本文整理汇总了Python中sklearn.cross_validation.cross_val_predict方法的典型用法代码示例。如果您正苦于以下问题:Python cross_validation.cross_val_predict方法的具体用法?Python cross_validation.cross_val_predict怎么用?Python cross_validation.cross_val_predict使用的例子?那么, 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.cross_validation
的用法示例。
在下文中一共展示了cross_validation.cross_val_predict方法的8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: get_logistic_regression_coefs_l2
# 需要导入模块: from sklearn import cross_validation [as 别名]
# 或者: from sklearn.cross_validation import cross_val_predict [as 别名]
def get_logistic_regression_coefs_l2(self, category,
clf=RidgeClassifierCV()):
''' Computes l2-penalized logistic regression score.
Parameters
----------
category : str
category name to score
category : str
category name to score
Returns
-------
(coefficient array, accuracy, majority class baseline accuracy)
'''
try:
from sklearn.cross_validation import cross_val_predict
except:
from sklearn.model_selection import cross_val_predict
y = self._get_mask_from_category(category)
X = TfidfTransformer().fit_transform(self._X)
clf.fit(X, y)
y_hat = cross_val_predict(clf, X, y)
acc, baseline = self._get_accuracy_and_baseline_accuracy(y, y_hat)
return clf.coef_[0], acc, baseline
示例2: get_logistic_regression_coefs_l1
# 需要导入模块: from sklearn import cross_validation [as 别名]
# 或者: from sklearn.cross_validation import cross_val_predict [as 别名]
def get_logistic_regression_coefs_l1(self, category,
clf=LassoCV(alphas=[0.1, 0.001],
max_iter=10000,
n_jobs=-1)):
''' Computes l1-penalized logistic regression score.
Parameters
----------
category : str
category name to score
Returns
-------
(coefficient array, accuracy, majority class baseline accuracy)
'''
try:
from sklearn.cross_validation import cross_val_predict
except:
from sklearn.model_selection import cross_val_predict
y = self._get_mask_from_category(category)
y_continuous = self._get_continuous_version_boolean_y(y)
# X = TfidfTransformer().fit_transform(self._X)
X = self._X
clf.fit(X, y_continuous)
y_hat = (cross_val_predict(clf, X, y_continuous) > 0)
acc, baseline = self._get_accuracy_and_baseline_accuracy(y, y_hat)
clf.fit(X, y_continuous)
return clf.coef_, acc, baseline
示例3: _generate_cross_val_predict_test
# 需要导入模块: from sklearn import cross_validation [as 别名]
# 或者: from sklearn.cross_validation import cross_val_predict [as 别名]
def _generate_cross_val_predict_test(X, y, est, pd_est, must_match):
def test(self):
self.assertEqual(
hasattr(est, 'predict'),
hasattr(pd_est, 'predict'))
if not hasattr(est, 'predict'):
return
pd_y_hat = pd_cross_val_predict(pd_est, X, y)
self.assertTrue(isinstance(pd_y_hat, pd.Series))
self.assertTrue(pd_y_hat.index.equals(X.index))
if must_match:
y_hat = cross_val_predict(est, X.as_matrix(), y.values)
np.testing.assert_allclose(pd_y_hat, y_hat)
return test
示例4: demo_getPerf
# 需要导入模块: from sklearn import cross_validation [as 别名]
# 或者: from sklearn.cross_validation import cross_val_predict [as 别名]
def demo_getPerf(X,y,Classifier,Classifier_label):
"""
Classifier_type: Sklearn model
Type of classifier to use, and it's parameters
Classifier_label: string
Descriptive Name of the classifier. e.g "Forest"
"""
results = {}
scores = cross_val_predict(Classifier, X, y, cv=10, n_jobs=-1)
results[label] = get_scores(scores,y,Classifier_label)
res_df = pd.DataFrame(results)
res_df.to_csv(outputFileName+"tsv", sep='\t')
示例5: test_cross_val_predict
# 需要导入模块: from sklearn import cross_validation [as 别名]
# 或者: from sklearn.cross_validation import cross_val_predict [as 别名]
def test_cross_val_predict():
boston = load_boston()
X, y = boston.data, boston.target
