class_docstr: Select predefined feature subsets.
class_docstr_tok: ["Select","predefined","feature","subsets","."]
docstr: Make subset after fit
  
  Parameters
  ----------
  X: numpy ndarray, {n_samples, n_features}
      New data, where n_samples is the number of samples and n_features is the number of features.
  
  Returns
  -------
  X_transformed: array-like, shape (n_samples, n_features + 1) or (n_samples, n_features + 1 + n_classes) for classifier with predict_proba attribute
      The transformed feature set.
docstr_tok: ["Make","subset","after","fit","Parameters","--","--","--","--","--","X",":","numpy","ndarray",",","{","n_samples",",","n_features","}","New","data",",","where","n_samples","is","the","number","of","samples","and","n_features","is","the","number","of","features",".","Returns","--","--","--","-","X_transformed",":","array-like",",","shape","(","n_samples",",","n_features","+","1",")","or","(","n_samples",",","n_features","+","1","+","n_classes",")","for","classifier","with","predict_proba","attribute","The","transformed","feature","set","."]
code: def transform(self, X):
      """Make subset after fit
  
      Parameters
      ----------
      X: numpy ndarray, {n_samples, n_features}
          New data, where n_samples is the number of samples and n_features is the number of features.
  
      Returns
      -------
      X_transformed: array-like, shape (n_samples, n_features + 1) or (n_samples, n_features + 1 + n_classes) for classifier with predict_proba attribute
          The transformed feature set.
      """
      if isinstance(X, pd.DataFrame):
          X_transformed = X[self.feat_list].values
      elif isinstance(X, np.ndarray):
          X_transformed = X[:, self.feat_list_idx]
  
      return X_transformed.astype(np.float64)
  
file_path: tpot/tpot/builtins/feature_set_selector.py
imports: import numpy
  import pandas
  import os, os.path
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 4 for calling the FeatureSetSelector obj's underlying member method code with example usage: obj.transform(X) and returns: X_transformed