scikit-learn · virchan · Dec 8, 2023 · Dec 10, 2023 · Dec 10, 2023 · Jan 26, 2024
diff --git a/sklearn/_temperature_scaling_test.py b/sklearn/_temperature_scaling_test.py
@@ -0,0 +1,71 @@
+'''
+This file is created to test if the custom 'TemperatureScaling' class runs properly,
+and serves as proof of work for the changes made to the scikit-learn repository.
+
+The file also includes examples related to developing a temperature scaling method
+for probability calibration in multi-class classification.
+
+
+References:
+-----------
+    .. [1]  https://github.com/scikit-learn/scikit-learn/issues/28574. Original issue
+            on Github.
+
+    .. [2]  On Calibration of Modern Neural Networks,
+            C. Guo, G. Pleiss, Y. Sun & K. Q. Weinberger, ICML 2017
+'''
+
+from sklearn.calibration_temperature import CalibratedClassifierCV_test
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+
+# We demonstrate with the Iris dataset, because
+# it is small, multi-class, and self-provided.
+X, y = datasets.load_iris(return_X_y=True)
+X_train, X_calib, y_train, y_calib = train_test_split(X, y)
+
+# Load the following classifiers for testing
+# - Support vector classifier
+# - Logistic regressor
+# - Decision tree classifier
+SV_classifier: SVC = SVC(probability=False)
+Logistic_classifier: LogisticRegression = LogisticRegression()
+Tree_classifier: DecisionTreeClassifier = DecisionTreeClassifier()
+
+# Initiate the temperature scaling calibrators for the classifiers
+SVC_scaled: CalibratedClassifierCV_test = CalibratedClassifierCV_test(SV_classifier,
+                                                                      cv=3,
+                                                                      method='temperature'
+                                                                      )
+Logistic_scaled: CalibratedClassifierCV_test = CalibratedClassifierCV_test(Logistic_classifier,
+                                                                           cv=7,
+                                                                           method='temperature'
+                                                                           )
+Tree_scaled: CalibratedClassifierCV_test = CalibratedClassifierCV_test(Tree_classifier,
+                                                                       cv=3,
+                                                                       method='temperature'
+                                                                       )
+
+# Calibrate the classifiers with temperature scaling
+# The calibrators are trained with the output of
+# `decision_function` for the support vector classifier
+# and logistic regression, while they are trained with
+# `predict_proba` for the decision tree classifier.
+SVC_scaled.fit(X_train,y_train)
+Logistic_scaled.fit(X_train,y_train)
+Tree_scaled.fit(X_train,y_train)
+
+print("Optimal Temperatures For Each Classifiers")
+print(f"{SVC_scaled.calibrated_classifiers_[0].calibrators[0].T_=}")
+print(f"{Logistic_scaled.calibrated_classifiers_[0].calibrators[0].T_=}")
+print(f"{Tree_scaled.calibrated_classifiers_[0].calibrators[0].T_=}")
+
+print('\n')
+print("Printing calibrated probabilities...")
+print(f"{SVC_scaled.predict_proba(X_calib)=}")
+print(f"{Logistic_scaled.predict_proba(X_calib)=}")
+print(f"{Tree_scaled.predict_proba(X_calib)=}")
+print(f"{y_calib=}")