx as nx +import numpy as np +import functools +#import multiprocessing +from matplotlib import pyplot as plt +from sklearn.model_selection import ParameterGrid + +sys.path.insert(0, "../") +sys.path.insert(0, "../../") +from libs import * +from gklearn.utils.utils import getSPGraph, direct_product +from gklearn.utils.graphdataset import get_dataset_attributes +from gklearn.utils.graphfiles import loadDataset +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + + +def spkernel(*args, + node_label='atom', + edge_weight=None, + node_kernels=None, + n_jobs=None, + chunksize=1): + """Calculate shortest-path kernels between graphs. + """ + # pre-process + Gn = args[0] if len(args) == 1 else [args[0], args[1]] + weight = None + if edge_weight is None: + print('\n None edge weight specified. Set all weight to 1.\n') + else: + try: + some_weight = list( + nx.get_edge_attributes(Gn[0], edge_weight).values())[0] + if isinstance(some_weight, (float, int)): + weight = edge_weight + else: + print( + '\n Edge weight with name %s is not float or integer. Set all weight to 1.\n' + % edge_weight) + except: + print( + '\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n' + % edge_weight) + ds_attrs = get_dataset_attributes( + Gn, + attr_names=['node_labeled', 'node_attr_dim', 'is_directed'], + node_label=node_label) + + # remove graphs with no edges, as no sp can be found in their structures, + # so the kernel between such a graph and itself will be zero. + len_gn = len(Gn) + Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_edges(G) != 0] + idx = [G[0] for G in Gn] + Gn = [G[1] for G in Gn] + if len(Gn) != len_gn: + print('\n %d graphs are removed as they don\'t contain edges.\n' % + (len_gn - len(Gn))) + + start_time = time.time() + + pool = Pool(n_jobs) + # get shortest path graphs of Gn + getsp_partial = partial(wrapper_getSPGraph, weight) + itr = zip(Gn, range(0, len(Gn))) + for i, g in tqdm( + pool.imap_unordered(getsp_partial, itr, chunksize), + desc='getting sp graphs', file=sys.stdout): + Gn[i] = g + pool.close() + pool.join() + + Kmatrix = np.zeros((len(Gn), len(Gn))) + + # ---- use pool.imap_unordered to parallel and track progress. ---- + def init_worker(gn_toshare): + global G_gn + G_gn = gn_toshare + do_partial = partial(wrapper_sp_do, ds_attrs, node_label, node_kernels) + itr = combinations_with_replacement(range(0, len(Gn)), 2) + with Pool(processes=n_jobs, initializer=init_worker, initargs=(Gn,)) as pool: + for i, j, kernel in tqdm(pool.imap_unordered(do_partial, itr, chunksize), + desc='calculating kernels', file=sys.stdout): + Kmatrix[i][j] = kernel + Kmatrix[j][i] = kernel + +# # ---- direct running, normally use single CPU core. ---- +# itr = combinations_with_replacement(range(0, len(Gn)), 2) +# for i, j in tqdm(itr, desc='calculating kernels', file=sys.stdout): +# kernel = spkernel_do(Gn[i], Gn[j], ds_attrs, node_label, node_kernels) +# Kmatrix[i][j] = kernel +# Kmatrix[j][i] = kernel + + run_time = time.time() - start_time + print( + "\n --- shortest path kernel matrix of size %d built in %s seconds ---" + % (len(Gn), run_time)) + + return Kmatrix, run_time, idx + + +def spkernel_do(g1, g2, ds_attrs, node_label, node_kernels): + + kernel = 0 + + # compute shortest path matrices first, method borrowed from FCSP. + if ds_attrs['node_labeled']: + # node symb and non-synb labeled + if ds_attrs['node_attr_dim'] > 0: + kn = node_kernels['mix'] + vk_dict = {} # shortest path matrices dict + for n1, n2 in product( + g1.nodes(data=True), g2.nodes(data=True)): + vk_dict[(n1[0], n2[0])] = kn( + n1[1][node_label], n2[1][node_label], + n1[1]['attributes'], n2[1]['attributes']) + # node symb labeled + else: + kn = node_kernels['symb'] + vk_dict = {} # shortest path matrices dict + for n1 in g1.nodes(data=True): + for n2 in g2.nodes(data=True): + vk_dict[(n1[0], n2[0])] = kn(n1[1][node_label], + n2[1][node_label]) + else: + # node non-synb labeled + if ds_attrs['node_attr_dim'] > 0: + kn = node_kernels['nsymb'] + vk_dict = {} # shortest path matrices dict + for n1 in g1.nodes(data=True): + for n2 in g2.nodes(data=True): + vk_dict[(n1[0], n2[0])] = kn(n1[1]['attributes'], + n2[1]['attributes']) + # node unlabeled + else: + for e1, e2 in product( + g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + kernel += 1 + return kernel + + # compute graph kernels + if ds_attrs['is_directed']: + for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + nk11, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[(e1[1], + e2[1])] + kn1 = nk11 * nk22 + kernel += kn1 + else: + for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + # each edge walk is counted twice, starting from both its extreme nodes. + nk11, nk12, nk21, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[( + e1[0], e2[1])], vk_dict[(e1[1], + e2[0])], vk_dict[(e1[1], + e2[1])] + kn1 = nk11 * nk22 + kn2 = nk12 * nk21 + kernel += kn1 + kn2 + + return kernel + + +def wrapper_sp_do(ds_attrs, node_label, node_kernels, itr): + i = itr[0] + j = itr[1] + return i, j, spkernel_do(G_gn[i], G_gn[j], ds_attrs, node_label, node_kernels) + + +def wrapper_getSPGraph(weight, itr_item): + g = itr_item[0] + i = itr_item[1] + return i, getSPGraph(g, edge_weight=weight) + + + +# +# +#def commonwalkkernel(*args, +# node_label='atom', +# edge_label='bond_type', +# n=None, +# weight=1, +# compute_method=None, +# n_jobs=None, +# chunksize=1): +# """Calculate common walk graph kernels between graphs. +# """ +# compute_method = compute_method.lower() +# # arrange all graphs in a list +# Gn = args[0] if len(args) == 1 else [args[0], args[1]] +# Kmatrix = np.zeros((len(Gn), len(Gn))) +# ds_attrs = get_dataset_attributes( +# Gn, +# attr_names=['node_labeled', 'edge_labeled', 'is_directed'], +# node_label=node_label, +# edge_label=edge_label) +# if not ds_attrs['node_labeled']: +# for G in Gn: +# nx.set_node_attributes(G, '0', 'atom') +# if not ds_attrs['edge_labeled']: +# for G in Gn: +# nx.set_edge_attributes(G, '0', 'bond_type') +# if not ds_attrs['is_directed']: # convert +# Gn = [G.to_directed() for G in Gn] +# +# start_time = time.time() +# +# # ---- use pool.imap_unordered to parallel and track progress. ---- +# pool = Pool(n_jobs) +# itr = combinations_with_replacement(range(0, len(Gn)), 2) +## len_itr = int(len(Gn) * (len(Gn) + 1) / 2) +## if len_itr < 100: +## chunksize, extra = divmod(len_itr, n_jobs * 4) +## if extra: +## chunksize += 1 +## else: +## chunksize = 100 +# +# # direct product graph method - exponential +# if compute_method == 'exp': +# do_partial = partial(_commonwalkkernel_exp, Gn, node_label, edge_label, +# weight) +# # direct product graph method - geometric +# elif compute_method == 'geo': +# do_partial = partial(_commonwalkkernel_geo, Gn, node_label, edge_label, +# weight) +# +# for i, j, kernel in tqdm( +# pool.imap_unordered(do_partial, itr, chunksize), +# desc='calculating kernels', +# file=sys.stdout): +# Kmatrix[i][j] = kernel +# Kmatrix[j][i] = kernel +# pool.close() +# pool.join() +# +# run_time = time.time() - start_time +# print( +# "\n --- kernel matrix of common walk kernel of size %d built in %s seconds ---" +# % (len(Gn), run_time)) +# +# return Kmatrix, run_time +# +# +#def _commonwalkkernel_exp(Gn, node_label, edge_label, beta, ij): +# """Calculate walk graph kernels up to n between 2 graphs using exponential +# series. +# """ +# i = ij[0] +# j = ij[1] +# g1 = Gn[i] +# g2 = Gn[j] +# +# # get tensor product / direct product +# gp = direct_product(g1, g2, node_label, edge_label) +# A = nx.adjacency_matrix(gp).todense() +# +# ew, ev = np.linalg.eig(A) +# D = np.zeros((len(ew), len(ew))) +# for i in range(len(ew)): +# D[i][i] = np.exp(beta * ew[i]) +# exp_D = ev * D * ev.T +# +# return i, j, exp_D.sum() +# +# +#def _commonwalkkernel_geo(Gn, node_label, edge_label, gamma, ij): +# """Calculate common walk graph kernels up to n between 2 graphs using +# geometric series. +# """ +# i = ij[0] +# j = ij[1] +# g1 = Gn[i] +# g2 = Gn[j] +# +# # get tensor product / direct product +# gp = direct_product(g1, g2, node_label, edge_label) +# A = nx.adjacency_matrix(gp).todense() +# mat = np.identity(len(A)) - gamma * A +# try: +# return i, j, mat.I.sum() +# except np.linalg.LinAlgError: +# return i, j, np.nan + + +#def structuralspkernel(*args, +# node_label='atom', +# edge_weight=None, +# edge_label='bond_type', +# node_kernels=None, +# edge_kernels=None, +# n_jobs=None, +# chunksize=1): +# """Calculate mean average structural shortest path kernels between graphs. +# """ +# # pre-process +# Gn = args[0] if len(args) == 1 else [args[0], args[1]] +# +# weight = None +# if edge_weight is None: +# print('\n None edge weight specified. Set all weight to 1.\n') +# else: +# try: +# some_weight = list( +# nx.get_edge_attributes(Gn[0], edge_weight).values())[0] +# if isinstance(some_weight, (float, int)): +# weight = edge_weight +# else: +# print( +# '\n Edge weight with name %s is not float or integer. Set all weight to 1.\n' +# % edge_weight) +# except: +# print( +# '\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n' +# % edge_weight) +# ds_attrs = get_dataset_attributes( +# Gn, +# attr_names=['node_labeled', 'node_attr_dim', 'edge_labeled', +# 'edge_attr_dim', 'is_directed'], +# node_label=node_label, edge_label=edge_label) +# +# start_time = time.time() +# +# # get shortest paths of each graph in Gn +# splist = [[] for _ in range(len(Gn))] +# pool = Pool(n_jobs) +# # get shortest path graphs of Gn +# getsp_partial = partial(wrap_getSP, Gn, weight, ds_attrs['is_directed']) +## if len(Gn) < 100: +## # use default chunksize as pool.map when iterable is less than 100 +## chunksize, extra = divmod(len(Gn), n_jobs * 4) +## if extra: +## chunksize += 1 +## else: +## chunksize = 100 +# # chunksize = 300 # int(len(list(itr)) / n_jobs) +# for i, sp in tqdm( +# pool.imap_unordered(getsp_partial, range(0, len(Gn)), chunksize), +# desc='getting shortest paths', +# file=sys.stdout): +# splist[i] = sp +# +# Kmatrix = np.zeros((len(Gn), len(Gn))) +# +# # ---- use pool.imap_unordered to parallel and track progress. ---- +# do_partial = partial(structuralspkernel_do, Gn, splist, ds_attrs, +# node_label, edge_label, node_kernels, edge_kernels) +# itr = combinations_with_replacement(range(0, len(Gn)), 2) +## len_itr = int(len(Gn) * (len(Gn) + 1) / 2) +## if len_itr < 100: +## chunksize, extra = divmod(len_itr, n_jobs * 4) +## if extra: +## chunksize += 1 +## else: +## chunksize = 100 +# for i, j, kernel in tqdm( +# pool.imap_unordered(do_partial, itr, chunksize), +# desc='calculating kernels', +# file=sys.stdout): +# Kmatrix[i][j] = kernel +# Kmatrix[j][i] = kernel +# pool.close() +# pool.join() +# +# run_time = time.time() - start_time +# print( +# "\n --- shortest path kernel matrix of size %d built in %s seconds ---" +# % (len(Gn), run_time)) +# +# return Kmatrix, run_time +# +# +#def structuralspkernel_do(Gn, splist, ds_attrs, node_label, edge_label, +# node_kernels, edge_kernels, ij): +# +# iglobal = ij[0] +# jglobal = ij[1] +# g1 = Gn[iglobal] +# g2 = Gn[jglobal] +# spl1 = splist[iglobal] +# spl2 = splist[jglobal] +# kernel = 0 +# +# try: +# # First, compute shortest path matrices, method borrowed from FCSP. +# if ds_attrs['node_labeled']: +# # node symb and non-synb labeled +# if ds_attrs['node_attr_dim'] > 0: +# kn = node_kernels['mix'] +# vk_dict = {} # shortest path matrices dict +# for n1, n2 in product( +# g1.nodes(data=True), g2.nodes(data=True)): +# vk_dict[(n1[0], n2[0])] = kn( +# n1[1][node_label], n2[1][node_label], +# [n1[1]['attributes']], [n2[1]['attributes']]) +# # node symb labeled +# else: +# kn = node_kernels['symb'] +# vk_dict = {} # shortest path matrices dict +# for n1 in g1.nodes(data=True): +# for n2 in g2.nodes(data=True): +# vk_dict[(n1[0], n2[0])] = kn(n1[1][node_label], +# n2[1][node_label]) +# else: +# # node non-synb labeled +# if ds_attrs['node_attr_dim'] > 0: +# kn = node_kernels['nsymb'] +# vk_dict = {} # shortest path matrices dict +# for n1 in g1.nodes(data=True): +# for n2 in g2.nodes(data=True): +# vk_dict[(n1[0], n2[0])] = kn([n1[1]['attributes']], +# [n2[1]['attributes']]) +# # node unlabeled +# else: +# vk_dict = {} +# +# # Then, compute kernels between all pairs of edges, which idea is an +# # extension of FCSP. It suits sparse graphs, which is the most case we +# # went though. For dense graphs, it would be slow. +# if ds_attrs['edge_labeled']: +# # edge symb and non-synb labeled +# if ds_attrs['edge_attr_dim'] > 0: +# ke = edge_kernels['mix'] +# ek_dict = {} # dict of edge kernels +# for e1, e2 in product( +# g1.edges(data=True), g2.edges(data=True)): +# ek_dict[((e1[0], e1[1]), (e2[0], e2[1]))] = ke( +# e1[2][edge_label], e2[2][edge_label], +# [e1[2]['attributes']], [e2[2]['attributes']]) +# # edge symb labeled +# else: +# ke = edge_kernels['symb'] +# ek_dict = {} +# for e1 in g1.edges(data=True): +# for e2 in g2.edges(data=True): +# ek_dict[((e1[0], e1[1]), (e2[0], e2[1]))] = ke( +# e1[2][edge_label], e2[2][edge_label]) +# else: +# # edge non-synb labeled +# if ds_attrs['edge_attr_dim'] > 0: +# ke = edge_kernels['nsymb'] +# ek_dict = {} +# for e1 in g1.edges(data=True): +# for e2 in g2.edges(data=True): +# ek_dict[((e1[0], e1[1]), (e2[0], e2[1]))] = kn( +# [e1[2]['attributes']], [e2[2]['attributes']]) +# # edge unlabeled +# else: +# ek_dict = {} +# +# # compute graph kernels +# if vk_dict: +# if ek_dict: +# for p1, p2 in product(spl1, spl2): +# if len(p1) == len(p2): +# kpath = vk_dict[(p1[0], p2[0])] +# if kpath: +# for idx in range(1, len(p1)): +# kpath *= vk_dict[(p1[idx], p2[idx])] * \ +# ek_dict[((p1[idx-1], p1[idx]), +# (p2[idx-1], p2[idx]))] +# if not kpath: +# break +# kernel += kpath # add up kernels of all paths +# else: +# for p1, p2 in product(spl1, spl2): +# if len(p1) == len(p2): +# kpath = vk_dict[(p1[0], p2[0])] +# if kpath: +# for idx in range(1, len(p1)): +# kpath *= vk_dict[(p1[idx], p2[idx])] +# if not kpath: +# break +# kernel += kpath # add up kernels of all paths +# else: +# if ek_dict: +# for p1, p2 in product(spl1, spl2): +# if len(p1) == len(p2): +# if len(p1) == 0: +# kernel += 1 +# else: +# kpath = 1 +# for idx in range(0, len(p1) - 1): +# kpath *= ek_dict[((p1[idx], p1[idx+1]), +# (p2[idx], p2[idx+1]))] +# if not kpath: +# break +# kernel += kpath # add up kernels of all paths +# else: +# for p1, p2 in product(spl1, spl2): +# if len(p1) == len(p2): +# kernel += 1 +# +# kernel = kernel / (len(spl1) * len(spl2)) # calculate mean average +# except KeyError: # missing labels or attributes +# pass +# +# return iglobal, jglobal, kernel +# +# +#def get_shortest_paths(G, weight, directed): +# """Get all shortest paths of a graph. +# """ +# sp = [] +# for n1, n2 in combinations(G.nodes(), 2): +# try: +# sptemp = nx.shortest_path(G, n1, n2, weight=weight) +# sp.append(sptemp) +# # each edge walk is counted twice, starting from both its extreme nodes. +# if not directed: +# sp.append(sptemp[::-1]) +# except nx.NetworkXNoPath: # nodes not connected +# # sp.append([]) +# pass +# # add single nodes as length 0 paths. +# sp += [[n] for n in G.nodes()] +# return sp +# +# +#def wrap_getSP(Gn, weight, directed, i): +# return i, get_shortest_paths(Gn[i], weight, directed) + + +def compute_gram_matrices(datafile, + estimator, + param_grid_precomputed, + datafile_y=None, + extra_params=None, + ds_name='ds-unknown', + n_jobs=1, + chunksize=1): + """ + + Parameters + ---------- + datafile : string + Path of dataset file. + estimator : function + kernel function used to estimate. This function needs to return a gram matrix. + param_grid_precomputed : dictionary + Dictionary with names (string) of parameters used to calculate gram matrices as keys and lists of parameter settings to try as values. This enables searching over any sequence of parameter settings. Params with length 1 will be omitted. + datafile_y : string + Path of file storing y data. This parameter is optional depending on the given dataset file. + """ + tqdm.monitor_interval = 0 + + # Load the dataset + dataset, y_all = loadDataset( + datafile, filename_y=datafile_y, extra_params=extra_params) + + # Grid of parameters with a discrete number of values for each. + param_list_precomputed = list(ParameterGrid(param_grid_precomputed)) + + gram_matrix_time = [ + ] # a list to store time to calculate gram matrices + + # calculate all gram matrices + for idx, params_out in enumerate(param_list_precomputed): + y = y_all[:] + params_out['n_jobs'] = n_jobs + params_out['chunksize'] = chunksize + rtn_data = estimator(dataset[:], **params_out) + Kmatrix = rtn_data[0] + current_run_time = rtn_data[1] + # for some kernels, some graphs in datasets may not meet the + # kernels' requirements for graph structure. These graphs are trimmed. + if len(rtn_data) == 3: + idx_trim = rtn_data[2] # the index of trimmed graph list + y = [y[idx] for idx in idx_trim] # trim y accordingly + + Kmatrix_diag = Kmatrix.diagonal().copy() + # remove graphs whose kernels with themselves are zeros + nb_g_ignore = 0 + for idx, diag in enumerate(Kmatrix_diag): + if diag == 0: + Kmatrix = np.delete(Kmatrix, (idx - nb_g_ignore), axis=0) + Kmatrix = np.delete(Kmatrix, (idx - nb_g_ignore), axis=1) + nb_g_ignore += 1 + # normalization + for i in range(len(Kmatrix)): + for j in range(i, len(Kmatrix)): + Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j]) + Kmatrix[j][i] = Kmatrix[i][j] + + gram_matrix_time.append(current_run_time) + + average_gram_matrix_time = np.mean(gram_matrix_time) + + return average_gram_matrix_time + + +dslist = [ + {'name': 'Acyclic', 'dataset': '../../datasets/acyclic/dataset_bps.ds', + 'task': 'regression'}, # node symb + {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds', 'task': 'regression', + 'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt', }, # contains single node graph, node symb + {'name': 'MAO', 'dataset': '../../datasets/MAO/dataset.ds', }, # node/edge symb + {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds', }, # unlabeled + {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG.mat', + 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, # node/edge symb + {'name': 'Letter-med', 'dataset': '../../datasets/Letter-med/Letter-med_A.txt'}, + # node symb/nsymb + {'name': 'ENZYMES', 'dataset': '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, + # node/edge symb +# {'name': 'Mutagenicity', 'dataset': '../../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# {'name': 'D&D', 'dataset': '../../datasets/D&D/DD.mat', +# 'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, # node symb +] + +fig, ax = plt.subplots() +ax.set_xscale('log', nonposx='clip') +ax.set_yscale('log', nonposy='clip') +ax.set_xlabel('parallel chunksize') +ax.set_ylabel('runtime($s$)') +ax.set_title('28 cpus') +ax.grid(axis='both') + +estimator = spkernel +if estimator.__name__ == 'spkernel': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'node_kernels': [ + {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]} + +elif estimator.__name__ == 'commonwalkkernel': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'compute_method': ['geo'], + 'weight': [1]} +elif estimator.__name__ == 'structuralspkernel': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'node_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}], + 'edge_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]} + +#list(range(10, 100, 20)) + +#chunklist = list(range(10, 100, 20)) + list(range(100, 1000, 200)) + \ +# list(range(1000, 10000, 2000)) + list(range(10000, 100000, 20000)) +# chunklist = list(range(300, 1000, 200)) + list(range(1000, 10000, 2000)) + list(range(10000, 100000, 20000)) +chunklist = list(range(10, 100, 10)) + list(range(100, 1000, 100)) + \ + list(range(1000, 10000, 1000)) + list(range(10000, 100000, 10000)) +#chunklist = list(range(1000, 10000, 1000)) +gmtmat = np.zeros((len(dslist), len(chunklist))) +cpus = 28 + +for idx1, ds in enumerate(dslist): + print() + print(ds['name']) + + for idx2, cs in enumerate(chunklist): + print(ds['name'], idx2, cs) + gmtmat[idx1][idx2] = compute_gram_matrices( + ds['dataset'], + estimator, + param_grid_precomputed, + + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] + if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=cpus, + chunksize=cs) + + print() + print(gmtmat[idx1, :]) + np.save('../test_parallel/' + estimator.__name__ + '.' + ds['name'] + '_' + + str(idx1), gmtmat[idx1, :]) + + p = ax.plot(chunklist, gmtmat[idx1, :], '.-', label=ds['name'], zorder=3) + ax.legend(loc='upper right', ncol=3, labelspacing=0.1, handletextpad=0.4, + columnspacing=0.6) + plt.savefig('../test_parallel/' + estimator.__name__ + str(idx1) + '_' + + str(cpus) + '.eps', format='eps', dpi=300) +# plt.show() \ No newline at end of file From c037ec82b65955d0af4736263bb50af25b1211f0 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:01 +0200 Subject: [PATCH 017/728] New translations test_networkx.ipynb (French) --- lang/fr/notebooks/tests/test_networkx.ipynb | 363 ++++++++++++++++++++ 1 file changed, 363 insertions(+) create mode 100644 lang/fr/notebooks/tests/test_networkx.ipynb diff --git a/lang/fr/notebooks/tests/test_networkx.ipynb b/lang/fr/notebooks/tests/test_networkx.ipynb new file mode 100644 index 0000000000..d46ba87487 --- /dev/null +++ b/lang/fr/notebooks/tests/test_networkx.ipynb @@ -0,0 +1,363 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(1, {}), (2, {}), (3, {})]\n", + "[(1, 2, {}), (2, 1, {}), (3, 1, {})]\n", + "{2: {}}\n" + ] + } + ], + "source": [ + "import networkx as nx\n", + "dg = nx.DiGraph()\n", + "dg.add_nodes_from([1, 2, 3])\n", + "dg.add_edges_from([(1, 2), (2, 1), (3, 1)])\n", + "print(dg.nodes(data=True))\n", + "print(dg.edges(data=True))\n", + "print(dg[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def loadCT(filename):\n", + " content = open(filename).read().splitlines()\n", + " G = nx.Graph(name=str(content[0]))\n", + " tmp = content[1].split(\" \")\n", + " if tmp[0] == '':\n", + " nb_nodes = int(tmp[1])\n", + " nb_edges = int(tmp[2])\n", + " else:\n", + " nb_nodes = int(tmp[0])\n", + " nb_edges = int(tmp[1])\n", + "\n", + " for i in range(0, nb_nodes):\n", + " tmp = content[i + 2].split(\" \")\n", + " tmp = [x for x in tmp if x != '']\n", + " G.add_node(i, label=tmp[3])\n", + "\n", + " for i in range(0, nb_edges):\n", + " tmp = content[i+G.number_of_nodes()+2].split(\" \")\n", + " tmp = [x for x in tmp if x != '']\n", + " G.add_edge(int(tmp[0]) - 1, int(tmp[1]) - 1, label=int(tmp[3]))\n", + " return G\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def loadDataset(filename):\n", + " from os.path import dirname, splitext\n", + "\n", + " dirname_dataset = dirname(filename)\n", + " extension = splitext(filename)[1][1:]\n", + " data = []\n", + " y = []\n", + " if(extension == \"ds\"):\n", + " content = open(filename).read().splitlines()\n", + " for i in range(0, len(content)):\n", + " tmp = content[i].split(' ')\n", + " data.append(loadCT(dirname_dataset + '/' + tmp[0].replace('#', '', 1)))\n", + " y.append(float(tmp[1]))\n", + " elif(extension == \"cxl\"):\n", + " import xml.etree.ElementTree as ET\n", + "\n", + " tree = ET.parse(filename)\n", + " root = tree.getroot()\n", + " data = []\n", + " y = []\n", + " for graph in root.iter('print'):\n", + " mol_filename = graph.attrib['file']\n", + " mol_class = graph.attrib['class']\n", + " data.append(loadGXL(dirname_dataset + '/' + mol_filename))\n", + " y.append(mol_class)\n", + "\n", + " return data, y" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "dataset, y = loadDataset(\"../../datasets/acyclic/dataset_bps.ds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import numpy as np\n", + "\n", + "\n", + "def getSPLengths(G1):\n", + " sp = nx.shortest_path(G1)\n", + " distances = np.zeros((G1.number_of_nodes(), G1.number_of_nodes()))\n", + " for i in np.keys():\n", + " for j in np[i].keys():\n", + " distances[i, j] = len(sp[i][j])-1\n", + "\n", + "def getSPGraph(G):\n", + " \"\"\"Transform graph G to its corresponding shortest-paths graph.\n", + " \n", + " Parameters\n", + " ----------\n", + " G : NetworkX graph\n", + " The graph to be tramsformed.\n", + " \n", + " Return\n", + " ------\n", + " S : NetworkX graph\n", + " The shortest-paths graph corresponding to G.\n", + " \n", + " Notes\n", + " ------\n", + " For an input graph G, its corresponding shortest-paths graph S contains the same set of nodes as G, while there exists an edge between all nodes in S which are connected by a walk in G. Every edge in S between two nodes is labeled by the shortest distance between these two nodes.\n", + " \n", + " References\n", + " ----------\n", + " [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.\n", + " \"\"\"\n", + " return floydTransformation(G)\n", + " \n", + "def floydTransformation(G):\n", + " \"\"\"Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.\n", + " \n", + " Parameters\n", + " ----------\n", + " G : NetworkX graph\n", + " The graph to be tramsformed.\n", + " \n", + " Return\n", + " ------\n", + " S : NetworkX graph\n", + " The shortest-paths graph corresponding to G.\n", + " \n", + " References\n", + " ----------\n", + " [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.\n", + " \"\"\"\n", + " spMatrix = nx.floyd_warshall_numpy(G) # @todo weigth label not considered\n", + " S = nx.Graph()\n", + " S.add_nodes_from(G)\n", + " for i in range(0, G.number_of_nodes()):\n", + " for j in range(0, G.number_of_nodes()):\n", + " print(spMatrix[i, j])\n", + " S.add_edge(i, j, cost = spMatrix[i, j])\n", + " return S" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. 2. 3. 1. 2.]]\n", + "{0: {0: [0], 3: [0, 3], 1: [0, 3, 1], 4: [0, 3, 4], 2: [0, 3, 4, 2]}, 1: {1: [1], 3: [1, 3], 0: [1, 3, 0], 4: [1, 3, 4], 2: [1, 3, 4, 2]}, 2: {2: [2], 4: [2, 4], 3: [2, 4, 3], 0: [2, 4, 3, 0], 1: [2, 4, 3, 1]}, 3: {3: [3], 0: [3, 0], 1: [3, 1], 4: [3, 4], 2: [3, 4, 2]}, 4: {4: [4], 2: [4, 2], 3: [4, 3], 0: [4, 3, 0], 1: [4, 3, 1]}}\n", + "[[0. 2. 3. 1. 2.]\n", + " [2. 0. 3. 1. 2.]\n", + " [3. 3. 0. 2. 1.]\n", + " [1. 1. 2. 0. 1.]\n", + " [2. 2. 1. 1. 0.]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dataset, y = loadDataset(\"../../datasets/acyclic/dataset_bps.ds\")\n", + "G1 = dataset[12]\n", + "\n", + "nx.draw_networkx(G1)\n", + "# print(list(dataset[12][4]))\n", + "\n", + "l = nx.shortest_path(G1)\n", + "\n", + "l2 = nx.floyd_warshall_numpy(G1)\n", + "print(np.array(l2[0]))\n", + "print(l)\n", + "print(l2)\n", + "from matplotlib import pyplot as plt\n", + "plt.show()\n", + "\n", + "S = getSPGraph(G1)\n", + "nx.draw_networkx(S)\n", + "pos = nx.spring_layout(S)\n", + "edge_labels = nx.get_edge_attributes(S,'cost')\n", + "print(edge_labels)\n", + "nx.draw_networkx_edge_labels(S, pos, edge_labels = edge_labels)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "G = dataset[155]\n", + "G.clear()\n", + "G.add_edges_from([(1, 2), (1, 3)])\n", + "G.add_node(1)\n", + "G.add_edge(1, 2)\n", + "G.add_node(\"spam\") # adds node \"spam\"\n", + "G.add_nodes_from(\"spam\") # adds 4 nodes: 's', 'p', 'a', 'm'\n", + "G.add_edge(3, 'm')\n", + "nx.draw_networkx(G)\n", + "plt.show()\n", + "\n", + "K_5 = nx.random_lobster(30, 0.9, 0.9)\n", + "nx.draw_networkx(K_5)\n", + "plt.show()\n", + "G.number_of_nodes()\n", + "G.number_of_edges()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 84abc27a9ce0e3817d8a3d203934e94cc60849a4 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:03 +0200 Subject: [PATCH 018/728] New translations test_modelselection.ipynb (French) --- .../notebooks/tests/test_modelselection.ipynb | 1574 +++++++++++++++++ 1 file changed, 1574 insertions(+) create mode 100644 lang/fr/notebooks/tests/test_modelselection.ipynb diff --git a/lang/fr/notebooks/tests/test_modelselection.ipynb b/lang/fr/notebooks/tests/test_modelselection.ipynb new file mode 100644 index 0000000000..a3e656a1a1 --- /dev/null +++ b/lang/fr/notebooks/tests/test_modelselection.ipynb @@ -0,0 +1,1574 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "getting sp graphs: 183it [00:00, 2868.18it/s]\n", + "calculating kernels: 16836it [00:03, 4962.86it/s]\n", + "\n", + " --- shortest path kernel matrix of size 183 built in 3.5163042545318604 seconds ---\n", + "\n", + "the gram matrix with parameters {'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 1} is: \n", + "\n", + "\n", + "\n", + "1 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 2it [00:00, 4.47it/s]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 1}]\n", + "best_params_in: [{'alpha': 0.0001}]\n", + "\n", + "best_val_perf: 9.922073568477266\n", + "best_val_std: 0.3829108688812842\n", + "final_performance: [8.039190309451554]\n", + "final_confidence: [2.8576078550320037]\n", + "train_performance: [6.285008316076738]\n", + "train_std: [0.23613211181729038]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 3.52±nans\n", + "time to calculate best gram matrix: 3.52±nans\n", + "total training time with all hyper-param choices: 4.34s\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " keepdims=keepdims)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import functools\n", + "sys.path.insert(0, \"../../\")\n", + "from gklearn.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n", + "from gklearn.kernels.spKernel import spkernel\n", + "from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct\n", + "\n", + "datafile = '../../datasets/acyclic/dataset_bps.ds'\n", + "estimator = spkernel\n", + "# hyper-parameters\n", + "mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)\n", + "param_grid_precomputed = {'node_kernels': [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]}\n", + "param_grid = {\"alpha\": np.logspace(-5, 5, num = 21, base = 10)}\n", + "\n", + "model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, \n", + " 'regression', NUM_TRIALS=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'o1': 1, 'o2': 2}, {'o1': 3, 'o2': 4}]\n", + "[{'i2': 6, 'i1': 5}, {'i2': 8, 'i1': 7}, {'i2': 10, 'i1': 9}]\n" + ] + }, + { + "data": { + "text/plain": [ + "[({'o1': 1, 'o2': 2}, {'o1': 3, 'o2': 4}),\n", + " ({'o1': 3, 'o2': 4}, {'o1': 1, 'o2': 2})]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = [ {'o1':1,'o2':2},{'o1':3,'o2':4}]\n", + "print(x)\n", + "y = [ {'i1':5,'i2':6},{'i1':7,'i2':8},{'i1':9,'i2':10}]\n", + "print(y)\n", + "from itertools import permutations\n", + "[item for item in permutations(x)]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically created module for IPython interactive environment\n", + "\n", + "0\n", + "{'mean_fit_time': array([0.00053245, 0.00036639, 0.00027829, 0.00026679, 0.00036031,\n", + " 0.00035846]), 'std_fit_time': array([2.35126922e-04, 6.76477532e-05, 1.11031151e-06, 1.48806431e-05,\n", + " 7.18533189e-05, 6.76927198e-05]), 'mean_score_time': array([0.00021952, 0.00019908, 0.00016928, 0.00015849, 0.00017059,\n", + " 0.00019705]), 'std_score_time': array([3.96984386e-05, 4.61193611e-05, 7.34855414e-07, 2.82289511e-06,\n", + " 1.66522193e-05, 2.63754426e-05]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, {'C': 100, 'gamma': 0.01}, {'C': 100, 'gamma': 0.1}], 'split0_test_score': array([0.85714286, 0.92857143, 0.92857143, 0.96428571, 0.96428571,\n", + " 0.92857143]), 'split1_test_score': array([0.89285714, 0.96428571, 0.96428571, 1. , 1. ,\n", + " 0.96428571]), 'split2_test_score': array([0.96428571, 1. , 1. , 1. , 1. ,\n", + " 1. ]), 'split3_test_score': array([0.82142857, 0.92857143, 0.92857143, 0.92857143, 0.92857143,\n", + " 0.92857143]), 'mean_test_score': array([0.88392857, 0.95535714, 0.95535714, 0.97321429, 0.97321429,\n", + " 0.95535714]), 'std_test_score': array([0.05282214, 0.02961272, 0.02961272, 0.02961272, 0.02961272,\n", + " 0.02961272]), 'rank_test_score': array([6, 3, 3, 1, 1, 3], dtype=int32), 'split0_train_score': array([0.91666667, 0.97619048, 0.97619048, 0.97619048, 0.97619048,\n", + " 0.98809524]), 'split1_train_score': array([0.91666667, 0.97619048, 0.96428571, 0.98809524, 0.98809524,\n", + " 0.98809524]), 'split2_train_score': array([0.9047619 , 0.97619048, 0.97619048, 0.96428571, 0.96428571,\n", + " 0.96428571]), 'split3_train_score': array([0.96428571, 1. , 1. , 1. , 1. ,\n", + " 0.98809524]), 'mean_train_score': array([0.92559524, 0.98214286, 0.97916667, 0.98214286, 0.98214286,\n", + " 0.98214286]), 'std_train_score': array([0.02286055, 0.01030983, 0.01297291, 0.01330993, 0.01330993,\n", + " 0.01030983])}\n", + "\n", + "1\n", + "{'mean_fit_time': array([0.00036663, 0.0002979 , 0.00025547, 0.0002926 , 0.00026369,\n", + " 0.0002656 ]), 'std_fit_time': array([2.12649960e-05, 2.13949957e-05, 4.63078609e-06, 4.18155790e-05,\n", + " 1.60335387e-05, 7.18625396e-06]), 'mean_score_time': array([0.00018156, 0.00017273, 0.00015974, 0.00017357, 0.00015533,\n", + " 0.00015408]), 'std_score_time': array([2.59263076e-06, 1.78372315e-05, 8.02872574e-06, 2.67129904e-05,\n", + " 1.22153172e-06, 7.79431869e-07]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, {'C': 100, 'gamma': 0.01}, {'C': 100, 'gamma': 0.1}], 'split0_test_score': array([0.89285714, 0.92857143, 0.92857143, 0.96428571, 0.96428571,\n", + " 0.92857143]), 'split1_test_score': array([0.82142857, 0.96428571, 0.96428571, 1. , 1. ,\n", + " 1. ]), 'split2_test_score': array([0.92857143, 1. , 1. , 1. , 1. ,\n", + " 0.96428571]), 'split3_test_score': array([0.89285714, 0.92857143, 0.89285714, 0.96428571, 0.96428571,\n", + " 0.96428571]), 'mean_test_score': array([0.88392857, 0.95535714, 0.94642857, 0.98214286, 0.98214286,\n", + " 0.96428571]), 'std_test_score': array([0.03891874, 0.02961272, 0.03992979, 0.01785714, 0.01785714,\n", + " 0.02525381]), 'rank_test_score': array([6, 4, 5, 1, 1, 3], dtype=int32), 'split0_train_score': array([0.92857143, 0.97619048, 0.97619048, 0.98809524, 0.98809524,\n", + " 0.98809524]), 'split1_train_score': array([0.91666667, 0.96428571, 0.96428571, 0.97619048, 0.97619048,\n", + " 0.97619048]), 'split2_train_score': array([0.92857143, 0.95238095, 0.95238095, 0.97619048, 0.98809524,\n", + " 0.97619048]), 'split3_train_score': array([0.91666667, 0.95238095, 0.95238095, 1. , 1. ,\n", + " 0.98809524]), 'mean_train_score': array([0.92261905, 0.96130952, 0.96130952, 0.98511905, 0.98809524,\n", + " 0.98214286]), 'std_train_score': array([0.00595238, 0.00987091, 0.00987091, 0.00987091, 0.00841794,\n", + " 0.00595238])}\n", + "\n", + "2\n", + "{'mean_fit_time': array([0.00041491, 0.00027972, 0.00026184, 0.00033522, 0.0002436 ,\n", + " 0.00027043]), 'std_fit_time': array([3.45070743e-05, 1.29256902e-05, 1.01382227e-05, 9.88078909e-05,\n", + " 8.90705614e-06, 2.57328515e-05]), 'mean_score_time': array([0.00019699, 0.00015837, 0.00018364, 0.00016433, 0.00014621,\n", + " 0.00014848]), 'std_score_time': array([1.55497312e-05, 1.34209755e-06, 4.97970678e-05, 1.56898401e-05,\n", + " 9.88431212e-07, 8.49235466e-07]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, {'C': 100, 'gamma': 0.01}, {'C': 100, 'gamma': 0.1}], 'split0_test_score': array([0.96428571, 1. , 1. , 1. , 1. ,\n", + " 1. ]), 'split1_test_score': array([0.85714286, 0.92857143, 0.92857143, 0.96428571, 0.96428571,\n", + " 0.96428571]), 'split2_test_score': array([0.92857143, 1. , 1. , 1. , 1. ,\n", + " 1. ]), 'split3_test_score': array([0.85714286, 0.89285714, 0.89285714, 0.89285714, 0.92857143,\n", + " 0.92857143]), 'mean_test_score': array([0.90178571, 0.95535714, 0.95535714, 0.96428571, 0.97321429,\n", + " 0.97321429]), 'std_test_score': array([0.04639422, 0.04639422, 0.04639422, 0.04374089, 0.02961272,\n", + " 0.02961272]), 'rank_test_score': array([6, 4, 4, 3, 1, 1], dtype=int32), 'split0_train_score': array([0.94047619, 0.96428571, 0.96428571, 0.96428571, 0.96428571,\n", + " 0.97619048]), 'split1_train_score': array([0.91666667, 0.97619048, 0.96428571, 0.97619048, 0.97619048,\n", + " 0.98809524]), 'split2_train_score': array([0.89285714, 0.95238095, 0.95238095, 0.97619048, 0.97619048,\n", + " 0.97619048]), 'split3_train_score': array([0.92857143, 0.98809524, 0.98809524, 1. , 0.98809524,\n", + " 1. ]), 'mean_train_score': array([0.91964286, 0.9702381 , 0.9672619 , 0.97916667, 0.97619048,\n", + " 0.98511905]), 'std_train_score': array([0.01760738, 0.01330993, 0.01297291, 0.01297291, 0.00841794,\n", + " 0.00987091])}\n", + "\n", + "3\n", + "{'mean_fit_time': array([0.00033492, 0.0002749 , 0.00025362, 0.00025046, 0.00024259,\n", + " 0.00024784]), 'std_fit_time': array([1.22110993e-05, 8.60786829e-06, 1.90268632e-06, 8.94069672e-06,\n", + " 1.44803716e-05, 9.40847565e-06]), 'mean_score_time': array([0.0001812 , 0.00015831, 0.0001632 , 0.00018615, 0.00014585,\n", + " 0.00014651]), 'std_score_time': array([1.21605162e-05, 1.35919502e-06, 1.42478791e-05, 6.11994010e-05,\n", + " 1.97686242e-07, 6.84805232e-07]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, {'C': 100, 'gamma': 0.01}, {'C': 100, 'gamma': 0.1}], 'split0_test_score': array([0.85714286, 0.92857143, 0.92857143, 0.96428571, 1. ,\n", + " 0.96428571]), 'split1_test_score': array([0.85714286, 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0.95833333, 0.98809524, 0.98511905,\n", + " 0.98809524]), 'std_train_score': array([0.01297291, 0.00987091, 0.01330993, 0.00841794, 0.01297291,\n", + " 0.01190476])}\n", + "\n", + "4\n", + "{'mean_fit_time': array([0.00034726, 0.00027776, 0.0002659 , 0.00026214, 0.00024235,\n", + " 0.00024557]), 'std_fit_time': array([7.59672831e-06, 6.21948121e-06, 8.52721472e-06, 5.29509361e-06,\n", + " 5.88971106e-06, 2.99212942e-06]), 'mean_score_time': array([0.00018328, 0.00016528, 0.00016391, 0.00015694, 0.00015819,\n", + " 0.00014925]), 'std_score_time': array([5.45341785e-06, 1.78714566e-06, 1.12461819e-06, 1.38380370e-06,\n", + " 4.00550309e-06, 8.92080638e-07]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, {'C': 100, 'gamma': 0.01}, {'C': 100, 'gamma': 0.1}], 'split0_test_score': array([1. , 1. , 1. , 1. , 1. ,\n", + " 0.96428571]), 'split1_test_score': array([0.89285714, 0.92857143, 0.92857143, 0.92857143, 0.96428571,\n", + " 0.96428571]), 'split2_test_score': array([0.92857143, 0.92857143, 0.96428571, 0.96428571, 1. ,\n", + " 0.96428571]), 'split3_test_score': array([0.57142857, 0.92857143, 0.92857143, 0.92857143, 0.89285714,\n", + " 0.89285714]), 'mean_test_score': array([0.84821429, 0.94642857, 0.95535714, 0.95535714, 0.96428571,\n", + " 0.94642857]), 'std_test_score': array([0.16439243, 0.03092948, 0.02961272, 0.02961272, 0.04374089,\n", + " 0.03092948]), 'rank_test_score': array([6, 4, 2, 2, 1, 4], dtype=int32), 'split0_train_score': array([0.89285714, 0.97619048, 0.97619048, 0.98809524, 0.98809524,\n", + " 0.98809524]), 'split1_train_score': array([0.94047619, 0.97619048, 0.97619048, 1. , 0.98809524,\n", + " 1. ]), 'split2_train_score': array([0.94047619, 0.97619048, 0.97619048, 0.98809524, 0.98809524,\n", + " 0.98809524]), 'split3_train_score': array([0.91666667, 0.96428571, 0.96428571, 0.98809524, 0.98809524,\n", + " 0.98809524]), 'mean_train_score': array([0.92261905, 0.97321429, 0.97321429, 0.99107143, 0.98809524,\n", + " 0.99107143]), 'std_train_score': array([0.01974181, 0.00515491, 0.00515491, 0.00515491, 0. ,\n", + " 0.00515491])}\n", + "\n", + "5\n", + "{'mean_fit_time': array([0.00037438, 0.00028503, 0.0002768 , 0.00026542, 0.00026023,\n", + " 0.00027144]), 'std_fit_time': array([4.16169772e-05, 7.12169506e-06, 1.29175793e-05, 1.24644985e-05,\n", + " 1.07069587e-05, 4.76091517e-06]), 'mean_score_time': array([0.00018823, 0.00016671, 0.00016403, 0.00015712, 0.0001545 ,\n", + " 0.00015604]), 'std_score_time': array([2.45756246e-06, 1.61153148e-06, 2.50623179e-06, 6.95103633e-07,\n", + " 2.53442426e-06, 2.43432258e-06]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, {'C': 100, 'gamma': 0.01}, {'C': 100, 'gamma': 0.1}], 'split0_test_score': array([0.78571429, 0.96428571, 0.96428571, 1. , 1. ,\n", + " 1. ]), 'split1_test_score': array([0.82142857, 0.89285714, 0.89285714, 0.92857143, 0.92857143,\n", + " 0.89285714]), 'split2_test_score': array([0.92857143, 1. , 1. , 1. , 1. ,\n", + " 1. ]), 'split3_test_score': array([0.92857143, 0.96428571, 0.96428571, 1. , 1. ,\n", + " 0.89285714]), 'mean_test_score': array([0.86607143, 0.95535714, 0.95535714, 0.98214286, 0.98214286,\n", + " 0.94642857]), 'std_test_score': array([0.06376275, 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1.34557403e-05, 9.11074105e-06]), 'mean_score_time': array([0.00018913, 0.00016457, 0.00017023, 0.00016582, 0.00015312,\n", + " 0.0001536 ]), 'std_score_time': array([1.62092856e-05, 1.56682008e-06, 1.34922589e-05, 1.07849824e-05,\n", + " 7.22667912e-07, 3.09714819e-07]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, {'C': 100, 'gamma': 0.01}, {'C': 100, 'gamma': 0.1}], 'split0_test_score': array([0.85714286, 0.96428571, 0.96428571, 0.96428571, 0.96428571,\n", + " 0.96428571]), 'split1_test_score': array([0.96428571, 1. , 0.96428571, 1. , 1. ,\n", + " 0.96428571]), 'split2_test_score': array([0.92857143, 1. , 1. , 1. , 1. ,\n", + " 1. ]), 'split3_test_score': array([0.92857143, 1. , 1. , 1. , 1. ,\n", + " 0.96428571]), 'mean_test_score': array([0.91964286, 0.99107143, 0.98214286, 0.99107143, 0.99107143,\n", + " 0.97321429]), 'std_test_score': array([0.03891874, 0.01546474, 0.01785714, 0.01546474, 0.01546474,\n", + " 0.01546474]), 'rank_test_score': array([6, 1, 4, 1, 1, 5], dtype=int32), 'split0_train_score': array([0.89285714, 0.98809524, 0.98809524, 1. , 1. ,\n", + " 1. ]), 'split1_train_score': array([0.96428571, 0.98809524, 0.98809524, 0.98809524, 0.98809524,\n", + " 0.98809524]), 'split2_train_score': array([0.92857143, 0.98809524, 0.97619048, 0.98809524, 0.98809524,\n", + " 0.98809524]), 'split3_train_score': array([0.95238095, 0.97619048, 0.97619048, 0.98809524, 0.98809524,\n", + " 0.98809524]), 'mean_train_score': array([0.93452381, 0.98511905, 0.98214286, 0.99107143, 0.99107143,\n", + " 0.99107143]), 'std_train_score': array([0.02727724, 0.00515491, 0.00595238, 0.00515491, 0.00515491,\n", + " 0.00515491])}\n", + "\n", + "7\n", + "{'mean_fit_time': array([0.00034738, 0.00028139, 0.00030065, 0.00026196, 0.00025433,\n", + " 0.00026512]), 'std_fit_time': array([2.23039275e-05, 5.79454027e-06, 6.06874812e-05, 6.98443351e-06,\n", + " 1.43758446e-05, 1.10845410e-05]), 'mean_score_time': array([0.00017852, 0.00016618, 0.0001632 , 0.00016266, 0.00015312,\n", + " 0.00015479]), 'std_score_time': array([2.90721617e-06, 1.46971083e-06, 2.00541065e-06, 1.30569549e-05,\n", + " 1.66359970e-06, 2.50268540e-06]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, 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, 1. ,\n", + " 1. ]), 'mean_train_score': array([0.86904762, 0.97916667, 0.98511905, 0.99404762, 0.99702381,\n", + " 0.99702381]), 'std_train_score': array([0.05120432, 0.00987091, 0.00515491, 0.00595238, 0.00515491,\n", + " 0.00515491])}\n", + "\n", + "8\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'mean_fit_time': array([0.00067562, 0.00042343, 0.00038534, 0.00038195, 0.00039953,\n", + " 0.00027204]), 'std_fit_time': array([1.49889683e-04, 3.03546273e-05, 1.74643643e-05, 1.06530693e-05,\n", + " 7.33150457e-05, 1.69947466e-05]), 'mean_score_time': array([0.00028437, 0.00026864, 0.00024623, 0.00024372, 0.0002315 ,\n", + " 0.00015861]), 'std_score_time': array([5.55748990e-05, 2.98201389e-05, 1.44350341e-05, 9.34615275e-06,\n", + " 6.64798903e-06, 2.83294550e-06]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, {'C': 100, 'gamma': 0.01}, {'C': 100, 'gamma': 0.1}], 'split0_test_score': array([0.89285714, 1. , 1. , 0.96428571, 0.96428571,\n", + " 0.96428571]), 'split1_test_score': array([0.89285714, 1. , 0.96428571, 1. , 1. ,\n", + " 1. ]), 'split2_test_score': array([0.89285714, 1. , 1. , 1. , 1. ,\n", + " 1. ]), 'split3_test_score': array([0.78571429, 1. , 1. , 1. , 1. ,\n", + " 1. ]), 'mean_test_score': array([0.86607143, 1. , 0.99107143, 0.99107143, 0.99107143,\n", + " 0.99107143]), 'std_test_score': array([0.04639422, 0. , 0.01546474, 0.01546474, 0.01546474,\n", + " 0.01546474]), 'rank_test_score': array([6, 1, 2, 2, 2, 2], dtype=int32), 'split0_train_score': array([0.91666667, 1. , 1. , 1. , 1. ,\n", + " 1. ]), 'split1_train_score': array([0.91666667, 0.97619048, 0.97619048, 0.98809524, 0.98809524,\n", + " 0.98809524]), 'split2_train_score': array([0.95238095, 0.98809524, 0.98809524, 0.98809524, 0.98809524,\n", + " 1. ]), 'split3_train_score': array([0.91666667, 1. , 1. , 0.98809524, 0.98809524,\n", + " 0.98809524]), 'mean_train_score': array([0.92559524, 0.99107143, 0.99107143, 0.99107143, 0.99107143,\n", + " 0.99404762]), 'std_train_score': array([0.01546474, 0.00987091, 0.00987091, 0.00515491, 0.00515491,\n", + " 0.00595238])}\n", + "\n", + "9\n", + "{'mean_fit_time': array([0.00042182, 0.00032252, 0.00031126, 0.00025773, 0.0002439 ,\n", + " 0.00026196]), 'std_fit_time': array([8.71577873e-05, 3.58894707e-05, 1.64262442e-05, 3.96449307e-06,\n", + " 4.98117981e-06, 1.21702993e-05]), 'mean_score_time': array([0.00018883, 0.0001893 , 0.00019503, 0.00015533, 0.00015396,\n", + " 0.00015324]), 'std_score_time': array([4.33436846e-06, 1.25877333e-05, 2.04790318e-05, 5.46285593e-07,\n", + " 7.79431869e-07, 3.12398855e-06]), 'param_C': masked_array(data=[1, 1, 10, 10, 100, 100],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'param_gamma': masked_array(data=[0.01, 0.1, 0.01, 0.1, 0.01, 0.1],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object), 'params': [{'C': 1, 'gamma': 0.01}, {'C': 1, 'gamma': 0.1}, {'C': 10, 'gamma': 0.01}, {'C': 10, 'gamma': 0.1}, {'C': 100, 'gamma': 0.01}, {'C': 100, 'gamma': 0.1}], 'split0_test_score': array([0.92857143, 0.92857143, 0.92857143, 0.96428571, 0.96428571,\n", + " 0.96428571]), 'split1_test_score': array([0.92857143, 0.96428571, 0.96428571, 1. , 1. ,\n", + " 0.92857143]), 'split2_test_score': array([1., 1., 1., 1., 1., 1.]), 'split3_test_score': array([0.89285714, 1. , 1. , 1. , 1. ,\n", + " 1. ]), 'mean_test_score': array([0.9375 , 0.97321429, 0.97321429, 0.99107143, 0.99107143,\n", + " 0.97321429]), 'std_test_score': array([0.03891874, 0.02961272, 0.02961272, 0.01546474, 0.01546474,\n", + " 0.02961272]), 'rank_test_score': array([6, 3, 3, 1, 1, 3], dtype=int32), 'split0_train_score': array([0.9047619 , 0.98809524, 0.98809524, 0.98809524, 0.98809524,\n", + " 1. ]), 'split1_train_score': array([0.9047619 , 0.96428571, 0.94047619, 0.98809524, 0.98809524,\n", + " 1. ]), 'split2_train_score': array([0.92857143, 0.96428571, 0.97619048, 0.98809524, 0.98809524,\n", + " 0.97619048]), 'split3_train_score': array([0.91666667, 0.95238095, 0.95238095, 0.97619048, 0.98809524,\n", + " 0.98809524]), 'mean_train_score': array([0.91369048, 0.9672619 , 0.96428571, 0.98511905, 0.98809524,\n", + " 0.99107143]), 'std_train_score': array([0.00987091, 0.01297291, 0.01882308, 0.00515491, 0. ,\n", + " 0.00987091])}\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.svm import SVC\n", + "from sklearn.model_selection import GridSearchCV, cross_val_score, KFold, train_test_split\n", + "import numpy as np\n", + "\n", + "print(__doc__)\n", + "\n", + "# Number of random trials\n", + "NUM_TRIALS = 10\n", + "\n", + "# Load the dataset\n", + "iris = load_iris()\n", + "X_iris = iris.data\n", + "y_iris = iris.target\n", + "\n", + "# Set up possible values of parameters to optimize over\n", + "p_grid = {\"C\": [1, 10, 100],\n", + " \"gamma\": [.01, .1]}\n", + "\n", + "# We will use a Support Vector Classifier with \"rbf\" kernel\n", + "svm = SVC(kernel=\"rbf\")\n", + "\n", + "# Arrays to store scores\n", + "val_scores = np.zeros((NUM_TRIALS,len(p_grid['C'])))\n", + "test_scores = np.zeros((NUM_TRIALS,len(p_grid['C'])))\n", + "\n", + "best_params = []\n", + "# Loop for each trial\n", + "for i in range(NUM_TRIALS): #Test set level\n", + " print()\n", + " print(i)\n", + " X_app,X_test,y_app,y_test = train_test_split(X_iris,y_iris)\n", + " inner_cv = KFold(n_splits=4, shuffle=True, random_state=i)\n", + " # Non_nested parameter search and scoring\n", + " clf = GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv)\n", + " clf.fit(X_app, y_app)\n", + " print(clf.cv_results_)\n", + " best_params.append(clf.best_params_)\n", + " val_scores[i] = clf.best_score_\n", + " test_scores[i] = clf.score(X_test,y_test)\n", + "\n", + "final_performance = np.mean(test_scores)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n", + " warnings.warn(CV_WARNING, FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.base import BaseEstimator\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.datasets.samples_generator import make_classification\n", + "from sklearn.svm import LinearSVC, SVC\n", + "from sklearn.metrics import f1_score, precision_score\n", + "\n", + "X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)\n", + "# compute the training kernel matrix corresponding to the linear kernel\n", + "K_train = np.dot(X_[:180], X_[:180].T)\n", + "y_train = y_[:180]\n", + "clf = SVC(kernel='precomputed')\n", + "cv = GridSearchCV(clf, {'C': [0.1, 1.0]})\n", + "cv.fit(K_train, y_train)\n", + "# compute the test kernel matrix\n", + "K_test = np.dot(X_[180:], X_[:180].T)\n", + "y_test = y_[180:]\n", + "y_pred = cv.predict(K_test)\n", + "np.mean(y_pred == y_test) >= 0" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2 3] [0 1]\n", + "[0 1] [2 3]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from sklearn.model_selection import KFold\n", + "\n", + "X = [\"a\", \"b\", \"c\", \"d\"]\n", + "kf = KFold(n_splits=2)\n", + "for train, test in kf.split(X):\n", + " print(\"%s %s\" % (train, test))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9733333333333334" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import datasets\n", + "from sklearn import svm\n", + "\n", + "iris = datasets.load_iris()\n", + "iris.data.shape, iris.target.shape\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " iris.data, iris.target, test_size=0.4, random_state=0)\n", + "\n", + "X_train.shape, y_train.shape\n", + "\n", + "X_test.shape, y_test.shape\n", + "\n", + "\n", + "clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)\n", + "clf.score(X_test, y_test)\n", + "\n", + "\n", + "from sklearn.model_selection import cross_val_score, cross_validate\n", + "clf = svm.SVC(kernel='linear', C=1)\n", + "scores = cross_validate(clf, iris.data, iris.target, cv=5, return_train_score=True)\n", + "scores \n", + "# print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n", + "\n", + "\n", + "from sklearn.model_selection import cross_val_predict\n", + "from sklearn.metrics import accuracy_score\n", + "predicted = cross_val_predict(clf, iris.data, iris.target, cv=10)\n", + "accuracy_score(iris.target, predicted) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically created module for IPython interactive environment\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=0, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 1, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.913 (+/-0.085) for {'C': 1, 'gamma': 0.01}\n", + "0.960 (+/-0.079) for {'C': 1, 'gamma': 0.1}\n", + "0.960 (+/-0.079) for {'C': 10, 'gamma': 0.01}\n", + "0.960 (+/-0.060) for {'C': 10, 'gamma': 0.1}\n", + "0.960 (+/-0.060) for {'C': 100, 'gamma': 0.01}\n", + "0.947 (+/-0.038) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=1, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.927 (+/-0.101) for {'C': 1, 'gamma': 0.01}\n", + "0.947 (+/-0.064) for {'C': 1, 'gamma': 0.1}\n", + "0.940 (+/-0.086) for {'C': 10, 'gamma': 0.01}\n", + "0.960 (+/-0.027) for {'C': 10, 'gamma': 0.1}\n", + "0.960 (+/-0.027) for {'C': 100, 'gamma': 0.01}\n", + "0.933 (+/-0.080) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=2, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.920 (+/-0.039) for {'C': 1, 'gamma': 0.01}\n", + "0.960 (+/-0.060) for {'C': 1, 'gamma': 0.1}\n", + "0.960 (+/-0.060) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.059) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.066) for {'C': 100, 'gamma': 0.01}\n", + "0.953 (+/-0.070) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=3, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.940 (+/-0.117) for {'C': 1, 'gamma': 0.01}\n", + "0.960 (+/-0.060) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.044) for {'C': 10, 'gamma': 0.01}\n", + "0.960 (+/-0.046) for {'C': 10, 'gamma': 0.1}\n", + "0.960 (+/-0.046) for {'C': 100, 'gamma': 0.01}\n", + "0.940 (+/-0.059) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=4, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.913 (+/-0.042) for {'C': 1, 'gamma': 0.01}\n", + "0.967 (+/-0.022) for {'C': 1, 'gamma': 0.1}\n", + "0.973 (+/-0.037) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.044) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.038) for {'C': 100, 'gamma': 0.01}\n", + "0.947 (+/-0.037) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=5, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.947 (+/-0.066) for {'C': 1, 'gamma': 0.01}\n", + "0.960 (+/-0.059) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.044) for {'C': 10, 'gamma': 0.01}\n", + "0.980 (+/-0.023) for {'C': 10, 'gamma': 0.1}\n", + "0.980 (+/-0.023) for {'C': 100, 'gamma': 0.01}\n", + "0.973 (+/-0.037) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=6, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.920 (+/-0.084) for {'C': 1, 'gamma': 0.01}\n", + "0.947 (+/-0.064) for {'C': 1, 'gamma': 0.1}\n", + "0.953 (+/-0.044) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.022) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.001) for {'C': 100, 'gamma': 0.01}\n", + "0.947 (+/-0.039) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=7, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.947 (+/-0.124) for {'C': 1, 'gamma': 0.01}\n", + "0.967 (+/-0.087) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.058) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.058) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.037) for {'C': 100, 'gamma': 0.01}\n", + "0.933 (+/-0.096) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=8, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.940 (+/-0.078) for {'C': 1, 'gamma': 0.01}\n", + "0.960 (+/-0.026) for {'C': 1, 'gamma': 0.1}\n", + "0.960 (+/-0.059) for {'C': 10, 'gamma': 0.01}\n", + "0.973 (+/-0.065) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.037) for {'C': 100, 'gamma': 0.01}\n", + "0.947 (+/-0.084) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=9, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.927 (+/-0.129) for {'C': 1, 'gamma': 0.01}\n", + "0.953 (+/-0.045) for {'C': 1, 'gamma': 0.1}\n", + "0.960 (+/-0.028) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.059) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.038) for {'C': 100, 'gamma': 0.01}\n", + "0.960 (+/-0.060) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=10, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.920 (+/-0.101) for {'C': 1, 'gamma': 0.01}\n", + "0.953 (+/-0.044) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.044) for {'C': 10, 'gamma': 0.01}\n", + "0.973 (+/-0.065) for {'C': 10, 'gamma': 0.1}\n", + "0.980 (+/-0.044) for {'C': 100, 'gamma': 0.01}\n", + "0.973 (+/-0.065) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=11, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.947 (+/-0.083) for {'C': 1, 'gamma': 0.01}\n", + "0.960 (+/-0.026) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.043) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.022) for {'C': 10, 'gamma': 0.1}\n", + "0.967 (+/-0.022) for {'C': 100, 'gamma': 0.01}\n", + "0.947 (+/-0.037) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=12, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 1, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.927 (+/-0.069) for {'C': 1, 'gamma': 0.01}\n", + "0.973 (+/-0.037) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.022) for {'C': 10, 'gamma': 0.01}\n", + "0.960 (+/-0.027) for {'C': 10, 'gamma': 0.1}\n", + "0.967 (+/-0.022) for {'C': 100, 'gamma': 0.01}\n", + "0.940 (+/-0.024) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=13, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.920 (+/-0.091) for {'C': 1, 'gamma': 0.01}\n", + "0.973 (+/-0.037) for {'C': 1, 'gamma': 0.1}\n", + "0.980 (+/-0.044) for {'C': 10, 'gamma': 0.01}\n", + "0.960 (+/-0.059) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.053) for {'C': 100, 'gamma': 0.01}\n", + "0.947 (+/-0.065) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=14, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.920 (+/-0.086) for {'C': 1, 'gamma': 0.01}\n", + "0.960 (+/-0.027) for {'C': 1, 'gamma': 0.1}\n", + "0.960 (+/-0.027) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.022) for {'C': 10, 'gamma': 0.1}\n", + "0.980 (+/-0.044) for {'C': 100, 'gamma': 0.01}\n", + "0.960 (+/-0.059) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=15, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 1, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.953 (+/-0.044) for {'C': 1, 'gamma': 0.01}\n", + "0.973 (+/-0.037) for {'C': 1, 'gamma': 0.1}\n", + "0.973 (+/-0.037) for {'C': 10, 'gamma': 0.01}\n", + "0.973 (+/-0.038) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.038) for {'C': 100, 'gamma': 0.01}\n", + "0.953 (+/-0.059) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=16, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.900 (+/-0.088) for {'C': 1, 'gamma': 0.01}\n", + "0.953 (+/-0.024) for {'C': 1, 'gamma': 0.1}\n", + "0.960 (+/-0.027) for {'C': 10, 'gamma': 0.01}\n", + "0.973 (+/-0.037) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.037) for {'C': 100, 'gamma': 0.01}\n", + "0.940 (+/-0.057) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=17, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.920 (+/-0.084) for {'C': 1, 'gamma': 0.01}\n", + "0.967 (+/-0.059) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.059) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.044) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.037) for {'C': 100, 'gamma': 0.01}\n", + "0.940 (+/-0.043) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=18, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 1, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.927 (+/-0.059) for {'C': 1, 'gamma': 0.01}\n", + "0.973 (+/-0.054) for {'C': 1, 'gamma': 0.1}\n", + "0.973 (+/-0.054) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.045) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.038) for {'C': 100, 'gamma': 0.01}\n", + "0.953 (+/-0.070) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=19, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.907 (+/-0.057) for {'C': 1, 'gamma': 0.01}\n", + "0.967 (+/-0.057) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.043) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.022) for {'C': 10, 'gamma': 0.1}\n", + "0.980 (+/-0.023) for {'C': 100, 'gamma': 0.01}\n", + "0.960 (+/-0.027) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=20, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.893 (+/-0.107) for {'C': 1, 'gamma': 0.01}\n", + "0.953 (+/-0.059) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.059) for {'C': 10, 'gamma': 0.01}\n", + "0.973 (+/-0.038) for {'C': 10, 'gamma': 0.1}\n", + "0.980 (+/-0.023) for {'C': 100, 'gamma': 0.01}\n", + "0.953 (+/-0.080) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=21, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.947 (+/-0.038) for {'C': 1, 'gamma': 0.01}\n", + "0.973 (+/-0.065) for {'C': 1, 'gamma': 0.1}\n", + "0.980 (+/-0.044) for {'C': 10, 'gamma': 0.01}\n", + "0.980 (+/-0.044) for {'C': 10, 'gamma': 0.1}\n", + "0.987 (+/-0.046) for {'C': 100, 'gamma': 0.01}\n", + "0.973 (+/-0.065) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=22, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.913 (+/-0.078) for {'C': 1, 'gamma': 0.01}\n", + "0.940 (+/-0.044) for {'C': 1, 'gamma': 0.1}\n", + "0.953 (+/-0.043) for {'C': 10, 'gamma': 0.01}\n", + "0.960 (+/-0.059) for {'C': 10, 'gamma': 0.1}\n", + "0.953 (+/-0.043) for {'C': 100, 'gamma': 0.01}\n", + "0.960 (+/-0.045) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=23, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.940 (+/-0.057) for {'C': 1, 'gamma': 0.01}\n", + "0.960 (+/-0.027) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.044) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.022) for {'C': 10, 'gamma': 0.1}\n", + "0.967 (+/-0.022) for {'C': 100, 'gamma': 0.01}\n", + "0.947 (+/-0.091) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=24, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.907 (+/-0.048) for {'C': 1, 'gamma': 0.01}\n", + "0.953 (+/-0.070) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.059) for {'C': 10, 'gamma': 0.01}\n", + "0.973 (+/-0.037) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.037) for {'C': 100, 'gamma': 0.01}\n", + "0.947 (+/-0.066) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=25, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.913 (+/-0.069) for {'C': 1, 'gamma': 0.01}\n", + "0.953 (+/-0.043) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.022) for {'C': 10, 'gamma': 0.01}\n", + "0.967 (+/-0.022) for {'C': 10, 'gamma': 0.1}\n", + "0.980 (+/-0.044) for {'C': 100, 'gamma': 0.01}\n", + "0.953 (+/-0.068) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=26, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 1, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.907 (+/-0.091) for {'C': 1, 'gamma': 0.01}\n", + "0.967 (+/-0.059) for {'C': 1, 'gamma': 0.1}\n", + "0.967 (+/-0.059) for {'C': 10, 'gamma': 0.01}\n", + "0.960 (+/-0.060) for {'C': 10, 'gamma': 0.1}\n", + "0.967 (+/-0.059) for {'C': 100, 'gamma': 0.01}\n", + "0.960 (+/-0.060) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=27, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 1, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.947 (+/-0.001) for {'C': 1, 'gamma': 0.01}\n", + "0.980 (+/-0.044) for {'C': 1, 'gamma': 0.1}\n", + "0.980 (+/-0.044) for {'C': 10, 'gamma': 0.01}\n", + "0.980 (+/-0.023) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.037) for {'C': 100, 'gamma': 0.01}\n", + "0.967 (+/-0.044) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "clf.params: {'cv': KFold(n_splits=4, random_state=28, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 100, 'gamma': 0.01}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.933 (+/-0.060) for {'C': 1, 'gamma': 0.01}\n", + "0.960 (+/-0.060) for {'C': 1, 'gamma': 0.1}\n", + "0.960 (+/-0.060) for {'C': 10, 'gamma': 0.01}\n", + "0.960 (+/-0.060) for {'C': 10, 'gamma': 0.1}\n", + "0.973 (+/-0.037) for {'C': 100, 'gamma': 0.01}\n", + "0.960 (+/-0.089) for {'C': 100, 'gamma': 0.1}\n", + "\n", + "clf.params: {'cv': KFold(n_splits=4, random_state=29, shuffle=True), 'error_score': 'raise-deprecating', 'estimator__C': 1.0, 'estimator__cache_size': 200, 'estimator__class_weight': None, 'estimator__coef0': 0.0, 'estimator__decision_function_shape': 'ovr', 'estimator__degree': 3, 'estimator__gamma': 'auto_deprecated', 'estimator__kernel': 'rbf', 'estimator__max_iter': -1, 'estimator__probability': False, 'estimator__random_state': None, 'estimator__shrinking': True, 'estimator__tol': 0.001, 'estimator__verbose': False, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False), 'fit_params': None, 'iid': 'warn', 'n_jobs': None, 'param_grid': {'C': [1, 10, 100], 'gamma': [0.01, 0.1]}, 'pre_dispatch': '2*n_jobs', 'refit': True, 'return_train_score': 'warn', 'scoring': None, 'verbose': 0}\n", + "\n", + "Best parameters set found on development set:\n", + "\n", + "{'C': 10, 'gamma': 0.1}\n", + "\n", + "Grid scores on development set:\n", + "\n", + "0.940 (+/-0.022) for {'C': 1, 'gamma': 0.01}\n", + "0.967 (+/-0.022) for {'C': 1, 'gamma': 0.1}\n", + "0.973 (+/-0.001) for {'C': 10, 'gamma': 0.01}\n", + "0.980 (+/-0.044) for {'C': 10, 'gamma': 0.1}\n", + "0.980 (+/-0.044) for {'C': 100, 'gamma': 0.01}\n", + "0.953 (+/-0.043) for {'C': 100, 'gamma': 0.1}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n", + "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average difference of 0.007742 with std. dev. of 0.007688.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.svm import SVC\n", + "from sklearn.model_selection import GridSearchCV, cross_val_score, KFold\n", + "import numpy as np\n", + "\n", + "print(__doc__)\n", + "\n", + "# Number of random trials\n", + "NUM_TRIALS = 30\n", + "\n", + "# Load the dataset\n", + "iris = load_iris()\n", + "X_iris = iris.data\n", + "y_iris = iris.target\n", + "\n", + "# Set up possible values of parameters to optimize over\n", + "p_grid = {\"C\": [1, 10, 100],\n", + " \"gamma\": [.01, .1]}\n", + "\n", + "# We will use a Support Vector Classifier with \"rbf\" kernel\n", + "svm = SVC(kernel=\"rbf\")\n", + "\n", + "# Arrays to store scores\n", + "non_nested_scores = np.zeros(NUM_TRIALS)\n", + "nested_scores = np.zeros(NUM_TRIALS)\n", + "\n", + "# Loop for each trial\n", + "for i in range(NUM_TRIALS):\n", + "\n", + " # Choose cross-validation techniques for the inner and outer loops,\n", + " # independently of the dataset.\n", + " # E.g \"LabelKFold\", \"LeaveOneOut\", \"LeaveOneLabelOut\", etc.\n", + " inner_cv = KFold(n_splits=4, shuffle=True, random_state=i)\n", + " outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)\n", + "\n", + " # Non_nested parameter search and scoring\n", + " clf = GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv)\n", + " clf.fit(X_iris, y_iris)\n", + " print('clf.params: ', clf.get_params())\n", + " print()\n", + " non_nested_scores[i] = clf.best_score_\n", + " \n", + " print(\"Best parameters set found on development set:\")\n", + " print()\n", + " print(clf.best_params_)\n", + " print()\n", + " print(\"Grid scores on development set:\")\n", + " print()\n", + " means = clf.cv_results_['mean_test_score']\n", + " stds = clf.cv_results_['std_test_score']\n", + " for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n", + " print(\"%0.3f (+/-%0.03f) for %r\"\n", + " % (mean, std * 2, params))\n", + " print()\n", + "\n", + " # Nested CV with parameter optimization\n", + " nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv)\n", + " nested_scores[i] = nested_score.mean()\n", + "\n", + "score_difference = non_nested_scores - nested_scores\n", + "\n", + "print(\"Average difference of {0:6f} with std. dev. of {1:6f}.\"\n", + " .format(score_difference.mean(), score_difference.std()))\n", + "\n", + "# Plot scores on each trial for nested and non-nested CV\n", + "plt.figure()\n", + "plt.subplot(211)\n", + "non_nested_scores_line, = plt.plot(non_nested_scores, color='r')\n", + "nested_line, = plt.plot(nested_scores, color='b')\n", + "plt.ylabel(\"score\", fontsize=\"14\")\n", + "plt.legend([non_nested_scores_line, nested_line],\n", + " [\"Non-Nested CV\", \"Nested CV\"],\n", + " bbox_to_anchor=(0, .4, .5, 0))\n", + "plt.title(\"Non-Nested and Nested Cross Validation on Iris Dataset\",\n", + " x=.5, y=1.1, fontsize=\"15\")\n", + "\n", + "# Plot bar chart of the difference.\n", + "plt.subplot(212)\n", + "difference_plot = plt.bar(range(NUM_TRIALS), score_difference)\n", + "plt.xlabel(\"Individual Trial #\")\n", + "plt.legend([difference_plot],\n", + " [\"Non-Nested CV - Nested CV Score\"],\n", + " bbox_to_anchor=(0, 1, .8, 0))\n", + "plt.ylabel(\"score difference\", fontsize=\"14\")\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From cd9b4c1977534a60d7963f3ccbd80aed6f5674c9 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:05 +0200 Subject: [PATCH 019/728] New translations test_lib.ipynb (French) --- lang/fr/notebooks/tests/test_lib.ipynb | 200 +++++++++++++++++++++++++ 1 file changed, 200 insertions(+) create mode 100644 lang/fr/notebooks/tests/test_lib.ipynb diff --git a/lang/fr/notebooks/tests/test_lib.ipynb b/lang/fr/notebooks/tests/test_lib.ipynb new file mode 100644 index 0000000000..29038128bf --- /dev/null +++ b/lang/fr/notebooks/tests/test_lib.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "autoscroll": false, + "ein.tags": "worksheet-0", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "# import paths\n", + "\n", + "import sys\n", + "sys.path.insert(0, \"../../\")\n", + "from gklearn.utils.graphfiles import loadDataset\n", + "# from ged.GED import ged\n", + "# from ged.costfunctions import RiesenCostFunction, BasicCostFunction\n", + "# from ged.bipartiteGED import computeBipartiteCostMatrix, getOptimalMapping" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "autoscroll": false, + "ein.tags": "worksheet-0", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n", + " if cb.is_numlike(alpha):\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 4]\n" + ] + } + ], + "source": [ + "import networkx as nx\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "dataset, y = loadDataset(\"../../datasets/acyclic/dataset_bps.ds\")\n", + "nx.draw_networkx(dataset[12])\n", + "plt.show()\n", + "print(list(dataset[12][3]))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "autoscroll": false, + "ein.tags": "worksheet-0", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'BasicCostFunction' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasicCostFunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mged_distances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'BasicCostFunction' is not defined" + ] + } + ], + "source": [ + "cf = BasicCostFunction(1,3,1,3)\n", + "N=len(dataset)\n", + "N=10\n", + "ged_distances = np.zeros((N,N))\n", + "for i in range(0,N):\n", + " for j in range(i,N):\n", + " ged_distances[j,i] = ged_distances[i,j] = ged(dataset[i],dataset[j],cf=cf, method='Riesen')[0]\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "autoscroll": false, + "ein.tags": "worksheet-0", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'ged' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mG2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mG2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mG2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Basic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mG2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Riesen'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'ged' is not defined" + ] + } + ], + "source": [ + "G1=dataset[12]\n", + "G2=dataset[12]\n", + "\n", + "print(ged(G1,G2))\n", + "print(ged(G1,G2,method='Basic'))\n", + "print(ged(G1,G2,method='Riesen'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "autoscroll": false, + "ein.tags": "worksheet-0", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: {0: [0], 3: [0, 3], 1: [0, 3, 1], 4: [0, 3, 4], 2: [0, 3, 4, 2]}, 1: {1: [1], 3: [1, 3], 0: [1, 3, 0], 4: [1, 3, 4], 2: [1, 3, 4, 2]}, 2: {2: [2], 4: [2, 4], 3: [2, 4, 3], 0: [2, 4, 3, 0], 1: [2, 4, 3, 1]}, 3: {3: [3], 0: [3, 0], 1: [3, 1], 4: [3, 4], 2: [3, 4, 2]}, 4: {4: [4], 2: [4, 2], 3: [4, 3], 0: [4, 3, 0], 1: [4, 3, 1]}}\n", + "[[0. 2. 3. 1. 2.]\n", + " [2. 0. 3. 1. 2.]\n", + " [3. 3. 0. 2. 1.]\n", + " [1. 1. 2. 0. 1.]\n", + " [2. 2. 1. 1. 0.]]\n" + ] + } + ], + "source": [ + "l = nx.shortest_path(G1)\n", + "print(l)\n", + "\n", + "distances = np.zeros((G1.number_of_nodes(),G1.number_of_nodes()))\n", + "for i in l.keys():\n", + " for j in l[i].keys():\n", + " distances[i,j] = len(l[i][j])-1\n", + "\n", + "print(distances)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "name": "test_lib.ipynb" + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 958d761f621f402e2dc632e0faec14bfb9bf035f Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:06 +0200 Subject: [PATCH 020/728] New translations memory_profile.ipynb (French) --- lang/fr/notebooks/tests/memory_profile.ipynb | 821 +++++++++++++++++++ 1 file changed, 821 insertions(+) create mode 100644 lang/fr/notebooks/tests/memory_profile.ipynb diff --git a/lang/fr/notebooks/tests/memory_profile.ipynb b/lang/fr/notebooks/tests/memory_profile.ipynb new file mode 100644 index 0000000000..cbf0c6f8de --- /dev/null +++ b/lang/fr/notebooks/tests/memory_profile.ipynb @@ -0,0 +1,821 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Acyclic\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "getting sp graphs: 183it [00:00, 1871.37it/s]\n", + "calculating kernels: 16836it [00:16, 1014.42it/s]\n", + "\n", + " --- shortest path kernel matrix of size 183 built in 16.947543382644653 seconds ---\n", + "\n", + "the gram matrix with parameters {'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8} is: \n", + "\n", + "\n", + "\n", + "1 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 30it [00:12, 2.03it/s]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8}]\n", + "best_params_in: [{'alpha': 1e-06}]\n", + "\n", + "best_val_perf: 9.55244065682399\n", + "best_val_std: 0.5574811966683159\n", + "final_performance: [9.724426192585643]\n", + "final_confidence: [2.999822095078807]\n", + "train_performance: [6.141755071354953]\n", + "train_std: [0.2732168016478284]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 16.95±nans\n", + "time to calculate best gram matrix: 16.95±nans\n", + "total training time with all hyper-param choices: 32.74s\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " keepdims=keepdims)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filename: ../../gklearn/utils/model_selection_precomputed.py\n", + "\n", + "Line # Mem usage Increment Line Contents\n", + "================================================\n", + " 24 115.2 MiB 115.2 MiB @profile\n", + " 25 def model_selection_for_precomputed_kernel(datafile,\n", + " 26 estimator,\n", + " 27 param_grid_precomputed,\n", + " 28 param_grid,\n", + " 29 model_type,\n", + " 30 NUM_TRIALS=30,\n", + " 31 datafile_y=None,\n", + " 32 extra_params=None,\n", + " 33 ds_name='ds-unknown',\n", + " 34 n_jobs=1,\n", + " 35 read_gm_from_file=False):\n", + " 36 \"\"\"Perform model selection, fitting and testing for precomputed kernels using nested cv. Print out neccessary data during the process then finally the results.\n", + " 37 \n", + " 38 Parameters\n", + " 39 ----------\n", + " 40 datafile : string\n", + " 41 Path of dataset file.\n", + " 42 estimator : function\n", + " 43 kernel function used to estimate. This function needs to return a gram matrix.\n", + " 44 param_grid_precomputed : dictionary\n", + " 45 Dictionary with names (string) of parameters used to calculate gram matrices as keys and lists of parameter settings to try as values. This enables searching over any sequence of parameter settings. Params with length 1 will be omitted.\n", + " 46 param_grid : dictionary\n", + " 47 Dictionary with names (string) of parameters used as penelties as keys and lists of parameter settings to try as values. This enables searching over any sequence of parameter settings. Params with length 1 will be omitted.\n", + " 48 model_type : string\n", + " 49 Typr of the problem, can be regression or classification.\n", + " 50 NUM_TRIALS : integer\n", + " 51 Number of random trials of outer cv loop. The default is 30.\n", + " 52 datafile_y : string\n", + " 53 Path of file storing y data. This parameter is optional depending on the given dataset file.\n", + " 54 read_gm_from_file : boolean\n", + " 55 Whether gram matrices are loaded from file.\n", + " 56 \n", + " 57 Examples\n", + " 58 --------\n", + " 59 >>> import numpy as np\n", + " 60 >>> import sys\n", + " 61 >>> sys.path.insert(0, \"../\")\n", + " 62 >>> from gklearn.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n", + " 63 >>> from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel\n", + " 64 >>>\n", + " 65 >>> datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n", + " 66 >>> estimator = weisfeilerlehmankernel\n", + " 67 >>> param_grid_precomputed = {'height': [0,1,2,3,4,5,6,7,8,9,10], 'base_kernel': ['subtree']}\n", + " 68 >>> param_grid = {\"alpha\": np.logspace(-2, 2, num = 10, base = 10)}\n", + " 69 >>>\n", + " 70 >>> model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, 'regression')\n", + " 71 \"\"\"\n", + " 72 115.2 MiB 0.0 MiB tqdm.monitor_interval = 0\n", + " 73 \n", + " 74 115.2 MiB 0.0 MiB results_dir = '../notebooks/results/' + estimator.__name__\n", + " 75 115.2 MiB 0.0 MiB if not os.path.exists(results_dir):\n", + " 76 os.makedirs(results_dir)\n", + " 77 # a string to save all the results.\n", + " 78 115.2 MiB 0.0 MiB str_fw = '###################### log time: ' + datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\") + '. ######################\\n\\n'\n", + " 79 115.2 MiB 0.0 MiB str_fw += '# This file contains results of ' + estimator.__name__ + ' on dataset ' + ds_name + ',\\n# including gram matrices, serial numbers for gram matrix figures and performance.\\n\\n'\n", + " 80 \n", + " 81 # setup the model type\n", + " 82 115.2 MiB 0.0 MiB model_type = model_type.lower()\n", + " 83 115.2 MiB 0.0 MiB if model_type != 'regression' and model_type != 'classification':\n", + " 84 raise Exception(\n", + " 85 'The model type is incorrect! Please choose from regression or classification.'\n", + " 86 )\n", + " 87 115.2 MiB 0.0 MiB print()\n", + " 88 115.2 MiB 0.0 MiB print('--- This is a %s problem ---' % model_type)\n", + " 89 115.2 MiB 0.0 MiB str_fw += 'This is a %s problem.\\n' % model_type\n", + " 90 \n", + " 91 # calculate gram matrices rather than read them from file.\n", + " 92 115.2 MiB 0.0 MiB if read_gm_from_file == False:\n", + " 93 # Load the dataset\n", + " 94 115.2 MiB 0.0 MiB print()\n", + " 95 115.2 MiB 0.0 MiB print('\\n1. Loading dataset from file...')\n", + " 96 115.2 MiB 0.0 MiB if isinstance(datafile, str):\n", + " 97 115.2 MiB 0.0 MiB dataset, y_all = loadDataset(\n", + " 98 116.3 MiB 1.1 MiB datafile, filename_y=datafile_y, extra_params=extra_params)\n", + " 99 else: # load data directly from variable.\n", + " 100 dataset = datafile\n", + " 101 y_all = datafile_y \n", + " 102 \n", + " 103 # import matplotlib.pyplot as plt\n", + " 104 # import networkx as nx\n", + " 105 # nx.draw_networkx(dataset[30])\n", + " 106 # plt.show()\n", + " 107 \n", + " 108 # Grid of parameters with a discrete number of values for each.\n", + " 109 116.3 MiB 0.0 MiB param_list_precomputed = list(ParameterGrid(param_grid_precomputed))\n", + " 110 116.3 MiB 0.0 MiB param_list = list(ParameterGrid(param_grid))\n", + " 111 \n", + " 112 116.3 MiB 0.0 MiB gram_matrices = [\n", + " 113 ] # a list to store gram matrices for all param_grid_precomputed\n", + " 114 116.3 MiB 0.0 MiB gram_matrix_time = [\n", + " 115 ] # a list to store time to calculate gram matrices\n", + " 116 116.3 MiB 0.0 MiB param_list_pre_revised = [\n", + " 117 ] # list to store param grids precomputed ignoring the useless ones\n", + " 118 \n", + " 119 # calculate all gram matrices\n", + " 120 116.3 MiB 0.0 MiB print()\n", + " 121 116.3 MiB 0.0 MiB print('2. Calculating gram matrices. This could take a while...')\n", + " 122 116.3 MiB 0.0 MiB str_fw += '\\nII. Gram matrices.\\n\\n'\n", + " 123 116.3 MiB 0.0 MiB tts = time.time() # start training time\n", + " 124 116.3 MiB 0.0 MiB nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)\n", + " 125 145.3 MiB 0.0 MiB for idx, params_out in enumerate(param_list_precomputed):\n", + " 126 116.3 MiB 0.0 MiB y = y_all[:]\n", + " 127 116.3 MiB 0.0 MiB params_out['n_jobs'] = n_jobs\n", + " 128 # print(dataset)\n", + " 129 # import networkx as nx\n", + " 130 # nx.draw_networkx(dataset[1])\n", + " 131 # plt.show()\n", + " 132 119.5 MiB 3.1 MiB rtn_data = estimator(dataset[:], **params_out)\n", + " 133 119.5 MiB 0.0 MiB Kmatrix = rtn_data[0]\n", + " 134 119.5 MiB 0.0 MiB current_run_time = rtn_data[1]\n", + " 135 # for some kernels, some graphs in datasets may not meet the \n", + " 136 # kernels' requirements for graph structure. These graphs are trimmed. \n", + " 137 119.5 MiB 0.0 MiB if len(rtn_data) == 3:\n", + " 138 119.5 MiB 0.0 MiB idx_trim = rtn_data[2] # the index of trimmed graph list\n", + " 139 119.5 MiB 0.0 MiB y = [y[idxt] for idxt in idx_trim] # trim y accordingly\n", + " 140 # Kmatrix = np.random.rand(2250, 2250)\n", + " 141 # current_run_time = 0.1\n", + " 142 \n", + " 143 # remove graphs whose kernels with themselves are zeros\n", + " 144 119.5 MiB 0.0 MiB Kmatrix_diag = Kmatrix.diagonal().copy()\n", + " 145 119.5 MiB 0.0 MiB nb_g_ignore = 0\n", + " 146 119.5 MiB 0.0 MiB for idxk, diag in enumerate(Kmatrix_diag):\n", + " 147 119.5 MiB 0.0 MiB if diag == 0:\n", + " 148 Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=0)\n", + " 149 Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=1)\n", + " 150 nb_g_ignore += 1\n", + " 151 # normalization\n", + " 152 119.5 MiB 0.0 MiB Kmatrix_diag = Kmatrix.diagonal().copy()\n", + " 153 119.5 MiB 0.0 MiB for i in range(len(Kmatrix)):\n", + " 154 119.5 MiB 0.0 MiB for j in range(i, len(Kmatrix)):\n", + " 155 119.5 MiB 0.0 MiB Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])\n", + " 156 119.5 MiB 0.0 MiB Kmatrix[j][i] = Kmatrix[i][j]\n", + " 157 \n", + " 158 119.5 MiB 0.0 MiB print()\n", + " 159 119.5 MiB 0.0 MiB if params_out == {}:\n", + " 160 print('the gram matrix is: ')\n", + " 161 str_fw += 'the gram matrix is:\\n\\n'\n", + " 162 else:\n", + " 163 119.5 MiB 0.0 MiB print('the gram matrix with parameters', params_out, 'is: \\n\\n')\n", + " 164 119.5 MiB 0.0 MiB str_fw += 'the gram matrix with parameters %s is:\\n\\n' % params_out\n", + " 165 119.5 MiB 0.0 MiB if len(Kmatrix) < 2:\n", + " 166 nb_gm_ignore += 1\n", + " 167 print('ignored, as at most only one of all its diagonal value is non-zero.')\n", + " 168 str_fw += 'ignored, as at most only one of all its diagonal value is non-zero.\\n\\n'\n", + " 169 else: \n", + " 170 119.5 MiB 0.0 MiB if np.isnan(Kmatrix).any(\n", + " 171 ): # if the matrix contains elements that are not numbers\n", + " 172 nb_gm_ignore += 1\n", + " 173 print('ignored, as it contains elements that are not numbers.')\n", + " 174 str_fw += 'ignored, as it contains elements that are not numbers.\\n\\n'\n", + " 175 else:\n", + " 176 # print(Kmatrix)\n", + " 177 119.5 MiB 0.0 MiB str_fw += np.array2string(\n", + " 178 119.5 MiB 0.0 MiB Kmatrix,\n", + " 179 119.5 MiB 0.0 MiB separator=',') + '\\n\\n'\n", + " 180 # separator=',',\n", + " 181 # threshold=np.inf,\n", + " 182 # floatmode='unique') + '\\n\\n'\n", + " 183 \n", + " 184 119.5 MiB 0.0 MiB fig_file_name = results_dir + '/GM[ds]' + ds_name\n", + " 185 119.5 MiB 0.0 MiB if params_out != {}:\n", + " 186 119.5 MiB 0.0 MiB fig_file_name += '[params]' + str(idx)\n", + " 187 120.3 MiB 0.7 MiB plt.imshow(Kmatrix)\n", + " 188 120.4 MiB 0.1 MiB plt.colorbar()\n", + " 189 145.3 MiB 24.9 MiB plt.savefig(fig_file_name + '.eps', format='eps', dpi=300)\n", + " 190 # plt.show()\n", + " 191 145.3 MiB 0.0 MiB plt.clf()\n", + " 192 145.3 MiB 0.0 MiB gram_matrices.append(Kmatrix)\n", + " 193 145.3 MiB 0.0 MiB gram_matrix_time.append(current_run_time)\n", + " 194 145.3 MiB 0.0 MiB param_list_pre_revised.append(params_out)\n", + " 195 145.3 MiB 0.0 MiB if nb_g_ignore > 0:\n", + " 196 print(', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore)\n", + " 197 str_fw += ', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore\n", + " 198 145.3 MiB 0.0 MiB print()\n", + " 199 145.3 MiB 0.0 MiB print(\n", + " 200 145.3 MiB 0.0 MiB '{} gram matrices are calculated, {} of which are ignored.'.format(\n", + " 201 145.3 MiB 0.0 MiB len(param_list_precomputed), nb_gm_ignore))\n", + " 202 145.3 MiB 0.0 MiB str_fw += '{} gram matrices are calculated, {} of which are ignored.\\n\\n'.format(len(param_list_precomputed), nb_gm_ignore)\n", + " 203 145.3 MiB 0.0 MiB str_fw += 'serial numbers of gram matrix figures and their corresponding parameters settings:\\n\\n'\n", + " 204 145.3 MiB 0.0 MiB str_fw += ''.join([\n", + " 205 145.3 MiB 0.0 MiB '{}: {}\\n'.format(idx, params_out)\n", + " 206 145.3 MiB 0.0 MiB for idx, params_out in enumerate(param_list_precomputed)\n", + " 207 ])\n", + " 208 \n", + " 209 145.3 MiB 0.0 MiB print()\n", + " 210 145.3 MiB 0.0 MiB if len(gram_matrices) == 0:\n", + " 211 print('all gram matrices are ignored, no results obtained.')\n", + " 212 str_fw += '\\nall gram matrices are ignored, no results obtained.\\n\\n'\n", + " 213 else:\n", + " 214 # save gram matrices to file.\n", + " 215 145.4 MiB 0.1 MiB np.savez(results_dir + '/' + ds_name + '.gm', \n", + " 216 145.4 MiB 0.0 MiB gms=gram_matrices, params=param_list_pre_revised, y=y, \n", + " 217 145.4 MiB 0.0 MiB gmtime=gram_matrix_time)\n", + " 218 \n", + " 219 145.4 MiB 0.0 MiB print(\n", + " 220 145.4 MiB 0.0 MiB '3. Fitting and predicting using nested cross validation. This could really take a while...'