class_docstr: Wrapper of scikit-learn one-class SVM Class with more functionalities.
  Unsupervised Outlier Detection.
  
  Estimate the support of a high-dimensional distribution.
  
  The implementation is based on libsvm.
  See http://scikit-learn.org/stable/modules/svm.html#svm-outlier-detection
  and :cite:`scholkopf2001estimating`.
  
  Parameters
  ----------
  kernel : string, optional (default='rbf')
       Specifies the kernel type to be used in the algorithm.
       It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
       a callable.
       If none is given, 'rbf' will be used. If a callable is given it is
       used to precompute the kernel matrix.
  
  nu : float, optional
      An upper bound on the fraction of training
      errors and a lower bound of the fraction of support
      vectors. Should be in the interval (0, 1]. By default 0.5
      will be taken.
  
  degree : int, optional (default=3)
      Degree of the polynomial kernel function ('poly').
      Ignored by all other kernels.
  
  gamma : float, optional (default='auto')
      Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
      If gamma is 'auto' then 1/n_features will be used instead.
  
  coef0 : float, optional (default=0.0)
      Independent term in kernel function.
      It is only significant in 'poly' and 'sigmoid'.
  
  tol : float, optional
      Tolerance for stopping criterion.
  
  shrinking : bool, optional
      Whether to use the shrinking heuristic.
  
  cache_size : float, optional
      Specify the size of the kernel cache (in MB).
  
  verbose : bool, default: False
      Enable verbose output. Note that this setting takes advantage of a
      per-process runtime setting in libsvm that, if enabled, may not work
      properly in a multithreaded context.
  
  max_iter : int, optional (default=-1)
      Hard limit on iterations within solver, or -1 for no limit.
  
  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.
  
  
  Attributes
  ----------
  support_ : array-like, shape = [n_SV]
      Indices of support vectors.
  
  support_vectors_ : array-like, shape = [nSV, n_features]
      Support vectors.
  
  dual_coef_ : array, shape = [1, n_SV]
      Coefficients of the support vectors in the decision function.
  
  coef_ : array, shape = [1, n_features]
      Weights assigned to the features (coefficients in the primal
      problem). This is only available in the case of a linear kernel.
  
      `coef_` is readonly property derived from `dual_coef_` and
      `support_vectors_`
  
  intercept_ : array, shape = [1,]
      Constant in the decision function.
  
  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_``.
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docstr: Fit detector. y is ignored in unsupervised methods.
  
  Parameters
  ----------
  X : numpy array of shape (n_samples, n_features)
      The input samples.
  
  y : Ignored
      Not used, present for API consistency by convention.
  
  sample_weight : array-like, shape (n_samples,)
      Per-sample weights. Rescale C per sample. Higher weights
      force the classifier to put more emphasis on these points.
  
  Returns
  -------
  self : object
      Fitted estimator.
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code: def fit(self, X, y=None, sample_weight=None, **params):
      """Fit detector. y is ignored in unsupervised methods.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      y : Ignored
          Not used, present for API consistency by convention.
  
      sample_weight : array-like, shape (n_samples,)
          Per-sample weights. Rescale C per sample. Higher weights
          force the classifier to put more emphasis on these points.
  
      Returns
      -------
      self : object
          Fitted estimator.
      """
      # validate inputs X and y (optional)
      X = check_array(X)
      self._set_n_classes(y)
  
      self.detector_ = OneClassSVM(
          kernel=self.kernel,
          degree=self.degree,
          gamma=self.gamma,
          coef0=self.coef0,
          tol=self.tol,
          nu=self.nu,
          shrinking=self.shrinking,
          cache_size=self.cache_size,
          verbose=self.verbose,
          max_iter=self.max_iter,
      )
      self.detector_.fit(
          X=X, y=y, sample_weight=sample_weight, **params
      )
  
      # invert decision_scores_. Outliers comes with higher outlier scores
      self.decision_scores_ = invert_order(
          self.detector_.decision_function(X)
      )
      self._process_decision_scores()
      return self
  
file_path: pyod/pyod/models/ocsvm.py
imports: from __future__ import division
  from __future__ import print_function
  from sklearn.svm import OneClassSVM
  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 2 for calling the OCSVM obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: self