class_docstr: Class for getting data.
class_docstr_tok: ["Class","for","getting","data","."]
docstr: Set up an experiment ground truth.
  Returns
  -------
  F : ndarray
      Full prediction array (train errors)
  P : ndarray
      Folded prediction array (test errors)
  Raises
  ------
  AssertionError :
      Raises assertion error if any weight vectors overlap or any
      predictions (as measured by columns in F and P) are judged to be
      equal.
code: def ground_truth(self, X, y, subsets=1, feature_prop=None):
      """Set up an experiment ground truth.
      Returns
      -------
      F : ndarray
          Full prediction array (train errors)
      P : ndarray
          Folded prediction array (test errors)
      Raises
      ------
      AssertionError :
          Raises assertion error if any weight vectors overlap or any
          predictions (as measured by columns in F and P) are judged to be
          equal.
      """
      P, weights_p = self._full_ests(X, y, subsets)
      if feature_prop:
          P = np.hstack([X[:, :feature_prop], P])
  
      if self.indexer.__class__.__name__.lower() == "fullindex":
          return (P, weights_p), (P, weights_p)
  
      F, weights_f = self._folded_ests(X, y, subsets)
      if feature_prop:
          r = X.shape[0] - F.shape[0]
          F = np.hstack([X[r:][:, :feature_prop], F])
  
      return (F, weights_f), (P, weights_p)
  
imports: from __future__ import division, print_function, with_statement
  import os
  import shutil
  import warnings
  from abc import abstractmethod
  import numpy
  from ..utils.utils import pickle_save, pickle_load
  from ..utils.dummy import OLS, LogisticRegression, Scale
  from ..externals.sklearn.base import clone
  from ..index import INDEXERS
  from ..ensemble.base import Sequential
  from ..estimators import LayerEnsemble
  from ..parallel import ParallelProcessing, Learner, Transformer, Layer, make_group, Pipeline
label_desc: Use image node_id 3 for calling the Data obj's underlying member method code with example usage: obj.ground_truth(X, y, subsets, feature_prop) and returns: F,  weights_f,  P,  weights_p, P,  weights_p,  P,  weights_p