\n", + " 221 )\n", + " 222 \n", + " 223 # ---- use pool.imap_unordered to parallel and track progress. ----\n", + " 224 # train_pref = []\n", + " 225 # val_pref = []\n", + " 226 # test_pref = []\n", + " 227 # def func_assign(result, var_to_assign):\n", + " 228 # for idx, itm in enumerate(var_to_assign):\n", + " 229 # itm.append(result[idx]) \n", + " 230 # trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, y, model_type)\n", + " 231 # \n", + " 232 # parallel_me(trial_do_partial, range(NUM_TRIALS), func_assign, \n", + " 233 # [train_pref, val_pref, test_pref], glbv=gram_matrices,\n", + " 234 # method='imap_unordered', n_jobs=n_jobs, chunksize=1,\n", + " 235 # itr_desc='cross validation')\n", + " 236 \n", + " 237 145.4 MiB 0.0 MiB def init_worker(gms_toshare):\n", + " 238 global G_gms\n", + " 239 G_gms = gms_toshare\n", + " 240 \n", + " 241 # gram_matrices = np.array(gram_matrices)\n", + " 242 # gms_shape = gram_matrices.shape\n", + " 243 # gms_array = Array('d', np.reshape(gram_matrices.copy(), -1, order='C'))\n", + " 244 # pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gms_array, gms_shape))\n", + " 245 145.4 MiB 0.0 MiB pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gram_matrices,))\n", + " 246 145.4 MiB 0.0 MiB trial_do_partial = partial(parallel_trial_do, param_list_pre_revised, param_list, y, model_type)\n", + " 247 145.4 MiB 0.0 MiB train_pref = []\n", + " 248 145.4 MiB 0.0 MiB val_pref = []\n", + " 249 145.4 MiB 0.0 MiB test_pref = []\n", + " 250 # if NUM_TRIALS < 1000 * n_jobs:\n", + " 251 # chunksize = int(NUM_TRIALS / n_jobs) + 1\n", + " 252 # else:\n", + " 253 # chunksize = 1000\n", + " 254 145.4 MiB 0.0 MiB chunksize = 1\n", + " 255 145.4 MiB 0.0 MiB for o1, o2, o3 in tqdm(pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout):\n", + " 256 145.4 MiB 0.0 MiB train_pref.append(o1)\n", + " 257 145.4 MiB 0.0 MiB val_pref.append(o2)\n", + " 258 145.4 MiB 0.0 MiB test_pref.append(o3)\n", + " 259 145.4 MiB 0.0 MiB pool.close()\n", + " 260 145.4 MiB 0.0 MiB pool.join()\n", + " 261 \n", + " 262 # # ---- use pool.map to parallel. ----\n", + " 263 # pool = Pool(n_jobs)\n", + " 264 # trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y[0:250], model_type)\n", + " 265 # result_perf = pool.map(trial_do_partial, range(NUM_TRIALS))\n", + " 266 # train_pref = [item[0] for item in result_perf]\n", + " 267 # val_pref = [item[1] for item in result_perf]\n", + " 268 # test_pref = [item[2] for item in result_perf]\n", + " 269 \n", + " 270 # # ---- direct running, normally use a single CPU core. ----\n", + " 271 # train_pref = []\n", + " 272 # val_pref = []\n", + " 273 # test_pref = []\n", + " 274 # for i in tqdm(range(NUM_TRIALS), desc='cross validation', file=sys.stdout):\n", + " 275 # o1, o2, o3 = trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, i)\n", + " 276 # train_pref.append(o1)\n", + " 277 # val_pref.append(o2)\n", + " 278 # test_pref.append(o3)\n", + " 279 # print()\n", + " 280 \n", + " 281 145.4 MiB 0.0 MiB print()\n", + " 282 145.4 MiB 0.0 MiB print('4. Getting final performance...')\n", + " 283 145.4 MiB 0.0 MiB str_fw += '\\nIII. Performance.\\n\\n'\n", + " 284 # averages and confidences of performances on outer trials for each combination of parameters\n", + " 285 145.4 MiB 0.0 MiB average_train_scores = np.mean(train_pref, axis=0)\n", + " 286 # print('val_pref: ', val_pref[0][0])\n", + " 287 145.4 MiB 0.0 MiB average_val_scores = np.mean(val_pref, axis=0)\n", + " 288 # print('test_pref: ', test_pref[0][0])\n", + " 289 145.4 MiB 0.0 MiB average_perf_scores = np.mean(test_pref, axis=0)\n", + " 290 # sample std is used here\n", + " 291 145.4 MiB 0.0 MiB std_train_scores = np.std(train_pref, axis=0, ddof=1)\n", + " 292 145.4 MiB 0.0 MiB std_val_scores = np.std(val_pref, axis=0, ddof=1)\n", + " 293 145.4 MiB 0.0 MiB std_perf_scores = np.std(test_pref, axis=0, ddof=1)\n", + " 294 \n", + " 295 145.4 MiB 0.0 MiB if model_type == 'regression':\n", + " 296 145.4 MiB 0.0 MiB best_val_perf = np.amin(average_val_scores)\n", + " 297 else:\n", + " 298 best_val_perf = np.amax(average_val_scores)\n", + " 299 # print('average_val_scores: ', average_val_scores)\n", + " 300 # print('best_val_perf: ', best_val_perf)\n", + " 301 # print()\n", + " 302 145.4 MiB 0.0 MiB best_params_index = np.where(average_val_scores == best_val_perf)\n", + " 303 # find smallest val std with best val perf.\n", + " 304 best_val_stds = [\n", + " 305 145.4 MiB 0.0 MiB std_val_scores[value][best_params_index[1][idx]]\n", + " 306 145.4 MiB 0.0 MiB for idx, value in enumerate(best_params_index[0])\n", + " 307 ]\n", + " 308 145.4 MiB 0.0 MiB min_val_std = np.amin(best_val_stds)\n", + " 309 145.4 MiB 0.0 MiB best_params_index = np.where(std_val_scores == min_val_std)\n", + " 310 best_params_out = [\n", + " 311 145.4 MiB 0.0 MiB param_list_pre_revised[i] for i in best_params_index[0]\n", + " 312 ]\n", + " 313 145.4 MiB 0.0 MiB best_params_in = [param_list[i] for i in best_params_index[1]]\n", + " 314 145.4 MiB 0.0 MiB print('best_params_out: ', best_params_out)\n", + " 315 145.4 MiB 0.0 MiB print('best_params_in: ', best_params_in)\n", + " 316 145.4 MiB 0.0 MiB print()\n", + " 317 145.4 MiB 0.0 MiB print('best_val_perf: ', best_val_perf)\n", + " 318 145.4 MiB 0.0 MiB print('best_val_std: ', min_val_std)\n", + " 319 145.4 MiB 0.0 MiB str_fw += 'best settings of hyper-params to build gram matrix: %s\\n' % best_params_out\n", + " 320 145.4 MiB 0.0 MiB str_fw += 'best settings of other hyper-params: %s\\n\\n' % best_params_in\n", + " 321 145.4 MiB 0.0 MiB str_fw += 'best_val_perf: %s\\n' % best_val_perf\n", + " 322 145.4 MiB 0.0 MiB str_fw += 'best_val_std: %s\\n' % min_val_std\n", + " 323 \n", + " 324 # print(best_params_index)\n", + " 325 # print(best_params_index[0])\n", + " 326 # print(average_perf_scores)\n", + " 327 final_performance = [\n", + " 328 145.4 MiB 0.0 MiB average_perf_scores[value][best_params_index[1][idx]]\n", + " 329 145.4 MiB 0.0 MiB for idx, value in enumerate(best_params_index[0])\n", + " 330 ]\n", + " 331 final_confidence = [\n", + " 332 145.4 MiB 0.0 MiB std_perf_scores[value][best_params_index[1][idx]]\n", + " 333 145.4 MiB 0.0 MiB for idx, value in enumerate(best_params_index[0])\n", + " 334 ]\n", + " 335 145.4 MiB 0.0 MiB print('final_performance: ', final_performance)\n", + " 336 145.4 MiB 0.0 MiB print('final_confidence: ', final_confidence)\n", + " 337 145.4 MiB 0.0 MiB str_fw += 'final_performance: %s\\n' % final_performance\n", + " 338 145.4 MiB 0.0 MiB str_fw += 'final_confidence: %s\\n' % final_confidence\n", + " 339 train_performance = [\n", + " 340 145.4 MiB 0.0 MiB average_train_scores[value][best_params_index[1][idx]]\n", + " 341 145.4 MiB 0.0 MiB for idx, value in enumerate(best_params_index[0])\n", + " 342 ]\n", + " 343 train_std = [\n", + " 344 145.4 MiB 0.0 MiB std_train_scores[value][best_params_index[1][idx]]\n", + " 345 145.4 MiB 0.0 MiB for idx, value in enumerate(best_params_index[0])\n", + " 346 ]\n", + " 347 145.4 MiB 0.0 MiB print('train_performance: %s' % train_performance)\n", + " 348 145.4 MiB 0.0 MiB print('train_std: ', train_std)\n", + " 349 145.4 MiB 0.0 MiB str_fw += 'train_performance: %s\\n' % train_performance\n", + " 350 145.4 MiB 0.0 MiB str_fw += 'train_std: %s\\n\\n' % train_std\n", + " 351 \n", + " 352 145.4 MiB 0.0 MiB print()\n", + " 353 145.4 MiB 0.0 MiB tt_total = time.time() - tts # training time for all hyper-parameters\n", + " 354 145.4 MiB 0.0 MiB average_gram_matrix_time = np.mean(gram_matrix_time)\n", + " 355 145.4 MiB 0.0 MiB std_gram_matrix_time = np.std(gram_matrix_time, ddof=1)\n", + " 356 best_gram_matrix_time = [\n", + " 357 145.4 MiB 0.0 MiB gram_matrix_time[i] for i in best_params_index[0]\n", + " 358 ]\n", + " 359 145.4 MiB 0.0 MiB ave_bgmt = np.mean(best_gram_matrix_time)\n", + " 360 145.4 MiB 0.0 MiB std_bgmt = np.std(best_gram_matrix_time, ddof=1)\n", + " 361 145.4 MiB 0.0 MiB print(\n", + " 362 145.4 MiB 0.0 MiB 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'\n", + " 363 145.4 MiB 0.0 MiB .format(average_gram_matrix_time, std_gram_matrix_time))\n", + " 364 145.4 MiB 0.0 MiB print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(\n", + " 365 145.4 MiB 0.0 MiB ave_bgmt, std_bgmt))\n", + " 366 145.4 MiB 0.0 MiB print(\n", + " 367 145.4 MiB 0.0 MiB 'total training time with all hyper-param choices: {:.2f}s'.format(\n", + " 368 145.4 MiB 0.0 MiB tt_total))\n", + " 369 145.4 MiB 0.0 MiB str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\\n'.format(average_gram_matrix_time, std_gram_matrix_time)\n", + " 370 145.4 MiB 0.0 MiB str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\\n'.format(ave_bgmt, std_bgmt)\n", + " 371 145.4 MiB 0.0 MiB str_fw += 'total training time with all hyper-param choices: {:.2f}s\\n\\n'.format(tt_total)\n", + " 372 \n", + " 373 # # save results to file\n", + " 374 # np.savetxt(results_name_pre + 'average_train_scores.dt',\n", + " 375 # average_train_scores)\n", + " 376 # np.savetxt(results_name_pre + 'average_val_scores', average_val_scores)\n", + " 377 # np.savetxt(results_name_pre + 'average_perf_scores.dt',\n", + " 378 # average_perf_scores)\n", + " 379 # np.savetxt(results_name_pre + 'std_train_scores.dt', std_train_scores)\n", + " 380 # np.savetxt(results_name_pre + 'std_val_scores.dt', std_val_scores)\n", + " 381 # np.savetxt(results_name_pre + 'std_perf_scores.dt', std_perf_scores)\n", + " 382 \n", + " 383 # np.save(results_name_pre + 'best_params_index', best_params_index)\n", + " 384 # np.save(results_name_pre + 'best_params_pre.dt', best_params_out)\n", + " 385 # np.save(results_name_pre + 'best_params_in.dt', best_params_in)\n", + " 386 # np.save(results_name_pre + 'best_val_perf.dt', best_val_perf)\n", + " 387 # np.save(results_name_pre + 'best_val_std.dt', best_val_std)\n", + " 388 # np.save(results_name_pre + 'final_performance.dt', final_performance)\n", + " 389 # np.save(results_name_pre + 'final_confidence.dt', final_confidence)\n", + " 390 # np.save(results_name_pre + 'train_performance.dt', train_performance)\n", + " 391 # np.save(results_name_pre + 'train_std.dt', train_std)\n", + " 392 \n", + " 393 # np.save(results_name_pre + 'gram_matrix_time.dt', gram_matrix_time)\n", + " 394 # np.save(results_name_pre + 'average_gram_matrix_time.dt',\n", + " 395 # average_gram_matrix_time)\n", + " 396 # np.save(results_name_pre + 'std_gram_matrix_time.dt',\n", + " 397 # std_gram_matrix_time)\n", + " 398 # np.save(results_name_pre + 'best_gram_matrix_time.dt',\n", + " 399 # best_gram_matrix_time)\n", + " 400 \n", + " 401 # print out as table.\n", + " 402 145.4 MiB 0.0 MiB from collections import OrderedDict\n", + " 403 145.4 MiB 0.0 MiB from tabulate import tabulate\n", + " 404 145.4 MiB 0.0 MiB table_dict = {}\n", + " 405 145.4 MiB 0.0 MiB if model_type == 'regression':\n", + " 406 145.6 MiB 0.0 MiB for param_in in param_list:\n", + " 407 145.6 MiB 0.2 MiB param_in['alpha'] = '{:.2e}'.format(param_in['alpha'])\n", + " 408 else:\n", + " 409 for param_in in param_list:\n", + " 410 param_in['C'] = '{:.2e}'.format(param_in['C'])\n", + " 411 145.6 MiB 0.0 MiB table_dict['params'] = [{**param_out, **param_in}\n", + " 412 145.6 MiB 0.0 MiB for param_in in param_list for param_out in param_list_pre_revised]\n", + " 413 table_dict['gram_matrix_time'] = [\n", + " 414 145.6 MiB 0.0 MiB '{:.2f}'.format(gram_matrix_time[index_out])\n", + " 415 145.6 MiB 0.0 MiB for param_in in param_list\n", + " 416 145.6 MiB 0.0 MiB for index_out, _ in enumerate(param_list_pre_revised)\n", + " 417 ]\n", + " 418 table_dict['valid_perf'] = [\n", + " 419 145.6 MiB 0.0 MiB '{:.2f}±{:.2f}'.format(average_val_scores[index_out][index_in],\n", + " 420 std_val_scores[index_out][index_in])\n", + " 421 145.6 MiB 0.0 MiB for index_in, _ in enumerate(param_list)\n", + " 422 145.6 MiB 0.0 MiB for index_out, _ in enumerate(param_list_pre_revised)\n", + " 423 ]\n", + " 424 table_dict['test_perf'] = [\n", + " 425 145.6 MiB 0.0 MiB '{:.2f}±{:.2f}'.format(average_perf_scores[index_out][index_in],\n", + " 426 std_perf_scores[index_out][index_in])\n", + " 427 145.6 MiB 0.0 MiB for index_in, _ in enumerate(param_list)\n", + " 428 145.6 MiB 0.0 MiB for index_out, _ in enumerate(param_list_pre_revised)\n", + " 429 ]\n", + " 430 table_dict['train_perf'] = [\n", + " 431 145.6 MiB 0.0 MiB '{:.2f}±{:.2f}'.format(average_train_scores[index_out][index_in],\n", + " 432 std_train_scores[index_out][index_in])\n", + " 433 145.6 MiB 0.0 MiB for index_in, _ in enumerate(param_list)\n", + " 434 145.6 MiB 0.0 MiB for index_out, _ in enumerate(param_list_pre_revised)\n", + " 435 ]\n", + " 436 keyorder = [\n", + " 437 145.6 MiB 0.0 MiB 'params', 'train_perf', 'valid_perf', 'test_perf',\n", + " 438 145.6 MiB 0.0 MiB 'gram_matrix_time'\n", + " 439 ]\n", + " 440 145.6 MiB 0.0 MiB print()\n", + " 441 145.6 MiB 0.0 MiB tb_print = tabulate(\n", + " 442 145.6 MiB 0.0 MiB OrderedDict(\n", + " 443 145.6 MiB 0.0 MiB sorted(table_dict.items(),\n", + " 444 145.6 MiB 0.0 MiB key=lambda i: keyorder.index(i[0]))),\n", + " 445 145.6 MiB 0.0 MiB headers='keys')\n", + " 446 # print(tb_print)\n", + " 447 145.6 MiB 0.0 MiB str_fw += 'table of performance v.s. hyper-params:\\n\\n%s\\n\\n' % tb_print\n", + " 448 \n", + " 449 # read gram matrices from file.\n", + " 450 else: \n", + " 451 # Grid of parameters with a discrete number of values for each.\n", + " 452 # param_list_precomputed = list(ParameterGrid(param_grid_precomputed))\n", + " 453 param_list = list(ParameterGrid(param_grid))\n", + " 454 \n", + " 455 # read gram matrices from file.\n", + " 456 print()\n", + " 457 print('2. Reading gram matrices from file...')\n", + " 458 str_fw += '\\nII. Gram matrices.\\n\\nGram matrices are read from file, see last log for detail.\\n'\n", + " 459 gmfile = np.load(results_dir + '/' + ds_name + '.gm.npz')\n", + " 460 gram_matrices = gmfile['gms'] # a list to store gram matrices for all param_grid_precomputed\n", + " 461 gram_matrix_time = gmfile['gmtime'] # time used to compute the gram matrices\n", + " 462 param_list_pre_revised = gmfile['params'] # list to store param grids precomputed ignoring the useless ones\n", + " 463 y = gmfile['y'].tolist()\n", + " 464 \n", + " 465 tts = time.time() # start training time\n", + " 466 # nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN) \n", + " 467 print(\n", + " 468 '3. Fitting and predicting using nested cross validation. This could really take a while...'\n", + " 469 )\n", + " 470 \n", + " 471 # ---- use pool.imap_unordered to parallel and track progress. ----\n", + " 472 def init_worker(gms_toshare):\n", + " 473 global G_gms\n", + " 474 G_gms = gms_toshare\n", + " 475 \n", + " 476 pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gram_matrices,))\n", + " 477 trial_do_partial = partial(parallel_trial_do, param_list_pre_revised, param_list, y, model_type)\n", + " 478 train_pref = []\n", + " 479 val_pref = []\n", + " 480 test_pref = []\n", + " 481 chunksize = 1\n", + " 482 for o1, o2, o3 in tqdm(pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout):\n", + " 483 train_pref.append(o1)\n", + " 484 val_pref.append(o2)\n", + " 485 test_pref.append(o3)\n", + " 486 pool.close()\n", + " 487 pool.join()\n", + " 488 \n", + " 489 # # ---- use pool.map to parallel. ----\n", + " 490 # result_perf = pool.map(trial_do_partial, range(NUM_TRIALS))\n", + " 491 # train_pref = [item[0] for item in result_perf]\n", + " 492 # val_pref = [item[1] for item in result_perf]\n", + " 493 # test_pref = [item[2] for item in result_perf]\n", + " 494 \n", + " 495 # # ---- use joblib.Parallel to parallel and track progress. ----\n", + " 496 # trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y, model_type)\n", + " 497 # result_perf = Parallel(n_jobs=n_jobs, verbose=10)(delayed(trial_do_partial)(trial) for trial in range(NUM_TRIALS))\n", + " 498 # train_pref = [item[0] for item in result_perf]\n", + " 499 # val_pref = [item[1] for item in result_perf]\n", + " 500 # test_pref = [item[2] for item in result_perf]\n", + " 501 \n", + " 502 # # ---- direct running, normally use a single CPU core. ----\n", + " 503 # train_pref = []\n", + " 504 # val_pref = []\n", + " 505 # test_pref = []\n", + " 506 # for i in tqdm(range(NUM_TRIALS), desc='cross validation', file=sys.stdout):\n", + " 507 # o1, o2, o3 = trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, i)\n", + " 508 # train_pref.append(o1)\n", + " 509 # val_pref.append(o2)\n", + " 510 # test_pref.append(o3)\n", + " 511 \n", + " 512 print()\n", + " 513 print('4. Getting final performance...')\n", + " 514 str_fw += '\\nIII. Performance.\\n\\n'\n", + " 515 # averages and confidences of performances on outer trials for each combination of parameters\n", + " 516 average_train_scores = np.mean(train_pref, axis=0)\n", + " 517 average_val_scores = np.mean(val_pref, axis=0)\n", + " 518 average_perf_scores = np.mean(test_pref, axis=0)\n", + " 519 # sample std is used here\n", + " 520 std_train_scores = np.std(train_pref, axis=0, ddof=1)\n", + " 521 std_val_scores = np.std(val_pref, axis=0, ddof=1)\n", + " 522 std_perf_scores = np.std(test_pref, axis=0, ddof=1)\n", + " 523 \n", + " 524 if model_type == 'regression':\n", + " 525 best_val_perf = np.amin(average_val_scores)\n", + " 526 else:\n", + " 527 best_val_perf = np.amax(average_val_scores)\n", + " 528 best_params_index = np.where(average_val_scores == best_val_perf)\n", + " 529 # find smallest val std with best val perf.\n", + " 530 best_val_stds = [\n", + " 531 std_val_scores[value][best_params_index[1][idx]]\n", + " 532 for idx, value in enumerate(best_params_index[0])\n", + " 533 ]\n", + " 534 min_val_std = np.amin(best_val_stds)\n", + " 535 best_params_index = np.where(std_val_scores == min_val_std)\n", + " 536 best_params_out = [\n", + " 537 param_list_pre_revised[i] for i in best_params_index[0]\n", + " 538 ]\n", + " 539 best_params_in = [param_list[i] for i in best_params_index[1]]\n", + " 540 print('best_params_out: ', best_params_out)\n", + " 541 print('best_params_in: ', best_params_in)\n", + " 542 print()\n", + " 543 print('best_val_perf: ', best_val_perf)\n", + " 544 print('best_val_std: ', min_val_std)\n", + " 545 str_fw += 'best settings of hyper-params to build gram matrix: %s\\n' % best_params_out\n", + " 546 str_fw += 'best settings of other hyper-params: %s\\n\\n' % best_params_in\n", + " 547 str_fw += 'best_val_perf: %s\\n' % best_val_perf\n", + " 548 str_fw += 'best_val_std: %s\\n' % min_val_std\n", + " 549 \n", + " 550 final_performance = [\n", + " 551 average_perf_scores[value][best_params_index[1][idx]]\n", + " 552 for idx, value in enumerate(best_params_index[0])\n", + " 553 ]\n", + " 554 final_confidence = [\n", + " 555 std_perf_scores[value][best_params_index[1][idx]]\n", + " 556 for idx, value in enumerate(best_params_index[0])\n", + " 557 ]\n", + " 558 print('final_performance: ', final_performance)\n", + " 559 print('final_confidence: ', final_confidence)\n", + " 560 str_fw += 'final_performance: %s\\n' % final_performance\n", + " 561 str_fw += 'final_confidence: %s\\n' % final_confidence\n", + " 562 train_performance = [\n", + " 563 average_train_scores[value][best_params_index[1][idx]]\n", + " 564 for idx, value in enumerate(best_params_index[0])\n", + " 565 ]\n", + " 566 train_std = [\n", + " 567 std_train_scores[value][best_params_index[1][idx]]\n", + " 568 for idx, value in enumerate(best_params_index[0])\n", + " 569 ]\n", + " 570 print('train_performance: %s' % train_performance)\n", + " 571 print('train_std: ', train_std)\n", + " 572 str_fw += 'train_performance: %s\\n' % train_performance\n", + " 573 str_fw += 'train_std: %s\\n\\n' % train_std\n", + " 574 \n", + " 575 print()\n", + " 576 average_gram_matrix_time = np.mean(gram_matrix_time)\n", + " 577 std_gram_matrix_time = np.std(gram_matrix_time, ddof=1)\n", + " 578 best_gram_matrix_time = [\n", + " 579 gram_matrix_time[i] for i in best_params_index[0]\n", + " 580 ]\n", + " 581 ave_bgmt = np.mean(best_gram_matrix_time)\n", + " 582 std_bgmt = np.std(best_gram_matrix_time, ddof=1)\n", + " 583 print(\n", + " 584 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'\n", + " 585 .format(average_gram_matrix_time, std_gram_matrix_time))\n", + " 586 print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(\n", + " 587 ave_bgmt, std_bgmt))\n", + " 588 tt_poster = time.time() - tts # training time with hyper-param choices who did not participate in calculation of gram matrices\n", + " 589 print(\n", + " 590 'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s'.format(\n", + " 591 tt_poster))\n", + " 592 print('total training time with all hyper-param choices: {:.2f}s'.format(\n", + " 593 tt_poster + np.sum(gram_matrix_time)))\n", + " 594 # str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\\n'.format(average_gram_matrix_time, std_gram_matrix_time)\n", + " 595 # str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\\n'.format(ave_bgmt, std_bgmt)\n", + " 596 str_fw += 'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s\\n\\n'.format(tt_poster)\n", + " 597 \n", + " 598 # print out as table.\n", + " 599 from collections import OrderedDict\n", + " 600 from tabulate import tabulate\n", + " 601 table_dict = {}\n", + " 602 if model_type == 'regression':\n", + " 603 for param_in in param_list:\n", + " 604 param_in['alpha'] = '{:.2e}'.format(param_in['alpha'])\n", + " 605 else:\n", + " 606 for param_in in param_list:\n", + " 607 param_in['C'] = '{:.2e}'.format(param_in['C'])\n", + " 608 table_dict['params'] = [{**param_out, **param_in}\n", + " 609 for param_in in param_list for param_out in param_list_pre_revised]\n", + " 610 # table_dict['gram_matrix_time'] = [\n", + " 611 # '{:.2f}'.format(gram_matrix_time[index_out])\n", + " 612 # for param_in in param_list\n", + " 613 # for index_out, _ in enumerate(param_list_pre_revised)\n", + " 614 # ]\n", + " 615 table_dict['valid_perf'] = [\n", + " 616 '{:.2f}±{:.2f}'.format(average_val_scores[index_out][index_in],\n", + " 617 std_val_scores[index_out][index_in])\n", + " 618 for index_in, _ in enumerate(param_list)\n", + " 619 for index_out, _ in enumerate(param_list_pre_revised)\n", + " 620 ]\n", + " 621 table_dict['test_perf'] = [\n", + " 622 '{:.2f}±{:.2f}'.format(average_perf_scores[index_out][index_in],\n", + " 623 std_perf_scores[index_out][index_in])\n", + " 624 for index_in, _ in enumerate(param_list)\n", + " 625 for index_out, _ in enumerate(param_list_pre_revised)\n", + " 626 ]\n", + " 627 table_dict['train_perf'] = [\n", + " 628 '{:.2f}±{:.2f}'.format(average_train_scores[index_out][index_in],\n", + " 629 std_train_scores[index_out][index_in])\n", + " 630 for index_in, _ in enumerate(param_list)\n", + " 631 for index_out, _ in enumerate(param_list_pre_revised)\n", + " 632 ]\n", + " 633 keyorder = [\n", + " 634 'params', 'train_perf', 'valid_perf', 'test_perf'\n", + " 635 ]\n", + " 636 print()\n", + " 637 tb_print = tabulate(\n", + " 638 OrderedDict(\n", + " 639 sorted(table_dict.items(),\n", + " 640 key=lambda i: keyorder.index(i[0]))),\n", + " 641 headers='keys')\n", + " 642 # print(tb_print)\n", + " 643 str_fw += 'table of performance v.s. hyper-params:\\n\\n%s\\n\\n' % tb_print\n", + " 644 \n", + " 645 # open file to save all results for this dataset.\n", + " 646 if not os.path.exists(results_dir):\n", + " 647 os.makedirs(results_dir)\n", + " 648 \n", + " 649 # open file to save all results for this dataset.\n", + " 650 145.6 MiB 0.0 MiB if not os.path.exists(results_dir + '/' + ds_name + '.output.txt'):\n", + " 651 with open(results_dir + '/' + ds_name + '.output.txt', 'w') as f:\n", + " 652 f.write(str_fw)\n", + " 653 else:\n", + " 654 145.6 MiB 0.0 MiB with open(results_dir + '/' + ds_name + '.output.txt', 'r+') as f:\n", + " 655 145.6 MiB 0.0 MiB content = f.read()\n", + " 656 145.6 MiB 0.0 MiB f.seek(0, 0)\n", + " 657 145.6 MiB 0.0 MiB f.write(str_fw + '\\n\\n\\n' + content)\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "import functools\n", + "import sys\n", + "sys.path.insert(0, \"../\")\n", + "sys.path.insert(0, \"../../\")\n", + "from libs import *\n", + "import multiprocessing\n", + "\n", + "from gklearn.kernels.spKernel import spkernel\n", + "from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct\n", + "#from gklearn.utils.model_selection_precomputed import trial_do\n", + "\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../../datasets/acyclic/dataset_bps.ds',\n", + " 'task': 'regression'}, # node symb\n", + "# {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds', 'task': 'regression',\n", + "# 'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt', }, \n", + "# # contains single node graph, node symb\n", + "# {'name': 'MAO', 'dataset': '../../datasets/MAO/dataset.ds', }, # node/edge symb\n", + "# {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds', }, # unlabeled\n", + "# {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG.mat',\n", + "# 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, # node/edge symb\n", + "# {'name': 'Letter-med', 'dataset': '../../datasets/Letter-med/Letter-med_A.txt'},\n", + "# # node nsymb\n", + "# {'name': 'ENZYMES', 'dataset': '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + "# # node symb/nsymb\n", + "# {'name': 'Mutagenicity', 'dataset': '../../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + "# # node/edge symb\n", + "# {'name': 'D&D', 'dataset': '../../datasets/D&D/DD.mat',\n", + "# 'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, # node symb\n", + "\n", + " # {'name': 'COIL-DEL', 'dataset': '../../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + " # # # {'name': 'BZR', 'dataset': '../../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n", + " # # # {'name': 'COX2', 'dataset': '../../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n", + " # {'name': 'Fingerprint', 'dataset': '../../datasets/Fingerprint/Fingerprint_A.txt'},\n", + " #\n", + " # # {'name': 'DHFR', 'dataset': '../../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'SYNTHETIC', 'dataset': '../../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'MSRC9', 'dataset': '../../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n", + " # # {'name': 'MSRC21', 'dataset': '../../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n", + " # # {'name': 'FIRSTMM_DB', 'dataset': '../../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n", + "\n", + " # # {'name': 'PROTEINS', 'dataset': '../../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'PROTEINS_full', 'dataset': '../../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'AIDS', 'dataset': '../../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n", + " # {'name': 'NCI1', 'dataset': '../../datasets/NCI1/NCI1.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI109', 'dataset': '../../datasets/NCI109/NCI109.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI-HIV', 'dataset': '../../datasets/NCI-HIV/AIDO99SD.sdf',\n", + " # 'dataset_y': '../../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n", + "\n", + " # # not working below\n", + " # {'name': 'PTC_FM', 'dataset': '../../datasets/PTC/Train/FM.ds',},\n", + " # {'name': 'PTC_FR', 'dataset': '../../datasets/PTC/Train/FR.ds',},\n", + " # {'name': 'PTC_MM', 'dataset': '../../datasets/PTC/Train/MM.ds',},\n", + " # {'name': 'PTC_MR', 'dataset': '../../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "estimator = spkernel\n", + "mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)\n", + "param_grid_precomputed = {'node_kernels': [\n", + " {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]}\n", + "param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n", + " {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n", + "\n", + "for ds in dslist:\n", + " print()\n", + " print(ds['name'])\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'],\n", + " estimator,\n", + " param_grid_precomputed,\n", + " (param_grid[1] if ('task' in ds and ds['task']\n", + " == 'regression') else param_grid[0]),\n", + " (ds['task'] if 'task' in ds else 'classification'),\n", + " NUM_TRIALS=30,\n", + " datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n", + " ds_name=ds['name'],\n", + " n_jobs=multiprocessing.cpu_count(),\n", + " read_gm_from_file=False)\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 4b090035e47a91e0770a52d23f38d8abf495aba2 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:08 +0200 Subject: [PATCH 021/728] New translations run_weisfeilerlehmankernel.py (French) --- .../notebooks/run_weisfeilerlehmankernel.py | 83 +++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 lang/fr/notebooks/run_weisfeilerlehmankernel.py diff --git a/lang/fr/notebooks/run_weisfeilerlehmankernel.py b/lang/fr/notebooks/run_weisfeilerlehmankernel.py new file mode 100644 index 0000000000..d84fa15c08 --- /dev/null +++ b/lang/fr/notebooks/run_weisfeilerlehmankernel.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Mar 21 11:19:33 2019 + +@author: ljia +""" + +from libs import * +import multiprocessing + +from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel + + + +dslist = [ +# {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', +# 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, +# # contains single node graph, node symb +# {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', +# 'task': 'regression'}, # node symb +# {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb +# {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled +# {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb + {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, + # node nsymb +# {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb +# {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, + # node symb/nsymb +# {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1_A.txt'}, # node symb +# {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109_A.txt'}, # node symb +# {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb +# {'name': 'monoterpenoides', 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'}, # node/edge symb + +# +# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# # node/edge symb + # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb + # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb + # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb + # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'}, + # + # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb + # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb + # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb + + # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb + # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf', + # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb + + # # not working below + # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',}, + # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',}, + # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',}, + # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',}, +] +estimator = weisfeilerlehmankernel +param_grid_precomputed = {'base_kernel': ['subtree'], + 'height': np.linspace(0, 10, 11)} +param_grid = [{'C': np.logspace(-10, 4, num=29, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +for ds in dslist: + print() + print(ds['name']) + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False, + verbose=True) + print() \ No newline at end of file From 1bce1813a60035efbbed78bcc41797b5b5d5ebfc Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:10 +0200 Subject: [PATCH 022/728] New translations run_weisfeilerlehmankernel.ipynb (French) --- .../run_weisfeilerlehmankernel.ipynb | 144 ++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 lang/fr/notebooks/run_weisfeilerlehmankernel.ipynb diff --git a/lang/fr/notebooks/run_weisfeilerlehmankernel.ipynb b/lang/fr/notebooks/run_weisfeilerlehmankernel.ipynb new file mode 100644 index 0000000000..785f163aa1 --- /dev/null +++ b/lang/fr/notebooks/run_weisfeilerlehmankernel.ipynb @@ -0,0 +1,144 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "MUTAG\n", + "\n", + "--- This is a classification problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " --- Weisfeiler-Lehman subtree kernel matrix of size 188 built in 0.14636015892028809 seconds ---\n", + "\n", + "the gram matrix with parameters {'base_kernel': 'subtree', 'height': 0.0, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " --- Weisfeiler-Lehman subtree kernel matrix of size 188 built in 0.2917311191558838 seconds ---\n", + "\n", + "the gram matrix with parameters {'base_kernel': 'subtree', 'height': 1.0, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n" + ] + } + ], + "source": [ + "#!/usr/bin/env python3\n", + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Mon Mar 21 11:19:33 2019\n", + "\n", + "@author: ljia\n", + "\"\"\"\n", + "\n", + "from libs import *\n", + "import multiprocessing\n", + "\n", + "from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel\n", + "from gklearn.utils.kernels import gaussiankernel, polynomialkernel\n", + "\n", + "\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n", + " 'task': 'regression'}, # node symb\n", + " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n", + " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, \n", + " # contains single node graph, node symb\n", + " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb\n", + " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled\n", + " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb\n", + " {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n", + " # node nsymb\n", + " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + " # node symb/nsymb\n", + "# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + "# # node/edge symb\n", + " {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb\n", + "\n", + " # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + " # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n", + " # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n", + " # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", + " #\n", + " # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n", + " # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n", + " # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n", + "\n", + " # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n", + " {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n", + " 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n", + " 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n", + " # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n", + "\n", + " # # not working below\n", + " # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n", + " # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n", + " # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n", + " # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "estimator = weisfeilerlehmankernel\n", + "param_grid_precomputed = {'base_kernel': ['subtree'], \n", + " 'height': np.linspace(0, 10, 11)}\n", + "param_grid = [{'C': np.logspace(-10, 4, num=29, base=10)},\n", + " {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n", + "\n", + "for ds in dslist:\n", + " print()\n", + " print(ds['name'])\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'],\n", + " estimator,\n", + " param_grid_precomputed,\n", + " (param_grid[1] if ('task' in ds and ds['task']\n", + " == 'regression') else param_grid[0]),\n", + " (ds['task'] if 'task' in ds else 'classification'),\n", + " NUM_TRIALS=30,\n", + " datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n", + " ds_name=ds['name'],\n", + " n_jobs=multiprocessing.cpu_count(),\n", + " read_gm_from_file=False,\n", + " verbose=True)\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From abc4e6b32d26cf731802583d8711ba3cbdff88dd Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:11 +0200 Subject: [PATCH 023/728] New translations run_untilhpathkernel.py (French) --- lang/fr/notebooks/run_untilhpathkernel.py | 83 +++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 lang/fr/notebooks/run_untilhpathkernel.py diff --git a/lang/fr/notebooks/run_untilhpathkernel.py b/lang/fr/notebooks/run_untilhpathkernel.py new file mode 100644 index 0000000000..00a4a40c43 --- /dev/null +++ b/lang/fr/notebooks/run_untilhpathkernel.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Oct 5 19:19:33 2018 + +@author: ljia +""" + +from libs import * +import multiprocessing + +from gklearn.kernels.untilHPathKernel import untilhpathkernel + +dslist = [ + {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', + 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, + # contains single node graph, node symb + {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', + 'task': 'regression'}, # node symb + {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb + {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled + {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb + {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, + # node nsymb + {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, + # node symb/nsymb + {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb + {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1_A.txt'}, # node symb + {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109_A.txt'}, # node symb + {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb +# {'name': 'monoterpenoides', 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'}, # node/edge symb +# +# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# # node/edge symb + # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb + # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb + # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb + # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'}, + # + # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb + # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb + # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb + + # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb + # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf', + # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb + + # # not working below + # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',}, + # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',}, + # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',}, + # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',}, +] +estimator = untilhpathkernel +#param_grid_precomputed = {'depth': np.linspace(3, 10, 8), # [2], +# 'k_func': [None]} # ['MinMax', 'tanimoto'], +param_grid_precomputed = {'depth': np.linspace(1, 10, 10), # [2], + 'k_func': ['MinMax', 'tanimoto'], # ['MinMax'], # + 'compute_method': ['trie']} # ['MinMax']} +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +for ds in dslist: + print() + print(ds['name']) + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False, + verbose=True) + print() From 70a18d44e69f30bd57537563dd15586d25e1c62a Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:13 +0200 Subject: [PATCH 024/728] New translations run_untilhpathkernel.ipynb (French) --- lang/fr/notebooks/run_untilhpathkernel.ipynb | 412 +++++++++++++++++++ 1 file changed, 412 insertions(+) create mode 100644 lang/fr/notebooks/run_untilhpathkernel.ipynb diff --git a/lang/fr/notebooks/run_untilhpathkernel.ipynb b/lang/fr/notebooks/run_untilhpathkernel.ipynb new file mode 100644 index 0000000000..1cce3fc2e8 --- /dev/null +++ b/lang/fr/notebooks/run_untilhpathkernel.ipynb @@ -0,0 +1,412 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Acyclic\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "getting paths: 183it [00:00, 22697.39it/s]\n", + "calculating kernels: 16836it [00:00, 371524.56it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 1 of size 183 built in 0.27962422370910645 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 35988.26it/s]\n", + "calculating kernels: 16836it [00:00, 444708.75it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 1 of size 183 built in 0.284440279006958 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 26474.81it/s]\n", + "calculating kernels: 16836it [00:00, 215084.65it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 2 of size 183 built in 0.2832369804382324 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 18360.43it/s]\n", + "calculating kernels: 16836it [00:00, 254309.18it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 2 of size 183 built in 0.28844165802001953 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 8687.30it/s]\n", + "calculating kernels: 16836it [00:00, 168741.96it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 3 of size 183 built in 0.38907885551452637 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 11379.65it/s]\n", + "calculating kernels: 16836it [00:00, 195770.23it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 3 of size 183 built in 0.39213061332702637 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 8062.50it/s]\n", + "calculating kernels: 16836it [00:00, 72349.59it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 4 of size 183 built in 0.512467622756958 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 10578.68it/s]\n", + "calculating kernels: 16836it [00:00, 133704.13it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 4 of size 183 built in 0.3866546154022217 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 9220.91it/s]\n", + "calculating kernels: 16836it [00:00, 98386.86it/s] \n", + "\n", + " --- kernel matrix of path kernel up to 5 of size 183 built in 0.38112974166870117 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 8493.03it/s]\n", + "calculating kernels: 16836it [00:00, 119698.11it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 5 of size 183 built in 0.38007307052612305 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 7385.55it/s]\n", + "calculating kernels: 16836it [00:00, 88347.09it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 6 of size 183 built in 0.3929023742675781 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 5394.24it/s]\n", + "calculating kernels: 16836it [00:00, 100946.78it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 6 of size 183 built in 0.3824801445007324 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 12457.52it/s]\n", + "calculating kernels: 16836it [00:00, 68995.02it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 7 of size 183 built in 0.49313783645629883 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 2829.00it/s]\n", + "calculating kernels: 16836it [00:00, 105515.66it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 7 of size 183 built in 0.35750555992126465 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 7427.43it/s]\n", + "calculating kernels: 16836it [00:00, 81607.79it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 8 of size 183 built in 0.4937615394592285 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 5660.08it/s]\n", + "calculating kernels: 16836it [00:00, 90014.85it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 8 of size 183 built in 0.36504673957824707 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 7548.83it/s]\n", + "calculating kernels: 16836it [00:00, 79498.55it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 9 of size 183 built in 0.47993040084838867 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 7319.90it/s]\n", + "calculating kernels: 16836it [00:00, 92310.24it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 9 of size 183 built in 0.3970515727996826 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 8318.60it/s]\n", + "calculating kernels: 16836it [00:00, 89934.38it/s] \n", + "\n", + " --- kernel matrix of path kernel up to 10 of size 183 built in 0.4861469268798828 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 183it [00:00, 2635.72it/s]\n", + "calculating kernels: 16836it [00:00, 90123.30it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 10 of size 183 built in 0.367603063583374 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + "20 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 30it [01:06, 1.11s/it]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True}]\n", + "best_params_in: [{'alpha': 0.01}]\n", + "\n", + "best_val_perf: 6.842702754673377\n", + "best_val_std: 0.3600238142615252\n", + "final_performance: [7.557191252340816]\n", + "final_confidence: [2.5849069582911595]\n", + "train_performance: [2.276370048287339]\n", + "train_std: [0.13830866732067562]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 0.39±0.07s\n", + "time to calculate best gram matrix: 0.28±0.00s\n", + "total training time with all hyper-param choices: 79.82s\n", + "\n", + "\n", + "\n", + "Alkane\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "getting paths: 150it [00:00, 31366.32it/s]\n", + "calculating kernels: 11325it [00:00, 509820.58it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 1 of size 150 built in 0.29791831970214844 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 150it [00:00, 30330.50it/s]\n", + "calculating kernels: 11325it [00:00, 655613.27it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 1 of size 150 built in 0.29232001304626465 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 150it [00:00, 27568.71it/s]\n", + "calculating kernels: 11325it [00:00, 780628.98it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 2 of size 150 built in 0.2590019702911377 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting paths: 150it [00:00, 17554.29it/s]\n", + "calculating kernels: 11325it [00:00, 320784.55it/s]\n", + "\n", + " --- kernel matrix of path kernel up to 2 of size 150 built in 0.3091611862182617 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n" + ] + } + ], + "source": [ + "# %load_ext line_profiler\n", + "# %matplotlib inline\n", + "from libs import *\n", + "import multiprocessing\n", + "\n", + "from gklearn.kernels.untilHPathKernel import untilhpathkernel\n", + "from gklearn.utils.kernels import deltakernel, kernelproduct\n", + "\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n", + " 'task': 'regression'}, # node symb\n", + " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n", + " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, \n", + " # contains single node graph, node symb\n", + " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb\n", + " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled\n", + " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb\n", + " {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n", + " # node nsymb\n", + " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + " # node symb/nsymb\n", + "# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + "# # node/edge symb\n", + "# {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb\n", + "\n", + " # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + " # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n", + " # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n", + " # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", + " #\n", + " # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n", + " # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n", + " # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n", + "\n", + " # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n", + " # {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n", + " # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n", + "\n", + " # # not working below\n", + " # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n", + " # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n", + " # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n", + " # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "estimator = untilhpathkernel\n", + "param_grid_precomputed = {'depth': np.linspace(1, 10, 10), # [2], \n", + " 'k_func': ['MinMax', 'tanimoto'],\n", + " 'compute_method': ['trie']} # ['MinMax']}\n", + "param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n", + " {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n", + "\n", + "for ds in dslist:\n", + " print()\n", + " print(ds['name'])\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'],\n", + " estimator,\n", + " param_grid_precomputed,\n", + " (param_grid[1] if ('task' in ds and ds['task']\n", + " == 'regression') else param_grid[0]),\n", + " (ds['task'] if 'task' in ds else 'classification'),\n", + " NUM_TRIALS=30,\n", + " datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n", + " ds_name=ds['name'],\n", + " n_jobs=multiprocessing.cpu_count(),\n", + " read_gm_from_file=False,\n", + " verbose=True)\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'line_profiler'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'line_profiler'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 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'line_profiler'" + ] + } + ], + "source": [ + "%load_ext line_profiler\n", + "\n", + "import sys\n", + "sys.path.insert(0, \"../\")\n", + "from gklearn.utils.utils import kernel_train_test\n", + "from gklearn.kernels.untildPathKernel import untildpathkernel\n", + "\n", + "import numpy as np\n", + "\n", + "datafile = '../../datasets/acyclic/Acyclic/dataset_bps.ds'\n", + "kernel_file_path = 'kernelmatrices_path_acyclic/'\n", + "\n", + "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', labeled = True, k_func = 'tanimoto')\n", + "\n", + "# kernel_train_test(datafile, kernel_file_path, treeletkernel, kernel_para, normalize = False)\n", + "\n", + "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n", + " hyper_name = 'depth', hyper_range = np.linspace(0, 20, 21), normalize = True)\n", + "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n", + " hyper_name = 'depth', hyper_range = np.linspace(0, 20, 21), normalize = False)\n", + "\n", + "kernel_para['k_func'] = 'minmax'\n", + "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n", + " hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = True)\n", + "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n", + " hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = False)\n", + "\n", + "# # kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)\n", + "\n", + "# kernel_para['depth'] = 10\n", + "# %lprun -f untildpathkernel \\\n", + "# kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 375948d9d339ada230062f2f13d46cf73ccb216a Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:14 +0200 Subject: [PATCH 025/728] New translations run_treeletkernel.py (French) --- lang/fr/notebooks/run_treeletkernel.py | 86 ++++++++++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 lang/fr/notebooks/run_treeletkernel.py diff --git a/lang/fr/notebooks/run_treeletkernel.py b/lang/fr/notebooks/run_treeletkernel.py new file mode 100644 index 0000000000..544f28b413 --- /dev/null +++ b/lang/fr/notebooks/run_treeletkernel.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Mar 21 11:19:33 2019 + +@author: ljia +""" + +from libs import * +import multiprocessing +import functools + +from gklearn.kernels.treeletKernel import treeletkernel +from gklearn.utils.kernels import gaussiankernel, polynomialkernel + +dslist = [ +# {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', +# 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, +# # contains single node graph, node symb +# {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', +# 'task': 'regression'}, # node symb +# {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb +# {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled +# {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb +# {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1_A.txt'}, # node symb +# {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109_A.txt'}, # node symb +# {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb +# {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, +# {'name': 'monoterpenoides', 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'}, # node/edge symb + # node symb/nsymb + {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb + {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, + # node nsymb +# +# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# # node/edge symb + # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb + # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb + # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb + # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'}, + # + # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb + # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb + # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb + + # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb + # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf', + # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb + + # # not working below + # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',}, + # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',}, + # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',}, + # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',}, +] +estimator = treeletkernel +gkernels = [functools.partial(gaussiankernel, gamma=1 / ga) +# for ga in np.linspace(1, 10, 10)] + for ga in np.logspace(0, 10, num=11, base=10)] +pkernels = [functools.partial(polynomialkernel, d=d, c=c) for d in range(1, 11) + for c in np.logspace(0, 10, num=11, base=10)] +param_grid_precomputed = {'sub_kernel': pkernels + gkernels} +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +for ds in dslist: + print() + print(ds['name']) + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False, + verbose=True) + print() From 0ac22fe91a391afd72a5b3051d1141e6fabd549b Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:16 +0200 Subject: [PATCH 026/728] New translations get_dataset_attributes.py (French) --- .../notebooks/utils/get_dataset_attributes.py | 62 +++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 lang/fr/notebooks/utils/get_dataset_attributes.py diff --git a/lang/fr/notebooks/utils/get_dataset_attributes.py b/lang/fr/notebooks/utils/get_dataset_attributes.py new file mode 100644 index 0000000000..911f573f5b --- /dev/null +++ b/lang/fr/notebooks/utils/get_dataset_attributes.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Oct 17 16:07:38 2018 + +@author: ljia +""" + +import sys +sys.path.insert(0, "../../") +from gklearn.utils.graphfiles import loadDataset +from gklearn.utils.graphdataset import get_dataset_attributes + +dslist = [ + {'name': 'Acyclic', 'dataset': '../../datasets/acyclic/dataset_bps.ds',}, + {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds', + 'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt',}, + {'name': 'MAO', 'dataset': '../../datasets/MAO/dataset.ds',}, + {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds',}, + {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG.mat', + 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, + {'name': 'Letter-med', 'dataset': '../../datasets/Letter-med/Letter-med_A.txt'}, + {'name': 'ENZYMES', 'dataset': '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, + {'name': 'Mutagenicity', 'dataset': '../../datasets/Mutagenicity/Mutagenicity_A.txt'}, + {'name': 'D&D', 'dataset': '../../datasets/D&D/DD.mat', + 'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, + {'name': 'AIDS', 'dataset': '../../datasets/AIDS/AIDS_A.txt'}, + {'name': 'FIRSTMM_DB', 'dataset': '../../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, + {'name': 'MSRC9', 'dataset': '../../datasets/MSRC_9_txt/MSRC_9_A.txt'}, + {'name': 'MSRC21', 'dataset': '../../datasets/MSRC_21_txt/MSRC_21_A.txt'}, + {'name': 'SYNTHETIC', 'dataset': '../../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, + {'name': 'BZR', 'dataset': '../../datasets/BZR_txt/BZR_A_sparse.txt'}, + {'name': 'COX2', 'dataset': '../../datasets/COX2_txt/COX2_A_sparse.txt'}, + {'name': 'DHFR', 'dataset': '../../datasets/DHFR_txt/DHFR_A_sparse.txt'}, + {'name': 'PROTEINS', 'dataset': '../../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, + {'name': 'PROTEINS_full', 'dataset': '../../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, + {'name': 'NCI1', 'dataset': '../../datasets/NCI1/NCI1.mat', + 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, + {'name': 'NCI109', 'dataset': '../../datasets/NCI109/NCI109.mat', + 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, + {'name': 'NCI-HIV', 'dataset': '../../datasets/NCI-HIV/AIDO99SD.sdf', + 'dataset_y': '../../datasets/NCI-HIV/aids_conc_may04.txt',}, + +# # not working below +# {'name': 'PTC_FM', 'dataset': '../../datasets/PTC/Train/FM.ds',}, +# {'name': 'PTC_FR', 'dataset': '../../datasets/PTC/Train/FR.ds',}, +# {'name': 'PTC_MM', 'dataset': '../../datasets/PTC/Train/MM.ds',}, +# {'name': 'PTC_MR', 'dataset': '../../datasets/PTC/Train/MR.ds',}, +] + +for ds in dslist: + dataset, y = loadDataset( + ds['dataset'], + filename_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None)) + attrs = get_dataset_attributes( + dataset, target=y, node_label='atom', edge_label='bond_type') + print() + print(ds['name'] + ':') + for atr in attrs: + print(atr, ':', attrs[atr]) + print() \ No newline at end of file From 099a1a35ccd4d76d0b6960a0943f98041c1fdfee Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:17 +0200 Subject: [PATCH 027/728] New translations .appveyor.yml (Chinese Simplified) --- lang/zh/.appveyor.yml | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 lang/zh/.appveyor.yml diff --git a/lang/zh/.appveyor.yml b/lang/zh/.appveyor.yml new file mode 100644 index 0000000000..d63af3a00f --- /dev/null +++ b/lang/zh/.appveyor.yml @@ -0,0 +1,29 @@ +--- +environment: + matrix: + - + PYTHON: "C:\\Python36" + - + PYTHON: "C:\\Python36-x64" + - + PYTHON: "C:\\Python37" + - + PYTHON: "C:\\Python37-x64" + - + PYTHON: "C:\\Python38" + - + PYTHON: "C:\\Python38-x64" +#skip_commits: +#files: +#- "*.yml" +#- "*.rst" +#- "LICENSE" +install: + - "%PYTHON%\\python.exe -m pip install -U pip" + - "%PYTHON%\\python.exe -m pip install wheel" + - "%PYTHON%\\python.exe -m pip install -r requirements.txt" + - "%PYTHON%\\python.exe -m pip install -U pytest" +build: false +test_script: + - "%PYTHON%\\python.exe setup.py bdist_wheel" + - "%PYTHON%\\python.exe -m pytest -v gklearn/tests/ --ignore=gklearn/tests/test_median_preimage_generator.py" From 62338f40fa3bf38c8ca265ca088330372bc0af56 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:19 +0200 Subject: [PATCH 028/728] New translations run_structuralspkernel.py (French) --- lang/fr/notebooks/run_structuralspkernel.py | 96 +++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 lang/fr/notebooks/run_structuralspkernel.py diff --git a/lang/fr/notebooks/run_structuralspkernel.py b/lang/fr/notebooks/run_structuralspkernel.py new file mode 100644 index 0000000000..612cfc41a0 --- /dev/null +++ b/lang/fr/notebooks/run_structuralspkernel.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Sep 28 16:37:29 2018 + +@author: ljia +""" + +import functools +from libs import * +import multiprocessing + +from gklearn.kernels.structuralspKernel import structuralspkernel +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + +dslist = [ +# {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', +# 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, +# # contains single node graph, node symb +# {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', +# 'task': 'regression'}, # node symb +# {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb +# {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled +# {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb +# {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, +# # node nsymb +# {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb +# {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1_A.txt'}, # node symb +# {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109_A.txt'}, # node symb +# {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, +# # node symb/nsymb + {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb +# {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt'}, +# # node nsymb symb +# +# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# # node/edge symb + # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb + # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb + # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb + # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'}, + # + # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb + # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb + # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb + + # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb + # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf', + # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb + + # # not working below + # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',}, + # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',}, + # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',}, + # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',}, +] +estimator = structuralspkernel + +## for non-symbolic labels. +#gkernels = [functools.partial(gaussiankernel, gamma=1 / ga) +# for ga in np.logspace(0, 10, num=11, base=10)] +#mixkernels = [functools.partial(kernelproduct, deltakernel, gk) for gk in gkernels] +#sub_kernels = [{'symb': deltakernel, 'nsymb': gkernels[i], 'mix': mixkernels[i]} +# for i in range(len(gkernels))] + +# for symbolic labels only. +#gaussiankernel = functools.partial(gaussiankernel, gamma=0.5) +mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) +sub_kernels = [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}] + +param_grid_precomputed = {'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, + 'compute_method': ['naive']} +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +for ds in dslist: + print() + print(ds['name']) + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False, + verbose=True) + print() From 410158ba36dac9d0525fc8b66993b89fb0354888 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:20 +0200 Subject: [PATCH 029/728] New translations all_ave_gm_times.svg (Chinese Simplified) --- .../docs/source/figures/all_ave_gm_times.svg | 2060 +++++++++++++++++ 1 file changed, 2060 insertions(+) create mode 100644 lang/zh/docs/source/figures/all_ave_gm_times.svg diff --git a/lang/zh/docs/source/figures/all_ave_gm_times.svg b/lang/zh/docs/source/figures/all_ave_gm_times.svg new file mode 100644 index 0000000000..5da69010bd --- /dev/null +++ b/lang/zh/docs/source/figures/all_ave_gm_times.svg @@ -0,0 +1,2060 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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gklearn.kernels.untilHPathKernel.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.kernels.untilHPathKernel.rst | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.kernels.untilHPathKernel.rst diff --git a/lang/zh/docs/source/gklearn.kernels.untilHPathKernel.rst b/lang/zh/docs/source/gklearn.kernels.untilHPathKernel.rst new file mode 100644 index 0000000000..76f39105bb --- /dev/null +++ b/lang/zh/docs/source/gklearn.kernels.untilHPathKernel.rst @@ -0,0 +1,7 @@ +gklearn.kernels.untilHPathKernel +================================ + +.. automodule:: gklearn.kernels.untilHPathKernel + :members: + :undoc-members: + :show-inheritance: From 48b6368f03562accf495ee2074a03fbda1c48733 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:24 +0200 Subject: [PATCH 031/728] New translations gklearn.kernels.treeletKernel.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.kernels.treeletKernel.rst | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.kernels.treeletKernel.rst diff --git a/lang/zh/docs/source/gklearn.kernels.treeletKernel.rst b/lang/zh/docs/source/gklearn.kernels.treeletKernel.rst new file mode 100644 index 0000000000..c88016dcb8 --- /dev/null +++ b/lang/zh/docs/source/gklearn.kernels.treeletKernel.rst @@ -0,0 +1,7 @@ +gklearn.kernels.treeletKernel +============================= + +.. automodule:: gklearn.kernels.treeletKernel + :members: + :undoc-members: + :show-inheritance: From 022da36093a856c27503a3d38000b9a9900be091 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:26 +0200 Subject: [PATCH 032/728] New translations gklearn.kernels.structuralspKernel.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.kernels.structuralspKernel.rst | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.kernels.structuralspKernel.rst diff --git a/lang/zh/docs/source/gklearn.kernels.structuralspKernel.rst b/lang/zh/docs/source/gklearn.kernels.structuralspKernel.rst new file mode 100644 index 0000000000..90c0fe3c2d --- /dev/null +++ b/lang/zh/docs/source/gklearn.kernels.structuralspKernel.rst @@ -0,0 +1,7 @@ +gklearn.kernels.structuralspKernel +================================== + +.. automodule:: gklearn.kernels.structuralspKernel + :members: + :undoc-members: + :show-inheritance: From 89627c10d9e11b0a74bdf43523820ddb5a6d4963 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:27 +0200 Subject: [PATCH 033/728] New translations gklearn.kernels.spKernel.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.kernels.spKernel.rst | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.kernels.spKernel.rst diff --git a/lang/zh/docs/source/gklearn.kernels.spKernel.rst b/lang/zh/docs/source/gklearn.kernels.spKernel.rst new file mode 100644 index 0000000000..d9da9bcdcf --- /dev/null +++ b/lang/zh/docs/source/gklearn.kernels.spKernel.rst @@ -0,0 +1,7 @@ +gklearn.kernels.spKernel +======================== + +.. automodule:: gklearn.kernels.spKernel + :members: + :undoc-members: + :show-inheritance: From 646e3c85a255d6900d2f390a96e29d728ed0b30f Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:29 +0200 Subject: [PATCH 034/728] New translations gklearn.kernels.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.kernels.rst | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.kernels.rst diff --git a/lang/zh/docs/source/gklearn.kernels.rst b/lang/zh/docs/source/gklearn.kernels.rst new file mode 100644 index 0000000000..404d2d3641 --- /dev/null +++ b/lang/zh/docs/source/gklearn.kernels.rst @@ -0,0 +1,19 @@ +gklearn.kernels +=============== + +.. automodule:: gklearn.kernels + :members: + :undoc-members: + :show-inheritance: + +.. toctree:: + + gklearn.kernels.commonWalkKernel + gklearn.kernels.marginalizedKernel + gklearn.kernels.randomWalkKernel + gklearn.kernels.spKernel + gklearn.kernels.structuralspKernel + gklearn.kernels.treeletKernel + gklearn.kernels.untilHPathKernel + gklearn.kernels.weisfeilerLehmanKernel + From 50954cc48a526d50c45034a27459b58bfc478c47 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:30 +0200 Subject: [PATCH 035/728] New translations gklearn.kernels.randomWalkKernel.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.kernels.randomWalkKernel.rst | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.kernels.randomWalkKernel.rst diff --git a/lang/zh/docs/source/gklearn.kernels.randomWalkKernel.rst b/lang/zh/docs/source/gklearn.kernels.randomWalkKernel.rst new file mode 100644 index 0000000000..f6a24d6618 --- /dev/null +++ b/lang/zh/docs/source/gklearn.kernels.randomWalkKernel.rst @@ -0,0 +1,7 @@ +gklearn.kernels.randomWalkKernel +================================ + +.. automodule:: gklearn.kernels.randomWalkKernel + :members: + :undoc-members: + :show-inheritance: From 891fd04aee5c266ec8783ff739b17899dfb99bb0 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:32 +0200 Subject: [PATCH 036/728] New translations gklearn.kernels.marginalizedKernel.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.kernels.marginalizedKernel.rst | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.kernels.marginalizedKernel.rst diff --git a/lang/zh/docs/source/gklearn.kernels.marginalizedKernel.rst b/lang/zh/docs/source/gklearn.kernels.marginalizedKernel.rst new file mode 100644 index 0000000000..70141f7a16 --- /dev/null +++ b/lang/zh/docs/source/gklearn.kernels.marginalizedKernel.rst @@ -0,0 +1,7 @@ +gklearn.kernels.marginalizedKernel +================================== + +.. automodule:: gklearn.kernels.marginalizedKernel + :members: + :undoc-members: + :show-inheritance: From 7addf224903030d85cd5432af450968a243ae326 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:33 +0200 Subject: [PATCH 037/728] New translations gklearn.kernels.commonWalkKernel.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.kernels.commonWalkKernel.rst | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.kernels.commonWalkKernel.rst diff --git a/lang/zh/docs/source/gklearn.kernels.commonWalkKernel.rst b/lang/zh/docs/source/gklearn.kernels.commonWalkKernel.rst new file mode 100644 index 0000000000..1b4b4d8d9d --- /dev/null +++ b/lang/zh/docs/source/gklearn.kernels.commonWalkKernel.rst @@ -0,0 +1,7 @@ +gklearn.kernels.commonWalkKernel +================================ + +.. automodule:: gklearn.kernels.commonWalkKernel + :members: + :undoc-members: + :show-inheritance: From 1cfc3b70d58a16bb061c4eebd165731dcc335573 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:35 +0200 Subject: [PATCH 038/728] New translations all_test_accuracy.svg (Chinese Simplified) --- .../docs/source/figures/all_test_accuracy.svg | 2113 +++++++++++++++++ 1 file changed, 2113 insertions(+) create mode 100644 lang/zh/docs/source/figures/all_test_accuracy.svg diff --git a/lang/zh/docs/source/figures/all_test_accuracy.svg b/lang/zh/docs/source/figures/all_test_accuracy.svg new file mode 100644 index 0000000000..db9baebe9b --- /dev/null +++ b/lang/zh/docs/source/figures/all_test_accuracy.svg @@ -0,0 +1,2113 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From fdec5af1e13f7900ab84770288bf484b7405626f Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:36 +0200 Subject: [PATCH 039/728] New translations experiments.rst (Chinese Simplified) --- lang/zh/docs/source/experiments.rst | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 lang/zh/docs/source/experiments.rst diff --git a/lang/zh/docs/source/experiments.rst b/lang/zh/docs/source/experiments.rst new file mode 100644 index 0000000000..de137c08c0 --- /dev/null +++ b/lang/zh/docs/source/experiments.rst @@ -0,0 +1,23 @@ +Experiments +=========== + +To exhibit the effectiveness and practicability of `graphkit-learn` library, we tested it on several benchmark datasets. See `(Kersting et al., 2016) `__ for details on these datasets. + +A two-layer nested cross-validation (CV) is applied to select and evaluate models, where outer CV randomly splits the dataset into 10 folds with 9 as validation set, and inner CV then randomly splits validation set to 10 folds with 9 as training set. The whole procedure is performed 30 times, and the average performance is computed over these trails. Possible parameters of a graph kernel are also tuned during this procedure. + +The machine used to execute the experiments is a cluster with 28 CPU cores of Intel(R) Xeon(R) E5-2680 v4 @ 2.40GHz, 252GB memory, and 64-bit operating system CentOS Linux release 7.3.1611. All results were run with Python 3.5.2. + +The figure below exhibits accuracies achieved by graph kernels implemented in `graphkit-learn` library. Each row corresponds to a dataset and each column to a graph kernel. Accuracies are in percentage for classification and in terms of errors of boiling points for regression (Alkane and +Acyclic datasets). Red color indicates a worse result and green a better one. Gray cells with the “inf” marker indicate that the computation of the graph kernel on the dataset is neglected due to much higher consumption of computational resources than other kernels. + +.. image:: figures/all_test_accuracy.svg + :width: 600 + :alt: accuracies + +The figure below displays computational time consumed to compute Gram matrices of each graph +kernels (in :math:`log10` of seconds) on each dataset. Colors have the same meaning as in the figure above. + +.. image:: figures/all_ave_gm_times.svg + :width: 600 + :alt: computational time + From c4b9edd4e83805f97b5681086edc3a14b5e786cb Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:38 +0200 Subject: [PATCH 040/728] New translations .coveragerc (Chinese Simplified) --- lang/zh/.coveragerc | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 lang/zh/.coveragerc diff --git a/lang/zh/.coveragerc b/lang/zh/.coveragerc new file mode 100644 index 0000000000..1acf8611f6 --- /dev/null +++ b/lang/zh/.coveragerc @@ -0,0 +1,4 @@ +[run] +omit = + gklearn/tests/* + gklearn/examples/* From 3ce6bebe5079d6f50b498e0298f30681c2e7ebfc Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:39 +0200 Subject: [PATCH 041/728] New translations conf.py (Chinese Simplified) --- lang/zh/docs/source/conf.py | 194 ++++++++++++++++++++++++++++++++++++ 1 file changed, 194 insertions(+) create mode 100644 lang/zh/docs/source/conf.py diff --git a/lang/zh/docs/source/conf.py b/lang/zh/docs/source/conf.py new file mode 100644 index 0000000000..b0fae5a482 --- /dev/null +++ b/lang/zh/docs/source/conf.py @@ -0,0 +1,194 @@ +# -*- coding: utf-8 -*- +# +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +sys.path.insert(0, os.path.abspath('.')) +# sys.path.insert(0, os.path.abspath('..')) +sys.path.insert(0, '../') +sys.path.insert(0, '../../') + +# -- Project information ----------------------------------------------------- + +project = 'graphkit-learn' +copyright = '2020, Linlin Jia' +author = 'Linlin Jia' + +# The short X.Y version +version = '' +# The full version, including alpha/beta/rc tags +release = '1.0.0' + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.doctest', + 'sphinx.ext.todo', + 'sphinx.ext.coverage', + 'sphinx.ext.mathjax', + 'sphinx.ext.ifconfig', + 'sphinx.ext.viewcode', + 'm2r', +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +source_suffix = ['.rst', '.md'] +# source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = None + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +# html_theme = 'alabaster' +html_theme = 'sphinx_rtd_theme' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'graphkit-learndoc' + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'graphkit-learn.tex', 'graphkit-learn Documentation', + 'Linlin Jia', 'manual'), +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'graphkit-learn', 'graphkit-learn Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'graphkit-learn', 'graphkit-learn Documentation', + author, 'graphkit-learn', 'One line description of project.', + 'Miscellaneous'), +] + + +# -- Options for Epub output ------------------------------------------------- + +# Bibliographic Dublin Core info. +epub_title = project + +# The unique identifier of the text. This can be a ISBN number +# or the project homepage. +# +# epub_identifier = '' + +# A unique identification for the text. +# +# epub_uid = '' + +# A list of files that should not be packed into the epub file. +epub_exclude_files = ['search.html'] + + +# -- Extension configuration ------------------------------------------------- + +# -- Options for todo extension ---------------------------------------------- + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = True + +add_module_names = False From 9774dcf15bae67998a1f268b2b0f577a02b519b5 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:41 +0200 Subject: [PATCH 042/728] New translations requirements.txt (Chinese Simplified) --- lang/zh/docs/requirements.txt | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 lang/zh/docs/requirements.txt diff --git a/lang/zh/docs/requirements.txt b/lang/zh/docs/requirements.txt new file mode 100644 index 0000000000..52189409e2 --- /dev/null +++ b/lang/zh/docs/requirements.txt @@ -0,0 +1,4 @@ +sphinx +m2r +nbsphinx +ipykernel From d4d92da08d33aed73ed1c5c974b9b1da03cf0df4 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:42 +0200 Subject: [PATCH 043/728] New translations make.bat (Chinese Simplified) --- lang/zh/docs/make.bat | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 lang/zh/docs/make.bat diff --git a/lang/zh/docs/make.bat b/lang/zh/docs/make.bat new file mode 100644 index 0000000000..543c6b13b4 --- /dev/null +++ b/lang/zh/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd From 27ff79275bd4f3251ceda5618968f98726684910 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:44 +0200 Subject: [PATCH 044/728] New translations commands.md (Chinese Simplified) --- lang/zh/docs/commands.md | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 lang/zh/docs/commands.md diff --git a/lang/zh/docs/commands.md b/lang/zh/docs/commands.md new file mode 100644 index 0000000000..ff7cc4cd79 --- /dev/null +++ b/lang/zh/docs/commands.md @@ -0,0 +1,5 @@ +sphinx-apidoc -o docs/ gklearn/ --separate + +sphinx-apidoc -o source/ ../gklearn/ --separate --force --module-first --no-toc + +make html From 76bc1606786a482afba16b38abc084e5c913d6a5 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:46 +0200 Subject: [PATCH 045/728] New translations Makefile (Chinese Simplified) --- lang/zh/docs/Makefile | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 lang/zh/docs/Makefile diff --git a/lang/zh/docs/Makefile b/lang/zh/docs/Makefile new file mode 100644 index 0000000000..69fe55ecfa --- /dev/null +++ b/lang/zh/docs/Makefile @@ -0,0 +1,19 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file From 32123b57b13c377fe6be07b06c6320aaf9d7557e Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:47 +0200 Subject: [PATCH 046/728] New translations Problems.md (Chinese Simplified) --- lang/zh/Problems.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 lang/zh/Problems.md diff --git a/lang/zh/Problems.md b/lang/zh/Problems.md new file mode 100644 index 0000000000..cb7dd1e1b1 --- /dev/null +++ b/lang/zh/Problems.md @@ -0,0 +1,23 @@ +# About graph kenrels. + +## (Random walk) Sylvester equation kernel. + +### ImportError: cannot import name 'frange' from 'matplotlib.mlab' + +You are using an outdated `control` with a recent `matplotlib`. `mlab.frange` was removed in `matplotlib-3.1.0`, and `control` removed the call in `control-0.8.2`. + +Update your `control` package. + +### Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so. + +The Intel Math Kernel Library (MKL) is missing or not properly set. I assume MKL is required by the `control` module. + +Install MKL. Then add the following to your path: + +``` +export PATH=/opt/intel/bin:$PATH + +export LD_LIBRARY_PATH=/opt/intel/lib/intel64:/opt/intel/mkl/lib/intel64:$LD_LIBRARY_PATH + +export LD_PRELOAD=/opt/intel/mkl/lib/intel64/libmkl_def.so:/opt/intel/mkl/lib/intel64/libmkl_avx2.so:/opt/intel/mkl/lib/intel64/libmkl_core.so:/opt/intel/mkl/lib/intel64/libmkl_intel_lp64.so:/opt/intel/mkl/lib/intel64/libmkl_intel_thread.so:/opt/intel/lib/intel64_lin/libiomp5.so +``` From 141f86177ee688f2836b4fd03373454519e9e1b0 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:49 +0200 Subject: [PATCH 047/728] New translations LICENSE (Chinese Simplified) --- lang/zh/LICENSE | 674 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 674 insertions(+) create mode 100644 lang/zh/LICENSE diff --git a/lang/zh/LICENSE b/lang/zh/LICENSE new file mode 100644 index 0000000000..94a9ed024d --- /dev/null +++ b/lang/zh/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. You can apply it to +your programs, too. + + When we speak of free software, we are referring to freedom, not +price. Our General Public Licenses are designed to make sure that you +have the freedom to distribute copies of free software (and charge for +them if you wish), that you receive source code or can get it if you +want it, that you can change the software or use pieces of it in new +free programs, and that you know you can do these things. + + To protect your rights, we need to prevent others from denying you +these rights or asking you to surrender the rights. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. From e7c60627fbd6197055799ccfe1d28c1d2b80c99f Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:50 +0200 Subject: [PATCH 048/728] New translations .travis.yml (Chinese Simplified) --- lang/zh/.travis.yml | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 lang/zh/.travis.yml diff --git a/lang/zh/.travis.yml b/lang/zh/.travis.yml new file mode 100644 index 0000000000..2e79bef8aa --- /dev/null +++ b/lang/zh/.travis.yml @@ -0,0 +1,21 @@ +--- +language: python +python: + - '3.6' + - '3.7' + - '3.8' +before_install: + - python --version + - pip install -U pip + - pip install -U pytest + - pip install codecov + - pip install coverage + - pip install pytest-cov +install: + - pip install -r requirements.txt + - pip install wheel +script: + - python setup.py bdist_wheel + - if [ $TRAVIS_PYTHON_VERSION == 3.6 ]; then pytest -v --cov-config=.coveragerc --cov-report term --cov=gklearn gklearn/tests/; else pytest -v --cov-config=.coveragerc --cov-report term --cov=gklearn gklearn/tests/ --ignore=gklearn/tests/test_median_preimage_generator.py; fi +after_success: + - codecov From 0c037a8887ba5a970990ab17ddd496c5deffa5e4 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:52 +0200 Subject: [PATCH 049/728] New translations .readthedocs.yml (Chinese Simplified) --- lang/zh/.readthedocs.yml | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 lang/zh/.readthedocs.yml diff --git a/lang/zh/.readthedocs.yml b/lang/zh/.readthedocs.yml new file mode 100644 index 0000000000..32329e3116 --- /dev/null +++ b/lang/zh/.readthedocs.yml @@ -0,0 +1,27 @@ +--- +#.readthedocs.yml +#Read the Docs configuration file +#See https://docs.readthedocs.io/en/stable/config-file/v2.html for details +#Required +version: 2 +#Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/source/conf.py +#Build documentation with MkDocs +#mkdocs: +#configuration: mkdocs.yml +#Optionally build your docs in additional formats such as PDF and ePub +formats: all +#Optionally set the version of Python and requirements required to build your docs +python: + version: 3.6 + install: + - + requirements: docs/requirements.txt + - + requirements: requirements.txt + - + method: pip + path: . + extra_requirements: + - docs From 6797af36d71fdaaa18bcff5aa37e1ddc1b44e7c2 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:54 +0200 Subject: [PATCH 050/728] New translations .gitignore (Chinese Simplified) --- lang/zh/.gitignore | 75 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 lang/zh/.gitignore diff --git a/lang/zh/.gitignore b/lang/zh/.gitignore new file mode 100644 index 0000000000..13141a3144 --- /dev/null +++ b/lang/zh/.gitignore @@ -0,0 +1,75 @@ +# Jupyter Notebook +.ipynb_checkpoints +datasets/* +!datasets/ds.py +!datasets/Alkane/ +!datasets/acyclic/ +!datasets/Acyclic/ +!datasets/MAO/ +!datasets/PAH/ +!datasets/MUTAG/ +!datasets/Letter-med/ +!datasets/ENZYMES_txt/ +!datasets/DD/ +!datasets/NCI1/ +!datasets/NCI109/ +!datasets/AIDS/ +!datasets/monoterpenoides/ +!datasets/Monoterpenoides/ +!datasets/Fingerprint/*.txt +!datasets/Cuneiform/*.txt +notebooks/results/* +notebooks/check_gm/* +notebooks/test_parallel/* +requirements/* +gklearn/model.py +gklearn/kernels/*_sym.py +*.npy +*.eps +*.dat +*.pyc + +gklearn/preimage/* +!gklearn/preimage/*.py +!gklearn/preimage/experiments/*.py +!gklearn/preimage/experiments/tools/*.py + +__pycache__ +##*# + +docs/build/* +!docs/build/latex/*.pdf +docs/log* + +*.egg-info +dist/ +build/ + +.coverage +htmlcov + +virtualenv + +.vscode/ + +# gedlibpy +gklearn/gedlib/build/ +gklearn/gedlib/build/__pycache__/ +gklearn/gedlib/collections/ +gklearn/gedlib/Median_Example/ +gklearn/gedlib/build/include/gedlib-master/median/collections/ +gklearn/gedlib/include/ +gklearn/gedlib/libgxlgedlib.so + +# misc +notebooks/preimage/ +notebooks/unfinished +gklearn/kernels/else/ +gklearn/kernels/unfinished/ +gklearn/kernels/.tags + +# pyenv +.python-version + +# docker travis debug. +ci.sh From 8c442a6f110b6d3587f436ffe1c0f70b4f02417a Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:55 +0200 Subject: [PATCH 051/728] New translations run_treeletkernel.ipynb (French) --- lang/fr/notebooks/run_treeletkernel.ipynb | 190 ++++++++++++++++++++++ 1 file changed, 190 insertions(+) create mode 100644 lang/fr/notebooks/run_treeletkernel.ipynb diff --git a/lang/fr/notebooks/run_treeletkernel.ipynb b/lang/fr/notebooks/run_treeletkernel.ipynb new file mode 100644 index 0000000000..f9e3a5f897 --- /dev/null +++ b/lang/fr/notebooks/run_treeletkernel.