class_docstr: Class for getting data.
class_docstr_tok: ["Class","for","getting","data","."]
docstr: Get ground truth for train and predict on full data.
code: def _full_ests(self, X, y, subsets=1):
      """Get ground truth for train and predict on full data."""
      ests, prep, n_ests, attr, labels = self._set_up_est(y)
      if subsets == 1:
          indexer = DummyPartition(np.arange(X.shape[0]))
      else:
          indexer = self.indexer
          indexer.fit(X)
  
      P = np.zeros(
          (X.shape[0], n_ests * subsets * labels), dtype=np.float
      )
  
      weights = list()
      col_id = {}
      col_ass = 0
  
      vps = ["%s__%s" % (c, e[0]) for c in prep.keys() for e in ests[c]]
      for meta_key in sorted(vps):
          key, est_name = meta_key.split("__")
          est = dict(ests[key])[est_name]
          for i, tri in enumerate(indexer.partition(as_array=True)):
              if "%s-%s-%s" % (i, key, est_name) not in col_id:
                  col_id["%s-%s-%s" % (i, key, est_name)] = col_ass
                  col_ass += labels
  
              # Transform input
              xtrain = X[tri]
              ytrain = y[tri]
  
              xtest = X
  
              for _, tr in prep[key]:
                  t = clone(tr)
                  xtrain = t.fit_transform(xtrain)
                  xtest = t.transform(xtest)
  
              # Fit est
              e = clone(est).fit(xtrain, ytrain)
              w = e.coef_
              weights.append(w.tolist())
  
              # Predict
              p = getattr(e, attr)(xtest)
              c = col_id["%s-%s-%s" % (i, key, est_name)]
              if labels == 1:
                  P[:, c] = p
              else:
                  P[:, c : c + labels] = p
  
      return P, weights
  
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 6 for calling the Data obj's underlying member method code with example usage: obj._full_ests(X, y, subsets) and returns: P,  weights