def test_predict_proba(): clf = TimeSeriesForestClassifier(n_estimators=2) clf.fit(X, y) proba = clf.predict_proba(X) assert proba.shape == (X.shape[0], n_classes) np.testing.assert_array_equal(np.ones(X.shape[0]), np.sum(proba, axis=1)) # test single row input y_proba = clf.predict_proba(X.iloc[[0], :]) assert y_proba.shape == (1, n_classes) y_pred = clf.predict(X.iloc[[0], :]) assert y_pred.shape == (1,)
y_train = pd.Series(y_train.reshape(-1)) y_test = pd.Series(y_test.reshape(-1)) # Timeseries random foreset for every column for i, col in enumerate(col_names[:2]): print(col) # Choose one feature X_train_step = X_train.iloc[:, [i]] X_test_step = X_test.iloc[:, [i]] # Time series forest clf classifier = TimeSeriesForestClassifier() classifier.fit(X_train_step, y_train) y_pred = classifier.predict(X_test_step) # Metrics print(f'accuracy_test: {accuracy_score(y_test, y_pred)}') print(f"recall_test: {recall_score(y_test, y_pred)}") print(f"precisoin_test: {precision_score(y_test, y_pred)}") print(f"f1_test: {f1_score(y_test, y_pred)}") # clf2 = pickle.loads(s) # clf2.predict(X_test[0:1]) # # KNeighbors Classifier # clf = KNeighborsTimeSeriesClassifier(n_neighbors=2,