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Acyclic\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "getting canonkeys: 183it [00:00, 2869.32it/s]\n", + "calculating kernels: 16836it [00:00, 289967.90it/s]\n", + "\n", + " --- treelet kernel matrix of size 183 built in 0.2736480236053467 seconds ---\n", + "\n", + "the gram matrix with parameters {'sub_kernel': , 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting canonkeys: 183it [00:00, 2431.05it/s]\n", + "calculating kernels: 16836it [00:00, 225177.06it/s]\n", + "\n", + " --- treelet kernel matrix of size 183 built in 0.2614881992340088 seconds ---\n", + "\n", + "the gram matrix with parameters {'sub_kernel': , 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + "2 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 30it [00:06, 4.34it/s]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'sub_kernel': , 'n_jobs': 8, 'verbose': True}]\n", + "best_params_in: [{'alpha': 0.01}]\n", + "\n", + "best_val_perf: 8.699254729880051\n", + "best_val_std: 0.6859488791023038\n", + "final_performance: [10.449041034883777]\n", + "final_confidence: [5.005824863496953]\n", + "train_performance: [1.3405521528763233]\n", + "train_std: [0.0923786919637616]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 0.27±0.01s\n", + "time to calculate best gram matrix: 0.26±0.00s\n", + "total training time with all hyper-param choices: 8.18s\n", + "\n", + "\n", + "\n", + "Alkane\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "getting canonkeys: 150it [00:00, 1460.40it/s]\n", + "calculating kernels: 11325it [00:00, 188753.18it/s]\n", + "\n", + " --- treelet kernel matrix of size 150 built in 0.452197790145874 seconds ---\n", + "\n", + "the gram matrix with parameters {'sub_kernel': , 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "getting canonkeys: 150it [00:00, 3273.02it/s]\n", + "calculating kernels: 11325it [00:00, 223074.04it/s]\n", + "\n", + " --- treelet kernel matrix of size 150 built in 0.2638716697692871 seconds ---\n", + "\n", + "the gram matrix with parameters {'sub_kernel': , 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + "2 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 1it [00:01, 1.41s/it]" + ] + } + ], + "source": [ + "from libs import *\n", + "import multiprocessing\n", + "\n", + "from gklearn.kernels.treeletKernel import treeletkernel\n", + "from gklearn.utils.kernels import gaussiankernel, polynomialkernel\n", + "\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n", + " 'task': 'regression'}, # node symb\n", + " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n", + " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, \n", + " # contains single node graph, node symb\n", + " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb\n", + " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled\n", + " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb\n", + "# {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n", + "# # node nsymb\n", + " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + " # node symb/nsymb\n", + "# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + "# # node/edge symb\n", + "# {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb\n", + "\n", + " # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + " # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n", + " # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n", + " # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", + " #\n", + " # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n", + " # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n", + " # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n", + "\n", + " # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n", + " {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n", + " # {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n", + " # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n", + "\n", + " # # not working below\n", + " # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n", + " # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n", + " # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n", + " # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "estimator = treeletkernel\n", + "param_grid_precomputed = {'sub_kernel': [gaussiankernel, polynomialkernel]}\n", + "param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n", + " {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n", + "\n", + "for ds in dslist:\n", + " print()\n", + " print(ds['name'])\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'],\n", + " estimator,\n", + " param_grid_precomputed,\n", + " (param_grid[1] if ('task' in ds and ds['task']\n", + " == 'regression') else param_grid[0]),\n", + " (ds['task'] if 'task' in ds else 'classification'),\n", + " NUM_TRIALS=30,\n", + " datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n", + " ds_name=ds['name'],\n", + " n_jobs=multiprocessing.cpu_count(),\n", + " read_gm_from_file=False,\n", + " verbose=True)\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 64fbfe7eb71773218adbd88717897e8a3a6c6e0f Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:56 +0200 Subject: [PATCH 052/728] New translations run_structuralspkernel.ipynb (French) --- .../fr/notebooks/run_structuralspkernel.ipynb | 219 ++++++++++++++++++ 1 file changed, 219 insertions(+) create mode 100644 lang/fr/notebooks/run_structuralspkernel.ipynb diff --git a/lang/fr/notebooks/run_structuralspkernel.ipynb b/lang/fr/notebooks/run_structuralspkernel.ipynb new file mode 100644 index 0000000000..79dcd0d225 --- /dev/null +++ b/lang/fr/notebooks/run_structuralspkernel.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Acyclic\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "getting shortest paths: 183it [00:00, 5323.35it/s]\n", + "calculating kernels: 16836it [00:02, 5980.75it/s]\n", + "\n", + " --- shortest path kernel matrix of size 183 built in 3.0884954929351807 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + "1 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 30it [00:03, 8.90it/s]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'compute_method': 'naive', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True}]\n", + "best_params_in: [{'alpha': 0.001}]\n", + "\n", + "best_val_perf: 12.857015647214508\n", + "best_val_std: 0.8860388066269581\n", + "final_performance: [12.157314781928168]\n", + "final_confidence: [2.5739406086892296]\n", + "train_performance: [3.773093745028789]\n", + "train_std: [0.12430822644728814]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 3.09±0.00s\n", + "time to calculate best gram matrix: 3.09±0.00s\n", + "total training time with all hyper-param choices: 6.84s\n", + "\n", + "\n", + "\n", + "Alkane\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "getting shortest paths: 150it [00:00, 5191.83it/s]\n", + "calculating kernels: 11325it [00:01, 7143.18it/s]\n", + "\n", + " --- shortest path kernel matrix of size 150 built in 1.7898523807525635 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + "1 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 30it [00:02, 10.59it/s]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'compute_method': 'naive', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True}]\n", + "best_params_in: [{'alpha': 0.1}]\n", + "\n", + "best_val_perf: 11.040598123045763\n", + "best_val_std: 0.31492017111536147\n", + "final_performance: [8.138193149138093]\n", + "final_confidence: [1.6238744767195439]\n", + "train_performance: [7.9412913127748235]\n", + "train_std: [0.18726339675217385]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 1.79±0.00s\n", + "time to calculate best gram matrix: 1.79±0.00s\n", + "total training time with all hyper-param choices: 5.00s\n", + "\n", + "\n", + "\n", + "MAO\n", + "\n", + "--- This is a classification problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "getting shortest paths: 68it [00:00, 536.19it/s]\n", + "calculating kernels: 0it [00:00, ?it/s]" + ] + } + ], + "source": [ + "import functools\n", + "from libs import *\n", + "import multiprocessing\n", + "\n", + "from gklearn.kernels.structuralspKernel import structuralspkernel\n", + "from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct\n", + "\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n", + " 'task': 'regression'}, # node symb\n", + " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n", + " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, \n", + " # contains single node graph, node symb\n", + " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb\n", + " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled\n", + " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb\n", + " {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n", + " # node nsymb\n", + " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + " # node symb/nsymb\n", + "# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + "# # node/edge symb\n", + "# {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb\n", + "\n", + " # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + " # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n", + " # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n", + " # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", + " #\n", + " # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb, missing values\n", + "# {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb, missing values\n", + " # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n", + "\n", + " # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n", + " # {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n", + " # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n", + "\n", + "# # not working below\n", + "# {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n", + " # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n", + " # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n", + " # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "estimator = structuralspkernel\n", + "mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)\n", + "param_grid_precomputed = {'node_kernels': \n", + " [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],\n", + " 'edge_kernels': \n", + " [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],\n", + " 'compute_method': ['naive']}\n", + "param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n", + " {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n", + "\n", + "for ds in dslist:\n", + " print()\n", + " print(ds['name'])\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'],\n", + " estimator,\n", + " param_grid_precomputed,\n", + " (param_grid[1] if ('task' in ds and ds['task']\n", + " == 'regression') else param_grid[0]),\n", + " (ds['task'] if 'task' in ds else 'classification'),\n", + " NUM_TRIALS=30,\n", + " datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n", + " ds_name=ds['name'],\n", + " n_jobs=multiprocessing.cpu_count(),\n", + " read_gm_from_file=False,\n", + " verbose=True)\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0bd30aa39e43ab08276b0bc3bf275b07b31388a6 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:15:58 +0200 Subject: [PATCH 053/728] New translations gklearn.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.rst | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.rst diff --git a/lang/zh/docs/source/gklearn.rst b/lang/zh/docs/source/gklearn.rst new file mode 100644 index 0000000000..d7de14a196 --- /dev/null +++ b/lang/zh/docs/source/gklearn.rst @@ -0,0 +1,13 @@ +gklearn +======= + +.. automodule:: gklearn + :members: + :undoc-members: + :show-inheritance: + +.. toctree:: + + gklearn.kernels + gklearn.utils + From 78d97e3af6500b118e988af1ae878945231f4b7d Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:00 +0200 Subject: [PATCH 054/728] New translations test_get_nb_edit_operations_symbolic_cml.py (French) --- ...est_get_nb_edit_operations_symbolic_cml.py | 122 ++++++++++++++++++ 1 file changed, 122 insertions(+) create mode 100644 lang/fr/gklearn/tests/ged/test_get_nb_edit_operations_symbolic_cml.py diff --git a/lang/fr/gklearn/tests/ged/test_get_nb_edit_operations_symbolic_cml.py b/lang/fr/gklearn/tests/ged/test_get_nb_edit_operations_symbolic_cml.py new file mode 100644 index 0000000000..aa40cca0c8 --- /dev/null +++ b/lang/fr/gklearn/tests/ged/test_get_nb_edit_operations_symbolic_cml.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Jul 6 12:08:24 2020 + +@author: ljia +""" +import random +import numpy as np + +def test_get_nb_edit_operations_symbolic_cml(): + """Test get_nb_edit_operations_symbolic_cml(). + """ + """**1. Get dataset.**""" + + from gklearn.utils import Dataset + + # Predefined dataset name, use dataset "MUTAG". + ds_name = 'MUTAG' + + # Initialize a Dataset. + dataset = Dataset() + # Load predefined dataset "MUTAG". + dataset.load_predefined_dataset(ds_name) + graph1 = dataset.graphs[0] + graph2 = dataset.graphs[1] + + """**2. Compute graph edit distance.**""" + +# try: + # Initialize label costs randomly. + node_label_costs, edge_label_costs = _initialize_label_costs(dataset) + + # Compute GEDs. + pi_forward, pi_backward, dis, node_labels, edge_labels = _compute_ged(dataset, node_label_costs, edge_label_costs) + + + # Compute numbers of edit operations. + + from gklearn.ged.util.util import get_nb_edit_operations_symbolic_cml + + n_edit_operations = get_nb_edit_operations_symbolic_cml(graph1, graph2, pi_forward, pi_backward, node_labels, edge_labels) + + assert np.abs((np.dot(np.concatenate((node_label_costs, edge_label_costs)), n_edit_operations) - dis) / dis) < 10e-6 + +# except Exception as exception: +# assert False, exception + + +def _initialize_label_costs(dataset): + node_label_costs = _initialize_node_label_costs(dataset) + edge_label_costs = _initialize_edge_label_costs(dataset) + return node_label_costs, edge_label_costs + + +def _initialize_node_label_costs(dataset): + # Get list of node labels. + nls = dataset.get_all_node_labels() + # Generate random costs. + nb_nl = int((len(nls) * (len(nls) - 1)) / 2 + 2 * len(nls)) + rand_costs = random.sample(range(1, 10 * nb_nl + 1), nb_nl) + rand_costs /= np.max(rand_costs) + + return rand_costs + + +def _initialize_edge_label_costs(dataset): + # Get list of edge labels. + els = dataset.get_all_edge_labels() + # Generate random costs. + nb_el = int((len(els) * (len(els) - 1)) / 2 + 2 * len(els)) + rand_costs = random.sample(range(1, 10 * nb_el + 1), nb_el) + rand_costs /= np.max(rand_costs) + + return rand_costs + + +def _compute_ged(dataset, node_label_costs, edge_label_costs): + from gklearn.ged.env import GEDEnv + from gklearn.ged.util.util import label_costs_to_matrix + import networkx as nx + + ged_env = GEDEnv() # initailize GED environment. + ged_env.set_edit_cost('CONSTANT', # GED cost type. + edit_cost_constants=[3, 3, 1, 3, 3, 1] # edit costs. + ) + for g in dataset.graphs: + ged_env.add_nx_graph(g, '') # add graphs + + node_labels = ged_env.get_all_node_labels() + edge_labels = ged_env.get_all_edge_labels() + listID = ged_env.get_all_graph_ids() # get list IDs of graphs + ged_env.set_label_costs(label_costs_to_matrix(node_label_costs, len(node_labels)), + label_costs_to_matrix(edge_label_costs, len(edge_labels))) + ged_env.init(init_type='LAZY_WITHOUT_SHUFFLED_COPIES') # initialize GED environment. + options = {'initialization_method': 'RANDOM', # or 'NODE', etc. + 'threads': 1 # parallel threads. + } + ged_env.set_method('BIPARTITE', # GED method. + options # options for GED method. + ) + ged_env.init_method() # initialize GED method. + + ged_env.run_method(listID[0], listID[1]) # run. + + pi_forward = ged_env.get_forward_map(listID[0], listID[1]) # forward map. + pi_backward = ged_env.get_backward_map(listID[0], listID[1]) # backward map. + dis = ged_env.get_upper_bound(listID[0], listID[1]) # GED bewteen two graphs. + + # make the map label correct (label remove map as np.inf) + nodes1 = [n for n in dataset.graphs[0].nodes()] + nodes2 = [n for n in dataset.graphs[1].nodes()] + nb1 = nx.number_of_nodes(dataset.graphs[0]) + nb2 = nx.number_of_nodes(dataset.graphs[1]) + pi_forward = [nodes2[pi] if pi < nb2 else np.inf for pi in pi_forward] + pi_backward = [nodes1[pi] if pi < nb1 else np.inf for pi in pi_backward] + + return pi_forward, pi_backward, dis, node_labels, edge_labels + + +if __name__ == "__main__": + test_get_nb_edit_operations_symbolic_cml() \ No newline at end of file From 2ab48581305afd51d51022f2c7af8b2f2fe753b2 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:02 +0200 Subject: [PATCH 055/728] New translations graph_synthesizer.py (French) --- lang/fr/gklearn/utils/graph_synthesizer.py | 53 ++++++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 lang/fr/gklearn/utils/graph_synthesizer.py diff --git a/lang/fr/gklearn/utils/graph_synthesizer.py b/lang/fr/gklearn/utils/graph_synthesizer.py new file mode 100644 index 0000000000..2c5f650bfe --- /dev/null +++ b/lang/fr/gklearn/utils/graph_synthesizer.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Sep 11 18:10:06 2020 + +@author: ljia +""" +import numpy as np +import networkx as nx +import random + + +class GraphSynthesizer(object): + + + def __init__(self): + pass + + + def random_graph(self, num_nodes, num_edges, num_node_labels=0, num_edge_labels=0, seed=None, directed=False, max_num_edges=None, all_edges=None): + g = nx.Graph() + if num_node_labels > 0: + node_labels = np.random.randint(0, high=num_node_labels, size=num_nodes) + for i in range(0, num_nodes): + g.add_node(str(i), atom=node_labels[i]) # @todo: update "atom". + else: + for i in range(0, num_nodes): + g.add_node(str(i)) + + if num_edge_labels > 0: + edge_labels = np.random.randint(0, high=num_edge_labels, size=num_edges) + for idx, i in enumerate(random.sample(range(0, max_num_edges), num_edges)): + node1, node2 = all_edges[i] + g.add_edge(str(node1), str(node2), bond_type=edge_labels[idx]) # @todo: update "bond_type". + else: + for i in random.sample(range(0, max_num_edges), num_edges): + node1, node2 = all_edges[i] + g.add_edge(str(node1), str(node2)) + + return g + + + def unified_graphs(self, num_graphs=1000, num_nodes=20, num_edges=40, num_node_labels=0, num_edge_labels=0, seed=None, directed=False): + max_num_edges = int((num_nodes - 1) * num_nodes / 2) + if num_edges > max_num_edges: + raise Exception('Too many edges.') + all_edges = [(i, j) for i in range(0, num_nodes) for j in range(i + 1, num_nodes)] # @todo: optimize. No directed graphs. + + graphs = [] + for idx in range(0, num_graphs): + graphs.append(self.random_graph(num_nodes, num_edges, num_node_labels=num_node_labels, num_edge_labels=num_edge_labels, seed=seed, directed=directed, max_num_edges=max_num_edges, all_edges=all_edges)) + + return graphs \ No newline at end of file From 6c3dee7d5cccf0538eaf4e8299897ab2d460f78b Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:03 +0200 Subject: [PATCH 056/728] New translations graph_files.py (French) --- lang/fr/gklearn/utils/graph_files.py | 833 +++++++++++++++++++++++++++ 1 file changed, 833 insertions(+) create mode 100644 lang/fr/gklearn/utils/graph_files.py diff --git a/lang/fr/gklearn/utils/graph_files.py b/lang/fr/gklearn/utils/graph_files.py new file mode 100644 index 0000000000..7de4ba0441 --- /dev/null +++ b/lang/fr/gklearn/utils/graph_files.py @@ -0,0 +1,833 @@ +""" Utilities function to manage graph files +""" +from os.path import dirname, splitext + + +def load_dataset(filename, filename_targets=None, gformat=None, **kwargs): + """Read graph data from filename and load them as NetworkX graphs. + + Parameters + ---------- + filename : string + The name of the file from where the dataset is read. + filename_y : string + The name of file of the targets corresponding to graphs. + extra_params : dict + Extra parameters only designated to '.mat' format. + + Return + ------ + data : List of NetworkX graph. + + y : List + + Targets corresponding to graphs. + + Notes + ----- + This function supports following graph dataset formats: + + 'ds': load data from .ds file. See comments of function loadFromDS for a example. + + 'cxl': load data from Graph eXchange Language file (.cxl file). See + `here `__ for detail. + + 'sdf': load data from structured data file (.sdf file). See + `here `__ + for details. + + 'mat': Load graph data from a MATLAB (up to version 7.1) .mat file. See + README in `downloadable file `__ + for details. + + 'txt': Load graph data from a special .txt file. See + `here `__ + for details. Note here filename is the name of either .txt file in + the dataset directory. + """ + extension = splitext(filename)[1][1:] + if extension == "ds": + data, y, label_names = load_from_ds(filename, filename_targets) + elif extension == "cxl": + dir_dataset = kwargs.get('dirname_dataset', None) + data, y, label_names = load_from_xml(filename, dir_dataset) + elif extension == 'xml': + dir_dataset = kwargs.get('dirname_dataset', None) + data, y, label_names = load_from_xml(filename, dir_dataset) + elif extension == "mat": + order = kwargs.get('order') + data, y, label_names = load_mat(filename, order) + elif extension == 'txt': + data, y, label_names = load_tud(filename) + + return data, y, label_names + + +def save_dataset(Gn, y, gformat='gxl', group=None, filename='gfile', **kwargs): + """Save list of graphs. + """ + import os + dirname_ds = os.path.dirname(filename) + if dirname_ds != '': + dirname_ds += '/' + if not os.path.exists(dirname_ds) : + os.makedirs(dirname_ds) + + if 'graph_dir' in kwargs: + graph_dir = kwargs['graph_dir'] + '/' + if not os.path.exists(graph_dir): + os.makedirs(graph_dir) + del kwargs['graph_dir'] + else: + graph_dir = dirname_ds + + if group == 'xml' and gformat == 'gxl': + with open(filename + '.xml', 'w') as fgroup: + fgroup.write("") + fgroup.write("\n") + fgroup.write("\n") + for idx, g in enumerate(Gn): + fname_tmp = "graph" + str(idx) + ".gxl" + save_gxl(g, graph_dir + fname_tmp, **kwargs) + fgroup.write("\n\t") + fgroup.write("\n") + fgroup.close() + + +def load_ct(filename): # @todo: this function is only tested on CTFile V2000; header not considered; only simple cases (atoms and bonds are considered.) + """load data from a Chemical Table (.ct) file. + + Notes + ------ + a typical example of data in .ct is like this: + + 3 2 <- number of nodes and edges + + 0.0000 0.0000 0.0000 C <- each line describes a node (x,y,z + label) + + 0.0000 0.0000 0.0000 C + + 0.0000 0.0000 0.0000 O + + 1 3 1 1 <- each line describes an edge : to, from, bond type, bond stereo + + 2 3 1 1 + + Check `CTFile Formats file `__ + for detailed format discription. + """ + import networkx as nx + from os.path import basename + g = nx.Graph() + with open(filename) as f: + content = f.read().splitlines() + g = nx.Graph(name=str(content[0]), filename=basename(filename)) # set name of the graph + + # read the counts line. + tmp = content[1].split(' ') + tmp = [x for x in tmp if x != ''] + nb_atoms = int(tmp[0].strip()) # number of atoms + nb_bonds = int(tmp[1].strip()) # number of bonds + count_line_tags = ['number_of_atoms', 'number_of_bonds', 'number_of_atom_lists', '', 'chiral_flag', 'number_of_stext_entries', '', '', '', '', 'number_of_properties', 'CT_version'] + i = 0 + while i < len(tmp): + if count_line_tags[i] != '': # if not obsoleted + g.graph[count_line_tags[i]] = tmp[i].strip() + i += 1 + + # read the atom block. + atom_tags = ['x', 'y', 'z', 'atom_symbol', 'mass_difference', 'charge', 'atom_stereo_parity', 'hydrogen_count_plus_1', 'stereo_care_box', 'valence', 'h0_designator', '', '', 'atom_atom_mapping_number', 'inversion_retention_flag', 'exact_change_flag'] + for i in range(0, nb_atoms): + tmp = content[i + 2].split(' ') + tmp = [x for x in tmp if x != ''] + g.add_node(i) + j = 0 + while j < len(tmp): + if atom_tags[j] != '': + g.nodes[i][atom_tags[j]] = tmp[j].strip() + j += 1 + + # read the bond block. + bond_tags = ['first_atom_number', 'second_atom_number', 'bond_type', 'bond_stereo', '', 'bond_topology', 'reacting_center_status'] + for i in range(0, nb_bonds): + tmp = content[i + g.number_of_nodes() + 2].split(' ') + tmp = [x for x in tmp if x != ''] + n1, n2 = int(tmp[0].strip()) - 1, int(tmp[1].strip()) - 1 + g.add_edge(n1, n2) + j = 2 + while j < len(tmp): + if bond_tags[j] != '': + g.edges[(n1, n2)][bond_tags[j]] = tmp[j].strip() + j += 1 + + # get label names. + label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []} + atom_symbolic = [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, None, None, 1, 1, 1] + for nd in g.nodes(): + for key in g.nodes[nd]: + if atom_symbolic[atom_tags.index(key)] == 1: + label_names['node_labels'].append(key) + else: + label_names['node_attrs'].append(key) + break + bond_symbolic = [None, None, 1, 1, None, 1, 1] + for ed in g.edges(): + for key in g.edges[ed]: + if bond_symbolic[bond_tags.index(key)] == 1: + label_names['edge_labels'].append(key) + else: + label_names['edge_attrs'].append(key) + break + + return g, label_names + + +def load_gxl(filename): # @todo: directed graphs. + from os.path import basename + import networkx as nx + import xml.etree.ElementTree as ET + + tree = ET.parse(filename) + root = tree.getroot() + index = 0 + g = nx.Graph(filename=basename(filename), name=root[0].attrib['id']) + dic = {} # used to retrieve incident nodes of edges + for node in root.iter('node'): + dic[node.attrib['id']] = index + labels = {} + for attr in node.iter('attr'): + labels[attr.attrib['name']] = attr[0].text + g.add_node(index, **labels) + index += 1 + + for edge in root.iter('edge'): + labels = {} + for attr in edge.iter('attr'): + labels[attr.attrib['name']] = attr[0].text + g.add_edge(dic[edge.attrib['from']], dic[edge.attrib['to']], **labels) + + # get label names. + label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []} + for node in root.iter('node'): + for attr in node.iter('attr'): + if attr[0].tag == 'int': # @todo: this maybe wrong, and slow. + label_names['node_labels'].append(attr.attrib['name']) + else: + label_names['node_attrs'].append(attr.attrib['name']) + break + for edge in root.iter('edge'): + for attr in edge.iter('attr'): + if attr[0].tag == 'int': # @todo: this maybe wrong, and slow. + label_names['edge_labels'].append(attr.attrib['name']) + else: + label_names['edge_attrs'].append(attr.attrib['name']) + break + + return g, label_names + + +def save_gxl(graph, filename, method='default', node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]): + if method == 'default': + gxl_file = open(filename, 'w') + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + if 'name' in graph.graph: + name = str(graph.graph['name']) + else: + name = 'dummy' + gxl_file.write("\n") + for v, attrs in graph.nodes(data=True): + gxl_file.write("") + for l_name in node_labels: + gxl_file.write("" + + str(attrs[l_name]) + "") + for a_name in node_attrs: + gxl_file.write("" + + str(attrs[a_name]) + "") + gxl_file.write("\n") + for v1, v2, attrs in graph.edges(data=True): + gxl_file.write("") + for l_name in edge_labels: + gxl_file.write("" + + str(attrs[l_name]) + "") + for a_name in edge_attrs: + gxl_file.write("" + + str(attrs[a_name]) + "") + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("") + gxl_file.close() + elif method == 'benoit': + import xml.etree.ElementTree as ET + root_node = ET.Element('gxl') + attr = dict() + attr['id'] = str(graph.graph['name']) + attr['edgeids'] = 'true' + attr['edgemode'] = 'undirected' + graph_node = ET.SubElement(root_node, 'graph', attrib=attr) + + for v in graph: + current_node = ET.SubElement(graph_node, 'node', attrib={'id': str(v)}) + for attr in graph.nodes[v].keys(): + cur_attr = ET.SubElement( + current_node, 'attr', attrib={'name': attr}) + cur_value = ET.SubElement(cur_attr, + graph.nodes[v][attr].__class__.__name__) + cur_value.text = graph.nodes[v][attr] + + for v1 in graph: + for v2 in graph[v1]: + if (v1 < v2): # Non oriented graphs + cur_edge = ET.SubElement( + graph_node, + 'edge', + attrib={ + 'from': str(v1), + 'to': str(v2) + }) + for attr in graph[v1][v2].keys(): + cur_attr = ET.SubElement( + cur_edge, 'attr', attrib={'name': attr}) + cur_value = ET.SubElement( + cur_attr, graph[v1][v2][attr].__class__.__name__) + cur_value.text = str(graph[v1][v2][attr]) + + tree = ET.ElementTree(root_node) + tree.write(filename) + elif method == 'gedlib': + # reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22 +# pass + gxl_file = open(filename, 'w') + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + for v, attrs in graph.nodes(data=True): + gxl_file.write("") + gxl_file.write("" + str(attrs['chem']) + "") + gxl_file.write("\n") + for v1, v2, attrs in graph.edges(data=True): + gxl_file.write("") + gxl_file.write("" + str(attrs['valence']) + "") +# gxl_file.write("" + "1" + "") + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("") + gxl_file.close() + elif method == 'gedlib-letter': + # reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22 + # and https://github.com/dbblumenthal/gedlib/blob/master/data/datasets/Letter/HIGH/AP1_0000.gxl + gxl_file = open(filename, 'w') + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + for v, attrs in graph.nodes(data=True): + gxl_file.write("") + gxl_file.write("" + str(attrs['attributes'][0]) + "") + gxl_file.write("" + str(attrs['attributes'][1]) + "") + gxl_file.write("\n") + for v1, v2, attrs in graph.edges(data=True): + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("") + gxl_file.close() + + +# def loadSDF(filename): +# """load data from structured data file (.sdf file). + +# Notes +# ------ +# A SDF file contains a group of molecules, represented in the similar way as in MOL format. +# Check `here `__ for detailed structure. +# """ +# import networkx as nx +# from os.path import basename +# from tqdm import tqdm +# import sys +# data = [] +# with open(filename) as f: +# content = f.read().splitlines() +# index = 0 +# pbar = tqdm(total=len(content) + 1, desc='load SDF', file=sys.stdout) +# while index < len(content): +# index_old = index + +# g = nx.Graph(name=content[index].strip()) # set name of the graph + +# tmp = content[index + 3] +# nb_nodes = int(tmp[:3]) # number of the nodes +# nb_edges = int(tmp[3:6]) # number of the edges + +# for i in range(0, nb_nodes): +# tmp = content[i + index + 4] +# g.add_node(i, atom=tmp[31:34].strip()) + +# for i in range(0, nb_edges): +# tmp = content[i + index + g.number_of_nodes() + 4] +# tmp = [tmp[i:i + 3] for i in range(0, len(tmp), 3)] +# g.add_edge( +# int(tmp[0]) - 1, int(tmp[1]) - 1, bond_type=tmp[2].strip()) + +# data.append(g) + +# index += 4 + g.number_of_nodes() + g.number_of_edges() +# while content[index].strip() != '$$$$': # seperator +# index += 1 +# index += 1 + +# pbar.update(index - index_old) +# pbar.update(1) +# pbar.close() + +# return data + + +def load_mat(filename, order): # @todo: need to be updated (auto order) or deprecated. + """Load graph data from a MATLAB (up to version 7.1) .mat file. + + Notes + ------ + A MAT file contains a struct array containing graphs, and a column vector lx containing a class label for each graph. + Check README in `downloadable file `__ for detailed structure. + """ + from scipy.io import loadmat + import numpy as np + import networkx as nx + data = [] + content = loadmat(filename) + # print(content) + # print('----') + for key, value in content.items(): + if key[0] == 'l': # class label + y = np.transpose(value)[0].tolist() + # print(y) + elif key[0] != '_': + # print(value[0][0][0]) + # print() + # print(value[0][0][1]) + # print() + # print(value[0][0][2]) + # print() + # if len(value[0][0]) > 3: + # print(value[0][0][3]) + # print('----') + # if adjacency matrix is not compressed / edge label exists + if order[1] == 0: + for i, item in enumerate(value[0]): + # print(item) + # print('------') + g = nx.Graph(name=i) # set name of the graph + nl = np.transpose(item[order[3]][0][0][0]) # node label + # print(item[order[3]]) + # print() + for index, label in enumerate(nl[0]): + g.add_node(index, label_1=str(label)) + el = item[order[4]][0][0][0] # edge label + for edge in el: + g.add_edge(edge[0] - 1, edge[1] - 1, label_1=str(edge[2])) + data.append(g) + else: +# from scipy.sparse import csc_matrix + for i, item in enumerate(value[0]): + # print(item) + # print('------') + g = nx.Graph(name=i) # set name of the graph + nl = np.transpose(item[order[3]][0][0][0]) # node label + # print(nl) + # print() + for index, label in enumerate(nl[0]): + g.add_node(index, label_1=str(label)) + sam = item[order[0]] # sparse adjacency matrix + index_no0 = sam.nonzero() + for col, row in zip(index_no0[0], index_no0[1]): + # print(col) + # print(row) + g.add_edge(col, row) + data.append(g) + # print(g.edges(data=True)) + + label_names = {'node_labels': ['label_1'], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []} + if order[1] == 0: + label_names['edge_labels'].append('label_1') + + return data, y, label_names + + +def load_tud(filename): + """Load graph data from TUD dataset files. + + Notes + ------ + The graph data is loaded from separate files. + Check README in `downloadable file `__, 2018 for detailed structure. + """ + import networkx as nx + from os import listdir + from os.path import dirname, basename + + + def get_infos_from_readme(frm): # @todo: add README (cuniform), maybe node/edge label maps. + """Get information from DS_label_readme.txt file. + """ + + def get_label_names_from_line(line): + """Get names of labels/attributes from a line. + """ + str_names = line.split('[')[1].split(']')[0] + names = str_names.split(',') + names = [attr.strip() for attr in names] + return names + + + def get_class_label_map(label_map_strings): + label_map = {} + for string in label_map_strings: + integer, label = string.split('\t') + label_map[int(integer.strip())] = label.strip() + return label_map + + + label_names = {'node_labels': [], 'node_attrs': [], + 'edge_labels': [], 'edge_attrs': []} + class_label_map = None + class_label_map_strings = [] + with open(frm) as rm: + content_rm = rm.read().splitlines() + i = 0 + while i < len(content_rm): + line = content_rm[i].strip() + # get node/edge labels and attributes. + if line.startswith('Node labels:'): + label_names['node_labels'] = get_label_names_from_line(line) + elif line.startswith('Node attributes:'): + label_names['node_attrs'] = get_label_names_from_line(line) + elif line.startswith('Edge labels:'): + label_names['edge_labels'] = get_label_names_from_line(line) + elif line.startswith('Edge attributes:'): + label_names['edge_attrs'] = get_label_names_from_line(line) + # get class label map. + elif line.startswith('Class labels were converted to integer values using this map:'): + i += 2 + line = content_rm[i].strip() + while line != '' and i < len(content_rm): + class_label_map_strings.append(line) + i += 1 + line = content_rm[i].strip() + class_label_map = get_class_label_map(class_label_map_strings) + i += 1 + + return label_names, class_label_map + + + # get dataset name. + dirname_dataset = dirname(filename) + filename = basename(filename) + fn_split = filename.split('_A') + ds_name = fn_split[0].strip() + + # load data file names + for name in listdir(dirname_dataset): + if ds_name + '_A' in name: + fam = dirname_dataset + '/' + name + elif ds_name + '_graph_indicator' in name: + fgi = dirname_dataset + '/' + name + elif ds_name + '_graph_labels' in name: + fgl = dirname_dataset + '/' + name + elif ds_name + '_node_labels' in name: + fnl = dirname_dataset + '/' + name + elif ds_name + '_edge_labels' in name: + fel = dirname_dataset + '/' + name + elif ds_name + '_edge_attributes' in name: + fea = dirname_dataset + '/' + name + elif ds_name + '_node_attributes' in name: + fna = dirname_dataset + '/' + name + elif ds_name + '_graph_attributes' in name: + fga = dirname_dataset + '/' + name + elif ds_name + '_label_readme' in name: + frm = dirname_dataset + '/' + name + # this is supposed to be the node attrs, make sure to put this as the last 'elif' + elif ds_name + '_attributes' in name: + fna = dirname_dataset + '/' + name + + # get labels and attributes names. + if 'frm' in locals(): + label_names, class_label_map = get_infos_from_readme(frm) + else: + label_names = {'node_labels': [], 'node_attrs': [], + 'edge_labels': [], 'edge_attrs': []} + class_label_map = None + + with open(fgi) as gi: + content_gi = gi.read().splitlines() # graph indicator + with open(fam) as am: + content_am = am.read().splitlines() # adjacency matrix + + # load targets. + if 'fgl' in locals(): + with open(fgl) as gl: + content_targets = gl.read().splitlines() # targets (classification) + targets = [float(i) for i in content_targets] + elif 'fga' in locals(): + with open(fga) as ga: + content_targets = ga.read().splitlines() # targets (regression) + targets = [int(i) for i in content_targets] + else: + raise Exception('Can not find targets file. Please make sure there is a "', ds_name, '_graph_labels.txt" or "', ds_name, '_graph_attributes.txt"', 'file in your dataset folder.') + if class_label_map is not None: + targets = [class_label_map[t] for t in targets] + + # create graphs and add nodes + data = [nx.Graph(name=str(i)) for i in range(0, len(content_targets))] + if 'fnl' in locals(): + with open(fnl) as nl: + content_nl = nl.read().splitlines() # node labels + for idx, line in enumerate(content_gi): + # transfer to int first in case of unexpected blanks + data[int(line) - 1].add_node(idx) + labels = [l.strip() for l in content_nl[idx].split(',')] + if label_names['node_labels'] == []: # @todo: need fix bug. + for i, label in enumerate(labels): + l_name = 'label_' + str(i) + data[int(line) - 1].nodes[idx][l_name] = label + label_names['node_labels'].append(l_name) + else: + for i, l_name in enumerate(label_names['node_labels']): + data[int(line) - 1].nodes[idx][l_name] = labels[i] + else: + for i, line in enumerate(content_gi): + data[int(line) - 1].add_node(i) + + # add edges + for line in content_am: + tmp = line.split(',') + n1 = int(tmp[0]) - 1 + n2 = int(tmp[1]) - 1 + # ignore edge weight here. + g = int(content_gi[n1]) - 1 + data[g].add_edge(n1, n2) + + # add edge labels + if 'fel' in locals(): + with open(fel) as el: + content_el = el.read().splitlines() + for idx, line in enumerate(content_el): + labels = [l.strip() for l in line.split(',')] + n = [int(i) - 1 for i in content_am[idx].split(',')] + g = int(content_gi[n[0]]) - 1 + if label_names['edge_labels'] == []: + for i, label in enumerate(labels): + l_name = 'label_' + str(i) + data[g].edges[n[0], n[1]][l_name] = label + label_names['edge_labels'].append(l_name) + else: + for i, l_name in enumerate(label_names['edge_labels']): + data[g].edges[n[0], n[1]][l_name] = labels[i] + + # add node attributes + if 'fna' in locals(): + with open(fna) as na: + content_na = na.read().splitlines() + for idx, line in enumerate(content_na): + attrs = [a.strip() for a in line.split(',')] + g = int(content_gi[idx]) - 1 + if label_names['node_attrs'] == []: + for i, attr in enumerate(attrs): + a_name = 'attr_' + str(i) + data[g].nodes[idx][a_name] = attr + label_names['node_attrs'].append(a_name) + else: + for i, a_name in enumerate(label_names['node_attrs']): + data[g].nodes[idx][a_name] = attrs[i] + + # add edge attributes + if 'fea' in locals(): + with open(fea) as ea: + content_ea = ea.read().splitlines() + for idx, line in enumerate(content_ea): + attrs = [a.strip() for a in line.split(',')] + n = [int(i) - 1 for i in content_am[idx].split(',')] + g = int(content_gi[n[0]]) - 1 + if label_names['edge_attrs'] == []: + for i, attr in enumerate(attrs): + a_name = 'attr_' + str(i) + data[g].edges[n[0], n[1]][a_name] = attr + label_names['edge_attrs'].append(a_name) + else: + for i, a_name in enumerate(label_names['edge_attrs']): + data[g].edges[n[0], n[1]][a_name] = attrs[i] + + return data, targets, label_names + + +def load_from_ds(filename, filename_targets): + """Load data from .ds file. + + Possible graph formats include: + + '.ct': see function load_ct for detail. + + '.gxl': see dunction load_gxl for detail. + + Note these graph formats are checked automatically by the extensions of + graph files. + """ + dirname_dataset = dirname(filename) + data = [] + y = [] + label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []} + with open(filename) as fn: + content = fn.read().splitlines() + extension = splitext(content[0].split(' ')[0])[1][1:] + if extension == 'ct': + load_file_fun = load_ct + elif extension == 'gxl' or extension == 'sdf': # @todo: .sdf not tested yet. + load_file_fun = load_gxl + + if filename_targets is None or filename_targets == '': + for i in range(0, len(content)): + tmp = content[i].split(' ') + # remove the '#'s in file names + g, l_names = load_file_fun(dirname_dataset + '/' + tmp[0].replace('#', '', 1)) + data.append(g) + __append_label_names(label_names, l_names) + y.append(float(tmp[1])) + else: # targets in a seperate file + for i in range(0, len(content)): + tmp = content[i] + # remove the '#'s in file names + g, l_names = load_file_fun(dirname_dataset + '/' + tmp.replace('#', '', 1)) + data.append(g) + __append_label_names(label_names, l_names) + + with open(filename_targets) as fnt: + content_y = fnt.read().splitlines() + # assume entries in filename and filename_targets have the same order. + for item in content_y: + tmp = item.split(' ') + # assume the 3rd entry in a line is y (for Alkane dataset) + y.append(float(tmp[2])) + + return data, y, label_names + + +# def load_from_cxl(filename): +# import xml.etree.ElementTree as ET +# +# dirname_dataset = dirname(filename) +# tree = ET.parse(filename) +# root = tree.getroot() +# data = [] +# y = [] +# for graph in root.iter('graph'): +# mol_filename = graph.attrib['file'] +# mol_class = graph.attrib['class'] +# data.append(load_gxl(dirname_dataset + '/' + mol_filename)) +# y.append(mol_class) + + +def load_from_xml(filename, dir_dataset=None): + import xml.etree.ElementTree as ET + + if dir_dataset is not None: + dir_dataset = dir_dataset + else: + dir_dataset = dirname(filename) + tree = ET.parse(filename) + root = tree.getroot() + data = [] + y = [] + label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []} + for graph in root.iter('graph'): + mol_filename = graph.attrib['file'] + mol_class = graph.attrib['class'] + g, l_names = load_gxl(dir_dataset + '/' + mol_filename) + data.append(g) + __append_label_names(label_names, l_names) + y.append(mol_class) + + return data, y, label_names + + +def __append_label_names(label_names, new_names): + for key, val in label_names.items(): + label_names[key] += [name for name in new_names[key] if name not in val] + + +if __name__ == '__main__': +# ### Load dataset from .ds file. +# # .ct files. +# ds = {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds', +# 'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt'} +# Gn, y = loadDataset(ds['dataset'], filename_y=ds['dataset_y']) +# ds_file = '../../datasets/Acyclic/dataset_bps.ds' # node symb +# Gn, targets, label_names = load_dataset(ds_file) +# ds_file = '../../datasets/MAO/dataset.ds' # node/edge symb +# Gn, targets, label_names = load_dataset(ds_file) +## ds = {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds'} # unlabeled +## Gn, y = loadDataset(ds['dataset']) +# print(Gn[1].graph) +# print(Gn[1].nodes(data=True)) +# print(Gn[1].edges(data=True)) +# print(targets[1]) + +# # .gxl file. +# ds_file = '../../datasets/monoterpenoides/dataset_10+.ds' # node/edge symb +# Gn, y, label_names = load_dataset(ds_file) +# print(Gn[1].graph) +# print(Gn[1].nodes(data=True)) +# print(Gn[1].edges(data=True)) +# print(y[1]) + + # .mat file. + ds_file = '../../datasets/MUTAG_mat/MUTAG.mat' + order = [0, 0, 3, 1, 2] + Gn, targets, label_names = load_dataset(ds_file, order=order) + print(Gn[1].graph) + print(Gn[1].nodes(data=True)) + print(Gn[1].edges(data=True)) + print(targets[1]) + +# ### Convert graph from one format to another. +# # .gxl file. +# import networkx as nx +# ds = {'name': 'monoterpenoides', +# 'dataset': '../../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb +# Gn, y = loadDataset(ds['dataset']) +# y = [int(i) for i in y] +# print(Gn[1].nodes(data=True)) +# print(Gn[1].edges(data=True)) +# print(y[1]) +# # Convert a graph to the proper NetworkX format that can be recognized by library gedlib. +# Gn_new = [] +# for G in Gn: +# G_new = nx.Graph() +# for nd, attrs in G.nodes(data=True): +# G_new.add_node(str(nd), chem=attrs['atom']) +# for nd1, nd2, attrs in G.edges(data=True): +# G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type']) +## G_new.add_edge(str(nd1), str(nd2)) +# Gn_new.append(G_new) +# print(Gn_new[1].nodes(data=True)) +# print(Gn_new[1].edges(data=True)) +# print(Gn_new[1]) +# filename = '/media/ljia/DATA/research-repo/codes/others/gedlib/tests_linlin/generated_datsets/monoterpenoides/gxl/monoterpenoides' +# xparams = {'method': 'gedlib'} +# saveDataset(Gn, y, gformat='gxl', group='xml', filename=filename, xparams=xparams) + + # save dataset. +# ds = {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG.mat', +# 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}} # node/edge symb +# Gn, y = loadDataset(ds['dataset'], extra_params=ds['extra_params']) +# saveDataset(Gn, y, group='xml', filename='temp/temp') + + # test - new way to add labels and attributes. +# dataset = '../../datasets/SYNTHETICnew/SYNTHETICnew_A.txt' +# filename = '../../datasets/Fingerprint/Fingerprint_A.txt' +# dataset = '../../datasets/Letter-med/Letter-med_A.txt' +# dataset = '../../datasets/AIDS/AIDS_A.txt' +# dataset = '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt' +# Gn, targets, label_names = load_dataset(filename) + pass From a2cae7c2261b70814a76e03acd303603f27d4a67 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:05 +0200 Subject: [PATCH 057/728] New translations dataset.py (French) --- lang/fr/gklearn/utils/dataset.py | 774 +++++++++++++++++++++++++++++++ 1 file changed, 774 insertions(+) create mode 100644 lang/fr/gklearn/utils/dataset.py diff --git a/lang/fr/gklearn/utils/dataset.py b/lang/fr/gklearn/utils/dataset.py new file mode 100644 index 0000000000..7201a0d7fb --- /dev/null +++ b/lang/fr/gklearn/utils/dataset.py @@ -0,0 +1,774 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Mar 26 18:48:27 2020 + +@author: ljia +""" +import numpy as np +import networkx as nx +from gklearn.utils.graph_files import load_dataset +import os + + +class Dataset(object): + + def __init__(self, filename=None, filename_targets=None, **kwargs): + if filename is None: + self.__graphs = None + self.__targets = None + self.__node_labels = None + self.__edge_labels = None + self.__node_attrs = None + self.__edge_attrs = None + else: + self.load_dataset(filename, filename_targets=filename_targets, **kwargs) + + self.__substructures = None + self.__node_label_dim = None + self.__edge_label_dim = None + self.__directed = None + self.__dataset_size = None + self.__total_node_num = None + self.__ave_node_num = None + self.__min_node_num = None + self.__max_node_num = None + self.__total_edge_num = None + self.__ave_edge_num = None + self.__min_edge_num = None + self.__max_edge_num = None + self.__ave_node_degree = None + self.__min_node_degree = None + self.__max_node_degree = None + self.__ave_fill_factor = None + self.__min_fill_factor = None + self.__max_fill_factor = None + self.__node_label_nums = None + self.__edge_label_nums = None + self.__node_attr_dim = None + self.__edge_attr_dim = None + self.__class_number = None + + + def load_dataset(self, filename, filename_targets=None, **kwargs): + self.__graphs, self.__targets, label_names = load_dataset(filename, filename_targets=filename_targets, **kwargs) + self.__node_labels = label_names['node_labels'] + self.__node_attrs = label_names['node_attrs'] + self.__edge_labels = label_names['edge_labels'] + self.__edge_attrs = label_names['edge_attrs'] + self.clean_labels() + + + def load_graphs(self, graphs, targets=None): + # this has to be followed by set_labels(). + self.__graphs = graphs + self.__targets = targets +# self.set_labels_attrs() # @todo + + + def load_predefined_dataset(self, ds_name): + current_path = os.path.dirname(os.path.realpath(__file__)) + '/' + if ds_name == 'Acyclic': + ds_file = current_path + '../../datasets/Acyclic/dataset_bps.ds' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'AIDS': + ds_file = current_path + '../../datasets/AIDS/AIDS_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Alkane': + ds_file = current_path + '../../datasets/Alkane/dataset.ds' + fn_targets = current_path + '../../datasets/Alkane/dataset_boiling_point_names.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file, filename_targets=fn_targets) + elif ds_name == 'COIL-DEL': + ds_file = current_path + '../../datasets/COIL-DEL/COIL-DEL_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'COIL-RAG': + ds_file = current_path + '../../datasets/COIL-RAG/COIL-RAG_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'COLORS-3': + ds_file = current_path + '../../datasets/COLORS-3/COLORS-3_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Cuneiform': + ds_file = current_path + '../../datasets/Cuneiform/Cuneiform_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'DD': + ds_file = current_path + '../../datasets/DD/DD_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'ENZYMES': + ds_file = current_path + '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Fingerprint': + ds_file = current_path + '../../datasets/Fingerprint/Fingerprint_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'FRANKENSTEIN': + ds_file = current_path + '../../datasets/FRANKENSTEIN/FRANKENSTEIN_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Letter-high': # node non-symb + ds_file = current_path + '../../datasets/Letter-high/Letter-high_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Letter-low': # node non-symb + ds_file = current_path + '../../datasets/Letter-low/Letter-low_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Letter-med': # node non-symb + ds_file = current_path + '../../datasets/Letter-med/Letter-med_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'MAO': + ds_file = current_path + '../../datasets/MAO/dataset.ds' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Monoterpenoides': + ds_file = current_path + '../../datasets/Monoterpenoides/dataset_10+.ds' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'MUTAG': + ds_file = current_path + '../../datasets/MUTAG/MUTAG_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'NCI1': + ds_file = current_path + '../../datasets/NCI1/NCI1_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'NCI109': + ds_file = current_path + '../../datasets/NCI109/NCI109_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'PAH': + ds_file = current_path + '../../datasets/PAH/dataset.ds' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'SYNTHETIC': + pass + elif ds_name == 'SYNTHETICnew': + ds_file = current_path + '../../datasets/SYNTHETICnew/SYNTHETICnew_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Synthie': + pass + else: + raise Exception('The dataset name "', ds_name, '" is not pre-defined.') + + self.__node_labels = label_names['node_labels'] + self.__node_attrs = label_names['node_attrs'] + self.__edge_labels = label_names['edge_labels'] + self.__edge_attrs = label_names['edge_attrs'] + self.clean_labels() + + + def set_labels(self, node_labels=[], node_attrs=[], edge_labels=[], edge_attrs=[]): + self.__node_labels = node_labels + self.__node_attrs = node_attrs + self.__edge_labels = edge_labels + self.__edge_attrs = edge_attrs + + + def set_labels_attrs(self, node_labels=None, node_attrs=None, edge_labels=None, edge_attrs=None): + # @todo: remove labels which have only one possible values. + if node_labels is None: + self.__node_labels = self.__graphs[0].graph['node_labels'] +# # graphs are considered node unlabeled if all nodes have the same label. +# infos.update({'node_labeled': is_nl if node_label_num > 1 else False}) + if node_attrs is None: + self.__node_attrs = self.__graphs[0].graph['node_attrs'] +# for G in Gn: +# for n in G.nodes(data=True): +# if 'attributes' in n[1]: +# return len(n[1]['attributes']) +# return 0 + if edge_labels is None: + self.__edge_labels = self.__graphs[0].graph['edge_labels'] +# # graphs are considered edge unlabeled if all edges have the same label. +# infos.update({'edge_labeled': is_el if edge_label_num > 1 else False}) + if edge_attrs is None: + self.__edge_attrs = self.__graphs[0].graph['edge_attrs'] +# for G in Gn: +# if nx.number_of_edges(G) > 0: +# for e in G.edges(data=True): +# if 'attributes' in e[2]: +# return len(e[2]['attributes']) +# return 0 + + + def get_dataset_infos(self, keys=None): + """Computes and returns the structure and property information of the graph dataset. + + Parameters + ---------- + keys : list + List of strings which indicate which informations will be returned. The + possible choices includes: + + 'substructures': sub-structures graphs contains, including 'linear', 'non + linear' and 'cyclic'. + + 'node_label_dim': whether vertices have symbolic labels. + + 'edge_label_dim': whether egdes have symbolic labels. + + 'directed': whether graphs in dataset are directed. + + 'dataset_size': number of graphs in dataset. + + 'total_node_num': total number of vertices of all graphs in dataset. + + 'ave_node_num': average number of vertices of graphs in dataset. + + 'min_node_num': minimum number of vertices of graphs in dataset. + + 'max_node_num': maximum number of vertices of graphs in dataset. + + 'total_edge_num': total number of edges of all graphs in dataset. + + 'ave_edge_num': average number of edges of graphs in dataset. + + 'min_edge_num': minimum number of edges of graphs in dataset. + + 'max_edge_num': maximum number of edges of graphs in dataset. + + 'ave_node_degree': average vertex degree of graphs in dataset. + + 'min_node_degree': minimum vertex degree of graphs in dataset. + + 'max_node_degree': maximum vertex degree of graphs in dataset. + + 'ave_fill_factor': average fill factor (number_of_edges / + (number_of_nodes ** 2)) of graphs in dataset. + + 'min_fill_factor': minimum fill factor of graphs in dataset. + + 'max_fill_factor': maximum fill factor of graphs in dataset. + + 'node_label_nums': list of numbers of symbolic vertex labels of graphs in dataset. + + 'edge_label_nums': list number of symbolic edge labels of graphs in dataset. + + 'node_attr_dim': number of dimensions of non-symbolic vertex labels. + Extracted from the 'attributes' attribute of graph nodes. + + 'edge_attr_dim': number of dimensions of non-symbolic edge labels. + Extracted from the 'attributes' attribute of graph edges. + + 'class_number': number of classes. Only available for classification problems. + + All informations above will be returned if `keys` is not given. + + Return + ------ + dict + Information of the graph dataset keyed by `keys`. + """ + infos = {} + + if keys == None: + keys = [ + 'substructures', + 'node_label_dim', + 'edge_label_dim', + 'directed', + 'dataset_size', + 'total_node_num', + 'ave_node_num', + 'min_node_num', + 'max_node_num', + 'total_edge_num', + 'ave_edge_num', + 'min_edge_num', + 'max_edge_num', + 'ave_node_degree', + 'min_node_degree', + 'max_node_degree', + 'ave_fill_factor', + 'min_fill_factor', + 'max_fill_factor', + 'node_label_nums', + 'edge_label_nums', + 'node_attr_dim', + 'edge_attr_dim', + 'class_number', + ] + + # dataset size + if 'dataset_size' in keys: + if self.__dataset_size is None: + self.__dataset_size = self.__get_dataset_size() + infos['dataset_size'] = self.__dataset_size + + # graph node number + if any(i in keys for i in ['total_node_num', 'ave_node_num', 'min_node_num', 'max_node_num']): + all_node_nums = self.__get_all_node_nums() + + if 'total_node_num' in keys: + if self.__total_node_num is None: + self.__total_node_num = self.__get_total_node_num(all_node_nums) + infos['total_node_num'] = self.__total_node_num + + if 'ave_node_num' in keys: + if self.__ave_node_num is None: + self.__ave_node_num = self.__get_ave_node_num(all_node_nums) + infos['ave_node_num'] = self.__ave_node_num + + if 'min_node_num' in keys: + if self.__min_node_num is None: + self.__min_node_num = self.__get_min_node_num(all_node_nums) + infos['min_node_num'] = self.__min_node_num + + if 'max_node_num' in keys: + if self.__max_node_num is None: + self.__max_node_num = self.__get_max_node_num(all_node_nums) + infos['max_node_num'] = self.__max_node_num + + # graph edge number + if any(i in keys for i in ['total_edge_num', 'ave_edge_num', 'min_edge_num', 'max_edge_num']): + all_edge_nums = self.__get_all_edge_nums() + + if 'total_edge_num' in keys: + if self.__total_edge_num is None: + self.__total_edge_num = self.__get_total_edge_num(all_edge_nums) + infos['total_edge_num'] = self.__total_edge_num + + if 'ave_edge_num' in keys: + if self.__ave_edge_num is None: + self.__ave_edge_num = self.__get_ave_edge_num(all_edge_nums) + infos['ave_edge_num'] = self.__ave_edge_num + + if 'max_edge_num' in keys: + if self.__max_edge_num is None: + self.__max_edge_num = self.__get_max_edge_num(all_edge_nums) + infos['max_edge_num'] = self.__max_edge_num + + if 'min_edge_num' in keys: + if self.__min_edge_num is None: + self.__min_edge_num = self.__get_min_edge_num(all_edge_nums) + infos['min_edge_num'] = self.__min_edge_num + + # label number + if 'node_label_dim' in keys: + if self.__node_label_dim is None: + self.__node_label_dim = self.__get_node_label_dim() + infos['node_label_dim'] = self.__node_label_dim + + if 'node_label_nums' in keys: + if self.__node_label_nums is None: + self.__node_label_nums = {} + for node_label in self.__node_labels: + self.__node_label_nums[node_label] = self.__get_node_label_num(node_label) + infos['node_label_nums'] = self.__node_label_nums + + if 'edge_label_dim' in keys: + if self.__edge_label_dim is None: + self.__edge_label_dim = self.__get_edge_label_dim() + infos['edge_label_dim'] = self.__edge_label_dim + + if 'edge_label_nums' in keys: + if self.__edge_label_nums is None: + self.__edge_label_nums = {} + for edge_label in self.__edge_labels: + self.__edge_label_nums[edge_label] = self.__get_edge_label_num(edge_label) + infos['edge_label_nums'] = self.__edge_label_nums + + if 'directed' in keys or 'substructures' in keys: + if self.__directed is None: + self.__directed = self.__is_directed() + infos['directed'] = self.__directed + + # node degree + if any(i in keys for i in ['ave_node_degree', 'max_node_degree', 'min_node_degree']): + all_node_degrees = self.__get_all_node_degrees() + + if 'ave_node_degree' in keys: + if self.__ave_node_degree is None: + self.__ave_node_degree = self.__get_ave_node_degree(all_node_degrees) + infos['ave_node_degree'] = self.__ave_node_degree + + if 'max_node_degree' in keys: + if self.__max_node_degree is None: + self.__max_node_degree = self.__get_max_node_degree(all_node_degrees) + infos['max_node_degree'] = self.__max_node_degree + + if 'min_node_degree' in keys: + if self.__min_node_degree is None: + self.__min_node_degree = self.__get_min_node_degree(all_node_degrees) + infos['min_node_degree'] = self.__min_node_degree + + # fill factor + if any(i in keys for i in ['ave_fill_factor', 'max_fill_factor', 'min_fill_factor']): + all_fill_factors = self.__get_all_fill_factors() + + if 'ave_fill_factor' in keys: + if self.__ave_fill_factor is None: + self.__ave_fill_factor = self.__get_ave_fill_factor(all_fill_factors) + infos['ave_fill_factor'] = self.__ave_fill_factor + + if 'max_fill_factor' in keys: + if self.__max_fill_factor is None: + self.__max_fill_factor = self.__get_max_fill_factor(all_fill_factors) + infos['max_fill_factor'] = self.__max_fill_factor + + if 'min_fill_factor' in keys: + if self.__min_fill_factor is None: + self.__min_fill_factor = self.__get_min_fill_factor(all_fill_factors) + infos['min_fill_factor'] = self.__min_fill_factor + + if 'substructures' in keys: + if self.__substructures is None: + self.__substructures = self.__get_substructures() + infos['substructures'] = self.__substructures + + if 'class_number' in keys: + if self.__class_number is None: + self.__class_number = self.__get_class_number() + infos['class_number'] = self.__class_number + + if 'node_attr_dim' in keys: + if self.__node_attr_dim is None: + self.__node_attr_dim = self.__get_node_attr_dim() + infos['node_attr_dim'] = self.__node_attr_dim + + if 'edge_attr_dim' in keys: + if self.__edge_attr_dim is None: + self.__edge_attr_dim = self.__get_edge_attr_dim() + infos['edge_attr_dim'] = self.__edge_attr_dim + + return infos + + + def print_graph_infos(self, infos): + from collections import OrderedDict + keys = list(infos.keys()) + print(OrderedDict(sorted(infos.items(), key=lambda i: keys.index(i[0])))) + + + def remove_labels(self, node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]): + node_labels = [item for item in node_labels if item in self.__node_labels] + edge_labels = [item for item in edge_labels if item in self.__edge_labels] + node_attrs = [item for item in node_attrs if item in self.__node_attrs] + edge_attrs = [item for item in edge_attrs if item in self.__edge_attrs] + + for g in self.__graphs: + for nd in g.nodes(): + for nl in node_labels: + del g.nodes[nd][nl] + for na in node_attrs: + del g.nodes[nd][na] + for ed in g.edges(): + for el in edge_labels: + del g.edges[ed][el] + for ea in edge_attrs: + del g.edges[ed][ea] + if len(node_labels) > 0: + self.__node_labels = [nl for nl in self.__node_labels if nl not in node_labels] + if len(edge_labels) > 0: + self.__edge_labels = [el for el in self.__edge_labels if el not in edge_labels] + if len(node_attrs) > 0: + self.__node_attrs = [na for na in self.__node_attrs if na not in node_attrs] + if len(edge_attrs) > 0: + self.__edge_attrs = [ea for ea in self.__edge_attrs if ea not in edge_attrs] + + + def clean_labels(self): + labels = [] + for name in self.__node_labels: + label = set() + for G in self.__graphs: + label = label | set(nx.get_node_attributes(G, name).values()) + if len(label) > 1: + labels.append(name) + break + if len(label) < 2: + for G in self.__graphs: + for nd in G.nodes(): + del G.nodes[nd][name] + self.__node_labels = labels + + labels = [] + for name in self.__edge_labels: + label = set() + for G in self.__graphs: + label = label | set(nx.get_edge_attributes(G, name).values()) + if len(label) > 1: + labels.append(name) + break + if len(label) < 2: + for G in self.__graphs: + for ed in G.edges(): + del G.edges[ed][name] + self.__edge_labels = labels + + labels = [] + for name in self.__node_attrs: + label = set() + for G in self.__graphs: + label = label | set(nx.get_node_attributes(G, name).values()) + if len(label) > 1: + labels.append(name) + break + if len(label) < 2: + for G in self.__graphs: + for nd in G.nodes(): + del G.nodes[nd][name] + self.__node_attrs = labels + + labels = [] + for name in self.__edge_attrs: + label = set() + for G in self.__graphs: + label = label | set(nx.get_edge_attributes(G, name).values()) + if len(label) > 1: + labels.append(name) + break + if len(label) < 2: + for G in self.__graphs: + for ed in G.edges(): + del G.edges[ed][name] + self.__edge_attrs = labels + + + def cut_graphs(self, range_): + self.__graphs = [self.__graphs[i] for i in range_] + if self.__targets is not None: + self.__targets = [self.__targets[i] for i in range_] + self.clean_labels() + + + def trim_dataset(self, edge_required=False): + if edge_required: + trimed_pairs = [(idx, g) for idx, g in enumerate(self.__graphs) if (nx.number_of_nodes(g) != 0 and nx.number_of_edges(g) != 0)] + else: + trimed_pairs = [(idx, g) for idx, g in enumerate(self.__graphs) if nx.number_of_nodes(g) != 0] + idx = [p[0] for p in trimed_pairs] + self.__graphs = [p[1] for p in trimed_pairs] + self.__targets = [self.__targets[i] for i in idx] + self.clean_labels() + + + def copy(self): + dataset = Dataset() + graphs = [g.copy() for g in self.__graphs] if self.__graphs is not None else None + target = self.__targets.copy() if self.__targets is not None else None + node_labels = self.__node_labels.copy() if self.__node_labels is not None else None + node_attrs = self.__node_attrs.copy() if self.__node_attrs is not None else None + edge_labels = self.__edge_labels.copy() if self.__edge_labels is not None else None + edge_attrs = self.__edge_attrs.copy() if self.__edge_attrs is not None else None + dataset.load_graphs(graphs, target) + dataset.set_labels(node_labels=node_labels, node_attrs=node_attrs, edge_labels=edge_labels, edge_attrs=edge_attrs) + # @todo: clean_labels and add other class members? + return dataset + + + def get_all_node_labels(self): + node_labels = [] + for g in self.__graphs: + for n in g.nodes(): + nl = tuple(g.nodes[n].items()) + if nl not in node_labels: + node_labels.append(nl) + return node_labels + + + def get_all_edge_labels(self): + edge_labels = [] + for g in self.__graphs: + for e in g.edges(): + el = tuple(g.edges[e].items()) + if el not in edge_labels: + edge_labels.append(el) + return edge_labels + + + def __get_dataset_size(self): + return len(self.__graphs) + + + def __get_all_node_nums(self): + return [nx.number_of_nodes(G) for G in self.__graphs] + + + def __get_total_node_nums(self, all_node_nums): + return np.sum(all_node_nums) + + + def __get_ave_node_num(self, all_node_nums): + return np.mean(all_node_nums) + + + def __get_min_node_num(self, all_node_nums): + return np.amin(all_node_nums) + + + def __get_max_node_num(self, all_node_nums): + return np.amax(all_node_nums) + + + def __get_all_edge_nums(self): + return [nx.number_of_edges(G) for G in self.__graphs] + + + def __get_total_edge_nums(self, all_edge_nums): + return np.sum(all_edge_nums) + + + def __get_ave_edge_num(self, all_edge_nums): + return np.mean(all_edge_nums) + + + def __get_min_edge_num(self, all_edge_nums): + return np.amin(all_edge_nums) + + + def __get_max_edge_num(self, all_edge_nums): + return np.amax(all_edge_nums) + + + def __get_node_label_dim(self): + return len(self.__node_labels) + + + def __get_node_label_num(self, node_label): + nl = set() + for G in self.__graphs: + nl = nl | set(nx.get_node_attributes(G, node_label).values()) + return len(nl) + + + def __get_edge_label_dim(self): + return len(self.__edge_labels) + + + def __get_edge_label_num(self, edge_label): + el = set() + for G in self.__graphs: + el = el | set(nx.get_edge_attributes(G, edge_label).values()) + return len(el) + + + def __is_directed(self): + return nx.is_directed(self.__graphs[0]) + + + def __get_all_node_degrees(self): + return [np.mean(list(dict(G.degree()).values())) for G in self.__graphs] + + + def __get_ave_node_degree(self, all_node_degrees): + return np.mean(all_node_degrees) + + + def __get_max_node_degree(self, all_node_degrees): + return np.amax(all_node_degrees) + + + def __get_min_node_degree(self, all_node_degrees): + return np.amin(all_node_degrees) + + + def __get_all_fill_factors(self): + """ + Get fill factor, the number of non-zero entries in the adjacency matrix. + + Returns + ------- + list[float] + List of fill factors for all graphs. + """ + return [nx.number_of_edges(G) / (nx.number_of_nodes(G) ** 2) for G in self.__graphs] + + + def __get_ave_fill_factor(self, all_fill_factors): + return np.mean(all_fill_factors) + + + def __get_max_fill_factor(self, all_fill_factors): + return np.amax(all_fill_factors) + + + def __get_min_fill_factor(self, all_fill_factors): + return np.amin(all_fill_factors) + + + def __get_substructures(self): + subs = set() + for G in self.__graphs: + degrees = list(dict(G.degree()).values()) + if any(i == 2 for i in degrees): + subs.add('linear') + if np.amax(degrees) >= 3: + subs.add('non linear') + if 'linear' in subs and 'non linear' in subs: + break + + if self.__directed: + for G in self.__graphs: + if len(list(nx.find_cycle(G))) > 0: + subs.add('cyclic') + break + # else: + # # @todo: this method does not work for big graph with large amount of edges like D&D, try a better way. + # upper = np.amin([nx.number_of_edges(G) for G in Gn]) * 2 + 10 + # for G in Gn: + # if (nx.number_of_edges(G) < upper): + # cyc = list(nx.simple_cycles(G.to_directed())) + # if any(len(i) > 2 for i in cyc): + # subs.add('cyclic') + # break + # if 'cyclic' not in subs: + # for G in Gn: + # cyc = list(nx.simple_cycles(G.to_directed())) + # if any(len(i) > 2 for i in cyc): + # subs.add('cyclic') + # break + + return subs + + + def __get_class_num(self): + return len(set(self.__targets)) + + + def __get_node_attr_dim(self): + return len(self.__node_attrs) + + + def __get_edge_attr_dim(self): + return len(self.__edge_attrs) + + + @property + def graphs(self): + return self.__graphs + + + @property + def targets(self): + return self.__targets + + + @property + def node_labels(self): + return self.__node_labels + + + @property + def edge_labels(self): + return self.__edge_labels + + + @property + def node_attrs(self): + return self.__node_attrs + + + @property + def edge_attrs(self): + return self.__edge_attrs + + +def split_dataset_by_target(dataset): + from gklearn.preimage.utils import get_same_item_indices + + graphs = dataset.graphs + targets = dataset.targets + datasets = [] + idx_targets = get_same_item_indices(targets) + for key, val in idx_targets.items(): + sub_graphs = [graphs[i] for i in val] + sub_dataset = Dataset() + sub_dataset.load_graphs(sub_graphs, [key] * len(val)) + node_labels = dataset.node_labels.copy() if dataset.node_labels is not None else None + node_attrs = dataset.node_attrs.copy() if dataset.node_attrs is not None else None + edge_labels = dataset.edge_labels.copy() if dataset.edge_labels is not None else None + edge_attrs = dataset.edge_attrs.copy() if dataset.edge_attrs is not None else None + sub_dataset.set_labels(node_labels=node_labels, node_attrs=node_attrs, edge_labels=edge_labels, edge_attrs=edge_attrs) + datasets.append(sub_dataset) + # @todo: clean_labels? + return datasets \ No newline at end of file From 712893f3b7de4c94481eca286631ae2678a20dcb Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:06 +0200 Subject: [PATCH 058/728] New translations __init__.py (French) --- lang/fr/gklearn/utils/__init__.py | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 lang/fr/gklearn/utils/__init__.py diff --git a/lang/fr/gklearn/utils/__init__.py b/lang/fr/gklearn/utils/__init__.py new file mode 100644 index 0000000000..7c120f3b8a --- /dev/null +++ b/lang/fr/gklearn/utils/__init__.py @@ -0,0 +1,27 @@ +# -*-coding:utf-8 -*- +"""gklearn - utils module + +Implement some methods to manage graphs + graphfiles.py : load .gxl and .ct files + utils.py : compute some properties on networkX graphs + + +""" + +# info +__version__ = "0.1" +__author__ = "Benoit Gaüzère" +__date__ = "November 2017" + +# from utils import graphfiles +# from utils import utils +from gklearn.utils.dataset import Dataset, split_dataset_by_target +from gklearn.utils.graph_files import load_dataset, save_dataset +from gklearn.utils.timer import Timer +from gklearn.utils.utils import get_graph_kernel_by_name +from gklearn.utils.utils import compute_gram_matrices_by_class +from gklearn.utils.utils import SpecialLabel, dummy_node, undefined_node, dummy_edge +from gklearn.utils.utils import normalize_gram_matrix, compute_distance_matrix +from gklearn.utils.trie import Trie +from gklearn.utils.knn import knn_cv, knn_classification +from gklearn.utils.model_selection_precomputed import model_selection_for_precomputed_kernel From ad27739b2fef717830c88cda9762993937e4d307 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:08 +0200 Subject: [PATCH 059/728] New translations test_tools.py (French) --- lang/fr/gklearn/tests/test_tools.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 lang/fr/gklearn/tests/test_tools.py diff --git a/lang/fr/gklearn/tests/test_tools.py b/lang/fr/gklearn/tests/test_tools.py new file mode 100644 index 0000000000..18778d4158 --- /dev/null +++ b/lang/fr/gklearn/tests/test_tools.py @@ -0,0 +1 @@ +print('Hello graphkit-learn!') From 038af81e2889aa47eb7794e79b0a51b9b5a7ced0 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:09 +0200 Subject: [PATCH 060/728] New translations test_median_preimage_generator.py (French) --- .../tests/test_median_preimage_generator.py | 74 +++++++++++++++++++ 1 file changed, 74 insertions(+) create mode 100644 lang/fr/gklearn/tests/test_median_preimage_generator.py diff --git a/lang/fr/gklearn/tests/test_median_preimage_generator.py b/lang/fr/gklearn/tests/test_median_preimage_generator.py new file mode 100644 index 0000000000..a376c7a917 --- /dev/null +++ b/lang/fr/gklearn/tests/test_median_preimage_generator.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jan 14 15:39:29 2020 + +@author: ljia +""" +import multiprocessing +import functools +from gklearn.preimage.utils import generate_median_preimages_by_class + + +def test_median_preimage_generator(): + """MAO, Treelet, using CONSTANT, symbolic only. + """ + from gklearn.utils.kernels import polynomialkernel + # set parameters. + ds_name = 'MAO' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 3, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + pkernel = functools.partial(polynomialkernel, d=4, c=1e+7) + kernel_options = {'name': 'Treelet', # + 'sub_kernel': pkernel, + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 1, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = ds_name + '.' + kernel_options['name'] + '.symb.pytest/' + irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert', 'random']: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + try: + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=range(0, 4)) + except Exception as exception: + assert False, exception \ No newline at end of file From 3df13c02bc2d328f1f63e3cc979740d7f208adfb Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:11 +0200 Subject: [PATCH 061/728] New translations test_graphkernels.py (French) --- lang/fr/gklearn/tests/test_graphkernels.py | 281 +++++++++++++++++++++ 1 file changed, 281 insertions(+) create mode 100644 lang/fr/gklearn/tests/test_graphkernels.py diff --git a/lang/fr/gklearn/tests/test_graphkernels.py b/lang/fr/gklearn/tests/test_graphkernels.py new file mode 100644 index 0000000000..e90a1d0a42 --- /dev/null +++ b/lang/fr/gklearn/tests/test_graphkernels.py @@ -0,0 +1,281 @@ +"""Tests of graph kernels. +""" + +import pytest +import multiprocessing + + +def chooseDataset(ds_name): + """Choose dataset according to name. + """ + from gklearn.utils.graphfiles import loadDataset + + # no node labels (and no edge labels). + if ds_name == 'Alkane': + ds_file = 'datasets/Alkane/dataset.ds' + ds_y = 'datasets/Alkane/dataset_boiling_point_names.txt' + Gn, y = loadDataset(ds_file, filename_y=ds_y) + for G in Gn: + for node in G.nodes: + del G.nodes[node]['attributes'] + # node symbolic labels. + elif ds_name == 'Acyclic': + ds_file = 'datasets/acyclic/dataset_bps.ds' + Gn, y = loadDataset(ds_file) + for G in Gn: + for node in G.nodes: + del G.nodes[node]['attributes'] + # node non-symbolic labels. + elif ds_name == 'Letter-med': + ds_file = 'datasets/Letter-med/Letter-med_A.txt' + Gn, y = loadDataset(ds_file) + # node symbolic and non-symbolic labels (and edge symbolic labels). + elif ds_name == 'AIDS': + ds_file = 'datasets/AIDS/AIDS_A.txt' + Gn, y = loadDataset(ds_file) + + # edge non-symbolic labels (no node labels). + elif ds_name == 'Fingerprint_edge': + import networkx as nx + ds_file = 'datasets/Fingerprint/Fingerprint_A.txt' + Gn, y = loadDataset(ds_file) + Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_edges(G) != 0] + idx = [G[0] for G in Gn] + Gn = [G[1] for G in Gn] + y = [y[i] for i in idx] + for G in Gn: + G.graph['node_attrs'] = [] + for node in G.nodes: + del G.nodes[node]['attributes'] + del G.nodes[node]['x'] + del G.nodes[node]['y'] + # edge non-symbolic labels (and node non-symbolic labels). + elif ds_name == 'Fingerprint': + import networkx as nx + ds_file = 'datasets/Fingerprint/Fingerprint_A.txt' + Gn, y = loadDataset(ds_file) + Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_edges(G) != 0] + idx = [G[0] for G in Gn] + Gn = [G[1] for G in Gn] + y = [y[i] for i in idx] + # edge symbolic and non-symbolic labels (and node symbolic and non-symbolic labels). + elif ds_name == 'Cuneiform': + import networkx as nx + ds_file = 'datasets/Cuneiform/Cuneiform_A.txt' + Gn, y = loadDataset(ds_file) + + Gn = Gn[0:3] + y = y[0:3] + + return Gn, y + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('weight,compute_method', [(0.01, 'geo'), (1, 'exp')]) +#@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_commonwalkkernel(ds_name, weight, compute_method): + """Test common walk kernel. + """ + from gklearn.kernels.commonWalkKernel import commonwalkkernel + + Gn, y = chooseDataset(ds_name) + + try: + Kmatrix, run_time, idx = commonwalkkernel(Gn, + node_label='atom', + edge_label='bond_type', + weight=weight, + compute_method=compute_method, +# parallel=parallel, + n_jobs=multiprocessing.cpu_count(), + verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('remove_totters', [True, False]) +#@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_marginalizedkernel(ds_name, remove_totters): + """Test marginalized kernel. + """ + from gklearn.kernels.marginalizedKernel import marginalizedkernel + + Gn, y = chooseDataset(ds_name) + + try: + Kmatrix, run_time = marginalizedkernel(Gn, + node_label='atom', + edge_label='bond_type', + p_quit=0.5, + n_iteration=2, + remove_totters=remove_totters, +# parallel=parallel, + n_jobs=multiprocessing.cpu_count(), + verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize( + 'compute_method,ds_name,sub_kernel', + [ +# ('sylvester', 'Alkane', None), +# ('conjugate', 'Alkane', None), +# ('conjugate', 'AIDS', None), +# ('fp', 'Alkane', None), +# ('fp', 'AIDS', None), + ('spectral', 'Alkane', 'exp'), + ('spectral', 'Alkane', 'geo'), + ] +) +#@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_randomwalkkernel(ds_name, compute_method, sub_kernel): + """Test random walk kernel kernel. + """ + from gklearn.kernels.randomWalkKernel import randomwalkkernel + from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + import functools + + Gn, y = chooseDataset(ds_name) + + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}] + try: + Kmatrix, run_time, idx = randomwalkkernel(Gn, + compute_method=compute_method, + weight=1e-3, + p=None, + q=None, + edge_weight=None, + node_kernels=sub_kernels, + edge_kernels=sub_kernels, + node_label='atom', + edge_label='bond_type', + sub_kernel=sub_kernel, +# parallel=parallel, + n_jobs=multiprocessing.cpu_count(), + verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint']) +#@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +@pytest.mark.parametrize('parallel', ['imap_unordered']) +def test_spkernel(ds_name, parallel): + """Test shortest path kernel. + """ + from gklearn.kernels.spKernel import spkernel + from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + import functools + + Gn, y = chooseDataset(ds_name) + + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + try: + Kmatrix, run_time, idx = spkernel(Gn, node_label='atom', + node_kernels=sub_kernels, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + except Exception as exception: + assert False, exception + + +#@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint']) +@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint', 'Fingerprint_edge', 'Cuneiform']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_structuralspkernel(ds_name, parallel): + """Test structural shortest path kernel. + """ + from gklearn.kernels.structuralspKernel import structuralspkernel + from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + import functools + + Gn, y = chooseDataset(ds_name) + + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + try: + Kmatrix, run_time = structuralspkernel(Gn, node_label='atom', + edge_label='bond_type', node_kernels=sub_kernels, + edge_kernels=sub_kernels, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), + verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +#@pytest.mark.parametrize('k_func', ['MinMax', 'tanimoto', None]) +@pytest.mark.parametrize('k_func', ['MinMax', 'tanimoto']) +@pytest.mark.parametrize('compute_method', ['trie', 'naive']) +def test_untilhpathkernel(ds_name, parallel, k_func, compute_method): + """Test path kernel up to length $h$. + """ + from gklearn.kernels.untilHPathKernel import untilhpathkernel + + Gn, y = chooseDataset(ds_name) + + try: + Kmatrix, run_time = untilhpathkernel(Gn, node_label='atom', + edge_label='bond_type', + depth=2, k_func=k_func, compute_method=compute_method, + parallel=parallel, + n_jobs=multiprocessing.cpu_count(), verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_treeletkernel(ds_name, parallel): + """Test treelet kernel. + """ + from gklearn.kernels.treeletKernel import treeletkernel + from gklearn.utils.kernels import polynomialkernel + import functools + + Gn, y = chooseDataset(ds_name) + + pkernel = functools.partial(polynomialkernel, d=2, c=1e5) + try: + Kmatrix, run_time = treeletkernel(Gn, + sub_kernel=pkernel, + node_label='atom', + edge_label='bond_type', + parallel=parallel, + n_jobs=multiprocessing.cpu_count(), + verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Acyclic']) +#@pytest.mark.parametrize('base_kernel', ['subtree', 'sp', 'edge']) +@pytest.mark.parametrize('base_kernel', ['subtree']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_weisfeilerlehmankernel(ds_name, parallel, base_kernel): + """Test Weisfeiler-Lehman kernel. + """ + from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel + + Gn, y = chooseDataset(ds_name) + + try: + Kmatrix, run_time = weisfeilerlehmankernel(Gn, + node_label='atom', + edge_label='bond_type', + height=2, + base_kernel=base_kernel, + parallel=parallel, + n_jobs=multiprocessing.cpu_count(), + verbose=True) + except Exception as exception: + assert False, exception + + +if __name__ == "__main__": +# test_spkernel('Alkane', 'imap_unordered') + test_structuralspkernel('Fingerprint_edge', 'imap_unordered') \ No newline at end of file From debaf8608d9820ed8c813e6cda2f391d6faad9d5 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:12 +0200 Subject: [PATCH 062/728] New translations test_graph_kernels.py (French) --- lang/fr/gklearn/tests/test_graph_kernels.py | 299 ++++++++++++++++++++ 1 file changed, 299 insertions(+) create mode 100644 lang/fr/gklearn/tests/test_graph_kernels.py diff --git a/lang/fr/gklearn/tests/test_graph_kernels.py b/lang/fr/gklearn/tests/test_graph_kernels.py new file mode 100644 index 0000000000..c6fcfbee08 --- /dev/null +++ b/lang/fr/gklearn/tests/test_graph_kernels.py @@ -0,0 +1,299 @@ +"""Tests of graph kernels. +""" + +import pytest +import multiprocessing + + +def chooseDataset(ds_name): + """Choose dataset according to name. + """ + from gklearn.utils import Dataset + + dataset = Dataset() + + # no node labels (and no edge labels). + if ds_name == 'Alkane': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=False) + irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} + dataset.remove_labels(**irrelevant_labels) + # node symbolic labels. + elif ds_name == 'Acyclic': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=False) + irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} + dataset.remove_labels(**irrelevant_labels) + # node non-symbolic labels. + elif ds_name == 'Letter-med': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=False) + # node symbolic and non-symbolic labels (and edge symbolic labels). + elif ds_name == 'AIDS': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=False) + # edge non-symbolic labels (no node labels). + elif ds_name == 'Fingerprint_edge': + dataset.load_predefined_dataset('Fingerprint') + dataset.trim_dataset(edge_required=True) + irrelevant_labels = {'edge_attrs': ['orient', 'angle']} + dataset.remove_labels(**irrelevant_labels) + # edge non-symbolic labels (and node non-symbolic labels). + elif ds_name == 'Fingerprint': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=True) + # edge symbolic and non-symbolic labels (and node symbolic and non-symbolic labels). + elif ds_name == 'Cuneiform': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=True) + + dataset.cut_graphs(range(0, 3)) + + return dataset + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('weight,compute_method', [(0.01, 'geo'), (1, 'exp')]) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_CommonWalk(ds_name, parallel, weight, compute_method): + """Test common walk kernel. + """ + from gklearn.kernels import CommonWalk + import networkx as nx + + dataset = chooseDataset(ds_name) + dataset.load_graphs([g for g in dataset.graphs if nx.number_of_nodes(g) > 1]) + + try: + graph_kernel = CommonWalk(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + weight=weight, + compute_method=compute_method) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('remove_totters', [False]) #[True, False]) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_Marginalized(ds_name, parallel, remove_totters): + """Test marginalized kernel. + """ + from gklearn.kernels import Marginalized + + dataset = chooseDataset(ds_name) + + try: + graph_kernel = Marginalized(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + p_quit=0.5, + n_iteration=2, + remove_totters=remove_totters) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + + except Exception as exception: + assert False, exception + + +# @pytest.mark.parametrize( +# 'compute_method,ds_name,sub_kernel', +# [ +# # ('sylvester', 'Alkane', None), +# # ('conjugate', 'Alkane', None), +# # ('conjugate', 'AIDS', None), +# # ('fp', 'Alkane', None), +# # ('fp', 'AIDS', None), +# ('spectral', 'Alkane', 'exp'), +# ('spectral', 'Alkane', 'geo'), +# ] +# ) +# #@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +# def test_randomwalkkernel(ds_name, compute_method, sub_kernel): +# """Test random walk kernel kernel. +# """ +# from gklearn.kernels.randomWalkKernel import randomwalkkernel +# from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct +# import functools + +# Gn, y = chooseDataset(ds_name) + +# mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) +# sub_kernels = [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}] +# try: +# Kmatrix, run_time, idx = randomwalkkernel(Gn, +# compute_method=compute_method, +# weight=1e-3, +# p=None, +# q=None, +# edge_weight=None, +# node_kernels=sub_kernels, +# edge_kernels=sub_kernels, +# node_label='atom', +# edge_label='bond_type', +# sub_kernel=sub_kernel, +# # parallel=parallel, +# n_jobs=multiprocessing.cpu_count(), +# verbose=True) +# except Exception as exception: +# assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_ShortestPath(ds_name, parallel): + """Test shortest path kernel. + """ + from gklearn.kernels import ShortestPath + from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + import functools + + dataset = chooseDataset(ds_name) + + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + try: + graph_kernel = ShortestPath(node_labels=dataset.node_labels, + node_attrs=dataset.node_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + node_kernels=sub_kernels) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + + except Exception as exception: + assert False, exception + + +#@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint']) +@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint', 'Fingerprint_edge', 'Cuneiform']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_StructuralSP(ds_name, parallel): + """Test structural shortest path kernel. + """ + from gklearn.kernels import StructuralSP + from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + import functools + + dataset = chooseDataset(ds_name) + + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + try: + graph_kernel = StructuralSP(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, + edge_attrs=dataset.edge_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + node_kernels=sub_kernels, + edge_kernels=sub_kernels) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +#@pytest.mark.parametrize('k_func', ['MinMax', 'tanimoto', None]) +@pytest.mark.parametrize('k_func', ['MinMax', 'tanimoto']) +@pytest.mark.parametrize('compute_method', ['trie', 'naive']) +def test_PathUpToH(ds_name, parallel, k_func, compute_method): + """Test path kernel up to length $h$. + """ + from gklearn.kernels import PathUpToH + + dataset = chooseDataset(ds_name) + + try: + graph_kernel = PathUpToH(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + depth=2, k_func=k_func, compute_method=compute_method) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_Treelet(ds_name, parallel): + """Test treelet kernel. + """ + from gklearn.kernels import Treelet + from gklearn.utils.kernels import polynomialkernel + import functools + + dataset = chooseDataset(ds_name) + + pkernel = functools.partial(polynomialkernel, d=2, c=1e5) + try: + graph_kernel = Treelet(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + sub_kernel=pkernel) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Acyclic']) +#@pytest.mark.parametrize('base_kernel', ['subtree', 'sp', 'edge']) +# @pytest.mark.parametrize('base_kernel', ['subtree']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_WLSubtree(ds_name, parallel): + """Test Weisfeiler-Lehman subtree kernel. + """ + from gklearn.kernels import WLSubtree + + dataset = chooseDataset(ds_name) + + try: + graph_kernel = WLSubtree(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + height=2) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + except Exception as exception: + assert False, exception + + +if __name__ == "__main__": +# test_spkernel('Alkane', 'imap_unordered') + test_StructuralSP('Fingerprint_edge', 'imap_unordered') \ No newline at end of file From 1e70f3f5f830af71f93a97d9ace1713f6b80c92c Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:14 +0200 Subject: [PATCH 063/728] New translations requirements.txt (French) --- lang/fr/gklearn/tests/requirements.txt | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 lang/fr/gklearn/tests/requirements.txt diff --git a/lang/fr/gklearn/tests/requirements.txt b/lang/fr/gklearn/tests/requirements.txt new file mode 100644 index 0000000000..5a423e0b30 --- /dev/null +++ b/lang/fr/gklearn/tests/requirements.txt @@ -0,0 +1,8 @@ +numpy +scipy +matplotlib +networkx +scikit-learn +tabulate +tqdm +pytest From c80bcf5829630877839eedb2c17b6f78fd0f98b8 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:16 +0200 Subject: [PATCH 064/728] New translations test_ged_env.py (French) --- lang/fr/gklearn/tests/ged/test_ged_env.py | 57 +++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 lang/fr/gklearn/tests/ged/test_ged_env.py diff --git a/lang/fr/gklearn/tests/ged/test_ged_env.py b/lang/fr/gklearn/tests/ged/test_ged_env.py new file mode 100644 index 0000000000..bf4a7e4ea9 --- /dev/null +++ b/lang/fr/gklearn/tests/ged/test_ged_env.py @@ -0,0 +1,57 @@ +"""Tests of GEDEnv. +""" + + +def test_GEDEnv(): + """Test GEDEnv. + """ + """**1. Get dataset.