class_docstr: Unsupervised Outlier Detection using the Local Outlier Factor (LOF).
  
  The anomaly score of each sample is called the Local Outlier Factor.
  It measures the local deviation of the density of a given sample with respect
  to its neighbors.
  It is local in that the anomaly score depends on how isolated the object
  is with respect to the surrounding neighborhood.
  More precisely, locality is given by k-nearest neighbors, whose distance
  is used to estimate the local density.
  By comparing the local density of a sample to the local densities of its
  neighbors, one can identify samples that have a substantially lower density
  than their neighbors. These are considered outliers.
  
  .. versionadded:: 0.19
  
  Parameters
  ----------
  n_neighbors : int, default=20
      Number of neighbors to use by default for :meth:`kneighbors` queries.
      If n_neighbors is larger than the number of samples provided,
      all samples will be used.
  
  algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
      Algorithm used to compute the nearest neighbors:
  
      - 'ball_tree' will use :class:`BallTree`
      - 'kd_tree' will use :class:`KDTree`
      - 'brute' will use a brute-force search.
      - 'auto' will attempt to decide the most appropriate algorithm
        based on the values passed to :meth:`fit` method.
  
      Note: fitting on sparse input will override the setting of
      this parameter, using brute force.
  
  leaf_size : int, default=30
      Leaf is size passed to :class:`BallTree` or :class:`KDTree`. This can
      affect the speed of the construction and query, as well as the memory
      required to store the tree. The optimal value depends on the
      nature of the problem.
  
  metric : str or callable, default='minkowski'
      Metric to use for distance computation. Default is "minkowski", which
      results in the standard Euclidean distance when p = 2. See the
      documentation of `scipy.spatial.distance
      <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
      the metrics listed in
      :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
      values.
  
      If metric is "precomputed", X is assumed to be a distance matrix and
      must be square during fit. X may be a :term:`sparse graph`, in which
      case only "nonzero" elements may be considered neighbors.
  
      If metric is a callable function, it takes two arrays representing 1D
      vectors as inputs and must return one value indicating the distance
      between those vectors. This works for Scipy's metrics, but is less
      efficient than passing the metric name as a string.
  
  p : float, default=2
      Parameter for the Minkowski metric from
      :func:`sklearn.metrics.pairwise_distances`. When p = 1, this
      is equivalent to using manhattan_distance (l1), and euclidean_distance
      (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
  
  metric_params : dict, default=None
      Additional keyword arguments for the metric function.
  
  contamination : 'auto' or float, default='auto'
      The amount of contamination of the data set, i.e. the proportion
      of outliers in the data set. When fitting this is used to define the
      threshold on the scores of the samples.
  
      - if 'auto', the threshold is determined as in the
        original paper,
      - if a float, the contamination should be in the range (0, 0.5].
  
      .. versionchanged:: 0.22
         The default value of ``contamination`` changed from 0.1
         to ``'auto'``.
  
  novelty : bool, default=False
      By default, LocalOutlierFactor is only meant to be used for outlier
      detection (novelty=False). Set novelty to True if you want to use
      LocalOutlierFactor for novelty detection. In this case be aware that
      you should only use predict, decision_function and score_samples
      on new unseen data and not on the training set; and note that the
      results obtained this way may differ from the standard LOF results.
  
      .. versionadded:: 0.20
  
  n_jobs : int, default=None
      The number of parallel jobs to run for neighbors search.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
  
  Attributes
  ----------
  negative_outlier_factor_ : ndarray of shape (n_samples,)
      The opposite LOF of the training samples. The higher, the more normal.
      Inliers tend to have a LOF score close to 1
      (``negative_outlier_factor_`` close to -1), while outliers tend to have
      a larger LOF score.
  
      The local outlier factor (LOF) of a sample captures its
      supposed 'degree of abnormality'.
      It is the average of the ratio of the local reachability density of
      a sample and those of its k-nearest neighbors.
  
  n_neighbors_ : int
      The actual number of neighbors used for :meth:`kneighbors` queries.
  
  offset_ : float
      Offset used to obtain binary labels from the raw scores.
      Observations having a negative_outlier_factor smaller than `offset_`
      are detected as abnormal.
      The offset is set to -1.5 (inliers score around -1), except when a
      contamination parameter different than "auto" is provided. In that
      case, the offset is defined in such a way we obtain the expected
      number of outliers in training.
  
      .. versionadded:: 0.20
  
  effective_metric_ : str
      The effective metric used for the distance computation.
  
  effective_metric_params_ : dict
      The effective additional keyword arguments for the metric function.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  n_samples_fit_ : int
      It is the number of samples in the fitted data.
  
  See Also
  --------
  sklearn.svm.OneClassSVM: Unsupervised Outlier Detection using
      Support Vector Machine.
  
  References
  ----------
  .. [1] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May).
         LOF: identifying density-based local outliers. In ACM sigmod record.
  
  Examples
  --------
  >>> 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)
  array([ 1,  1, -1,  1])
  >>> clf.negative_outlier_factor_
  array([ -0.9821...,  -1.0370..., -73.3697...,  -0.9821...])
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code: def _check_novelty_decision_function(self):
      if not self.novelty:
          msg = (
              "decision_function is not available when novelty=False. "
              "Use novelty=True if you want to use LOF for novelty "
              "detection and compute decision_function for new unseen "
              "data. Note that the opposite LOF of the training samples "
              "is always available by considering the "
              "negative_outlier_factor_ attribute."
          )
          raise AttributeError(msg)
      return True
  
imports: import warnings
  from numbers import Real
  import numpy
  from ..base import OutlierMixin, _fit_context
  from ..utils import check_array
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.metaestimators import available_if
  from ..utils.validation import check_is_fitted
  from ._base import KNeighborsMixin, NeighborsBase
label_desc: Use image node_id 8 for calling the LocalOutlierFactor obj's underlying member method code with example usage: obj._check_novelty_decision_function() and returns: True