本文整理汇总了Python中sklearn.model_selection.RandomizedSearchCV.score方法的典型用法代码示例。如果您正苦于以下问题：Python RandomizedSearchCV.score方法的具体用法？Python RandomizedSearchCV.score怎么用？Python RandomizedSearchCV.score使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.model_selection.RandomizedSearchCV的用法示例。
在下文中一共展示了RandomizedSearchCV.score方法的1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_large_grid
# 需要导入模块: from sklearn.model_selection import RandomizedSearchCV [as 别名]
# 或者: from sklearn.model_selection.RandomizedSearchCV import score [as 别名]
def test_large_grid():
"""In this test, we purposely overfit a RandomForest to completely random data
in order to assert that the test error will far supercede the train error.
"""
if not SK18:
custom_cv = KFold(n=y_train.shape[0], n_folds=3, shuffle=True, random_state=42)
else:
custom_cv = KFold(n_splits=3, shuffle=True, random_state=42)
# define the pipe
pipe = Pipeline([
('scaler', SelectiveScaler()),
('pca', SelectivePCA(weight=True)),
('rf', RandomForestClassifier(random_state=42))
])
# define hyper parameters
hp = {
'scaler__scaler': [StandardScaler(), RobustScaler(), MinMaxScaler()],
'pca__whiten': [True, False],
'pca__weight': [True, False],
'pca__n_components': uniform(0.75, 0.15),
'rf__n_estimators': randint(5, 10),
'rf__max_depth': randint(5, 15)
}
# define the grid
grid = RandomizedSearchCV(pipe, hp, n_iter=2, scoring='accuracy', n_jobs=1, cv=custom_cv, random_state=42)
# this will fail because we haven't fit yet
assert_fails(grid.score, (ValueError, AttributeError), X_train, y_train)
# fit the grid
grid.fit(X_train, y_train)
# score for coverage -- this might warn...
with warnings.catch_warnings():
warnings.simplefilter("ignore")
grid.score(X_train, y_train)
# coverage:
assert grid._estimator_type == 'classifier'
# get predictions
tr_pred, te_pred = grid.predict(X_train), grid.predict(X_test)
# evaluate score (SHOULD be better than random...)
accuracy_score(y_train, tr_pred), accuracy_score(y_test, te_pred)
# grid score reports:
# assert fails for bad percentile
assert_fails(report_grid_score_detail, ValueError, **{'random_search': grid, 'percentile': 0.0})
assert_fails(report_grid_score_detail, ValueError, **{'random_search': grid, 'percentile': 1.0})
# assert fails for bad y_axis
assert_fails(report_grid_score_detail, ValueError, **{'random_search': grid, 'y_axis': 'bad_axis'})
# assert passes otherwise
report_grid_score_detail(grid, charts=True, percentile=0.95) # just ensure percentile works