**""" + + from gklearn.utils import Dataset + + # Predefined dataset name, use dataset "MUTAG". + ds_name = 'MUTAG' + + # Initialize a Dataset. + dataset = Dataset() + # Load predefined dataset "MUTAG". + dataset.load_predefined_dataset(ds_name) + graph1 = dataset.graphs[0] + graph2 = dataset.graphs[1] + + """**2. Compute graph edit distance.**""" + + try: + from gklearn.ged.env import GEDEnv + + ged_env = GEDEnv() # initailize GED environment. + ged_env.set_edit_cost('CONSTANT', # GED cost type. + edit_cost_constants=[3, 3, 1, 3, 3, 1] # edit costs. + ) + ged_env.add_nx_graph(graph1, '') # add graph1 + ged_env.add_nx_graph(graph2, '') # add graph2 + listID = ged_env.get_all_graph_ids() # get list IDs of graphs + ged_env.init(init_type='LAZY_WITHOUT_SHUFFLED_COPIES') # initialize GED environment. + options = {'initialization_method': 'RANDOM', # or 'NODE', etc. + 'threads': 1 # parallel threads. + } + ged_env.set_method('BIPARTITE', # GED method. + options # options for GED method. + ) + ged_env.init_method() # initialize GED method. + + ged_env.run_method(listID[0], listID[1]) # run. + + pi_forward = ged_env.get_forward_map(listID[0], listID[1]) # forward map. + pi_backward = ged_env.get_backward_map(listID[0], listID[1]) # backward map. + dis = ged_env.get_upper_bound(listID[0], listID[1]) # GED bewteen two graphs. + + import networkx as nx + assert len(pi_forward) == nx.number_of_nodes(graph1), len(pi_backward) == nx.number_of_nodes(graph2) + + except Exception as exception: + assert False, exception + + +if __name__ == "__main__": + test_GEDEnv() \ No newline at end of file From e940a05941bc423291d64282d2192ece4999bba2 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:18 +0200 Subject: [PATCH 065/728] New translations graphfiles.py (French) --- lang/fr/gklearn/utils/graphfiles.py | 762 ++++++++++++++++++++++++++++ 1 file changed, 762 insertions(+) create mode 100644 lang/fr/gklearn/utils/graphfiles.py diff --git a/lang/fr/gklearn/utils/graphfiles.py b/lang/fr/gklearn/utils/graphfiles.py new file mode 100644 index 0000000000..862cda1142 --- /dev/null +++ b/lang/fr/gklearn/utils/graphfiles.py @@ -0,0 +1,762 @@ +""" Utilities function to manage graph files +""" +from os.path import dirname, splitext + +def loadCT(filename): + """load data from a Chemical Table (.ct) file. + + Notes + ------ + a typical example of data in .ct is like this: + + 3 2 <- number of nodes and edges + + 0.0000 0.0000 0.0000 C <- each line describes a node (x,y,z + label) + + 0.0000 0.0000 0.0000 C + + 0.0000 0.0000 0.0000 O + + 1 3 1 1 <- each line describes an edge : to, from, bond type, bond stereo + + 2 3 1 1 + + Check `CTFile Formats file `__ + for detailed format discription. + """ + import networkx as nx + from os.path import basename + g = nx.Graph() + with open(filename) as f: + content = f.read().splitlines() + g = nx.Graph( + name = str(content[0]), + filename = basename(filename)) # set name of the graph + tmp = content[1].split(" ") + if tmp[0] == '': + nb_nodes = int(tmp[1]) # number of the nodes + nb_edges = int(tmp[2]) # number of the edges + else: + nb_nodes = int(tmp[0]) + nb_edges = int(tmp[1]) + # patch for compatibility : label will be removed later + for i in range(0, nb_nodes): + tmp = content[i + 2].split(" ") + tmp = [x for x in tmp if x != ''] + g.add_node(i, atom=tmp[3].strip(), + label=[item.strip() for item in tmp[3:]], + attributes=[item.strip() for item in tmp[0:3]]) + for i in range(0, nb_edges): + tmp = content[i + g.number_of_nodes() + 2].split(" ") + tmp = [x for x in tmp if x != ''] + g.add_edge(int(tmp[0]) - 1, int(tmp[1]) - 1, + bond_type=tmp[2].strip(), + label=[item.strip() for item in tmp[2:]]) + return g + + +def loadGXL(filename): + from os.path import basename + import networkx as nx + import xml.etree.ElementTree as ET + + tree = ET.parse(filename) + root = tree.getroot() + index = 0 + g = nx.Graph(filename=basename(filename), name=root[0].attrib['id']) + dic = {} # used to retrieve incident nodes of edges + for node in root.iter('node'): + dic[node.attrib['id']] = index + labels = {} + for attr in node.iter('attr'): + labels[attr.attrib['name']] = attr[0].text + if 'chem' in labels: + labels['label'] = labels['chem'] + labels['atom'] = labels['chem'] + g.add_node(index, **labels) + index += 1 + + for edge in root.iter('edge'): + labels = {} + for attr in edge.iter('attr'): + labels[attr.attrib['name']] = attr[0].text + if 'valence' in labels: + labels['label'] = labels['valence'] + labels['bond_type'] = labels['valence'] + g.add_edge(dic[edge.attrib['from']], dic[edge.attrib['to']], **labels) + return g + + +def saveGXL(graph, filename, method='default', node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]): + if method == 'default': + gxl_file = open(filename, 'w') + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + if 'name' in graph.graph: + name = str(graph.graph['name']) + else: + name = 'dummy' + gxl_file.write("\n") + for v, attrs in graph.nodes(data=True): + gxl_file.write("") + for l_name in node_labels: + gxl_file.write("" + + str(attrs[l_name]) + "") + for a_name in node_attrs: + gxl_file.write("" + + str(attrs[a_name]) + "") + gxl_file.write("\n") + for v1, v2, attrs in graph.edges(data=True): + gxl_file.write("") + for l_name in edge_labels: + gxl_file.write("" + + str(attrs[l_name]) + "") + for a_name in edge_attrs: + gxl_file.write("" + + str(attrs[a_name]) + "") + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("") + gxl_file.close() + elif method == 'benoit': + import xml.etree.ElementTree as ET + root_node = ET.Element('gxl') + attr = dict() + attr['id'] = str(graph.graph['name']) + attr['edgeids'] = 'true' + attr['edgemode'] = 'undirected' + graph_node = ET.SubElement(root_node, 'graph', attrib=attr) + + for v in graph: + current_node = ET.SubElement(graph_node, 'node', attrib={'id': str(v)}) + for attr in graph.nodes[v].keys(): + cur_attr = ET.SubElement( + current_node, 'attr', attrib={'name': attr}) + cur_value = ET.SubElement(cur_attr, + graph.nodes[v][attr].__class__.__name__) + cur_value.text = graph.nodes[v][attr] + + for v1 in graph: + for v2 in graph[v1]: + if (v1 < v2): # Non oriented graphs + cur_edge = ET.SubElement( + graph_node, + 'edge', + attrib={ + 'from': str(v1), + 'to': str(v2) + }) + for attr in graph[v1][v2].keys(): + cur_attr = ET.SubElement( + cur_edge, 'attr', attrib={'name': attr}) + cur_value = ET.SubElement( + cur_attr, graph[v1][v2][attr].__class__.__name__) + cur_value.text = str(graph[v1][v2][attr]) + + tree = ET.ElementTree(root_node) + tree.write(filename) + elif method == 'gedlib': + # reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22 +# pass + gxl_file = open(filename, 'w') + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + for v, attrs in graph.nodes(data=True): + gxl_file.write("") + gxl_file.write("" + str(attrs['chem']) + "") + gxl_file.write("\n") + for v1, v2, attrs in graph.edges(data=True): + gxl_file.write("") + gxl_file.write("" + str(attrs['valence']) + "") +# gxl_file.write("" + "1" + "") + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("") + gxl_file.close() + elif method == 'gedlib-letter': + # reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22 + # and https://github.com/dbblumenthal/gedlib/blob/master/data/datasets/Letter/HIGH/AP1_0000.gxl + gxl_file = open(filename, 'w') + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("\n") + for v, attrs in graph.nodes(data=True): + gxl_file.write("") + gxl_file.write("" + str(attrs['attributes'][0]) + "") + gxl_file.write("" + str(attrs['attributes'][1]) + "") + gxl_file.write("\n") + for v1, v2, attrs in graph.edges(data=True): + gxl_file.write("\n") + gxl_file.write("\n") + gxl_file.write("") + gxl_file.close() + + +def loadSDF(filename): + """load data from structured data file (.sdf file). + + Notes + ------ + A SDF file contains a group of molecules, represented in the similar way as in MOL format. + Check `here `__ for detailed structure. + """ + import networkx as nx + from os.path import basename + from tqdm import tqdm + import sys + data = [] + with open(filename) as f: + content = f.read().splitlines() + index = 0 + pbar = tqdm(total=len(content) + 1, desc='load SDF', file=sys.stdout) + while index < len(content): + index_old = index + + g = nx.Graph(name=content[index].strip()) # set name of the graph + + tmp = content[index + 3] + nb_nodes = int(tmp[:3]) # number of the nodes + nb_edges = int(tmp[3:6]) # number of the edges + + for i in range(0, nb_nodes): + tmp = content[i + index + 4] + g.add_node(i, atom=tmp[31:34].strip()) + + for i in range(0, nb_edges): + tmp = content[i + index + g.number_of_nodes() + 4] + tmp = [tmp[i:i + 3] for i in range(0, len(tmp), 3)] + g.add_edge( + int(tmp[0]) - 1, int(tmp[1]) - 1, bond_type=tmp[2].strip()) + + data.append(g) + + index += 4 + g.number_of_nodes() + g.number_of_edges() + while content[index].strip() != '$$$$': # seperator + index += 1 + index += 1 + + pbar.update(index - index_old) + pbar.update(1) + pbar.close() + + return data + + +def loadMAT(filename, extra_params): + """Load graph data from a MATLAB (up to version 7.1) .mat file. + + Notes + ------ + A MAT file contains a struct array containing graphs, and a column vector lx containing a class label for each graph. + Check README in `downloadable file `__ for detailed structure. + """ + from scipy.io import loadmat + import numpy as np + import networkx as nx + data = [] + content = loadmat(filename) + order = extra_params['am_sp_al_nl_el'] + # print(content) + # print('----') + for key, value in content.items(): + if key[0] == 'l': # class label + y = np.transpose(value)[0].tolist() + # print(y) + elif key[0] != '_': + # print(value[0][0][0]) + # print() + # print(value[0][0][1]) + # print() + # print(value[0][0][2]) + # print() + # if len(value[0][0]) > 3: + # print(value[0][0][3]) + # print('----') + # if adjacency matrix is not compressed / edge label exists + if order[1] == 0: + for i, item in enumerate(value[0]): + # print(item) + # print('------') + g = nx.Graph(name=i) # set name of the graph + nl = np.transpose(item[order[3]][0][0][0]) # node label + # print(item[order[3]]) + # print() + for index, label in enumerate(nl[0]): + g.add_node(index, atom=str(label)) + el = item[order[4]][0][0][0] # edge label + for edge in el: + g.add_edge( + edge[0] - 1, edge[1] - 1, bond_type=str(edge[2])) + data.append(g) + else: + from scipy.sparse import csc_matrix + for i, item in enumerate(value[0]): + # print(item) + # print('------') + g = nx.Graph(name=i) # set name of the graph + nl = np.transpose(item[order[3]][0][0][0]) # node label + # print(nl) + # print() + for index, label in enumerate(nl[0]): + g.add_node(index, atom=str(label)) + sam = item[order[0]] # sparse adjacency matrix + index_no0 = sam.nonzero() + for col, row in zip(index_no0[0], index_no0[1]): + # print(col) + # print(row) + g.add_edge(col, row) + data.append(g) + # print(g.edges(data=True)) + return data, y + + +def loadTXT(filename): + """Load graph data from a .txt file. + + Notes + ------ + The graph data is loaded from separate files. + Check README in `downloadable file `__, 2018 for detailed structure. + """ +# import numpy as np + import networkx as nx + from os import listdir + from os.path import dirname, basename + + + def get_label_names(frm): + """Get label names from DS_label_readme.txt file. + """ + + def get_names_from_line(line): + """Get names of labels/attributes from a line. + """ + str_names = line.split('[')[1].split(']')[0] + names = str_names.split(',') + names = [attr.strip() for attr in names] + return names + + + label_names = {'node_labels': [], 'node_attrs': [], + 'edge_labels': [], 'edge_attrs': []} + content_rm = open(frm).read().splitlines() + for line in content_rm: + line = line.strip() + if line.startswith('Node labels:'): + label_names['node_labels'] = get_names_from_line(line) + elif line.startswith('Node attributes:'): + label_names['node_attrs'] = get_names_from_line(line) + elif line.startswith('Edge labels:'): + label_names['edge_labels'] = get_names_from_line(line) + elif line.startswith('Edge attributes:'): + label_names['edge_attrs'] = get_names_from_line(line) + return label_names + + + # get dataset name. + dirname_dataset = dirname(filename) + filename = basename(filename) + fn_split = filename.split('_A') + ds_name = fn_split[0].strip() + + # load data file names + for name in listdir(dirname_dataset): + if ds_name + '_A' in name: + fam = dirname_dataset + '/' + name + elif ds_name + '_graph_indicator' in name: + fgi = dirname_dataset + '/' + name + elif ds_name + '_graph_labels' in name: + fgl = dirname_dataset + '/' + name + elif ds_name + '_node_labels' in name: + fnl = dirname_dataset + '/' + name + elif ds_name + '_edge_labels' in name: + fel = dirname_dataset + '/' + name + elif ds_name + '_edge_attributes' in name: + fea = dirname_dataset + '/' + name + elif ds_name + '_node_attributes' in name: + fna = dirname_dataset + '/' + name + elif ds_name + '_graph_attributes' in name: + fga = dirname_dataset + '/' + name + elif ds_name + '_label_readme' in name: + frm = dirname_dataset + '/' + name + # this is supposed to be the node attrs, make sure to put this as the last 'elif' + elif ds_name + '_attributes' in name: + fna = dirname_dataset + '/' + name + + # get labels and attributes names. + if 'frm' in locals(): + label_names = get_label_names(frm) + else: + label_names = {'node_labels': [], 'node_attrs': [], + 'edge_labels': [], 'edge_attrs': []} + + content_gi = open(fgi).read().splitlines() # graph indicator + content_am = open(fam).read().splitlines() # adjacency matrix + content_gl = open(fgl).read().splitlines() # graph labels + + # create graphs and add nodes + data = [nx.Graph(name=str(i), + node_labels=label_names['node_labels'], + node_attrs=label_names['node_attrs'], + edge_labels=label_names['edge_labels'], + edge_attrs=label_names['edge_attrs']) for i in range(0, len(content_gl))] + if 'fnl' in locals(): + content_nl = open(fnl).read().splitlines() # node labels + for idx, line in enumerate(content_gi): + # transfer to int first in case of unexpected blanks + data[int(line) - 1].add_node(idx) + labels = [l.strip() for l in content_nl[idx].split(',')] + data[int(line) - 1].nodes[idx]['atom'] = str(int(labels[0])) # @todo: this should be removed after. + if data[int(line) - 1].graph['node_labels'] == []: + for i, label in enumerate(labels): + l_name = 'label_' + str(i) + data[int(line) - 1].nodes[idx][l_name] = label + data[int(line) - 1].graph['node_labels'].append(l_name) + else: + for i, l_name in enumerate(data[int(line) - 1].graph['node_labels']): + data[int(line) - 1].nodes[idx][l_name] = labels[i] + else: + for i, line in enumerate(content_gi): + data[int(line) - 1].add_node(i) + + # add edges + for line in content_am: + tmp = line.split(',') + n1 = int(tmp[0]) - 1 + n2 = int(tmp[1]) - 1 + # ignore edge weight here. + g = int(content_gi[n1]) - 1 + data[g].add_edge(n1, n2) + + # add edge labels + if 'fel' in locals(): + content_el = open(fel).read().splitlines() + for idx, line in enumerate(content_el): + labels = [l.strip() for l in line.split(',')] + n = [int(i) - 1 for i in content_am[idx].split(',')] + g = int(content_gi[n[0]]) - 1 + data[g].edges[n[0], n[1]]['bond_type'] = labels[0] # @todo: this should be removed after. + if data[g].graph['edge_labels'] == []: + for i, label in enumerate(labels): + l_name = 'label_' + str(i) + data[g].edges[n[0], n[1]][l_name] = label + data[g].graph['edge_labels'].append(l_name) + else: + for i, l_name in enumerate(data[g].graph['edge_labels']): + data[g].edges[n[0], n[1]][l_name] = labels[i] + + # add node attributes + if 'fna' in locals(): + content_na = open(fna).read().splitlines() + for idx, line in enumerate(content_na): + attrs = [a.strip() for a in line.split(',')] + g = int(content_gi[idx]) - 1 + data[g].nodes[idx]['attributes'] = attrs # @todo: this should be removed after. + if data[g].graph['node_attrs'] == []: + for i, attr in enumerate(attrs): + a_name = 'attr_' + str(i) + data[g].nodes[idx][a_name] = attr + data[g].graph['node_attrs'].append(a_name) + else: + for i, a_name in enumerate(data[g].graph['node_attrs']): + data[g].nodes[idx][a_name] = attrs[i] + + # add edge attributes + if 'fea' in locals(): + content_ea = open(fea).read().splitlines() + for idx, line in enumerate(content_ea): + attrs = [a.strip() for a in line.split(',')] + n = [int(i) - 1 for i in content_am[idx].split(',')] + g = int(content_gi[n[0]]) - 1 + data[g].edges[n[0], n[1]]['attributes'] = attrs # @todo: this should be removed after. + if data[g].graph['edge_attrs'] == []: + for i, attr in enumerate(attrs): + a_name = 'attr_' + str(i) + data[g].edges[n[0], n[1]][a_name] = attr + data[g].graph['edge_attrs'].append(a_name) + else: + for i, a_name in enumerate(data[g].graph['edge_attrs']): + data[g].edges[n[0], n[1]][a_name] = attrs[i] + + # load y + y = [int(i) for i in content_gl] + + return data, y + + +def loadDataset(filename, filename_y=None, extra_params=None): + """Read graph data from filename and load them as NetworkX graphs. + + Parameters + ---------- + filename : string + The name of the file from where the dataset is read. + filename_y : string + The name of file of the targets corresponding to graphs. + extra_params : dict + Extra parameters only designated to '.mat' format. + + Return + ------ + data : List of NetworkX graph. + + y : List + + Targets corresponding to graphs. + + Notes + ----- + This function supports following graph dataset formats: + + 'ds': load data from .ds file. See comments of function loadFromDS for a example. + + 'cxl': load data from Graph eXchange Language file (.cxl file). See + `here `__ for detail. + + 'sdf': load data from structured data file (.sdf file). See + `here `__ + for details. + + 'mat': Load graph data from a MATLAB (up to version 7.1) .mat file. See + README in `downloadable file `__ + for details. + + 'txt': Load graph data from a special .txt file. See + `here `__ + for details. Note here filename is the name of either .txt file in + the dataset directory. + """ + extension = splitext(filename)[1][1:] + if extension == "ds": + data, y = loadFromDS(filename, filename_y) + elif extension == "cxl": + import xml.etree.ElementTree as ET + + dirname_dataset = dirname(filename) + tree = ET.parse(filename) + root = tree.getroot() + data = [] + y = [] + for graph in root.iter('graph'): + mol_filename = graph.attrib['file'] + mol_class = graph.attrib['class'] + data.append(loadGXL(dirname_dataset + '/' + mol_filename)) + y.append(mol_class) + elif extension == 'xml': + data, y = loadFromXML(filename, extra_params) + elif extension == "sdf": +# import numpy as np + from tqdm import tqdm + import sys + + data = loadSDF(filename) + + y_raw = open(filename_y).read().splitlines() + y_raw.pop(0) + tmp0 = [] + tmp1 = [] + for i in range(0, len(y_raw)): + tmp = y_raw[i].split(',') + tmp0.append(tmp[0]) + tmp1.append(tmp[1].strip()) + + y = [] + for i in tqdm(range(0, len(data)), desc='ajust data', file=sys.stdout): + try: + y.append(tmp1[tmp0.index(data[i].name)].strip()) + except ValueError: # if data[i].name not in tmp0 + data[i] = [] + data = list(filter(lambda a: a != [], data)) + elif extension == "mat": + data, y = loadMAT(filename, extra_params) + elif extension == 'txt': + data, y = loadTXT(filename) + # print(len(y)) + # print(y) + # print(data[0].nodes(data=True)) + # print('----') + # print(data[0].edges(data=True)) + # for g in data: + # print(g.nodes(data=True)) + # print('----') + # print(g.edges(data=True)) + + return data, y + + +def loadFromXML(filename, extra_params): + import xml.etree.ElementTree as ET + + if extra_params: + dirname_dataset = extra_params + else: + dirname_dataset = dirname(filename) + tree = ET.parse(filename) + root = tree.getroot() + data = [] + y = [] + for graph in root.iter('graph'): + mol_filename = graph.attrib['file'] + mol_class = graph.attrib['class'] + data.append(loadGXL(dirname_dataset + '/' + mol_filename)) + y.append(mol_class) + + return data, y + + +def loadFromDS(filename, filename_y): + """Load data from .ds file. + + Possible graph formats include: + + '.ct': see function loadCT for detail. + + '.gxl': see dunction loadGXL for detail. + + Note these graph formats are checked automatically by the extensions of + graph files. + """ + dirname_dataset = dirname(filename) + data = [] + y = [] + content = open(filename).read().splitlines() + extension = splitext(content[0].split(' ')[0])[1][1:] + if filename_y is None or filename_y == '': + if extension == 'ct': + for i in range(0, len(content)): + tmp = content[i].split(' ') + # remove the '#'s in file names + data.append( + loadCT(dirname_dataset + '/' + tmp[0].replace('#', '', 1))) + y.append(float(tmp[1])) + elif extension == 'gxl': + for i in range(0, len(content)): + tmp = content[i].split(' ') + # remove the '#'s in file names + data.append( + loadGXL(dirname_dataset + '/' + tmp[0].replace('#', '', 1))) + y.append(float(tmp[1])) + else: # y in a seperate file + if extension == 'ct': + for i in range(0, len(content)): + tmp = content[i] + # remove the '#'s in file names + data.append( + loadCT(dirname_dataset + '/' + tmp.replace('#', '', 1))) + elif extension == 'gxl': + for i in range(0, len(content)): + tmp = content[i] + # remove the '#'s in file names + data.append( + loadGXL(dirname_dataset + '/' + tmp.replace('#', '', 1))) + + content_y = open(filename_y).read().splitlines() + # assume entries in filename and filename_y have the same order. + for item in content_y: + tmp = item.split(' ') + # assume the 3rd entry in a line is y (for Alkane dataset) + y.append(float(tmp[2])) + + return data, y + + +def saveDataset(Gn, y, gformat='gxl', group=None, filename='gfile', xparams=None): + """Save list of graphs. + """ + import os + dirname_ds = os.path.dirname(filename) + if dirname_ds != '': + dirname_ds += '/' + if not os.path.exists(dirname_ds) : + os.makedirs(dirname_ds) + + if xparams is not None and 'graph_dir' in xparams: + graph_dir = xparams['graph_dir'] + '/' + if not os.path.exists(graph_dir): + os.makedirs(graph_dir) + else: + graph_dir = dirname_ds + + if group == 'xml' and gformat == 'gxl': + kwargs = {'method': xparams['method']} if xparams is not None else {} + with open(filename + '.xml', 'w') as fgroup: + fgroup.write("") + fgroup.write("\n") + fgroup.write("\n") + for idx, g in enumerate(Gn): + fname_tmp = "graph" + str(idx) + ".gxl" + saveGXL(g, graph_dir + fname_tmp, **kwargs) + fgroup.write("\n\t") + fgroup.write("\n") + fgroup.close() + + +if __name__ == '__main__': +# ### Load dataset from .ds file. +# # .ct files. +# ds = {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds', +# 'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt'} +# Gn, y = loadDataset(ds['dataset'], filename_y=ds['dataset_y']) +## ds = {'name': 'Acyclic', 'dataset': '../../datasets/acyclic/dataset_bps.ds'} # node symb +## Gn, y = loadDataset(ds['dataset']) +## ds = {'name': 'MAO', 'dataset': '../../datasets/MAO/dataset.ds'} # node/edge symb +## Gn, y = loadDataset(ds['dataset']) +## ds = {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds'} # unlabeled +## Gn, y = loadDataset(ds['dataset']) +# print(Gn[1].nodes(data=True)) +# print(Gn[1].edges(data=True)) +# print(y[1]) + +# # .gxl file. +# ds = {'name': 'monoterpenoides', +# 'dataset': '../../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb +# Gn, y = loadDataset(ds['dataset']) +# print(Gn[1].nodes(data=True)) +# print(Gn[1].edges(data=True)) +# print(y[1]) + +# ### Convert graph from one format to another. +# # .gxl file. +# import networkx as nx +# ds = {'name': 'monoterpenoides', +# 'dataset': '../../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb +# Gn, y = loadDataset(ds['dataset']) +# y = [int(i) for i in y] +# print(Gn[1].nodes(data=True)) +# print(Gn[1].edges(data=True)) +# print(y[1]) +# # Convert a graph to the proper NetworkX format that can be recognized by library gedlib. +# Gn_new = [] +# for G in Gn: +# G_new = nx.Graph() +# for nd, attrs in G.nodes(data=True): +# G_new.add_node(str(nd), chem=attrs['atom']) +# for nd1, nd2, attrs in G.edges(data=True): +# G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type']) +## G_new.add_edge(str(nd1), str(nd2)) +# Gn_new.append(G_new) +# print(Gn_new[1].nodes(data=True)) +# print(Gn_new[1].edges(data=True)) +# print(Gn_new[1]) +# filename = '/media/ljia/DATA/research-repo/codes/others/gedlib/tests_linlin/generated_datsets/monoterpenoides/gxl/monoterpenoides' +# xparams = {'method': 'gedlib'} +# saveDataset(Gn, y, gformat='gxl', group='xml', filename=filename, xparams=xparams) + + # save dataset. +# ds = {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG.mat', +# 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}} # node/edge symb +# Gn, y = loadDataset(ds['dataset'], extra_params=ds['extra_params']) +# saveDataset(Gn, y, group='xml', filename='temp/temp') + + # test - new way to add labels and attributes. +# dataset = '../../datasets/SYNTHETICnew/SYNTHETICnew_A.txt' +# dataset = '../../datasets/Fingerprint/Fingerprint_A.txt' +# dataset = '../../datasets/Letter-med/Letter-med_A.txt' +# dataset = '../../datasets/AIDS/AIDS_A.txt' +# dataset = '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt' +# Gn, y_all = loadDataset(dataset) + pass \ No newline at end of file From ff30033dcde076a4128070f99a63acc2d8892af1 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:19 +0200 Subject: [PATCH 066/728] New translations __init__.py (French) --- lang/fr/gklearn/tests/__init__.py | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 lang/fr/gklearn/tests/__init__.py diff --git a/lang/fr/gklearn/tests/__init__.py b/lang/fr/gklearn/tests/__init__.py new file mode 100644 index 0000000000..be702d9c06 --- /dev/null +++ b/lang/fr/gklearn/tests/__init__.py @@ -0,0 +1,8 @@ +# -*-coding:utf-8 -*- +"""tests - tests module. +""" + +# info +__version__ = "0.1" +__author__ = "Linlin Jia" +__date__ = "March 2020" From 0c906ca37efa25dd395a9a076768440cd379d2a3 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:21 +0200 Subject: [PATCH 067/728] New translations visualization.py (French) --- lang/fr/gklearn/preimage/visualization.py | 585 ++++++++++++++++++++++ 1 file changed, 585 insertions(+) create mode 100644 lang/fr/gklearn/preimage/visualization.py diff --git a/lang/fr/gklearn/preimage/visualization.py b/lang/fr/gklearn/preimage/visualization.py new file mode 100644 index 0000000000..81b814beb5 --- /dev/null +++ b/lang/fr/gklearn/preimage/visualization.py @@ -0,0 +1,585 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Dec 19 17:16:23 2019 + +@author: ljia +""" +import numpy as np +from sklearn.manifold import TSNE, Isomap +import matplotlib.pyplot as plt +from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset +from tqdm import tqdm + +from gklearn.utils.graphfiles import loadDataset, loadGXL +from gklearn.preimage.utils import kernel_distance_matrix, compute_kernel, dis_gstar, get_same_item_indices + + +def visualize_graph_dataset(dis_measure, visual_method, draw_figure, + draw_params={}, dis_mat=None, Gn=None, + median_set=None): + + + def draw_zoomed_axes(Gn_embedded, ax): + margin = 0.01 + if dis_measure == 'graph-kernel': + index = -2 + elif dis_measure == 'ged': + index = -1 + x1 = np.min(Gn_embedded[median_set + [index], 0]) - margin * np.max(Gn_embedded) + x2 = np.max(Gn_embedded[median_set + [index], 0]) + margin * np.max(Gn_embedded) + y1 = np.min(Gn_embedded[median_set + [index], 1]) - margin * np.max(Gn_embedded) + y2 = np.max(Gn_embedded[median_set + [index], 1]) + margin * np.max(Gn_embedded) + if (x1 < 0 and y1 < 0) or ((x1 > 0 and y1 > 0)): + loc = 2 + else: + loc = 3 + axins = zoomed_inset_axes(ax, 4, loc=loc) # zoom-factor: 2.5, location: upper-left + draw_figure(axins, Gn_embedded, dis_measure=dis_measure, + median_set=median_set, **draw_params) + axins.set_xlim(x1, x2) # apply the x-limits + axins.set_ylim(y1, y2) # apply the y-limits + plt.yticks(visible=False) + plt.xticks(visible=False) + loc1 = 1 if loc == 2 else 3 + mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") + + + if dis_mat is None: + if dis_measure == 'graph-kernel': + gkernel = 'untilhpathkernel' + node_label = 'atom' + edge_label = 'bond_type' + dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, + Kmatrix=None, gkernel=gkernel) + elif dis_measure == 'ged': + pass + + if visual_method == 'tsne': + Gn_embedded = TSNE(n_components=2, metric='precomputed').fit_transform(dis_mat) + elif visual_method == 'isomap': + Gn_embedded = Isomap(n_components=2, metric='precomputed').fit_transform(dis_mat) + print(Gn_embedded.shape) + fig, ax = plt.subplots() + draw_figure(plt, Gn_embedded, dis_measure=dis_measure, legend=True, + median_set=median_set, **draw_params) +# draw_zoomed_axes(Gn_embedded, ax) + plt.show() + plt.clf() + + return + + +def draw_figure(ax, Gn_embedded, dis_measure=None, y_idx=None, legend=False, + median_set=None): + from matplotlib import colors as mcolors + colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)) +# colors = ['#08306b', '#08519c', '#2171b5', '#4292c6', '#6baed6', '#9ecae1', +# '#c6dbef', '#deebf7'] +# for i, values in enumerate(y_idx.values()): +# for item in values: +## ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c=colors[i]) # , c='b') +# ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c='b') +# ax.scatter(Gn_embedded[:,0], Gn_embedded[:,1], c='b') + h1 = ax.scatter(Gn_embedded[median_set, 0], Gn_embedded[median_set, 1], c='b') + if dis_measure == 'graph-kernel': + h2 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='darkorchid') # \psi + h3 = ax.scatter(Gn_embedded[-2, 0], Gn_embedded[-2, 1], c='gold') # gen median + h4 = ax.scatter(Gn_embedded[-3, 0], Gn_embedded[-3, 1], c='r') #c='g', marker='+') # set median + elif dis_measure == 'ged': + h3 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='gold') # gen median + h4 = ax.scatter(Gn_embedded[-2, 0], Gn_embedded[-2, 1], c='r') #c='g', marker='+') # set median + if legend: +# fig.subplots_adjust(bottom=0.17) + if dis_measure == 'graph-kernel': + ax.legend([h1, h2, h3, h4], + ['k closest graphs', 'true median', 'gen median', 'set median']) + elif dis_measure == 'ged': + ax.legend([h1, h3, h4], ['k closest graphs', 'gen median', 'set median']) +# fig.legend(handles, labels, loc='lower center', ncol=2, frameon=False) # , ncol=5, labelspacing=0.1, handletextpad=0.4, columnspacing=0.6) +# plt.savefig('symbolic_and_non_comparison_vertical_short.eps', format='eps', dpi=300, transparent=True, +# bbox_inches='tight') +# plt.show() + + +############################################################################### + +def visualize_distances_in_kernel(): + + ds = {'name': 'monoterpenoides', + 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb + Gn, y_all = loadDataset(ds['dataset']) +# Gn = Gn[0:50] + fname_medians = 'expert.treelet' + # add set median. + fname_sm = 'results/test_k_closest_graphs/set_median.' + fname_medians + '.gxl' + set_median = loadGXL(fname_sm) + Gn.append(set_median) + # add generalized median (estimated pre-image.) + fname_gm = 'results/test_k_closest_graphs/gen_median.' + fname_medians + '.gxl' + gen_median = loadGXL(fname_gm) + Gn.append(gen_median) + + # compute distance matrix + median_set = [22, 29, 54, 74] + gkernel = 'treeletkernel' + node_label = 'atom' + edge_label = 'bond_type' + Gn_median_set = [Gn[i].copy() for i in median_set] + Kmatrix_median = compute_kernel(Gn + Gn_median_set, gkernel, node_label, + edge_label, True) + Kmatrix = Kmatrix_median[0:len(Gn), 0:len(Gn)] + dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, + Kmatrix=Kmatrix, gkernel=gkernel) + print('average distances: ', np.mean(np.mean(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) + print('min distances: ', np.min(np.min(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) + print('max distances: ', np.max(np.max(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) + + # add distances for the image of exact median \psi. + dis_k_median_list = [] + for idx, g in enumerate(Gn): + dis_k_median_list.append(dis_gstar(idx, range(len(Gn), len(Gn) + len(Gn_median_set)), + [1 / len(Gn_median_set)] * len(Gn_median_set), + Kmatrix_median, withterm3=False)) + dis_mat_median = np.zeros((len(Gn) + 1, len(Gn) + 1)) + for i in range(len(Gn)): + for j in range(i, len(Gn)): + dis_mat_median[i, j] = dis_mat[i, j] + dis_mat_median[j, i] = dis_mat_median[i, j] + for i in range(len(Gn)): + dis_mat_median[i, -1] = dis_k_median_list[i] + dis_mat_median[-1, i] = dis_k_median_list[i] + + # get indices by classes. + y_idx = get_same_item_indices(y_all) + + # visualization. +# visualize_graph_dataset('graph-kernel', 'tsne', Gn) +# visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, +# draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median) + visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, + draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median, + median_set=median_set) + + +def visualize_distances_in_ged(): + from gklearn.preimage.fitDistance import compute_geds + from gklearn.preimage.ged import GED + ds = {'name': 'monoterpenoides', + 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb + Gn, y_all = loadDataset(ds['dataset']) +# Gn = Gn[0:50] + # add set median. + fname_medians = 'expert.treelet' + fname_sm = 'preimage/results/test_k_closest_graphs/set_median.' + fname_medians + '.gxl' + set_median = loadGXL(fname_sm) + Gn.append(set_median) + # add generalized median (estimated pre-image.) + fname_gm = 'preimage/results/test_k_closest_graphs/gen_median.' + fname_medians + '.gxl' + gen_median = loadGXL(fname_gm) + Gn.append(gen_median) + + # compute/load ged matrix. +# # compute. +## k = 4 +## edit_costs = [0.16229209837639536, 0.06612870523413916, 0.04030113378793905, 0.20723547009415202, 0.3338607220394598, 0.27054392518077297] +# edit_costs = [3, 3, 1, 3, 3, 1] +## edit_costs = [7, 3, 5, 9, 2, 6] +# algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' +# params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', +# 'algo_options': algo_options, 'stabilizer': None, +# 'edit_cost_constant': edit_costs} +# _, ged_mat, _ = compute_geds(Gn, params_ged=params_ged, parallel=True) +# np.savez('results/test_k_closest_graphs/ged_mat.' + fname_medians + '.with_medians.gm', ged_mat=ged_mat) + # load from file. + gmfile = np.load('results/test_k_closest_graphs/ged_mat.' + fname_medians + '.with_medians.gm.npz') + ged_mat = gmfile['ged_mat'] +# # change medians. +# edit_costs = [3, 3, 1, 3, 3, 1] +# algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' +# params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', +# 'algo_options': algo_options, 'stabilizer': None, +# 'edit_cost_constant': edit_costs} +# for idx in tqdm(range(len(Gn) - 2), desc='computing GEDs', file=sys.stdout): +# dis, _, _ = GED(Gn[idx], set_median, **params_ged) +# ged_mat[idx, -2] = dis +# ged_mat[-2, idx] = dis +# dis, _, _ = GED(Gn[idx], gen_median, **params_ged) +# ged_mat[idx, -1] = dis +# ged_mat[-1, idx] = dis +# np.savez('results/test_k_closest_graphs/ged_mat.' + fname_medians + '.with_medians.gm', +# ged_mat=ged_mat) + + + # get indices by classes. + y_idx = get_same_item_indices(y_all) + + # visualization. + median_set = [22, 29, 54, 74] + visualize_graph_dataset('ged', 'tsne', draw_figure, + draw_params={'y_idx': y_idx}, dis_mat=ged_mat, + median_set=median_set) + +############################################################################### + + +def visualize_distances_in_kernel_monoterpenoides(): + import os + + ds = {'dataset': '../datasets/monoterpenoides/dataset_10+.ds', + 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/'} # node/edge symb + Gn_original, y_all = loadDataset(ds['dataset']) +# Gn = Gn[0:50] + + # compute distance matrix +# median_set = [22, 29, 54, 74] + gkernel = 'treeletkernel' + fit_method = 'expert' + node_label = 'atom' + edge_label = 'bond_type' + ds_name = 'monoterpenoides' + fname_medians = fit_method + '.' + gkernel + dir_output = 'results/xp_monoterpenoides/' + repeat = 0 + + # get indices by classes. + y_idx = get_same_item_indices(y_all) + for i, (y, values) in enumerate(y_idx.items()): + print('\ny =', y) + k = len(values) + + Gn = [Gn_original[g].copy() for g in values] + # add set median. + fname_sm = dir_output + 'medians/' + str(int(y)) + '/set_median.k' + str(int(k)) \ + + '.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' + set_median = loadGXL(fname_sm) + Gn.append(set_median) + # add generalized median (estimated pre-image.) + fname_gm = dir_output + 'medians/' + str(int(y)) + '/gen_median.k' + str(int(k)) \ + + '.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' + gen_median = loadGXL(fname_gm) + Gn.append(gen_median) + + # compute distance matrix + median_set = range(0, len(values)) + + Gn_median_set = [Gn[i].copy() for i in median_set] + Kmatrix_median = compute_kernel(Gn + Gn_median_set, gkernel, node_label, + edge_label, False) + Kmatrix = Kmatrix_median[0:len(Gn), 0:len(Gn)] + dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, + Kmatrix=Kmatrix, gkernel=gkernel) + print('average distances: ', np.mean(np.mean(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) + print('min distances: ', np.min(np.min(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) + print('max distances: ', np.max(np.max(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) + + # add distances for the image of exact median \psi. + dis_k_median_list = [] + for idx, g in enumerate(Gn): + dis_k_median_list.append(dis_gstar(idx, range(len(Gn), len(Gn) + len(Gn_median_set)), + [1 / len(Gn_median_set)] * len(Gn_median_set), + Kmatrix_median, withterm3=False)) + dis_mat_median = np.zeros((len(Gn) + 1, len(Gn) + 1)) + for i in range(len(Gn)): + for j in range(i, len(Gn)): + dis_mat_median[i, j] = dis_mat[i, j] + dis_mat_median[j, i] = dis_mat_median[i, j] + for i in range(len(Gn)): + dis_mat_median[i, -1] = dis_k_median_list[i] + dis_mat_median[-1, i] = dis_k_median_list[i] + + + # visualization. +# visualize_graph_dataset('graph-kernel', 'tsne', Gn) +# visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, +# draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median) + visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, + draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median, + median_set=median_set) + + +def visualize_distances_in_ged_monoterpenoides(): + from gklearn.preimage.fitDistance import compute_geds + from gklearn.preimage.ged import GED + import os + + ds = {'dataset': '../datasets/monoterpenoides/dataset_10+.ds', + 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/'} # node/edge symb + Gn_original, y_all = loadDataset(ds['dataset']) +# Gn = Gn[0:50] + + # compute distance matrix +# median_set = [22, 29, 54, 74] + gkernel = 'treeletkernel' + fit_method = 'expert' + ds_name = 'monoterpenoides' + fname_medians = fit_method + '.' + gkernel + dir_output = 'results/xp_monoterpenoides/' + repeat = 0 +# edit_costs = [0.16229209837639536, 0.06612870523413916, 0.04030113378793905, 0.20723547009415202, 0.3338607220394598, 0.27054392518077297] + edit_costs = [3, 3, 1, 3, 3, 1] +# edit_costs = [7, 3, 5, 9, 2, 6] + + # get indices by classes. + y_idx = get_same_item_indices(y_all) + for i, (y, values) in enumerate(y_idx.items()): + print('\ny =', y) + k = len(values) + + Gn = [Gn_original[g].copy() for g in values] + # add set median. + fname_sm = dir_output + 'medians/' + str(int(y)) + '/set_median.k' + str(int(k)) \ + + '.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' + set_median = loadGXL(fname_sm) + Gn.append(set_median) + # add generalized median (estimated pre-image.) + fname_gm = dir_output + 'medians/' + str(int(y)) + '/gen_median.k' + str(int(k)) \ + + '.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' + gen_median = loadGXL(fname_gm) + Gn.append(gen_median) + + + # compute/load ged matrix. + # compute. + algo_options = '--threads 1 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' + params_ged = {'dataset': ds_name, 'lib': 'gedlibpy', 'cost': 'CONSTANT', + 'method': 'IPFP', 'algo_options': algo_options, + 'stabilizer': None, 'edit_cost_constant': edit_costs} + _, ged_mat, _ = compute_geds(Gn, params_ged=params_ged, parallel=True) + np.savez(dir_output + 'ged_mat.' + fname_medians + '.y' + str(int(y)) \ + + '.with_medians.gm', ged_mat=ged_mat) +# # load from file. +# gmfile = np.load('dir_output + 'ged_mat.' + fname_medians + '.y' + str(int(y)) + '.with_medians.gm.npz') +# ged_mat = gmfile['ged_mat'] +# # change medians. +# algo_options = '--threads 1 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' +# params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', +# 'algo_options': algo_options, 'stabilizer': None, +# 'edit_cost_constant': edit_costs} +# for idx in tqdm(range(len(Gn) - 2), desc='computing GEDs', file=sys.stdout): +# dis, _, _ = GED(Gn[idx], set_median, **params_ged) +# ged_mat[idx, -2] = dis +# ged_mat[-2, idx] = dis +# dis, _, _ = GED(Gn[idx], gen_median, **params_ged) +# ged_mat[idx, -1] = dis +# ged_mat[-1, idx] = dis +# np.savez(dir_output + 'ged_mat.' + fname_medians + '.y' + str(int(y)) + '.with_medians.gm', +# ged_mat=ged_mat) + + # visualization. + median_set = range(0, len(values)) + visualize_graph_dataset('ged', 'tsne', draw_figure, + draw_params={'y_idx': y_idx}, dis_mat=ged_mat, + median_set=median_set) + + +############################################################################### + + +def visualize_distances_in_kernel_letter_h(): + + ds = {'dataset': 'cpp_ext/data/collections/Letter.xml', + 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb + Gn_original, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir']) +# Gn = Gn[0:50] + + # compute distance matrix +# median_set = [22, 29, 54, 74] + gkernel = 'structuralspkernel' + fit_method = 'expert' + node_label = None + edge_label = None + ds_name = 'letter-h' + fname_medians = fit_method + '.' + gkernel + dir_output = 'results/xp_letter_h/' + k = 150 + repeat = 0 + + # get indices by classes. + y_idx = get_same_item_indices(y_all) + for i, (y, values) in enumerate(y_idx.items()): + print('\ny =', y) + + Gn = [Gn_original[g].copy() for g in values] + # add set median. + fname_sm = dir_output + 'medians/' + y + '/set_median.k' + str(int(k)) \ + + '.y' + y + '.repeat' + str(repeat) + '.gxl' + set_median = loadGXL(fname_sm) + Gn.append(set_median) + # add generalized median (estimated pre-image.) + fname_gm = dir_output + 'medians/' + y + '/gen_median.k' + str(int(k)) \ + + '.y' + y + '.repeat' + str(repeat) + '.gxl' + gen_median = loadGXL(fname_gm) + Gn.append(gen_median) + + # compute distance matrix + median_set = range(0, len(values)) + + Gn_median_set = [Gn[i].copy() for i in median_set] + Kmatrix_median = compute_kernel(Gn + Gn_median_set, gkernel, node_label, + edge_label, False) + Kmatrix = Kmatrix_median[0:len(Gn), 0:len(Gn)] + dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, + Kmatrix=Kmatrix, gkernel=gkernel) + print('average distances: ', np.mean(np.mean(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) + print('min distances: ', np.min(np.min(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) + print('max distances: ', np.max(np.max(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) + + # add distances for the image of exact median \psi. + dis_k_median_list = [] + for idx, g in enumerate(Gn): + dis_k_median_list.append(dis_gstar(idx, range(len(Gn), len(Gn) + len(Gn_median_set)), + [1 / len(Gn_median_set)] * len(Gn_median_set), + Kmatrix_median, withterm3=False)) + dis_mat_median = np.zeros((len(Gn) + 1, len(Gn) + 1)) + for i in range(len(Gn)): + for j in range(i, len(Gn)): + dis_mat_median[i, j] = dis_mat[i, j] + dis_mat_median[j, i] = dis_mat_median[i, j] + for i in range(len(Gn)): + dis_mat_median[i, -1] = dis_k_median_list[i] + dis_mat_median[-1, i] = dis_k_median_list[i] + + + # visualization. +# visualize_graph_dataset('graph-kernel', 'tsne', Gn) +# visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, +# draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median) + visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, + draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median, + median_set=median_set) + + +def visualize_distances_in_ged_letter_h(): + from fitDistance import compute_geds + from preimage.test_k_closest_graphs import reform_attributes + + ds = {'dataset': 'cpp_ext/data/collections/Letter.xml', + 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb + Gn_original, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir']) +# Gn = Gn[0:50] + + # compute distance matrix +# median_set = [22, 29, 54, 74] + gkernel = 'structuralspkernel' + fit_method = 'expert' + ds_name = 'letter-h' + fname_medians = fit_method + '.' + gkernel + dir_output = 'results/xp_letter_h/' + k = 150 + repeat = 0 +# edit_costs = [0.16229209837639536, 0.06612870523413916, 0.04030113378793905, 0.20723547009415202, 0.3338607220394598, 0.27054392518077297] + edit_costs = [3, 3, 1, 3, 3, 1] +# edit_costs = [7, 3, 5, 9, 2, 6] + + # get indices by classes. + y_idx = get_same_item_indices(y_all) + for i, (y, values) in enumerate(y_idx.items()): + print('\ny =', y) + + Gn = [Gn_original[g].copy() for g in values] + # add set median. + fname_sm = dir_output + 'medians/' + y + '/set_median.k' + str(int(k)) \ + + '.y' + y + '.repeat' + str(repeat) + '.gxl' + set_median = loadGXL(fname_sm) + Gn.append(set_median) + # add generalized median (estimated pre-image.) + fname_gm = dir_output + 'medians/' + y + '/gen_median.k' + str(int(k)) \ + + '.y' + y + '.repeat' + str(repeat) + '.gxl' + gen_median = loadGXL(fname_gm) + Gn.append(gen_median) + + + # compute/load ged matrix. + # compute. + algo_options = '--threads 1 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' + params_ged = {'dataset': 'Letter', 'lib': 'gedlibpy', 'cost': 'CONSTANT', + 'method': 'IPFP', 'algo_options': algo_options, + 'stabilizer': None, 'edit_cost_constant': edit_costs} + for g in Gn: + reform_attributes(g) + _, ged_mat, _ = compute_geds(Gn, params_ged=params_ged, parallel=True) + np.savez(dir_output + 'ged_mat.' + fname_medians + '.y' + y + '.with_medians.gm', ged_mat=ged_mat) +# # load from file. +# gmfile = np.load('dir_output + 'ged_mat.' + fname_medians + '.y' + y + '.with_medians.gm.npz') +# ged_mat = gmfile['ged_mat'] +# # change medians. +# algo_options = '--threads 1 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' +# params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', +# 'algo_options': algo_options, 'stabilizer': None, +# 'edit_cost_constant': edit_costs} +# for idx in tqdm(range(len(Gn) - 2), desc='computing GEDs', file=sys.stdout): +# dis, _, _ = GED(Gn[idx], set_median, **params_ged) +# ged_mat[idx, -2] = dis +# ged_mat[-2, idx] = dis +# dis, _, _ = GED(Gn[idx], gen_median, **params_ged) +# ged_mat[idx, -1] = dis +# ged_mat[-1, idx] = dis +# np.savez(dir_output + 'ged_mat.' + fname_medians + '.y' + y + '.with_medians.gm', +# ged_mat=ged_mat) + + + # visualization. + median_set = range(0, len(values)) + visualize_graph_dataset('ged', 'tsne', draw_figure, + draw_params={'y_idx': y_idx}, dis_mat=ged_mat, + median_set=median_set) + + +if __name__ == '__main__': + visualize_distances_in_kernel_letter_h() +# visualize_distances_in_ged_letter_h() +# visualize_distances_in_kernel_monoterpenoides() +# visualize_distances_in_kernel_monoterpenoides() +# visualize_distances_in_kernel() +# visualize_distances_in_ged() + + + + + + + +#def draw_figure_dis_k(ax, Gn_embedded, y_idx=None, legend=False): +# from matplotlib import colors as mcolors +# colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)) +## colors = ['#08306b', '#08519c', '#2171b5', '#4292c6', '#6baed6', '#9ecae1', +## '#c6dbef', '#deebf7'] +# for i, values in enumerate(y_idx.values()): +# for item in values: +## ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c=colors[i]) # , c='b') +# ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c='b') +# h1 = ax.scatter(Gn_embedded[[12, 13, 22, 29], 0], Gn_embedded[[12, 13, 22, 29], 1], c='r') +# h2 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='darkorchid') # \psi +# h3 = ax.scatter(Gn_embedded[-2, 0], Gn_embedded[-2, 1], c='gold') # gen median +# h4 = ax.scatter(Gn_embedded[-3, 0], Gn_embedded[-3, 1], c='r', marker='+') # set median +# if legend: +## fig.subplots_adjust(bottom=0.17) +# ax.legend([h1, h2, h3, h4], ['k clostest graphs', 'true median', 'gen median', 'set median']) +## fig.legend(handles, labels, loc='lower center', ncol=2, frameon=False) # , ncol=5, labelspacing=0.1, handletextpad=0.4, columnspacing=0.6) +## plt.savefig('symbolic_and_non_comparison_vertical_short.eps', format='eps', dpi=300, transparent=True, +## bbox_inches='tight') +## plt.show() + + + +#def draw_figure_ged(ax, Gn_embedded, y_idx=None, legend=False): +# from matplotlib import colors as mcolors +# colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)) +## colors = ['#08306b', '#08519c', '#2171b5', '#4292c6', '#6baed6', '#9ecae1', +## '#c6dbef', '#deebf7'] +# for i, values in enumerate(y_idx.values()): +# for item in values: +## ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c=colors[i]) # , c='b') +# ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c='b') +# h1 = ax.scatter(Gn_embedded[[12, 13, 22, 29], 0], Gn_embedded[[12, 13, 22, 29], 1], c='r') +## h2 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='darkorchid') # \psi +# h3 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='gold') # gen median +# h4 = ax.scatter(Gn_embedded[-2, 0], Gn_embedded[-2, 1], c='r', marker='+') # set median +# if legend: +## fig.subplots_adjust(bottom=0.17) +# ax.legend([h1, h3, h4], ['k clostest graphs', 'gen median', 'set median']) +## fig.legend(handles, labels, loc='lower center', ncol=2, frameon=False) # , ncol=5, labelspacing=0.1, handletextpad=0.4, columnspacing=0.6) +## plt.savefig('symbolic_and_non_comparison_vertical_short.eps', format='eps', dpi=300, transparent=True, +## bbox_inches='tight') +## plt.show() \ No newline at end of file From 37a77f9a3649904db8504d6b9a715bb4e7898faf Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:22 +0200 Subject: [PATCH 068/728] New translations utils.py (French) --- lang/fr/gklearn/preimage/utils.py | 516 ++++++++++++++++++++++++++++++ 1 file changed, 516 insertions(+) create mode 100644 lang/fr/gklearn/preimage/utils.py diff --git a/lang/fr/gklearn/preimage/utils.py b/lang/fr/gklearn/preimage/utils.py new file mode 100644 index 0000000000..d4d5d05b9c --- /dev/null +++ b/lang/fr/gklearn/preimage/utils.py @@ -0,0 +1,516 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Oct 17 19:05:07 2019 + +Useful functions. +@author: ljia +""" +#import networkx as nx + +import multiprocessing +import numpy as np + +from gklearn.kernels.marginalizedKernel import marginalizedkernel +from gklearn.kernels.untilHPathKernel import untilhpathkernel +from gklearn.kernels.spKernel import spkernel +import functools +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct, polynomialkernel +from gklearn.kernels.structuralspKernel import structuralspkernel +from gklearn.kernels.treeletKernel import treeletkernel +from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel +from gklearn.utils import Dataset +import csv +import networkx as nx +import os + + +def generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=True, save_medians=True, plot_medians=True, load_gm='auto', dir_save='', irrelevant_labels=None, edge_required=False, cut_range=None): + import os.path + from gklearn.preimage import MedianPreimageGenerator + from gklearn.utils import split_dataset_by_target + from gklearn.utils.graphfiles import saveGXL + + # 1. get dataset. + print('1. getting dataset...') + dataset_all = Dataset() + dataset_all.load_predefined_dataset(ds_name) + dataset_all.trim_dataset(edge_required=edge_required) + if irrelevant_labels is not None: + dataset_all.remove_labels(**irrelevant_labels) + if cut_range is not None: + dataset_all.cut_graphs(cut_range) + datasets = split_dataset_by_target(dataset_all) + + if save_results: + # create result files. + print('creating output files...') + fn_output_detail, fn_output_summary = __init_output_file_preimage(ds_name, kernel_options['name'], mpg_options['fit_method'], dir_save) + + sod_sm_list = [] + sod_gm_list = [] + dis_k_sm_list = [] + dis_k_gm_list = [] + dis_k_gi_min_list = [] + time_optimize_ec_list = [] + time_generate_list = [] + time_total_list = [] + itrs_list = [] + converged_list = [] + num_updates_ecc_list = [] + mge_decrease_order_list = [] + mge_increase_order_list = [] + mge_converged_order_list = [] + nb_sod_sm2gm = [0, 0, 0] + nb_dis_k_sm2gm = [0, 0, 0] + nb_dis_k_gi2sm = [0, 0, 0] + nb_dis_k_gi2gm = [0, 0, 0] + dis_k_max_list = [] + dis_k_min_list = [] + dis_k_mean_list = [] + if load_gm == 'auto': + gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' + gmfile_exist = os.path.isfile(os.path.abspath(gm_fname)) + if gmfile_exist: + gmfile = np.load(gm_fname, allow_pickle=True) # @todo: may not be safe. + gram_matrix_unnorm_list = [item for item in gmfile['gram_matrix_unnorm_list']] + time_precompute_gm_list = gmfile['run_time_list'].tolist() + else: + gram_matrix_unnorm_list = [] + time_precompute_gm_list = [] + elif not load_gm: + gram_matrix_unnorm_list = [] + time_precompute_gm_list = [] + else: + gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' + gmfile = np.load(gm_fname, allow_pickle=True) # @todo: may not be safe. + gram_matrix_unnorm_list = [item for item in gmfile['gram_matrix_unnorm_list']] + time_precompute_gm_list = gmfile['run_time_list'].tolist() +# repeats_better_sod_sm2gm = [] +# repeats_better_dis_k_sm2gm = [] +# repeats_better_dis_k_gi2sm = [] +# repeats_better_dis_k_gi2gm = [] + + print('starting generating preimage for each class of target...') + idx_offset = 0 + for idx, dataset in enumerate(datasets): + target = dataset.targets[0] + print('\ntarget =', target, '\n') +# if target != 1: +# continue + + num_graphs = len(dataset.graphs) + if num_graphs < 2: + print('\nnumber of graphs = ', num_graphs, ', skip.\n') + idx_offset += 1 + continue + + # 2. set parameters. + print('2. initializing mpg and setting parameters...') + if load_gm: + if gmfile_exist: + mpg_options['gram_matrix_unnorm'] = gram_matrix_unnorm_list[idx - idx_offset] + mpg_options['runtime_precompute_gm'] = time_precompute_gm_list[idx - idx_offset] + mpg = MedianPreimageGenerator() + mpg.dataset = dataset + mpg.set_options(**mpg_options.copy()) + mpg.kernel_options = kernel_options.copy() + mpg.ged_options = ged_options.copy() + mpg.mge_options = mge_options.copy() + + # 3. compute median preimage. + print('3. computing median preimage...') + mpg.run() + results = mpg.get_results() + + # 4. compute pairwise kernel distances. + print('4. computing pairwise kernel distances...') + _, dis_k_max, dis_k_min, dis_k_mean = mpg.graph_kernel.compute_distance_matrix() + dis_k_max_list.append(dis_k_max) + dis_k_min_list.append(dis_k_min) + dis_k_mean_list.append(dis_k_mean) + + # 5. save results (and median graphs). + print('5. saving results (and median graphs)...') + # write result detail. + if save_results: + print('writing results to files...') + sod_sm2gm = get_relations(np.sign(results['sod_gen_median'] - results['sod_set_median'])) + dis_k_sm2gm = get_relations(np.sign(results['k_dis_gen_median'] - results['k_dis_set_median'])) + dis_k_gi2sm = get_relations(np.sign(results['k_dis_set_median'] - results['k_dis_dataset'])) + dis_k_gi2gm = get_relations(np.sign(results['k_dis_gen_median'] - results['k_dis_dataset'])) + + f_detail = open(dir_save + fn_output_detail, 'a') + csv.writer(f_detail).writerow([ds_name, kernel_options['name'], + ged_options['edit_cost'], ged_options['method'], + ged_options['attr_distance'], mpg_options['fit_method'], + num_graphs, target, 1, + results['sod_set_median'], results['sod_gen_median'], + results['k_dis_set_median'], results['k_dis_gen_median'], + results['k_dis_dataset'], sod_sm2gm, dis_k_sm2gm, + dis_k_gi2sm, dis_k_gi2gm, results['edit_cost_constants'], + results['runtime_precompute_gm'], results['runtime_optimize_ec'], + results['runtime_generate_preimage'], results['runtime_total'], + results['itrs'], results['converged'], + results['num_updates_ecc'], + results['mge']['num_decrease_order'] > 0, # @todo: not suitable for multi-start mge + results['mge']['num_increase_order'] > 0, + results['mge']['num_converged_descents'] > 0]) + f_detail.close() + + # compute result summary. + sod_sm_list.append(results['sod_set_median']) + sod_gm_list.append(results['sod_gen_median']) + dis_k_sm_list.append(results['k_dis_set_median']) + dis_k_gm_list.append(results['k_dis_gen_median']) + dis_k_gi_min_list.append(results['k_dis_dataset']) + time_precompute_gm_list.append(results['runtime_precompute_gm']) + time_optimize_ec_list.append(results['runtime_optimize_ec']) + time_generate_list.append(results['runtime_generate_preimage']) + time_total_list.append(results['runtime_total']) + itrs_list.append(results['itrs']) + converged_list.append(results['converged']) + num_updates_ecc_list.append(results['num_updates_ecc']) + mge_decrease_order_list.append(results['mge']['num_decrease_order'] > 0) + mge_increase_order_list.append(results['mge']['num_increase_order'] > 0) + mge_converged_order_list.append(results['mge']['num_converged_descents'] > 0) + # # SOD SM -> GM + if results['sod_set_median'] > results['sod_gen_median']: + nb_sod_sm2gm[0] += 1 + # repeats_better_sod_sm2gm.append(1) + elif results['sod_set_median'] == results['sod_gen_median']: + nb_sod_sm2gm[1] += 1 + elif results['sod_set_median'] < results['sod_gen_median']: + nb_sod_sm2gm[2] += 1 + # # dis_k SM -> GM + if results['k_dis_set_median'] > results['k_dis_gen_median']: + nb_dis_k_sm2gm[0] += 1 + # repeats_better_dis_k_sm2gm.append(1) + elif results['k_dis_set_median'] == results['k_dis_gen_median']: + nb_dis_k_sm2gm[1] += 1 + elif results['k_dis_set_median'] < results['k_dis_gen_median']: + nb_dis_k_sm2gm[2] += 1 + # # dis_k gi -> SM + if results['k_dis_dataset'] > results['k_dis_set_median']: + nb_dis_k_gi2sm[0] += 1 + # repeats_better_dis_k_gi2sm.append(1) + elif results['k_dis_dataset'] == results['k_dis_set_median']: + nb_dis_k_gi2sm[1] += 1 + elif results['k_dis_dataset'] < results['k_dis_set_median']: + nb_dis_k_gi2sm[2] += 1 + # # dis_k gi -> GM + if results['k_dis_dataset'] > results['k_dis_gen_median']: + nb_dis_k_gi2gm[0] += 1 + # repeats_better_dis_k_gi2gm.append(1) + elif results['k_dis_dataset'] == results['k_dis_gen_median']: + nb_dis_k_gi2gm[1] += 1 + elif results['k_dis_dataset'] < results['k_dis_gen_median']: + nb_dis_k_gi2gm[2] += 1 + + # write result summary for each letter. + f_summary = open(dir_save + fn_output_summary, 'a') + csv.writer(f_summary).writerow([ds_name, kernel_options['name'], + ged_options['edit_cost'], ged_options['method'], + ged_options['attr_distance'], mpg_options['fit_method'], + num_graphs, target, + results['sod_set_median'], results['sod_gen_median'], + results['k_dis_set_median'], results['k_dis_gen_median'], + results['k_dis_dataset'], sod_sm2gm, dis_k_sm2gm, + dis_k_gi2sm, dis_k_gi2gm, + results['runtime_precompute_gm'], results['runtime_optimize_ec'], + results['runtime_generate_preimage'], results['runtime_total'], + results['itrs'], results['converged'], + results['num_updates_ecc'], + results['mge']['num_decrease_order'] > 0, # @todo: not suitable for multi-start mge + results['mge']['num_increase_order'] > 0, + results['mge']['num_converged_descents'] > 0, + nb_sod_sm2gm, + nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm]) + f_summary.close() + + # save median graphs. + if save_medians: + if not os.path.exists(dir_save + 'medians/'): + os.makedirs(dir_save + 'medians/') + print('Saving median graphs to files...') + fn_pre_sm = dir_save + 'medians/set_median.' + mpg_options['fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) + saveGXL(mpg.set_median, fn_pre_sm + '.gxl', method='default', + node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs) + fn_pre_gm = dir_save + 'medians/gen_median.' + mpg_options['fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) + saveGXL(mpg.gen_median, fn_pre_gm + '.gxl', method='default', + node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs) + fn_best_dataset = dir_save + 'medians/g_best_dataset.' + mpg_options['fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) + saveGXL(mpg.best_from_dataset, fn_best_dataset + '.gxl', method='default', + node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs) + + # plot median graphs. + if plot_medians and save_medians: + if ged_options['edit_cost'] == 'LETTER2' or ged_options['edit_cost'] == 'LETTER' or ds_name == 'Letter-high' or ds_name == 'Letter-med' or ds_name == 'Letter-low': + draw_Letter_graph(mpg.set_median, fn_pre_sm) + draw_Letter_graph(mpg.gen_median, fn_pre_gm) + draw_Letter_graph(mpg.best_from_dataset, fn_best_dataset) + + if (load_gm == 'auto' and not gmfile_exist) or not load_gm: + gram_matrix_unnorm_list.append(mpg.gram_matrix_unnorm) + + # write result summary for each class. + if save_results: + sod_sm_mean = np.mean(sod_sm_list) + sod_gm_mean = np.mean(sod_gm_list) + dis_k_sm_mean = np.mean(dis_k_sm_list) + dis_k_gm_mean = np.mean(dis_k_gm_list) + dis_k_gi_min_mean = np.mean(dis_k_gi_min_list) + time_precompute_gm_mean = np.mean(time_precompute_gm_list) + time_optimize_ec_mean = np.mean(time_optimize_ec_list) + time_generate_mean = np.mean(time_generate_list) + time_total_mean = np.mean(time_total_list) + itrs_mean = np.mean(itrs_list) + num_converged = np.sum(converged_list) + num_updates_ecc_mean = np.mean(num_updates_ecc_list) + num_mge_decrease_order = np.sum(mge_decrease_order_list) + num_mge_increase_order = np.sum(mge_increase_order_list) + num_mge_converged = np.sum(mge_converged_order_list) + sod_sm2gm_mean = get_relations(np.sign(sod_gm_mean - sod_sm_mean)) + dis_k_sm2gm_mean = get_relations(np.sign(dis_k_gm_mean - dis_k_sm_mean)) + dis_k_gi2sm_mean = get_relations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean)) + dis_k_gi2gm_mean = get_relations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean)) + f_summary = open(dir_save + fn_output_summary, 'a') + csv.writer(f_summary).writerow([ds_name, kernel_options['name'], + ged_options['edit_cost'], ged_options['method'], + ged_options['attr_distance'], mpg_options['fit_method'], + num_graphs, 'all', + sod_sm_mean, sod_gm_mean, dis_k_sm_mean, dis_k_gm_mean, + dis_k_gi_min_mean, sod_sm2gm_mean, dis_k_sm2gm_mean, + dis_k_gi2sm_mean, dis_k_gi2gm_mean, + time_precompute_gm_mean, time_optimize_ec_mean, + time_generate_mean, time_total_mean, itrs_mean, + num_converged, num_updates_ecc_mean, + num_mge_decrease_order, num_mge_increase_order, + num_mge_converged]) + f_summary.close() + + # save total pairwise kernel distances. + dis_k_max = np.max(dis_k_max_list) + dis_k_min = np.min(dis_k_min_list) + dis_k_mean = np.mean(dis_k_mean_list) + print('The maximum pairwise distance in kernel space:', dis_k_max) + print('The minimum pairwise distance in kernel space:', dis_k_min) + print('The average pairwise distance in kernel space:', dis_k_mean) + + # write Gram matrices to file. + if (load_gm == 'auto' and not gmfile_exist) or not load_gm: + np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm_list=gram_matrix_unnorm_list, run_time_list=time_precompute_gm_list) + + print('\ncomplete.\n') + + +def __init_output_file_preimage(ds_name, gkernel, fit_method, dir_output): + if not os.path.exists(dir_output): + os.makedirs(dir_output) +# fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' + fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.csv' + f_detail = open(dir_output + fn_output_detail, 'a') + csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'edit cost', + 'GED method', 'attr distance', 'fit method', 'num graphs', + 'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', + 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', + 'dis_k gi -> GM', 'edit cost constants', 'time precompute gm', + 'time optimize ec', 'time generate preimage', 'time total', + 'itrs', 'converged', 'num updates ecc', 'mge decrease order', + 'mge increase order', 'mge converged']) + f_detail.close() + +# fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' + fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.csv' + f_summary = open(dir_output + fn_output_summary, 'a') + csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'edit cost', + 'GED method', 'attr distance', 'fit method', 'num graphs', + 'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', + 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', + 'dis_k gi -> GM', 'time precompute gm', 'time optimize ec', + 'time generate preimage', 'time total', 'itrs', 'num converged', + 'num updates ecc', 'mge num decrease order', 'mge num increase order', + 'mge num converged', '# SOD SM -> GM', '# dis_k SM -> GM', + '# dis_k gi -> SM', '# dis_k gi -> GM']) +# 'repeats better SOD SM -> GM', +# 'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM', +# 'repeats better dis_k gi -> GM']) + f_summary.close() + + return fn_output_detail, fn_output_summary + + +def get_relations(sign): + if sign == -1: + return 'better' + elif sign == 0: + return 'same' + elif sign == 1: + return 'worse' + + +#Dessin median courrant +def draw_Letter_graph(graph, file_prefix): + import matplotlib + matplotlib.use('agg') + import matplotlib.pyplot as plt + plt.figure() + pos = {} + for n in graph.nodes: + pos[n] = np.array([float(graph.nodes[n]['x']),float(graph.nodes[n]['y'])]) + nx.draw_networkx(graph, pos) + plt.savefig(file_prefix + '.eps', format='eps', dpi=300) +# plt.show() + plt.clf() + plt.close() + + +def remove_edges(Gn): + for G in Gn: + for _, _, attrs in G.edges(data=True): + attrs.clear() + + +def dis_gstar(idx_g, idx_gi, alpha, Kmatrix, term3=0, withterm3=True): + term1 = Kmatrix[idx_g, idx_g] + term2 = 0 + for i, a in enumerate(alpha): + term2 += a * Kmatrix[idx_g, idx_gi[i]] + term2 *= 2 + if withterm3 == False: + for i1, a1 in enumerate(alpha): + for i2, a2 in enumerate(alpha): + term3 += a1 * a2 * Kmatrix[idx_gi[i1], idx_gi[i2]] + return np.sqrt(term1 - term2 + term3) + + +def compute_k_dis(idx_g, idx_gi, alphas, Kmatrix, term3=0, withterm3=True): + term1 = Kmatrix[idx_g, idx_g] + term2 = 0 + for i, a in enumerate(alphas): + term2 += a * Kmatrix[idx_g, idx_gi[i]] + term2 *= 2 + if withterm3 == False: + for i1, a1 in enumerate(alphas): + for i2, a2 in enumerate(alphas): + term3 += a1 * a2 * Kmatrix[idx_gi[i1], idx_gi[i2]] + return np.sqrt(term1 - term2 + term3) + + +def compute_kernel(Gn, graph_kernel, node_label, edge_label, verbose, parallel='imap_unordered'): + if graph_kernel == 'marginalizedkernel': + Kmatrix, _ = marginalizedkernel(Gn, node_label=node_label, edge_label=edge_label, + p_quit=0.03, n_iteration=10, remove_totters=False, + n_jobs=multiprocessing.cpu_count(), verbose=verbose) + elif graph_kernel == 'untilhpathkernel': + Kmatrix, _ = untilhpathkernel(Gn, node_label=node_label, edge_label=edge_label, + depth=7, k_func='MinMax', compute_method='trie', + parallel=parallel, + n_jobs=multiprocessing.cpu_count(), verbose=verbose) + elif graph_kernel == 'spkernel': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + Kmatrix = np.empty((len(Gn), len(Gn))) +# Kmatrix[:] = np.nan + Kmatrix, _, idx = spkernel(Gn, node_label=node_label, node_kernels= + {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}, + n_jobs=multiprocessing.cpu_count(), verbose=verbose) +# for i, row in enumerate(idx): +# for j, col in enumerate(idx): +# Kmatrix[row, col] = Kmatrix_tmp[i, j] + elif graph_kernel == 'structuralspkernel': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + Kmatrix, _ = structuralspkernel(Gn, node_label=node_label, + edge_label=edge_label, node_kernels=sub_kernels, + edge_kernels=sub_kernels, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), + verbose=verbose) + elif graph_kernel == 'treeletkernel': + pkernel = functools.partial(polynomialkernel, d=2, c=1e5) +# pkernel = functools.partial(gaussiankernel, gamma=1e-6) + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + Kmatrix, _ = treeletkernel(Gn, node_label=node_label, edge_label=edge_label, + sub_kernel=pkernel, parallel=parallel, + n_jobs=multiprocessing.cpu_count(), verbose=verbose) + elif graph_kernel == 'weisfeilerlehmankernel': + Kmatrix, _ = weisfeilerlehmankernel(Gn, node_label=node_label, edge_label=edge_label, + height=4, base_kernel='subtree', parallel=None, + n_jobs=multiprocessing.cpu_count(), verbose=verbose) + else: + raise Exception('The graph kernel "', graph_kernel, '" is not defined.') + + # normalization + Kmatrix_diag = Kmatrix.diagonal().copy() + for i in range(len(Kmatrix)): + for j in range(i, len(Kmatrix)): + Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j]) + Kmatrix[j][i] = Kmatrix[i][j] + return Kmatrix + + +def gram2distances(Kmatrix): + dmatrix = np.zeros((len(Kmatrix), len(Kmatrix))) + for i1 in range(len(Kmatrix)): + for i2 in range(len(Kmatrix)): + dmatrix[i1, i2] = Kmatrix[i1, i1] + Kmatrix[i2, i2] - 2 * Kmatrix[i1, i2] + dmatrix = np.sqrt(dmatrix) + return dmatrix + + +def kernel_distance_matrix(Gn, node_label, edge_label, Kmatrix=None, + gkernel=None, verbose=True): + import warnings + warnings.warn('gklearn.preimage.utils.kernel_distance_matrix is deprecated, use gklearn.kernels.graph_kernel.compute_distance_matrix or gklearn.utils.compute_distance_matrix instead', DeprecationWarning) + dis_mat = np.empty((len(Gn), len(Gn))) + if Kmatrix is None: + Kmatrix = compute_kernel(Gn, gkernel, node_label, edge_label, verbose) + for i in range(len(Gn)): + for j in range(i, len(Gn)): + dis = Kmatrix[i, i] + Kmatrix[j, j] - 2 * Kmatrix[i, j] + if dis < 0: + if dis > -1e-10: + dis = 0 + else: + raise ValueError('The distance is negative.') + dis_mat[i, j] = np.sqrt(dis) + dis_mat[j, i] = dis_mat[i, j] + dis_max = np.max(np.max(dis_mat)) + dis_min = np.min(np.min(dis_mat[dis_mat != 0])) + dis_mean = np.mean(np.mean(dis_mat)) + return dis_mat, dis_max, dis_min, dis_mean + + +def get_same_item_indices(ls): + """Get the indices of the same items in a list. Return a dict keyed by items. + """ + idx_dict = {} + for idx, item in enumerate(ls): + if item in idx_dict: + idx_dict[item].append(idx) + else: + idx_dict[item] = [idx] + return idx_dict + + +def k_nearest_neighbors_to_median_in_kernel_space(Gn, Kmatrix=None, gkernel=None, + node_label=None, edge_label=None): + dis_k_all = [] # distance between g_star and each graph. + alpha = [1 / len(Gn)] * len(Gn) + if Kmatrix is None: + Kmatrix = compute_kernel(Gn, gkernel, node_label, edge_label, True) + term3 = 0 + for i1, a1 in enumerate(alpha): + for i2, a2 in enumerate(alpha): + term3 += a1 * a2 * Kmatrix[idx_gi[i1], idx_gi[i2]] + for ig, g in tqdm(enumerate(Gn_init), desc='computing distances', file=sys.stdout): + dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix, term3=term3) + dis_all.append(dtemp) + + +def normalize_distance_matrix(D): + max_value = np.amax(D) + min_value = np.amin(D) + return (D - min_value) / (max_value - min_value) \ No newline at end of file From 60e1f555cbc501c6684b98481e4ee04942b0b0b9 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:24 +0200 Subject: [PATCH 069/728] New translations remove_best_graph.py (French) --- lang/fr/gklearn/preimage/remove_best_graph.py | 423 ++++++++++++++++++ 1 file changed, 423 insertions(+) create mode 100644 lang/fr/gklearn/preimage/remove_best_graph.py diff --git a/lang/fr/gklearn/preimage/remove_best_graph.py b/lang/fr/gklearn/preimage/remove_best_graph.py new file mode 100644 index 0000000000..d6be2a6dc0 --- /dev/null +++ b/lang/fr/gklearn/preimage/remove_best_graph.py @@ -0,0 +1,423 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wen May 27 14:27:15 2020 + +@author: ljia +""" +import numpy as np +import csv +import os +import os.path +from gklearn.utils import Dataset +from gklearn.preimage import MedianPreimageGenerator +from gklearn.utils import normalize_gram_matrix +from gklearn.utils import split_dataset_by_target +from gklearn.preimage.utils import compute_k_dis +from gklearn.utils.graphfiles import saveGXL +import networkx as nx + + +def remove_best_graph(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=True, save_medians=True, plot_medians=True, load_gm='auto', dir_save='', irrelevant_labels=None, edge_required=False, cut_range=None): + """Remove the best graph from the median set w.r.t. distance in kernel space, and to see if it is possible to generate the removed graph using the graphs left in the median set. + """ + # 1. get dataset. + print('1. getting dataset...') + dataset_all = Dataset() + dataset_all.load_predefined_dataset(ds_name) + dataset_all.trim_dataset(edge_required=edge_required) + if irrelevant_labels is not None: + dataset_all.remove_labels(**irrelevant_labels) + if cut_range is not None: + dataset_all.cut_graphs(cut_range) + datasets = split_dataset_by_target(dataset_all) + + if save_results: + # create result files. + print('creating output files...') + fn_output_detail, fn_output_summary = __init_output_file(ds_name, kernel_options['name'], mpg_options['fit_method'], dir_save) + else: + fn_output_detail, fn_output_summary = None, None + + # 2. compute/load Gram matrix a priori. + print('2. computing/loading Gram matrix...') + gram_matrix_unnorm_list, time_precompute_gm_list = __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, datasets) + + sod_sm_list = [] + sod_gm_list = [] + dis_k_sm_list = [] + dis_k_gm_list = [] + dis_k_gi_min_list = [] + time_optimize_ec_list = [] + time_generate_list = [] + time_total_list = [] + itrs_list = [] + converged_list = [] + num_updates_ecc_list = [] + mge_decrease_order_list = [] + mge_increase_order_list = [] + mge_converged_order_list = [] + nb_sod_sm2gm = [0, 0, 0] + nb_dis_k_sm2gm = [0, 0, 0] + nb_dis_k_gi2sm = [0, 0, 0] + nb_dis_k_gi2gm = [0, 0, 0] + dis_k_max_list = [] + dis_k_min_list = [] + dis_k_mean_list = [] + best_dis_list = [] + print('starting experiment for each class of target...') + idx_offset = 0 + for idx, dataset in enumerate(datasets): + target = dataset.targets[0] + print('\ntarget =', target, '\n') +# if target != 1: +# continue + + num_graphs = len(dataset.graphs) + if num_graphs < 2: + print('\nnumber of graphs = ', num_graphs, ', skip.\n') + idx_offset += 1 + continue + + # 3. get the best graph and remove it from median set. + print('3. getting and removing the best graph...') + gram_matrix_unnorm = gram_matrix_unnorm_list[idx - idx_offset] + best_index, best_dis, best_graph = __get_best_graph([g.copy() for g in dataset.graphs], normalize_gram_matrix(gram_matrix_unnorm.copy())) + median_set_new = [dataset.graphs[i] for i in range(len(dataset.graphs)) if i != best_index] + num_graphs -= 1 + if num_graphs == 1: + continue + best_dis_list.append(best_dis) + + dataset.load_graphs(median_set_new, targets=None) + gram_matrix_unnorm_new = np.delete(gram_matrix_unnorm, best_index, axis=0) + gram_matrix_unnorm_new = np.delete(gram_matrix_unnorm_new, best_index, axis=1) + + # 4. set parameters. + print('4. initializing mpg and setting parameters...') + mpg_options['gram_matrix_unnorm'] = gram_matrix_unnorm_new + mpg_options['runtime_precompute_gm'] = time_precompute_gm_list[idx - idx_offset] + mpg = MedianPreimageGenerator() + mpg.dataset = dataset + mpg.set_options(**mpg_options.copy()) + mpg.kernel_options = kernel_options.copy() + mpg.ged_options = ged_options.copy() + mpg.mge_options = mge_options.copy() + + # 5. compute median preimage. + print('5. computing median preimage...') + mpg.run() + results = mpg.get_results() + + # 6. compute pairwise kernel distances. + print('6. computing pairwise kernel distances...') + _, dis_k_max, dis_k_min, dis_k_mean = mpg.graph_kernel.compute_distance_matrix() + dis_k_max_list.append(dis_k_max) + dis_k_min_list.append(dis_k_min) + dis_k_mean_list.append(dis_k_mean) + + # 7. save results (and median graphs). + print('7. saving results (and median graphs)...') + # write result detail. + if save_results: + print('writing results to files...') + sod_sm2gm = get_relations(np.sign(results['sod_gen_median'] - results['sod_set_median'])) + dis_k_sm2gm = get_relations(np.sign(results['k_dis_gen_median'] - results['k_dis_set_median'])) + dis_k_gi2sm = get_relations(np.sign(results['k_dis_set_median'] - results['k_dis_dataset'])) + dis_k_gi2gm = get_relations(np.sign(results['k_dis_gen_median'] - results['k_dis_dataset'])) + + f_detail = open(dir_save + fn_output_detail, 'a') + csv.writer(f_detail).writerow([ds_name, kernel_options['name'], + ged_options['edit_cost'], ged_options['method'], + ged_options['attr_distance'], mpg_options['fit_method'], + num_graphs, target, 1, + results['sod_set_median'], results['sod_gen_median'], + results['k_dis_set_median'], results['k_dis_gen_median'], + results['k_dis_dataset'], best_dis, best_index, + sod_sm2gm, dis_k_sm2gm, + dis_k_gi2sm, dis_k_gi2gm, results['edit_cost_constants'], + results['runtime_precompute_gm'], results['runtime_optimize_ec'], + results['runtime_generate_preimage'], results['runtime_total'], + results['itrs'], results['converged'], + results['num_updates_ecc'], + results['mge']['num_decrease_order'] > 0, # @todo: not suitable for multi-start mge + results['mge']['num_increase_order'] > 0, + results['mge']['num_converged_descents'] > 0]) + f_detail.close() + + # compute result summary. + sod_sm_list.append(results['sod_set_median']) + sod_gm_list.append(results['sod_gen_median']) + dis_k_sm_list.append(results['k_dis_set_median']) + dis_k_gm_list.append(results['k_dis_gen_median']) + dis_k_gi_min_list.append(results['k_dis_dataset']) + time_precompute_gm_list.append(results['runtime_precompute_gm']) + time_optimize_ec_list.append(results['runtime_optimize_ec']) + time_generate_list.append(results['runtime_generate_preimage']) + time_total_list.append(results['runtime_total']) + itrs_list.append(results['itrs']) + converged_list.append(results['converged']) + num_updates_ecc_list.append(results['num_updates_ecc']) + mge_decrease_order_list.append(results['mge']['num_decrease_order'] > 0) + mge_increase_order_list.append(results['mge']['num_increase_order'] > 0) + mge_converged_order_list.append(results['mge']['num_converged_descents'] > 0) + # # SOD SM -> GM + if results['sod_set_median'] > results['sod_gen_median']: + nb_sod_sm2gm[0] += 1 + # repeats_better_sod_sm2gm.append(1) + elif results['sod_set_median'] == results['sod_gen_median']: + nb_sod_sm2gm[1] += 1 + elif results['sod_set_median'] < results['sod_gen_median']: + nb_sod_sm2gm[2] += 1 + # # dis_k SM -> GM + if results['k_dis_set_median'] > results['k_dis_gen_median']: + nb_dis_k_sm2gm[0] += 1 + # repeats_better_dis_k_sm2gm.append(1) + elif results['k_dis_set_median'] == results['k_dis_gen_median']: + nb_dis_k_sm2gm[1] += 1 + elif results['k_dis_set_median'] < results['k_dis_gen_median']: + nb_dis_k_sm2gm[2] += 1 + # # dis_k gi -> SM + if results['k_dis_dataset'] > results['k_dis_set_median']: + nb_dis_k_gi2sm[0] += 1 + # repeats_better_dis_k_gi2sm.append(1) + elif results['k_dis_dataset'] == results['k_dis_set_median']: + nb_dis_k_gi2sm[1] += 1 + elif results['k_dis_dataset'] < results['k_dis_set_median']: + nb_dis_k_gi2sm[2] += 1 + # # dis_k gi -> GM + if results['k_dis_dataset'] > results['k_dis_gen_median']: + nb_dis_k_gi2gm[0] += 1 + # repeats_better_dis_k_gi2gm.append(1) + elif results['k_dis_dataset'] == results['k_dis_gen_median']: + nb_dis_k_gi2gm[1] += 1 + elif results['k_dis_dataset'] < results['k_dis_gen_median']: + nb_dis_k_gi2gm[2] += 1 + + # write result summary for each letter. + f_summary = open(dir_save + fn_output_summary, 'a') + csv.writer(f_summary).writerow([ds_name, kernel_options['name'], + ged_options['edit_cost'], ged_options['method'], + ged_options['attr_distance'], mpg_options['fit_method'], + num_graphs, target, + results['sod_set_median'], results['sod_gen_median'], + results['k_dis_set_median'], results['k_dis_gen_median'], + results['k_dis_dataset'], best_dis, best_index, + sod_sm2gm, dis_k_sm2gm, + dis_k_gi2sm, dis_k_gi2gm, + results['runtime_precompute_gm'], results['runtime_optimize_ec'], + results['runtime_generate_preimage'], results['runtime_total'], + results['itrs'], results['converged'], + results['num_updates_ecc'], + results['mge']['num_decrease_order'] > 0, # @todo: not suitable for multi-start mge + results['mge']['num_increase_order'] > 0, + results['mge']['num_converged_descents'] > 0, + nb_sod_sm2gm, + nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm]) + f_summary.close() + + # save median graphs. + if save_medians: + if not os.path.exists(dir_save + 'medians/'): + os.makedirs(dir_save + 'medians/') + print('Saving median graphs to files...') + fn_pre_sm = dir_save + 'medians/set_median.' + mpg_options['fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) + saveGXL(mpg.set_median, fn_pre_sm + '.gxl', method='default', + node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs) + fn_pre_gm = dir_save + 'medians/gen_median.' + mpg_options['fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) + saveGXL(mpg.gen_median, fn_pre_gm + '.gxl', method='default', + node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs) + fn_best_dataset = dir_save + 'medians/g_best_dataset.' + mpg_options['fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) + saveGXL(best_graph, fn_best_dataset + '.gxl', method='default', + node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs) + fn_best_median_set = dir_save + 'medians/g_best_median_set.' + mpg_options['fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) + saveGXL(mpg.best_from_dataset, fn_best_median_set + '.gxl', method='default', + node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs) + + # plot median graphs. + if plot_medians and save_medians: + if ged_options['edit_cost'] == 'LETTER2' or ged_options['edit_cost'] == 'LETTER' or ds_name == 'Letter-high' or ds_name == 'Letter-med' or ds_name == 'Letter-low': + draw_Letter_graph(mpg.set_median, fn_pre_sm) + draw_Letter_graph(mpg.gen_median, fn_pre_gm) + draw_Letter_graph(mpg.best_from_dataset, fn_best_dataset) + + # write result summary for each letter. + if save_results: + sod_sm_mean = np.mean(sod_sm_list) + sod_gm_mean = np.mean(sod_gm_list) + dis_k_sm_mean = np.mean(dis_k_sm_list) + dis_k_gm_mean = np.mean(dis_k_gm_list) + dis_k_gi_min_mean = np.mean(dis_k_gi_min_list) + best_dis_mean = np.mean(best_dis_list) + time_precompute_gm_mean = np.mean(time_precompute_gm_list) + time_optimize_ec_mean = np.mean(time_optimize_ec_list) + time_generate_mean = np.mean(time_generate_list) + time_total_mean = np.mean(time_total_list) + itrs_mean = np.mean(itrs_list) + num_converged = np.sum(converged_list) + num_updates_ecc_mean = np.mean(num_updates_ecc_list) + num_mge_decrease_order = np.sum(mge_decrease_order_list) + num_mge_increase_order = np.sum(mge_increase_order_list) + num_mge_converged = np.sum(mge_converged_order_list) + sod_sm2gm_mean = get_relations(np.sign(sod_gm_mean - sod_sm_mean)) + dis_k_sm2gm_mean = get_relations(np.sign(dis_k_gm_mean - dis_k_sm_mean)) + dis_k_gi2sm_mean = get_relations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean)) + dis_k_gi2gm_mean = get_relations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean)) + f_summary = open(dir_save + fn_output_summary, 'a') + csv.writer(f_summary).writerow([ds_name, kernel_options['name'], + ged_options['edit_cost'], ged_options['method'], + ged_options['attr_distance'], mpg_options['fit_method'], + num_graphs, 'all', + sod_sm_mean, sod_gm_mean, dis_k_sm_mean, dis_k_gm_mean, + dis_k_gi_min_mean, best_dis_mean, '-', + sod_sm2gm_mean, dis_k_sm2gm_mean, + dis_k_gi2sm_mean, dis_k_gi2gm_mean, + time_precompute_gm_mean, time_optimize_ec_mean, + time_generate_mean, time_total_mean, itrs_mean, + num_converged, num_updates_ecc_mean, + num_mge_decrease_order, num_mge_increase_order, + num_mge_converged]) + f_summary.close() + + # save total pairwise kernel distances. + dis_k_max = np.max(dis_k_max_list) + dis_k_min = np.min(dis_k_min_list) + dis_k_mean = np.mean(dis_k_mean_list) + print('The maximum pairwise distance in kernel space:', dis_k_max) + print('The minimum pairwise distance in kernel space:', dis_k_min) + print('The average pairwise distance in kernel space:', dis_k_mean) + + print('\ncomplete.