本文整理汇总了Python中sklearn.covariance.EllipticEnvelope.score方法的典型用法代码示例。如果您正苦于以下问题：Python EllipticEnvelope.score方法的具体用法？Python EllipticEnvelope.score怎么用？Python EllipticEnvelope.score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.covariance.EllipticEnvelope的用法示例。
在下文中一共展示了EllipticEnvelope.score方法的4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: anomaly_detection
# 需要导入模块: from sklearn.covariance import EllipticEnvelope [as 别名]
# 或者: from sklearn.covariance.EllipticEnvelope import score [as 别名]
def anomaly_detection(features, labels):
# In this function, I try to use anomaly detection method (using mutivariate gaussian distribution) to identify poi-s
non_pois = features[labels==0]
pois = features[labels==1]
print "non poi size", non_pois.shape, pois.shape, features.shape
## Spliting data to train, test and cross validation set for anomaly detection
split1 = produce_spliting_array(non_pois.shape[0], .75 )
X_train = non_pois[split1==1]
X_intermediate = non_pois[split1==0]
print "size intermediate", X_intermediate.shape
split2 = produce_spliting_array(X_intermediate.shape[0], .5 )
X_test = X_intermediate[split2==1]
label_test = np.zeros((X_test.shape[0],), dtype=np.int) - 1
X_cv = X_intermediate[split2==0]
label_cv = np.zeros((X_cv.shape[0],), dtype=np.int) - 1
split3 = produce_spliting_array(pois.shape[0], .5 )
X_test = np.vstack((X_test, pois[split3==1]))
label_test = np.hstack((label_test, np.ones(sum(split3), dtype=np.int)))
X_cv = np.vstack((X_cv, pois[split3==0]))
label_cv = np.hstack((label_cv, np.ones(sum(split3==0), dtype=np.int)))
print "size X_train", X_train.shape
print "size test data", X_test.shape, label_test.shape
print "size cv data", X_cv.shape, label_cv.shape
print "size splits", len(split1), len(split2), len(split3)
from sklearn.covariance import EllipticEnvelope
detector = EllipticEnvelope(contamination=.85)
detector.fit(X_train)
pred_cv = detector.predict(X_cv)
print pred_cv
print label_cv
print detector.score(X_cv, label_cv)示例2: test_outlier_detection
# 需要导入模块: from sklearn.covariance import EllipticEnvelope [as 别名]
# 或者: from sklearn.covariance.EllipticEnvelope import score [as 别名]
def test_outlier_detection():
"""
"""
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
clf.fit(X)
y_pred = clf.predict(X)
assert_array_almost_equal(clf.decision_function(X, raw_mahalanobis=True), clf.mahalanobis(X - clf.location_))
assert_almost_equal(clf.score(X, np.ones(100)), (100 - y_pred[y_pred == -1].size) / 100.0)示例3: test_outlier_detection
# 需要导入模块: from sklearn.covariance import EllipticEnvelope [as 别名]
# 或者: from sklearn.covariance.EllipticEnvelope import score [as 别名]
def test_outlier_detection():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
assert_raises(NotFittedError, clf.predict, X)
assert_raises(NotFittedError, clf.decision_function, X)
clf.fit(X)
y_pred = clf.predict(X)
decision = clf.decision_function(X, raw_values=True)
decision_transformed = clf.decision_function(X, raw_values=False)
assert_array_almost_equal(decision, clf.mahalanobis(X))
assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
assert_almost_equal(clf.score(X, np.ones(100)), (100 - y_pred[y_pred == -1].size) / 100.0)
assert sum(y_pred == -1) == sum(decision_transformed < 0)示例4: test_elliptic_envelope
# 需要导入模块: from sklearn.covariance import EllipticEnvelope [as 别名]
# 或者: from sklearn.covariance.EllipticEnvelope import score [as 别名]
def test_elliptic_envelope():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
assert_raises(NotFittedError, clf.predict, X)
assert_raises(NotFittedError, clf.decision_function, X)
clf.fit(X)
y_pred = clf.predict(X)
scores = clf.score_samples(X)
decisions = clf.decision_function(X)
assert_array_almost_equal(
scores, -clf.mahalanobis(X))
assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
assert_almost_equal(clf.score(X, np.ones(100)),
(100 - y_pred[y_pred == -1].size) / 100.)
assert(sum(y_pred == -1) == sum(decisions < 0))