Merge pull request #64 from IntelPython/update_tests

update test to avoid deprecation warning

Author: vtavana

.github/workflows/conda-package.yml

@@ -1,6 +1,10 @@
 name: Conda package
 
-on: push
+on:
+    push:
+      branches:
+        - master
+    pull_request:
 
 permissions: read-all
 

README.md

@@ -3,28 +3,28 @@
 
 # `mkl_umath`
 
-`mkl_umath._ufuncs` exposes [Intel(R) Math Kernel Library](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html)
+`mkl_umath._ufuncs` exposes [Intel® OneAPI Math Kernel Library (OneMKL)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html)
 powered version of loops used in the patched version of [NumPy](https://numpy.org), that used to be included in
-[Intel(R) Distribution for Python*](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html).
+[Intel® Distribution for Python*](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html).
 
 Patches were factored out per community feedback ([NEP-36](https://numpy.org/neps/nep-0036-fair-play.html)).
 
-`mkl_umath` started as a part of Intel (R) Distribution for Python* optimizations to NumPy, and is now being released 
-as a stand-alone package. It can be installed into conda environment using 
+`mkl_umath` started as a part of Intel® Distribution for Python* optimizations to NumPy, and is now being released 
+as a stand-alone package. It can be installed into conda environment using:
 
 ```
    conda install -c https://software.repos.intel.com/python/conda mkl_umath
 ```
 
 ---
 
-To install mkl_umath Pypi package please use following command:
+To install mkl_umath PyPI package please use following command:
 
 ```
    python -m pip install --i https://software.repos.intel.com/python/pypi -extra-index-url https://pypi.org/simple mkl_umath
 ```
 
-If command above installs NumPy package from the Pypi, please use following command to install Intel optimized NumPy wheel package from Intel Pypi Cloud:
+If command above installs NumPy package from the PyPI, please use the following command to install Intel optimized NumPy wheel package from Intel PyPI Cloud:
 
 ```
    python -m pip install --i https://software.repos.intel.com/python/pypi -extra-index-url https://pypi.org/simple mkl_umath numpy==<numpy_version>
@@ -36,7 +36,7 @@ Where `<numpy_version>` should be the latest version from https://software.repos
 
 ## Building
 
-Intel(R) C compiler and Intel(R) Math Kernel Library are required to build `mkl_umath` from source:
+Intel® C compiler and Intel® OneMKL are required to build `mkl_umath` from source:
 
 ```sh
 # ensure that MKL is installed into Python prefix, Intel LLVM compiler is activated

mkl_umath/tests/test_basic.py

@@ -26,7 +26,6 @@
 import pytest
 import numpy as np
 import mkl_umath._ufuncs as mu
-import numpy.core.umath as nu
 
 np.random.seed(42)
 
@@ -56,10 +55,10 @@ def get_args(args_str):
 for umath in umaths:
     mkl_umath = getattr(mu, umath)
     types = mkl_umath.types
-    for type in types:
-        args_str = type[:type.find('->')]
+    for type_ in types:
+        args_str = type_[:type_.find('->')]
         args = get_args(args_str)
-        generated_cases[(umath, type)] = args
+        generated_cases[(umath, type_)] = args
 
 additional_cases = {
     ('arccosh', 'f->f'): (np.single(np.random.random_sample() + 1),),
@@ -68,23 +67,20 @@ def get_args(args_str):
 
 test_cases = {**generated_cases, **additional_cases}
 
-@pytest.mark.parametrize("case", list(test_cases.keys()))
+def get_id(val):
+    return val.__str__()
+
+@pytest.mark.parametrize("case", test_cases, ids=get_id)
 def test_umath(case):
-    umath, type = case
+    umath, _ = case
     args = test_cases[case]
     mkl_umath = getattr(mu, umath)
-    np_umath = getattr(nu, umath)
-    print('*'*80)
-    print(f"Testing {umath} with type {type}")
-    print("args:", args)
+    np_umath = getattr(np, umath)
 
     mkl_res = mkl_umath(*args)
     np_res = np_umath(*args)
-    
-    print("mkl res:", mkl_res)
-    print("npy res:", np_res)
-    
-    assert np.allclose(mkl_res, np_res), f"Results for {umath} do not match"
+       
+    assert np.allclose(mkl_res, np_res), f"Results for '{umath}': mkl_res: {mkl_res}, np_res: {np_res}"
 
 def test_cases_count():
     print("Test cases count:", len(test_cases))