def test_base_estimator_meta_estimator(): # Check that a meta-estimator relying on an estimator implementing # `predict_proba` will work even if it does expose this method before being # fitted. # Non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/19119 base_estimator = StackingClassifier( estimators=[ ("svc_1", SVC(probability=True)), ("svc_2", SVC(probability=True)), ], final_estimator=SVC(probability=True), cv=2, ) assert hasattr(base_estimator, "predict_proba") clf = SelfTrainingClassifier(base_estimator=base_estimator) clf.fit(X_train, y_train_missing_labels) clf.predict_proba(X_test) base_estimator = StackingClassifier( estimators=[ ("svc_1", SVC(probability=False)), ("svc_2", SVC(probability=False)), ], final_estimator=SVC(probability=False), cv=2, ) assert not hasattr(base_estimator, "predict_proba") clf = SelfTrainingClassifier(base_estimator=base_estimator) with pytest.raises(AttributeError): clf.fit(X_train, y_train_missing_labels)
def self_training_clf(self, base_classifier, X_train, y_train, threshold= None, max_iter = None,verbose = None): """ Train self-training classifier from scikit-learn >= 0.24.1 Parameters ___________ base_classifier: Supervised classifier implementing both fit and predict_proba X_train: Scaled feature matrix of the training set y_train: Class label of the training set threshold (float): The decision threshold for use with criterion='threshold'. Should be in [0, 1) max_iter (int): Maximum number of iterations allowed. Should be greater than or equal to 0 verbose (bool): Enable verbose output Returns _____________ Predicted labels and probability """ # Self training model model = SelfTrainingClassifier(base_classifier,threshold= threshold, max_iter = max_iter, verbose = verbose) # Fit the training set model.fit(X_train, y_train) # Predict the labels of the unlabeled data points predicted_labels = model.predict(X_train) # Predict probability predicted_proba = model.predict_proba(X_train) return predicted_labels, predicted_proba
def test_classification(base_estimator, selection_crit): # Check classification for various parameter settings. # Also assert that predictions for strings and numerical labels are equal. # Also test for multioutput classification threshold = 0.75 max_iter = 10 st = SelfTrainingClassifier(base_estimator, max_iter=max_iter, threshold=threshold, criterion=selection_crit) st.fit(X_train, y_train_missing_labels) pred = st.predict(X_test) proba = st.predict_proba(X_test) st_string = SelfTrainingClassifier(base_estimator, max_iter=max_iter, criterion=selection_crit, threshold=threshold) st_string.fit(X_train, y_train_missing_strings) pred_string = st_string.predict(X_test) proba_string = st_string.predict_proba(X_test) assert_array_equal(np.vectorize(mapping.get)(pred), pred_string) assert_array_equal(proba, proba_string) assert st.termination_condition_ == st_string.termination_condition_ # Check consistency between labeled_iter, n_iter and max_iter labeled = y_train_missing_labels != -1 # assert that labeled samples have labeled_iter = 0 assert_array_equal(st.labeled_iter_ == 0, labeled) # assert that labeled samples do not change label during training assert_array_equal(y_train_missing_labels[labeled], st.transduction_[labeled]) # assert that the max of the iterations is less than the total amount of # iterations assert np.max(st.labeled_iter_) <= st.n_iter_ <= max_iter assert np.max(st_string.labeled_iter_) <= st_string.n_iter_ <= max_iter # check shapes assert st.labeled_iter_.shape == st.transduction_.shape assert st_string.labeled_iter_.shape == st_string.transduction_.shape