\n') + + +def __get_best_graph(Gn, gram_matrix): + k_dis_list = [] + for idx in range(len(Gn)): + k_dis_list.append(compute_k_dis(idx, range(0, len(Gn)), [1 / len(Gn)] * len(Gn), gram_matrix, withterm3=False)) + best_index = np.argmin(k_dis_list) + best_dis = k_dis_list[best_index] + best_graph = Gn[best_index].copy() + return best_index, best_dis, best_graph + + +def get_relations(sign): + if sign == -1: + return 'better' + elif sign == 0: + return 'same' + elif sign == 1: + return 'worse' + + +def __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, datasets): + if load_gm == 'auto': + gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' + gmfile_exist = os.path.isfile(os.path.abspath(gm_fname)) + if gmfile_exist: + gmfile = np.load(gm_fname, allow_pickle=True) # @todo: may not be safe. + gram_matrix_unnorm_list = [item for item in gmfile['gram_matrix_unnorm_list']] + time_precompute_gm_list = gmfile['run_time_list'].tolist() + else: + gram_matrix_unnorm_list = [] + time_precompute_gm_list = [] + for dataset in datasets: + gram_matrix_unnorm, time_precompute_gm = __compute_gram_matrix_unnorm(dataset, kernel_options) + gram_matrix_unnorm_list.append(gram_matrix_unnorm) + time_precompute_gm_list.append(time_precompute_gm) + np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm_list=gram_matrix_unnorm_list, run_time_list=time_precompute_gm_list) + elif not load_gm: + gram_matrix_unnorm_list = [] + time_precompute_gm_list = [] + for dataset in datasets: + gram_matrix_unnorm, time_precompute_gm = __compute_gram_matrix_unnorm(dataset, kernel_options) + gram_matrix_unnorm_list.append(gram_matrix_unnorm) + time_precompute_gm_list.append(time_precompute_gm) + np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm_list=gram_matrix_unnorm_list, run_time_list=time_precompute_gm_list) + else: + gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' + gmfile = np.load(gm_fname, allow_pickle=True) # @todo: may not be safe. + gram_matrix_unnorm_list = [item for item in gmfile['gram_matrix_unnorm_list']] + time_precompute_gm_list = gmfile['run_time_list'].tolist() + + return gram_matrix_unnorm_list, time_precompute_gm_list + + +def __get_graph_kernel(dataset, kernel_options): + from gklearn.utils.utils import get_graph_kernel_by_name + graph_kernel = get_graph_kernel_by_name(kernel_options['name'], + node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, + edge_attrs=dataset.edge_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + kernel_options=kernel_options) + return graph_kernel + + +def __compute_gram_matrix_unnorm(dataset, kernel_options): + from gklearn.utils.utils import get_graph_kernel_by_name + graph_kernel = get_graph_kernel_by_name(kernel_options['name'], + node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, + edge_attrs=dataset.edge_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + kernel_options=kernel_options) + + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, **kernel_options) + gram_matrix_unnorm = graph_kernel.gram_matrix_unnorm + + return gram_matrix_unnorm, run_time + + +def __init_output_file(ds_name, gkernel, fit_method, dir_output): + if not os.path.exists(dir_output): + os.makedirs(dir_output) + fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.csv' + f_detail = open(dir_output + fn_output_detail, 'a') + csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'edit cost', + 'GED method', 'attr distance', 'fit method', 'num graphs', + 'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', + 'min dis_k gi', 'best kernel dis', 'best graph index', + 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', + 'dis_k gi -> GM', 'edit cost constants', 'time precompute gm', + 'time optimize ec', 'time generate preimage', 'time total', + 'itrs', 'converged', 'num updates ecc', 'mge decrease order', + 'mge increase order', 'mge converged']) + f_detail.close() + + fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.csv' + f_summary = open(dir_output + fn_output_summary, 'a') + csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'edit cost', + 'GED method', 'attr distance', 'fit method', 'num graphs', + 'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', + 'min dis_k gi', 'best kernel dis', 'best graph index', + 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', + 'dis_k gi -> GM', 'time precompute gm', 'time optimize ec', + 'time generate preimage', 'time total', 'itrs', 'num converged', + 'num updates ecc', 'mge num decrease order', 'mge num increase order', + 'mge num converged', '# SOD SM -> GM', '# dis_k SM -> GM', + '# dis_k gi -> SM', '# dis_k gi -> GM']) + f_summary.close() + + return fn_output_detail, fn_output_summary + + +#Dessin median courrant +def draw_Letter_graph(graph, file_prefix): + import matplotlib + matplotlib.use('agg') + import matplotlib.pyplot as plt + plt.figure() + pos = {} + for n in graph.nodes: + pos[n] = np.array([float(graph.nodes[n]['x']),float(graph.nodes[n]['y'])]) + nx.draw_networkx(graph, pos) + plt.savefig(file_prefix + '.eps', format='eps', dpi=300) +# plt.show() + plt.clf() + plt.close() \ No newline at end of file From b76805e166cbb196b2a0a134f5401f8f2a2d0e93 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:26 +0200 Subject: [PATCH 070/728] New translations random_preimage_generator.py (French) --- .../preimage/random_preimage_generator.py | 389 ++++++++++++++++++ 1 file changed, 389 insertions(+) create mode 100644 lang/fr/gklearn/preimage/random_preimage_generator.py diff --git a/lang/fr/gklearn/preimage/random_preimage_generator.py b/lang/fr/gklearn/preimage/random_preimage_generator.py new file mode 100644 index 0000000000..cb2851921f --- /dev/null +++ b/lang/fr/gklearn/preimage/random_preimage_generator.py @@ -0,0 +1,389 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri May 29 14:29:52 2020 + +@author: ljia +""" + +import numpy as np +import time +import sys +from tqdm import tqdm +import multiprocessing +import networkx as nx +from multiprocessing import Pool +from functools import partial +from gklearn.preimage import PreimageGenerator +from gklearn.preimage.utils import compute_k_dis +from gklearn.utils import Timer +from gklearn.utils.utils import get_graph_kernel_by_name +# from gklearn.utils.dataset import Dataset + + +class RandomPreimageGenerator(PreimageGenerator): + + def __init__(self, dataset=None): + PreimageGenerator.__init__(self, dataset=dataset) + # arguments to set. + self.__k = 5 # number of nearest neighbors of phi in D_N. + self.__r_max = 10 # maximum number of iterations. + self.__l = 500 # numbers of graphs generated for each graph in D_k U {g_i_hat}. + self.__alphas = None # weights of linear combinations of points in kernel space. + self.__parallel = True + self.__n_jobs = multiprocessing.cpu_count() + self.__time_limit_in_sec = 0 + self.__max_itrs = 20 + # values to compute. + self.__runtime_generate_preimage = None + self.__runtime_total = None + self.__preimage = None + self.__best_from_dataset = None + self.__k_dis_preimage = None + self.__k_dis_dataset = None + self.__itrs = 0 + self.__converged = False # @todo + self.__num_updates = 0 + # values that can be set or to be computed. + self.__gram_matrix_unnorm = None + self.__runtime_precompute_gm = None + + + def set_options(self, **kwargs): + self._kernel_options = kwargs.get('kernel_options', {}) + self._graph_kernel = kwargs.get('graph_kernel', None) + self._verbose = kwargs.get('verbose', 2) + self.__k = kwargs.get('k', 5) + self.__r_max = kwargs.get('r_max', 10) + self.__l = kwargs.get('l', 500) + self.__alphas = kwargs.get('alphas', None) + self.__parallel = kwargs.get('parallel', True) + self.__n_jobs = kwargs.get('n_jobs', multiprocessing.cpu_count()) + self.__time_limit_in_sec = kwargs.get('time_limit_in_sec', 0) + self.__max_itrs = kwargs.get('max_itrs', 20) + self.__gram_matrix_unnorm = kwargs.get('gram_matrix_unnorm', None) + self.__runtime_precompute_gm = kwargs.get('runtime_precompute_gm', None) + + + def run(self): + self._graph_kernel = get_graph_kernel_by_name(self._kernel_options['name'], + node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + node_attrs=self._dataset.node_attrs, + edge_attrs=self._dataset.edge_attrs, + ds_infos=self._dataset.get_dataset_infos(keys=['directed']), + kernel_options=self._kernel_options) + + # record start time. + start = time.time() + + # 1. precompute gram matrix. + if self.__gram_matrix_unnorm is None: + gram_matrix, run_time = self._graph_kernel.compute(self._dataset.graphs, **self._kernel_options) + self.__gram_matrix_unnorm = self._graph_kernel.gram_matrix_unnorm + end_precompute_gm = time.time() + self.__runtime_precompute_gm = end_precompute_gm - start + else: + if self.__runtime_precompute_gm is None: + raise Exception('Parameter "runtime_precompute_gm" must be given when using pre-computed Gram matrix.') + self._graph_kernel.gram_matrix_unnorm = self.__gram_matrix_unnorm + if self._kernel_options['normalize']: + self._graph_kernel.gram_matrix = self._graph_kernel.normalize_gm(np.copy(self.__gram_matrix_unnorm)) + else: + self._graph_kernel.gram_matrix = np.copy(self.__gram_matrix_unnorm) + end_precompute_gm = time.time() + start -= self.__runtime_precompute_gm + + # 2. compute k nearest neighbors of phi in D_N. + if self._verbose >= 2: + print('\nstart computing k nearest neighbors of phi in D_N...\n') + D_N = self._dataset.graphs + if self.__alphas is None: + self.__alphas = [1 / len(D_N)] * len(D_N) + k_dis_list = [] # distance between g_star and each graph. + term3 = 0 + for i1, a1 in enumerate(self.__alphas): + for i2, a2 in enumerate(self.__alphas): + term3 += a1 * a2 * self._graph_kernel.gram_matrix[i1, i2] + for idx in range(len(D_N)): + k_dis_list.append(compute_k_dis(idx, range(0, len(D_N)), self.__alphas, self._graph_kernel.gram_matrix, term3=term3, withterm3=True)) + + # sort. + sort_idx = np.argsort(k_dis_list) + dis_gs = [k_dis_list[idis] for idis in sort_idx[0:self.__k]] # the k shortest distances. + nb_best = len(np.argwhere(dis_gs == dis_gs[0]).flatten().tolist()) + g0hat_list = [D_N[idx].copy() for idx in sort_idx[0:nb_best]] # the nearest neighbors of phi in D_N + self.__best_from_dataset = g0hat_list[0] # get the first best graph if there are muitlple. + self.__k_dis_dataset = dis_gs[0] + + if self.__k_dis_dataset == 0: # get the exact pre-image. + end_generate_preimage = time.time() + self.__runtime_generate_preimage = end_generate_preimage - end_precompute_gm + self.__runtime_total = end_generate_preimage - start + self.__preimage = self.__best_from_dataset.copy() + self.__k_dis_preimage = self.__k_dis_dataset + if self._verbose: + print() + print('=============================================================================') + print('The exact pre-image is found from the input dataset.') + print('-----------------------------------------------------------------------------') + print('Distance in kernel space for the best graph from dataset and for preimage:', self.__k_dis_dataset) + print('Time to pre-compute Gram matrix:', self.__runtime_precompute_gm) + print('Time to generate pre-images:', self.__runtime_generate_preimage) + print('Total time:', self.__runtime_total) + print('=============================================================================') + print() + return + + dhat = dis_gs[0] # the nearest distance + Gk = [D_N[ig].copy() for ig in sort_idx[0:self.__k]] # the k nearest neighbors + Gs_nearest = [nx.convert_node_labels_to_integers(g) for g in Gk] # [g.copy() for g in Gk] + + # 3. start iterations. + if self._verbose >= 2: + print('starting iterations...') + gihat_list = [] + dihat_list = [] + r = 0 + dis_of_each_itr = [dhat] + if self.__parallel: + self._kernel_options['parallel'] = None + self.__itrs = 0 + self.__num_updates = 0 + timer = Timer(self.__time_limit_in_sec) + while not self.__termination_criterion_met(timer, self.__itrs, r): + print('\n- r =', r) + found = False + dis_bests = dis_gs + dihat_list + + # compute numbers of edges to be inserted/deleted. + # @todo what if the log is negetive? how to choose alpha (scalar)? + fdgs_list = np.array(dis_bests) + if np.min(fdgs_list) < 1: # in case the log is negetive. + fdgs_list /= np.min(fdgs_list) + fdgs_list = [int(item) for item in np.ceil(np.log(fdgs_list))] + if np.min(fdgs_list) < 1: # in case the log is smaller than 1. + fdgs_list = np.array(fdgs_list) + 1 + # expand the number of modifications to increase the possiblity. + nb_vpairs_list = [nx.number_of_nodes(g) * (nx.number_of_nodes(g) - 1) for g in (Gs_nearest + gihat_list)] + nb_vpairs_min = np.min(nb_vpairs_list) + idx_fdgs_max = np.argmax(fdgs_list) + fdgs_max_old = fdgs_list[idx_fdgs_max] + fdgs_max = fdgs_max_old + nb_modif = 1 + for idx, nb in enumerate(range(nb_vpairs_min, nb_vpairs_min - fdgs_max, -1)): + nb_modif *= nb / (fdgs_max - idx) + while fdgs_max < nb_vpairs_min and nb_modif < self.__l: + fdgs_max += 1 + nb_modif *= (nb_vpairs_min - fdgs_max + 1) / fdgs_max + nb_increase = int(fdgs_max - fdgs_max_old) + if nb_increase > 0: + fdgs_list += 1 + + + for ig, gs in enumerate(Gs_nearest + gihat_list): + if self._verbose >= 2: + print('-- computing', ig + 1, 'graphs out of', len(Gs_nearest) + len(gihat_list)) + gnew, dhat, found = self.__generate_l_graphs(gs, fdgs_list[ig], dhat, ig, found, term3) + + if found: + r = 0 + gihat_list = [gnew] + dihat_list = [dhat] + else: + r += 1 + + dis_of_each_itr.append(dhat) + self.__itrs += 1 + if self._verbose >= 2: + print('Total number of iterations is', self.__itrs, '.') + print('The preimage is updated', self.__num_updates, 'times.') + print('The shortest distances for previous iterations are', dis_of_each_itr, '.') + + + # get results and print. + end_generate_preimage = time.time() + self.__runtime_generate_preimage = end_generate_preimage - end_precompute_gm + self.__runtime_total = end_generate_preimage - start + self.__preimage = (g0hat_list[0] if len(gihat_list) == 0 else gihat_list[0]) + self.__k_dis_preimage = dhat + if self._verbose: + print() + print('=============================================================================') + print('Finished generation of preimages.') + print('-----------------------------------------------------------------------------') + print('Distance in kernel space for the best graph from dataset:', self.__k_dis_dataset) + print('Distance in kernel space for the preimage:', self.__k_dis_preimage) + print('Total number of iterations for optimizing:', self.__itrs) + print('Total number of updating preimage:', self.__num_updates) + print('Time to pre-compute Gram matrix:', self.__runtime_precompute_gm) + print('Time to generate pre-images:', self.__runtime_generate_preimage) + print('Total time:', self.__runtime_total) + print('=============================================================================') + print() + + + def __generate_l_graphs(self, g_init, fdgs, dhat, ig, found, term3): + if self.__parallel: + gnew, dhat, found = self.__generate_l_graphs_parallel(g_init, fdgs, dhat, ig, found, term3) + else: + gnew, dhat, found = self.__generate_l_graphs_series(g_init, fdgs, dhat, ig, found, term3) + return gnew, dhat, found + + + def __generate_l_graphs_series(self, g_init, fdgs, dhat, ig, found, term3): + gnew = None + updated = False + for trial in range(0, self.__l): + if self._verbose >= 2: + print('---', trial + 1, 'trial out of', self.__l) + + gtemp, dnew = self.__do_trial(g_init, fdgs, term3, trial) + + # get the better graph preimage. + if dnew <= dhat: # @todo: the new distance is smaller or also equal? + if dhat - dnew > 1e-6: + if self._verbose >= 2: + print('trial =', str(trial)) + print('\nI am smaller!') + print('index (as in D_k U {gihat} =', str(ig)) + print('distance:', dhat, '->', dnew) + updated = True + else: + if self._verbose >= 2: + print('I am equal!') + dhat = dnew + gnew = gtemp.copy() + found = True # found better or equally good graph. + + if updated: + self.__num_updates += 1 + + return gnew, dhat, found + + + def __generate_l_graphs_parallel(self, g_init, fdgs, dhat, ig, found, term3): + gnew = None + len_itr = self.__l + gnew_list = [None] * len_itr + dnew_list = [None] * len_itr + itr = range(0, len_itr) + n_jobs = multiprocessing.cpu_count() + if len_itr < 100 * n_jobs: + chunksize = int(len_itr / n_jobs) + 1 + else: + chunksize = 100 + do_fun = partial(self._generate_graph_parallel, g_init, fdgs, term3) + pool = Pool(processes=n_jobs) + if self._verbose >= 2: + iterator = tqdm(pool.imap_unordered(do_fun, itr, chunksize), + desc='Generating l graphs', file=sys.stdout) + else: + iterator = pool.imap_unordered(do_fun, itr, chunksize) + for idx, gnew, dnew in iterator: + gnew_list[idx] = gnew + dnew_list[idx] = dnew + pool.close() + pool.join() + + # check if get the better graph preimage. + idx_min = np.argmin(dnew_list) + dnew = dnew_list[idx_min] + if dnew <= dhat: # @todo: the new distance is smaller or also equal? + if dhat - dnew > 1e-6: # @todo: use a proportion and watch out for 0. + if self._verbose >= 2: + print('I am smaller!') + print('index (as in D_k U {gihat}) =', str(ig)) + print('distance:', dhat, '->', dnew, '\n') + self.__num_updates += 1 + else: + if self._verbose >= 2: + print('I am equal!') + dhat = dnew + gnew = gnew_list[idx_min] + found = True # found better graph. + + return gnew, dhat, found + + + def _generate_graph_parallel(self, g_init, fdgs, term3, itr): + trial = itr + gtemp, dnew = self.__do_trial(g_init, fdgs, term3, trial) + return trial, gtemp, dnew + + + def __do_trial(self, g_init, fdgs, term3, trial): + # add and delete edges. + gtemp = g_init.copy() + seed = (trial + int(time.time())) % (2 ** 32 - 1) + rdm_state = np.random.RandomState(seed=seed) + # which edges to change. + # @todo: should we use just half of the adjacency matrix for undirected graphs? + nb_vpairs = nx.number_of_nodes(g_init) * (nx.number_of_nodes(g_init) - 1) + # @todo: what if fdgs is bigger than nb_vpairs? + idx_change = rdm_state.randint(0, high=nb_vpairs, size=(fdgs if + fdgs < nb_vpairs else nb_vpairs)) +# print(idx_change) + for item in idx_change: + node1 = int(item / (nx.number_of_nodes(g_init) - 1)) + node2 = (item - node1 * (nx.number_of_nodes(g_init) - 1)) + if node2 >= node1: # skip the self pair. + node2 += 1 + # @todo: is the randomness correct? + if not gtemp.has_edge(node1, node2): + gtemp.add_edge(node1, node2) + else: + gtemp.remove_edge(node1, node2) + + # compute new distances. + kernels_to_gtmp, _ = self._graph_kernel.compute(gtemp, self._dataset.graphs, **self._kernel_options) + kernel_gtmp, _ = self._graph_kernel.compute(gtemp, gtemp, **self._kernel_options) + if self._kernel_options['normalize']: + kernels_to_gtmp = [kernels_to_gtmp[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_gtmp) for i in range(len(kernels_to_gtmp))] # normalize + kernel_gtmp = 1 + # @todo: not correct kernel value + gram_with_gtmp = np.concatenate((np.array([kernels_to_gtmp]), np.copy(self._graph_kernel.gram_matrix)), axis=0) + gram_with_gtmp = np.concatenate((np.array([[kernel_gtmp] + kernels_to_gtmp]).T, gram_with_gtmp), axis=1) + dnew = compute_k_dis(0, range(1, 1 + len(self._dataset.graphs)), self.__alphas, gram_with_gtmp, term3=term3, withterm3=True) + + return gtemp, dnew + + + def get_results(self): + results = {} + results['runtime_precompute_gm'] = self.__runtime_precompute_gm + results['runtime_generate_preimage'] = self.__runtime_generate_preimage + results['runtime_total'] = self.__runtime_total + results['k_dis_dataset'] = self.__k_dis_dataset + results['k_dis_preimage'] = self.__k_dis_preimage + results['itrs'] = self.__itrs + results['num_updates'] = self.__num_updates + return results + + + def __termination_criterion_met(self, timer, itr, r): + if timer.expired() or (itr >= self.__max_itrs if self.__max_itrs >= 0 else False): +# if self.__state == AlgorithmState.TERMINATED: +# self.__state = AlgorithmState.INITIALIZED + return True + return (r >= self.__r_max if self.__r_max >= 0 else False) +# return converged or (itrs_without_update > self.__max_itrs_without_update if self.__max_itrs_without_update >= 0 else False) + + + @property + def preimage(self): + return self.__preimage + + + @property + def best_from_dataset(self): + return self.__best_from_dataset + + + @property + def gram_matrix_unnorm(self): + return self.__gram_matrix_unnorm + + @gram_matrix_unnorm.setter + def gram_matrix_unnorm(self, value): + self.__gram_matrix_unnorm = value \ No newline at end of file From e25e0812caa7916e1e17e25907dd5ccbc8411139 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:28 +0200 Subject: [PATCH 071/728] New translations preimage_generator.py (French) --- .../fr/gklearn/preimage/preimage_generator.py | 49 +++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 lang/fr/gklearn/preimage/preimage_generator.py diff --git a/lang/fr/gklearn/preimage/preimage_generator.py b/lang/fr/gklearn/preimage/preimage_generator.py new file mode 100644 index 0000000000..306eb1299f --- /dev/null +++ b/lang/fr/gklearn/preimage/preimage_generator.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Mar 26 18:26:36 2020 + +@author: ljia +""" +# from gklearn.utils import Dataset + +class PreimageGenerator(object): + + def __init__(self, dataset=None): + # arguments to set. + self._dataset = None if dataset is None else dataset + self._kernel_options = {} + self._graph_kernel = None + self._verbose = 2 + + @property + def dataset(self): + return self._dataset + + @dataset.setter + def dataset(self, value): + self._dataset = value + + + @property + def kernel_options(self): + return self._kernel_options + + @kernel_options.setter + def kernel_options(self, value): + self._kernel_options = value + + + @property + def graph_kernel(self): + return self._graph_kernel + + + @property + def verbose(self): + return self._verbose + + @verbose.setter + def verbose(self, value): + self._verbose = value + From eb0c548f2264116927703caa904a5681b1ecb04c Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:29 +0200 Subject: [PATCH 072/728] New translations median_preimage_generator_py.py (French) --- .../preimage/median_preimage_generator_py.py | 1035 +++++++++++++++++ 1 file changed, 1035 insertions(+) create mode 100644 lang/fr/gklearn/preimage/median_preimage_generator_py.py diff --git a/lang/fr/gklearn/preimage/median_preimage_generator_py.py b/lang/fr/gklearn/preimage/median_preimage_generator_py.py new file mode 100644 index 0000000000..cdc7a3cf5a --- /dev/null +++ b/lang/fr/gklearn/preimage/median_preimage_generator_py.py @@ -0,0 +1,1035 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Mar 26 18:27:22 2020 + +@author: ljia +""" +import numpy as np +import time +import random +import multiprocessing +import networkx as nx +import cvxpy as cp +from gklearn.preimage import PreimageGenerator +from gklearn.preimage.utils import compute_k_dis +from gklearn.ged.util import compute_geds_cml +from gklearn.ged.median import MedianGraphEstimatorPy +from gklearn.ged.median import constant_node_costs, mge_options_to_string +from gklearn.ged.env import GEDEnv +from gklearn.utils import Timer +from gklearn.utils.utils import get_graph_kernel_by_name + + +class MedianPreimageGeneratorPy(PreimageGenerator): + """Generator median preimages using the pure Python version of GEDEnv. + """ + + def __init__(self, dataset=None): + PreimageGenerator.__init__(self, dataset=dataset) + # arguments to set. + self.__mge = None + self.__ged_options = {} + self.__mge_options = {} + self.__fit_method = 'k-graphs' + self.__init_ecc = None + self.__parallel = True + self.__n_jobs = multiprocessing.cpu_count() + self.__ds_name = None + self.__time_limit_in_sec = 0 + self.__max_itrs = 100 + self.__max_itrs_without_update = 3 + self.__epsilon_residual = 0.01 + self.__epsilon_ec = 0.1 + self.__allow_zeros = False + self.__triangle_rule = True + # values to compute. + self.__runtime_optimize_ec = None + self.__runtime_generate_preimage = None + self.__runtime_total = None + self.__set_median = None + self.__gen_median = None + self.__best_from_dataset = None + self.__sod_set_median = None + self.__sod_gen_median = None + self.__k_dis_set_median = None + self.__k_dis_gen_median = None + self.__k_dis_dataset = None + self.__itrs = 0 + self.__converged = False + self.__num_updates_ecc = 0 + # values that can be set or to be computed. + self.__edit_cost_constants = [] + self.__gram_matrix_unnorm = None + self.__runtime_precompute_gm = None + + + def set_options(self, **kwargs): + self._kernel_options = kwargs.get('kernel_options', {}) + self._graph_kernel = kwargs.get('graph_kernel', None) + self._verbose = kwargs.get('verbose', 2) + self.__ged_options = kwargs.get('ged_options', {}) + self.__mge_options = kwargs.get('mge_options', {}) + self.__fit_method = kwargs.get('fit_method', 'k-graphs') + self.__init_ecc = kwargs.get('init_ecc', None) + self.__edit_cost_constants = kwargs.get('edit_cost_constants', []) + self.__parallel = kwargs.get('parallel', True) + self.__n_jobs = kwargs.get('n_jobs', multiprocessing.cpu_count()) + self.__ds_name = kwargs.get('ds_name', None) + self.__time_limit_in_sec = kwargs.get('time_limit_in_sec', 0) + self.__max_itrs = kwargs.get('max_itrs', 100) + self.__max_itrs_without_update = kwargs.get('max_itrs_without_update', 3) + self.__epsilon_residual = kwargs.get('epsilon_residual', 0.01) + self.__epsilon_ec = kwargs.get('epsilon_ec', 0.1) + self.__gram_matrix_unnorm = kwargs.get('gram_matrix_unnorm', None) + self.__runtime_precompute_gm = kwargs.get('runtime_precompute_gm', None) + self.__allow_zeros = kwargs.get('allow_zeros', False) + self.__triangle_rule = kwargs.get('triangle_rule', True) + + + def run(self): + self._graph_kernel = get_graph_kernel_by_name(self._kernel_options['name'], + node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + node_attrs=self._dataset.node_attrs, + edge_attrs=self._dataset.edge_attrs, + ds_infos=self._dataset.get_dataset_infos(keys=['directed']), + kernel_options=self._kernel_options) + + # record start time. + start = time.time() + + # 1. precompute gram matrix. + if self.__gram_matrix_unnorm is None: + gram_matrix, run_time = self._graph_kernel.compute(self._dataset.graphs, **self._kernel_options) + self.__gram_matrix_unnorm = self._graph_kernel.gram_matrix_unnorm + end_precompute_gm = time.time() + self.__runtime_precompute_gm = end_precompute_gm - start + else: + if self.__runtime_precompute_gm is None: + raise Exception('Parameter "runtime_precompute_gm" must be given when using pre-computed Gram matrix.') + self._graph_kernel.gram_matrix_unnorm = self.__gram_matrix_unnorm + if self._kernel_options['normalize']: + self._graph_kernel.gram_matrix = self._graph_kernel.normalize_gm(np.copy(self.__gram_matrix_unnorm)) + else: + self._graph_kernel.gram_matrix = np.copy(self.__gram_matrix_unnorm) + end_precompute_gm = time.time() + start -= self.__runtime_precompute_gm + + if self.__fit_method != 'k-graphs' and self.__fit_method != 'whole-dataset': + start = time.time() + self.__runtime_precompute_gm = 0 + end_precompute_gm = start + + # 2. optimize edit cost constants. + self.__optimize_edit_cost_constants() + end_optimize_ec = time.time() + self.__runtime_optimize_ec = end_optimize_ec - end_precompute_gm + + # 3. compute set median and gen median using optimized edit costs. + if self._verbose >= 2: + print('\nstart computing set median and gen median using optimized edit costs...\n') + self.__gmg_bcu() + end_generate_preimage = time.time() + self.__runtime_generate_preimage = end_generate_preimage - end_optimize_ec + self.__runtime_total = end_generate_preimage - start + if self._verbose >= 2: + print('medians computed.') + print('SOD of the set median: ', self.__sod_set_median) + print('SOD of the generalized median: ', self.__sod_gen_median) + + # 4. compute kernel distances to the true median. + if self._verbose >= 2: + print('\nstart computing distances to true median....\n') + self.__compute_distances_to_true_median() + + # 5. print out results. + if self._verbose: + print() + print('================================================================================') + print('Finished generation of preimages.') + print('--------------------------------------------------------------------------------') + print('The optimized edit cost constants:', self.__edit_cost_constants) + print('SOD of the set median:', self.__sod_set_median) + print('SOD of the generalized median:', self.__sod_gen_median) + print('Distance in kernel space for set median:', self.__k_dis_set_median) + print('Distance in kernel space for generalized median:', self.__k_dis_gen_median) + print('Minimum distance in kernel space for each graph in median set:', self.__k_dis_dataset) + print('Time to pre-compute Gram matrix:', self.__runtime_precompute_gm) + print('Time to optimize edit costs:', self.__runtime_optimize_ec) + print('Time to generate pre-images:', self.__runtime_generate_preimage) + print('Total time:', self.__runtime_total) + print('Total number of iterations for optimizing:', self.__itrs) + print('Total number of updating edit costs:', self.__num_updates_ecc) + print('Is optimization of edit costs converged:', self.__converged) + print('================================================================================') + print() + + + def get_results(self): + results = {} + results['edit_cost_constants'] = self.__edit_cost_constants + results['runtime_precompute_gm'] = self.__runtime_precompute_gm + results['runtime_optimize_ec'] = self.__runtime_optimize_ec + results['runtime_generate_preimage'] = self.__runtime_generate_preimage + results['runtime_total'] = self.__runtime_total + results['sod_set_median'] = self.__sod_set_median + results['sod_gen_median'] = self.__sod_gen_median + results['k_dis_set_median'] = self.__k_dis_set_median + results['k_dis_gen_median'] = self.__k_dis_gen_median + results['k_dis_dataset'] = self.__k_dis_dataset + results['itrs'] = self.__itrs + results['converged'] = self.__converged + results['num_updates_ecc'] = self.__num_updates_ecc + results['mge'] = {} + results['mge']['num_decrease_order'] = self.__mge.get_num_times_order_decreased() + results['mge']['num_increase_order'] = self.__mge.get_num_times_order_increased() + results['mge']['num_converged_descents'] = self.__mge.get_num_converged_descents() + return results + + + def __optimize_edit_cost_constants(self): + """fit edit cost constants. + """ + if self.__fit_method == 'random': # random + if self.__ged_options['edit_cost'] == 'LETTER': + self.__edit_cost_constants = random.sample(range(1, 1000), 3) + self.__edit_cost_constants = [item * 0.001 for item in self.__edit_cost_constants] + elif self.__ged_options['edit_cost'] == 'LETTER2': + random.seed(time.time()) + self.__edit_cost_constants = random.sample(range(1, 1000), 5) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + self.__edit_cost_constants = random.sample(range(1, 1000), 6) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + if self._dataset.node_attrs == []: + self.__edit_cost_constants[2] = 0 + if self._dataset.edge_attrs == []: + self.__edit_cost_constants[5] = 0 + else: + self.__edit_cost_constants = random.sample(range(1, 1000), 6) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + if self._verbose >= 2: + print('edit cost constants used:', self.__edit_cost_constants) + elif self.__fit_method == 'expert': # expert + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__edit_cost_constants = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__edit_cost_constants = [0.675, 0.675, 0.75, 0.425, 0.425] + else: + self.__edit_cost_constants = [3, 3, 1, 3, 3, 1] + else: + self.__edit_cost_constants = self.__init_ecc + elif self.__fit_method == 'k-graphs': + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__init_ecc = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__init_ecc = [0.675, 0.675, 0.75, 0.425, 0.425] + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + self.__init_ecc = [0, 0, 1, 1, 1, 0] + if self._dataset.node_attrs == []: + self.__init_ecc[2] = 0 + if self._dataset.edge_attrs == []: + self.__init_ecc[5] = 0 + else: + self.__init_ecc = [3, 3, 1, 3, 3, 1] + # optimize on the k-graph subset. + self.__optimize_ecc_by_kernel_distances() + elif self.__fit_method == 'whole-dataset': + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__init_ecc = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__init_ecc = [0.675, 0.675, 0.75, 0.425, 0.425] + else: + self.__init_ecc = [3, 3, 1, 3, 3, 1] + # optimizeon the whole set. + self.__optimize_ecc_by_kernel_distances() + elif self.__fit_method == 'precomputed': + pass + + + def __optimize_ecc_by_kernel_distances(self): + # compute distances in feature space. + dis_k_mat, _, _, _ = self._graph_kernel.compute_distance_matrix() + dis_k_vec = [] + for i in range(len(dis_k_mat)): + # for j in range(i, len(dis_k_mat)): + for j in range(i + 1, len(dis_k_mat)): + dis_k_vec.append(dis_k_mat[i, j]) + dis_k_vec = np.array(dis_k_vec) + + # init ged. + if self._verbose >= 2: + print('\ninitial:') + time0 = time.time() + graphs = [self.__clean_graph(g) for g in self._dataset.graphs] + self.__edit_cost_constants = self.__init_ecc + options = self.__ged_options.copy() + options['edit_cost_constants'] = self.__edit_cost_constants # @todo + options['node_labels'] = self._dataset.node_labels + options['edge_labels'] = self._dataset.edge_labels + options['node_attrs'] = self._dataset.node_attrs + options['edge_attrs'] = self._dataset.edge_attrs + ged_vec_init, ged_mat, n_edit_operations = compute_geds_cml(graphs, options=options, parallel=self.__parallel, verbose=(self._verbose > 1)) + residual_list = [np.sqrt(np.sum(np.square(np.array(ged_vec_init) - dis_k_vec)))] + time_list = [time.time() - time0] + edit_cost_list = [self.__init_ecc] + nb_cost_mat = np.array(n_edit_operations) + nb_cost_mat_list = [nb_cost_mat] + if self._verbose >= 2: + print('Current edit cost constants:', self.__edit_cost_constants) + print('Residual list:', residual_list) + + # run iteration from initial edit costs. + self.__converged = False + itrs_without_update = 0 + self.__itrs = 0 + self.__num_updates_ecc = 0 + timer = Timer(self.__time_limit_in_sec) + while not self.__termination_criterion_met(self.__converged, timer, self.__itrs, itrs_without_update): + if self._verbose >= 2: + print('\niteration', self.__itrs + 1) + time0 = time.time() + # "fit" geds to distances in feature space by tuning edit costs using theLeast Squares Method. +# np.savez('results/xp_fit_method/fit_data_debug' + str(self.__itrs) + '.gm', +# nb_cost_mat=nb_cost_mat, dis_k_vec=dis_k_vec, +# n_edit_operations=n_edit_operations, ged_vec_init=ged_vec_init, +# ged_mat=ged_mat) + self.__edit_cost_constants, _ = self.__update_ecc(nb_cost_mat, dis_k_vec) + for i in range(len(self.__edit_cost_constants)): + if -1e-9 <= self.__edit_cost_constants[i] <= 1e-9: + self.__edit_cost_constants[i] = 0 + if self.__edit_cost_constants[i] < 0: + raise ValueError('The edit cost is negative.') + # for i in range(len(self.__edit_cost_constants)): + # if self.__edit_cost_constants[i] < 0: + # self.__edit_cost_constants[i] = 0 + + # compute new GEDs and numbers of edit operations. + options = self.__ged_options.copy() # np.array([self.__edit_cost_constants[0], self.__edit_cost_constants[1], 0.75]) + options['edit_cost_constants'] = self.__edit_cost_constants # @todo + options['node_labels'] = self._dataset.node_labels + options['edge_labels'] = self._dataset.edge_labels + options['node_attrs'] = self._dataset.node_attrs + options['edge_attrs'] = self._dataset.edge_attrs + ged_vec, ged_mat, n_edit_operations = compute_geds_cml(graphs, options=options, parallel=self.__parallel, verbose=(self._verbose > 1)) + residual_list.append(np.sqrt(np.sum(np.square(np.array(ged_vec) - dis_k_vec)))) + time_list.append(time.time() - time0) + edit_cost_list.append(self.__edit_cost_constants) + nb_cost_mat = np.array(n_edit_operations) + nb_cost_mat_list.append(nb_cost_mat) + + # check convergency. + ec_changed = False + for i, cost in enumerate(self.__edit_cost_constants): + if cost == 0: + if edit_cost_list[-2][i] > self.__epsilon_ec: + ec_changed = True + break + elif abs(cost - edit_cost_list[-2][i]) / cost > self.__epsilon_ec: + ec_changed = True + break +# if abs(cost - edit_cost_list[-2][i]) > self.__epsilon_ec: +# ec_changed = True +# break + residual_changed = False + if residual_list[-1] == 0: + if residual_list[-2] > self.__epsilon_residual: + residual_changed = True + elif abs(residual_list[-1] - residual_list[-2]) / residual_list[-1] > self.__epsilon_residual: + residual_changed = True + self.__converged = not (ec_changed or residual_changed) + if self.__converged: + itrs_without_update += 1 + else: + itrs_without_update = 0 + self.__num_updates_ecc += 1 + + # print current states. + if self._verbose >= 2: + print() + print('-------------------------------------------------------------------------') + print('States of iteration', self.__itrs + 1) + print('-------------------------------------------------------------------------') +# print('Time spend:', self.__runtime_optimize_ec) + print('Total number of iterations for optimizing:', self.__itrs + 1) + print('Total number of updating edit costs:', self.__num_updates_ecc) + print('Was optimization of edit costs converged:', self.__converged) + print('Did edit costs change:', ec_changed) + print('Did residual change:', residual_changed) + print('Iterations without update:', itrs_without_update) + print('Current edit cost constants:', self.__edit_cost_constants) + print('Residual list:', residual_list) + print('-------------------------------------------------------------------------') + + self.__itrs += 1 + + + def __termination_criterion_met(self, converged, timer, itr, itrs_without_update): + if timer.expired() or (itr >= self.__max_itrs if self.__max_itrs >= 0 else False): +# if self.__state == AlgorithmState.TERMINATED: +# self.__state = AlgorithmState.INITIALIZED + return True + return converged or (itrs_without_update > self.__max_itrs_without_update if self.__max_itrs_without_update >= 0 else False) + + + def __update_ecc(self, nb_cost_mat, dis_k_vec, rw_constraints='inequality'): + # if self.__ds_name == 'Letter-high': + if self.__ged_options['edit_cost'] == 'LETTER': + raise Exception('Cannot compute for cost "LETTER".') + pass + # # method 1: set alpha automatically, just tune c_vir and c_eir by + # # LMS using cvxpy. + # alpha = 0.5 + # coeff = 100 # np.max(alpha * nb_cost_mat[:,4] / dis_k_vec) + ## if np.count_nonzero(nb_cost_mat[:,4]) == 0: + ## alpha = 0.75 + ## else: + ## alpha = np.min([dis_k_vec / c_vs for c_vs in nb_cost_mat[:,4] if c_vs != 0]) + ## alpha = alpha * 0.99 + # param_vir = alpha * (nb_cost_mat[:,0] + nb_cost_mat[:,1]) + # param_eir = (1 - alpha) * (nb_cost_mat[:,4] + nb_cost_mat[:,5]) + # nb_cost_mat_new = np.column_stack((param_vir, param_eir)) + # dis_new = coeff * dis_k_vec - alpha * nb_cost_mat[:,3] + # + # x = cp.Variable(nb_cost_mat_new.shape[1]) + # cost = cp.sum_squares(nb_cost_mat_new * x - dis_new) + # constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]] + # prob = cp.Problem(cp.Minimize(cost), constraints) + # prob.solve() + # edit_costs_new = x.value + # edit_costs_new = np.array([edit_costs_new[0], edit_costs_new[1], alpha]) + # residual = np.sqrt(prob.value) + + # # method 2: tune c_vir, c_eir and alpha by nonlinear programming by + # # scipy.optimize.minimize. + # w0 = nb_cost_mat[:,0] + nb_cost_mat[:,1] + # w1 = nb_cost_mat[:,4] + nb_cost_mat[:,5] + # w2 = nb_cost_mat[:,3] + # w3 = dis_k_vec + # func_min = lambda x: np.sum((w0 * x[0] * x[3] + w1 * x[1] * (1 - x[2]) \ + # + w2 * x[2] - w3 * x[3]) ** 2) + # bounds = ((0, None), (0., None), (0.5, 0.5), (0, None)) + # res = minimize(func_min, [0.9, 1.7, 0.75, 10], bounds=bounds) + # edit_costs_new = res.x[0:3] + # residual = res.fun + + # method 3: tune c_vir, c_eir and alpha by nonlinear programming using cvxpy. + + + # # method 4: tune c_vir, c_eir and alpha by QP function + # # scipy.optimize.least_squares. An initial guess is required. + # w0 = nb_cost_mat[:,0] + nb_cost_mat[:,1] + # w1 = nb_cost_mat[:,4] + nb_cost_mat[:,5] + # w2 = nb_cost_mat[:,3] + # w3 = dis_k_vec + # func = lambda x: (w0 * x[0] * x[3] + w1 * x[1] * (1 - x[2]) \ + # + w2 * x[2] - w3 * x[3]) ** 2 + # res = optimize.root(func, [0.9, 1.7, 0.75, 100]) + # edit_costs_new = res.x + # residual = None + elif self.__ged_options['edit_cost'] == 'LETTER2': + # # 1. if c_vi != c_vr, c_ei != c_er. + # nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + # x = cp.Variable(nb_cost_mat_new.shape[1]) + # cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + ## # 1.1 no constraints. + ## constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]] + # # 1.2 c_vs <= c_vi + c_vr. + # constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + # np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + ## # 2. if c_vi == c_vr, c_ei == c_er. + ## nb_cost_mat_new = nb_cost_mat[:,[0,3,4]] + ## nb_cost_mat_new[:,0] += nb_cost_mat[:,1] + ## nb_cost_mat_new[:,2] += nb_cost_mat[:,5] + ## x = cp.Variable(nb_cost_mat_new.shape[1]) + ## cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + ## # 2.1 no constraints. + ## constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]] + ### # 2.2 c_vs <= c_vi + c_vr. + ### constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + ### np.array([2.0, -1.0, 0.0]).T@x >= 0.0] + # + # prob = cp.Problem(cp.Minimize(cost_fun), constraints) + # prob.solve() + # edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]] + # edit_costs_new = np.array(edit_costs_new) + # residual = np.sqrt(prob.value) + if not self.__triangle_rule and self.__allow_zeros: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif self.__triangle_rule and self.__allow_zeros: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01, + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif not self.__triangle_rule and not self.__allow_zeros: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + prob.solve() + edit_costs_new = x.value + residual = np.sqrt(prob.value) + # elif method == 'inequality_modified': + # # c_vs <= c_vi + c_vr. + # nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + # x = cp.Variable(nb_cost_mat_new.shape[1]) + # cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + # constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + # np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + # prob = cp.Problem(cp.Minimize(cost_fun), constraints) + # prob.solve() + # # use same costs for insertion and removal rather than the fitted costs. + # edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]] + # edit_costs_new = np.array(edit_costs_new) + # residual = np.sqrt(prob.value) + elif self.__triangle_rule and not self.__allow_zeros: + # c_vs <= c_vi + c_vr. + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif rw_constraints == '2constraints': # @todo: rearrange it later. + # c_vs <= c_vi + c_vr and c_vi == c_vr, c_ei == c_er. + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0, + np.array([1.0, -1.0, 0.0, 0.0, 0.0]).T@x == 0.0, + np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + prob.solve() + edit_costs_new = x.value + residual = np.sqrt(prob.value) + + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + is_n_attr = np.count_nonzero(nb_cost_mat[:,2]) + is_e_attr = np.count_nonzero(nb_cost_mat[:,5]) + + if self.__ds_name == 'SYNTHETICnew': # @todo: rearrenge this later. + # nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + nb_cost_mat_new = nb_cost_mat[:,[2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + # constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + # np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0] + # constraints = [x >= [0.0001 for i in range(nb_cost_mat_new.shape[1])]] + constraints = [x >= [0.0001 for i in range(nb_cost_mat_new.shape[1])], + np.array([0.0, 1.0, -1.0]).T@x == 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + prob.solve() + # print(x.value) + edit_costs_new = np.concatenate((np.array([0.0, 0.0]), x.value, + np.array([0.0]))) + residual = np.sqrt(prob.value) + + elif not self.__triangle_rule and self.__allow_zeros: + if is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif is_n_attr and not is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value, np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:])) + residual = np.sqrt(prob.value) + else: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), + x.value[2:], np.array([0.0]))) + residual = np.sqrt(prob.value) + elif self.__triangle_rule and self.__allow_zeros: + if is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif is_n_attr and not is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01, + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value, np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:])) + residual = np.sqrt(prob.value) + else: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), + x.value[2:], np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not self.__triangle_rule and not self.__allow_zeros: + if is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif is_n_attr and not is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value, np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:])) + residual = np.sqrt(prob.value) + else: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), + x.value[2:], np.array([0.0]))) + residual = np.sqrt(prob.value) + elif self.__triangle_rule and not self.__allow_zeros: + # c_vs <= c_vi + c_vr. + if is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif is_n_attr and not is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value, np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:])) + residual = np.sqrt(prob.value) + else: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), + x.value[2:], np.array([0.0]))) + residual = np.sqrt(prob.value) + + elif self.__ged_options['edit_cost'] == 'CONSTANT': # @todo: node/edge may not labeled. + if not self.__triangle_rule and self.__allow_zeros: + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif self.__triangle_rule and self.__allow_zeros: + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif not self.__triangle_rule and not self.__allow_zeros: + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif self.__triangle_rule and not self.__allow_zeros: + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + else: + raise Exception('The edit cost "', self.__ged_options['edit_cost'], '" is not supported for update progress.') + # # method 1: simple least square method. + # edit_costs_new, residual, _, _ = np.linalg.lstsq(nb_cost_mat, dis_k_vec, + # rcond=None) + + # # method 2: least square method with x_i >= 0. + # edit_costs_new, residual = optimize.nnls(nb_cost_mat, dis_k_vec) + + # method 3: solve as a quadratic program with constraints. + # P = np.dot(nb_cost_mat.T, nb_cost_mat) + # q_T = -2 * np.dot(dis_k_vec.T, nb_cost_mat) + # G = -1 * np.identity(nb_cost_mat.shape[1]) + # h = np.array([0 for i in range(nb_cost_mat.shape[1])]) + # A = np.array([1 for i in range(nb_cost_mat.shape[1])]) + # b = 1 + # x = cp.Variable(nb_cost_mat.shape[1]) + # prob = cp.Problem(cp.Minimize(cp.quad_form(x, P) + q_T@x), + # [G@x <= h]) + # prob.solve() + # edit_costs_new = x.value + # residual = prob.value - np.dot(dis_k_vec.T, dis_k_vec) + + # G = -1 * np.identity(nb_cost_mat.shape[1]) + # h = np.array([0 for i in range(nb_cost_mat.shape[1])]) + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])], + # np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + + # method 4: + + return edit_costs_new, residual + + + def __execute_cvx(self, prob): + try: + prob.solve(verbose=(self._verbose>=2)) + except MemoryError as error0: + if self._verbose >= 2: + print('\nUsing solver "OSQP" caused a memory error.') + print('the original error message is\n', error0) + print('solver status: ', prob.status) + print('trying solver "CVXOPT" instead...\n') + try: + prob.solve(solver=cp.CVXOPT, verbose=(self._verbose>=2)) + except Exception as error1: + if self._verbose >= 2: + print('\nAn error occured when using solver "CVXOPT".') + print('the original error message is\n', error1) + print('solver status: ', prob.status) + print('trying solver "MOSEK" instead. Notice this solver is commercial and a lisence is required.\n') + prob.solve(solver=cp.MOSEK, verbose=(self._verbose>=2)) + else: + if self._verbose >= 2: + print('solver status: ', prob.status) + else: + if self._verbose >= 2: + print('solver status: ', prob.status) + if self._verbose >= 2: + print() + + + def __gmg_bcu(self): + """ + The local search algorithm based on block coordinate update (BCU) for estimating a generalized median graph (GMG). + + Returns + ------- + None. + + """ + # Set up the ged environment. + ged_env = GEDEnv() # @todo: maybe create a ged_env as a private varible. + # gedlibpy.restart_env() + ged_env.set_edit_cost(self.__ged_options['edit_cost'], edit_cost_constants=self.__edit_cost_constants) + graphs = [self.__clean_graph(g) for g in self._dataset.graphs] + for g in graphs: + ged_env.add_nx_graph(g, '') + graph_ids = ged_env.get_all_graph_ids() + set_median_id = ged_env.add_graph('set_median') + gen_median_id = ged_env.add_graph('gen_median') + ged_env.init(init_type=self.__ged_options['init_option']) + + # Set up the madian graph estimator. + self.__mge = MedianGraphEstimatorPy(ged_env, constant_node_costs(self.__ged_options['edit_cost'])) + self.__mge.set_refine_method(self.__ged_options['method'], self.__ged_options) + options = self.__mge_options.copy() + if not 'seed' in options: + options['seed'] = int(round(time.time() * 1000)) # @todo: may not work correctly for possible parallel usage. + options['parallel'] = self.__parallel + + # Select the GED algorithm. + self.__mge.set_options(mge_options_to_string(options)) + self.__mge.set_label_names(node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + node_attrs=self._dataset.node_attrs, + edge_attrs=self._dataset.edge_attrs) + ged_options = self.__ged_options.copy() + if self.__parallel: + ged_options['threads'] = 1 + self.__mge.set_init_method(ged_options['method'], ged_options) + self.__mge.set_descent_method(ged_options['method'], ged_options) + + # Run the estimator. + self.__mge.run(graph_ids, set_median_id, gen_median_id) + + # Get SODs. + self.__sod_set_median = self.__mge.get_sum_of_distances('initialized') + self.__sod_gen_median = self.__mge.get_sum_of_distances('converged') + + # Get median graphs. + self.__set_median = ged_env.get_nx_graph(set_median_id) + self.__gen_median = ged_env.get_nx_graph(gen_median_id) + + + def __compute_distances_to_true_median(self): + # compute distance in kernel space for set median. + kernels_to_sm, _ = self._graph_kernel.compute(self.__set_median, self._dataset.graphs, **self._kernel_options) + kernel_sm, _ = self._graph_kernel.compute(self.__set_median, self.__set_median, **self._kernel_options) + if self._kernel_options['normalize']: + kernels_to_sm = [kernels_to_sm[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_sm) for i in range(len(kernels_to_sm))] # normalize + kernel_sm = 1 + # @todo: not correct kernel value + gram_with_sm = np.concatenate((np.array([kernels_to_sm]), np.copy(self._graph_kernel.gram_matrix)), axis=0) + gram_with_sm = np.concatenate((np.array([[kernel_sm] + kernels_to_sm]).T, gram_with_sm), axis=1) + self.__k_dis_set_median = compute_k_dis(0, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_sm, withterm3=False) + + # compute distance in kernel space for generalized median. + kernels_to_gm, _ = self._graph_kernel.compute(self.__gen_median, self._dataset.graphs, **self._kernel_options) + kernel_gm, _ = self._graph_kernel.compute(self.__gen_median, self.__gen_median, **self._kernel_options) + if self._kernel_options['normalize']: + kernels_to_gm = [kernels_to_gm[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_gm) for i in range(len(kernels_to_gm))] # normalize + kernel_gm = 1 + gram_with_gm = np.concatenate((np.array([kernels_to_gm]), np.copy(self._graph_kernel.gram_matrix)), axis=0) + gram_with_gm = np.concatenate((np.array([[kernel_gm] + kernels_to_gm]).T, gram_with_gm), axis=1) + self.__k_dis_gen_median = compute_k_dis(0, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_gm, withterm3=False) + + # compute distance in kernel space for each graph in median set. + k_dis_median_set = [] + for idx in range(len(self._dataset.graphs)): + k_dis_median_set.append(compute_k_dis(idx+1, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_gm, withterm3=False)) + idx_k_dis_median_set_min = np.argmin(k_dis_median_set) + self.__k_dis_dataset = k_dis_median_set[idx_k_dis_median_set_min] + self.__best_from_dataset = self._dataset.graphs[idx_k_dis_median_set_min].copy() + + if self._verbose >= 2: + print() + print('distance in kernel space for set median:', self.__k_dis_set_median) + print('distance in kernel space for generalized median:', self.__k_dis_gen_median) + print('minimum distance in kernel space for each graph in median set:', self.__k_dis_dataset) + print('distance in kernel space for each graph in median set:', k_dis_median_set) + + +# def __clean_graph(self, G, node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]): + def __clean_graph(self, G): # @todo: this may not be needed when datafile is updated. + """ + Cleans node and edge labels and attributes of the given graph. + """ + G_new = nx.Graph(**G.graph) + for nd, attrs in G.nodes(data=True): + G_new.add_node(str(nd)) # @todo: should we keep this as str()? + for l_name in self._dataset.node_labels: + G_new.nodes[str(nd)][l_name] = str(attrs[l_name]) + for a_name in self._dataset.node_attrs: + G_new.nodes[str(nd)][a_name] = str(attrs[a_name]) + for nd1, nd2, attrs in G.edges(data=True): + G_new.add_edge(str(nd1), str(nd2)) + for l_name in self._dataset.edge_labels: + G_new.edges[str(nd1), str(nd2)][l_name] = str(attrs[l_name]) + for a_name in self._dataset.edge_attrs: + G_new.edges[str(nd1), str(nd2)][a_name] = str(attrs[a_name]) + return G_new + + + @property + def mge(self): + return self.__mge + + @property + def ged_options(self): + return self.__ged_options + + @ged_options.setter + def ged_options(self, value): + self.__ged_options = value + + + @property + def mge_options(self): + return self.__mge_options + + @mge_options.setter + def mge_options(self, value): + self.__mge_options = value + + + @property + def fit_method(self): + return self.__fit_method + + @fit_method.setter + def fit_method(self, value): + self.__fit_method = value + + + @property + def init_ecc(self): + return self.__init_ecc + + @init_ecc.setter + def init_ecc(self, value): + self.__init_ecc = value + + + @property + def set_median(self): + return self.__set_median + + + @property + def gen_median(self): + return self.__gen_median + + + @property + def best_from_dataset(self): + return self.__best_from_dataset + + + @property + def gram_matrix_unnorm(self): + return self.__gram_matrix_unnorm + + @gram_matrix_unnorm.setter + def gram_matrix_unnorm(self, value): + self.__gram_matrix_unnorm = value \ No newline at end of file From 86d2610638e7d53a645e9e55512cce3bad99e9cb Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:31 +0200 Subject: [PATCH 073/728] New translations median_preimage_generator_cml.py (French) --- .../preimage/median_preimage_generator_cml.py | 520 ++++++++++++++++++ 1 file changed, 520 insertions(+) create mode 100644 lang/fr/gklearn/preimage/median_preimage_generator_cml.py diff --git a/lang/fr/gklearn/preimage/median_preimage_generator_cml.py b/lang/fr/gklearn/preimage/median_preimage_generator_cml.py new file mode 100644 index 0000000000..e6bca92da2 --- /dev/null +++ b/lang/fr/gklearn/preimage/median_preimage_generator_cml.py @@ -0,0 +1,520 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 16 16:04:46 2020 + +@author: ljia +""" +import numpy as np +import time +import random +import multiprocessing +import networkx as nx +from gklearn.preimage import PreimageGenerator +from gklearn.preimage.utils import compute_k_dis +from gklearn.ged.env import GEDEnv +from gklearn.ged.learning import CostMatricesLearner +from gklearn.ged.median import MedianGraphEstimatorCML +from gklearn.ged.median import constant_node_costs, mge_options_to_string +from gklearn.utils.utils import get_graph_kernel_by_name +from gklearn.ged.util import label_costs_to_matrix + + +class MedianPreimageGeneratorCML(PreimageGenerator): + """Generator median preimages by cost matrices learning using the pure Python version of GEDEnv. Works only for symbolic labeled graphs. + """ + + def __init__(self, dataset=None): + PreimageGenerator.__init__(self, dataset=dataset) + ### arguments to set. + self.__mge = None + self.__ged_options = {} + self.__mge_options = {} +# self.__fit_method = 'k-graphs' + self.__init_method = 'random' + self.__init_ecc = None + self.__parallel = True + self.__n_jobs = multiprocessing.cpu_count() + self.__ds_name = None + # for cml. + self.__time_limit_in_sec = 0 + self.__max_itrs = 100 + self.__max_itrs_without_update = 3 + self.__epsilon_residual = 0.01 + self.__epsilon_ec = 0.1 + self.__allow_zeros = True +# self.__triangle_rule = True + ### values to compute. + self.__runtime_optimize_ec = None + self.__runtime_generate_preimage = None + self.__runtime_total = None + self.__set_median = None + self.__gen_median = None + self.__best_from_dataset = None + self.__sod_set_median = None + self.__sod_gen_median = None + self.__k_dis_set_median = None + self.__k_dis_gen_median = None + self.__k_dis_dataset = None + self.__node_label_costs = None + self.__edge_label_costs = None + # for cml. + self.__itrs = 0 + self.__converged = False + self.__num_updates_ecs = 0 + ### values that can be set or to be computed. + self.__edit_cost_constants = [] + self.__gram_matrix_unnorm = None + self.__runtime_precompute_gm = None + + + def set_options(self, **kwargs): + self._kernel_options = kwargs.get('kernel_options', {}) + self._graph_kernel = kwargs.get('graph_kernel', None) + self._verbose = kwargs.get('verbose', 2) + self.__ged_options = kwargs.get('ged_options', {}) + self.__mge_options = kwargs.get('mge_options', {}) +# self.__fit_method = kwargs.get('fit_method', 'k-graphs') + self.__init_method = kwargs.get('init_method', 'random') + self.__init_ecc = kwargs.get('init_ecc', None) + self.__edit_cost_constants = kwargs.get('edit_cost_constants', []) + self.__parallel = kwargs.get('parallel', True) + self.__n_jobs = kwargs.get('n_jobs', multiprocessing.cpu_count()) + self.__ds_name = kwargs.get('ds_name', None) + self.__time_limit_in_sec = kwargs.get('time_limit_in_sec', 0) + self.__max_itrs = kwargs.get('max_itrs', 100) + self.__max_itrs_without_update = kwargs.get('max_itrs_without_update', 3) + self.__epsilon_residual = kwargs.get('epsilon_residual', 0.01) + self.__epsilon_ec = kwargs.get('epsilon_ec', 0.1) + self.__gram_matrix_unnorm = kwargs.get('gram_matrix_unnorm', None) + self.__runtime_precompute_gm = kwargs.get('runtime_precompute_gm', None) + self.__allow_zeros = kwargs.get('allow_zeros', True) +# self.__triangle_rule = kwargs.get('triangle_rule', True) + + + def run(self): + self._graph_kernel = get_graph_kernel_by_name(self._kernel_options['name'], + node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + node_attrs=self._dataset.node_attrs, + edge_attrs=self._dataset.edge_attrs, + ds_infos=self._dataset.get_dataset_infos(keys=['directed']), + kernel_options=self._kernel_options) + + # record start time. + start = time.time() + + # 1. precompute gram matrix. + if self.__gram_matrix_unnorm is None: + gram_matrix, run_time = self._graph_kernel.compute(self._dataset.graphs, **self._kernel_options) + self.__gram_matrix_unnorm = self._graph_kernel.gram_matrix_unnorm + end_precompute_gm = time.time() + self.__runtime_precompute_gm = end_precompute_gm - start + else: + if self.__runtime_precompute_gm is None: + raise Exception('Parameter "runtime_precompute_gm" must be given when using pre-computed Gram matrix.') + self._graph_kernel.gram_matrix_unnorm = self.__gram_matrix_unnorm + if self._kernel_options['normalize']: + self._graph_kernel.gram_matrix = self._graph_kernel.normalize_gm(np.copy(self.__gram_matrix_unnorm)) + else: + self._graph_kernel.gram_matrix = np.copy(self.__gram_matrix_unnorm) + end_precompute_gm = time.time() + start -= self.__runtime_precompute_gm + +# if self.__fit_method != 'k-graphs' and self.__fit_method != 'whole-dataset': +# start = time.time() +# self.__runtime_precompute_gm = 0 +# end_precompute_gm = start + + # 2. optimize edit cost constants. + self.__optimize_edit_cost_vector() + end_optimize_ec = time.time() + self.__runtime_optimize_ec = end_optimize_ec - end_precompute_gm + + # 3. compute set median and gen median using optimized edit costs. + if self._verbose >= 2: + print('\nstart computing set median and gen median using optimized edit costs...\n') + self.__gmg_bcu() + end_generate_preimage = time.time() + self.__runtime_generate_preimage = end_generate_preimage - end_optimize_ec + self.__runtime_total = end_generate_preimage - start + if self._verbose >= 2: + print('medians computed.') + print('SOD of the set median: ', self.__sod_set_median) + print('SOD of the generalized median: ', self.__sod_gen_median) + + # 4. compute kernel distances to the true median. + if self._verbose >= 2: + print('\nstart computing distances to true median....\n') + self.__compute_distances_to_true_median() + + # 5. print out results. + if self._verbose: + print() + print('================================================================================') + print('Finished generation of preimages.') + print('--------------------------------------------------------------------------------') + print('The optimized edit costs:', self.__edit_cost_constants) + print('SOD of the set median:', self.__sod_set_median) + print('SOD of the generalized median:', self.__sod_gen_median) + print('Distance in kernel space for set median:', self.__k_dis_set_median) + print('Distance in kernel space for generalized median:', self.__k_dis_gen_median) + print('Minimum distance in kernel space for each graph in median set:', self.__k_dis_dataset) + print('Time to pre-compute Gram matrix:', self.__runtime_precompute_gm) + print('Time to optimize edit costs:', self.__runtime_optimize_ec) + print('Time to generate pre-images:', self.__runtime_generate_preimage) + print('Total time:', self.__runtime_total) + print('Total number of iterations for optimizing:', self.__itrs) + print('Total number of updating edit costs:', self.__num_updates_ecs) + print('Is optimization of edit costs converged:', self.__converged) + print('================================================================================') + print() + + + def get_results(self): + results = {} + results['edit_cost_constants'] = self.__edit_cost_constants + results['runtime_precompute_gm'] = self.__runtime_precompute_gm + results['runtime_optimize_ec'] = self.__runtime_optimize_ec + results['runtime_generate_preimage'] = self.__runtime_generate_preimage + results['runtime_total'] = self.__runtime_total + results['sod_set_median'] = self.__sod_set_median + results['sod_gen_median'] = self.__sod_gen_median + results['k_dis_set_median'] = self.__k_dis_set_median + results['k_dis_gen_median'] = self.__k_dis_gen_median + results['k_dis_dataset'] = self.__k_dis_dataset + results['itrs'] = self.__itrs + results['converged'] = self.__converged + results['num_updates_ecc'] = self.__num_updates_ecs + results['mge'] = {} + results['mge']['num_decrease_order'] = self.__mge.get_num_times_order_decreased() + results['mge']['num_increase_order'] = self.__mge.get_num_times_order_increased() + results['mge']['num_converged_descents'] = self.__mge.get_num_converged_descents() + return results + + + def __optimize_edit_cost_vector(self): + """Learn edit cost vector. + """ + # Initialize label costs randomly. + if self.__init_method == 'random': + # Initialize label costs. + self.__initialize_label_costs() + + # Optimize edit cost matrices. + self.__optimize_ecm_by_kernel_distances() + # Initialize all label costs with the same value. + elif self.__init_method == 'uniform': # random + pass + + elif self.__fit_method == 'random': # random + if self.__ged_options['edit_cost'] == 'LETTER': + self.__edit_cost_constants = random.sample(range(1, 1000), 3) + self.__edit_cost_constants = [item * 0.001 for item in self.__edit_cost_constants] + elif self.__ged_options['edit_cost'] == 'LETTER2': + random.seed(time.time()) + self.__edit_cost_constants = random.sample(range(1, 1000), 5) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + self.__edit_cost_constants = random.sample(range(1, 1000), 6) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + if self._dataset.node_attrs == []: + self.__edit_cost_constants[2] = 0 + if self._dataset.edge_attrs == []: + self.__edit_cost_constants[5] = 0 + else: + self.__edit_cost_constants = random.sample(range(1, 1000), 6) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + if self._verbose >= 2: + print('edit cost constants used:', self.__edit_cost_constants) + elif self.__fit_method == 'expert': # expert + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__edit_cost_constants = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__edit_cost_constants = [0.675, 0.675, 0.75, 0.425, 0.425] + else: + self.__edit_cost_constants = [3, 3, 1, 3, 3, 1] + else: + self.__edit_cost_constants = self.__init_ecc + elif self.__fit_method == 'k-graphs': + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__init_ecc = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__init_ecc = [0.675, 0.675, 0.75, 0.425, 0.425] + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + self.__init_ecc = [0, 0, 1, 1, 1, 0] + if self._dataset.node_attrs == []: + self.__init_ecc[2] = 0 + if self._dataset.edge_attrs == []: + self.__init_ecc[5] = 0 + else: + self.__init_ecc = [3, 3, 1, 3, 3, 1] + # optimize on the k-graph subset. + self.__optimize_ecm_by_kernel_distances() + elif self.__fit_method == 'whole-dataset': + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__init_ecc = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__init_ecc = [0.675, 0.675, 0.75, 0.425, 0.425] + else: + self.__init_ecc = [3, 3, 1, 3, 3, 1] + # optimizeon the whole set. + self.__optimize_ecc_by_kernel_distances() + elif self.__fit_method == 'precomputed': + pass + + + def __initialize_label_costs(self): + self.__initialize_node_label_costs() + self.__initialize_edge_label_costs() + + + def __initialize_node_label_costs(self): + # Get list of node labels. + nls = self._dataset.get_all_node_labels() + # Generate random costs. + nb_nl = int((len(nls) * (len(nls) - 1)) / 2 + 2 * len(nls)) + rand_costs = random.sample(range(1, 10 * nb_nl + 1), nb_nl) + rand_costs /= np.max(rand_costs) # @todo: maybe not needed. + self.__node_label_costs = rand_costs + + + def __initialize_edge_label_costs(self): + # Get list of edge labels. + els = self._dataset.get_all_edge_labels() + # Generate random costs. + nb_el = int((len(els) * (len(els) - 1)) / 2 + 2 * len(els)) + rand_costs = random.sample(range(1, 10 * nb_el + 1), nb_el) + rand_costs /= np.max(rand_costs) # @todo: maybe not needed. + self.__edge_label_costs = rand_costs + + + def __optimize_ecm_by_kernel_distances(self): + # compute distances in feature space. + dis_k_mat, _, _, _ = self._graph_kernel.compute_distance_matrix() + dis_k_vec = [] + for i in range(len(dis_k_mat)): + # for j in range(i, len(dis_k_mat)): + for j in range(i + 1, len(dis_k_mat)): + dis_k_vec.append(dis_k_mat[i, j]) + dis_k_vec = np.array(dis_k_vec) + + # Set GEDEnv options. +# graphs = [self.__clean_graph(g) for g in self._dataset.graphs] +# self.__edit_cost_constants = self.__init_ecc + options = self.__ged_options.copy() + options['edit_cost_constants'] = self.__edit_cost_constants # @todo: not needed. + options['node_labels'] = self._dataset.node_labels + options['edge_labels'] = self._dataset.edge_labels +# options['node_attrs'] = self._dataset.node_attrs +# options['edge_attrs'] = self._dataset.edge_attrs + options['node_label_costs'] = self.__node_label_costs + options['edge_label_costs'] = self.__edge_label_costs + + # Learner cost matrices. + # Initialize cost learner. + cml = CostMatricesLearner(edit_cost='CONSTANT', triangle_rule=False, allow_zeros=True, parallel=self.__parallel, verbose=self._verbose) # @todo + cml.set_update_params(time_limit_in_sec=self.__time_limit_in_sec, max_itrs=self.__max_itrs, max_itrs_without_update=self.__max_itrs_without_update, epsilon_residual=self.__epsilon_residual, epsilon_ec=self.__epsilon_ec) + # Run cost learner. + cml.update(dis_k_vec, self._dataset.graphs, options) + + # Get results. + results = cml.get_results() + self.__converged = results['converged'] + self.__itrs = results['itrs'] + self.__num_updates_ecs = results['num_updates_ecs'] + cost_list = results['cost_list'] + self.__node_label_costs = cost_list[-1][0:len(self.__node_label_costs)] + self.__edge_label_costs = cost_list[-1][len(self.__node_label_costs):] + + + def __gmg_bcu(self): + """ + The local search algorithm based on block coordinate update (BCU) for estimating a generalized median graph (GMG). + + Returns + ------- + None. + + """ + # Set up the ged environment. + ged_env = GEDEnv() # @todo: maybe create a ged_env as a private varible. + # gedlibpy.restart_env() + ged_env.set_edit_cost(self.__ged_options['edit_cost'], edit_cost_constants=self.__edit_cost_constants) + graphs = [self.__clean_graph(g) for g in self._dataset.graphs] + for g in graphs: + ged_env.add_nx_graph(g, '') + graph_ids = ged_env.get_all_graph_ids() + + node_labels = ged_env.get_all_node_labels() + edge_labels = ged_env.get_all_edge_labels() + node_label_costs = label_costs_to_matrix(self.__node_label_costs, len(node_labels)) + edge_label_costs = label_costs_to_matrix(self.__edge_label_costs, len(edge_labels)) + ged_env.set_label_costs(node_label_costs, edge_label_costs) + + set_median_id = ged_env.add_graph('set_median') + gen_median_id = ged_env.add_graph('gen_median') + ged_env.init(init_type=self.__ged_options['init_option']) + + # Set up the madian graph estimator. + self.__mge = MedianGraphEstimatorCML(ged_env, constant_node_costs(self.__ged_options['edit_cost'])) + self.__mge.set_refine_method(self.__ged_options['method'], self.__ged_options) + options = self.__mge_options.copy() + if not 'seed' in options: + options['seed'] = int(round(time.time() * 1000)) # @todo: may not work correctly for possible parallel usage. + options['parallel'] = self.__parallel + + # Select the GED algorithm. + self.__mge.set_options(mge_options_to_string(options)) + self.__mge.set_label_names(node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + node_attrs=self._dataset.node_attrs, + edge_attrs=self._dataset.edge_attrs) + ged_options = self.__ged_options.copy() + if self.__parallel: + ged_options['threads'] = 1 + self.__mge.set_init_method(ged_options['method'], ged_options) + self.__mge.set_descent_method(ged_options['method'], ged_options) + + # Run the estimator. + self.__mge.run(graph_ids, set_median_id, gen_median_id) + + # Get SODs. + self.__sod_set_median = self.__mge.get_sum_of_distances('initialized') + self.__sod_gen_median = self.__mge.get_sum_of_distances('converged') + + # Get median graphs. + self.__set_median = ged_env.get_nx_graph(set_median_id) + self.__gen_median = ged_env.get_nx_graph(gen_median_id) + + + def __compute_distances_to_true_median(self): + # compute distance in kernel space for set median. + kernels_to_sm, _ = self._graph_kernel.compute(self.__set_median, self._dataset.graphs, **self._kernel_options) + kernel_sm, _ = self._graph_kernel.compute(self.__set_median, self.__set_median, **self._kernel_options) + if self._kernel_options['normalize']: + kernels_to_sm = [kernels_to_sm[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_sm) for i in range(len(kernels_to_sm))] # normalize + kernel_sm = 1 + # @todo: not correct kernel value + gram_with_sm = np.concatenate((np.array([kernels_to_sm]), np.copy(self._graph_kernel.gram_matrix)), axis=0) + gram_with_sm = np.concatenate((np.array([[kernel_sm] + kernels_to_sm]).T, gram_with_sm), axis=1) + self.__k_dis_set_median = compute_k_dis(0, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_sm, withterm3=False) + + # compute distance in kernel space for generalized median. + kernels_to_gm, _ = self._graph_kernel.compute(self.__gen_median, self._dataset.graphs, **self._kernel_options) + kernel_gm, _ = self._graph_kernel.compute(self.__gen_median, self.__gen_median, **self._kernel_options) + if self._kernel_options['normalize']: + kernels_to_gm = [kernels_to_gm[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_gm) for i in range(len(kernels_to_gm))] # normalize + kernel_gm = 1 + gram_with_gm = np.concatenate((np.array([kernels_to_gm]), np.copy(self._graph_kernel.gram_matrix)), axis=0) + gram_with_gm = np.concatenate((np.array([[kernel_gm] + kernels_to_gm]).T, gram_with_gm), axis=1) + self.__k_dis_gen_median = compute_k_dis(0, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_gm, withterm3=False) + + # compute distance in kernel space for each graph in median set. + k_dis_median_set = [] + for idx in range(len(self._dataset.graphs)): + k_dis_median_set.append(compute_k_dis(idx+1, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_gm, withterm3=False)) + idx_k_dis_median_set_min = np.argmin(k_dis_median_set) + self.__k_dis_dataset = k_dis_median_set[idx_k_dis_median_set_min] + self.__best_from_dataset = self._dataset.graphs[idx_k_dis_median_set_min].copy() + + if self._verbose >= 2: + print() + print('distance in kernel space for set median:', self.__k_dis_set_median) + print('distance in kernel space for generalized median:', self.__k_dis_gen_median) + print('minimum distance in kernel space for each graph in median set:', self.__k_dis_dataset) + print('distance in kernel space for each graph in median set:', k_dis_median_set) + + +# def __clean_graph(self, G, node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]): + def __clean_graph(self, G): # @todo: this may not be needed when datafile is updated. + """ + Cleans node and edge labels and attributes of the given graph. + """ + G_new = nx.Graph(**G.graph) + for nd, attrs in G.nodes(data=True): + G_new.add_node(str(nd)) # @todo: should we keep this as str()? + for l_name in self._dataset.node_labels: + G_new.nodes[str(nd)][l_name] = str(attrs[l_name]) + for a_name in self._dataset.node_attrs: + G_new.nodes[str(nd)][a_name] = str(attrs[a_name]) + for nd1, nd2, attrs in G.edges(data=True): + G_new.add_edge(str(nd1), str(nd2)) + for l_name in self._dataset.edge_labels: + G_new.edges[str(nd1), str(nd2)][l_name] = str(attrs[l_name]) + for a_name in self._dataset.edge_attrs: + G_new.edges[str(nd1), str(nd2)][a_name] = str(attrs[a_name]) + return G_new + + + @property + def mge(self): + return self.__mge + + @property + def ged_options(self): + return self.__ged_options + + @ged_options.setter + def ged_options(self, value): + self.__ged_options = value + + + @property + def mge_options(self): + return self.__mge_options + + @mge_options.setter + def mge_options(self, value): + self.__mge_options = value + + + @property + def fit_method(self): + return self.__fit_method + + @fit_method.setter + def fit_method(self, value): + self.__fit_method = value + + + @property + def init_ecc(self): + return self.__init_ecc + + @init_ecc.setter + def init_ecc(self, value): + self.__init_ecc = value + + + @property + def set_median(self): + return self.__set_median + + + @property + def gen_median(self): + return self.__gen_median + + + @property + def best_from_dataset(self): + return self.__best_from_dataset + + + @property + def gram_matrix_unnorm(self): + return self.__gram_matrix_unnorm + + @gram_matrix_unnorm.setter + def gram_matrix_unnorm(self, value): + self.__gram_matrix_unnorm = value \ No newline at end of file From 9b529c295297d1c131df6f81da71eec0c0a9b732 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:33 +0200 Subject: [PATCH 074/728] New translations median_preimage_generator.py (French) --- .../preimage/median_preimage_generator.py | 1033 +++++++++++++++++ 1 file changed, 1033 insertions(+) create mode 100644 lang/fr/gklearn/preimage/median_preimage_generator.py diff --git a/lang/fr/gklearn/preimage/median_preimage_generator.py b/lang/fr/gklearn/preimage/median_preimage_generator.py new file mode 100644 index 0000000000..657ee4ec58 --- /dev/null +++ b/lang/fr/gklearn/preimage/median_preimage_generator.py @@ -0,0 +1,1033 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Mar 26 18:27:22 2020 + +@author: ljia +""" +import numpy as np +import time +import random +import multiprocessing +import networkx as nx +import cvxpy as cp +from gklearn.preimage import PreimageGenerator +from gklearn.preimage.utils import compute_k_dis +from gklearn.ged.util import compute_geds, ged_options_to_string +from gklearn.ged.median import MedianGraphEstimator +from gklearn.ged.median import constant_node_costs,mge_options_to_string +from gklearn.gedlib import librariesImport, gedlibpy +from gklearn.utils import Timer +from gklearn.utils.utils import get_graph_kernel_by_name + + +class MedianPreimageGenerator(PreimageGenerator): + + def __init__(self, dataset=None): + PreimageGenerator.__init__(self, dataset=dataset) + # arguments to set. + self.__mge = None + self.__ged_options = {} + self.__mge_options = {} + self.__fit_method = 'k-graphs' + self.__init_ecc = None + self.__parallel = True + self.__n_jobs = multiprocessing.cpu_count() + self.__ds_name = None + self.__time_limit_in_sec = 0 + self.__max_itrs = 100 + self.__max_itrs_without_update = 3 + self.__epsilon_residual = 0.01 + self.__epsilon_ec = 0.1 + self.__allow_zeros = False + self.__triangle_rule = True + # values to compute. + self.__runtime_optimize_ec = None + self.__runtime_generate_preimage = None + self.__runtime_total = None + self.__set_median = None + self.__gen_median = None + self.__best_from_dataset = None + self.__sod_set_median = None + self.__sod_gen_median = None + self.__k_dis_set_median = None + self.__k_dis_gen_median = None + self.__k_dis_dataset = None + self.__itrs = 0 + self.__converged = False + self.__num_updates_ecc = 0 + # values that can be set or to be computed. + self.__edit_cost_constants = [] + self.__gram_matrix_unnorm = None + self.__runtime_precompute_gm = None + + + def set_options(self, **kwargs): + self._kernel_options = kwargs.get('kernel_options', {}) + self._graph_kernel = kwargs.get('graph_kernel', None) + self._verbose = kwargs.get('verbose', 2) + self.__ged_options = kwargs.get('ged_options', {}) + self.__mge_options = kwargs.get('mge_options', {}) + self.__fit_method = kwargs.get('fit_method', 'k-graphs') + self.__init_ecc = kwargs.get('init_ecc', None) + self.__edit_cost_constants = kwargs.get('edit_cost_constants', []) + self.__parallel = kwargs.get('parallel', True) + self.__n_jobs = kwargs.get('n_jobs', multiprocessing.cpu_count()) + self.__ds_name = kwargs.get('ds_name', None) + self.__time_limit_in_sec = kwargs.get('time_limit_in_sec', 0) + self.__max_itrs = kwargs.get('max_itrs', 100) + self.__max_itrs_without_update = kwargs.get('max_itrs_without_update', 3) + self.__epsilon_residual = kwargs.get('epsilon_residual', 0.01) + self.__epsilon_ec = kwargs.get('epsilon_ec', 0.1) + self.__gram_matrix_unnorm = kwargs.get('gram_matrix_unnorm', None) + self.__runtime_precompute_gm = kwargs.get('runtime_precompute_gm', None) + self.__allow_zeros = kwargs.get('allow_zeros', False) + self.__triangle_rule = kwargs.get('triangle_rule', True) + + + def run(self): + self._graph_kernel = get_graph_kernel_by_name(self._kernel_options['name'], + node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + node_attrs=self._dataset.node_attrs, + edge_attrs=self._dataset.edge_attrs, + ds_infos=self._dataset.get_dataset_infos(keys=['directed']), + kernel_options=self._kernel_options) + + # record start time. + start = time.time() + + # 1. precompute gram matrix. + if self.