def test_LinearDiscriminantAnalysis(self): helper = Helper() # Test with dataset type: sklearn DataBunch # Load up data iris = load_iris() X_train, y_train, X_test, y_test = iris.data[:120], iris.target[:120], iris.data[120:], iris.target[120:] # Bring in the default LinearDiscriminantAnalysis model model_name = 'lda' actual_model = helper.getBuiltModel(model_name) # Explicity create the model we expect to get from getBuiltModel call expected_model = LinearDiscriminantAnalysis() # Make sure the models are the same type before continuing self.assertEqual( type(actual_model), type(expected_model) ) # Train this default model on the iris dataset actual_model.fit(X_train, y_train) # Get default model accuracy on testing set actual_accuracy = actual_model.score(X_test, y_test) # Complete the same process, however we make the model explicitly expected_model.fit(X_train, y_train) expected_accuracy = expected_model.score(X_test, y_test) # Make sure that the accuracy reported from both models is the same self.assertEqual( actual_accuracy, expected_accuracy ) # Test with dataset type: pandas DataFrame # Load up data iris_df = load_iris(as_frame=True).frame X = iris_df.loc[:, iris_df.columns != 'target'] y = iris_df['target'] X_train, y_train = X.iloc[:120], y.iloc[:120] X_test, y_test = X.iloc[120:], y.iloc[120:] # Bring in the default LinearDiscriminantAnalysis model model_name = 'lda' actual_model = helper.getBuiltModel(model_name) # Explicity create the model we expect to get from getBuiltModel call expected_model = LinearDiscriminantAnalysis() # Make sure the models are the same type before continuing self.assertEqual( type(actual_model), type(expected_model) ) # Train this default model on the iris dataset actual_model.fit(X_train, y_train) # Get default model accuracy on testing set actual_accuracy = actual_model.score(X_test, y_test) # Complete the same process, however we make the model explicitly expected_model.fit(X_train, y_train) expected_accuracy = expected_model.score(X_test, y_test) # Make sure that the accuracy reported from both models is the same self.assertEqual( actual_accuracy, expected_accuracy )
def test_getBuiltModel(self): helper = Helper() model_name = 'rf:classifier' actual = helper.getBuiltModel(model_name) expected = RandomForestClassifier(random_state=0) self.assertEqual( type(actual), type(expected) ) try: model_name = 'jonathan' model = helper.getBuiltModel(model_name) fail(self) except ValueError as ve: self.assertEqual( str(ve), 'No default model found for jonathan')
def test_AdaBoostRegressor(self): helper = Helper() # Load up data X, y = load_boston(return_X_y=True) X_train, y_train, X_test, y_test = X[:405], y[:405], X[405:], y[405:] # Bring in the default RandomForestClassifier model model_name = 'ab:regressor' actual_model = helper.getBuiltModel(model_name) # Explicity create the model we expect to get from getBuiltModel call expected_model = AdaBoostRegressor(random_state=0) # Make sure the models are the same type before continuing self.assertEqual( type(actual_model), type(expected_model) ) # Train this default model on the iris dataset actual_model.fit(X_train, y_train) # Get default model accuracy on testing set actual_accuracy = actual_model.score(X_test, y_test) # Complete the same process, however we make the model explicitly expected_model.fit(X_train, y_train) expected_accuracy = expected_model.score(X_test, y_test) # Make sure that the accuracy reported from both models is the same self.assertEqual( actual_accuracy, expected_accuracy )
def test_LogisticRegression(self): helper = Helper() # Load up data iris = load_iris() X_train, y_train, X_test, y_test = iris.data[:120], iris.target[:120], iris.data[120:], iris.target[120:] # Bring in the default RandomForestClassifier model model_name = 'lr' actual_model = helper.getBuiltModel(model_name) # Explicity create the model we expect to get from getBuiltModel call expected_model = LogisticRegression(random_state=0) # Make sure the models are the same type before continuing self.assertEqual( type(actual_model), type(expected_model) ) # Train this default model on the iris dataset actual_model.fit(X_train, y_train) # Get default model accuracy on testing set actual_accuracy = actual_model.score(X_test, y_test) # Complete the same process, however we make the model explicitly expected_model.fit(X_train, y_train) expected_accuracy = expected_model.score(X_test, y_test) # Make sure that the accuracy reported from both models is the same self.assertEqual( actual_accuracy, expected_accuracy )
