class_docstr: Sampling class for outlier detection.
  
  Sugiyama, M., Borgwardt, K. M.: Rapid Distance-Based Outlier Detection via
  Sampling, Advances in Neural Information Processing Systems (NIPS 2013),
  467-475, 2013.
  
  See :cite:`sugiyama2013rapid` for details.
  
  Parameters
  ----------
  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.
  
  subset_size : float in (0., 1.0) or int (0, n_samples), optional (default=20)
      The size of subset of the data set.
      Sampling subset from the data set is performed only once.
  
  metric : string or callable, default 'minkowski'
      metric to use for distance computation. Any metric from scikit-learn
      or scipy.spatial.distance can be used.
  
      If metric is a callable function, it is called on each
      pair of instances (rows) and the resulting value recorded. The callable
      should take two arrays as input and return one value indicating the
      distance between them. This works for Scipy's metrics, but is less
      efficient than passing the metric name as a string.
  
      Distance matrices are not supported.
  
      Valid values for metric are:
  
      - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
        'manhattan']
  
      - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
        'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
        'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',
        'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
        'sqeuclidean', 'yule']
  
      See the documentation for scipy.spatial.distance for details on these
      metrics.
  
  metric_params : dict, optional (default = None)
      Additional keyword arguments for the metric function.
  
  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`.
  
  Attributes
  ----------
  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_``.
class_docstr_tok: ["Sampling","class","for","outlier","detection",".","Sugiyama",",","M.",",","Borgwardt",",","K.","M.",":","Rapid","Distance-Based","Outlier","Detection","via","Sampling",",","Advances","in","Neural","Information","Processing","Systems","(","NIPS","2013",")",",","467-475",",","2013",".","See",":","cite",":","`","sugiyama2013rapid","`","for","details",".","Parameters","--","--","--","--","--","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",".","subset_size",":","float","in","(","0.",",","1.0",")","or","int","(","0",",","n_samples",")",",","optional","(","default=20",")","The","size","of","subset","of","the","data","set",".","Sampling","subset","from","the","data","set","is","performed","only","once",".","metric",":","string","or","callable",",","default","'minkowski'","metric","to","use","for","distance","computation",".","Any","metric","from","scikit-learn","or","scipy.spatial.distance","can","be","used",".","If","metric","is","a","callable","function",",","it","is","called","on","each","pair","of","instances","(","rows",")","and","the","resulting","value","recorded",".","The","callable","should","take","two","arrays","as","input","and","return","one","value","indicating","the","distance","between","them",".","This","works","for","Scipy","'s","metrics",",","but","is","less","efficient","than","passing","the","metric","name","as","a","string",".","Distance","matrices","are","not","supported",".","Valid","values","for","metric","are",":","-","from","scikit-learn",":","[","'cityblock","'",",","'cosine","'",",","'euclidean","'",",","'l1","'",",","'l2","'",",","'manhattan","'","]","-","from","scipy.spatial.distance",":","[","'braycurtis","'",",","'canberra","'",",","'chebyshev","'",",","'correlation","'",",","'dice","'",",","'hamming","'",",","'jaccard","'",",","'kulsinski","'",",","'mahalanobis","'",",","'matching","'",",","'minkowski","'",",","'rogerstanimoto","'",",","'russellrao","'",",","'seuclidean","'",",","'sokalmichener","'",",","'sokalsneath","'",",","'sqeuclidean","'",",","'yule","'","]","See","the","documentation","for","scipy.spatial.distance","for","details","on","these","metrics",".","metric_params",":","dict",",","optional","(","default","=","None",")","Additional","keyword","arguments","for","the","metric","function",".","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","`",".","Attributes","--","--","--","--","--","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_","``","."]
code: def __init__(
      self,
      contamination=0.1,
      subset_size=20,
      metric="minkowski",
      metric_params=None,
      random_state=None,
  ):
      super().__init__(contamination=contamination)
      self.subset_size = subset_size
      self.metric = metric
      self.metric_params = metric_params
      self.random_state = check_random_state(random_state)
      self.dist = None
      self.subset = None
      self.decision_scores_ = None
  
file_path: pyod/pyod/models/sampling.py
imports: from __future__ import division, print_function
  import numpy
  from sklearn.utils import check_array, check_random_state
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
  from ..utils.utility import _get_sklearn_version
label_desc: Use image node_id 1 to create a new Sampling object from inherited base classes: BaseDetector with example: obj = Sampling(contamination, subset_size, metric, metric_params, random_state)