__gram_matrix_unnorm is None: + gram_matrix, run_time = self._graph_kernel.compute(self._dataset.graphs, **self._kernel_options) + self.__gram_matrix_unnorm = self._graph_kernel.gram_matrix_unnorm + end_precompute_gm = time.time() + self.__runtime_precompute_gm = end_precompute_gm - start + else: + if self.__runtime_precompute_gm is None: + raise Exception('Parameter "runtime_precompute_gm" must be given when using pre-computed Gram matrix.') + self._graph_kernel.gram_matrix_unnorm = self.__gram_matrix_unnorm + if self._kernel_options['normalize']: + self._graph_kernel.gram_matrix = self._graph_kernel.normalize_gm(np.copy(self.__gram_matrix_unnorm)) + else: + self._graph_kernel.gram_matrix = np.copy(self.__gram_matrix_unnorm) + end_precompute_gm = time.time() + start -= self.__runtime_precompute_gm + + if self.__fit_method != 'k-graphs' and self.__fit_method != 'whole-dataset': + start = time.time() + self.__runtime_precompute_gm = 0 + end_precompute_gm = start + + # 2. optimize edit cost constants. + self.__optimize_edit_cost_constants() + end_optimize_ec = time.time() + self.__runtime_optimize_ec = end_optimize_ec - end_precompute_gm + + # 3. compute set median and gen median using optimized edit costs. + if self._verbose >= 2: + print('\nstart computing set median and gen median using optimized edit costs...\n') + self.__gmg_bcu() + end_generate_preimage = time.time() + self.__runtime_generate_preimage = end_generate_preimage - end_optimize_ec + self.__runtime_total = end_generate_preimage - start + if self._verbose >= 2: + print('medians computed.') + print('SOD of the set median: ', self.__sod_set_median) + print('SOD of the generalized median: ', self.__sod_gen_median) + + # 4. compute kernel distances to the true median. + if self._verbose >= 2: + print('\nstart computing distances to true median....\n') + self.__compute_distances_to_true_median() + + # 5. print out results. + if self._verbose: + print() + print('================================================================================') + print('Finished generation of preimages.') + print('--------------------------------------------------------------------------------') + print('The optimized edit cost constants:', self.__edit_cost_constants) + print('SOD of the set median:', self.__sod_set_median) + print('SOD of the generalized median:', self.__sod_gen_median) + print('Distance in kernel space for set median:', self.__k_dis_set_median) + print('Distance in kernel space for generalized median:', self.__k_dis_gen_median) + print('Minimum distance in kernel space for each graph in median set:', self.__k_dis_dataset) + print('Time to pre-compute Gram matrix:', self.__runtime_precompute_gm) + print('Time to optimize edit costs:', self.__runtime_optimize_ec) + print('Time to generate pre-images:', self.__runtime_generate_preimage) + print('Total time:', self.__runtime_total) + print('Total number of iterations for optimizing:', self.__itrs) + print('Total number of updating edit costs:', self.__num_updates_ecc) + print('Is optimization of edit costs converged:', self.__converged) + print('================================================================================') + print() + + + def get_results(self): + results = {} + results['edit_cost_constants'] = self.__edit_cost_constants + results['runtime_precompute_gm'] = self.__runtime_precompute_gm + results['runtime_optimize_ec'] = self.__runtime_optimize_ec + results['runtime_generate_preimage'] = self.__runtime_generate_preimage + results['runtime_total'] = self.__runtime_total + results['sod_set_median'] = self.__sod_set_median + results['sod_gen_median'] = self.__sod_gen_median + results['k_dis_set_median'] = self.__k_dis_set_median + results['k_dis_gen_median'] = self.__k_dis_gen_median + results['k_dis_dataset'] = self.__k_dis_dataset + results['itrs'] = self.__itrs + results['converged'] = self.__converged + results['num_updates_ecc'] = self.__num_updates_ecc + results['mge'] = {} + results['mge']['num_decrease_order'] = self.__mge.get_num_times_order_decreased() + results['mge']['num_increase_order'] = self.__mge.get_num_times_order_increased() + results['mge']['num_converged_descents'] = self.__mge.get_num_converged_descents() + return results + + + def __optimize_edit_cost_constants(self): + """fit edit cost constants. + """ + if self.__fit_method == 'random': # random + if self.__ged_options['edit_cost'] == 'LETTER': + self.__edit_cost_constants = random.sample(range(1, 1000), 3) + self.__edit_cost_constants = [item * 0.001 for item in self.__edit_cost_constants] + elif self.__ged_options['edit_cost'] == 'LETTER2': + random.seed(time.time()) + self.__edit_cost_constants = random.sample(range(1, 1000), 5) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + self.__edit_cost_constants = random.sample(range(1, 1000), 6) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + if self._dataset.node_attrs == []: + self.__edit_cost_constants[2] = 0 + if self._dataset.edge_attrs == []: + self.__edit_cost_constants[5] = 0 + else: + self.__edit_cost_constants = random.sample(range(1, 1000), 6) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + if self._verbose >= 2: + print('edit cost constants used:', self.__edit_cost_constants) + elif self.__fit_method == 'expert': # expert + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__edit_cost_constants = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__edit_cost_constants = [0.675, 0.675, 0.75, 0.425, 0.425] + else: + self.__edit_cost_constants = [3, 3, 1, 3, 3, 1] + else: + self.__edit_cost_constants = self.__init_ecc + elif self.__fit_method == 'k-graphs': + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__init_ecc = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__init_ecc = [0.675, 0.675, 0.75, 0.425, 0.425] + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + self.__init_ecc = [0, 0, 1, 1, 1, 0] + if self._dataset.node_attrs == []: + self.__init_ecc[2] = 0 + if self._dataset.edge_attrs == []: + self.__init_ecc[5] = 0 + else: + self.__init_ecc = [3, 3, 1, 3, 3, 1] + # optimize on the k-graph subset. + self.__optimize_ecc_by_kernel_distances() + elif self.__fit_method == 'whole-dataset': + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__init_ecc = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__init_ecc = [0.675, 0.675, 0.75, 0.425, 0.425] + else: + self.__init_ecc = [3, 3, 1, 3, 3, 1] + # optimizeon the whole set. + self.__optimize_ecc_by_kernel_distances() + elif self.__fit_method == 'precomputed': + pass + + + def __optimize_ecc_by_kernel_distances(self): + # compute distances in feature space. + dis_k_mat, _, _, _ = self._graph_kernel.compute_distance_matrix() + dis_k_vec = [] + for i in range(len(dis_k_mat)): + # for j in range(i, len(dis_k_mat)): + for j in range(i + 1, len(dis_k_mat)): + dis_k_vec.append(dis_k_mat[i, j]) + dis_k_vec = np.array(dis_k_vec) + + # init ged. + if self._verbose >= 2: + print('\ninitial:') + time0 = time.time() + graphs = [self.__clean_graph(g) for g in self._dataset.graphs] + self.__edit_cost_constants = self.__init_ecc + options = self.__ged_options.copy() + options['edit_cost_constants'] = self.__edit_cost_constants # @todo + options['node_labels'] = self._dataset.node_labels + options['edge_labels'] = self._dataset.edge_labels + options['node_attrs'] = self._dataset.node_attrs + options['edge_attrs'] = self._dataset.edge_attrs + ged_vec_init, ged_mat, n_edit_operations = compute_geds(graphs, options=options, parallel=self.__parallel, verbose=(self._verbose > 1)) + residual_list = [np.sqrt(np.sum(np.square(np.array(ged_vec_init) - dis_k_vec)))] + time_list = [time.time() - time0] + edit_cost_list = [self.__init_ecc] + nb_cost_mat = np.array(n_edit_operations) + nb_cost_mat_list = [nb_cost_mat] + if self._verbose >= 2: + print('Current edit cost constants:', self.__edit_cost_constants) + print('Residual list:', residual_list) + + # run iteration from initial edit costs. + self.__converged = False + itrs_without_update = 0 + self.__itrs = 0 + self.__num_updates_ecc = 0 + timer = Timer(self.__time_limit_in_sec) + while not self.__termination_criterion_met(self.__converged, timer, self.__itrs, itrs_without_update): + if self._verbose >= 2: + print('\niteration', self.__itrs + 1) + time0 = time.time() + # "fit" geds to distances in feature space by tuning edit costs using theLeast Squares Method. +# np.savez('results/xp_fit_method/fit_data_debug' + str(self.__itrs) + '.gm', +# nb_cost_mat=nb_cost_mat, dis_k_vec=dis_k_vec, +# n_edit_operations=n_edit_operations, ged_vec_init=ged_vec_init, +# ged_mat=ged_mat) + self.__edit_cost_constants, _ = self.__update_ecc(nb_cost_mat, dis_k_vec) + for i in range(len(self.__edit_cost_constants)): + if -1e-9 <= self.__edit_cost_constants[i] <= 1e-9: + self.__edit_cost_constants[i] = 0 + if self.__edit_cost_constants[i] < 0: + raise ValueError('The edit cost is negative.') + # for i in range(len(self.__edit_cost_constants)): + # if self.__edit_cost_constants[i] < 0: + # self.__edit_cost_constants[i] = 0 + + # compute new GEDs and numbers of edit operations. + options = self.__ged_options.copy() # np.array([self.__edit_cost_constants[0], self.__edit_cost_constants[1], 0.75]) + options['edit_cost_constants'] = self.__edit_cost_constants # @todo + options['node_labels'] = self._dataset.node_labels + options['edge_labels'] = self._dataset.edge_labels + options['node_attrs'] = self._dataset.node_attrs + options['edge_attrs'] = self._dataset.edge_attrs + ged_vec, ged_mat, n_edit_operations = compute_geds(graphs, options=options, parallel=self.__parallel, verbose=(self._verbose > 1)) + residual_list.append(np.sqrt(np.sum(np.square(np.array(ged_vec) - dis_k_vec)))) + time_list.append(time.time() - time0) + edit_cost_list.append(self.__edit_cost_constants) + nb_cost_mat = np.array(n_edit_operations) + nb_cost_mat_list.append(nb_cost_mat) + + # check convergency. + ec_changed = False + for i, cost in enumerate(self.__edit_cost_constants): + if cost == 0: + if edit_cost_list[-2][i] > self.__epsilon_ec: + ec_changed = True + break + elif abs(cost - edit_cost_list[-2][i]) / cost > self.__epsilon_ec: + ec_changed = True + break +# if abs(cost - edit_cost_list[-2][i]) > self.__epsilon_ec: +# ec_changed = True +# break + residual_changed = False + if residual_list[-1] == 0: + if residual_list[-2] > self.__epsilon_residual: + residual_changed = True + elif abs(residual_list[-1] - residual_list[-2]) / residual_list[-1] > self.__epsilon_residual: + residual_changed = True + self.__converged = not (ec_changed or residual_changed) + if self.__converged: + itrs_without_update += 1 + else: + itrs_without_update = 0 + self.__num_updates_ecc += 1 + + # print current states. + if self._verbose >= 2: + print() + print('-------------------------------------------------------------------------') + print('States of iteration', self.__itrs + 1) + print('-------------------------------------------------------------------------') +# print('Time spend:', self.__runtime_optimize_ec) + print('Total number of iterations for optimizing:', self.__itrs + 1) + print('Total number of updating edit costs:', self.__num_updates_ecc) + print('Was optimization of edit costs converged:', self.__converged) + print('Did edit costs change:', ec_changed) + print('Did residual change:', residual_changed) + print('Iterations without update:', itrs_without_update) + print('Current edit cost constants:', self.__edit_cost_constants) + print('Residual list:', residual_list) + print('-------------------------------------------------------------------------') + + self.__itrs += 1 + + + def __termination_criterion_met(self, converged, timer, itr, itrs_without_update): + if timer.expired() or (itr >= self.__max_itrs if self.__max_itrs >= 0 else False): +# if self.__state == AlgorithmState.TERMINATED: +# self.__state = AlgorithmState.INITIALIZED + return True + return converged or (itrs_without_update > self.__max_itrs_without_update if self.__max_itrs_without_update >= 0 else False) + + + def __update_ecc(self, nb_cost_mat, dis_k_vec, rw_constraints='inequality'): + # if self.__ds_name == 'Letter-high': + if self.__ged_options['edit_cost'] == 'LETTER': + raise Exception('Cannot compute for cost "LETTER".') + pass + # # method 1: set alpha automatically, just tune c_vir and c_eir by + # # LMS using cvxpy. + # alpha = 0.5 + # coeff = 100 # np.max(alpha * nb_cost_mat[:,4] / dis_k_vec) + ## if np.count_nonzero(nb_cost_mat[:,4]) == 0: + ## alpha = 0.75 + ## else: + ## alpha = np.min([dis_k_vec / c_vs for c_vs in nb_cost_mat[:,4] if c_vs != 0]) + ## alpha = alpha * 0.99 + # param_vir = alpha * (nb_cost_mat[:,0] + nb_cost_mat[:,1]) + # param_eir = (1 - alpha) * (nb_cost_mat[:,4] + nb_cost_mat[:,5]) + # nb_cost_mat_new = np.column_stack((param_vir, param_eir)) + # dis_new = coeff * dis_k_vec - alpha * nb_cost_mat[:,3] + # + # x = cp.Variable(nb_cost_mat_new.shape[1]) + # cost = cp.sum_squares(nb_cost_mat_new * x - dis_new) + # constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]] + # prob = cp.Problem(cp.Minimize(cost), constraints) + # prob.solve() + # edit_costs_new = x.value + # edit_costs_new = np.array([edit_costs_new[0], edit_costs_new[1], alpha]) + # residual = np.sqrt(prob.value) + + # # method 2: tune c_vir, c_eir and alpha by nonlinear programming by + # # scipy.optimize.minimize. + # w0 = nb_cost_mat[:,0] + nb_cost_mat[:,1] + # w1 = nb_cost_mat[:,4] + nb_cost_mat[:,5] + # w2 = nb_cost_mat[:,3] + # w3 = dis_k_vec + # func_min = lambda x: np.sum((w0 * x[0] * x[3] + w1 * x[1] * (1 - x[2]) \ + # + w2 * x[2] - w3 * x[3]) ** 2) + # bounds = ((0, None), (0., None), (0.5, 0.5), (0, None)) + # res = minimize(func_min, [0.9, 1.7, 0.75, 10], bounds=bounds) + # edit_costs_new = res.x[0:3] + # residual = res.fun + + # method 3: tune c_vir, c_eir and alpha by nonlinear programming using cvxpy. + + + # # method 4: tune c_vir, c_eir and alpha by QP function + # # scipy.optimize.least_squares. An initial guess is required. + # w0 = nb_cost_mat[:,0] + nb_cost_mat[:,1] + # w1 = nb_cost_mat[:,4] + nb_cost_mat[:,5] + # w2 = nb_cost_mat[:,3] + # w3 = dis_k_vec + # func = lambda x: (w0 * x[0] * x[3] + w1 * x[1] * (1 - x[2]) \ + # + w2 * x[2] - w3 * x[3]) ** 2 + # res = optimize.root(func, [0.9, 1.7, 0.75, 100]) + # edit_costs_new = res.x + # residual = None + elif self.__ged_options['edit_cost'] == 'LETTER2': + # # 1. if c_vi != c_vr, c_ei != c_er. + # nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + # x = cp.Variable(nb_cost_mat_new.shape[1]) + # cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + ## # 1.1 no constraints. + ## constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]] + # # 1.2 c_vs <= c_vi + c_vr. + # constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + # np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + ## # 2. if c_vi == c_vr, c_ei == c_er. + ## nb_cost_mat_new = nb_cost_mat[:,[0,3,4]] + ## nb_cost_mat_new[:,0] += nb_cost_mat[:,1] + ## nb_cost_mat_new[:,2] += nb_cost_mat[:,5] + ## x = cp.Variable(nb_cost_mat_new.shape[1]) + ## cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + ## # 2.1 no constraints. + ## constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]] + ### # 2.2 c_vs <= c_vi + c_vr. + ### constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + ### np.array([2.0, -1.0, 0.0]).T@x >= 0.0] + # + # prob = cp.Problem(cp.Minimize(cost_fun), constraints) + # prob.solve() + # edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]] + # edit_costs_new = np.array(edit_costs_new) + # residual = np.sqrt(prob.value) + if not self.__triangle_rule and self.__allow_zeros: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif self.__triangle_rule and self.__allow_zeros: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01, + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif not self.__triangle_rule and not self.__allow_zeros: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + prob.solve() + edit_costs_new = x.value + residual = np.sqrt(prob.value) + # elif method == 'inequality_modified': + # # c_vs <= c_vi + c_vr. + # nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + # x = cp.Variable(nb_cost_mat_new.shape[1]) + # cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + # constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + # np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + # prob = cp.Problem(cp.Minimize(cost_fun), constraints) + # prob.solve() + # # use same costs for insertion and removal rather than the fitted costs. + # edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]] + # edit_costs_new = np.array(edit_costs_new) + # residual = np.sqrt(prob.value) + elif self.__triangle_rule and not self.__allow_zeros: + # c_vs <= c_vi + c_vr. + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif rw_constraints == '2constraints': # @todo: rearrange it later. + # c_vs <= c_vi + c_vr and c_vi == c_vr, c_ei == c_er. + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0, + np.array([1.0, -1.0, 0.0, 0.0, 0.0]).T@x == 0.0, + np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + prob.solve() + edit_costs_new = x.value + residual = np.sqrt(prob.value) + + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + is_n_attr = np.count_nonzero(nb_cost_mat[:,2]) + is_e_attr = np.count_nonzero(nb_cost_mat[:,5]) + + if self.__ds_name == 'SYNTHETICnew': # @todo: rearrenge this later. + # nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + nb_cost_mat_new = nb_cost_mat[:,[2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + # constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + # np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0] + # constraints = [x >= [0.0001 for i in range(nb_cost_mat_new.shape[1])]] + constraints = [x >= [0.0001 for i in range(nb_cost_mat_new.shape[1])], + np.array([0.0, 1.0, -1.0]).T@x == 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + prob.solve() + # print(x.value) + edit_costs_new = np.concatenate((np.array([0.0, 0.0]), x.value, + np.array([0.0]))) + residual = np.sqrt(prob.value) + + elif not self.__triangle_rule and self.__allow_zeros: + if is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif is_n_attr and not is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value, np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:])) + residual = np.sqrt(prob.value) + else: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), + x.value[2:], np.array([0.0]))) + residual = np.sqrt(prob.value) + elif self.__triangle_rule and self.__allow_zeros: + if is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif is_n_attr and not is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01, + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value, np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:])) + residual = np.sqrt(prob.value) + else: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), + x.value[2:], np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not self.__triangle_rule and not self.__allow_zeros: + if is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif is_n_attr and not is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value, np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:])) + residual = np.sqrt(prob.value) + else: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), + x.value[2:], np.array([0.0]))) + residual = np.sqrt(prob.value) + elif self.__triangle_rule and not self.__allow_zeros: + # c_vs <= c_vi + c_vr. + if is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif is_n_attr and not is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value, np.array([0.0]))) + residual = np.sqrt(prob.value) + elif not is_n_attr and is_e_attr: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], + np.array([0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:])) + residual = np.sqrt(prob.value) + else: + nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]] + x = cp.Variable(nb_cost_mat_new.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat_new @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), + x.value[2:], np.array([0.0]))) + residual = np.sqrt(prob.value) + + elif self.__ged_options['edit_cost'] == 'CONSTANT': # @todo: node/edge may not labeled. + if not self.__triangle_rule and self.__allow_zeros: + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif self.__triangle_rule and self.__allow_zeros: + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])], + np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01, + np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01, + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif not self.__triangle_rule and not self.__allow_zeros: + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])]] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + elif self.__triangle_rule and not self.__allow_zeros: + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])], + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + else: + raise Exception('The edit cost "', self.__ged_options['edit_cost'], '" is not supported for update progress.') + # # method 1: simple least square method. + # edit_costs_new, residual, _, _ = np.linalg.lstsq(nb_cost_mat, dis_k_vec, + # rcond=None) + + # # method 2: least square method with x_i >= 0. + # edit_costs_new, residual = optimize.nnls(nb_cost_mat, dis_k_vec) + + # method 3: solve as a quadratic program with constraints. + # P = np.dot(nb_cost_mat.T, nb_cost_mat) + # q_T = -2 * np.dot(dis_k_vec.T, nb_cost_mat) + # G = -1 * np.identity(nb_cost_mat.shape[1]) + # h = np.array([0 for i in range(nb_cost_mat.shape[1])]) + # A = np.array([1 for i in range(nb_cost_mat.shape[1])]) + # b = 1 + # x = cp.Variable(nb_cost_mat.shape[1]) + # prob = cp.Problem(cp.Minimize(cp.quad_form(x, P) + q_T@x), + # [G@x <= h]) + # prob.solve() + # edit_costs_new = x.value + # residual = prob.value - np.dot(dis_k_vec.T, dis_k_vec) + + # G = -1 * np.identity(nb_cost_mat.shape[1]) + # h = np.array([0 for i in range(nb_cost_mat.shape[1])]) + x = cp.Variable(nb_cost_mat.shape[1]) + cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) + constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])], + # np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] + np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, + np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] + prob = cp.Problem(cp.Minimize(cost_fun), constraints) + self.__execute_cvx(prob) + edit_costs_new = x.value + residual = np.sqrt(prob.value) + + # method 4: + + return edit_costs_new, residual + + + def __execute_cvx(self, prob): + try: + prob.solve(verbose=(self._verbose>=2)) + except MemoryError as error0: + if self._verbose >= 2: + print('\nUsing solver "OSQP" caused a memory error.') + print('the original error message is\n', error0) + print('solver status: ', prob.status) + print('trying solver "CVXOPT" instead...\n') + try: + prob.solve(solver=cp.CVXOPT, verbose=(self._verbose>=2)) + except Exception as error1: + if self._verbose >= 2: + print('\nAn error occured when using solver "CVXOPT".') + print('the original error message is\n', error1) + print('solver status: ', prob.status) + print('trying solver "MOSEK" instead. Notice this solver is commercial and a lisence is required.\n') + prob.solve(solver=cp.MOSEK, verbose=(self._verbose>=2)) + else: + if self._verbose >= 2: + print('solver status: ', prob.status) + else: + if self._verbose >= 2: + print('solver status: ', prob.status) + if self._verbose >= 2: + print() + + + def __gmg_bcu(self): + """ + The local search algorithm based on block coordinate update (BCU) for estimating a generalized median graph (GMG). + + Returns + ------- + None. + + """ + # Set up the ged environment. + ged_env = gedlibpy.GEDEnv() # @todo: maybe create a ged_env as a private varible. + # gedlibpy.restart_env() + ged_env.set_edit_cost(self.__ged_options['edit_cost'], edit_cost_constant=self.__edit_cost_constants) + graphs = [self.__clean_graph(g) for g in self._dataset.graphs] + for g in graphs: + ged_env.add_nx_graph(g, '') + graph_ids = ged_env.get_all_graph_ids() + set_median_id = ged_env.add_graph('set_median') + gen_median_id = ged_env.add_graph('gen_median') + ged_env.init(init_option=self.__ged_options['init_option']) + + # Set up the madian graph estimator. + self.__mge = MedianGraphEstimator(ged_env, constant_node_costs(self.__ged_options['edit_cost'])) + self.__mge.set_refine_method(self.__ged_options['method'], ged_options_to_string(self.__ged_options)) + options = self.__mge_options.copy() + if not 'seed' in options: + options['seed'] = int(round(time.time() * 1000)) # @todo: may not work correctly for possible parallel usage. + options['parallel'] = self.__parallel + + # Select the GED algorithm. + self.__mge.set_options(mge_options_to_string(options)) + self.__mge.set_label_names(node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + node_attrs=self._dataset.node_attrs, + edge_attrs=self._dataset.edge_attrs) + ged_options = self.__ged_options.copy() + if self.__parallel: + ged_options['threads'] = 1 + self.__mge.set_init_method(ged_options['method'], ged_options_to_string(ged_options)) + self.__mge.set_descent_method(ged_options['method'], ged_options_to_string(ged_options)) + + # Run the estimator. + self.__mge.run(graph_ids, set_median_id, gen_median_id) + + # Get SODs. + self.__sod_set_median = self.__mge.get_sum_of_distances('initialized') + self.__sod_gen_median = self.__mge.get_sum_of_distances('converged') + + # Get median graphs. + self.__set_median = ged_env.get_nx_graph(set_median_id) + self.__gen_median = ged_env.get_nx_graph(gen_median_id) + + + def __compute_distances_to_true_median(self): + # compute distance in kernel space for set median. + kernels_to_sm, _ = self._graph_kernel.compute(self.__set_median, self._dataset.graphs, **self._kernel_options) + kernel_sm, _ = self._graph_kernel.compute(self.__set_median, self.__set_median, **self._kernel_options) + if self._kernel_options['normalize']: + kernels_to_sm = [kernels_to_sm[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_sm) for i in range(len(kernels_to_sm))] # normalize + kernel_sm = 1 + # @todo: not correct kernel value + gram_with_sm = np.concatenate((np.array([kernels_to_sm]), np.copy(self._graph_kernel.gram_matrix)), axis=0) + gram_with_sm = np.concatenate((np.array([[kernel_sm] + kernels_to_sm]).T, gram_with_sm), axis=1) + self.__k_dis_set_median = compute_k_dis(0, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_sm, withterm3=False) + + # compute distance in kernel space for generalized median. + kernels_to_gm, _ = self._graph_kernel.compute(self.__gen_median, self._dataset.graphs, **self._kernel_options) + kernel_gm, _ = self._graph_kernel.compute(self.__gen_median, self.__gen_median, **self._kernel_options) + if self._kernel_options['normalize']: + kernels_to_gm = [kernels_to_gm[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_gm) for i in range(len(kernels_to_gm))] # normalize + kernel_gm = 1 + gram_with_gm = np.concatenate((np.array([kernels_to_gm]), np.copy(self._graph_kernel.gram_matrix)), axis=0) + gram_with_gm = np.concatenate((np.array([[kernel_gm] + kernels_to_gm]).T, gram_with_gm), axis=1) + self.__k_dis_gen_median = compute_k_dis(0, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_gm, withterm3=False) + + # compute distance in kernel space for each graph in median set. + k_dis_median_set = [] + for idx in range(len(self._dataset.graphs)): + k_dis_median_set.append(compute_k_dis(idx+1, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_gm, withterm3=False)) + idx_k_dis_median_set_min = np.argmin(k_dis_median_set) + self.__k_dis_dataset = k_dis_median_set[idx_k_dis_median_set_min] + self.__best_from_dataset = self._dataset.graphs[idx_k_dis_median_set_min].copy() + + if self._verbose >= 2: + print() + print('distance in kernel space for set median:', self.__k_dis_set_median) + print('distance in kernel space for generalized median:', self.__k_dis_gen_median) + print('minimum distance in kernel space for each graph in median set:', self.__k_dis_dataset) + print('distance in kernel space for each graph in median set:', k_dis_median_set) + + +# def __clean_graph(self, G, node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]): + def __clean_graph(self, G): # @todo: this may not be needed when datafile is updated. + """ + Cleans node and edge labels and attributes of the given graph. + """ + G_new = nx.Graph(**G.graph) + for nd, attrs in G.nodes(data=True): + G_new.add_node(str(nd)) # @todo: should we keep this as str()? + for l_name in self._dataset.node_labels: + G_new.nodes[str(nd)][l_name] = str(attrs[l_name]) + for a_name in self._dataset.node_attrs: + G_new.nodes[str(nd)][a_name] = str(attrs[a_name]) + for nd1, nd2, attrs in G.edges(data=True): + G_new.add_edge(str(nd1), str(nd2)) + for l_name in self._dataset.edge_labels: + G_new.edges[str(nd1), str(nd2)][l_name] = str(attrs[l_name]) + for a_name in self._dataset.edge_attrs: + G_new.edges[str(nd1), str(nd2)][a_name] = str(attrs[a_name]) + return G_new + + + @property + def mge(self): + return self.__mge + + @property + def ged_options(self): + return self.__ged_options + + @ged_options.setter + def ged_options(self, value): + self.__ged_options = value + + + @property + def mge_options(self): + return self.__mge_options + + @mge_options.setter + def mge_options(self, value): + self.__mge_options = value + + + @property + def fit_method(self): + return self.__fit_method + + @fit_method.setter + def fit_method(self, value): + self.__fit_method = value + + + @property + def init_ecc(self): + return self.__init_ecc + + @init_ecc.setter + def init_ecc(self, value): + self.__init_ecc = value + + + @property + def set_median(self): + return self.__set_median + + + @property + def gen_median(self): + return self.__gen_median + + + @property + def best_from_dataset(self): + return self.__best_from_dataset + + + @property + def gram_matrix_unnorm(self): + return self.__gram_matrix_unnorm + + @gram_matrix_unnorm.setter + def gram_matrix_unnorm(self, value): + self.__gram_matrix_unnorm = value \ No newline at end of file From 6cb561739f4cc664a75df084727a11b3069cb556 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:34 +0200 Subject: [PATCH 075/728] New translations kernel_knn_cv.py (French) --- lang/fr/gklearn/preimage/kernel_knn_cv.py | 418 ++++++++++++++++++++++ 1 file changed, 418 insertions(+) create mode 100644 lang/fr/gklearn/preimage/kernel_knn_cv.py diff --git a/lang/fr/gklearn/preimage/kernel_knn_cv.py b/lang/fr/gklearn/preimage/kernel_knn_cv.py new file mode 100644 index 0000000000..073fa31d4b --- /dev/null +++ b/lang/fr/gklearn/preimage/kernel_knn_cv.py @@ -0,0 +1,418 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue May 12 12:52:15 2020 + +@author: ljia +""" +import numpy as np +import csv +import os +import os.path +from gklearn.utils import Dataset +from sklearn.model_selection import ShuffleSplit +from gklearn.preimage import MedianPreimageGenerator +from gklearn.utils import normalize_gram_matrix, compute_distance_matrix +from gklearn.preimage.utils import get_same_item_indices +from gklearn.utils.knn import knn_classification +from gklearn.preimage.utils import compute_k_dis + + +def kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=True, load_gm='auto', dir_save='', irrelevant_labels=None, edge_required=False, cut_range=None): + + # 1. get dataset. + print('1. getting dataset...') + dataset_all = Dataset() + dataset_all.load_predefined_dataset(ds_name) + dataset_all.trim_dataset(edge_required=edge_required) + if irrelevant_labels is not None: + dataset_all.remove_labels(**irrelevant_labels) + if cut_range is not None: + dataset_all.cut_graphs(cut_range) + + if save_results: + # create result files. + print('creating output files...') + fn_output_detail, fn_output_summary = __init_output_file_knn(ds_name, kernel_options['name'], mpg_options['fit_method'], dir_save) + else: + fn_output_detail, fn_output_summary = None, None + + # 2. compute/load Gram matrix a priori. + print('2. computing/loading Gram matrix...') + gram_matrix_unnorm, time_precompute_gm = __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all) + + # 3. perform k-nn CV. + print('3. performing k-nn CV...') + if train_examples == 'k-graphs' or train_examples == 'expert' or train_examples == 'random': + __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary) + + elif train_examples == 'best-dataset': + __kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary) + + elif train_examples == 'trainset': + __kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary) + + print('\ncomplete.\n') + + +def __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary): + Gn = dataset_all.graphs + y_all = dataset_all.targets + n_neighbors, n_splits, test_size = knn_options['n_neighbors'], knn_options['n_splits'], knn_options['test_size'] + + # get shuffles. + train_indices, test_indices, train_nums, y_app = __get_shuffles(y_all, n_splits, test_size) + + accuracies = [[], [], []] + for trial in range(len(train_indices)): + print('\ntrial =', trial) + + train_index = train_indices[trial] + test_index = test_indices[trial] + G_app = [Gn[i] for i in train_index] + G_test = [Gn[i] for i in test_index] + y_test = [y_all[i] for i in test_index] + gm_unnorm_trial = gram_matrix_unnorm[train_index,:][:,train_index].copy() + + # compute pre-images for each class. + medians = [[], [], []] + train_nums_tmp = [0] + train_nums + print('\ncomputing pre-image for each class...\n') + for i_class in range(len(train_nums_tmp) - 1): + print(i_class + 1, 'of', len(train_nums_tmp) - 1, 'classes:') + i_start = int(np.sum(train_nums_tmp[0:i_class + 1])) + i_end = i_start + train_nums_tmp[i_class + 1] + median_set = G_app[i_start:i_end] + + dataset = dataset_all.copy() + dataset.load_graphs([g.copy() for g in median_set], targets=None) + mge_options['update_order'] = True + mpg_options['gram_matrix_unnorm'] = gm_unnorm_trial[i_start:i_end,i_start:i_end].copy() + mpg_options['runtime_precompute_gm'] = 0 + set_median, gen_median_uo = __generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options) + mge_options['update_order'] = False + mpg_options['gram_matrix_unnorm'] = gm_unnorm_trial[i_start:i_end,i_start:i_end].copy() + mpg_options['runtime_precompute_gm'] = 0 + _, gen_median = __generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options) + medians[0].append(set_median) + medians[1].append(gen_median) + medians[2].append(gen_median_uo) + + # for each set of medians. + print('\nperforming k-nn...') + for i_app, G_app in enumerate(medians): + # compute dis_mat between medians. + dataset = dataset_all.copy() + dataset.load_graphs([g.copy() for g in G_app], targets=None) + gm_app_unnorm, _ = __compute_gram_matrix_unnorm(dataset, kernel_options.copy()) + + # compute the entire Gram matrix. + graph_kernel = __get_graph_kernel(dataset.copy(), kernel_options.copy()) + kernels_to_medians = [] + for g in G_app: + kernels_to_median, _ = graph_kernel.compute(g, G_test, **kernel_options.copy()) + kernels_to_medians.append(kernels_to_median) + kernels_to_medians = np.array(kernels_to_medians) + gm_all = np.concatenate((gm_app_unnorm, kernels_to_medians), axis=1) + gm_all = np.concatenate((gm_all, np.concatenate((kernels_to_medians.T, gram_matrix_unnorm[test_index,:][:,test_index].copy()), axis=1)), axis=0) + + gm_all = normalize_gram_matrix(gm_all.copy()) + dis_mat, _, _, _ = compute_distance_matrix(gm_all) + + N = len(G_app) + + d_app = dis_mat[range(N),:][:,range(N)].copy() + + d_test = np.zeros((N, len(test_index))) + for i in range(N): + for j in range(len(test_index)): + d_test[i, j] = dis_mat[i, j] + + accuracies[i_app].append(knn_classification(d_app, d_test, y_app, y_test, n_neighbors, verbose=True, text=train_examples)) + + # write result detail. + if save_results: + f_detail = open(dir_save + fn_output_detail, 'a') + print('writing results to files...') + for i, median_type in enumerate(['set-median', 'gen median', 'gen median uo']): + csv.writer(f_detail).writerow([ds_name, kernel_options['name'], + train_examples + ': ' + median_type, trial, + knn_options['n_neighbors'], + len(gm_all), knn_options['test_size'], + accuracies[i][-1][0], accuracies[i][-1][1]]) + f_detail.close() + + results = {} + results['ave_perf_train'] = [np.mean([i[0] for i in j], axis=0) for j in accuracies] + results['std_perf_train'] = [np.std([i[0] for i in j], axis=0, ddof=1) for j in accuracies] + results['ave_perf_test'] = [np.mean([i[1] for i in j], axis=0) for j in accuracies] + results['std_perf_test'] = [np.std([i[1] for i in j], axis=0, ddof=1) for j in accuracies] + + # write result summary for each letter. + if save_results: + f_summary = open(dir_save + fn_output_summary, 'a') + for i, median_type in enumerate(['set-median', 'gen median', 'gen median uo']): + csv.writer(f_summary).writerow([ds_name, kernel_options['name'], + train_examples + ': ' + median_type, + knn_options['n_neighbors'], + knn_options['test_size'], results['ave_perf_train'][i], + results['ave_perf_test'][i], results['std_perf_train'][i], + results['std_perf_test'][i], time_precompute_gm]) + f_summary.close() + + +def __kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary): + Gn = dataset_all.graphs + y_all = dataset_all.targets + n_neighbors, n_splits, test_size = knn_options['n_neighbors'], knn_options['n_splits'], knn_options['test_size'] + + # get shuffles. + train_indices, test_indices, train_nums, y_app = __get_shuffles(y_all, n_splits, test_size) + + accuracies = [] + for trial in range(len(train_indices)): + print('\ntrial =', trial) + + train_index = train_indices[trial] + test_index = test_indices[trial] + G_app = [Gn[i] for i in train_index] + G_test = [Gn[i] for i in test_index] + y_test = [y_all[i] for i in test_index] + gm_unnorm_trial = gram_matrix_unnorm[train_index,:][:,train_index].copy() + + # get best graph from trainset according to distance in kernel space for each class. + best_graphs = [] + train_nums_tmp = [0] + train_nums + print('\ngetting best graph from trainset for each class...') + for i_class in range(len(train_nums_tmp) - 1): + print(i_class + 1, 'of', len(train_nums_tmp) - 1, 'classes.') + i_start = int(np.sum(train_nums_tmp[0:i_class + 1])) + i_end = i_start + train_nums_tmp[i_class + 1] + G_class = G_app[i_start:i_end] + gm_unnorm_class = gm_unnorm_trial[i_start:i_end,i_start:i_end] + gm_class = normalize_gram_matrix(gm_unnorm_class.copy()) + + k_dis_list = [] + for idx in range(len(G_class)): + k_dis_list.append(compute_k_dis(idx, range(0, len(G_class)), [1 / len(G_class)] * len(G_class), gm_class, withterm3=False)) + idx_k_dis_min = np.argmin(k_dis_list) + best_graphs.append(G_class[idx_k_dis_min].copy()) + + + # perform k-nn. + print('\nperforming k-nn...') + # compute dis_mat between medians. + dataset = dataset_all.copy() + dataset.load_graphs([g.copy() for g in best_graphs], targets=None) + gm_app_unnorm, _ = __compute_gram_matrix_unnorm(dataset, kernel_options.copy()) + + # compute the entire Gram matrix. + graph_kernel = __get_graph_kernel(dataset.copy(), kernel_options.copy()) + kernels_to_best_graphs = [] + for g in best_graphs: + kernels_to_best_graph, _ = graph_kernel.compute(g, G_test, **kernel_options.copy()) + kernels_to_best_graphs.append(kernels_to_best_graph) + kernels_to_best_graphs = np.array(kernels_to_best_graphs) + gm_all = np.concatenate((gm_app_unnorm, kernels_to_best_graphs), axis=1) + gm_all = np.concatenate((gm_all, np.concatenate((kernels_to_best_graphs.T, gram_matrix_unnorm[test_index,:][:,test_index].copy()), axis=1)), axis=0) + + gm_all = normalize_gram_matrix(gm_all.copy()) + dis_mat, _, _, _ = compute_distance_matrix(gm_all) + + N = len(best_graphs) + + d_app = dis_mat[range(N),:][:,range(N)].copy() + + d_test = np.zeros((N, len(test_index))) + for i in range(N): + for j in range(len(test_index)): + d_test[i, j] = dis_mat[i, j] + + accuracies.append(knn_classification(d_app, d_test, y_app, y_test, n_neighbors, verbose=True, text=train_examples)) + + # write result detail. + if save_results: + f_detail = open(dir_save + fn_output_detail, 'a') + print('writing results to files...') + csv.writer(f_detail).writerow([ds_name, kernel_options['name'], + train_examples, trial, + knn_options['n_neighbors'], + len(gm_all), knn_options['test_size'], + accuracies[-1][0], accuracies[-1][1]]) + f_detail.close() + + results = {} + results['ave_perf_train'] = np.mean([i[0] for i in accuracies], axis=0) + results['std_perf_train'] = np.std([i[0] for i in accuracies], axis=0, ddof=1) + results['ave_perf_test'] = np.mean([i[1] for i in accuracies], axis=0) + results['std_perf_test'] = np.std([i[1] for i in accuracies], axis=0, ddof=1) + + # write result summary for each letter. + if save_results: + f_summary = open(dir_save + fn_output_summary, 'a') + csv.writer(f_summary).writerow([ds_name, kernel_options['name'], + train_examples, + knn_options['n_neighbors'], + knn_options['test_size'], results['ave_perf_train'], + results['ave_perf_test'], results['std_perf_train'], + results['std_perf_test'], time_precompute_gm]) + f_summary.close() + + +def __kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary): + y_all = dataset_all.targets + n_neighbors, n_splits, test_size = knn_options['n_neighbors'], knn_options['n_splits'], knn_options['test_size'] + + # compute distance matrix. + gram_matrix = normalize_gram_matrix(gram_matrix_unnorm.copy()) + dis_mat, _, _, _ = compute_distance_matrix(gram_matrix) + + # get shuffles. + train_indices, test_indices, _, _ = __get_shuffles(y_all, n_splits, test_size) + + accuracies = [] + for trial in range(len(train_indices)): + print('\ntrial =', trial) + + train_index = train_indices[trial] + test_index = test_indices[trial] + y_app = [y_all[i] for i in train_index] + y_test = [y_all[i] for i in test_index] + + N = len(train_index) + + d_app = dis_mat[train_index,:][:,train_index].copy() + + d_test = np.zeros((N, len(test_index))) + for i in range(N): + for j in range(len(test_index)): + d_test[i, j] = dis_mat[train_index[i], test_index[j]] + + accuracies.append(knn_classification(d_app, d_test, y_app, y_test, n_neighbors, verbose=True, text=train_examples)) + + # write result detail. + if save_results: + print('writing results to files...') + f_detail = open(dir_save + fn_output_detail, 'a') + csv.writer(f_detail).writerow([ds_name, kernel_options['name'], + train_examples, trial, knn_options['n_neighbors'], + len(gram_matrix), knn_options['test_size'], + accuracies[-1][0], accuracies[-1][1]]) + f_detail.close() + + results = {} + results['ave_perf_train'] = np.mean([i[0] for i in accuracies], axis=0) + results['std_perf_train'] = np.std([i[0] for i in accuracies], axis=0, ddof=1) + results['ave_perf_test'] = np.mean([i[1] for i in accuracies], axis=0) + results['std_perf_test'] = np.std([i[1] for i in accuracies], axis=0, ddof=1) + + # write result summary for each letter. + if save_results: + f_summary = open(dir_save + fn_output_summary, 'a') + csv.writer(f_summary).writerow([ds_name, kernel_options['name'], + train_examples, knn_options['n_neighbors'], + knn_options['test_size'], results['ave_perf_train'], + results['ave_perf_test'], results['std_perf_train'], + results['std_perf_test'], time_precompute_gm]) + f_summary.close() + + +def __get_shuffles(y_all, n_splits, test_size): + rs = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=0) + train_indices = [[] for _ in range(n_splits)] + test_indices = [[] for _ in range(n_splits)] + idx_targets = get_same_item_indices(y_all) + train_nums = [] + keys = [] + for key, item in idx_targets.items(): + i = 0 + for train_i, test_i in rs.split(item): # @todo: careful when parallel. + train_indices[i] += [item[idx] for idx in train_i] + test_indices[i] += [item[idx] for idx in test_i] + i += 1 + train_nums.append(len(train_i)) + keys.append(key) + return train_indices, test_indices, train_nums, keys + + +def __generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options): + mpg = MedianPreimageGenerator() + mpg.dataset = dataset.copy() + mpg.set_options(**mpg_options.copy()) + mpg.kernel_options = kernel_options.copy() + mpg.ged_options = ged_options.copy() + mpg.mge_options = mge_options.copy() + mpg.run() + return mpg.set_median, mpg.gen_median + + +def __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all): + if load_gm == 'auto': + gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' + gmfile_exist = os.path.isfile(os.path.abspath(gm_fname)) + if gmfile_exist: + gmfile = np.load(gm_fname, allow_pickle=True) # @todo: may not be safe. + gram_matrix_unnorm = gmfile['gram_matrix_unnorm'] + time_precompute_gm = float(gmfile['run_time']) + else: + gram_matrix_unnorm, time_precompute_gm = __compute_gram_matrix_unnorm(dataset_all, kernel_options) + np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm=gram_matrix_unnorm, run_time=time_precompute_gm) + elif not load_gm: + gram_matrix_unnorm, time_precompute_gm = __compute_gram_matrix_unnorm(dataset_all, kernel_options) + np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm=gram_matrix_unnorm, run_time=time_precompute_gm) + else: + gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' + gmfile = np.load(gm_fname, allow_pickle=True) + gram_matrix_unnorm = gmfile['gram_matrix_unnorm'] + time_precompute_gm = float(gmfile['run_time']) + + return gram_matrix_unnorm, time_precompute_gm + + +def __get_graph_kernel(dataset, kernel_options): + from gklearn.utils.utils import get_graph_kernel_by_name + graph_kernel = get_graph_kernel_by_name(kernel_options['name'], + node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, + edge_attrs=dataset.edge_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + kernel_options=kernel_options) + return graph_kernel + + +def __compute_gram_matrix_unnorm(dataset, kernel_options): + from gklearn.utils.utils import get_graph_kernel_by_name + graph_kernel = get_graph_kernel_by_name(kernel_options['name'], + node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, + edge_attrs=dataset.edge_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + kernel_options=kernel_options) + + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, **kernel_options) + gram_matrix_unnorm = graph_kernel.gram_matrix_unnorm + + return gram_matrix_unnorm, run_time + + +def __init_output_file_knn(ds_name, gkernel, fit_method, dir_output): + if not os.path.exists(dir_output): + os.makedirs(dir_output) + fn_output_detail = 'results_detail_knn.' + ds_name + '.' + gkernel + '.csv' + f_detail = open(dir_output + fn_output_detail, 'a') + csv.writer(f_detail).writerow(['dataset', 'graph kernel', + 'train examples', 'trial', 'num neighbors', 'num graphs', 'test size', + 'perf train', 'perf test']) + f_detail.close() + + fn_output_summary = 'results_summary_knn.' + ds_name + '.' + gkernel + '.csv' + f_summary = open(dir_output + fn_output_summary, 'a') + csv.writer(f_summary).writerow(['dataset', 'graph kernel', + 'train examples', 'num neighbors', 'test size', + 'ave perf train', 'ave perf test', + 'std perf train', 'std perf test', 'time precompute gm']) + f_summary.close() + + return fn_output_detail, fn_output_summary \ No newline at end of file From 12d80d22ada6b27dbe171c055a3e10cfe8bc5419 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:36 +0200 Subject: [PATCH 076/728] New translations graphdataset.py (French) --- lang/fr/gklearn/utils/graphdataset.py | 419 ++++++++++++++++++++++++++ 1 file changed, 419 insertions(+) create mode 100644 lang/fr/gklearn/utils/graphdataset.py diff --git a/lang/fr/gklearn/utils/graphdataset.py b/lang/fr/gklearn/utils/graphdataset.py new file mode 100644 index 0000000000..4c64fd0136 --- /dev/null +++ b/lang/fr/gklearn/utils/graphdataset.py @@ -0,0 +1,419 @@ +""" Obtain all kinds of attributes of a graph dataset. + +This file is for old version of graphkit-learn. +""" + + +def get_dataset_attributes(Gn, + target=None, + attr_names=[], + node_label=None, + edge_label=None): + """Returns the structure and property information of the graph dataset Gn. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs whose information will be returned. + + target : list + The list of classification targets corresponding to Gn. Only works for + classification problems. + + attr_names : list + List of strings which indicate which informations will be returned. The + possible choices includes: + + 'substructures': sub-structures Gn contains, including 'linear', 'non + linear' and 'cyclic'. + + 'node_labeled': whether vertices have symbolic labels. + + 'edge_labeled': whether egdes have symbolic labels. + + 'is_directed': whether graphs in Gn are directed. + + 'dataset_size': number of graphs in Gn. + + 'ave_node_num': average number of vertices of graphs in Gn. + + 'min_node_num': minimum number of vertices of graphs in Gn. + + 'max_node_num': maximum number of vertices of graphs in Gn. + + 'ave_edge_num': average number of edges of graphs in Gn. + + 'min_edge_num': minimum number of edges of graphs in Gn. + + 'max_edge_num': maximum number of edges of graphs in Gn. + + 'ave_node_degree': average vertex degree of graphs in Gn. + + 'min_node_degree': minimum vertex degree of graphs in Gn. + + 'max_node_degree': maximum vertex degree of graphs in Gn. + + 'ave_fill_factor': average fill factor (number_of_edges / + (number_of_nodes ** 2)) of graphs in Gn. + + 'min_fill_factor': minimum fill factor of graphs in Gn. + + 'max_fill_factor': maximum fill factor of graphs in Gn. + + 'node_label_num': number of symbolic vertex labels. + + 'edge_label_num': number of symbolic edge labels. + + 'node_attr_dim': number of dimensions of non-symbolic vertex labels. + Extracted from the 'attributes' attribute of graph nodes. + + 'edge_attr_dim': number of dimensions of non-symbolic edge labels. + Extracted from the 'attributes' attribute of graph edges. + + 'class_number': number of classes. Only available for classification problems. + + node_label : string + Node attribute used as label. The default node label is atom. Mandatory + when 'node_labeled' or 'node_label_num' is required. + + edge_label : string + Edge attribute used as label. The default edge label is bond_type. + Mandatory when 'edge_labeled' or 'edge_label_num' is required. + + Return + ------ + attrs : dict + Value for each property. + """ + import networkx as nx + import numpy as np + + attrs = {} + + def get_dataset_size(Gn): + return len(Gn) + + def get_all_node_num(Gn): + return [nx.number_of_nodes(G) for G in Gn] + + def get_ave_node_num(all_node_num): + return np.mean(all_node_num) + + def get_min_node_num(all_node_num): + return np.amin(all_node_num) + + def get_max_node_num(all_node_num): + return np.amax(all_node_num) + + def get_all_edge_num(Gn): + return [nx.number_of_edges(G) for G in Gn] + + def get_ave_edge_num(all_edge_num): + return np.mean(all_edge_num) + + def get_min_edge_num(all_edge_num): + return np.amin(all_edge_num) + + def get_max_edge_num(all_edge_num): + return np.amax(all_edge_num) + + def is_node_labeled(Gn): + return False if node_label is None else True + + def get_node_label_num(Gn): + nl = set() + for G in Gn: + nl = nl | set(nx.get_node_attributes(G, node_label).values()) + return len(nl) + + def is_edge_labeled(Gn): + return False if edge_label is None else True + + def get_edge_label_num(Gn): + el = set() + for G in Gn: + el = el | set(nx.get_edge_attributes(G, edge_label).values()) + return len(el) + + def is_directed(Gn): + return nx.is_directed(Gn[0]) + + def get_ave_node_degree(Gn): + return np.mean([np.mean(list(dict(G.degree()).values())) for G in Gn]) + + def get_max_node_degree(Gn): + return np.amax([np.mean(list(dict(G.degree()).values())) for G in Gn]) + + def get_min_node_degree(Gn): + return np.amin([np.mean(list(dict(G.degree()).values())) for G in Gn]) + + # get fill factor, the number of non-zero entries in the adjacency matrix. + def get_ave_fill_factor(Gn): + return np.mean([nx.number_of_edges(G) / (nx.number_of_nodes(G) + * nx.number_of_nodes(G)) for G in Gn]) + + def get_max_fill_factor(Gn): + return np.amax([nx.number_of_edges(G) / (nx.number_of_nodes(G) + * nx.number_of_nodes(G)) for G in Gn]) + + def get_min_fill_factor(Gn): + return np.amin([nx.number_of_edges(G) / (nx.number_of_nodes(G) + * nx.number_of_nodes(G)) for G in Gn]) + + def get_substructures(Gn): + subs = set() + for G in Gn: + degrees = list(dict(G.degree()).values()) + if any(i == 2 for i in degrees): + subs.add('linear') + if np.amax(degrees) >= 3: + subs.add('non linear') + if 'linear' in subs and 'non linear' in subs: + break + + if is_directed(Gn): + for G in Gn: + if len(list(nx.find_cycle(G))) > 0: + subs.add('cyclic') + break + # else: + # # @todo: this method does not work for big graph with large amount of edges like D&D, try a better way. + # upper = np.amin([nx.number_of_edges(G) for G in Gn]) * 2 + 10 + # for G in Gn: + # if (nx.number_of_edges(G) < upper): + # cyc = list(nx.simple_cycles(G.to_directed())) + # if any(len(i) > 2 for i in cyc): + # subs.add('cyclic') + # break + # if 'cyclic' not in subs: + # for G in Gn: + # cyc = list(nx.simple_cycles(G.to_directed())) + # if any(len(i) > 2 for i in cyc): + # subs.add('cyclic') + # break + + return subs + + def get_class_num(target): + return len(set(target)) + + def get_node_attr_dim(Gn): + for G in Gn: + for n in G.nodes(data=True): + if 'attributes' in n[1]: + return len(n[1]['attributes']) + return 0 + + def get_edge_attr_dim(Gn): + for G in Gn: + if nx.number_of_edges(G) > 0: + for e in G.edges(data=True): + if 'attributes' in e[2]: + return len(e[2]['attributes']) + return 0 + + if attr_names == []: + attr_names = [ + 'substructures', + 'node_labeled', + 'edge_labeled', + 'is_directed', + 'dataset_size', + 'ave_node_num', + 'min_node_num', + 'max_node_num', + 'ave_edge_num', + 'min_edge_num', + 'max_edge_num', + 'ave_node_degree', + 'min_node_degree', + 'max_node_degree', + 'ave_fill_factor', + 'min_fill_factor', + 'max_fill_factor', + 'node_label_num', + 'edge_label_num', + 'node_attr_dim', + 'edge_attr_dim', + 'class_number', + ] + + # dataset size + if 'dataset_size' in attr_names: + + attrs.update({'dataset_size': get_dataset_size(Gn)}) + + # graph node number + if any(i in attr_names + for i in ['ave_node_num', 'min_node_num', 'max_node_num']): + + all_node_num = get_all_node_num(Gn) + + if 'ave_node_num' in attr_names: + + attrs.update({'ave_node_num': get_ave_node_num(all_node_num)}) + + if 'min_node_num' in attr_names: + + attrs.update({'min_node_num': get_min_node_num(all_node_num)}) + + if 'max_node_num' in attr_names: + + attrs.update({'max_node_num': get_max_node_num(all_node_num)}) + + # graph edge number + if any(i in attr_names for i in + ['ave_edge_num', 'min_edge_num', 'max_edge_num']): + + all_edge_num = get_all_edge_num(Gn) + + if 'ave_edge_num' in attr_names: + + attrs.update({'ave_edge_num': get_ave_edge_num(all_edge_num)}) + + if 'max_edge_num' in attr_names: + + attrs.update({'max_edge_num': get_max_edge_num(all_edge_num)}) + + if 'min_edge_num' in attr_names: + + attrs.update({'min_edge_num': get_min_edge_num(all_edge_num)}) + + # label number + if any(i in attr_names for i in ['node_labeled', 'node_label_num']): + is_nl = is_node_labeled(Gn) + node_label_num = get_node_label_num(Gn) + + if 'node_labeled' in attr_names: + # graphs are considered node unlabeled if all nodes have the same label. + attrs.update({'node_labeled': is_nl if node_label_num > 1 else False}) + + if 'node_label_num' in attr_names: + attrs.update({'node_label_num': node_label_num}) + + if any(i in attr_names for i in ['edge_labeled', 'edge_label_num']): + is_el = is_edge_labeled(Gn) + edge_label_num = get_edge_label_num(Gn) + + if 'edge_labeled' in attr_names: + # graphs are considered edge unlabeled if all edges have the same label. + attrs.update({'edge_labeled': is_el if edge_label_num > 1 else False}) + + if 'edge_label_num' in attr_names: + attrs.update({'edge_label_num': edge_label_num}) + + if 'is_directed' in attr_names: + attrs.update({'is_directed': is_directed(Gn)}) + + if 'ave_node_degree' in attr_names: + attrs.update({'ave_node_degree': get_ave_node_degree(Gn)}) + + if 'max_node_degree' in attr_names: + attrs.update({'max_node_degree': get_max_node_degree(Gn)}) + + if 'min_node_degree' in attr_names: + attrs.update({'min_node_degree': get_min_node_degree(Gn)}) + + if 'ave_fill_factor' in attr_names: + attrs.update({'ave_fill_factor': get_ave_fill_factor(Gn)}) + + if 'max_fill_factor' in attr_names: + attrs.update({'max_fill_factor': get_max_fill_factor(Gn)}) + + if 'min_fill_factor' in attr_names: + attrs.update({'min_fill_factor': get_min_fill_factor(Gn)}) + + if 'substructures' in attr_names: + attrs.update({'substructures': get_substructures(Gn)}) + + if 'class_number' in attr_names: + attrs.update({'class_number': get_class_num(target)}) + + if 'node_attr_dim' in attr_names: + attrs['node_attr_dim'] = get_node_attr_dim(Gn) + + if 'edge_attr_dim' in attr_names: + attrs['edge_attr_dim'] = get_edge_attr_dim(Gn) + + from collections import OrderedDict + return OrderedDict( + sorted(attrs.items(), key=lambda i: attr_names.index(i[0]))) + + +def load_predefined_dataset(ds_name): + import os + from gklearn.utils.graphfiles import loadDataset + + current_path = os.path.dirname(os.path.realpath(__file__)) + '/' + if ds_name == 'Acyclic': + ds_file = current_path + '../../datasets/Acyclic/dataset_bps.ds' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'AIDS': + ds_file = current_path + '../../datasets/AIDS/AIDS_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'Alkane': + ds_file = current_path + '../../datasets/Alkane/dataset.ds' + fn_targets = current_path + '../../datasets/Alkane/dataset_boiling_point_names.txt' + graphs, targets = loadDataset(ds_file, filename_y=fn_targets) + elif ds_name == 'COIL-DEL': + ds_file = current_path + '../../datasets/COIL-DEL/COIL-DEL_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'COIL-RAG': + ds_file = current_path + '../../datasets/COIL-RAG/COIL-RAG_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'COLORS-3': + ds_file = current_path + '../../datasets/COLORS-3/COLORS-3_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'Cuneiform': + ds_file = current_path + '../../datasets/Cuneiform/Cuneiform_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'DD': + ds_file = current_path + '../../datasets/DD/DD_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'ENZYMES': + ds_file = current_path + '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'Fingerprint': + ds_file = current_path + '../../datasets/Fingerprint/Fingerprint_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'FRANKENSTEIN': + ds_file = current_path + '../../datasets/FRANKENSTEIN/FRANKENSTEIN_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'Letter-high': # node non-symb + ds_file = current_path + '../../datasets/Letter-high/Letter-high_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'Letter-low': # node non-symb + ds_file = current_path + '../../datasets/Letter-low/Letter-low_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'Letter-med': # node non-symb + ds_file = current_path + '../../datasets/Letter-med/Letter-med_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'MAO': + ds_file = current_path + '../../datasets/MAO/dataset.ds' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'Monoterpenoides': + ds_file = current_path + '../../datasets/Monoterpenoides/dataset_10+.ds' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'MUTAG': + ds_file = current_path + '../../datasets/MUTAG/MUTAG_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'NCI1': + ds_file = current_path + '../../datasets/NCI1/NCI1_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'NCI109': + ds_file = current_path + '../../datasets/NCI109/NCI109_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'PAH': + ds_file = current_path + '../../datasets/PAH/dataset.ds' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'SYNTHETIC': + pass + elif ds_name == 'SYNTHETICnew': + ds_file = current_path + '../../datasets/SYNTHETICnew/SYNTHETICnew_A.txt' + graphs, targets = loadDataset(ds_file) + elif ds_name == 'Synthie': + pass + else: + raise Exception('The dataset name "', ds_name, '" is not pre-defined.') + + return graphs, targets \ No newline at end of file From edc7fe114d43cd3fc709e0f9c93a39a1e1f80622 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:38 +0200 Subject: [PATCH 077/728] New translations isNotebook.py (French) --- lang/fr/gklearn/utils/isNotebook.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 lang/fr/gklearn/utils/isNotebook.py diff --git a/lang/fr/gklearn/utils/isNotebook.py b/lang/fr/gklearn/utils/isNotebook.py new file mode 100644 index 0000000000..0491a36bcd --- /dev/null +++ b/lang/fr/gklearn/utils/isNotebook.py @@ -0,0 +1,16 @@ +""" Functions for python system. +""" + +def isNotebook(): + """check if code is executed in the IPython notebook. + """ + try: + shell = get_ipython().__class__.__name__ + if shell == 'ZMQInteractiveShell': + return True # Jupyter notebook or qtconsole + elif shell == 'TerminalInteractiveShell': + return False # Terminal running IPython + else: + return False # Other type (?) + except NameError: + return False # Probably standard Python interpreter \ No newline at end of file From f0b9ae1268f624a84df2728a7ac36ccf7103419e Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:39 +0200 Subject: [PATCH 078/728] New translations run_spkernel.py (French) --- lang/fr/notebooks/run_spkernel.py | 83 +++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 lang/fr/notebooks/run_spkernel.py diff --git a/lang/fr/notebooks/run_spkernel.py b/lang/fr/notebooks/run_spkernel.py new file mode 100644 index 0000000000..1822a1a356 --- /dev/null +++ b/lang/fr/notebooks/run_spkernel.py @@ -0,0 +1,83 @@ +import functools +from libs import * +import multiprocessing + +from gklearn.kernels.spKernel import spkernel +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct +#from gklearn.utils.model_selection_precomputed import trial_do + +# datasets +dslist = [ +# {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', +# 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, +# # contains single node graph, node symb +# {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', +# 'task': 'regression'}, # node symb +# {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb +# {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled +# {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb +# {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, +# # node nsymb +# {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, +# # node symb/nsymb +# {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb + {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1_A.txt'}, # node symb + {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109_A.txt'}, # node symb + {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb +# {'name': 'monoterpenoides', 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'}, # node/edge +# {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt'}, +# # node nsymb symb +# +# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# # node/edge symb + # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb + # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb + # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb + # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'}, + # + # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb + # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb + # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb + + # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb + # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf', + # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb + + # # not working below + # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',}, + # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',}, + # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',}, + # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',}, +] +estimator = spkernel +# hyper-parameters +#gaussiankernel = functools.partial(gaussiankernel, gamma=0.5) +mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) +param_grid_precomputed = {'node_kernels': [ + {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]} +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +# for each dataset, do model selection. +for ds in dslist: + print() + print(ds['name']) + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), +# n_jobs=7, + read_gm_from_file=False, + verbose=True) + print() From 8bba88fa3961686d98be559b68c50b980dfe9100 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:41 +0200 Subject: [PATCH 079/728] New translations run_rwalk_symonly.py (French) --- lang/fr/notebooks/else/run_rwalk_symonly.py | 70 +++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 lang/fr/notebooks/else/run_rwalk_symonly.py diff --git a/lang/fr/notebooks/else/run_rwalk_symonly.py b/lang/fr/notebooks/else/run_rwalk_symonly.py new file mode 100644 index 0000000000..8c929e6731 --- /dev/null +++ b/lang/fr/notebooks/else/run_rwalk_symonly.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sun Dec 23 16:56:44 2018 + +@author: ljia +""" + +import functools +from libs import * +import multiprocessing + +from gklearn.kernels.rwalk_sym import randomwalkkernel +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + +import numpy as np + + +dslist = [ + {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, + # node nsymb + {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, + # node symb/nsymb +] +estimator = randomwalkkernel +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +for ds in dslist: + print() + print(ds['name']) + for compute_method in ['conjugate', 'fp']: + if compute_method == 'sylvester': + param_grid_precomputed = {'compute_method': ['sylvester'], +# 'weight': np.linspace(0.01, 0.10, 10)} + 'weight': np.logspace(-1, -10, num=10, base=10)} + elif compute_method == 'conjugate': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'compute_method': ['conjugate'], + 'node_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}], + 'edge_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}], + 'weight': np.logspace(-1, -10, num=10, base=10)} + elif compute_method == 'fp': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'compute_method': ['fp'], + 'node_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}], + 'edge_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}], + 'weight': np.logspace(-3, -10, num=8, base=10)} + elif compute_method == 'spectral': + param_grid_precomputed = {'compute_method': ['spectral'], + 'weight': np.logspace(-1, -10, num=10, base=10), + 'sub_kernel': ['geo', 'exp']} + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False) + print() \ No newline at end of file From ccaeab04a5ed328e27de1d43bb13c833bcd992bc Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:42 +0200 Subject: [PATCH 080/728] New translations run_spkernel.ipynb (French) --- lang/fr/notebooks/run_spkernel.ipynb | 264 +++++++++++++++++++++++++++ 1 file changed, 264 insertions(+) create mode 100644 lang/fr/notebooks/run_spkernel.ipynb diff --git a/lang/fr/notebooks/run_spkernel.ipynb b/lang/fr/notebooks/run_spkernel.ipynb new file mode 100644 index 0000000000..b7fa0f451d --- /dev/null +++ b/lang/fr/notebooks/run_spkernel.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Acyclic\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "getting sp graphs: 183it [00:00, 11704.68it/s]\n", + "calculating kernels: 16836it [00:00, 17085.14it/s]\n", + "\n", + " --- shortest path kernel matrix of size 183 built in 1.2640743255615234 seconds ---\n", + "\n", + "the gram matrix with parameters {'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + "1 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 30it [00:03, 8.84it/s]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True}]\n", + "best_params_in: [{'alpha': 1e-10}]\n", + "\n", + "best_val_perf: 9.687399048018559\n", + "best_val_std: 0.654180946161292\n", + "final_performance: [9.411656660355659]\n", + "final_confidence: [2.500437167823725]\n", + "train_performance: [6.168480355249007]\n", + "train_std: [0.2541557651056269]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 1.26±0.00s\n", + "time to calculate best gram matrix: 1.26±0.00s\n", + "total training time with all hyper-param choices: 5.09s\n", + "\n", + "\n", + "\n", + "Alkane\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "\n", + " 1 graphs are removed as they don't contain edges.\n", + "\n", + "getting sp graphs: 149it [00:00, 7096.72it/s]\n", + "calculating kernels: 11175it [00:00, 19504.73it/s]\n", + "\n", + " --- shortest path kernel matrix of size 149 built in 0.7957959175109863 seconds ---\n", + "\n", + "the gram matrix with parameters {'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + "1 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 30it [00:02, 10.74it/s]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True}]\n", + "best_params_in: [{'alpha': 1e-05}]\n", + "\n", + "best_val_perf: 8.745832519261795\n", + "best_val_std: 0.26293501071192543\n", + "final_performance: [7.942686332248635]\n", + "final_confidence: [1.617779657027359]\n", + "train_performance: [7.860965083396337]\n", + "train_std: [0.16888913664254188]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 0.80±0.00s\n", + "time to calculate best gram matrix: 0.80±0.00s\n", + "total training time with all hyper-param choices: 3.90s\n", + "\n", + "\n", + "\n", + "MAO\n", + "\n", + "--- This is a classification problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "getting sp graphs: 68it [00:00, 2292.58it/s]\n", + "calculating kernels: 2346it [00:02, 873.39it/s]\n", + "\n", + " --- shortest path kernel matrix of size 68 built in 2.986046075820923 seconds ---\n", + "\n", + "the gram matrix with parameters {'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + "1 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 30it [00:02, 11.85it/s]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True}]\n", + "best_params_in: [{'C': 3162.2776601683795}]\n", + "\n", + "best_val_perf: 0.8780158730158729\n", + "best_val_std: 0.028162670831398482\n", + "final_performance: [0.8714285714285714]\n", + "final_confidence: [0.09446318571439967]\n", + "train_performance: [0.9740729517396185]\n", + "train_std: [0.007872630412568218]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 2.99±0.00s\n", + "time to calculate best gram matrix: 2.99±0.00s\n", + "total training time with all hyper-param choices: 5.93s\n", + "\n", + "\n", + "\n", + "PAH\n", + "\n", + "--- This is a classification problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "getting sp graphs: 94it [00:00, 2131.93it/s]\n", + "calculating kernels: 1501it [00:01, 78.00it/s]" + ] + } + ], + "source": [ + "import functools\n", + "from libs import *\n", + "import multiprocessing\n", + "\n", + "from gklearn.kernels.spKernel import spkernel\n", + "from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct\n", + "#from gklearn.utils.model_selection_precomputed import trial_do\n", + "\n", + "# datasets\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n", + " 'task': 'regression'}, # node symb\n", + " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n", + " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, \n", + " # contains single node graph, node symb\n", + " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb\n", + " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled\n", + " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb\n", + " {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n", + " # node nsymb\n", + " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + " # node symb/nsymb\n", + "# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + "# # node/edge symb\n", + "# {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb\n", + "#\n", + "# {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + "# {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", + "# {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n", + "# {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n", + "# {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n", + "#\n", + "# {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n", + "# {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n", + "# 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + "# {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n", + "# 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + "# {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n", + "# 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n", + "\n", + " # # not working below\n", + " # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n", + " # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n", + " # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n", + " # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "estimator = spkernel\n", + "# hyper-parameters\n", + "mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)\n", + "param_grid_precomputed = {'node_kernels': [\n", + " {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]}\n", + "param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n", + " {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n", + "\n", + "# for each dataset, do model selection.\n", + "for ds in dslist:\n", + " print()\n", + " print(ds['name'])\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'],\n", + " estimator,\n", + " param_grid_precomputed,\n", + " (param_grid[1] if ('task' in ds and ds['task']\n", + " == 'regression') else param_grid[0]),\n", + " (ds['task'] if 'task' in ds else 'classification'),\n", + " NUM_TRIALS=30,\n", + " datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n", + " ds_name=ds['name'],\n", + " n_jobs=multiprocessing.cpu_count(),\n", + " read_gm_from_file=False,\n", + " verbose=True)\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d1133d28db42656877a60021a258d8bf3e5375a5 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:44 +0200 Subject: [PATCH 081/728] New translations run_randomwalkkernel.py (French) --- lang/fr/notebooks/run_randomwalkkernel.py | 116 ++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 lang/fr/notebooks/run_randomwalkkernel.py diff --git a/lang/fr/notebooks/run_randomwalkkernel.py b/lang/fr/notebooks/run_randomwalkkernel.py new file mode 100644 index 0000000000..f6e85f38a1 --- /dev/null +++ b/lang/fr/notebooks/run_randomwalkkernel.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Nov 22 17:02:28 2018 + +@author: ljia +""" + +import functools +from libs import * +import multiprocessing + +from gklearn.kernels.randomWalkKernel import randomwalkkernel +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + +import numpy as np + + +dslist = [ +# {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', +# 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, +# # contains single node graph, node symb +# {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', +# 'task': 'regression'}, # node symb +# {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb +# {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled +# {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb +# {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, +# # node nsymb +# {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, +# # node symb/nsymb + {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb + {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1_A.txt'}, # node symb + {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109_A.txt'}, # node symb + {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb + +# +# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# # node/edge symb + # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb + # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb + # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb + # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'}, + # + # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb + # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb + # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb + + # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb + # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf', + # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb + + # # not working below + # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',}, + # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',}, + # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',}, + # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',}, +] +estimator = randomwalkkernel +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + + +## for non-symbolic labels. +#gkernels = [functools.partial(gaussiankernel, gamma=1 / ga) +# for ga in np.logspace(0, 10, num=11, base=10)] +#mixkernels = [functools.partial(kernelproduct, deltakernel, gk) for gk in gkernels] +#sub_kernels = [{'symb': deltakernel, 'nsymb': gkernels[i], 'mix': mixkernels[i]} +# for i in range(len(gkernels))] + +# for symbolic labels only. +#gaussiankernel = functools.partial(gaussiankernel, gamma=0.5) +mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) +sub_kernels = [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}] + +for ds in dslist: + print() + print(ds['name']) + for compute_method in ['sylvester', 'conjugate', 'fp', 'spectral']: +# for compute_method in ['conjugate', 'fp']: + if compute_method == 'sylvester': + param_grid_precomputed = {'compute_method': ['sylvester'], +# 'weight': np.linspace(0.01, 0.10, 10)} + 'weight': np.logspace(-1, -10, num=10, base=10)} + elif compute_method == 'conjugate': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'compute_method': ['conjugate'], + 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, + 'weight': np.logspace(-1, -10, num=10, base=10)} + elif compute_method == 'fp': + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + param_grid_precomputed = {'compute_method': ['fp'], + 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, + 'weight': np.logspace(-3, -10, num=8, base=10)} + elif compute_method == 'spectral': + param_grid_precomputed = {'compute_method': ['spectral'], + 'weight': np.logspace(-1, -10, num=10, base=10), + 'sub_kernel': ['geo', 'exp']} + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False, + verbose=True) + print() From e1aa816ea24a70b8ec30c12c0bd878486905b030 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:46 +0200 Subject: [PATCH 082/728] New translations run_randomwalkkernel.ipynb (French) --- lang/fr/notebooks/run_randomwalkkernel.ipynb | 333 +++++++++++++++++++ 1 file changed, 333 insertions(+) create mode 100644 lang/fr/notebooks/run_randomwalkkernel.ipynb diff --git a/lang/fr/notebooks/run_randomwalkkernel.ipynb b/lang/fr/notebooks/run_randomwalkkernel.ipynb new file mode 100644 index 0000000000..a5652da89d --- /dev/null +++ b/lang/fr/notebooks/run_randomwalkkernel.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Acyclic\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5308.25it/s]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "../gklearn/kernels/randomWalkKernel.py:108: UserWarning: All labels are ignored.\n", + " warnings.warn('All labels are ignored.')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calculating kernels: 16836it [00:00, 65408.89it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.4157981872558594 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.1, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5205.09it/s]\n", + "calculating kernels: 16836it [00:00, 73715.56it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.36714887619018555 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.01, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5344.96it/s]\n", + "calculating kernels: 16836it [00:00, 68817.65it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.3666379451751709 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.001, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5295.73it/s]\n", + "calculating kernels: 16836it [00:00, 74865.49it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.36979222297668457 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.0001, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5040.80it/s]\n", + "calculating kernels: 16836it [00:00, 70923.54it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.3692610263824463 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-05, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5326.60it/s]\n", + "calculating kernels: 16836it [00:00, 73697.55it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.37317800521850586 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-06, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5705.98it/s]\n", + "calculating kernels: 16836it [00:00, 64238.65it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.36565732955932617 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-07, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4833.15it/s]\n", + "calculating kernels: 16836it [00:00, 69971.77it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.37798523902893066 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-08, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4170.94it/s]\n", + "calculating kernels: 16836it [00:00, 64187.38it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.39433860778808594 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-09, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5273.43it/s]\n", + "calculating kernels: 16836it [00:00, 69555.28it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 0.3833920955657959 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-10, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + "10 gram matrices are calculated, 0 of which are ignored.\n", + "\n", + "3. Fitting and predicting using nested cross validation. This could really take a while...\n", + "cross validation: 30it [00:33, 1.11s/it]\n", + "\n", + "4. Getting final performance...\n", + "best_params_out: [{'compute_method': 'sylvester', 'weight': 0.01, 'n_jobs': 8, 'verbose': True}]\n", + "best_params_in: [{'alpha': 1e-10}]\n", + "\n", + "best_val_perf: 31.76835551233969\n", + "best_val_std: 0.43269972907929183\n", + "final_performance: [32.391882524496765]\n", + "final_confidence: [2.6542337929023336]\n", + "train_performance: [30.70127313658435]\n", + "train_std: [0.31861204198126475]\n", + "\n", + "time to calculate gram matrix with different hyper-params: 0.38±0.02s\n", + "time to calculate best gram matrix: 0.37±0.00s\n", + "total training time with all hyper-param choices: 40.53s\n", + "\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "reindex vertices: 100%|██████████| 183/183 [00:00<00:00, 28950.24it/s]\n", + "calculating kernels: 16836it [00:02, 6540.43it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 2.6675093173980713 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'conjugate', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'weight': 0.1, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "reindex vertices: 100%|██████████| 183/183 [00:00<00:00, 28019.19it/s]\n", + "calculating kernels: 16836it [00:02, 7963.48it/s]\n", + "\n", + " --- kernel matrix of random walk kernel of size 183 built in 2.2675061225891113 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'conjugate', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'weight': 0.01, 'n_jobs': 8, 'verbose': True} is: \n", + "\n", + "\n", + "\n", + " None edge weight specified. Set all weight to 1.