class_docstr: Isolation Forest Algorithm.
  
  Return the anomaly score of each sample using the IsolationForest algorithm
  
  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.
  
  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.
  
  Read more in the :ref:`User Guide <isolation_forest>`.
  
  Parameters
  ----------
  n_estimators : int, default=100
      The number of base estimators in the ensemble.
  
  max_samples : "auto", int or float, 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 : 'auto' or float, default='auto'
      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 scores of the samples.
  
          - If 'auto', the threshold is determined as in the
            original paper.
          - If float, the contamination should be in the range (0, 0.5].
  
  max_features : int or float, 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, 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.
  
  random_state : int, RandomState instance or None, default=None
      Controls the pseudo-randomness of the selection of the feature
      and split values for each branching step and each tree in the forest.
  
      Pass an int for reproducible results across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  warm_start : bool, default=False
      When set to ``True``, reuse the solution of the previous call to fit
      and add more estimators to the ensemble, otherwise, just fit a whole
      new forest. See :term:`the Glossary <warm_start>`.
  
  Attributes
  ----------
  base_estimator_ : ExtraTreeRegressor instance
      The child estimator template used to create the collection of
      fitted sub-estimators.
  
  estimators_ : list of ExtraTreeRegressor instances
      The collection of fitted sub-estimators.
  
  estimators_features_ : list of ndarray
      The subset of drawn features for each base estimator.
  
  max_samples_ : int
      The actual number of samples.
  
  offset_ : float
      Offset used to define the decision function from the raw scores. We
      have the relation: ``decision_function = score_samples - offset_``.
      ``offset_`` is defined as follows. When the contamination parameter is
      set to "auto", the offset is equal to -0.5 as the scores of inliers are
      close to 0 and the scores of outliers are close to -1. When a
      contamination parameter different than "auto" is provided, the offset
      is defined in such a way we obtain the expected number of outliers
      (samples with decision function < 0) in training.
  
  Notes
  -----
  The implementation is based on an ensemble of ExtraTreeRegressor. The
  maximum depth of each tree is set to ``ceil(log_2(n))`` where
  :math:`n` is the number of samples used to build the tree
  (see (Liu et al., 2008) for more details).
  
  References
  ----------
  .. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
         Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.
  .. [2] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation-based
         anomaly detection." ACM Transactions on Knowledge Discovery from
         Data (TKDD) 6.1 (2012): 3.
  
  See Also
  ----------
  sklearn.covariance.EllipticEnvelope : An object for detecting outliers in a
      Gaussian distributed dataset.
  sklearn.svm.OneClassSVM : Unsupervised Outlier Detection.
      Estimate the support of a high-dimensional distribution.
      The implementation is based on libsvm.
  sklearn.neighbors.LocalOutlierFactor : Unsupervised Outlier Detection
      using Local Outlier Factor (LOF).
  
  Examples
  --------
  >>> from mars.learn.ensemble import IsolationForest
  >>> X = [[-1.1], [0.3], [0.5], [100]]
  >>> clf = IsolationForest(random_state=0).fit(X)
  >>> clf.predict([[0.1], [0], [90]])
  array([ 1,  1, -1])
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docstr: Average anomaly score of X of the base classifiers.
  
  The anomaly score of an input sample is computed as
  the mean anomaly score of the trees in the forest.
  
  The measure of normality of an observation given a tree is the depth
  of the leaf containing this observation, which is equivalent to
  the number of splittings required to isolate this point. In case of
  several observations n_left in the leaf, the average path length of
  a n_left samples isolation tree is added.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      The input samples. Internally, it will be converted to
      ``dtype=np.float32`` and if a sparse matrix is provided
      to a sparse ``csr_matrix``.
  
  Returns
  -------
  scores : ndarray of shape (n_samples,)
      The anomaly score of the input samples.
      The lower, the more abnormal. Negative scores represent outliers,
      positive scores represent inliers.
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code: def decision_function(self, X, session=None, run_kwargs=None):
      """
      Average anomaly score of X of the base classifiers.
  
      The anomaly score of an input sample is computed as
      the mean anomaly score of the trees in the forest.
  
      The measure of normality of an observation given a tree is the depth
      of the leaf containing this observation, which is equivalent to
      the number of splittings required to isolate this point. In case of
      several observations n_left in the leaf, the average path length of
      a n_left samples isolation tree is added.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          The input samples. Internally, it will be converted to
          ``dtype=np.float32`` and if a sparse matrix is provided
          to a sparse ``csr_matrix``.
  
      Returns
      -------
      scores : ndarray of shape (n_samples,)
          The anomaly score of the input samples.
          The lower, the more abnormal. Negative scores represent outliers,
          positive scores represent inliers.
      """
      # We subtract self.offset_ to make 0 be the threshold value for being
      # an outlier:
  
      decision_func = self._decision_function(X)
      return execute(
          decision_func, session=session, **(run_kwargs or dict())
      )
  
imports: import numbers
  import warnings
  from typing import Union
  import numpy
  from sklearn.base import OutlierMixin
  from sklearn.tree import ExtraTreeRegressor
  from sklearn.utils import check_array, gen_batches
  from ...None import tensor
  from ...lib.sparse import issparse
  from ...session import execute
  from ...tensor.utils import check_random_state
  from ..utils import get_chunk_n_rows, convert_to_tensor_or_dataframe
  from ..utils.validation import _num_samples, check_is_fitted
  from ._bagging import BaseBagging, BaggingPredictionOperand, PredictionType
label_desc: Use image node_id 5 for calling the IsolationForest obj's underlying member method code with example usage: obj.decision_function(X, session, run_kwargs) and returns: execute