class_docstr: Convenience class to let singletask models be fit on multitask data.
  
  This wrapper class groups a set of singletask `SklearnModel` objects to
  create a multitask model. This class exists primarily to facilitate
  benchmarking.
  
  Note
  ----
  This current implementation is only functional for sklearn models.
class_docstr_tok: ["Convenience","class","to","let","singletask","models","be","fit","on","multitask","data",".","This","wrapper","class","groups","a","set","of","singletask","`","SklearnModel","`","objects","to","create","a","multitask","model",".","This","class","exists","primarily","to","facilitate","benchmarking",".","Note","--","--","This","current","implementation","is","only","functional","for","sklearn","models","."]
docstr: Concatenates results from all singletask models.
docstr_tok: ["Concatenates","results","from","all","singletask","models","."]
code: def predict_on_batch(self, X):
      """Concatenates results from all singletask models."""
      y_preds = []
      for ind, task in enumerate(self.tasks):
          task_model = self.model_builder(self.task_model_dirs[task])
          task_model.reload()
  
          y_preds.append(task_model.predict_on_batch(X))
      y_pred = np.stack(y_preds, axis=1)
      return y_pred
  
file_path: deepchem/deepchem/models/multitask.py
imports: import os
  import numpy
  import shutil
  import logging
  from deepchem.models import Model
  from deepchem.data import DiskDataset
  from deepchem.trans import undo_transforms
label_desc: Use image node_id 5 for calling the SingletaskToMultitask obj's underlying member method code with example usage: obj.predict_on_batch(X) and returns: y_pred