python异常值处理sklearn的调用

Sklearn异常检测模型一览

 Robust covariance:

　　https://scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope

# Robust convariance
import numpy as np
from sklearn.covariance import EllipticEnvelope
true_cov = np.array([[.8, .3],
[.3, .4]])
X = np.random.RandomState(0).multivariate_normal(mean=[0, 0],
cov=true_cov,
size=500)
cov = EllipticEnvelope(random_state=0).fit(X)
# predict returns 1 for an inlier and -1 for an outlier
cov.predict([[0, 0],
[3, 3]])

One-Class SVM:

　　https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM

# SVM有监督
from sklearn.svm import OneClassSVM
X = [[0], [0.44], [100], [0.46], [1]]
clf = OneClassSVM(gamma='auto').fit(X)
clf.predict(X)

# clf.score_samples(X)

Isolation Forest:

　　https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest　

# IsolationForest
from sklearn.ensemble import IsolationForest
# X = [[-1.1], [0.3], [0.5], [100]]
X=[[3910], [70], [3910], [3920], [3890]]
clf = IsolationForest(random_state=0).fit(X)
clf.predict(X)
# clf.fit([[0.1], [1400], [90],[1210]])

Local Outlier Factor:

　　https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor

# Local Outlier Factor
import numpy as np
from sklearn.neighbors import LocalOutlierFactor
X = [[-1.1], [0.2], [101.1], [0.3]]
clf = LocalOutlierFactor(n_neighbors=2)
clf.fit_predict(X)

clf.negative_outlier_factor_

参考：https://blog.csdn.net/hustqb/article/details/75216241

sklearn原文：https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html