refactor(validation): rename validation module to evaluation

Author: nmaarnio

docs/evaluation/calculate_base_metrics.md

@@ -0,0 +1,3 @@
+# Calculate base metrics
+
+::: eis_toolkit.evaluation.calculate_base_metrics

docs/evaluation/classification_label_evaluation.md

@@ -0,0 +1,3 @@
+# Classification label evaluation
+
+::: eis_toolkit.evaluation.classification_label_evaluation

docs/evaluation/classification_probability_evaluation.md

@@ -0,0 +1,3 @@
+# Classification probability evaluation
+
+::: eis_toolkit.evaluation.classification_probability_evaluation

docs/evaluation/plot_confusion_matrix.md

@@ -0,0 +1,3 @@
+# Plot confusion matrix
+
+::: eis_toolkit.evaluation.plot_confusion_matrix

docs/validation/plot_nn_model_performance.md docs/evaluation/plot_nn_model_performance.md

@@ -1,3 +1,3 @@
 # Plot neural network training performance (accuracy and loss)
 
-::: eis_toolkit.validation.plot_nn_model_performance
+::: eis_toolkit.evaluation.plot_nn_model_performance

docs/evaluation/plot_prediction_area_curves.md

@@ -0,0 +1,3 @@
+# Plot prediction-area (P-A) curves
+
+::: eis_toolkit.evaluation.plot_prediction_area_curves

docs/evaluation/plot_rate_curve.md

@@ -0,0 +1,3 @@
+# Plot rate curve
+
+::: eis_toolkit.evaluation.plot_rate_curve

docs/validation/calculate_base_metrics.md

@@ -2879,7 +2879,7 @@ def winsorize_transform_cli(
     typer.echo(f"Winsorize transform completed, writing raster to {output_raster}.")
 
 
-# ---VALIDATION ---
+# ---EVALUATION ---
 # TODO
 
 

docs/validation/classification_label_evaluation.md

@@ -13,13 +13,13 @@
 from sklearn.base import BaseEstimator
 from sklearn.model_selection import KFold, LeaveOneOut, StratifiedKFold, train_test_split
 
+from eis_toolkit.evaluation.scoring import score_predictions
 from eis_toolkit.exceptions import (
     InvalidParameterValueException,
     NonMatchingParameterLengthsException,
     NonMatchingRasterMetadataException,
 )
 from eis_toolkit.utilities.checks.raster import check_raster_grids
-from eis_toolkit.validation.scoring import score_predictions
 from eis_toolkit.vector_processing.rasterize_vector import rasterize_vector
 
 SPLIT = "split"

docs/validation/classification_probability_evaluation.md

@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Validation funtions\n",
+    "# Evaluation funtions\n",
     "\n",
-    "Purpose of this notebook is to demonstrate and test validation functions"
+    "Purpose of this notebook is to demonstrate and test evaluation functions"
    ]
   },
   {
@@ -23,9 +23,9 @@
     "\n",
     "import sys\n",
     "sys.path.insert(0, \"..\")\n",
-    "from eis_toolkit.validation.calculate_base_metrics import calculate_base_metrics\n",
-    "from eis_toolkit.validation.plot_rate_curve import plot_rate_curve\n",
-    "from eis_toolkit.validation.plot_prediction_area_curves import plot_prediction_area_curves"
+    "from eis_toolkit.evaluation.calculate_base_metrics import calculate_base_metrics\n",
+    "from eis_toolkit.evaluation.plot_rate_curve import plot_rate_curve\n",
+    "from eis_toolkit.evaluation.plot_prediction_area_curves import plot_prediction_area_curves"
    ]
   },
   {

docs/validation/plot_confusion_matrix.md

@@ -5,8 +5,8 @@
 import pytest
 import rasterio
 
+from eis_toolkit.evaluation.calculate_base_metrics import calculate_base_metrics
 from eis_toolkit.exceptions import GeometryTypeException, NonMatchingCrsException
-from eis_toolkit.validation.calculate_base_metrics import calculate_base_metrics
 
 test_dir = Path(__file__).parent.parent
 raster_path = test_dir.joinpath("data/remote/small_raster.tif")

docs/validation/plot_prediction_area_curves.md

@@ -2,8 +2,8 @@
 import numpy as np
 import pytest
 
+from eis_toolkit.evaluation.plot_confusion_matrix import plot_confusion_matrix
 from eis_toolkit.exceptions import InvalidDataShapeException
-from eis_toolkit.validation.plot_confusion_matrix import plot_confusion_matrix
 
 
 def test_plot_confusion_matrix():

docs/validation/plot_rate_curve.md

@@ -4,10 +4,10 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 
+from eis_toolkit.evaluation.plot_nn_model_performance import plot_nn_model_accuracy, plot_nn_model_loss
 from eis_toolkit.exceptions import InvalidDatasetException, InvalidDataShapeException
 from eis_toolkit.prediction.mlp import train_MLP_classifier
 from eis_toolkit.transformations.one_hot_encoding import one_hot_encode
-from eis_toolkit.validation.plot_nn_model_performance import plot_nn_model_accuracy, plot_nn_model_loss
 
 X_IRIS, Y_IRIS = load_iris(return_X_y=True)
 X_DIABETES, Y_DIABETES = load_diabetes(return_X_y=True)

eis_toolkit/cli.py

@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
 
+from eis_toolkit.evaluation.plot_prediction_area_curves import _get_pa_intersection, plot_prediction_area_curves
 from eis_toolkit.exceptions import InvalidParameterValueException
-from eis_toolkit.validation.plot_prediction_area_curves import _get_pa_intersection, plot_prediction_area_curves
 
 
 def test_get_pa_intersection():

eis_toolkit/validation/__init__.py eis_toolkit/evaluation/__init__.py

@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
 
+from eis_toolkit.evaluation.plot_rate_curve import plot_rate_curve
 from eis_toolkit.exceptions import InvalidParameterValueException
-from eis_toolkit.validation.plot_rate_curve import plot_rate_curve
 
 
 def test_plot_rate_curve_x_values_out_of_bound():

eis_toolkit/validation/calculate_base_metrics.py eis_toolkit/evaluation/calculate_base_metrics.py

@@ -1,9 +1,9 @@
 from sklearn.datasets import make_classification
 from sklearn.model_selection import train_test_split
 
+from eis_toolkit.evaluation.scoring import score_predictions
 from eis_toolkit.prediction.machine_learning_predict import predict_classifier
 from eis_toolkit.prediction.random_forests import random_forest_classifier_train
-from eis_toolkit.validation.scoring import score_predictions
 
 X, y = make_classification(n_samples=200, n_features=20, n_informative=2, n_redundant=10, random_state=42)
 X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

eis_toolkit/validation/classification_label_evaluation.py eis_toolkit/evaluation/classification_label_evaluation.py

@@ -6,6 +6,7 @@
 from sklearn.datasets import load_iris
 from sklearn.ensemble import RandomForestClassifier
 
+from eis_toolkit.evaluation.scoring import score_predictions
 from eis_toolkit.exceptions import InvalidParameterValueException, NonMatchingParameterLengthsException
 from eis_toolkit.prediction.machine_learning_general import (
     _train_and_validate_sklearn_model,
@@ -14,7 +15,6 @@
     split_data,
 )
 from eis_toolkit.prediction.machine_learning_predict import predict_classifier
-from eis_toolkit.validation.scoring import score_predictions
 
 TEST_DIR = Path(__file__).parent.parent