class_docstr: A collection of simple detector combination methods.
  
  Parameters
  ----------
  base_estimators : list, length must be greater than 1
      Base unsupervised outlier detectors from PyOD. (Note: requires fit and
      decision_function methods)
  
  method : str, optional (default='average')
      Combination method: {'average', 'maximization',
      'median'}. Pass in weights of detector for weighted version.
  
  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.
  
  standardization : bool, optional (default=True)
      If True, perform standardization first to convert
      prediction score to zero mean and unit variance.
      See http://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html
  
  weights : numpy array of shape (1, n_detectors)
      detector weights.
  
  pre_fitted : bool, optional (default=False)
      Whether the base detectors are trained. If True, `fit`
      process may be skipped.
  
  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_``.
docstr: Internal function to generate and combine scores.
  
  Parameters
  ----------
  X : numpy array of shape (n_samples, n_features)
      The input samples.
  
  Returns
  -------
  agg_score: numpy array of shape (n_samples,)
      Aggregated scores.
docstr_tok: ["Internal","function","to","generate","and","combine","scores",".","Parameters","--","--","--","--","--","X",":","numpy","array","of","shape","(","n_samples",",","n_features",")","The","input","samples",".","Returns","--","--","--","-","agg_score",":","numpy","array","of","shape","(","n_samples",",",")","Aggregated","scores","."]
code: def _create_scores(self, X):
      """Internal function to generate and combine scores.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      Returns
      -------
      agg_score: numpy array of shape (n_samples,)
          Aggregated scores.
      """
      all_scores = np.zeros([X.shape[0], self.n_base_estimators_])
  
      for i, clf in enumerate(self.base_estimators):
          if hasattr(clf, "decision_function"):
              all_scores[:, i] = clf.decision_function(X)
          else:
              raise ValueError(
                  "{clf} does not have decision_function.".format(
                      clf=clf
                  )
              )
  
      if self.standardization:
          all_scores = standardizer(all_scores)
      if self.method == "average":
          agg_score = average(
              all_scores, estimator_weights=self.weights
          )
      if self.method == "maximization":
          agg_score = maximization(all_scores)
      if self.method == "median":
          agg_score = median(all_scores)
  
      return agg_score
  
file_path: combo/combo/models/detector_comb.py
imports: import numpy
  from sklearn.utils import check_array
  from sklearn.utils import column_or_1d
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import standardizer
  from .base import BaseAggregator
  from .score_comb import average, maximization, median
label_desc: Use image node_id 3 for calling the SimpleDetectorAggregator obj's underlying member method code with example usage: obj._create_scores(X) and returns: agg_score