class_docstr: Wrapper of scikit-learn Isolation Forest with more functionalities.
  
  The IsolationForest 'isolates' observations by randomly selecting a
  feature and then randomly selecting a split value between the maximum and
  minimum values of the selected feature.
  See :cite:`liu2008isolation,liu2012isolation` for details.
  
  Since recursive partitioning can be represented by a tree structure, the
  number of splittings required to isolate a sample is equivalent to the path
  length from the root node to the terminating node.
  
  This path length, averaged over a forest of such random trees, is a
  measure of normality and our decision function.
  
  Random partitioning produces noticeably shorter paths for anomalies.
  Hence, when a forest of random trees collectively produce shorter path
  lengths for particular samples, they are highly likely to be anomalies.
  
  Parameters
  ----------
  n_estimators : int, optional (default=100)
          The number of base estimators in the ensemble.
  
  max_samples : int or float, optional (default="auto")
          The number of samples to draw from X to train each base estimator.
  
                  - If int, then draw `max_samples` samples.
                  - If float, then draw `max_samples * X.shape[0]` samples.
                  - If "auto", then `max_samples=min(256, n_samples)`.
  
          If max_samples is larger than the number of samples provided,
          all samples will be used for all trees (no sampling).
  
  contamination : float in (0., 0.5), optional (default=0.1)
          The amount of contamination of the data set, i.e. the proportion
          of outliers in the data set. Used when fitting to define the threshold
          on the decision function.
  
  max_features : int or float, optional (default=1.0)
          The number of features to draw from X to train each base estimator.
  
                  - If int, then draw `max_features` features.
                  - If float, then draw `max_features * X.shape[1]` features.
  
  bootstrap : bool, optional (default=False)
          If True, individual trees are fit on random subsets of the training
          data sampled with replacement. If False, sampling without replacement
          is performed.
  
  n_jobs : integer, optional (default=1)
          The number of jobs to run in parallel for both `fit` and `predict`.
          If -1, then the number of jobs is set to the number of cores.
  
  behaviour : str, default='old'
          Behaviour of the ``decision_function`` which can be either 'old' or
          'new'. Passing ``behaviour='new'`` makes the ``decision_function``
          change to match other anomaly detection algorithm API which will be
          the default behaviour in the future. As explained in details in the
          ``offset_`` attribute documentation, the ``decision_function`` becomes
          dependent on the contamination parameter, in such a way that 0 becomes
          its natural threshold to detect outliers.
  
          .. versionadded:: 0.7.0
             ``behaviour`` is added in 0.7.0 for back-compatibility purpose.
  
          .. deprecated:: 0.20
             ``behaviour='old'`` is deprecated in sklearn 0.20 and will not be
             possible in 0.22.
  
          .. deprecated:: 0.22
             ``behaviour`` parameter will be deprecated in sklearn 0.22 and
             removed in 0.24.
  
          .. warning::
                  Only applicable for sklearn 0.20 above.
  
  random_state : int, RandomState instance or None, optional (default=None)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
  verbose : int, optional (default=0)
          Controls the verbosity of the tree building process.
  
  Attributes
  ----------
  estimators_ : list of DecisionTreeClassifier
          The collection of fitted sub-estimators.
  
  estimators_samples_ : list of arrays
          The subset of drawn samples (i.e., the in-bag samples) for each base
          estimator.
  
  max_samples_ : integer
          The actual number of samples
  
  decision_scores_ : numpy array of shape (n_samples,)
          The outlier scores of the training data.
          The higher, the more abnormal. Outliers tend to have higher
          scores. This value is available once the detector is
          fitted.
  
  threshold_ : float
          The threshold is based on ``contamination``. It is the
          ``n_samples * contamination`` most abnormal samples in
          ``decision_scores_``. The threshold is calculated for generating
          binary outlier labels.
  
  labels_ : int, either 0 or 1
          The binary labels of the training data. 0 stands for inliers
          and 1 for outliers/anomalies. It is generated by applying
          ``threshold_`` on ``decision_scores_``.
docstr: The impurity-based feature importance. The higher, the more
  important the feature. The importance of a feature is computed as the
  (normalized) total reduction of the criterion brought by that feature.
  It is also known as the Gini importance.
  
  .. warning::
  impurity-based feature importance can be misleading for
  high cardinality features (many unique values). See
  https://scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html
  as an alternative.
  
  Returns
  -------
  feature_importances_ : ndarray of shape (n_features,)
          The values of this array sum to 1, unless all trees are single node
          trees consisting of only the root node, in which case it will be an
          array of zeros.
docstr_tok: ["The","impurity-based","feature","importance",".","The","higher",",","the","more","important","the","feature",".","The","importance","of","a","feature","is","computed","as","the","(","normalized",")","total","reduction","of","the","criterion","brought","by","that","feature",".","It","is","also","known","as","the","Gini","importance",".","..","warning",":",":","impurity-based","feature","importance","can","be","misleading","for","high","cardinality","features","(","many","unique","values",")",".","See","https",":","\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.inspection.permutation_importance.html","as","an","alternative",".","Returns","--","--","--","-","feature_importances_",":","ndarray","of","shape","(","n_features",",",")","The","values","of","this","array","sum","to","1",",","unless","all","trees","are","single","node","trees","consisting","of","only","the","root","node",",","in","which","case","it","will","be","an","array","of","zeros","."]
code: def feature_importances_(self):
      """The impurity-based feature importance. The higher, the more
      important the feature. The importance of a feature is computed as the
      (normalized) total reduction of the criterion brought by that feature.
      It is also known as the Gini importance.
  
      .. warning::
      impurity-based feature importance can be misleading for
      high cardinality features (many unique values). See
      https://scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html
      as an alternative.
  
      Returns
      -------
      feature_importances_ : ndarray of shape (n_features,)
              The values of this array sum to 1, unless all trees are single node
              trees consisting of only the root node, in which case it will be an
              array of zeros.
      """
      check_is_fitted(self)
      all_importances = Parallel(n_jobs=self.n_jobs)(
          delayed(getattr)(tree, "feature_importances_")
          for tree in self.detector_.estimators_
          if tree.tree_.node_count > 1
      )
  
      if not all_importances:
          return np.zeros(self.n_features_in_, dtype=np.float64)
  
      all_importances = np.mean(
          all_importances, axis=0, dtype=np.float64
      )
      return all_importances / np.sum(all_importances)
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from joblib import Parallel
  from joblib.parallel import delayed
  from sklearn.ensemble import IsolationForest
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
  from ..utils.utility import invert_order
label_desc: Use image node_id 10 for calling the IForest obj's underlying member method code with example usage: obj.feature_importances_() and returns: unknown, np