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","."]
code: def __init__(self, tasks, model_builder, model_dir=None):
      super(SingletaskToMultitask, self).__init__(
          self, model_dir=model_dir
      )
      self.tasks = tasks
      self.task_model_dirs = {}
      self.model_builder = model_builder
      logger.info("About to initialize singletask to multitask model")
      for task in self.tasks:
          task_model_dir = os.path.join(self.model_dir, str(task))
          if not os.path.exists(task_model_dir):
              os.makedirs(task_model_dir)
          logger.info("Initializing directory for task %s" % task)
          self.task_model_dirs[task] = task_model_dir
  
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 1 to create a new SingletaskToMultitask object from inherited base classes: Model with example: obj = SingletaskToMultitask(tasks, model_builder, model_dir)