使用带IsolationForest的GridSearchCV查找异常值

Question

我想用IsolationForest来查找异常值。我想用GridSearchCV找到模型的最佳参数。问题是我总是得到同样的错误：

TypeError: If no scoring is specified, the estimator passed should have a 'score' method. The estimator IsolationForest(behaviour='old', bootstrap=False, contamination='legacy',
                max_features=1.0, max_samples='auto', n_estimators=100,
                n_jobs=None, random_state=None, verbose=0, warm_start=False) does not.

这似乎是个问题，因为IsolationForest没有{}方法。 有办法解决这个问题吗？ 还有没有办法给隔离林打分？ 这是我的代码：

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Answer 1

您需要创建自己的评分函数，因为IsolationForest没有内置score方法。相反，您可以使用score_samples中提供的score_samples函数（可以看作是score的代理）并创建自己的记分器，如here所述，并将其传递给GridSearchCV。我修改了您的代码以执行以下操作：

import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.model_selection import GridSearchCV

df = pd.DataFrame({'first': [-112,0,1,28,5,6,3,5,4,2,7,5,1,3,2,2,5,2,42,84,13,43,13],
                   'second': [42,1,2,85,2,4,6,8,3,5,7,3,64,1,4,1,2,4,13,1,0,40,9],
                   'third': [3,4,7,74,3,8,2,4,7,1,53,6,5,5,59,0,5,12,65,4,3,4,11],
                   'result': [5,2,3,0.04,3,4,3,125,6,6,0.8,9,1,4,59,12,1,4,0,8,5,4,1]})

x = df.iloc[:,:-1]

tuned = {'n_estimators':[70,80], 'max_samples':['auto'],
     'contamination':['legacy'], 'max_features':[1],
     'bootstrap':[True], 'n_jobs':[None,1,2], 'behaviour':['old'],
     'random_state':[None,1,], 'verbose':[0,1,2], 'warm_start':[True]}  

def scorer_f(estimator, X):   #your own scorer
      return np.mean(estimator.score_samples(X))

#or you could use a lambda aexpression as shown below
#scorer = lambda est, data: np.mean(est.score_samples(data)) 

isolation_forest = GridSearchCV(IsolationForest(), tuned, scoring=scorer_f)
model = isolation_forest.fit(x)

SAMPLE OUTPUT

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希望这有帮助！在

Answer 2

我相信评分是指GridSearchCV对象，而不是IsolationForest。在

如果它是“None”（默认值），它将尝试使用estimators评分，正如您所说的，它不存在。尝试在GridSearchCV对象中使用一个适合您的问题的available scoring metrics