Hands-on Scikit-Learn for Machine Learning Applications by David Paper

Author:David Paper
Language: eng
Format: epub
ISBN: 9781484253731
Publisher: Apress

def get_scores(model, Xtest, ytest):

y_pred = model.predict(Xtest)

return np.sqrt(mean_squared_error(ytest, y_pred)),\

model.__class__.__name__

if __name__ == "__main__":

br = '\n'

X = np.load('data/X_boston.npy')

y = np.load('data/y_boston.npy')

print ('feature shape', X.shape)

print ('target shape', y.shape, br)

X_train, X_test, y_train, y_test = train_test_split( X, y, random_state=0)

print ('rmse:')

rfr = RandomForestRegressor(random_state=0, n_estimators=100)

rfr.fit(X_train, y_train)

rmse, rfr_name = get_scores(rfr, X_test, y_test)

print (rmse, '(' + rfr_name + ')')

lr = LinearRegression().fit(X_train, y_train)

rmse, lr_name = get_scores(lr, X_test, y_test)

print (rmse, '(' + lr_name + ')')

ridge = Ridge(random_state=0).fit(X_train, y_train)

rmse, ridge_name = get_scores(ridge, X_test, y_test)

print (rmse, '(' + ridge_name + ')')

lasso = Lasso(random_state=0).fit(X_train, y_train)

rmse, lasso_name = get_scores(lasso, X_test, y_test)

print (rmse, '(' + lasso_name + ')')

en = ElasticNet(random_state=0).fit(X_train, y_train)

rmse, en_name = get_scores(en, X_test, y_test)

print (rmse, '(' + en_name + ')')

scaler = StandardScaler()

X_train_std = scaler.fit_transform(X_train)

X_test_std = scaler.fit_transform(X_test)

sgdr_std = SGDRegressor(random_state=0, max_iter=1000, tol=0.001)

sgdr_std.fit(X_train_std, y_train)

rmse, sgdr_name = get_scores(sgdr_std, X_test_std, y_test)

print (rmse, '(' + sgdr_name + ' - scaled)')

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