def test_fwls_regressor(self):
feature_func = lambda x: np.ones(x.shape)
bclf = LinearRegression()
clfs = [
RandomForestRegressor(n_estimators=50, random_state=1),
GradientBoostingRegressor(n_estimators=25, random_state=1),
Ridge(random_state=1)
]
# Friedman1
X, y = datasets.make_friedman1(n_samples=1200,
random_state=1,
noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
sr = FWLSRegressor(bclf,
clfs,
feature_func,
n_folds=3,
verbose=0,
oob_score_flag=True)
sr.fit(X_train, y_train)
mse = mean_squared_error(y_test, sr.predict(X_test))
assert_less(mse, 6.0)
def test_fwls_regressor(self):
feature_func = lambda x: np.ones(x.shape)
bclf = LinearRegression()
clfs = [RandomForestRegressor(n_estimators=50, random_state=1),
GradientBoostingRegressor(n_estimators=25, random_state=1),
Ridge(random_state=1)]
# Friedman1
X, y = datasets.make_friedman1(n_samples=1200,
random_state=1,
noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
sr = FWLSRegressor(bclf,
clfs,
feature_func,
n_folds=3,
verbose=0,
oob_score_flag=True)
sr.fit(X_train, y_train)
mse = mean_squared_error(y_test, sr.predict(X_test))
assert_less(mse, 6.0)
boston = datasets.load_boston()
X = preprocessing.StandardScaler().fit_transform(boston.data)
Y = boston.target
X_train = X[:200]
Y_train = Y[:200]
X_test = X[200:]
Y_test = Y[200:]
breg = LinearRegression()
regs = [RandomForestRegressor(n_estimators=50, random_state=1),
GradientBoostingRegressor(n_estimators=25, random_state=1),
Ridge(),
ExtraTreesRegressor(n_estimators=50),
]
feature_func = lambda x: np.c_[np.ones((x.shape[0], 1)),
x[:, 1].reshape((x.shape[0], 1)),
x[:, 6].reshape((x.shape[0], 1)),]
sr = FWLSRegressor(breg,
regs,
feature_func,
n_folds=3,
verbose=0,
oob_score_flag=False)
sr.fit(X_train, Y_train)
score = metrics.mean_squared_error(sr.predict(X_test), Y_test)
print ("MSE of stacked regressor: %f" % score)
boston = datasets.load_boston()
X = preprocessing.StandardScaler().fit_transform(boston.data)
Y = boston.target
X_train = X[:200]
Y_train = Y[:200]
X_test = X[200:]
Y_test = Y[200:]
breg = LinearRegression()
regs = [RandomForestRegressor(n_estimators=50, random_state=1),
GradientBoostingRegressor(n_estimators=25, random_state=1),
Ridge(),
ExtraTreesRegressor(n_estimators=50),
]
feature_func = lambda x: np.c_[np.ones((x.shape[0], 1)),
x[:, 1].reshape((x.shape[0], 1)),
x[:, 6].reshape((x.shape[0], 1)),]
sr = FWLSRegressor(breg,
regs,
feature_func,
n_folds=3,
verbose=0,
oob_score_flag=False)
sr.fit(X_train, Y_train)
score = metrics.mean_squared_error(sr.predict(X_test), Y_test)
print ("MSE of stacked regressor: %f" % score)