本文整理汇总了Python中sklearn.model_selection.GridSearchCV.transform方法的典型用法代码示例。如果您正苦于以下问题：Python GridSearchCV.transform方法的具体用法？Python GridSearchCV.transform怎么用？Python GridSearchCV.transform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.GridSearchCV的用法示例。
在下文中一共展示了GridSearchCV.transform方法的2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_grid_search
# 需要导入模块: from sklearn.model_selection import GridSearchCV [as 别名]
# 或者: from sklearn.model_selection.GridSearchCV import transform [as 别名]
def test_grid_search():
# Test that the best estimator contains the right value for foo_param
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, verbose=3)
# make sure it selects the smallest parameter in case of ties
old_stdout = sys.stdout
sys.stdout = StringIO()
grid_search.fit(X, y)
sys.stdout = old_stdout
assert_equal(grid_search.best_estimator_.foo_param, 2)
assert_array_equal(grid_search.results_["param_foo_param"].data, [1, 2, 3])
# Smoke test the score etc:
grid_search.score(X, y)
grid_search.predict_proba(X)
grid_search.decision_function(X)
grid_search.transform(X)
# Test exception handling on scoring
grid_search.scoring = 'sklearn'
assert_raises(ValueError, grid_search.fit, X, y)示例2: Decimal
# 需要导入模块: from sklearn.model_selection import GridSearchCV [as 别名]
# 或者: from sklearn.model_selection.GridSearchCV import transform [as 别名]
from decimal import Decimal
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
fraudInstanceData = pd.read_csv("FraudInstanceData.csv", header=0, index_col=0)
maritalStatuses = pd.get_dummies(fraudInstanceData["Marital Status"])
accomodationTypes = pd.get_dummies(fraudInstanceData["Accomodation Type"])
fraudInstanceData = fraudInstanceData.drop('Marital Status', axis=1)
fraudInstanceData = fraudInstanceData.drop('Accomodation Type', axis=1)
fraudInstanceData = fraudInstanceData.join(maritalStatuses)
fraudInstanceData = fraudInstanceData.join(accomodationTypes)
currencyToMoney = lambda c: Decimal(sub(r'[^\d.]', '', c))
fraudInstanceData['Claim Amount'] = fraudInstanceData["Claim Amount"].apply(currencyToMoney)
y = fraudInstanceData.iloc[:, 1]
X = fraudInstanceData.iloc[:, 1:]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=23)
pipeline = Pipeline([('feature_selection', SelectFromModel(LogisticRegression(penalty="l1"))),
('regression', LogisticRegression())])
grid_cv = GridSearchCV(pipeline, {}, cv=10)
grid_cv.fit(X_train, y_train)
selected_feature = grid_cv.transform(X_train.co)
y_pred = grid_cv.predict(X_test)
print(grid_cv.score(X_test, y_pred))
print(confusion_matrix(y_test, y_pred))