本文整理汇总了Python中sklearn.covariance.EllipticEnvelope.mahalanobis方法的典型用法代码示例。如果您正苦于以下问题：Python EllipticEnvelope.mahalanobis方法的具体用法？Python EllipticEnvelope.mahalanobis怎么用？Python EllipticEnvelope.mahalanobis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.mahalanobis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: test_outlier_detection
# 需要导入模块: from sklearn.covariance import EllipticEnvelope [as 别名]
# 或者: from sklearn.covariance.EllipticEnvelope import mahalanobis [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_values=True), 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.)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 mahalanobis [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3: calc
# 需要导入模块: from sklearn.covariance import EllipticEnvelope [as 别名]
# 或者: from sklearn.covariance.EllipticEnvelope import mahalanobis [as 别名]
def calc(self,outliers_fraction):
data, dqs, raw = self.get_data()
clf = EllipticEnvelope(contamination=outliers_fraction)
X = zip(data['Tbandwidth'],data['Tlatency'],data['Tframerate'])
clf.fit(X)
#data['y_pred'] = clf.decision_function(X).ravel()
#data['y_pred'] = clf.decision_function(X).ravel()
#threshold = np.percentile(data['y_pred'],100 * outliers_fraction)
data['MDist']=clf.mahalanobis(X)
#picking "bad" outliers, not good ones
outliers = chi2_outliers(data, [.8,.9,.95], 3)
#print outliers
outliers = [i[i['Tbandwidth']<i['Tlatency']] for i in outliers]
#outliers = data[data['y_pred']<threshold]
#data['y_pred'] = data['y_pred'] > threshold
#outliers = [x[['ticketid','MDist']].merge(raw, how='inner').drop_duplicates() for x in outliers]
#print raw
#outliers = [raw[raw['ticketid'].isin(j['ticketid'])] for j in outliers]
outliers = [k[k['Tframerate']<(k['Tframerate'].mean()+k['Tframerate'].std())] for k in outliers] #making sure we don't remove aberrantly good framrates
outliers = [t.sort_values(by='MDist', ascending=False).drop_duplicates().drop(['Tbandwidth','Tlatency','Tframerate'],axis=1) for t in outliers]
#dqs = raw[raw['ticketid'].isin(dqs['ticketid'])]
#data = data.sort_values('MDist', ascending=False).drop_duplicates()
return outliers, dqs, data.sort_values(by='MDist', ascending=False).drop_duplicates().drop(['Tbandwidth','Tlatency','Tframerate'],axis=1)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 mahalanobis [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))