def test_pipe_regression(): # no context data, single time series X = [np.random.rand(1000, 10)] y = [np.random.rand(1000)] pipe = Pype([('seg', SegmentXY()), ('ftr', FeatureRep()), ('ridge', Ridge())]) pipe.fit(X, y) pipe.transform_predict(X, y) pipe.predict(X) pipe.score(X, y) # context data, single time seres Xt = [np.random.rand(1000, 10)] Xc = [np.random.rand(3)] X = TS_Data(Xt, Xc) y = [np.random.rand(1000)] pipe.fit(X, y) pipe.transform_predict(X, y) pipe.predict(X) pipe.score(X, y) # multiple time seres Xt = [ np.random.rand(1000, 10), np.random.rand(100, 10), np.random.rand(500, 10) ] Xc = np.random.rand(3, 3) X = TS_Data(Xt, Xc) y = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)] pipe.fit(X, y) pipe.transform_predict(X, y) pipe.predict(X) pipe.score(X, y) # cross val Xt = np.array([np.random.rand(1000, 10)] * 5) Xc = np.random.rand(5, 3) X = TS_Data(Xt, Xc) y = np.array([np.random.rand(1000)] * 5) cross_validate(pipe, X, y, cv=3) # transform pipe pipe = Pype([('seg', SegmentXY()), ('ftr', FeatureRep()), ('scaler', StandardScaler())]) Xt = [ np.random.rand(1000, 10), np.random.rand(100, 10), np.random.rand(500, 10) ] Xc = np.random.rand(3, 3) X = TS_Data(Xt, Xc) y = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)] pipe.fit(X, y) pipe.transform(X, y) pipe.fit_transform(X, y)
n_jobs=6, verbose=10))) ]) #scorer=scorer1 X_train, X_test, y_train, y_test, matlab_train, matlab_test = train_test_split( new_features_seg_included, new_labels_seg_included, new_matlab_seg_included, test_size=0.10, random_state=10) clf.fit(X_train, y_train) #clf1.fit(X_train, y_train) score = clf.score(X_test, y_test) predict = clf.predict(X_test) test1, test2 = clf.transform_predict(X_test, y_test) train1, train2 = clf.transform_predict(X_train, y_train) test21 = inverse_transform(test2, y_test, chosenwidth, 1, order='F') test11 = inverse_transform(test1, y_test, chosenwidth, 1, order='F') train11 = inverse_transform(train1, y_train, chosenwidth, 1, order='F') train21 = inverse_transform(train2, y_train, chosenwidth, 1, order='F') # and do a cross validation scoring = make_scorer(f1_score, average='macro') if change_labels == 1 and conservative == 1: freeze2 = [np.argmax(test21[i]) for i in range(len(test21))] freeze2_conserve = [ test21[i].shape[0] - np.argmax(np.flip(np.int_(np.logical_not(test21[i]))))
def test_pipe_PadTrunc(): # no context data, single time series X = [np.random.rand(1000, 10)] y = [5] pipe = Pype([('trunc', PadTrunc()), ('ftr', FeatureRep()), ('rf', RandomForestClassifier(n_estimators=10))]) pipe.fit(X, y) pipe.transform_predict(X, y) pipe.predict(X) pipe.score(X, y) # context data, single time seres Xt = [np.random.rand(1000, 10)] Xc = [np.random.rand(3)] X = TS_Data(Xt, Xc) y = [5] pipe.fit(X, y) pipe.transform_predict(X, y) pipe.predict(X) pipe.score(X, y) # multiple time series Xt = [ np.random.rand(1000, 10), np.random.rand(100, 10), np.random.rand(500, 10) ] Xc = np.random.rand(3, 3) X = TS_Data(Xt, Xc) y = [1, 2, 3] pipe.fit(X, y) pipe.transform_predict(X, y) pipe.predict(X) pipe.score(X, y) # univariate data Xt = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)] Xc = np.random.rand(3) X = TS_Data(Xt, Xc) y = [1, 2, 3] pipe.fit(X, y) pipe.transform_predict(X, y) pipe.predict(X) pipe.score(X, y) # transform pipe pipe = Pype([('trunc', PadTrunc()), ('ftr', FeatureRep()), ('scaler', StandardScaler())]) Xt = [ np.random.rand(1000, 10), np.random.rand(100, 10), np.random.rand(500, 10) ] Xc = np.random.rand(3, 3) X = TS_Data(Xt, Xc) y = [1, 2, 3] pipe.fit(X, y) pipe.transform(X, y) pipe.fit_transform(X, y)