cv = cval.KFold(len(boston.target))
est = Ridge()
# Naive loop (should be same as cross_val_predict):
preds2 = np.zeros_like(y)
for train, test in cv:
est.fit(X[train], y[train])
preds2[test] = est.predict(X[test])
preds = cval.cross_val_predict(est, X, y, cv=cv)
assert_array_almost_equal(preds, preds2)
preds = cval.cross_val_predict(est, X, y)
assert_equal(len(preds), len(y))
cv = cval.LeaveOneOut(len(y))
preds = cval.cross_val_predict(est, X, y, cv=cv)
assert_equal(len(preds), len(y))
Xsp = X.copy()
Xsp *= (Xsp > np.median(Xsp))
Xsp = coo_matrix(Xsp)
preds = cval.cross_val_predict(est, Xsp, y)
assert_array_almost_equal(len(preds), len(y))
preds = cval.cross_val_predict(KMeans(), X)
assert_equal(len(preds), len(y))
def bad_cv():
for i in range(4):
yield np.array([0, 1, 2, 3]), np.array([4, 5, 6, 7, 8])
assert_raises(ValueError, cval.cross_val_predict, est, X, y, cv=bad_cv())
示例6: test_cross_val_predict_input_types
# 需要导入模块: from sklearn import cross_validation [as 别名]
# 或者: from sklearn.cross_validation import cross_val_predict [as 别名]
def test_cross_val_predict_input_types():
clf = Ridge()
# Smoke test
predictions = cval.cross_val_predict(clf, X, y)
assert_equal(predictions.shape, (10,))
# test with multioutput y
with ignore_warnings(category=ConvergenceWarning):
predictions = cval.cross_val_predict(clf, X_sparse, X)
assert_equal(predictions.shape, (10, 2))
predictions = cval.cross_val_predict(clf, X_sparse, y)
assert_array_equal(predictions.shape, (10,))
# test with multioutput y
with ignore_warnings(category=ConvergenceWarning):
predictions = cval.cross_val_predict(clf, X_sparse, X)
assert_array_equal(predictions.shape, (10, 2))
# test with X and y as list
list_check = lambda x: isinstance(x, list)
clf = CheckingClassifier(check_X=list_check)
predictions = cval.cross_val_predict(clf, X.tolist(), y.tolist())
clf = CheckingClassifier(check_y=list_check)
predictions = cval.cross_val_predict(clf, X, y.tolist())
# test with 3d X and
X_3d = X[:, :, np.newaxis]
check_3d = lambda x: x.ndim == 3
clf = CheckingClassifier(check_X=check_3d)
predictions = cval.cross_val_predict(clf, X_3d, y)
assert_array_equal(predictions.shape, (10,))
示例7: test_cross_val_predict_pandas
# 需要导入模块: from sklearn import cross_validation [as 别名]
# 或者: from sklearn.cross_validation import cross_val_predict [as 别名]
def test_cross_val_predict_pandas():
# check cross_val_score doesn't destroy pandas dataframe
types = [(MockDataFrame, MockDataFrame)]
try:
from pandas import Series, DataFrame
types.append((Series, DataFrame))
except ImportError:
pass
for TargetType, InputFeatureType in types:
# X dataframe, y series
X_df, y_ser = InputFeatureType(X), TargetType(y)
check_df = lambda x: isinstance(x, InputFeatureType)
check_series = lambda x: isinstance(x, TargetType)
clf = CheckingClassifier(check_X=check_df, check_y=check_series)
cval.cross_val_predict(clf, X_df, y_ser)
示例8: test_cross_val_predict_sparse_prediction
# 需要导入模块: from sklearn import cross_validation [as 别名]
# 或者: from sklearn.cross_validation import cross_val_predict [as 别名]
def test_cross_val_predict_sparse_prediction():
# check that cross_val_predict gives same result for sparse and dense input
X, y = make_multilabel_classification(n_classes=2, n_labels=1,
allow_unlabeled=False,
return_indicator=True,
random_state=1)
X_sparse = csr_matrix(X)
y_sparse = csr_matrix(y)
classif = OneVsRestClassifier(SVC(kernel='linear'))
preds = cval.cross_val_predict(classif, X, y, cv=10)
preds_sparse = cval.cross_val_predict(classif, X_sparse, y_sparse, cv=10)
preds_sparse = preds_sparse.toarray()
assert_array_almost_equal(preds_sparse, preds)