我正在更新一些代码以与最新版本的scikit-learn（0.21.2）一起使用。在该代码中，我使用IsolationForest并在拟合后访问.threshold_属性。但是，此属性已被弃用。人们有没有其他选择？
这是"old"的行为。我可以访问.threshold_，但会获得DeprecationWarnings。
from sklearn.ensemble import IsolationForest
from sklearn import datasets
import numpy as np
dataset = datasets.load_iris()
np.random.seed(10)
X_train = dataset.data[:,:2]
X_test = np.random.random((10,2))*np.array([6,4])+np.array([3,1]) # fake observations
clf = IsolationForest(max_samples=100, contamination=0.1, random_state=1001, behaviour="old")
clf.fit(X_train)
#> /Users/ncg/anaconda3/envs/sklearntest/lib/python3.6/site-packages/sklearn/ensemble/iforest.py:247: FutureWarning: behaviour="old" is deprecated and will be removed in version 0.22. Please use behaviour="new", which makes the decision_function change to match other anomaly detection algorithm API.
#> FutureWarning)
#> IsolationForest(behaviour='old', bootstrap=False, contamination=0.1,
#> max_features=1.0, max_samples=100, n_estimators=100,
#> n_jobs=None, random_state=1001, verbose=0, warm_start=False)
y_pred_test = clf.predict(X_test)
#> /Users/ncg/anaconda3/envs/sklearntest/lib/python3.6/site-packages/sklearn/ensemble/iforest.py:415: DeprecationWarning: threshold_ attribute is deprecated in 0.20 and will be removed in 0.22.
#> " be removed in 0.22.", DeprecationWarning)
print(clf.threshold_)
#> -0.06984862813837053
#> /Users/ncg/anaconda3/envs/sklearntest/lib/python3.6/site-packages/sklearn/ensemble/iforest.py:415: DeprecationWarning: threshold_ attribute is deprecated in 0.20 and will be removed in 0.22.
#> " be removed in 0.22.", DeprecationWarning)
这是"new"的行为。没有DeprecationWarning，但没有.threshold_。
from sklearn.ensemble import IsolationForest
from sklearn import datasets
import numpy as np
dataset = datasets.load_iris()
np.random.seed(10)
X_train = dataset.data[:,:2]
X_test = np.random.random((10,2))*np.array([6,4])+np.array([3,1]) # fake observations
clf = IsolationForest(max_samples=100, contamination=0.1, random_state=1001, behaviour="new")
clf.fit(X_train)
#> IsolationForest(behaviour='new', bootstrap=False, contamination=0.1,
#> max_features=1.0, max_samples=100, n_estimators=100,
#> n_jobs=None, random_state=1001, verbose=0, warm_start=False)
y_pred_test = clf.predict(X_test)
print(clf.threshold_)
#> Traceback (most recent call last):
#> <ipython-input-13-27950b57c945> in <module>()
#> ----> 1 print(clf.threshold_)
#> ~/anaconda3/envs/sklearntest/lib/python3.6/site-packages/sklearn/ensemble/iforest.py in threshold_(self)
#> 410 def threshold_(self):
#> 411 if self.behaviour != 'old':
#> --> 412 raise AttributeError("threshold_ attribute does not exist when "
#> 413 "behaviour != 'old'")
#> 414 warn("threshold_ attribute is deprecated in 0.20 and will"
#> AttributeError: threshold_ attribute does not exist when behaviour != 'old'
即使使用新行为，是否仍可以访问.threshold_？