\n", + "\n", + "reindex vertices: 100%|██████████| 183/183 [00:00<00:00, 23036.63it/s]\n", + "calculating kernels: 12801it [00:01, 8043.11it/s]" + ] + } + ], + "source": [ + "# %load_ext line_profiler\n", + "# %matplotlib inline\n", + "import functools\n", + "from libs import *\n", + "import multiprocessing\n", + "\n", + "from gklearn.kernels.randomWalkKernel import randomwalkkernel\n", + "from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct\n", + "\n", + "import numpy as np\n", + "\n", + "\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n", + " 'task': 'regression'}, # node symb\n", + " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n", + " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, \n", + " # contains single node graph, node symb\n", + " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb\n", + " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled\n", + " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb\n", + " {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n", + " # node nsymb\n", + " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + " # node symb/nsymb\n", + "# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + "# # node/edge symb\n", + "# {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb\n", + "\n", + " # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + " # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n", + " # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n", + " # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", + " #\n", + " # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb, missing values\n", + "# {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb, missing values\n", + " # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n", + "\n", + " # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n", + " # {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n", + " # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n", + "\n", + "# # not working below\n", + "# {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n", + " # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n", + " # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n", + " # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "estimator = randomwalkkernel\n", + "param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n", + " {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n", + "\n", + "for ds in dslist:\n", + " print()\n", + " print(ds['name'])\n", + " for compute_method in ['sylvester', 'conjugate', 'fp', 'spectral']:\n", + " if compute_method == 'sylvester':\n", + " param_grid_precomputed = {'compute_method': ['sylvester'],\n", + "# 'weight': np.linspace(0.01, 0.10, 10)}\n", + " 'weight': np.logspace(-1, -10, num=10, base=10)}\n", + " elif compute_method == 'conjugate':\n", + " mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)\n", + " param_grid_precomputed = {'compute_method': ['conjugate'], \n", + " 'node_kernels': \n", + " [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],\n", + " 'edge_kernels': \n", + " [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],\n", + " 'weight': np.logspace(-1, -10, num=10, base=10)}\n", + " elif compute_method == 'fp':\n", + " mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)\n", + " param_grid_precomputed = {'compute_method': ['fp'], \n", + " 'node_kernels': \n", + " [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],\n", + " 'edge_kernels': \n", + " [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],\n", + " 'weight': np.logspace(-3, -10, num=8, base=10)}\n", + " elif compute_method == 'spectral':\n", + " param_grid_precomputed = {'compute_method': ['spectral'],\n", + " 'weight': np.logspace(-1, -10, num=10, base=10),\n", + " 'sub_kernel': ['geo', 'exp']}\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'],\n", + " estimator,\n", + " param_grid_precomputed,\n", + " (param_grid[1] if ('task' in ds and ds['task']\n", + " == 'regression') else param_grid[0]),\n", + " (ds['task'] if 'task' in ds else 'classification'),\n", + " NUM_TRIALS=30,\n", + " datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n", + " ds_name=ds['name'],\n", + " n_jobs=multiprocessing.cpu_count(),\n", + " read_gm_from_file=False,\n", + " verbose=True)\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 706c3b0c0464c06cadc5899b8e30045037d79a5a Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:47 +0200 Subject: [PATCH 083/728] New translations run_marginalizedkernel.py (French) --- lang/fr/notebooks/run_marginalizedkernel.py | 83 +++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 lang/fr/notebooks/run_marginalizedkernel.py diff --git a/lang/fr/notebooks/run_marginalizedkernel.py b/lang/fr/notebooks/run_marginalizedkernel.py new file mode 100644 index 0000000000..998fb6b8f7 --- /dev/null +++ b/lang/fr/notebooks/run_marginalizedkernel.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Sep 28 18:58:47 2018 + +@author: ljia +""" + +from libs import * +import multiprocessing + +from gklearn.kernels.marginalizedKernel import marginalizedkernel + +dslist = [ +# {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', +# 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, +# # contains single node graph, node symb +# {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', +# 'task': 'regression'}, # node symb +# {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb +# {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled +# {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb +# {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, +# # node nsymb +# {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, +# # node symb/nsymb + {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb + {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1_A.txt'}, # node symb + {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109_A.txt'}, # node symb + {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb +# {'name': 'monoterpenoides', 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'}, # node/edge symb +# +# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# # node/edge symb + # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb + # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb + # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb + # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'}, + # + # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb + # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb + # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb + + # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb + # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf', + # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb + + # # not working below + # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',}, + # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',}, + # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',}, + # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',}, +] +estimator = marginalizedkernel +#param_grid_precomputed = {'p_quit': np.linspace(0.1, 0.3, 3), +# 'n_iteration': np.linspace(1, 1, 1), +param_grid_precomputed = {'p_quit': np.linspace(0.1, 0.9, 9), + 'n_iteration': np.linspace(1, 19, 7), + 'remove_totters': [False]} +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +for ds in dslist: + print() + print(ds['name']) + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False, + verbose=True) + print() From ee0db76b2762309044e8ffdd0da4d46ce8d85a48 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:49 +0200 Subject: [PATCH 084/728] New translations run_marginalizedkernel.ipynb (French) --- .../fr/notebooks/run_marginalizedkernel.ipynb | 198 ++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100644 lang/fr/notebooks/run_marginalizedkernel.ipynb diff --git a/lang/fr/notebooks/run_marginalizedkernel.ipynb b/lang/fr/notebooks/run_marginalizedkernel.ipynb new file mode 100644 index 0000000000..82b9d013a0 --- /dev/null +++ b/lang/fr/notebooks/run_marginalizedkernel.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Acyclic\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "calculating kernels: 16836it [00:00, 18811.60it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 1.0535125732421875 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.1, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:00, 18469.31it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 1.037832498550415 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.2, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:00, 17905.98it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 1.030360460281372 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.30000000000000004, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:00, 17494.74it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 1.0369200706481934 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.4, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:00, 18481.51it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 1.0335497856140137 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.5, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:00, 18173.29it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 1.0338375568389893 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.6, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:00, 18516.39it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 1.0297644138336182 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.7000000000000001, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:00, 18708.47it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 1.0404298305511475 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.8, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:00, 18376.82it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 1.0408570766448975 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.9, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:08, 1984.14it/s]\n", + "\n", + " --- marginalized kernel matrix of size 183 built in 8.540878295898438 seconds ---\n", + "\n", + "the gram matrix with parameters {'n_iteration': 4.0, 'p_quit': 0.1, 'remove_totters': False, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 14901it [00:07, 1221.99it/s]" + ] + } + ], + "source": [ + "# %load_ext line_profiler\n", + "# %matplotlib inline\n", + "from libs import *\n", + "import multiprocessing\n", + "\n", + "from gklearn.kernels.marginalizedKernel import marginalizedkernel\n", + "\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n", + " 'task': 'regression'}, # node symb\n", + " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n", + " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, \n", + " # contains single node graph, node symb\n", + " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb\n", + " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled\n", + " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb\n", + " {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n", + " # node nsymb\n", + " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + " # node symb/nsymb\n", + "# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + "# # node/edge symb\n", + "# {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb\n", + "\n", + " # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + " # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n", + " # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n", + " # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", + " #\n", + " # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n", + " # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n", + " # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n", + "\n", + " # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n", + " # {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n", + " # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n", + "\n", + " # # not working below\n", + " # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n", + " # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n", + " # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n", + " # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "estimator = marginalizedkernel\n", + "#param_grid_precomputed = {'p_quit': np.linspace(0.1, 0.3, 3),\n", + "# 'n_iteration': np.linspace(1, 1, 1),\n", + "param_grid_precomputed = {'p_quit': np.linspace(0.1, 0.9, 9),\n", + " 'n_iteration': np.linspace(1, 19, 7), \n", + " 'remove_totters': [False]}\n", + "param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n", + " {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n", + "\n", + "for ds in dslist:\n", + " print()\n", + " print(ds['name'])\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'],\n", + " estimator,\n", + " param_grid_precomputed,\n", + " (param_grid[1] if ('task' in ds and ds['task']\n", + " == 'regression') else param_grid[0]),\n", + " (ds['task'] if 'task' in ds else 'classification'),\n", + " NUM_TRIALS=30,\n", + " datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n", + " ds_name=ds['name'],\n", + " n_jobs=multiprocessing.cpu_count(),\n", + " read_gm_from_file=False,\n", + " verbose=True)\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From aedabe30a41b22cf0825edb3c3aee8a4f796dfcd Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:50 +0200 Subject: [PATCH 085/728] New translations run_commonwalkkernel.py (French) --- lang/fr/notebooks/run_commonwalkkernel.py | 83 +++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 lang/fr/notebooks/run_commonwalkkernel.py diff --git a/lang/fr/notebooks/run_commonwalkkernel.py b/lang/fr/notebooks/run_commonwalkkernel.py new file mode 100644 index 0000000000..1422a4d7f7 --- /dev/null +++ b/lang/fr/notebooks/run_commonwalkkernel.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Sep 28 17:01:13 2018 + +@author: ljia +""" + +from libs import * +import multiprocessing + +from gklearn.kernels.commonWalkKernel import commonwalkkernel + +dslist = [ +# {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', +# 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, +# # contains single node graph, node symb +# {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', +# 'task': 'regression'}, # node symb +# {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb +# {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled +# {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb +# {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, +# # node nsymb +# {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb +# {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, + # node symb/nsymb + {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1_A.txt'}, # node symb + {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109_A.txt'}, # node symb + {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb +# +# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, +# # node/edge symb + # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb + # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb + # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb + # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'}, + # + # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb + # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb + # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb + + # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb + # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf', + # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb + + # # not working below + # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',}, + # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',}, + # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',}, + # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',}, +] +estimator = commonwalkkernel +#param_grid_precomputed = [{'compute_method': ['geo'], +# 'weight': np.linspace(0.01, 0.15, 15)}, +## 'weight': np.logspace(-1, -10, num=10, base=10)}, +# {'compute_method': ['exp'], 'weight': range(0, 15)}] +param_grid_precomputed = [{'compute_method': ['geo'], + 'weight': np.linspace(0.01, 0.15, 15)}] +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +for ds in dslist: + print() + print(ds['name']) + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False, + verbose=True) + print() From 3eb9f5a00225da406e0579474ef0be8b7c3c974a Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:52 +0200 Subject: [PATCH 086/728] New translations run_commonwalkkernel.ipynb (French) --- lang/fr/notebooks/run_commonwalkkernel.ipynb | 166 +++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 lang/fr/notebooks/run_commonwalkkernel.ipynb diff --git a/lang/fr/notebooks/run_commonwalkkernel.ipynb b/lang/fr/notebooks/run_commonwalkkernel.ipynb new file mode 100644 index 0000000000..c13109b434 --- /dev/null +++ b/lang/fr/notebooks/run_commonwalkkernel.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Acyclic\n", + "\n", + "--- This is a regression problem ---\n", + "\n", + "\n", + "1. Loading dataset from file...\n", + "\n", + "2. Calculating gram matrices. This could take a while...\n", + "calculating kernels: 16836it [00:04, 3664.86it/s]\n", + "\n", + " --- kernel matrix of common walk kernel of size 183 built in 4.640817880630493 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.01, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:04, 3737.97it/s]\n", + "\n", + " --- kernel matrix of common walk kernel of size 183 built in 4.647830486297607 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.019999999999999997, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:04, 3385.68it/s]\n", + "\n", + " --- kernel matrix of common walk kernel of size 183 built in 5.04192042350769 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.03, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:04, 3624.84it/s]\n", + "\n", + " --- kernel matrix of common walk kernel of size 183 built in 4.778176546096802 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.039999999999999994, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 16836it [00:04, 3619.18it/s]\n", + "\n", + " --- kernel matrix of common walk kernel of size 183 built in 4.761460542678833 seconds ---\n", + "\n", + "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.049999999999999996, 'n_jobs': 8} is: \n", + "\n", + "\n", + "calculating kernels: 10201it [00:02, 3556.34it/s]" + ] + } + ], + "source": [ + "# %load_ext line_profiler\n", + "# %matplotlib inline\n", + "from libs import *\n", + "import multiprocessing\n", + "from sklearn.metrics.pairwise import rbf_kernel\n", + "\n", + "from gklearn.kernels.commonWalkKernel import commonwalkkernel\n", + "from gklearn.utils.kernels import deltakernel, kernelproduct\n", + "\n", + "dslist = [\n", + " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n", + " 'task': 'regression'}, # node symb\n", + " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n", + " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, \n", + " # contains single node graph, node symb\n", + " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb\n", + " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled\n", + " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb\n", + " {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n", + " # node nsymb\n", + " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n", + " # node symb/nsymb\n", + "# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n", + "# # node/edge symb\n", + "# {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb\n", + "\n", + " # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + " # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n", + " # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n", + " # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", + " #\n", + " # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n", + " # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n", + " # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n", + "\n", + " # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n", + " # # {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n", + " # {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n", + " # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n", + " # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n", + " # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n", + "\n", + " # # not working below\n", + " # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n", + " # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n", + " # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n", + " # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n", + "]\n", + "estimator = commonwalkkernel\n", + "param_grid_precomputed = [{'compute_method': ['geo'], \n", + " 'weight': np.linspace(0.01, 0.15, 15)},\n", + "# 'weight': np.logspace(-1, -10, num=10, base=10)},\n", + " {'compute_method': ['exp'], 'weight': range(0, 15)}]\n", + "param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n", + " {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n", + "\n", + "for ds in dslist:\n", + " print()\n", + " print(ds['name'])\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'],\n", + " estimator,\n", + " param_grid_precomputed,\n", + " (param_grid[1] if ('task' in ds and ds['task']\n", + " == 'regression') else param_grid[0]),\n", + " (ds['task'] if 'task' in ds else 'classification'),\n", + " NUM_TRIALS=30,\n", + " datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n", + " extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n", + " ds_name=ds['name'],\n", + " n_jobs=multiprocessing.cpu_count(),\n", + " read_gm_from_file=False,\n", + " verbose=True)\n", + " print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9babef75c1ec5b49eecd0162e18fc1eb63c47700 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:54 +0200 Subject: [PATCH 087/728] New translations libs.py (French) --- lang/fr/notebooks/libs.py | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 lang/fr/notebooks/libs.py diff --git a/lang/fr/notebooks/libs.py b/lang/fr/notebooks/libs.py new file mode 100644 index 0000000000..55d6d2564a --- /dev/null +++ b/lang/fr/notebooks/libs.py @@ -0,0 +1,8 @@ +import sys +import pathlib +sys.path.insert(0, "../") + +import numpy as np + +from gklearn.utils.model_selection_precomputed import model_selection_for_precomputed_kernel +from datasets.ds import dslist \ No newline at end of file From cb0fa07ae9b695551eb37e2532d9d83c9a50b706 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:55 +0200 Subject: [PATCH 088/728] New translations run_ssp_symonly.py (French) --- lang/fr/notebooks/else/run_ssp_symonly.py | 47 +++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 lang/fr/notebooks/else/run_ssp_symonly.py diff --git a/lang/fr/notebooks/else/run_ssp_symonly.py b/lang/fr/notebooks/else/run_ssp_symonly.py new file mode 100644 index 0000000000..081d5968dc --- /dev/null +++ b/lang/fr/notebooks/else/run_ssp_symonly.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sun Dec 23 16:40:52 2018 + +@author: ljia +""" + +import functools +from libs import * +import multiprocessing + +from gklearn.kernels.ssp_sym import structuralspkernel +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + +dslist = [ + {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, + # node nsymb + {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, + # node symb/nsymb +] +estimator = structuralspkernel +mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) +param_grid_precomputed = {'node_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}], + 'edge_kernels': + [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]} +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +for ds in dslist: + print() + print(ds['name']) + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False) + print() \ No newline at end of file From 1b2928c2b0dd1730cb8d677a6a574f85a656c375 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:57 +0200 Subject: [PATCH 089/728] New translations run_sp_symonly.py (French) --- lang/fr/notebooks/else/run_sp_symonly.py | 61 ++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 lang/fr/notebooks/else/run_sp_symonly.py diff --git a/lang/fr/notebooks/else/run_sp_symonly.py b/lang/fr/notebooks/else/run_sp_symonly.py new file mode 100644 index 0000000000..427dd42cf6 --- /dev/null +++ b/lang/fr/notebooks/else/run_sp_symonly.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Dec 21 17:59:28 2018 + +@author: ljia +""" + +import functools +from libs import * +import multiprocessing + +from gklearn.kernels.sp_sym import spkernel +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct +#from gklearn.utils.model_selection_precomputed import trial_do + +dslist = [ + {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'}, + # node nsymb + {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, + # node symb/nsymb + + # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb + # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb + # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb + # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'}, + # + # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb + # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb + # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb + + # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb + # # {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb +] +estimator = spkernel +mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) +param_grid_precomputed = {'node_kernels': [ + {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]} +param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)}, + {'alpha': np.logspace(-10, 10, num=41, base=10)}] + +for ds in dslist: + print() + print(ds['name']) + model_selection_for_precomputed_kernel( + ds['dataset'], + estimator, + param_grid_precomputed, + (param_grid[1] if ('task' in ds and ds['task'] + == 'regression') else param_grid[0]), + (ds['task'] if 'task' in ds else 'classification'), + NUM_TRIALS=30, + datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), + extra_params=(ds['extra_params'] if 'extra_params' in ds else None), + ds_name=ds['name'], + n_jobs=multiprocessing.cpu_count(), + read_gm_from_file=False) + print() From bbe16c46abd77d82cad996e9505eb7d497025d06 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:59 +0200 Subject: [PATCH 090/728] New translations job_test.sl (French) --- lang/fr/notebooks/else/job_test.sl | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 lang/fr/notebooks/else/job_test.sl diff --git a/lang/fr/notebooks/else/job_test.sl b/lang/fr/notebooks/else/job_test.sl new file mode 100644 index 0000000000..dcff855429 --- /dev/null +++ b/lang/fr/notebooks/else/job_test.sl @@ -0,0 +1,12 @@ +#!/bin/bash +# +#SBATCH --job-name=test +#SBATCH --output=res.txt +#SBATCH --partition=long +# +#SBATCH --ntasks=1 +#SBATCH --time=10:00 +#SBATCH --mem-per-cpu=100 + +srun hostname +srun sleep 60 From b1cd3804ca4894a5cb0e7dce29142059525983df Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:00 +0200 Subject: [PATCH 091/728] New translations kernels.py (French) --- lang/fr/gklearn/utils/kernels.py | 158 +++++++++++++++++++++++++++++++ 1 file changed, 158 insertions(+) create mode 100644 lang/fr/gklearn/utils/kernels.py diff --git a/lang/fr/gklearn/utils/kernels.py b/lang/fr/gklearn/utils/kernels.py new file mode 100644 index 0000000000..49ce04b138 --- /dev/null +++ b/lang/fr/gklearn/utils/kernels.py @@ -0,0 +1,158 @@ +"""Those who are not graph kernels. We can be kernels for nodes or edges! +These kernels are defined between pairs of vectors. +""" +import numpy as np + +def deltakernel(x, y): + """Delta kernel. Return 1 if x == y, 0 otherwise. + + Parameters + ---------- + x, y : any + Two parts to compare. + + Return + ------ + kernel : integer + Delta kernel. + + References + ---------- + [1] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between + labeled graphs. In Proceedings of the 20th International Conference on + Machine Learning, Washington, DC, United States, 2003. + """ + return x == y #(1 if condition else 0) + + +def gaussiankernel(x, y, gamma=None): + """Gaussian kernel. + Compute the rbf (gaussian) kernel between x and y: + + K(x, y) = exp(-gamma ||x-y||^2). + + Read more in the `User Guide of scikit-learn library `__. + + Parameters + ---------- + x, y : array + + gamma : float, default None + If None, defaults to 1.0 / n_features + + Returns + ------- + kernel : float + """ + if gamma is None: + gamma = 1.0 / len(x) + + xt = np.array([float(itm) for itm in x]) # @todo: move this to dataset or datafile to speed up. + yt = np.array([float(itm) for itm in y]) + kernel = xt - yt + kernel = kernel ** 2 + kernel = np.sum(kernel) + kernel *= -gamma + kernel = np.exp(kernel) + return kernel + + +def polynomialkernel(x, y, d=1, c=0): + """Polynomial kernel. + Compute the polynomial kernel between x and y: + + K(x, y) = ^d + c. + + Parameters + ---------- + x, y : array + + d : integer, default 1 + + c : float, default 0 + + Returns + ------- + kernel : float + """ + return np.dot(x, y) ** d + c + + +def linearkernel(x, y): + """Polynomial kernel. + Compute the polynomial kernel between x and y: + + K(x, y) = . + + Parameters + ---------- + x, y : array + + d : integer, default 1 + + c : float, default 0 + + Returns + ------- + kernel : float + """ + return np.dot(x, y) + + +def kernelsum(k1, k2, d11, d12, d21=None, d22=None, lamda1=1, lamda2=1): + """Sum of a pair of kernels. + + k = lamda1 * k1(d11, d12) + lamda2 * k2(d21, d22) + + Parameters + ---------- + k1, k2 : function + A pair of kernel functions. + d11, d12: + Inputs of k1. If d21 or d22 is None, apply d11, d12 to both k1 and k2. + d21, d22: + Inputs of k2. + lamda1, lamda2: float + Coefficients of the product. + + Return + ------ + kernel : integer + + """ + if d21 == None or d22 == None: + kernel = lamda1 * k1(d11, d12) + lamda2 * k2(d11, d12) + else: + kernel = lamda1 * k1(d11, d12) + lamda2 * k2(d21, d22) + return kernel + + +def kernelproduct(k1, k2, d11, d12, d21=None, d22=None, lamda=1): + """Product of a pair of kernels. + + k = lamda * k1(d11, d12) * k2(d21, d22) + + Parameters + ---------- + k1, k2 : function + A pair of kernel functions. + d11, d12: + Inputs of k1. If d21 or d22 is None, apply d11, d12 to both k1 and k2. + d21, d22: + Inputs of k2. + lamda: float + Coefficient of the product. + + Return + ------ + kernel : integer + """ + if d21 == None or d22 == None: + kernel = lamda * k1(d11, d12) * k2(d11, d12) + else: + kernel = lamda * k1(d11, d12) * k2(d21, d22) + return kernel + + +if __name__ == '__main__': + o = polynomialkernel([1, 2], [3, 4], 2, 3) From 635e22f8eefb1c7aa44d40ef94e35d5b4011c33f Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:02 +0200 Subject: [PATCH 092/728] New translations job_graphkernels.sl (French) --- lang/fr/notebooks/else/job_graphkernels.sl | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 lang/fr/notebooks/else/job_graphkernels.sl diff --git a/lang/fr/notebooks/else/job_graphkernels.sl b/lang/fr/notebooks/else/job_graphkernels.sl new file mode 100644 index 0000000000..6f982eda55 --- /dev/null +++ b/lang/fr/notebooks/else/job_graphkernels.sl @@ -0,0 +1,19 @@ +#!/bin/bash + +#SBATCH --exclusive +#SBATCH --job-name="graphkernels" +#SBATCH --partition=tcourt +#SBATCH --mail-type=ALL +#SBATCH --mail-user=jajupmochi@gmail.com +#SBATCH --output=output_graphkernels.txt +#SBATCH --error=error_graphkernels.txt +# +#SBATCH --ntasks=1 +#SBATCH --nodes=2 +#SBATCH --cpus-per-task=56 +#SBATCH --time=24:00:00 +#SBATCH --mem-per-cpu=4000 + +srun hostname +srun cd /home/2017018/ljia01/graphkit-learn/notebooks +srun python3 run_spkernel.py From 0644b089b95f5e6dc6317a5316d68c4b8068dd33 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:04 +0200 Subject: [PATCH 093/728] New translations compute_sspkernel_for_syntheticnew.py (French) --- .../compute_sspkernel_for_syntheticnew.py | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 lang/fr/notebooks/else/compute_sspkernel_for_syntheticnew.py diff --git a/lang/fr/notebooks/else/compute_sspkernel_for_syntheticnew.py b/lang/fr/notebooks/else/compute_sspkernel_for_syntheticnew.py new file mode 100644 index 0000000000..6c13ef3b0a --- /dev/null +++ b/lang/fr/notebooks/else/compute_sspkernel_for_syntheticnew.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sun Dec 23 16:40:52 2018 + +@author: ljia +""" +import sys +import numpy as np +import networkx as nx + +sys.path.insert(0, "../") +from gklearn.utils.graphfiles import loadDataset +from gklearn.utils.model_selection_precomputed import compute_gram_matrices +from gklearn.kernels.structuralspKernel import structuralspkernel +from sklearn.model_selection import ParameterGrid + +from libs import * +import multiprocessing +import functools +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + + +if __name__ == "__main__": + # load dataset. + print('getting dataset and computing kernel distance matrix first...') + ds_name = 'SYNTHETICnew' + gkernel = 'structuralspkernel' + dataset = '../datasets/SYNTHETICnew/SYNTHETICnew_A.txt' + Gn, y_all = loadDataset(dataset) + + for G in Gn: + G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl' + + # compute/read Gram matrix and pair distances. + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + Kmatrix, run_time = structuralspkernel(Gn, node_label=None, edge_label=None, + node_kernels=sub_kernels, edge_kernels=sub_kernels, + parallel=None, # parallel='imap_unordered', + n_jobs=multiprocessing.cpu_count(), + verbose=True) + + # normalization + Kmatrix_diag = Kmatrix.diagonal().copy() + for i in range(len(Kmatrix)): + for j in range(i, len(Kmatrix)): + Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j]) + Kmatrix[j][i] = Kmatrix[i][j] + + np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', + Kmatrix=Kmatrix, run_time=run_time) + + print('complete!') \ No newline at end of file From a408759473e2afbc25803f30dc9856d66b83ebb9 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:05 +0200 Subject: [PATCH 094/728] New translations compute_spkernel_for_syntheticnew.py (French) --- .../else/compute_spkernel_for_syntheticnew.py | 52 +++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 lang/fr/notebooks/else/compute_spkernel_for_syntheticnew.py diff --git a/lang/fr/notebooks/else/compute_spkernel_for_syntheticnew.py b/lang/fr/notebooks/else/compute_spkernel_for_syntheticnew.py new file mode 100644 index 0000000000..339cc63545 --- /dev/null +++ b/lang/fr/notebooks/else/compute_spkernel_for_syntheticnew.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sun Dec 23 16:40:52 2018 + +@author: ljia +""" +import sys +import numpy as np +import networkx as nx + +sys.path.insert(0, "../") +from gklearn.utils.graphfiles import loadDataset +from gklearn.utils.model_selection_precomputed import compute_gram_matrices +from gklearn.kernels.spKernel import spkernel +from sklearn.model_selection import ParameterGrid + +from libs import * +import multiprocessing +import functools +from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + + +if __name__ == "__main__": + # load dataset. + print('getting dataset and computing kernel distance matrix first...') + ds_name = 'SYNTHETICnew' + gkernel = 'spkernel' + dataset = '../datasets/SYNTHETICnew/SYNTHETICnew_A.txt' + Gn, y_all = loadDataset(dataset) + + for G in Gn: + G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl' + + # compute/read Gram matrix and pair distances. + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + Kmatrix = np.empty((len(Gn), len(Gn))) + Kmatrix, run_time, idx = spkernel(Gn, node_label=None, node_kernels= + {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}, + n_jobs=multiprocessing.cpu_count(), verbose=True) + + # normalization + Kmatrix_diag = Kmatrix.diagonal().copy() + for i in range(len(Kmatrix)): + for j in range(i, len(Kmatrix)): + Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j]) + Kmatrix[j][i] = Kmatrix[i][j] + + np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', + Kmatrix=Kmatrix, run_time=run_time) + + print('complete!') From c74c87e0cb46d3f7927d2493fe93f1ae25e495a1 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:07 +0200 Subject: [PATCH 095/728] New translations utils.py (French) --- lang/fr/gklearn/utils/utils.py | 605 +++++++++++++++++++++++++++++++++ 1 file changed, 605 insertions(+) create mode 100644 lang/fr/gklearn/utils/utils.py diff --git a/lang/fr/gklearn/utils/utils.py b/lang/fr/gklearn/utils/utils.py new file mode 100644 index 0000000000..c32169de26 --- /dev/null +++ b/lang/fr/gklearn/utils/utils.py @@ -0,0 +1,605 @@ +import networkx as nx +import numpy as np +from copy import deepcopy +from enum import Enum, unique +#from itertools import product + +# from tqdm import tqdm + + +def getSPLengths(G1): + sp = nx.shortest_path(G1) + distances = np.zeros((G1.number_of_nodes(), G1.number_of_nodes())) + for i in sp.keys(): + for j in sp[i].keys(): + distances[i, j] = len(sp[i][j]) - 1 + return distances + + +def getSPGraph(G, edge_weight=None): + """Transform graph G to its corresponding shortest-paths graph. + + Parameters + ---------- + G : NetworkX graph + The graph to be tramsformed. + edge_weight : string + edge attribute corresponding to the edge weight. + + Return + ------ + S : NetworkX graph + The shortest-paths graph corresponding to G. + + Notes + ------ + For an input graph G, its corresponding shortest-paths graph S contains the same set of nodes as G, while there exists an edge between all nodes in S which are connected by a walk in G. Every edge in S between two nodes is labeled by the shortest distance between these two nodes. + + References + ---------- + .. [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE. + """ + return floydTransformation(G, edge_weight=edge_weight) + + +def floydTransformation(G, edge_weight=None): + """Transform graph G to its corresponding shortest-paths graph using Floyd-transformation. + + Parameters + ---------- + G : NetworkX graph + The graph to be tramsformed. + edge_weight : string + edge attribute corresponding to the edge weight. The default edge weight is bond_type. + + Return + ------ + S : NetworkX graph + The shortest-paths graph corresponding to G. + + References + ---------- + .. [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE. + """ + spMatrix = nx.floyd_warshall_numpy(G, weight=edge_weight) + S = nx.Graph() + S.add_nodes_from(G.nodes(data=True)) + ns = list(G.nodes()) + for i in range(0, G.number_of_nodes()): + for j in range(i + 1, G.number_of_nodes()): + if spMatrix[i, j] != np.inf: + S.add_edge(ns[i], ns[j], cost=spMatrix[i, j]) + return S + + +def get_shortest_paths(G, weight, directed): + """Get all shortest paths of a graph. + + Parameters + ---------- + G : NetworkX graphs + The graphs whose paths are calculated. + weight : string/None + edge attribute used as weight to calculate the shortest path. + directed: boolean + Whether graph is directed. + + Return + ------ + sp : list of list + List of shortest paths of the graph, where each path is represented by a list of nodes. + """ + from itertools import combinations + sp = [] + for n1, n2 in combinations(G.nodes(), 2): + try: + spltemp = list(nx.all_shortest_paths(G, n1, n2, weight=weight)) + except nx.NetworkXNoPath: # nodes not connected + pass + else: + sp += spltemp + # each edge walk is counted twice, starting from both its extreme nodes. + if not directed: + sp += [sptemp[::-1] for sptemp in spltemp] + + # add single nodes as length 0 paths. + sp += [[n] for n in G.nodes()] + return sp + + +def untotterTransformation(G, node_label, edge_label): + """Transform graph G according to Mahé et al.'s work to filter out tottering patterns of marginalized kernel and tree pattern kernel. + + Parameters + ---------- + G : NetworkX graph + The graph to be tramsformed. + node_label : string + node attribute used as label. The default node label is 'atom'. + edge_label : string + edge attribute used as label. The default edge label is 'bond_type'. + + Return + ------ + gt : NetworkX graph + The transformed graph corresponding to G. + + References + ---------- + .. [1] Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert. Extensions of marginalized graph kernels. In Proceedings of the twenty-first international conference on Machine learning, page 70. ACM, 2004. + """ + # arrange all graphs in a list + G = G.to_directed() + gt = nx.Graph() + gt.graph = G.graph + gt.add_nodes_from(G.nodes(data=True)) + for edge in G.edges(): + gt.add_node(edge) + gt.nodes[edge].update({node_label: G.nodes[edge[1]][node_label]}) + gt.add_edge(edge[0], edge) + gt.edges[edge[0], edge].update({ + edge_label: + G[edge[0]][edge[1]][edge_label] + }) + for neighbor in G[edge[1]]: + if neighbor != edge[0]: + gt.add_edge(edge, (edge[1], neighbor)) + gt.edges[edge, (edge[1], neighbor)].update({ + edge_label: + G[edge[1]][neighbor][edge_label] + }) + # nx.draw_networkx(gt) + # plt.show() + + # relabel nodes using consecutive integers for convenience of kernel calculation. + gt = nx.convert_node_labels_to_integers( + gt, first_label=0, label_attribute='label_orignal') + return gt + + +def direct_product(G1, G2, node_label, edge_label): + """Return the direct/tensor product of directed graphs G1 and G2. + + Parameters + ---------- + G1, G2 : NetworkX graph + The original graphs. + node_label : string + node attribute used as label. The default node label is 'atom'. + edge_label : string + edge attribute used as label. The default edge label is 'bond_type'. + + Return + ------ + gt : NetworkX graph + The direct product graph of G1 and G2. + + Notes + ----- + This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to the direct product graph. + + References + ---------- + .. [1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003. + """ + # arrange all graphs in a list + from itertools import product + # G = G.to_directed() + gt = nx.DiGraph() + # add nodes + for u, v in product(G1, G2): + if G1.nodes[u][node_label] == G2.nodes[v][node_label]: + gt.add_node((u, v)) + gt.nodes[(u, v)].update({node_label: G1.nodes[u][node_label]}) + # add edges, faster for sparse graphs (no so many edges), which is the most case for now. + for (u1, v1), (u2, v2) in product(G1.edges, G2.edges): + if (u1, u2) in gt and ( + v1, v2 + ) in gt and G1.edges[u1, v1][edge_label] == G2.edges[u2, + v2][edge_label]: + gt.add_edge((u1, u2), (v1, v2)) + gt.edges[(u1, u2), (v1, v2)].update({ + edge_label: + G1.edges[u1, v1][edge_label] + }) + + # # add edges, faster for dense graphs (a lot of edges, complete graph would be super). + # for u, v in product(gt, gt): + # if (u[0], v[0]) in G1.edges and ( + # u[1], v[1] + # ) in G2.edges and G1.edges[u[0], + # v[0]][edge_label] == G2.edges[u[1], + # v[1]][edge_label]: + # gt.add_edge((u[0], u[1]), (v[0], v[1])) + # gt.edges[(u[0], u[1]), (v[0], v[1])].update({ + # edge_label: + # G1.edges[u[0], v[0]][edge_label] + # }) + + # relabel nodes using consecutive integers for convenience of kernel calculation. + # gt = nx.convert_node_labels_to_integers( + # gt, first_label=0, label_attribute='label_orignal') + return gt + + +def direct_product_graph(G1, G2, node_labels, edge_labels): + """Return the direct/tensor product of directed graphs G1 and G2. + + Parameters + ---------- + G1, G2 : NetworkX graph + The original graphs. + node_labels : list + A list of node attributes used as labels. + edge_labels : list + A list of edge attributes used as labels. + + Return + ------ + gt : NetworkX graph + The direct product graph of G1 and G2. + + Notes + ----- + This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to the direct product graph. + + References + ---------- + .. [1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003. + """ + # arrange all graphs in a list + from itertools import product + # G = G.to_directed() + gt = nx.DiGraph() + # add nodes + for u, v in product(G1, G2): + label1 = tuple(G1.nodes[u][nl] for nl in node_labels) + label2 = tuple(G2.nodes[v][nl] for nl in node_labels) + if label1 == label2: + gt.add_node((u, v), node_label=label1) + + # add edges, faster for sparse graphs (no so many edges), which is the most case for now. + for (u1, v1), (u2, v2) in product(G1.edges, G2.edges): + if (u1, u2) in gt and (v1, v2) in gt: + label1 = tuple(G1.edges[u1, v1][el] for el in edge_labels) + label2 = tuple(G2.edges[u2, v2][el] for el in edge_labels) + if label1 == label2: + gt.add_edge((u1, u2), (v1, v2), edge_label=label1) + + + # # add edges, faster for dense graphs (a lot of edges, complete graph would be super). + # for u, v in product(gt, gt): + # if (u[0], v[0]) in G1.edges and ( + # u[1], v[1] + # ) in G2.edges and G1.edges[u[0], + # v[0]][edge_label] == G2.edges[u[1], + # v[1]][edge_label]: + # gt.add_edge((u[0], u[1]), (v[0], v[1])) + # gt.edges[(u[0], u[1]), (v[0], v[1])].update({ + # edge_label: + # G1.edges[u[0], v[0]][edge_label] + # }) + + # relabel nodes using consecutive integers for convenience of kernel calculation. + # gt = nx.convert_node_labels_to_integers( + # gt, first_label=0, label_attribute='label_orignal') + return gt + + +def graph_deepcopy(G): + """Deep copy a graph, including deep copy of all nodes, edges and + attributes of the graph, nodes and edges. + + Note + ---- + It is the same as the NetworkX function graph.copy(), as far as I know. + """ + # add graph attributes. + labels = {} + for k, v in G.graph.items(): + labels[k] = deepcopy(v) + if G.is_directed(): + G_copy = nx.DiGraph(**labels) + else: + G_copy = nx.Graph(**labels) + + # add nodes + for nd, attrs in G.nodes(data=True): + labels = {} + for k, v in attrs.items(): + labels[k] = deepcopy(v) + G_copy.add_node(nd, **labels) + + # add edges. + for nd1, nd2, attrs in G.edges(data=True): + labels = {} + for k, v in attrs.items(): + labels[k] = deepcopy(v) + G_copy.add_edge(nd1, nd2, **labels) + + return G_copy + + +def graph_isIdentical(G1, G2): + """Check if two graphs are identical, including: same nodes, edges, node + labels/attributes, edge labels/attributes. + + Notes + ----- + 1. The type of graphs has to be the same. + + 2. Global/Graph attributes are neglected as they may contain names for graphs. + """ + # check nodes. + nlist1 = [n for n in G1.nodes(data=True)] + nlist2 = [n for n in G2.nodes(data=True)] + if not nlist1 == nlist2: + return False + # check edges. + elist1 = [n for n in G1.edges(data=True)] + elist2 = [n for n in G2.edges(data=True)] + if not elist1 == elist2: + return False + # check graph attributes. + + return True + + +def get_node_labels(Gn, node_label): + """Get node labels of dataset Gn. + """ + nl = set() + for G in Gn: + nl = nl | set(nx.get_node_attributes(G, node_label).values()) + return nl + + +def get_edge_labels(Gn, edge_label): + """Get edge labels of dataset Gn. + """ + el = set() + for G in Gn: + el = el | set(nx.get_edge_attributes(G, edge_label).values()) + return el + + +def get_graph_kernel_by_name(name, node_labels=None, edge_labels=None, node_attrs=None, edge_attrs=None, ds_infos=None, kernel_options={}): + if name == 'Marginalized': + from gklearn.kernels import Marginalized + graph_kernel = Marginalized(node_labels=node_labels, + edge_labels=edge_labels, + ds_infos=ds_infos, + **kernel_options) + elif name == 'ShortestPath': + from gklearn.kernels import ShortestPath + graph_kernel = ShortestPath(node_labels=node_labels, + node_attrs=node_attrs, + ds_infos=ds_infos, + **kernel_options) + elif name == 'StructuralSP': + from gklearn.kernels import StructuralSP + graph_kernel = StructuralSP(node_labels=node_labels, + edge_labels=edge_labels, + node_attrs=node_attrs, + edge_attrs=edge_attrs, + ds_infos=ds_infos, + **kernel_options) + elif name == 'PathUpToH': + from gklearn.kernels import PathUpToH + graph_kernel = PathUpToH(node_labels=node_labels, + edge_labels=edge_labels, + ds_infos=ds_infos, + **kernel_options) + elif name == 'Treelet': + from gklearn.kernels import Treelet + graph_kernel = Treelet(node_labels=node_labels, + edge_labels=edge_labels, + ds_infos=ds_infos, + **kernel_options) + elif name == 'WLSubtree': + from gklearn.kernels import WLSubtree + graph_kernel = WLSubtree(node_labels=node_labels, + edge_labels=edge_labels, + ds_infos=ds_infos, + **kernel_options) + elif name == 'WeisfeilerLehman': + from gklearn.kernels import WeisfeilerLehman + graph_kernel = WeisfeilerLehman(node_labels=node_labels, + edge_labels=edge_labels, + ds_infos=ds_infos, + **kernel_options) + else: + raise Exception('The graph kernel given is not defined. Possible choices include: "StructuralSP", "ShortestPath", "PathUpToH", "Treelet", "WLSubtree", "WeisfeilerLehman".') + + return graph_kernel + + +def compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, dir_save='', irrelevant_labels=None, edge_required=False): + import os + from gklearn.utils import Dataset, split_dataset_by_target + + # 1. get dataset. + print('1. getting dataset...') + dataset_all = Dataset() + dataset_all.load_predefined_dataset(ds_name) + dataset_all.trim_dataset(edge_required=edge_required) + if not irrelevant_labels is None: + dataset_all.remove_labels(**irrelevant_labels) +# dataset_all.cut_graphs(range(0, 10)) + datasets = split_dataset_by_target(dataset_all) + + gram_matrix_unnorm_list = [] + run_time_list = [] + + print('start generating preimage for each class of target...') + for idx, dataset in enumerate(datasets): + target = dataset.targets[0] + print('\ntarget =', target, '\n') + + # 2. initialize graph kernel. + print('2. initializing graph kernel and setting parameters...') + graph_kernel = get_graph_kernel_by_name(kernel_options['name'], + node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, + edge_attrs=dataset.edge_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + kernel_options=kernel_options) + + # 3. compute gram matrix. + print('3. computing gram matrix...') + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, **kernel_options) + gram_matrix_unnorm = graph_kernel.gram_matrix_unnorm + + gram_matrix_unnorm_list.append(gram_matrix_unnorm) + run_time_list.append(run_time) + + # 4. save results. + print() + print('4. saving results...') + if save_results: + if not os.path.exists(dir_save): + os.makedirs(dir_save) + np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm_list=gram_matrix_unnorm_list, run_time_list=run_time_list) + + print('\ncomplete.') + + +def find_paths(G, source_node, length): + """Find all paths with a certain length those start from a source node. + A recursive depth first search is applied. + + Parameters + ---------- + G : NetworkX graphs + The graph in which paths are searched. + source_node : integer + The number of the node from where all paths start. + length : integer + The length of paths. + + Return + ------ + path : list of list + List of paths retrieved, where each path is represented by a list of nodes. + """ + if length == 0: + return [[source_node]] + path = [[source_node] + path for neighbor in G[source_node] \ + for path in find_paths(G, neighbor, length - 1) if source_node not in path] + return path + + +def find_all_paths(G, length, is_directed): + """Find all paths with a certain length in a graph. A recursive depth first + search is applied. + + Parameters + ---------- + G : NetworkX graphs + The graph in which paths are searched. + length : integer + The length of paths. + + Return + ------ + path : list of list + List of paths retrieved, where each path is represented by a list of nodes. + """ + all_paths = [] + for node in G: + all_paths.extend(find_paths(G, node, length)) + + if not is_directed: + # For each path, two presentations are retrieved from its two extremities. + # Remove one of them. + all_paths_r = [path[::-1] for path in all_paths] + for idx, path in enumerate(all_paths[:-1]): + for path2 in all_paths_r[idx+1::]: + if path == path2: + all_paths[idx] = [] + break + all_paths = list(filter(lambda a: a != [], all_paths)) + + return all_paths + + +def get_mlti_dim_node_attrs(G, attr_names): + attributes = [] + for nd, attrs in G.nodes(data=True): + attributes.append(tuple(attrs[aname] for aname in attr_names)) + return attributes + + +def get_mlti_dim_edge_attrs(G, attr_names): + attributes = [] + for ed, attrs in G.edges(data=True): + attributes.append(tuple(attrs[aname] for aname in attr_names)) + return attributes + + +def normalize_gram_matrix(gram_matrix): + diag = gram_matrix.diagonal().copy() + for i in range(len(gram_matrix)): + for j in range(i, len(gram_matrix)): + gram_matrix[i][j] /= np.sqrt(diag[i] * diag[j]) + gram_matrix[j][i] = gram_matrix[i][j] + return gram_matrix + + +def compute_distance_matrix(gram_matrix): + dis_mat = np.empty((len(gram_matrix), len(gram_matrix))) + for i in range(len(gram_matrix)): + for j in range(i, len(gram_matrix)): + dis = gram_matrix[i, i] + gram_matrix[j, j] - 2 * gram_matrix[i, j] + if dis < 0: + if dis > -1e-10: + dis = 0 + else: + raise ValueError('The distance is negative.') + dis_mat[i, j] = np.sqrt(dis) + dis_mat[j, i] = dis_mat[i, j] + dis_max = np.max(np.max(dis_mat)) + dis_min = np.min(np.min(dis_mat[dis_mat != 0])) + dis_mean = np.mean(np.mean(dis_mat)) + return dis_mat, dis_max, dis_min, dis_mean + + +def dummy_node(): + """ + /*! + * @brief Returns a dummy node. + * @return ID of dummy node. + */ + """ + return np.inf # @todo: in GEDLIB, this is the max - 1 rather than max, I don't know why. + + +def undefined_node(): + """ + /*! + * @brief Returns an undefined node. + * @return ID of undefined node. + */ + + """ + return np.inf + + +def dummy_edge(): + """ + /*! + * @brief Returns a dummy edge. + * @return ID of dummy edge. + */ + + """ + return np.inf + + +@unique +class SpecialLabel(Enum): + """can be used to define special labels. + """ + DUMMY = 1 # The dummy label. + # DUMMY = auto # enum.auto does not exist in Python 3.5. \ No newline at end of file From 7e2bbb87d9e6eb4c03a1389377452850dc1bbb85 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:08 +0200 Subject: [PATCH 096/728] New translations trie.py (French) --- lang/fr/gklearn/utils/trie.py | 113 ++++++++++++++++++++++++++++++++++ 1 file changed, 113 insertions(+) create mode 100644 lang/fr/gklearn/utils/trie.py diff --git a/lang/fr/gklearn/utils/trie.py b/lang/fr/gklearn/utils/trie.py new file mode 100644 index 0000000000..8f02e0f019 --- /dev/null +++ b/lang/fr/gklearn/utils/trie.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Jan 30 10:48:49 2019 + +Trie (prefix tree) +@author: ljia +@references: +`NLP: Build a Trie Data structure from scratch with python `__, 2019.1 +""" + +import pickle +import json + +""" Trie class +""" +class Trie: + """ + """ + # init Trie class + def __init__(self): + self.root = self.getNode() + + def getNode(self): + return {"isEndOfWord": False, "children": {}} + + def insertWord(self, word): + current = self.root + for ch in word: + + if ch in current["children"]: + node = current["children"][ch] + else: + node = self.getNode() + current["children"][ch] = node + + current = node + current["isEndOfWord"] = True + if 'count' in current: + current['count'] += 1 + else: + current['count'] = 1 + + def searchWord(self, word): + current = self.root + for ch in word: + if ch not in current["children"]: + return 0 + node = current["children"][ch] + + current = node + if 'count' in current: + return current["count"] + else: + return 0 + + def searchWordPrefix(self, word): + current = self.root + for ch in word: + if not current["children"].has_key(ch): + return False + node = current["children"][ch] + + current = node + # return True if children contain keys and values + return bool(current["children"]) + + def deleteWord(self, word): + self._delete(self.root, word, 0) + + def _delete(self, current, word, index): + if(index == len(word)): + if not current["isEndOfWord"]: + return False + current["isEndOfWord"] = False + return len(current["children"].keys()) == 0 + + ch = word[index] + if not current["children"].has_key(ch): + return False + node = current["children"][ch] + + should_delete_current_node = self._delete(node, word, index + 1) + + if should_delete_current_node: + current["children"].pop(ch) + return len(current["children"].keys()) == 0 + + return False + + def save_to_pickle(self, file_name): + f = open(file_name + ".pkl", "wb") + pickle.dump(self.root, f) + f.close() + + def load_from_pickle(self, file_name): + f = open(file_name + ".pkl", "rb") + self.root = pickle.load(f) + f.close() + + def to_json(self): + return json.dump(self.root) + + def save_to_json(self, file_name): + json_data = json.dumps(self.root) + f = open(file_name + ".json", "w") + f.write(json_data) + f.close() + + def load_from_json(self, file_name): + json_file = open(file_name + ".json", "r") + self.root = json.load(json_file) + json_file.close() From 6e0f1d1c59078d6a920737ea051a737fbe152682 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:10 +0200 Subject: [PATCH 097/728] New translations timer.py (French) --- lang/fr/gklearn/utils/timer.py | 40 ++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 lang/fr/gklearn/utils/timer.py diff --git a/lang/fr/gklearn/utils/timer.py b/lang/fr/gklearn/utils/timer.py new file mode 100644 index 0000000000..b1cecec324 --- /dev/null +++ b/lang/fr/gklearn/utils/timer.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Mar 23 09:52:50 2020 + +@author: ljia +""" +import time + +class Timer(object): + """A timer class that can be used by methods that support time limits. + + Note + ---- + This is the Python implementation of `the C++ code in GEDLIB `__. + """ + + def __init__(self, time_limit_in_sec): + """Constructs a timer for a given time limit. + + Parameters + ---------- + time_limit_in_sec : string + The time limit in seconds. + """ + self.__time_limit_in_sec = time_limit_in_sec + self.__start_time = time.time() + + + def expired(self): + """Checks if the time limit has expired. + + Return + ------ + Boolean true if the time limit has expired and false otherwise. +""" + if self.__time_limit_in_sec > 0: + runtime = time.time() - self.__start_time + return runtime >= self.__time_limit_in_sec + return False \ No newline at end of file From 8e3150c0bbf3a01936f5757f3d8fcf493b381b03 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:11 +0200 Subject: [PATCH 098/728] New translations parallel.py (French) --- lang/fr/gklearn/utils/parallel.py | 66 +++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 lang/fr/gklearn/utils/parallel.py diff --git a/lang/fr/gklearn/utils/parallel.py b/lang/fr/gklearn/utils/parallel.py new file mode 100644 index 0000000000..71bb47c36e --- /dev/null +++ b/lang/fr/gklearn/utils/parallel.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Dec 11 11:39:46 2018 +Parallel aid functions. +@author: ljia +""" +import multiprocessing +from multiprocessing import Pool +from tqdm import tqdm +import sys + +def parallel_me(func, func_assign, var_to_assign, itr, len_itr=None, init_worker=None, + glbv=None, method=None, n_jobs=None, chunksize=None, itr_desc='', + verbose=True): + ''' + ''' + if method == 'imap_unordered': + if glbv: # global varibles required. +# def init_worker(v_share): +# global G_var +# G_var = v_share + if n_jobs == None: + n_jobs = multiprocessing.cpu_count() + with Pool(processes=n_jobs, initializer=init_worker, + initargs=glbv) as pool: + if chunksize is None: + if len_itr < 100 * n_jobs: + chunksize = int(len_itr / n_jobs) + 1 + else: + chunksize = 100 + for result in (tqdm(pool.imap_unordered(func, itr, chunksize), + desc=itr_desc, file=sys.stdout) if verbose else + pool.imap_unordered(func, itr, chunksize)): + func_assign(result, var_to_assign) + pool.close() + pool.join() + else: + if n_jobs == None: + n_jobs = multiprocessing.cpu_count() + with Pool(processes=n_jobs) as pool: + if chunksize is None: + if len_itr < 100 * n_jobs: + chunksize = int(len_itr / n_jobs) + 1 + else: + chunksize = 100 + for result in (tqdm(pool.imap_unordered(func, itr, chunksize), + desc=itr_desc, file=sys.stdout) if verbose else + pool.imap_unordered(func, itr, chunksize)): + func_assign(result, var_to_assign) + pool.close() + pool.join() + + +def parallel_gm(func, Kmatrix, Gn, init_worker=None, glbv=None, + method='imap_unordered', n_jobs=None, chunksize=None, + verbose=True): # @todo: Gn seems not necessary. + from itertools import combinations_with_replacement + def func_assign(result, var_to_assign): + var_to_assign[result[0]][result[1]] = result[2] + var_to_assign[result[1]][result[0]] = result[2] + itr = combinations_with_replacement(range(0, len(Gn)), 2) + len_itr = int(len(Gn) * (len(Gn) + 1) / 2) + parallel_me(func, func_assign, Kmatrix, itr, len_itr=len_itr, + init_worker=init_worker, glbv=glbv, method=method, n_jobs=n_jobs, + chunksize=chunksize, itr_desc='calculating kernels', verbose=verbose) \ No newline at end of file From 144c1036df852015c63973487022d2ccceddde90 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:13 +0200 Subject: [PATCH 099/728] New translations model_selection_precomputed.py (French) --- .../utils/model_selection_precomputed.py | 957 ++++++++++++++++++ 1 file changed, 957 insertions(+) create mode 100644 lang/fr/gklearn/utils/model_selection_precomputed.py diff --git a/lang/fr/gklearn/utils/model_selection_precomputed.py b/lang/fr/gklearn/utils/model_selection_precomputed.py new file mode 100644 index 0000000000..1252f12386 --- /dev/null +++ b/lang/fr/gklearn/utils/model_selection_precomputed.py @@ -0,0 +1,957 @@ +import numpy as np +import matplotlib +matplotlib.use('Agg') +from matplotlib import pyplot as plt +from sklearn.kernel_ridge import KernelRidge +from sklearn.svm import SVC +from sklearn.metrics import accuracy_score, mean_squared_error +from sklearn.model_selection import KFold, train_test_split, ParameterGrid + +#from joblib import Parallel, delayed +from multiprocessing import Pool, Array +from functools import partial +import sys +import os +import time +import datetime +#from os.path import basename, splitext +from gklearn.utils.graphfiles import loadDataset +from tqdm import tqdm + +#from memory_profiler import profile + +#@profile +def model_selection_for_precomputed_kernel(datafile, + estimator, + param_grid_precomputed, + param_grid, + model_type, + NUM_TRIALS=30, + datafile_y=None, + extra_params=None, + ds_name='ds-unknown', + output_dir='outputs/', + n_jobs=1, + read_gm_from_file=False, + verbose=True): + """Perform model selection, fitting and testing for precomputed kernels + using nested CV. Print out neccessary data during the process then finally + the results. + + Parameters + ---------- + datafile : string + Path of dataset file. + estimator : function + kernel function used to estimate. This function needs to return a gram matrix. + param_grid_precomputed : dictionary + Dictionary with names (string) of parameters used to calculate gram + matrices as keys and lists of parameter settings to try as values. This + enables searching over any sequence of parameter settings. Params with + length 1 will be omitted. + param_grid : dictionary + Dictionary with names (string) of parameters used as penelties as keys + and lists of parameter settings to try as values. This enables + searching over any sequence of parameter settings. Params with length 1 + will be omitted. + model_type : string + Type of the problem, can be 'regression' or 'classification'. + NUM_TRIALS : integer + Number of random trials of the outer CV loop. The default is 30. + datafile_y : string + Path of file storing y data. This parameter is optional depending on + the given dataset file. + extra_params : dict + Extra parameters for loading dataset. See function gklearn.utils. + graphfiles.loadDataset for detail. + ds_name : string + Name of the dataset. + n_jobs : int + Number of jobs for parallelization. + read_gm_from_file : boolean + Whether gram matrices are loaded from a file. + + Examples + -------- + >>> import numpy as np + >>> from gklearn.utils.model_selection_precomputed import model_selection_for_precomputed_kernel + >>> from gklearn.kernels.untilHPathKernel import untilhpathkernel + >>> + >>> datafile = '../datasets/MUTAG/MUTAG_A.txt' + >>> estimator = untilhpathkernel + >>> param_grid_precomputed = {’depth’: np.linspace(1, 10, 10), ’k_func’: + [’MinMax’, ’tanimoto’], ’compute_method’: [’trie’]} + >>> # ’C’ for classification problems and ’alpha’ for regression problems. + >>> param_grid = [{’C’: np.logspace(-10, 10, num=41, base=10)}, {’alpha’: + np.logspace(-10, 10, num=41, base=10)}] + >>> + >>> model_selection_for_precomputed_kernel(datafile, estimator, + param_grid_precomputed, param_grid[0], 'classification', ds_name=’MUTAG’) + """ + tqdm.monitor_interval = 0 + + output_dir += estimator.__name__ + if not os.path.exists(output_dir): + os.makedirs(output_dir) + # a string to save all the results. + str_fw = '###################### log time: ' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + '. ######################\n\n' + str_fw += '# This file contains results of ' + estimator.__name__ + ' on dataset ' + ds_name + ',\n# including gram matrices, serial numbers for gram matrix figures and performance.\n\n' + + # setup the model type + model_type = model_type.lower() + if model_type != 'regression' and model_type != 'classification': + raise Exception( + 'The model type is incorrect! Please choose from regression or classification.' + ) + if verbose: + print() + print('--- This is a %s problem ---' % model_type) + str_fw += 'This is a %s problem.\n' % model_type + + # calculate gram matrices rather than read them from file. + if read_gm_from_file == False: + # Load the dataset + if verbose: + print() + print('\n1. Loading dataset from file...') + if isinstance(datafile, str): + dataset, y_all = loadDataset( + datafile, filename_y=datafile_y, extra_params=extra_params) + else: # load data directly from variable. + dataset = datafile + y_all = datafile_y + + # import matplotlib.pyplot as plt + # import networkx as nx + # nx.draw_networkx(dataset[30]) + # plt.show() + + # Grid of parameters with a discrete number of values for each. + param_list_precomputed = list(ParameterGrid(param_grid_precomputed)) + param_list = list(ParameterGrid(param_grid)) + + gram_matrices = [ + ] # a list to store gram matrices for all param_grid_precomputed + gram_matrix_time = [ + ] # a list to store time to calculate gram matrices + param_list_pre_revised = [ + ] # list to store param grids precomputed ignoring the useless ones + + # calculate all gram matrices + if verbose: + print() + print('2. Calculating gram matrices. This could take a while...') + str_fw += '\nII. Gram matrices.\n\n' + tts = time.time() # start training time + nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN) + for idx, params_out in enumerate(param_list_precomputed): + y = y_all[:] + params_out['n_jobs'] = n_jobs + params_out['verbose'] = verbose +# print(dataset) +# import networkx as nx +# nx.draw_networkx(dataset[1]) +# plt.show() + rtn_data = estimator(dataset[:], **params_out) + Kmatrix = rtn_data[0] + current_run_time = rtn_data[1] + # for some kernels, some graphs in datasets may not meet the + # kernels' requirements for graph structure. These graphs are trimmed. + if len(rtn_data) == 3: + idx_trim = rtn_data[2] # the index of trimmed graph list + y = [y[idxt] for idxt in idx_trim] # trim y accordingly +# Kmatrix = np.random.rand(2250, 2250) +# current_run_time = 0.1 + + # remove graphs whose kernels with themselves are zeros + # @todo: y not changed accordingly? + Kmatrix_diag = Kmatrix.diagonal().copy() + nb_g_ignore = 0 + for idxk, diag in enumerate(Kmatrix_diag): + if diag == 0: + Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=0) + Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=1) + nb_g_ignore += 1 + # normalization + # @todo: works only for undirected graph? + Kmatrix_diag = Kmatrix.diagonal().copy() + for i in range(len(Kmatrix)): + for j in range(i, len(Kmatrix)): + Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j]) + Kmatrix[j][i] = Kmatrix[i][j] + if verbose: + print() + if params_out == {}: + if verbose: + print('the gram matrix is: ') + str_fw += 'the gram matrix is:\n\n' + else: + if verbose: + print('the gram matrix with parameters', params_out, 'is: \n\n') + str_fw += 'the gram matrix with parameters %s is:\n\n' % params_out + if len(Kmatrix) < 2: + nb_gm_ignore += 1 + if verbose: + print('ignored, as at most only one of all its diagonal value is non-zero.') + str_fw += 'ignored, as at most only one of all its diagonal value is non-zero.\n\n' + else: + if np.isnan(Kmatrix).any( + ): # if the matrix contains elements that are not numbers + nb_gm_ignore += 1 + if verbose: + print('ignored, as it contains elements that are not numbers.') + str_fw += 'ignored, as it contains elements that are not numbers.\n\n' + else: +# print(Kmatrix) + str_fw += np.array2string( + Kmatrix, + separator=',') + '\n\n' +# separator=',', +# threshold=np.inf, +# floatmode='unique') + '\n\n' + + fig_file_name = output_dir + '/GM[ds]' + ds_name + if params_out != {}: + fig_file_name += '[params]' + str(idx) + plt.imshow(Kmatrix) + plt.colorbar() + plt.savefig(fig_file_name + '.eps', format='eps', dpi=300) +# plt.show() + plt.clf() + gram_matrices.append(Kmatrix) + gram_matrix_time.append(current_run_time) + param_list_pre_revised.append(params_out) + if nb_g_ignore > 0: + if verbose: + print(', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore) + str_fw += ', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore + if verbose: + print() + print( + '{} gram matrices are calculated, {} of which are ignored.'.format( + len(param_list_precomputed), nb_gm_ignore)) + str_fw += '{} gram matrices are calculated, {} of which are ignored.\n\n'.format(len(param_list_precomputed), nb_gm_ignore) + str_fw += 'serial numbers of gram matrix figures and their corresponding parameters settings:\n\n' + str_fw += ''.join([ + '{}: {}\n'.format(idx, params_out) + for idx, params_out in enumerate(param_list_precomputed) + ]) + + if verbose: + print() + if len(gram_matrices) == 0: + if verbose: + print('all gram matrices are ignored, no results obtained.') + str_fw += '\nall gram matrices are ignored, no results obtained.\n\n' + else: + # save gram matrices to file. +# np.savez(output_dir + '/' + ds_name + '.gm', +# gms=gram_matrices, params=param_list_pre_revised, y=y, +# gmtime=gram_matrix_time) + if verbose: + print( + '3. Fitting and predicting using nested cross validation. This could really take a while...' + ) + + # ---- use pool.imap_unordered to parallel and track progress. ---- +# train_pref = [] +# val_pref = [] +# test_pref = [] +# def func_assign(result, var_to_assign): +# for idx, itm in enumerate(var_to_assign): +# itm.append(result[idx]) +# trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, y, model_type) +# +# parallel_me(trial_do_partial, range(NUM_TRIALS), func_assign, +# [train_pref, val_pref, test_pref], glbv=gram_matrices, +# method='imap_unordered', n_jobs=n_jobs, chunksize=1, +# itr_desc='cross validation') + + def init_worker(gms_toshare): + global G_gms + G_gms = gms_toshare + +# gram_matrices = np.array(gram_matrices) +# gms_shape = gram_matrices.shape +# gms_array = Array('d', np.reshape(gram_matrices.copy(), -1, order='C')) +# pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gms_array, gms_shape)) + pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gram_matrices,)) + trial_do_partial = partial(parallel_trial_do, param_list_pre_revised, param_list, y, model_type) + train_pref = [] + val_pref = [] + test_pref = [] +# if NUM_TRIALS < 1000 * n_jobs: +# chunksize = int(NUM_TRIALS / n_jobs) + 1 +# else: +# chunksize = 1000 + chunksize = 1 + if verbose: + iterator = tqdm(pool.imap_unordered(trial_do_partial, + range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout) + else: + iterator = pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize) + for o1, o2, o3 in iterator: + train_pref.append(o1) + val_pref.append(o2) + test_pref.append(o3) + pool.close() + pool.join() + +# # ---- use pool.map to parallel. ---- +# pool = Pool(n_jobs) +# trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y[0:250], model_type) +# result_perf = pool.map(trial_do_partial, range(NUM_TRIALS)) +# train_pref = [item[0] for item in result_perf] +# val_pref = [item[1] for item in result_perf] +# test_pref = [item[2] for item in result_perf] + +# # ---- direct running, normally use a single CPU core. ---- +# train_pref = [] +# val_pref = [] +# test_pref = [] +# for i in tqdm(range(NUM_TRIALS), desc='cross validation', file=sys.stdout): +# o1, o2, o3 = trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, i) +# train_pref.append(o1) +# val_pref.append(o2) +# test_pref.append(o3) +# print() + + if verbose: + print() + print('4. Getting final performance...') + str_fw += '\nIII. Performance.\n\n' + # averages and confidences of performances on outer trials for each combination of parameters + average_train_scores = np.mean(train_pref, axis=0) +# print('val_pref: ', val_pref[0][0]) + average_val_scores = np.mean(val_pref, axis=0) +# print('test_pref: ', test_pref[0][0]) + average_perf_scores = np.mean(test_pref, axis=0) + # sample std is used here + std_train_scores = np.std(train_pref, axis=0, ddof=1) + std_val_scores = np.std(val_pref, axis=0, ddof=1) + std_perf_scores = np.std(test_pref, axis=0, ddof=1) + + if model_type == 'regression': + best_val_perf = np.amin(average_val_scores) + else: + best_val_perf = np.amax(average_val_scores) +# print('average_val_scores: ', average_val_scores) +# print('best_val_perf: ', best_val_perf) +# print() + best_params_index = np.where(average_val_scores == best_val_perf) + # find smallest val std with best val perf. + best_val_stds = [ + std_val_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + min_val_std = np.amin(best_val_stds) + best_params_index = np.where(std_val_scores == min_val_std) + best_params_out = [ + param_list_pre_revised[i] for i in best_params_index[0] + ] + best_params_in = [param_list[i] for i in best_params_index[1]] + if verbose: + print('best_params_out: ', best_params_out) + print('best_params_in: ', best_params_in) + print() + print('best_val_perf: ', best_val_perf) + print('best_val_std: ', min_val_std) + str_fw += 'best settings of hyper-params to build gram matrix: %s\n' % best_params_out + str_fw += 'best settings of other hyper-params: %s\n\n' % best_params_in + str_fw += 'best_val_perf: %s\n' % best_val_perf + str_fw += 'best_val_std: %s\n' % min_val_std + +# print(best_params_index) +# print(best_params_index[0]) +# print(average_perf_scores) + final_performance = [ + average_perf_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + final_confidence = [ + std_perf_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + if verbose: + print('final_performance: ', final_performance) + print('final_confidence: ', final_confidence) + str_fw += 'final_performance: %s\n' % final_performance + str_fw += 'final_confidence: %s\n' % final_confidence + train_performance = [ + average_train_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + train_std = [ + std_train_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + if verbose: + print('train_performance: %s' % train_performance) + print('train_std: ', train_std) + str_fw += 'train_performance: %s\n' % train_performance + str_fw += 'train_std: %s\n\n' % train_std + + if verbose: + print() + tt_total = time.time() - tts # training time for all hyper-parameters + average_gram_matrix_time = np.mean(gram_matrix_time) + std_gram_matrix_time = np.std(gram_matrix_time, ddof=1) if len(gram_matrix_time) > 1 else 0 + best_gram_matrix_time = [ + gram_matrix_time[i] for i in best_params_index[0] + ] + ave_bgmt = np.mean(best_gram_matrix_time) + std_bgmt = np.std(best_gram_matrix_time, ddof=1) if len(best_gram_matrix_time) > 1 else 0 + if verbose: + print('time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s' + .format(average_gram_matrix_time, std_gram_matrix_time)) + print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format( + ave_bgmt, std_bgmt)) + print('total training time with all hyper-param choices: {:.2f}s'.format( + tt_total)) + str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\n'.format(average_gram_matrix_time, std_gram_matrix_time) + str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\n'.format(ave_bgmt, std_bgmt) + str_fw += 'total training time with all hyper-param choices: {:.2f}s\n\n'.format(tt_total) + + # # save results to file + # np.savetxt(results_name_pre + 'average_train_scores.dt', + # average_train_scores) + # np.savetxt(results_name_pre + 'average_val_scores', average_val_scores) + # np.savetxt(results_name_pre + 'average_perf_scores.dt', + # average_perf_scores) + # np.savetxt(results_name_pre + 'std_train_scores.dt', std_train_scores) + # np.savetxt(results_name_pre + 'std_val_scores.dt', std_val_scores) + # np.savetxt(results_name_pre + 'std_perf_scores.dt', std_perf_scores) + + # np.save(results_name_pre + 'best_params_index', best_params_index) + # np.save(results_name_pre + 'best_params_pre.dt', best_params_out) + # np.save(results_name_pre + 'best_params_in.dt', best_params_in) + # np.save(results_name_pre + 'best_val_perf.dt', best_val_perf) + # np.save(results_name_pre + 'best_val_std.dt', best_val_std) + # np.save(results_name_pre + 'final_performance.dt', final_performance) + # np.save(results_name_pre + 'final_confidence.dt', final_confidence) + # np.save(results_name_pre + 'train_performance.dt', train_performance) + # np.save(results_name_pre + 'train_std.dt', train_std) + + # np.save(results_name_pre + 'gram_matrix_time.dt', gram_matrix_time) + # np.save(results_name_pre + 'average_gram_matrix_time.dt', + # average_gram_matrix_time) + # np.save(results_name_pre + 'std_gram_matrix_time.dt', + # std_gram_matrix_time) + # np.save(results_name_pre + 'best_gram_matrix_time.dt', + # best_gram_matrix_time) + + # read gram matrices from file. + else: + # Grid of parameters with a discrete number of values for each. +# param_list_precomputed = list(ParameterGrid(param_grid_precomputed)) + param_list = list(ParameterGrid(param_grid)) + + # read gram matrices from file. + if verbose: + print() + print('2. Reading gram matrices from file...') + str_fw += '\nII. Gram matrices.\n\nGram matrices are read from file, see last log for detail.\n' + gmfile = np.load(output_dir + '/' + ds_name + '.gm.npz') + gram_matrices = gmfile['gms'] # a list to store gram matrices for all param_grid_precomputed + gram_matrix_time = gmfile['gmtime'] # time used to compute the gram matrices + param_list_pre_revised = gmfile['params'] # list to store param grids precomputed ignoring the useless ones + y = gmfile['y'].tolist() + + tts = time.time() # start training time +# nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN) + if verbose: + print( + '3. Fitting and predicting using nested cross validation. This could really take a while...' + ) + + # ---- use pool.imap_unordered to parallel and track progress. ---- + def init_worker(gms_toshare): + global G_gms + G_gms = gms_toshare + + pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gram_matrices,)) + trial_do_partial = partial(parallel_trial_do, param_list_pre_revised, param_list, y, model_type) + train_pref = [] + val_pref = [] + test_pref = [] + chunksize = 1 + if verbose: + iterator = tqdm(pool.imap_unordered(trial_do_partial, + range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout) + else: + iterator = pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize) + for o1, o2, o3 in iterator: + train_pref.append(o1) + val_pref.append(o2) + test_pref.append(o3) + pool.close() + pool.join() + + # # ---- use pool.map to parallel. ---- + # result_perf = pool.map(trial_do_partial, range(NUM_TRIALS)) + # train_pref = [item[0] for item in result_perf] + # val_pref = [item[1] for item in result_perf] + # test_pref = [item[2] for item in result_perf] + + # # ---- use joblib.Parallel to parallel and track progress. ---- + # trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y, model_type) + # result_perf = Parallel(n_jobs=n_jobs, verbose=10)(delayed(trial_do_partial)(trial) for trial in range(NUM_TRIALS)) + # train_pref = [item[0] for item in result_perf] + # val_pref = [item[1] for item in result_perf] + # test_pref = [item[2] for item in result_perf] + +# # ---- direct running, normally use a single CPU core. ---- +# train_pref = [] +# val_pref = [] +# test_pref = [] +# for i in tqdm(range(NUM_TRIALS), desc='cross validation', file=sys.stdout): +# o1, o2, o3 = trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, i) +# train_pref.append(o1) +# val_pref.append(o2) +# test_pref.append(o3) + + if verbose: + print() + print('4. Getting final performance...') + str_fw += '\nIII. Performance.