本文整理汇总了Python中mlxtend.regressor.StackingRegressor类的典型用法代码示例。如果您正苦于以下问题：Python StackingRegressor类的具体用法？Python StackingRegressor怎么用？Python StackingRegressor使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了StackingRegressor类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_get_coeff_fail
def test_get_coeff_fail():
lr = LinearRegression()
svr_rbf = SVR(kernel='rbf')
ridge = Ridge(random_state=1)
stregr = StackingRegressor(regressors=[ridge, lr],
meta_regressor=svr_rbf)
stregr = stregr.fit(X1, y)
got = stregr.coef_示例2: test_get_coeff
def test_get_coeff():
lr = LinearRegression()
svr_lin = SVR(kernel='linear')
ridge = Ridge(random_state=1)
stregr = StackingRegressor(regressors=[svr_lin, lr],
meta_regressor=ridge)
stregr.fit(X1, y)
got = stregr.coef_
expect = np.array([0.4874216, 0.45518317])
assert_almost_equal(got, expect)示例3: test_predict_meta_features
def test_predict_meta_features():
lr = LinearRegression()
svr_rbf = SVR(kernel='rbf')
ridge = Ridge(random_state=1)
stregr = StackingRegressor(regressors=[lr, ridge],
meta_regressor=svr_rbf)
X_train, X_test, y_train, y_test = train_test_split(X2, y, test_size=0.3)
stregr.fit(X_train, y_train)
test_meta_features = stregr.predict(X_test)
assert test_meta_features.shape[0] == X_test.shape[0]示例4: test_get_intercept
def test_get_intercept():
lr = LinearRegression()
svr_lin = SVR(kernel='linear')
ridge = Ridge(random_state=1)
stregr = StackingRegressor(regressors=[svr_lin, lr],
meta_regressor=ridge)
stregr.fit(X1, y)
got = stregr.intercept_
expect = 0.024
assert round(got, 3) == expect示例5: test_multivariate_class
def test_multivariate_class():
lr = LinearRegression()
ridge = Ridge(random_state=1)
meta = LinearRegression(normalize=True)
stregr = StackingRegressor(regressors=[lr, ridge],
meta_regressor=meta)
stregr.fit(X2, y2).predict(X2)
mse = 0.122
got = np.mean((stregr.predict(X2) - y2) ** 2)
assert round(got, 3) == mse示例6: test_different_models
def test_different_models():
lr = LinearRegression()
svr_lin = SVR(kernel='linear')
ridge = Ridge(random_state=1)
svr_rbf = SVR(kernel='rbf')
stregr = StackingRegressor(regressors=[svr_lin, lr, ridge],
meta_regressor=svr_rbf)
y_pred = stregr.fit(X1, y).predict(X1)
mse = 0.214
got = np.mean((stregr.predict(X1) - y) ** 2)
assert round(got, 3) == mse示例7: test_train_meta_features_
def test_train_meta_features_():
lr = LinearRegression()
svr_rbf = SVR(kernel='rbf')
ridge = Ridge(random_state=1)
stregr = StackingRegressor(regressors=[lr, ridge],
meta_regressor=svr_rbf,
store_train_meta_features=True)
X_train, X_test, y_train, y_test = train_test_split(X2, y, test_size=0.3)
stregr.fit(X_train, y_train)
train_meta_features = stregr.train_meta_features_
assert train_meta_features.shape[0] == X_train.shape[0]示例8: test_get_coeff_fail
def test_get_coeff_fail():
lr = LinearRegression()
svr_rbf = SVR(kernel='rbf', gamma='auto')
ridge = Ridge(random_state=1)
stregr = StackingRegressor(regressors=[ridge, lr],
meta_regressor=svr_rbf)
with pytest.raises(AttributeError):
stregr = stregr.fit(X1, y)
r = stregr.coef_
assert r示例9: test_multivariate
def test_multivariate():
lr = LinearRegression()
svr_lin = SVR(kernel='linear')
ridge = Ridge(random_state=1)
svr_rbf = SVR(kernel='rbf')
stregr = StackingRegressor(regressors=[svr_lin, lr, ridge],
meta_regressor=svr_rbf)
stregr.fit(X2, y).predict(X2)
mse = 0.218
got = np.mean((stregr.predict(X2) - y) ** 2)
assert round(got, 3) == mse示例10: test_multivariate_class
def test_multivariate_class():
lr = LinearRegression()
ridge = Ridge(random_state=1)
meta = LinearRegression(normalize=True)
stregr = StackingRegressor(regressors=[lr, ridge],
meta_regressor=meta)
stregr.fit(X2, y2).predict(X2)
mse = 0.12
got = np.mean((stregr.predict(X2) - y2) ** 2.)
