def regression(): # Generate a random regression problem X, y = make_regression(n_samples=5000, n_features=25, n_informative=25, n_targets=1, random_state=100, noise=0.05) y *= 0.01 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111) model = NeuralNet( layers=[ Dense(64, Parameters(init='normal')), Activation('linear'), Dense(32, Parameters(init='normal')), Activation('linear'), Dense(1), ], loss='mse', optimizer=Adam(), metric='mse', batch_size=256, max_epochs=15, ) model.fit(X_train, y_train) predictions = model.predict(X_test) print("regression mse", mean_squared_error(y_test, predictions.flatten()))
def regression(): # Generate a random regression problem X, y = make_regression( n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5 ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1111) model = knn.KNNRegressor(k=5, distance_func=distance.euclidean) model.fit(X_train, y_train) predictions = model.predict(X_test) print("regression mse", mean_squared_error(y_test, predictions))
def regression(): # Generate a random regression problem X, y = make_regression( n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5 ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111) model = RandomForestRegressor(n_estimators=50, max_depth=10, max_features=3) model.fit(X_train, y_train) predictions = model.predict(X_test) print("regression, mse: %s" % mean_squared_error(y_test.flatten(), predictions.flatten()))
def regression(): # Generate a random regression problem X, y = make_regression(n_samples=10000, n_features=100, n_informative=75, n_targets=1, noise=0.05, random_state=1111, bias=0.5) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1111) model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03) model.fit(X_train, y_train) predictions = model.predict(X_test) print('regression mse', mean_squared_error(y_test, predictions))
def regression(): # Generate a random regression problem X, y = make_regression(n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1111) model = knn.KNNRegressor(k=5, distance_func=distance.euclidean) model.fit(X_train, y_train) predictions = model.predict(X_test) print('regression mse', mean_squared_error(y_test, predictions))
def regression(): # Generate a random regression problem X, y = make_regression(n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111) model = GradientBoostingRegressor(n_estimators=25, max_depth=5, max_features=3, ) model.fit(X_train, y_train) predictions = model.predict(X_test) print('regression, mse: %s' % mean_squared_error(y_test.flatten(), predictions.flatten()))
def regression(): X, y = make_regression(n_samples=10000, n_features=100, n_informative=75, n_targets=1, noise=0.05, random_state=8888, bias=0.5) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1111) model = LinearRegression(lr=0.001, max_iters=2000, penalty='l2', C=0.03) model.fit(X_train, y_train) predictions = model.predict(X_test) print('Regression MSE', mean_squared_error(y_test, predictions))
def test_mlp(): model = NeuralNet( layers=[ Dense(16, Parameters(init='normal')), Activation('linear'), Dense(8, Parameters(init='normal')), Activation('linear'), Dense(1), ], loss='mse', optimizer=Adam(), metric='mse', batch_size=64, max_epochs=150, ) model.fit(X_train, y_train) predictions = model.predict(X_test) assert mean_squared_error(y_test, predictions.flatten()) < 1.0
def regression(): # Generate a random regression problem X, y = make_regression(n_samples=10000, n_features=100, n_informative=75, n_targets=1, noise=0.05, random_state=1111, bias=0.5) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1111) model = LinearRegression(lr=0.01, max_iters=2000, penalty="l2", C=0.03) model.fit(X_train, y_train) predictions = model.predict(X_test) print("regression mse", mean_squared_error(y_test, predictions))
def regression(): X, y = make_regression(n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111) model = GradientBoostingRegressor( n_estimators=25, max_depth=5, max_features=3, ) model.fit(X_train, y_train) predictions = model.predict(X_test) print('regression, mse: %s' % mean_squared_error(y_test.flatten(), predictions.flatten()))
def test_knn(): model = KNNRegressor(k=5) model.fit(X_train, y_train) predictions = model.predict(X_test) assert mean_squared_error(y_test, predictions) < 10000
def test_linear(): model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03) model.fit(X_train, y_train) predictions = model.predict(X_test) assert mean_squared_error(y_test, predictions) < 0.25