\n\n' + # averages and confidences of performances on outer trials for each combination of parameters + average_train_scores = np.mean(train_pref, axis=0) + average_val_scores = np.mean(val_pref, axis=0) + average_perf_scores = np.mean(test_pref, axis=0) + # sample std is used here + std_train_scores = np.std(train_pref, axis=0, ddof=1) + std_val_scores = np.std(val_pref, axis=0, ddof=1) + std_perf_scores = np.std(test_pref, axis=0, ddof=1) + + if model_type == 'regression': + best_val_perf = np.amin(average_val_scores) + else: + best_val_perf = np.amax(average_val_scores) + best_params_index = np.where(average_val_scores == best_val_perf) + # find smallest val std with best val perf. + best_val_stds = [ + std_val_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + min_val_std = np.amin(best_val_stds) + best_params_index = np.where(std_val_scores == min_val_std) + best_params_out = [ + param_list_pre_revised[i] for i in best_params_index[0] + ] + best_params_in = [param_list[i] for i in best_params_index[1]] + if verbose: + print('best_params_out: ', best_params_out) + print('best_params_in: ', best_params_in) + print() + print('best_val_perf: ', best_val_perf) + print('best_val_std: ', min_val_std) + str_fw += 'best settings of hyper-params to build gram matrix: %s\n' % best_params_out + str_fw += 'best settings of other hyper-params: %s\n\n' % best_params_in + str_fw += 'best_val_perf: %s\n' % best_val_perf + str_fw += 'best_val_std: %s\n' % min_val_std + + final_performance = [ + average_perf_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + final_confidence = [ + std_perf_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + if verbose: + print('final_performance: ', final_performance) + print('final_confidence: ', final_confidence) + str_fw += 'final_performance: %s\n' % final_performance + str_fw += 'final_confidence: %s\n' % final_confidence + train_performance = [ + average_train_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + train_std = [ + std_train_scores[value][best_params_index[1][idx]] + for idx, value in enumerate(best_params_index[0]) + ] + if verbose: + print('train_performance: %s' % train_performance) + print('train_std: ', train_std) + str_fw += 'train_performance: %s\n' % train_performance + str_fw += 'train_std: %s\n\n' % train_std + + if verbose: + print() + average_gram_matrix_time = np.mean(gram_matrix_time) + std_gram_matrix_time = np.std(gram_matrix_time, ddof=1) if len(gram_matrix_time) > 1 else 0 + best_gram_matrix_time = [ + gram_matrix_time[i] for i in best_params_index[0] + ] + ave_bgmt = np.mean(best_gram_matrix_time) + std_bgmt = np.std(best_gram_matrix_time, ddof=1) if len(best_gram_matrix_time) > 1 else 0 + if verbose: + print( + 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s' + .format(average_gram_matrix_time, std_gram_matrix_time)) + print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format( + ave_bgmt, std_bgmt)) + tt_poster = time.time() - tts # training time with hyper-param choices who did not participate in calculation of gram matrices + if verbose: + print( + 'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s'.format( + tt_poster)) + print('total training time with all hyper-param choices: {:.2f}s'.format( + tt_poster + np.sum(gram_matrix_time))) +# str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\n'.format(average_gram_matrix_time, std_gram_matrix_time) +# str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\n'.format(ave_bgmt, std_bgmt) + str_fw += 'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s\n\n'.format(tt_poster) + + # open file to save all results for this dataset. + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # print out results as table. + str_fw += printResultsInTable(param_list, param_list_pre_revised, average_val_scores, + std_val_scores, average_perf_scores, std_perf_scores, + average_train_scores, std_train_scores, gram_matrix_time, + model_type, verbose) + + # open file to save all results for this dataset. + if not os.path.exists(output_dir + '/' + ds_name + '.output.txt'): + with open(output_dir + '/' + ds_name + '.output.txt', 'w') as f: + f.write(str_fw) + else: + with open(output_dir + '/' + ds_name + '.output.txt', 'r+') as f: + content = f.read() + f.seek(0, 0) + f.write(str_fw + '\n\n\n' + content) + + +def trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, trial): # Test set level + +# # get gram matrices from global variables. +# gram_matrices = np.reshape(G_gms.copy(), G_gms_shape, order='C') + + # Arrays to store scores + train_pref = np.zeros((len(param_list_pre_revised), len(param_list))) + val_pref = np.zeros((len(param_list_pre_revised), len(param_list))) + test_pref = np.zeros((len(param_list_pre_revised), len(param_list))) + + # randomness added to seeds of split function below. "high" is "size" times + # 10 so that at least 10 different random output will be yielded. Remove + # these lines if identical outputs is required. + rdm_out = np.random.RandomState(seed=None) + rdm_seed_out_l = rdm_out.uniform(high=len(param_list_pre_revised) * 10, + size=len(param_list_pre_revised)) +# print(trial, rdm_seed_out_l) +# print() + # loop for each outer param tuple + for index_out, params_out in enumerate(param_list_pre_revised): + # get gram matrices from global variables. +# gm_now = G_gms[index_out * G_gms_shape[1] * G_gms_shape[2]:(index_out + 1) * G_gms_shape[1] * G_gms_shape[2]] +# gm_now = np.reshape(gm_now.copy(), (G_gms_shape[1], G_gms_shape[2]), order='C') + gm_now = gram_matrices[index_out].copy() + + # split gram matrix and y to app and test sets. + indices = range(len(y)) + # The argument "random_state" in function "train_test_split" can not be + # set to None, because it will use RandomState instance used by + # np.random, which is possible for multiple subprocesses to inherit the + # same seed if they forked at the same time, leading to identical + # random variates for different subprocesses. Instead, we use "trial" + # and "index_out" parameters to generate different seeds for different + # trials/subprocesses and outer loops. "rdm_seed_out_l" is used to add + # randomness into seeds, so that it yields a different output every + # time the program is run. To yield identical outputs every time, + # remove the second line below. Same method is used to the "KFold" + # function in the inner loop. + rdm_seed_out = (trial + 1) * (index_out + 1) + rdm_seed_out = (rdm_seed_out + int(rdm_seed_out_l[index_out])) % (2 ** 32 - 1) +# print(trial, rdm_seed_out) + X_app, X_test, y_app, y_test, idx_app, idx_test = train_test_split( + gm_now, y, indices, test_size=0.1, + random_state=rdm_seed_out, shuffle=True) +# print(trial, idx_app, idx_test) +# print() + X_app = X_app[:, idx_app] + X_test = X_test[:, idx_app] + y_app = np.array(y_app) + y_test = np.array(y_test) + + rdm_seed_in_l = rdm_out.uniform(high=len(param_list) * 10, + size=len(param_list)) + # loop for each inner param tuple + for index_in, params_in in enumerate(param_list): +# if trial == 0: +# print(index_out, index_in) +# print('params_in: ', params_in) +# st = time.time() + rdm_seed_in = (trial + 1) * (index_out + 1) * (index_in + 1) +# print("rdm_seed_in1: ", trial, index_in, rdm_seed_in) + rdm_seed_in = (rdm_seed_in + int(rdm_seed_in_l[index_in])) % (2 ** 32 - 1) +# print("rdm_seed_in2: ", trial, index_in, rdm_seed_in) + inner_cv = KFold(n_splits=10, shuffle=True, random_state=rdm_seed_in) + current_train_perf = [] + current_valid_perf = [] + current_test_perf = [] + + # For regression use the Kernel Ridge method +# try: + if model_type == 'regression': + kr = KernelRidge(kernel='precomputed', **params_in) + # loop for each split on validation set level + # validation set level + for train_index, valid_index in inner_cv.split(X_app): +# print("train_index, valid_index: ", trial, index_in, train_index, valid_index) +# if trial == 0: +# print('train_index: ', train_index) +# print('valid_index: ', valid_index) +# print('idx_test: ', idx_test) +# print('y_app[train_index]: ', y_app[train_index]) +# print('X_app[train_index, :][:, train_index]: ', X_app[train_index, :][:, train_index]) +# print('X_app[valid_index, :][:, train_index]: ', X_app[valid_index, :][:, train_index]) + kr.fit(X_app[train_index, :][:, train_index], + y_app[train_index]) + + # predict on the train, validation and test set + y_pred_train = kr.predict( + X_app[train_index, :][:, train_index]) + y_pred_valid = kr.predict( + X_app[valid_index, :][:, train_index]) +# if trial == 0: +# print('y_pred_valid: ', y_pred_valid) +# print() + y_pred_test = kr.predict( + X_test[:, train_index]) + + # root mean squared errors + current_train_perf.append( + np.sqrt( + mean_squared_error( + y_app[train_index], y_pred_train))) + current_valid_perf.append( + np.sqrt( + mean_squared_error( + y_app[valid_index], y_pred_valid))) +# if trial == 0: +# print(mean_squared_error( +# y_app[valid_index], y_pred_valid)) + current_test_perf.append( + np.sqrt( + mean_squared_error( + y_test, y_pred_test))) + # For clcassification use SVM + else: + svc = SVC(kernel='precomputed', cache_size=200, + verbose=False, **params_in) + # loop for each split on validation set level + # validation set level + for train_index, valid_index in inner_cv.split(X_app): +# np.savez("bug.npy",X_app[train_index, :][:, train_index],y_app[train_index]) +# if trial == 0: +# print('train_index: ', train_index) +# print('valid_index: ', valid_index) +# print('idx_test: ', idx_test) +# print('y_app[train_index]: ', y_app[train_index]) +# print('X_app[train_index, :][:, train_index]: ', X_app[train_index, :][:, train_index]) +# print('X_app[valid_index, :][:, train_index]: ', X_app[valid_index, :][:, train_index]) + svc.fit(X_app[train_index, :][:, train_index], + y_app[train_index]) + + # predict on the train, validation and test set + y_pred_train = svc.predict( + X_app[train_index, :][:, train_index]) + y_pred_valid = svc.predict( + X_app[valid_index, :][:, train_index]) + y_pred_test = svc.predict( + X_test[:, train_index]) + + # root mean squared errors + current_train_perf.append( + accuracy_score(y_app[train_index], + y_pred_train)) + current_valid_perf.append( + accuracy_score(y_app[valid_index], + y_pred_valid)) + current_test_perf.append( + accuracy_score(y_test, y_pred_test)) +# except ValueError: +# print(sys.exc_info()[0]) +# print(params_out, params_in) + + # average performance on inner splits + train_pref[index_out][index_in] = np.mean( + current_train_perf) + val_pref[index_out][index_in] = np.mean( + current_valid_perf) + test_pref[index_out][index_in] = np.mean( + current_test_perf) +# print(time.time() - st) +# if trial == 0: +# print('val_pref: ', val_pref) +# print('test_pref: ', test_pref) + + return train_pref, val_pref, test_pref + +def parallel_trial_do(param_list_pre_revised, param_list, y, model_type, trial): + train_pref, val_pref, test_pref = trial_do(param_list_pre_revised, + param_list, G_gms, y, + model_type, trial) + return train_pref, val_pref, test_pref + + +def compute_gram_matrices(dataset, y, estimator, param_list_precomputed, + output_dir, ds_name, + n_jobs=1, str_fw='', verbose=True): + gram_matrices = [ + ] # a list to store gram matrices for all param_grid_precomputed + gram_matrix_time = [ + ] # a list to store time to calculate gram matrices + param_list_pre_revised = [ + ] # list to store param grids precomputed ignoring the useless ones + + nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN) + for idx, params_out in enumerate(param_list_precomputed): + params_out['n_jobs'] = n_jobs +# print(dataset) +# import networkx as nx +# nx.draw_networkx(dataset[1]) +# plt.show() + rtn_data = estimator(dataset[:], **params_out) + Kmatrix = rtn_data[0] + current_run_time = rtn_data[1] + # for some kernels, some graphs in datasets may not meet the + # kernels' requirements for graph structure. These graphs are trimmed. + if len(rtn_data) == 3: + idx_trim = rtn_data[2] # the index of trimmed graph list + y = [y[idxt] for idxt in idx_trim] # trim y accordingly + + Kmatrix_diag = Kmatrix.diagonal().copy() + # remove graphs whose kernels with themselves are zeros + nb_g_ignore = 0 + for idxk, diag in enumerate(Kmatrix_diag): + if diag == 0: + Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=0) + Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=1) + nb_g_ignore += 1 + # normalization + for i in range(len(Kmatrix)): + for j in range(i, len(Kmatrix)): + Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j]) + Kmatrix[j][i] = Kmatrix[i][j] + + if verbose: + print() + if params_out == {}: + if verbose: + print('the gram matrix is: ') + str_fw += 'the gram matrix is:\n\n' + else: + if verbose: + print('the gram matrix with parameters', params_out, 'is: ') + str_fw += 'the gram matrix with parameters %s is:\n\n' % params_out + if len(Kmatrix) < 2: + nb_gm_ignore += 1 + if verbose: + print('ignored, as at most only one of all its diagonal value is non-zero.') + str_fw += 'ignored, as at most only one of all its diagonal value is non-zero.\n\n' + else: + if np.isnan(Kmatrix).any( + ): # if the matrix contains elements that are not numbers + nb_gm_ignore += 1 + if verbose: + print('ignored, as it contains elements that are not numbers.') + str_fw += 'ignored, as it contains elements that are not numbers.\n\n' + else: +# print(Kmatrix) + str_fw += np.array2string( + Kmatrix, + separator=',') + '\n\n' +# separator=',', +# threshold=np.inf, +# floatmode='unique') + '\n\n' + + fig_file_name = output_dir + '/GM[ds]' + ds_name + if params_out != {}: + fig_file_name += '[params]' + str(idx) + plt.imshow(Kmatrix) + plt.colorbar() + plt.savefig(fig_file_name + '.eps', format='eps', dpi=300) +# plt.show() + plt.clf() + gram_matrices.append(Kmatrix) + gram_matrix_time.append(current_run_time) + param_list_pre_revised.append(params_out) + if nb_g_ignore > 0: + if verbose: + print(', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore) + str_fw += ', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore + if verbose: + print() + print( + '{} gram matrices are calculated, {} of which are ignored.'.format( + len(param_list_precomputed), nb_gm_ignore)) + str_fw += '{} gram matrices are calculated, {} of which are ignored.\n\n'.format(len(param_list_precomputed), nb_gm_ignore) + str_fw += 'serial numbers of gram matrix figures and their corresponding parameters settings:\n\n' + str_fw += ''.join([ + '{}: {}\n'.format(idx, params_out) + for idx, params_out in enumerate(param_list_precomputed) + ]) + + return gram_matrices, gram_matrix_time, param_list_pre_revised, y, str_fw + + +def read_gram_matrices_from_file(output_dir, ds_name): + gmfile = np.load(output_dir + '/' + ds_name + '.gm.npz') + gram_matrices = gmfile['gms'] # a list to store gram matrices for all param_grid_precomputed + param_list_pre_revised = gmfile['params'] # list to store param grids precomputed ignoring the useless ones + y = gmfile['y'].tolist() + return gram_matrices, param_list_pre_revised, y + + +def printResultsInTable(param_list, param_list_pre_revised, average_val_scores, + std_val_scores, average_perf_scores, std_perf_scores, + average_train_scores, std_train_scores, gram_matrix_time, + model_type, verbose): + from collections import OrderedDict + from tabulate import tabulate + table_dict = {} + if model_type == 'regression': + for param_in in param_list: + param_in['alpha'] = '{:.2e}'.format(param_in['alpha']) + else: + for param_in in param_list: + param_in['C'] = '{:.2e}'.format(param_in['C']) + table_dict['params'] = [{**param_out, **param_in} + for param_in in param_list for param_out in param_list_pre_revised] + table_dict['gram_matrix_time'] = [ + '{:.2f}'.format(gram_matrix_time[index_out]) + for param_in in param_list + for index_out, _ in enumerate(param_list_pre_revised) + ] + table_dict['valid_perf'] = [ + '{:.2f}±{:.2f}'.format(average_val_scores[index_out][index_in], + std_val_scores[index_out][index_in]) + for index_in, _ in enumerate(param_list) + for index_out, _ in enumerate(param_list_pre_revised) + ] + table_dict['test_perf'] = [ + '{:.2f}±{:.2f}'.format(average_perf_scores[index_out][index_in], + std_perf_scores[index_out][index_in]) + for index_in, _ in enumerate(param_list) + for index_out, _ in enumerate(param_list_pre_revised) + ] + table_dict['train_perf'] = [ + '{:.2f}±{:.2f}'.format(average_train_scores[index_out][index_in], + std_train_scores[index_out][index_in]) + for index_in, _ in enumerate(param_list) + for index_out, _ in enumerate(param_list_pre_revised) + ] + + keyorder = [ + 'params', 'train_perf', 'valid_perf', 'test_perf', + 'gram_matrix_time' + ] + if verbose: + print() + tb_print = tabulate(OrderedDict(sorted(table_dict.items(), + key=lambda i: keyorder.index(i[0]))), headers='keys') +# print(tb_print) + return 'table of performance v.s. hyper-params:\n\n%s\n\n' % tb_print \ No newline at end of file From e7691b31e928f2ce2dea5b69dc5d2460e945ad84 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:15 +0200 Subject: [PATCH 100/728] New translations logger2file.py (French) --- lang/fr/gklearn/utils/logger2file.py | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 lang/fr/gklearn/utils/logger2file.py diff --git a/lang/fr/gklearn/utils/logger2file.py b/lang/fr/gklearn/utils/logger2file.py new file mode 100644 index 0000000000..2c7fc0b586 --- /dev/null +++ b/lang/fr/gklearn/utils/logger2file.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Nov 8 14:21:25 2019 + +@author: ljia +""" + +import sys +import time + +class Logger(object): + def __init__(self): + self.terminal = sys.stdout + self.log = open("log." + str(time.time()) + ".log", "a") + + def write(self, message): + self.terminal.write(message) + self.log.write(message) + + def flush(self): + #this flush method is needed for python 3 compatibility. + #this handles the flush command by doing nothing. + #you might want to specify some extra behavior here. + pass + +sys.stdout = Logger() \ No newline at end of file From d0ec6ec1a2b3fcb88f97068a07c765d8b0b4ae18 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:16 +0200 Subject: [PATCH 101/728] New translations knn.py (French) --- lang/fr/gklearn/utils/knn.py | 141 +++++++++++++++++++++++++++++++++++ 1 file changed, 141 insertions(+) create mode 100644 lang/fr/gklearn/utils/knn.py diff --git a/lang/fr/gklearn/utils/knn.py b/lang/fr/gklearn/utils/knn.py new file mode 100644 index 0000000000..81419be52d --- /dev/null +++ b/lang/fr/gklearn/utils/knn.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon May 11 11:03:01 2020 + +@author: ljia +""" +import numpy as np +from sklearn.model_selection import ShuffleSplit +from sklearn.neighbors import KNeighborsClassifier +from sklearn.metrics import accuracy_score +from gklearn.utils.utils import get_graph_kernel_by_name +# from gklearn.preimage.utils import get_same_item_indices + +def sum_squares(a, b): + """ + Return the sum of squares of the difference between a and b, aka MSE + """ + return np.sum([(a[i] - b[i])**2 for i in range(len(a))]) + + +def euclid_d(x, y): + """ + 1D euclidean distance + """ + return np.sqrt((x-y)**2) + + +def man_d(x, y): + """ + 1D manhattan distance + """ + return np.abs((x-y)) + + +def knn_regression(D_app, D_test, y_app, y_test, n_neighbors, verbose=True, text=None): + + from sklearn.neighbors import KNeighborsRegressor + knn = KNeighborsRegressor(n_neighbors=n_neighbors, metric='precomputed') + knn.fit(D_app, y_app) + y_pred = knn.predict(D_app) + y_pred_test = knn.predict(D_test.T) + perf_app = np.sqrt(sum_squares(y_pred, y_app)/len(y_app)) + perf_test = np.sqrt(sum_squares(y_pred_test, y_test)/len(y_test)) + + if (verbose): + print("Learning error with {} train examples : {}".format(text, perf_app)) + print("Test error with {} train examples : {}".format(text, perf_test)) + + return perf_app, perf_test + + +def knn_classification(d_app, d_test, y_app, y_test, n_neighbors, verbose=True, text=None): + knn = KNeighborsClassifier(n_neighbors=n_neighbors, metric='precomputed') + knn.fit(d_app, y_app) + y_pred = knn.predict(d_app) + y_pred_test = knn.predict(d_test.T) + perf_app = accuracy_score(y_app, y_pred) + perf_test = accuracy_score(y_test, y_pred_test) + + if (verbose): + print("Learning accuracy with {} costs : {}".format(text, perf_app)) + print("Test accuracy with {} costs : {}".format(text, perf_test)) + + return perf_app, perf_test + + +def knn_cv(dataset, kernel_options, trainset=None, n_neighbors=1, n_splits=50, test_size=0.9, verbose=True): + ''' + Perform a knn classification cross-validation on given dataset. + ''' +# Gn = dataset.graphs + y_all = dataset.targets + + # compute kernel distances. + dis_mat = __compute_kernel_distances(dataset, kernel_options, trainset=trainset) + + + rs = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=0) +# train_indices = [[] for _ in range(n_splits)] +# test_indices = [[] for _ in range(n_splits)] +# idx_targets = get_same_item_indices(y_all) +# for key, item in idx_targets.items(): +# i = 0 +# for train_i, test_i in rs.split(item): # @todo: careful when parallel. +# train_indices[i] += [item[idx] for idx in train_i] +# test_indices[i] += [item[idx] for idx in test_i] +# i += 1 + + accuracies = [] +# for trial in range(len(train_indices)): +# train_index = train_indices[trial] +# test_index = test_indices[trial] + for train_index, test_index in rs.split(y_all): +# print(train_index, test_index) +# G_app = [Gn[i] for i in train_index] +# G_test = [Gn[i] for i in test_index] + y_app = [y_all[i] for i in train_index] + y_test = [y_all[i] for i in test_index] + + N = len(train_index) + + d_app = dis_mat.copy() + d_app = d_app[train_index,:] + d_app = d_app[:,train_index] + + d_test = np.zeros((N, len(test_index))) + + for i in range(N): + for j in range(len(test_index)): + d_test[i, j] = dis_mat[train_index[i], test_index[j]] + + accuracies.append(knn_classification(d_app, d_test, y_app, y_test, n_neighbors, verbose=verbose, text='')) + + results = {} + results['ave_perf_train'] = np.mean([i[0] for i in accuracies], axis=0) + results['std_perf_train'] = np.std([i[0] for i in accuracies], axis=0, ddof=1) + results['ave_perf_test'] = np.mean([i[1] for i in accuracies], axis=0) + results['std_perf_test'] = np.std([i[1] for i in accuracies], axis=0, ddof=1) + + return results + + +def __compute_kernel_distances(dataset, kernel_options, trainset=None): + graph_kernel = get_graph_kernel_by_name(kernel_options['name'], + node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, + edge_attrs=dataset.edge_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + kernel_options=kernel_options) + + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, **kernel_options) + + dis_mat, _, _, _ = graph_kernel.compute_distance_matrix() + + if trainset is not None: + gram_matrix_unnorm = graph_kernel.gram_matrix_unnorm + + + return dis_mat \ No newline at end of file From 252ab9243145f6f6b8d897581c919a99564a9f6d Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:18 +0200 Subject: [PATCH 102/728] New translations gklearn.kernels.weisfeilerLehmanKernel.rst (Chinese Simplified) --- .../docs/source/gklearn.kernels.weisfeilerLehmanKernel.rst | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.kernels.weisfeilerLehmanKernel.rst diff --git a/lang/zh/docs/source/gklearn.kernels.weisfeilerLehmanKernel.rst b/lang/zh/docs/source/gklearn.kernels.weisfeilerLehmanKernel.rst new file mode 100644 index 0000000000..f5797a2217 --- /dev/null +++ b/lang/zh/docs/source/gklearn.kernels.weisfeilerLehmanKernel.rst @@ -0,0 +1,7 @@ +gklearn.kernels.weisfeilerLehmanKernel +====================================== + +.. automodule:: gklearn.kernels.weisfeilerLehmanKernel + :members: + :undoc-members: + :show-inheritance: From 196febf40ec58c103f8c7ddead1c0a6a2020ff04 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:19 +0200 Subject: [PATCH 103/728] New translations gklearn.utils.graphdataset.rst (Chinese Simplified) --- lang/zh/docs/source/gklearn.utils.graphdataset.rst | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 lang/zh/docs/source/gklearn.utils.graphdataset.rst diff --git a/lang/zh/docs/source/gklearn.utils.graphdataset.rst b/lang/zh/docs/source/gklearn.utils.graphdataset.rst new file mode 100644 index 0000000000..4e2aae17db --- /dev/null +++ b/lang/zh/docs/source/gklearn.utils.graphdataset.rst @@ -0,0 +1,7 @@ +gklearn.utils.graphdataset +========================== + +.. automodule:: gklearn.utils.graphdataset + :members: + :undoc-members: + :show-inheritance: From 831d3ee5620a026bafd20868af1b96e6dd87bf60 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:21 +0200 Subject: [PATCH 104/728] New translations xp_simple_preimage.py (French) --- .../experiments/xp_simple_preimage.py | 176 ++++++++++++++++++ 1 file changed, 176 insertions(+) create mode 100644 lang/fr/gklearn/preimage/experiments/xp_simple_preimage.py diff --git a/lang/fr/gklearn/preimage/experiments/xp_simple_preimage.py b/lang/fr/gklearn/preimage/experiments/xp_simple_preimage.py new file mode 100644 index 0000000000..a8ce79ec6c --- /dev/null +++ b/lang/fr/gklearn/preimage/experiments/xp_simple_preimage.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Fri Jun 12 10:30:17 2020 + +@author: ljia + +This script constructs simple preimages to test preimage methods and find bugs and shortcomings in them. +""" + + +def xp_simple_preimage(): + import numpy as np + + """**1. Get dataset.**""" + + from gklearn.utils import Dataset, split_dataset_by_target + + # Predefined dataset name, use dataset "MAO". + ds_name = 'MAO' + # The node/edge labels that will not be used in the computation. + irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} + + # Initialize a Dataset. + dataset_all = Dataset() + # Load predefined dataset "MAO". + dataset_all.load_predefined_dataset(ds_name) + # Remove irrelevant labels. + dataset_all.remove_labels(**irrelevant_labels) + # Split the whole dataset according to the classification targets. + datasets = split_dataset_by_target(dataset_all) + # Get the first class of graphs, whose median preimage will be computed. + dataset = datasets[0] + len(dataset.graphs) + + """**2. Set parameters.**""" + + import multiprocessing + + # Parameters for MedianPreimageGenerator (our method). + mpg_options = {'fit_method': 'k-graphs', # how to fit edit costs. "k-graphs" means use all graphs in median set when fitting. + 'init_ecc': [4, 4, 2, 1, 1, 1], # initial edit costs. + 'ds_name': ds_name, # name of the dataset. + 'parallel': True, # whether the parallel scheme is to be used. + 'time_limit_in_sec': 0, # maximum time limit to compute the preimage. If set to 0 then no limit. + 'max_itrs': 10, # maximum iteration limit to optimize edit costs. If set to 0 then no limit. + 'max_itrs_without_update': 3, # If the times that edit costs is not update is more than this number, then the optimization stops. + 'epsilon_residual': 0.01, # In optimization, the residual is only considered changed if the change is bigger than this number. + 'epsilon_ec': 0.1, # In optimization, the edit costs are only considered changed if the changes are bigger than this number. + 'verbose': 2 # whether to print out results. + } + # Parameters for graph kernel computation. + kernel_options = {'name': 'PathUpToH', # use path kernel up to length h. + 'depth': 9, + 'k_func': 'MinMax', + 'compute_method': 'trie', + 'parallel': 'imap_unordered', # or None + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, # whether to use normalized Gram matrix to optimize edit costs. + 'verbose': 2 # whether to print out results. + } + # Parameters for GED computation. + ged_options = {'method': 'IPFP', # use IPFP huristic. + 'initialization_method': 'RANDOM', # or 'NODE', etc. + 'initial_solutions': 10, # when bigger than 1, then the method is considered mIPFP. + 'edit_cost': 'CONSTANT', # use CONSTANT cost. + 'attr_distance': 'euclidean', # the distance between non-symbolic node/edge labels is computed by euclidean distance. + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), # parallel threads. Do not work if mpg_options['parallel'] = False. + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES' + } + # Parameters for MedianGraphEstimator (Boria's method). + mge_options = {'init_type': 'MEDOID', # how to initial median (compute set-median). "MEDOID" is to use the graph with smallest SOD. + 'random_inits': 10, # number of random initialization when 'init_type' = 'RANDOM'. + 'time_limit': 600, # maximum time limit to compute the generalized median. If set to 0 then no limit. + 'verbose': 2, # whether to print out results. + 'refine': False # whether to refine the final SODs or not. + } + print('done.') + + """**3. Compute the Gram matrix and distance matrix.**""" + + from gklearn.utils.utils import get_graph_kernel_by_name + + # Get a graph kernel instance. + graph_kernel = get_graph_kernel_by_name(kernel_options['name'], + node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + kernel_options=kernel_options) + # Compute Gram matrix. + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, **kernel_options) + + # Compute distance matrix. + from gklearn.utils import compute_distance_matrix + dis_mat, _, _, _ = compute_distance_matrix(gram_matrix) + + print('done.') + + """**4. Find the candidate graph.**""" + + from gklearn.preimage.utils import compute_k_dis + + # Number of the nearest neighbors. + k_neighbors = 10 + + # For each graph G in dataset, compute the distance between its image \Phi(G) and the mean of its neighbors' images. + dis_min = np.inf # the minimum distance between possible \Phi(G) and the mean of its neighbors. + for idx, G in enumerate(dataset.graphs): + # Find the k nearest neighbors of G. + dis_list = dis_mat[idx] # distance between \Phi(G) and image of each graphs. + idx_sort = np.argsort(dis_list) # sort distances and get the sorted indices. + idx_nearest = idx_sort[1:k_neighbors+1] # indices of the k-nearest neighbors. + dis_k_nearest = [dis_list[i] for i in idx_nearest] # k-nearest distances, except the 0. + G_k_nearest = [dataset.graphs[i] for i in idx_nearest] # k-nearest neighbors. + + # Compute the distance between \Phi(G) and the mean of its neighbors. + dis_tmp = compute_k_dis(idx, # the index of G in Gram matrix. + idx_nearest, # the indices of the neighbors + [1 / k_neighbors] * k_neighbors, # coefficients for neighbors. + gram_matrix, + withterm3=False) + # Check if the new distance is smallers. + if dis_tmp < dis_min: + dis_min = dis_tmp + G_cand = G + G_neighbors = G_k_nearest + + print('The minimum distance is', dis_min) + + """**5. Run median preimage generator.**""" + + from gklearn.preimage import MedianPreimageGenerator + + # Set the dataset as the k-nearest neighbors. + dataset.load_graphs(G_neighbors) + + # Create median preimage generator instance. + mpg = MedianPreimageGenerator() + # Add dataset. + mpg.dataset = dataset + # Set parameters. + mpg.set_options(**mpg_options.copy()) + mpg.kernel_options = kernel_options.copy() + mpg.ged_options = ged_options.copy() + mpg.mge_options = mge_options.copy() + # Run. + mpg.run() + + """**4. Get results.**""" + + # Get results. + import pprint + pp = pprint.PrettyPrinter(indent=4) # pretty print + results = mpg.get_results() + pp.pprint(results) + + draw_graph(mpg.set_median) + draw_graph(mpg.gen_median) + draw_graph(G_cand) + + +# Draw generated graphs. +def draw_graph(graph): + import matplotlib.pyplot as plt + import networkx as nx + plt.figure() + pos = nx.spring_layout(graph) + nx.draw(graph, pos, node_size=500, labels=nx.get_node_attributes(graph, 'atom_symbol'), font_color='w', width=3, with_labels=True) + plt.show() + plt.clf() + plt.close() + + +if __name__ == '__main__': + xp_simple_preimage() \ No newline at end of file From 24cbed8625cb7d3685412a68b82862d796950217 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:22 +0200 Subject: [PATCH 105/728] New translations index.rst (Chinese Simplified) --- .../gedlib/documentation/source/index.rst | 36 +++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 lang/zh/gklearn/gedlib/documentation/source/index.rst diff --git a/lang/zh/gklearn/gedlib/documentation/source/index.rst b/lang/zh/gklearn/gedlib/documentation/source/index.rst new file mode 100644 index 0000000000..42b70672a5 --- /dev/null +++ b/lang/zh/gklearn/gedlib/documentation/source/index.rst @@ -0,0 +1,36 @@ +.. Python_GedLib documentation master file, created by + sphinx-quickstart on Thu Jun 13 16:10:06 2019. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + + +Welcome to GEDLIBPY's documentation! +========================================= + +This module allow to use a C++ library for edit distance between graphs (GedLib) with Python. + +Before using, please read the first section to ensure a good start with the library. Then, you can follow some examples or informations about each method. + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + readme + editcost + examples + doc + +Authors +~~~~~~~~ + +* David Blumenthal for C++ module +* Natacha Lambert for Python module + +Copyright (C) 2019 by all the authors + +Indices and tables +~~~~~~~~~~~~~~~~~~~~~ + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` From 250f5ff71c7da4ba4248d668f5e78f93d29281ad Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:24 +0200 Subject: [PATCH 106/728] New translations libfann.so.2 (Chinese Simplified) --- lang/zh/gklearn/gedlib/lib/fann/libfann.so.2 | 1 + 1 file changed, 1 insertion(+) create mode 100644 lang/zh/gklearn/gedlib/lib/fann/libfann.so.2 diff --git a/lang/zh/gklearn/gedlib/lib/fann/libfann.so.2 b/lang/zh/gklearn/gedlib/lib/fann/libfann.so.2 new file mode 100644 index 0000000000..2de4601b8d --- /dev/null +++ b/lang/zh/gklearn/gedlib/lib/fann/libfann.so.2 @@ -0,0 +1 @@ +libfann.so.2.2.0 \ 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zMP=DHD#|Q=TUb)R(8@1cS`A4BF*Cpvxx35t5(cdE+HwemR&^6{9zpa!qe z$P)ZZpB2DV!wH)oStuV87M4MYep8|nahA}}Bt>CC0ozqlRZ$9CYN8YjFf=irj}C*C zk8d0_W>?=!{?mWyyDOGxVvmRV>3v8V4iLNsShno?<>T#B?6K7Hdi{-t^GG>UUZnVcM_vnQ4e+tgfFo{H9*{=DHo;*r<;+cYdY*_3wjUt{UB>#_LL(a1f%$C>t&fAVq{ Date: Sun, 4 Oct 2020 19:17:29 +0200 Subject: [PATCH 109/728] New translations libdoublefann.so.2 (Chinese Simplified) --- lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so.2 | 1 + 1 file changed, 1 insertion(+) create mode 100644 lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so.2 diff --git a/lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so.2 b/lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so.2 new file mode 100644 index 0000000000..825749323d --- /dev/null +++ b/lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so.2 @@ -0,0 +1 @@ +libdoublefann.so.2.2.0 \ No newline at end of file From 3e33ecd94d1e78189405a7869c123e2e7995cd64 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:31 +0200 Subject: [PATCH 110/728] New translations libdoublefann.so (Chinese Simplified) --- lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so | 1 + 1 file changed, 1 insertion(+) create mode 100644 lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so diff --git a/lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so b/lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so new file mode 100644 index 0000000000..ad9e2a8596 --- /dev/null +++ b/lang/zh/gklearn/gedlib/lib/fann/libdoublefann.so @@ -0,0 +1 @@ +libdoublefann.so.2 \ No newline at end of file From 55bc3978ab34acbe8c3ab22e9ce0567f461ab6b0 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:32 +0200 Subject: [PATCH 111/728] New translations gedlibpy.pyx (Chinese Simplified) --- lang/zh/gklearn/gedlib/gedlibpy.pyx | 1581 +++++++++++++++++++++++++++ 1 file changed, 1581 insertions(+) create mode 100644 lang/zh/gklearn/gedlib/gedlibpy.pyx diff --git a/lang/zh/gklearn/gedlib/gedlibpy.pyx b/lang/zh/gklearn/gedlib/gedlibpy.pyx new file mode 100644 index 0000000000..7fb7e846ae --- /dev/null +++ b/lang/zh/gklearn/gedlib/gedlibpy.pyx @@ -0,0 +1,1581 @@ +# distutils: language = c++ + +""" + Python GedLib module + ====================== + + This module allow to use a C++ library for edit distance between graphs (GedLib) with Python. + + + Authors + ------------------- + + David Blumenthal + Natacha Lambert + Linlin Jia + + Copyright (C) 2019-2020 by all the authors + + Classes & Functions + ------------------- + +""" + +################################# +##DECLARATION OF C++ INTERFACES## +################################# + + +#Types imports for C++ compatibility +from libcpp.vector cimport vector +from libcpp.string cimport string +from libcpp.map cimport map +from libcpp cimport bool +from libcpp.pair cimport pair +from libcpp.list cimport list + +#Long unsigned int equivalent +cimport numpy as cnp +ctypedef cnp.npy_uint32 UINT32_t +from cpython cimport array + + +cdef extern from "src/GedLibBind.hpp" namespace "pyged": + + cdef vector[string] getEditCostStringOptions() except + + cdef vector[string] getMethodStringOptions() except + + cdef vector[string] getInitStringOptions() except + + cdef size_t getDummyNode() except + + + cdef cppclass PyGEDEnv: + PyGEDEnv() except + + bool isInitialized() except + + void restartEnv() except + + void loadGXLGraph(string pathFolder, string pathXML, bool node_type, bool edge_type) except + + pair[size_t,size_t] getGraphIds() except + + vector[size_t] getAllGraphIds() except + + string getGraphClass(size_t id) except + + string getGraphName(size_t id) except + + size_t addGraph(string name, string classe) except + + void addNode(size_t graphId, string nodeId, map[string, string] nodeLabel) except + + void addEdge(size_t graphId, string tail, string head, map[string, string] edgeLabel, bool ignoreDuplicates) except + + void clearGraph(size_t graphId) except + + size_t getGraphInternalId(size_t graphId) except + + size_t getGraphNumNodes(size_t graphId) except + + size_t getGraphNumEdges(size_t graphId) except + + vector[string] getGraphOriginalNodeIds(size_t graphId) except + + vector[map[string, string]] getGraphNodeLabels(size_t graphId) except + + map[pair[size_t, size_t], map[string, string]] getGraphEdges(size_t graphId) except + + vector[vector[size_t]] getGraphAdjacenceMatrix(size_t graphId) except + + void setEditCost(string editCost, vector[double] editCostConstant) except + + void setPersonalEditCost(vector[double] editCostConstant) except + + void initEnv(string initOption, bool print_to_stdout) except + + void setMethod(string method, string options) except + + void initMethod() except + + double getInitime() except + + void runMethod(size_t g, size_t h) except + + double getUpperBound(size_t g, size_t h) except + + double getLowerBound(size_t g, size_t h) except + + vector[cnp.npy_uint64] getForwardMap(size_t g, size_t h) except + + vector[cnp.npy_uint64] getBackwardMap(size_t g, size_t h) except + + size_t getNodeImage(size_t g, size_t h, size_t nodeId) except + + size_t getNodePreImage(size_t g, size_t h, size_t nodeId) except + + double getInducedCost(size_t g, size_t h) except + + vector[pair[size_t,size_t]] getNodeMap(size_t g, size_t h) except + + vector[vector[int]] getAssignmentMatrix(size_t g, size_t h) except + + vector[vector[cnp.npy_uint64]] getAllMap(size_t g, size_t h) except + + double getRuntime(size_t g, size_t h) except + + bool quasimetricCosts() except + + vector[vector[size_t]] hungarianLSAP(vector[vector[size_t]] matrixCost) except + + vector[vector[double]] hungarianLSAPE(vector[vector[double]] matrixCost) except + + # added by Linlin Jia. + size_t getNumNodeLabels() except + + map[string, string] getNodeLabel(size_t label_id) except + + size_t getNumEdgeLabels() except + + map[string, string] getEdgeLabel(size_t label_id) except + +# size_t getNumNodes(size_t graph_id) except + + double getAvgNumNodes() except + + double getNodeRelCost(map[string, string] & node_label_1, map[string, string] & node_label_2) except + + double getNodeDelCost(map[string, string] & node_label) except + + double getNodeInsCost(map[string, string] & node_label) except + + map[string, string] getMedianNodeLabel(vector[map[string, string]] & node_labels) except + + double getEdgeRelCost(map[string, string] & edge_label_1, map[string, string] & edge_label_2) except + + double getEdgeDelCost(map[string, string] & edge_label) except + + double getEdgeInsCost(map[string, string] & edge_label) except + + map[string, string] getMedianEdgeLabel(vector[map[string, string]] & edge_labels) except + + string getInitType() except + +# double getNodeCost(size_t label1, size_t label2) except + + double computeInducedCost(size_t g_id, size_t h_id, vector[pair[size_t,size_t]]) except + + + +############################# +##CYTHON WRAPPER INTERFACES## +############################# + +# import cython +import numpy as np +import networkx as nx +from gklearn.ged.env import NodeMap + +# import librariesImport +from ctypes import * +import os +lib1 = cdll.LoadLibrary(os.path.dirname(os.path.realpath(__file__)) + '/lib/fann/libdoublefann.so') +lib2 = cdll.LoadLibrary(os.path.dirname(os.path.realpath(__file__)) + '/lib/libsvm.3.22/libsvm.so') +lib3 = cdll.LoadLibrary(os.path.dirname(os.path.realpath(__file__)) + '/lib/nomad/libnomad.so') +lib4 = cdll.LoadLibrary(os.path.dirname(os.path.realpath(__file__)) + '/lib/nomad/libsgtelib.so') + + +def get_edit_cost_options() : + """ + Searchs the differents edit cost functions and returns the result. + + :return: The list of edit cost functions + :rtype: list[string] + + .. warning:: This function is useless for an external use. Please use directly list_of_edit_cost_options. + .. note:: Prefer the list_of_edit_cost_options attribute of this module. + """ + + return [option.decode('utf-8') for option in getEditCostStringOptions()] + + +def get_method_options() : + """ + Searchs the differents method for edit distance computation between graphs and returns the result. + + :return: The list of method to compute the edit distance between graphs + :rtype: list[string] + + .. warning:: This function is useless for an external use. Please use directly list_of_method_options. + .. note:: Prefer the list_of_method_options attribute of this module. + """ + return [option.decode('utf-8') for option in getMethodStringOptions()] + + +def get_init_options() : + """ + Searchs the differents initialization parameters for the environment computation for graphs and returns the result. + + :return: The list of options to initialize the computation environment + :rtype: list[string] + + .. warning:: This function is useless for an external use. Please use directly list_of_init_options. + .. note:: Prefer the list_of_init_options attribute of this module. + """ + return [option.decode('utf-8') for option in getInitStringOptions()] + + +def get_dummy_node() : + """ + Returns the ID of a dummy node. + + :return: The ID of the dummy node (18446744073709551614 for my computer, the hugest number possible) + :rtype: size_t + + .. note:: A dummy node is used when a node isn't associated to an other node. + """ + return getDummyNode() + + +# @cython.auto_pickle(True) +cdef class GEDEnv: + """Cython wrapper class for C++ class PyGEDEnv + """ +# cdef PyGEDEnv c_env # Hold a C++ instance which we're wrapping + cdef PyGEDEnv* c_env # hold a pointer to the C++ instance which we're wrapping + + + def __cinit__(self): +# self.c_env = PyGEDEnv() + self.c_env = new PyGEDEnv() + + + def __dealloc__(self): + del self.c_env + + +# def __reduce__(self): +# # return GEDEnv, (self.c_env,) +# return GEDEnv, tuple() + + + def is_initialized(self) : + """ + Checks and returns if the computation environment is initialized or not. + + :return: True if it's initialized, False otherwise + :rtype: bool + + .. note:: This function exists for internals verifications but you can use it for your code. + """ + return self.c_env.isInitialized() + + + def restart_env(self) : + """ + Restarts the environment variable. All data related to it will be delete. + + .. warning:: This function deletes all graphs, computations and more so make sure you don't need anymore your environment. + .. note:: You can now delete and add somes graphs after initialization so you can avoid this function. + """ + self.c_env.restartEnv() + + + def load_GXL_graphs(self, path_folder, path_XML, node_type, edge_type) : + """ + Loads some GXL graphes on the environment which is in a same folder, and present in the XMLfile. + + :param path_folder: The folder's path which contains GXL graphs + :param path_XML: The XML's path which indicates which graphes you want to load + :param node_type: Select if nodes are labeled or unlabeled + :param edge_type: Select if edges are labeled or unlabeled + :type path_folder: string + :type path_XML: string + :type node_type: bool + :type edge_type: bool + + + .. note:: You can call this function multiple times if you want, but not after an init call. + """ + self.c_env.loadGXLGraph(path_folder.encode('utf-8'), path_XML.encode('utf-8'), node_type, edge_type) + + + def graph_ids(self) : + """ + Searchs the first and last IDs of the loaded graphs in the environment. + + :return: The pair of the first and the last graphs Ids + :rtype: tuple(size_t, size_t) + + .. note:: Prefer this function if you have huges structures with lots of graphs. + """ + return self.c_env.getGraphIds() + + + def get_all_graph_ids(self) : + """ + Searchs all the IDs of the loaded graphs in the environment. + + :return: The list of all graphs's Ids + :rtype: list[size_t] + + .. note:: The last ID is equal to (number of graphs - 1). The order correspond to the loading order. + """ + return self.c_env.getAllGraphIds() + + + def get_graph_class(self, id) : + """ + Returns the class of a graph with its ID. + + :param id: The ID of the wanted graph + :type id: size_t + :return: The class of the graph which correpond to the ID + :rtype: string + + .. seealso:: get_graph_class() + .. note:: An empty string can be a class. + """ + return self.c_env.getGraphClass(id) + + + def get_graph_name(self, id) : + """ + Returns the name of a graph with its ID. + + :param id: The ID of the wanted graph + :type id: size_t + :return: The name of the graph which correpond to the ID + :rtype: string + + .. seealso:: get_graph_class() + .. note:: An empty string can be a name. + """ + return self.c_env.getGraphName(id).decode('utf-8') + + + def add_graph(self, name="", classe="") : + """ + Adds a empty graph on the environment, with its name and its class. Nodes and edges will be add in a second time. + + :param name: The name of the new graph, an empty string by default + :param classe: The class of the new graph, an empty string by default + :type name: string + :type classe: string + :return: The ID of the newly graphe + :rtype: size_t + + .. seealso::add_node(), add_edge() , add_symmetrical_edge() + .. note:: You can call this function without parameters. You can also use this function after initialization, call init() after you're finished your modifications. + """ + return self.c_env.addGraph(name.encode('utf-8'), classe.encode('utf-8')) + + + def add_node(self, graph_id, node_id, node_label): + """ + Adds a node on a graph selected by its ID. A ID and a label for the node is required. + + :param graph_id: The ID of the wanted graph + :param node_id: The ID of the new node + :param node_label: The label of the new node + :type graph_id: size_t + :type node_id: string + :type node_label: dict{string : string} + + .. seealso:: add_graph(), add_edge(), add_symmetrical_edge() + .. note:: You can also use this function after initialization, but only on a newly added graph. Call init() after you're finished your modifications. + """ + self.c_env.addNode(graph_id, node_id.encode('utf-8'), encode_your_map(node_label)) + + + def add_edge(self, graph_id, tail, head, edge_label, ignore_duplicates=True) : + """ + Adds an edge on a graph selected by its ID. + + :param graph_id: The ID of the wanted graph + :param tail: The ID of the tail node for the new edge + :param head: The ID of the head node for the new edge + :param edge_label: The label of the new edge + :param ignore_duplicates: If True, duplicate edges are ignored, otherwise it's raise an error if an existing edge is added. True by default + :type graph_id: size_t + :type tail: string + :type head: string + :type edge_label: dict{string : string} + :type ignore_duplicates: bool + + .. seealso:: add_graph(), add_node(), add_symmetrical_edge() + .. note:: You can also use this function after initialization, but only on a newly added graph. Call init() after you're finished your modifications. + """ + self.c_env.addEdge(graph_id, tail.encode('utf-8'), head.encode('utf-8'), encode_your_map(edge_label), ignore_duplicates) + + + def add_symmetrical_edge(self, graph_id, tail, head, edge_label) : + """ + Adds a symmetrical edge on a graph selected by its ID. + + :param graph_id: The ID of the wanted graph + :param tail: The ID of the tail node for the new edge + :param head: The ID of the head node for the new edge + :param edge_label: The label of the new edge + :type graph_id: size_t + :type tail: string + :type head: string + :type edge_label: dict{string : string} + + .. seealso:: add_graph(), add_node(), add_edge() + .. note:: You can also use this function after initialization, but only on a newly added graph. Call init() after you're finished your modifications. + """ + tailB = tail.encode('utf-8') + headB = head.encode('utf-8') + edgeLabelB = encode_your_map(edge_label) + self.c_env.addEdge(graph_id, tailB, headB, edgeLabelB, True) + self.c_env.addEdge(graph_id, headB, tailB, edgeLabelB, True) + + + def clear_graph(self, graph_id) : + """ + Deletes a graph, selected by its ID, to the environment. + + :param graph_id: The ID of the wanted graph + :type graph_id: size_t + + .. note:: Call init() after you're finished your modifications. + """ + self.c_env.clearGraph(graph_id) + + + def get_graph_internal_id(self, graph_id) : + """ + Searchs and returns the internal Id of a graph, selected by its ID. + + :param graph_id: The ID of the wanted graph + :type graph_id: size_t + :return: The internal ID of the selected graph + :rtype: size_t + + .. seealso:: get_graph_num_nodes(), get_graph_num_edges(), get_original_node_ids(), get_graph_node_labels(), get_graph_edges(), get_graph_adjacence_matrix() + .. note:: These functions allow to collect all the graph's informations. + """ + return self.c_env.getGraphInternalId(graph_id) + + + def get_graph_num_nodes(self, graph_id) : + """ + Searchs and returns the number of nodes on a graph, selected by its ID. + + :param graph_id: The ID of the wanted graph + :type graph_id: size_t + :return: The number of nodes on the selected graph + :rtype: size_t + + .. seealso:: get_graph_internal_id(), get_graph_num_edges(), get_original_node_ids(), get_graph_node_labels(), get_graph_edges(), get_graph_adjacence_matrix() + .. note:: These functions allow to collect all the graph's informations. + """ + return self.c_env.getGraphNumNodes(graph_id) + + + def get_graph_num_edges(self, graph_id) : + """ + Searchs and returns the number of edges on a graph, selected by its ID. + + :param graph_id: The ID of the wanted graph + :type graph_id: size_t + :return: The number of edges on the selected graph + :rtype: size_t + + .. seealso:: get_graph_internal_id(), get_graph_num_nodes(), get_original_node_ids(), get_graph_node_labels(), get_graph_edges(), get_graph_adjacence_matrix() + .. note:: These functions allow to collect all the graph's informations. + """ + return self.c_env.getGraphNumEdges(graph_id) + + + def get_original_node_ids(self, graph_id) : + """ + Searchs and returns all th Ids of nodes on a graph, selected by its ID. + + :param graph_id: The ID of the wanted graph + :type graph_id: size_t + :return: The list of IDs's nodes on the selected graph + :rtype: list[string] + + .. seealso::get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_graph_node_labels(), get_graph_edges(), get_graph_adjacence_matrix() + .. note:: These functions allow to collect all the graph's informations. + """ + return [gid.decode('utf-8') for gid in self.c_env.getGraphOriginalNodeIds(graph_id)] + + + def get_graph_node_labels(self, graph_id) : + """ + Searchs and returns all the labels of nodes on a graph, selected by its ID. + + :param graph_id: The ID of the wanted graph + :type graph_id: size_t + :return: The list of nodes' labels on the selected graph + :rtype: list[dict{string : string}] + + .. seealso:: get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_original_node_ids(), get_graph_edges(), get_graph_adjacence_matrix() + .. note:: These functions allow to collect all the graph's informations. + """ + return [decode_your_map(node_label) for node_label in self.c_env.getGraphNodeLabels(graph_id)] + + + def get_graph_edges(self, graph_id) : + """ + Searchs and returns all the edges on a graph, selected by its ID. + + :param graph_id: The ID of the wanted graph + :type graph_id: size_t + :return: The list of edges on the selected graph + :rtype: dict{tuple(size_t, size_t) : dict{string : string}} + + .. seealso::get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_original_node_ids(), get_graph_node_labels(), get_graph_adjacence_matrix() + .. note:: These functions allow to collect all the graph's informations. + """ + return decode_graph_edges(self.c_env.getGraphEdges(graph_id)) + + + def get_graph_adjacence_matrix(self, graph_id) : + """ + Searchs and returns the adjacence list of a graph, selected by its ID. + + :param graph_id: The ID of the wanted graph + :type graph_id: size_t + :return: The adjacence list of the selected graph + :rtype: list[list[size_t]] + + .. seealso:: get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_original_node_ids(), get_graph_node_labels(), get_graph_edges() + .. note:: These functions allow to collect all the graph's informations. + """ + return self.c_env.getGraphAdjacenceMatrix(graph_id) + + + def set_edit_cost(self, edit_cost, edit_cost_constant = []) : + """ + Sets an edit cost function to the environment, if it exists. + + :param edit_cost: The name of the edit cost function + :type edit_cost: string + :param edi_cost_constant: The parameters you will add to the editCost, empty by default + :type edit_cost_constant: list + + .. seealso:: list_of_edit_cost_options + .. note:: Try to make sure the edit cost function exists with list_of_edit_cost_options, raise an error otherwise. + """ + if edit_cost in list_of_edit_cost_options: + edit_cost_b = edit_cost.encode('utf-8') + self.c_env.setEditCost(edit_cost_b, edit_cost_constant) + else: + raise EditCostError("This edit cost function doesn't exist, please see list_of_edit_cost_options for selecting a edit cost function") + + + def set_personal_edit_cost(self, edit_cost_constant = []) : + """ + Sets an personal edit cost function to the environment. + + :param edit_cost_constant: The parameters you will add to the editCost, empty by default + :type edit_cost_constant: list + + .. seealso:: list_of_edit_cost_options, set_edit_cost() + .. note::You have to modify the C++ function to use it. Please see the documentation to add your Edit Cost function. + """ + self.c_env.setPersonalEditCost(edit_cost_constant) + + + def init(self, init_option='EAGER_WITHOUT_SHUFFLED_COPIES', print_to_stdout=False) : + """ + Initializes the environment with the chosen edit cost function and graphs. + + :param init_option: The name of the init option, "EAGER_WITHOUT_SHUFFLED_COPIES" by default + :type init_option: string + + .. seealso:: list_of_init_options + .. warning:: No modification were allowed after initialization. Try to make sure your choices is correct. You can though clear or add a graph, but recall init() after that. + .. note:: Try to make sure the option exists with list_of_init_options or choose no options, raise an error otherwise. + """ + if init_option in list_of_init_options: + init_option_b = init_option.encode('utf-8') + self.c_env.initEnv(init_option_b, print_to_stdout) + else: + raise InitError("This init option doesn't exist, please see list_of_init_options for selecting an option. You can choose any options.") + + + def set_method(self, method, options="") : + """ + Sets a computation method to the environment, if its exists. + + :param method: The name of the computation method + :param options: The options of the method (like bash options), an empty string by default + :type method: string + :type options: string + + .. seealso:: init_method(), list_of_method_options + .. note:: Try to make sure the edit cost function exists with list_of_method_options, raise an error otherwise. Call init_method() after your set. + """ + if method in list_of_method_options: + method_b = method.encode('utf-8') + self.c_env.setMethod(method_b, options.encode('utf-8')) + else: + raise MethodError("This method doesn't exist, please see list_of_method_options for selecting a method") + + + def init_method(self) : + """ + Inits the environment with the set method. + + .. seealso:: set_method(), list_of_method_options + .. note:: Call this function after set the method. You can't launch computation or change the method after that. + """ + self.c_env.initMethod() + + + def get_init_time(self) : + """ + Returns the initialization time. + + :return: The initialization time + :rtype: double + """ + return self.c_env.getInitime() + + + def run_method(self, g, h) : + """ + Computes the edit distance between two graphs g and h, with the edit cost function and method computation selected. + + :param g: The Id of the first graph to compare + :param h: The Id of the second graph to compare + :type g: size_t + :type h: size_t + + .. seealso:: get_upper_bound(), get_lower_bound(), get_forward_map(), get_backward_map(), get_runtime(), quasimetric_cost() + .. note:: This function only compute the distance between two graphs, without returning a result. Use the differents function to see the result between the two graphs. + """ + self.c_env.runMethod(g, h) + + + def get_upper_bound(self, g, h) : + """ + Returns the upper bound of the edit distance cost between two graphs g and h. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: The upper bound of the edit distance cost + :rtype: double + + .. seealso:: run_method(), get_lower_bound(), get_forward_map(), get_backward_map(), get_runtime(), quasimetric_cost() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: The upper bound is equivalent to the result of the pessimist edit distance cost. Methods are heuristics so the library can't compute the real perfect result because it's NP-Hard problem. + """ + return self.c_env.getUpperBound(g, h) + + + def get_lower_bound(self, g, h) : + """ + Returns the lower bound of the edit distance cost between two graphs g and h. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: The lower bound of the edit distance cost + :rtype: double + + .. seealso:: run_method(), get_upper_bound(), get_forward_map(), get_backward_map(), get_runtime(), quasimetric_cost() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: This function can be ignored, because lower bound doesn't have a crucial utility. + """ + return self.c_env.getLowerBound(g, h) + + + def get_forward_map(self, g, h) : + """ + Returns the forward map (or the half of the adjacence matrix) between nodes of the two indicated graphs. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: The forward map to the adjacence matrix between nodes of the two graphs + :rtype: list[npy_uint32] + + .. seealso:: run_method(), get_upper_bound(), get_lower_bound(), get_backward_map(), get_runtime(), quasimetric_cost(), get_node_map(), get_assignment_matrix() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: I don't know how to connect the two map to reconstruct the adjacence matrix. Please come back when I know how it's work ! + """ + return self.c_env.getForwardMap(g, h) + + + def get_backward_map(self, g, h) : + """ + Returns the backward map (or the half of the adjacence matrix) between nodes of the two indicated graphs. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: The backward map to the adjacence matrix between nodes of the two graphs + :rtype: list[npy_uint32] + + .. seealso:: run_method(), get_upper_bound(), get_lower_bound(), get_forward_map(), get_runtime(), quasimetric_cost(), get_node_map(), get_assignment_matrix() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: I don't know how to connect the two map to reconstruct the adjacence matrix. Please come back when I know how it's work ! + """ + return self.c_env.getBackwardMap(g, h) + + + def get_node_image(self, g, h, node_id) : + """ + Returns the node's image in the adjacence matrix, if it exists. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :param node_id: The ID of the node which you want to see the image + :type g: size_t + :type h: size_t + :type node_id: size_t + :return: The ID of the image node + :rtype: size_t + + .. seealso:: run_method(), get_forward_map(), get_backward_map(), get_node_pre_image(), get_node_map(), get_assignment_matrix() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: Use BackwardMap's Node to find its images ! You can also use get_forward_map() and get_backward_map(). + + """ + return self.c_env.getNodeImage(g, h, node_id) + + + def get_node_pre_image(self, g, h, node_id) : + """ + Returns the node's preimage in the adjacence matrix, if it exists. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :param node_id: The ID of the node which you want to see the preimage + :type g: size_t + :type h: size_t + :type node_id: size_t + :return: The ID of the preimage node + :rtype: size_t + + .. seealso:: run_method(), get_forward_map(), get_backward_map(), get_node_image(), get_node_map(), get_assignment_matrix() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: Use ForwardMap's Node to find its images ! You can also use get_forward_map() and get_backward_map(). + + """ + return self.c_env.getNodePreImage(g, h, node_id) + + + def get_induced_cost(self, g, h) : + """ + Returns the induced cost between the two indicated graphs. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: The induced cost between the two indicated graphs + :rtype: double + + .. seealso:: run_method(), get_forward_map(), get_backward_map(), get_node_image(), get_node_map(), get_assignment_matrix() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: Use ForwardMap's Node to find its images ! You can also use get_forward_map() and get_backward_map(). + + """ + return self.c_env.getInducedCost(g, h) + + + def get_node_map(self, g, h) : + """ + Returns the Node Map, like C++ NodeMap. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: The Node Map between the two selected graph. + :rtype: gklearn.ged.env.NodeMap. + + .. seealso:: run_method(), get_forward_map(), get_backward_map(), get_node_image(), get_node_pre_image(), get_assignment_matrix() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: This function creates datas so use it if necessary, however you can understand how assignement works with this example. + """ + map_as_relation = self.c_env.getNodeMap(g, h) + induced_cost = self.c_env.getInducedCost(g, h) # @todo: the C++ implementation for this function in GedLibBind.ipp re-call get_node_map() once more, this is not neccessary. + source_map = [item.first if item.first < len(map_as_relation) else np.inf for item in map_as_relation] # item.first < len(map_as_relation) is not exactly correct. +# print(source_map) + target_map = [item.second if item.second < len(map_as_relation) else np.inf for item in map_as_relation] +# print(target_map) + num_node_source = len([item for item in source_map if item != np.inf]) +# print(num_node_source) + num_node_target = len([item for item in target_map if item != np.inf]) +# print(num_node_target) + + node_map = NodeMap(num_node_source, num_node_target) +# print(node_map.get_forward_map(), node_map.get_backward_map()) + for i in range(len(source_map)): + node_map.add_assignment(source_map[i], target_map[i]) + node_map.set_induced_cost(induced_cost) + + return node_map + + + def get_assignment_matrix(self, g, h) : + """ + Returns the Assignment Matrix between two selected graphs g and h. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: The Assignment Matrix between the two selected graph. + :rtype: list[list[int]] + + .. seealso:: run_method(), get_forward_map(), get_backward_map(), get_node_image(), get_node_pre_image(), get_node_map() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: This function creates datas so use it if necessary. + """ + return self.c_env.getAssignmentMatrix(g, h) + + + def get_all_map(self, g, h) : + """ + Returns a vector which contains the forward and the backward maps between nodes of the two indicated graphs. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: The forward and backward maps to the adjacence matrix between nodes of the two graphs + :rtype: list[list[npy_uint32]] + + .. seealso:: run_method(), get_upper_bound(), get_lower_bound(), get_forward_map(), get_backward_map(), get_runtime(), quasimetric_cost() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: This function duplicates data so please don't use it. I also don't know how to connect the two map to reconstruct the adjacence matrix. Please come back when I know how it's work ! + """ + return self.c_env.getAllMap(g, h) + + + def get_runtime(self, g, h) : + """ + Returns the runtime to compute the edit distance cost between two graphs g and h + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: The runtime of the computation of edit distance cost between the two selected graphs + :rtype: double + + .. seealso:: run_method(), get_upper_bound(), get_lower_bound(), get_forward_map(), get_backward_map(), quasimetric_cost() + .. warning:: run_method() between the same two graph must be called before this function. + .. note:: Python is a bit longer than C++ due to the functions's encapsulate. + """ + return self.c_env.getRuntime(g,h) + + + def quasimetric_cost(self) : + """ + Checks and returns if the edit costs are quasimetric. + + :param g: The Id of the first compared graph + :param h: The Id of the second compared graph + :type g: size_t + :type h: size_t + :return: True if it's verified, False otherwise + :rtype: bool + + .. seealso:: run_method(), get_upper_bound(), get_lower_bound(), get_forward_map(), get_backward_map(), get_runtime() + .. warning:: run_method() between the same two graph must be called before this function. + """ + return self.c_env.quasimetricCosts() + + + def hungarian_LSAP(self, matrix_cost) : + """ + Applies the hungarian algorithm (LSAP) on a matrix Cost. + + :param matrix_cost: The matrix Cost + :type matrix_cost: vector[vector[size_t]] + :return: The values of rho, varrho, u and v, in this order + :rtype: vector[vector[size_t]] + + .. seealso:: hungarian_LSAPE() + """ + return self.c_env.hungarianLSAP(matrix_cost) + + + def hungarian_LSAPE(self, matrix_cost) : + """ + Applies the hungarian algorithm (LSAPE) on a matrix Cost. + + :param matrix_cost: The matrix Cost + :type matrix_cost: vector[vector[double]] + :return: The values of rho, varrho, u and v, in this order + :rtype: vector[vector[double]] + + .. seealso:: hungarian_LSAP() + """ + return self.c_env.hungarianLSAPE(matrix_cost) + + + def add_random_graph(self, name, classe, list_of_nodes, list_of_edges, ignore_duplicates=True) : + """ + Add a Graph (not GXL) on the environment. Be careful to respect the same format as GXL graphs for labelling nodes and edges. + + :param name: The name of the graph to add, can be an empty string + :param classe: The classe of the graph to add, can be an empty string + :param list_of_nodes: The list of nodes to add + :param list_of_edges: The list of edges to add + :param ignore_duplicates: If True, duplicate edges are ignored, otherwise it's raise an error if an existing edge is added. True by default + :type name: string + :type classe: string + :type list_of_nodes: list[tuple(size_t, dict{string : string})] + :type list_of_edges: list[tuple(tuple(size_t,size_t), dict{string : string})] + :type ignore_duplicates: bool + :return: The ID of the newly added graphe + :rtype: size_t + + .. note:: The graph must respect the GXL structure. Please see how a GXL graph is construct. + + """ + id = self.add_graph(name, classe) + for node in list_of_nodes: + self.add_node(id, node[0], node[1]) + for edge in list_of_edges: + self.add_edge(id, edge[0], edge[1], edge[2], ignore_duplicates) + return id + + + def add_nx_graph(self, g, classe, ignore_duplicates=True) : + """ + Add a Graph (made by networkx) on the environment. Be careful to respect the same format as GXL graphs for labelling nodes and edges. + + :param g: The graph to add (networkx graph) + :param ignore_duplicates: If True, duplicate edges are ignored, otherwise it's raise an error if an existing edge is added. True by default + :type g: networkx.graph + :type ignore_duplicates: bool + :return: The ID of the newly added graphe + :rtype: size_t + + .. note:: The NX graph must respect the GXL structure. Please see how a GXL graph is construct. + + """ + id = self.add_graph(g.name, classe) + for node in g.nodes: + self.add_node(id, str(node), g.nodes[node]) + for edge in g.edges: + self.add_edge(id, str(edge[0]), str(edge[1]), g.get_edge_data(edge[0], edge[1]), ignore_duplicates) + return id + + + def compute_ged_on_two_graphs(self, g1, g2, edit_cost, method, options, init_option="EAGER_WITHOUT_SHUFFLED_COPIES") : + """ + Computes the edit distance between two NX graphs. + + :param g1: The first graph to add and compute + :param g2: The second graph to add and compute + :param edit_cost: The name of the edit cost function + :param method: The name of the computation method + :param options: The options of the method (like bash options), an empty string by default + :param init_option: The name of the init option, "EAGER_WITHOUT_SHUFFLED_COPIES" by default + :type g1: networksx.graph + :type g2: networksx.graph + :type edit_cost: string + :type method: string + :type options: string + :type init_option: string + :return: The edit distance between the two graphs and the nodeMap between them. + :rtype: double, list[tuple(size_t, size_t)] + + .. seealso:: list_of_edit_cost_options, list_of_method_options, list_of_init_options + .. note:: Make sure each parameter exists with your architecture and these lists : list_of_edit_cost_options, list_of_method_options, list_of_init_options. The structure of graphs must be similar as GXL. + + """ + if self.is_initialized() : + self.restart_env() + + g = self.add_nx_graph(g1, "") + h = self.add_nx_graph(g2, "") + + self.set_edit_cost(edit_cost) + self.init(init_option) + + self.set_method(method, options) + self.init_method() + + resDistance = 0 + resMapping = [] + self.run_method(g, h) + resDistance = self.get_upper_bound(g, h) + resMapping = self.get_node_map(g, h) + + return resDistance, resMapping + + + def compute_edit_distance_on_nx_graphs(self, dataset, classes, edit_cost, method, options, init_option="EAGER_WITHOUT_SHUFFLED_COPIES") : + """ + + Computes all the edit distance between each NX graphs on the dataset. + + :param dataset: The list of graphs to add and compute + :param classes: The classe of all the graph, can be an empty string + :param edit_cost: The name of the edit cost function + :param method: The name of the computation method + :param options: The options of the method (like bash options), an empty string by default + :param init_option: The name of the init option, "EAGER_WITHOUT_SHUFFLED_COPIES" by default + :type dataset: list[networksx.graph] + :type classes: string + :type edit_cost: string + :type method: string + :type options: string + :type init_option: string + :return: Two matrix, the first with edit distances between graphs and the second the nodeMap between graphs. The result between g and h is one the [g][h] coordinates. + :rtype: list[list[double]], list[list[list[tuple(size_t, size_t)]]] + + .. seealso:: list_of_edit_cost_options, list_of_method_options, list_of_init_options + .. note:: Make sure each parameter exists with your architecture and these lists : list_of_edit_cost_options, list_of_method_options, list_of_init_options. The structure of graphs must be similar as GXL. + + """ + if self.is_initialized() : + self.restart_env() + + print("Loading graphs in progress...") + for graph in dataset : + self.add_nx_graph(graph, classes) + listID = self.graph_ids() + print("Graphs loaded ! ") + print("Number of graphs = " + str(listID[1])) + + self.set_edit_cost(edit_cost) + print("Initialization in progress...") + self.init(init_option) + print("Initialization terminated !") + + self.set_method(method, options) + self.init_method() + + resDistance = [[]] + resMapping = [[]] + for g in range(listID[0], listID[1]) : + print("Computation between graph " + str(g) + " with all the others including himself.") + for h in range(listID[0], listID[1]) : + #print("Computation between graph " + str(g) + " and graph " + str(h)) + self.run_method(g, h) + resDistance[g][h] = self.get_upper_bound(g, h) + resMapping[g][h] = self.get_node_map(g, h) + + print("Finish ! The return contains edit distances and NodeMap but you can check the result with graphs'ID until you restart the environment") + return resDistance, resMapping + + + def compute_edit_distance_on_GXl_graphs(self, path_folder, path_XML, edit_cost, method, options="", init_option="EAGER_WITHOUT_SHUFFLED_COPIES") : + """ + Computes all the edit distance between each GXL graphs on the folder and the XMl file. + + :param path_folder: The folder's path which contains GXL graphs + :param path_XML: The XML's path which indicates which graphes you want to load + :param edit_cost: The name of the edit cost function + :param method: The name of the computation method + :param options: The options of the method (like bash options), an empty string by default + :param init_option: The name of the init option, "EAGER_WITHOUT_SHUFFLED_COPIES" by default + :type path_folder: string + :type path_XML: string + :type edit_cost: string + :type method: string + :type options: string + :type init_option: string + :return: The list of the first and last-1 ID of graphs + :rtype: tuple(size_t, size_t) + + .. seealso:: list_of_edit_cost_options, list_of_method_options, list_of_init_options + .. note:: Make sure each parameter exists with your architecture and these lists : list_of_edit_cost_options, list_of_method_options, list_of_init_options. + + """ + + if self.is_initialized() : + self.restart_env() + + print("Loading graphs in progress...") + self.load_GXL_graphs(path_folder, path_XML) + listID = self.graph_ids() + print("Graphs loaded ! ") + print("Number of graphs = " + str(listID[1])) + + self.set_edit_cost(edit_cost) + print("Initialization in progress...") + self.init(init_option) + print("Initialization terminated !") + + self.set_method(method, options) + self.init_method() + + #res = [] + for g in range(listID[0], listID[1]) : + print("Computation between graph " + str(g) + " with all the others including himself.") + for h in range(listID[0], listID[1]) : + #print("Computation between graph " + str(g) + " and graph " + str(h)) + self.run_method(g,h) + #res.append((get_upper_bound(g,h), get_node_map(g,h), get_runtime(g,h))) + + #return res + + print ("Finish ! You can check the result with each ID of graphs ! There are in the return") + print ("Please don't restart the environment or recall this function, you will lose your results !") + return listID + + + def get_num_node_labels(self): + """ + Returns the number of node labels. + + :return: Number of pairwise different node labels contained in the environment. + :rtype: size_t + + .. note:: If 1 is returned, the nodes are unlabeled. + """ + return self.c_env.getNumNodeLabels() + + + def get_node_label(self, label_id): + """ + Returns node label. + + :param label_id: ID of node label that should be returned. Must be between 1 and get_num_node_labels(). + :type label_id: size_t + :return: Node label for selected label ID. + :rtype: dict{string : string} + """ + return decode_your_map(self.c_env.getNodeLabel(label_id)) + + + def get_num_edge_labels(self): + """ + Returns the number of edge labels. + + :return: Number of pairwise different edge labels contained in the environment. + :rtype: size_t + + .. note:: If 1 is returned, the edges are unlabeled. + """ + return self.c_env.getNumEdgeLabels() + + + def get_edge_label(self, label_id): + """ + Returns edge label. + + :param label_id: ID of edge label that should be returned. Must be between 1 and get_num_edge_labels(). + :type label_id: size_t + :return: Edge label for selected label ID. + :rtype: dict{string : string} + """ + return decode_your_map(self.c_env.getEdgeLabel(label_id)) + + +# def get_num_nodes(self, graph_id): +# """ +# Returns the number of nodes. +# +# :param graph_id: ID of an input graph that has been added to the environment. +# :type graph_id: size_t +# :return: Number of nodes in the graph. +# :rtype: size_t +# """ +# return self.c_env.getNumNodes(graph_id) + + def get_avg_num_nodes(self): + """ + Returns average number of nodes. + + :return: Average number of nodes of the graphs contained in the environment. + :rtype: double + """ + return self.c_env.getAvgNumNodes() + + def get_node_rel_cost(self, node_label_1, node_label_2): + """ + Returns node relabeling cost. + + :param node_label_1: First node label. + :param node_label_2: Second node label. + :type node_label_1: dict{string : string} + :type node_label_2: dict{string : string} + :return: Node relabeling cost for the given node labels. + :rtype: double + """ + return self.c_env.getNodeRelCost(encode_your_map(node_label_1), encode_your_map(node_label_2)) + + + def get_node_del_cost(self, node_label): + """ + Returns node deletion cost. + + :param node_label: Node label. + :type node_label: dict{string : string} + :return: Cost of deleting node with given label. + :rtype: double + """ + return self.c_env.getNodeDelCost(encode_your_map(node_label)) + + + def get_node_ins_cost(self, node_label): + """ + Returns node insertion cost. + + :param node_label: Node label. + :type node_label: dict{string : string} + :return: Cost of inserting node with given label. + :rtype: double + """ + return self.c_env.getNodeInsCost(encode_your_map(node_label)) + + + def get_median_node_label(self, node_labels): + """ + Computes median node label. + + :param node_labels: The node labels whose median should be computed. + :type node_labels: list[dict{string : string}] + :return: Median of the given node labels. + :rtype: dict{string : string} + """ + node_labels_b = [encode_your_map(node_label) for node_label in node_labels] + return decode_your_map(self.c_env.getMedianNodeLabel(node_labels_b)) + + + def get_edge_rel_cost(self, edge_label_1, edge_label_2): + """ + Returns edge relabeling cost. + + :param edge_label_1: First edge label. + :param edge_label_2: Second edge label. + :type edge_label_1: dict{string : string} + :type edge_label_2: dict{string : string} + :return: Edge relabeling cost for the given edge labels. + :rtype: double + """ + return self.c_env.getEdgeRelCost(encode_your_map(edge_label_1), encode_your_map(edge_label_2)) + + + def get_edge_del_cost(self, edge_label): + """ + Returns edge deletion cost. + + :param edge_label: Edge label. + :type edge_label: dict{string : string} + :return: Cost of deleting edge with given label. + :rtype: double + """ + return self.c_env.getEdgeDelCost(encode_your_map(edge_label)) + + + def get_edge_ins_cost(self, edge_label): + """ + Returns edge insertion cost. + + :param edge_label: Edge label. + :type edge_label: dict{string : string} + :return: Cost of inserting edge with given label. + :rtype: double + """ + return self.c_env.getEdgeInsCost(encode_your_map(edge_label)) + + + def get_median_edge_label(self, edge_labels): + """ + Computes median edge label. + + :param edge_labels: The edge labels whose median should be computed. + :type edge_labels: list[dict{string : string}] + :return: Median of the given edge labels. + :rtype: dict{string : string} + """ + edge_labels_b = [encode_your_map(edge_label) for edge_label in edge_labels] + return decode_your_map(self.c_env.getMedianEdgeLabel(edge_label_b)) + + + def get_nx_graph(self, graph_id, adj_matrix=True, adj_lists=False, edge_list=False): # @todo + """ + Get graph with id `graph_id` in the form of the NetworkX Graph. + + Parameters + ---------- + graph_id : int + ID of the selected graph. + + adj_matrix : bool + Set to `True` to construct an adjacency matrix `adj_matrix` and a hash-map `edge_labels`, which has a key for each pair `(i,j)` such that `adj_matrix[i][j]` equals 1. No effect for now. + + adj_lists : bool + No effect for now. + + edge_list : bool + No effect for now. + + Returns + ------- + NetworkX Graph object + The obtained graph. + """ + graph = nx.Graph() + graph.graph['id'] = graph_id + + nb_nodes = self.get_graph_num_nodes(graph_id) + original_node_ids = self.get_original_node_ids(graph_id) + node_labels = self.get_graph_node_labels(graph_id) +# print(original_node_ids) +# print(node_labels) + graph.graph['original_node_ids'] = original_node_ids + + for node_id in range(0, nb_nodes): + graph.add_node(node_id, **node_labels[node_id]) +# graph.nodes[node_id]['original_node_id'] = original_node_ids[node_id] + + edges = self.get_graph_edges(graph_id) + for (head, tail), labels in edges.items(): + graph.add_edge(head, tail, **labels) +# print(edges) + + return graph + + + def get_init_type(self): + """ + Returns the initialization type of the last initialization in string. + + Returns + ------- + string + Initialization type in string. + """ + return self.c_env.getInitType().decode('utf-8') + + +# def get_node_cost(self, label1, label2): +# """ +# Returns node relabeling, insertion, or deletion cost. + +# Parameters +# ---------- +# label1 : int +# First node label. +# +# label2 : int +# Second node label. +# +# Returns +# ------- +# Node relabeling cost if `label1` and `label2` are both different from `ged::dummy_label()`, node insertion cost if `label1` equals `ged::dummy_label` and `label2` does not, node deletion cost if `label1` does not equal `ged::dummy_label` and `label2` does, and 0 otherwise. +# """ +# return self.c_env.getNodeCost(label1, label2) + + + def load_nx_graph(self, nx_graph, graph_id, graph_name='', graph_class=''): + """ + Loads NetworkX Graph into the GED environment. + + Parameters + ---------- + nx_graph : NetworkX Graph object + The graph that should be loaded. + + graph_id : int or None + The ID of a graph contained the environment (overwrite existing graph) or add new graph if `None`. + + graph_name : string, optional + The name of newly added graph. The default is ''. Has no effect unless `graph_id` equals `None`. + + graph_class : string, optional + The class of newly added graph. The default is ''. Has no effect unless `graph_id` equals `None`. + + Returns + ------- + int + The ID of the newly loaded graph. + """ + if graph_id is None: + graph_id = self.add_graph(graph_name, graph_class) + else: + self.clear_graph(graph_id) + for node in nx_graph.nodes: + self.add_node(graph_id, str(node), nx_graph.nodes[node]) + for edge in nx_graph.edges: + self.add_edge(graph_id, str(edge[0]), str(edge[1]), nx_graph.get_edge_data(edge[0], edge[1])) + return graph_id + + + def compute_induced_cost(self, g_id, h_id, node_map): + """ + Computes the edit cost between two graphs induced by a node map. + + Parameters + ---------- + g_id : int + ID of input graph. + h_id : int + ID of input graph. + node_map: gklearn.ged.env.NodeMap. + The NodeMap instance whose reduced cost will be computed and re-assigned. + + Returns + ------- + None. + """ + relation = [] + node_map.as_relation(relation) +# print(relation) + dummy_node = get_dummy_node() +# print(dummy_node) + for i, val in enumerate(relation): + val1 = dummy_node if val[0] == np.inf else val[0] + val2 = dummy_node if val[1] == np.inf else val[1] + relation[i] = tuple((val1, val2)) +# print(relation) + induced_cost = self.c_env.computeInducedCost(g_id, h_id, relation) + node_map.set_induced_cost(induced_cost) + + +##################################################################### +##LISTS OF EDIT COST FUNCTIONS, METHOD COMPUTATION AND INIT OPTIONS## +##################################################################### + +list_of_edit_cost_options = get_edit_cost_options() +list_of_method_options = get_method_options() +list_of_init_options = get_init_options() + + +##################### +##ERRORS MANAGEMENT## +##################### + +class Error(Exception): + """ + Class for error's management. This one is general. + """ + pass + + +class EditCostError(Error) : + """ + Class for Edit Cost Error. Raise an error if an edit cost function doesn't exist in the library (not in list_of_edit_cost_options). + + :attribute message: The message to print when an error is detected. + :type message: string + """ + def __init__(self, message): + """ + Inits the error with its message. + + :param message: The message to print when the error is detected + :type message: string + """ + self.message = message + + +class MethodError(Error) : + """ + Class for Method Error. Raise an error if a computation method doesn't exist in the library (not in list_of_method_options). + + :attribute message: The message to print when an error is detected. + :type message: string + """ + def __init__(self, message): + """ + Inits the error with its message. + + :param message: The message to print when the error is detected + :type message: string + """ + self.message = message + + +class InitError(Error) : + """ + Class for Init Error. Raise an error if an init option doesn't exist in the library (not in list_of_init_options). + + :attribute message: The message to print when an error is detected. + :type message: string + """ + def __init__(self, message): + """ + Inits the error with its message. + + :param message: The message to print when the error is detected + :type message: string + """ + self.message = message + + +######################################### +##PYTHON FUNCTIONS FOR SOME COMPUTATION## +######################################### + +def encode_your_map(map_u): + """ + Encodes Python unicode strings in dictionnary `map` to utf-8 byte strings for C++ functions. + + :param map_b: The map to encode + :type map_b: dict{string : string} + :return: The encoded map + :rtype: dict{'b'string : 'b'string} + + .. note:: This function is used for type connection. + + """ + res = {} + for key, value in map_u.items(): + res[key.encode('utf-8')] = value.encode('utf-8') + return res + + +def decode_your_map(map_b): + """ + Decodes utf-8 byte strings in `map` from C++ functions to Python unicode strings. + + :param map_b: The map to decode + :type map_b: dict{'b'string : 'b'string} + :return: The decoded map + :rtype: dict{string : string} + + .. note:: This function is used for type connection. + + """ + res = {} + for key, value in map_b.items(): + res[key.decode('utf-8')] = value.decode('utf-8') + return res + + +def decode_graph_edges(map_edge_b): + """ + Decode utf-8 byte strings in graph edges `map` from C++ functions to Python unicode strings. + + Parameters + ---------- + map_edge_b : dict{tuple(size_t, size_t) : dict{'b'string : 'b'string}} + The map to decode. + + Returns + ------- + dict{tuple(size_t, size_t) : dict{string : string}} + The decoded map. + + Notes + ----- + This is a helper function for function `GEDEnv.get_graph_edges()`. + """ + map_edges = {} + for key, value in map_edge_b.items(): + map_edges[key] = decode_your_map(value) + return map_edges + + + + + + + +# cdef extern from "src/GedLibBind.h" namespace "shapes": +# cdef cppclass Rectangle: +# Rectangle() except + +# Rectangle(int, int, int, int) except + +# int x0, y0, x1, y1 +# int getArea() +# void getSize(int* width, int* height) +# void move(int, int) + + +# # Create a Cython extension type which holds a C++ instance +# # as an attribute and create a bunch of forwarding methods +# # Python extension type. +# cdef class PyRectangle: +# cdef Rectangle c_rect # Hold a C++ instance which we're wrapping + +# def __cinit__(self, int x0, int y0, int x1, int y1): +# self.c_rect = Rectangle(x0, y0, x1, y1) + +# def get_area(self): +# return self.c_rect.getArea() + +# def get_size(self): +# cdef int width, height +# self.c_rect.getSize(&width, &height) +# return width, height + +# def move(self, dx, dy): +# self.c_rect.move(dx, dy) + +# # Attribute access +# @property +# def x0(self): +# return self.c_rect.x0 +# @x0.setter +# def x0(self, x0): +# self.c_rect.x0 = x0 + +# # Attribute access +# @property +# def x1(self): +# return self.c_rect.x1 +# @x1.setter +# def x1(self, x1): +# self.c_rect.x1 = x1 + +# # Attribute access +# @property +# def y0(self): +# return self.c_rect.y0 +# @y0.setter +# def y0(self, y0): +# self.c_rect.y0 = y0 + +# # Attribute access +# @property +# def y1(self): +# return self.c_rect.y1 +# @y1.setter +# def y1(self, y1): +# self.c_rect.y1 = y1 \ No newline at end of file From 83d62f46c913ef3d0eead52a09b5a4d388b28dfb Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:17:37 +0200 Subject: [PATCH 112/728] New translations gedlibpy.cpython-36m-x86_64-linux-gnu.so (Chinese Simplified) --- .../gedlibpy.cpython-36m-x86_64-linux-gnu.so | Bin 0 -> 33390352 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 lang/zh/gklearn/gedlib/gedlibpy.cpython-36m-x86_64-linux-gnu.so diff --git a/lang/zh/gklearn/gedlib/gedlibpy.cpython-36m-x86_64-linux-gnu.so 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