# there seems to be an issue with the following test on Windows
# sometimes via Appveyor
assert round(got, 2) == mse, got示例11: test_weight_ones
def test_weight_ones():
# sample weight of ones should produce equivalent outcome as no weight
lr = LinearRegression()
svr_lin = SVR(kernel='linear', gamma='auto')
ridge = Ridge(random_state=1)
svr_rbf = SVR(kernel='rbf', gamma='auto')
stregr = StackingRegressor(regressors=[svr_lin, lr, ridge],
meta_regressor=svr_rbf)
pred1 = stregr.fit(X1, y).predict(X1)
pred2 = stregr.fit(X1, y, sample_weight=np.ones(40)).predict(X1)
maxdiff = np.max(np.abs(pred1 - pred2))
assert maxdiff < 1e-3, "max diff is %.4f" % maxdiff示例12: test_weight_unsupported_meta
def test_weight_unsupported_meta():
# meta regressor with no support for
# sample_weight should raise error
lr = LinearRegression()
svr_lin = SVR(kernel='linear', gamma='auto')
ridge = Ridge(random_state=1)
lasso = Lasso(random_state=1)
stregr = StackingRegressor(regressors=[svr_lin, lr, ridge],
meta_regressor=lasso)
with pytest.raises(TypeError):
stregr.fit(X1, y, sample_weight=w).predict(X1)示例13: test_weight_unsupported_regressor
def test_weight_unsupported_regressor():
# including regressor that does not support
# sample_weight should raise error
lr = LinearRegression()
svr_lin = SVR(kernel='linear', gamma='auto')
ridge = Ridge(random_state=1)
svr_rbf = SVR(kernel='rbf', gamma='auto')
lasso = Lasso(random_state=1)
stregr = StackingRegressor(regressors=[svr_lin, lr, ridge, lasso],
meta_regressor=svr_rbf)
with pytest.raises(TypeError):
stregr.fit(X1, y, sample_weight=w).predict(X1)示例14: test_features_in_secondary
def test_features_in_secondary():
lr = LinearRegression()
svr_lin = SVR(kernel='linear', gamma='auto')
rf = RandomForestRegressor(n_estimators=10, random_state=2)
ridge = Ridge(random_state=0)
svr_rbf = SVR(kernel='rbf', gamma='auto')
stack = StackingRegressor(regressors=[svr_lin, lr, ridge, rf],
meta_regressor=svr_rbf,
use_features_in_secondary=True)
stack.fit(X1, y).predict(X1)
mse = 0.14
got = np.mean((stack.predict(X1) - y) ** 2)
print(got)
assert round(got, 2) == mse
stack = StackingRegressor(regressors=[svr_lin, lr, ridge, rf],
meta_regressor=svr_rbf,
use_features_in_secondary=False)
# dense
stack.fit(X1, y).predict(X1)
mse = 0.12
got = np.mean((stack.predict(X1) - y) ** 2)
print(got)
assert round(got, 2) == mse示例15: test_sample_weight
def test_sample_weight():
lr = LinearRegression()
svr_lin = SVR(kernel='linear', gamma='auto')
ridge = Ridge(random_state=1)
svr_rbf = SVR(kernel='rbf', gamma='auto')
stregr = StackingRegressor(regressors=[svr_lin, lr, ridge],
meta_regressor=svr_rbf)
pred1 = stregr.fit(X1, y, sample_weight=w).predict(X1)
mse = 0.22
got = np.mean((stregr.predict(X1) - y) ** 2)
assert round(got, 2) == mse
# make sure that this is not equivalent to the model with no weight
pred2 = stregr.fit(X1, y).predict(X1)
maxdiff = np.max(np.abs(pred1 - pred2))
assert maxdiff > 1e-3, "max diff is %.4f" % maxdiff