adding in multitask fixes

import tempfile

import numpy as np

import shutil

from deepchem.utils.save import log

import logging

from deepchem.models import Model

from deepchem.data import DiskDataset

from deepchem.trans import undo_transforms

logger = logging.getLogger(__name__)

class SingletaskToMultitask(Model):

  """

  Warning: This current implementation is only functional for sklearn models.

  """

  def __init__(self, tasks, model_builder, model_dir=None, verbose=True):

  def __init__(self, tasks, model_builder, model_dir=None):

    super(SingletaskToMultitask, self).__init__(

        self, model_dir=model_dir, verbose=verbose)

        self, model_dir=model_dir)

    self.tasks = tasks

    self.task_model_dirs = {}

    self.model_builder = model_builder

    log("About to initialize singletask to multitask model", self.verbose)

    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)

      log("Initializing directory for task %s" % task, self.verbose)

      logger.info("Initializing directory for task %s" % task)

      self.task_model_dirs[task] = task_model_dir

  def _create_task_datasets(self, dataset):

      task_data_dirs.append(task_data_dir)

    task_datasets = self._to_singletask(dataset, task_data_dirs)

    for task, task_dataset in zip(self.tasks, task_datasets):

      log(

      logger.info(

          "Dataset for task %s has shape %s" % (task,

                                                str(task_dataset.get_shape())),

          self.verbose)

                                                str(task_dataset.get_shape())))

    return task_datasets

  @staticmethod

    """Transforms a multitask dataset to a collection of singletask datasets."""

    tasks = dataset.get_task_names()

    assert len(tasks) == len(task_dirs)

    log("Splitting multitask dataset into singletask datasets", dataset.verbose)

    logger.info("Splitting multitask dataset into singletask datasets")

    task_datasets = [

        DiskDataset.create_dataset([], task_dirs[task_num], [task])

        for (task_num, task) in enumerate(tasks)

    ]

    #task_metadata_rows = {task: [] for task in tasks}

    for shard_num, (X, y, w, ids) in enumerate(dataset.itershards()):

      log("Processing shard %d" % shard_num, dataset.verbose)

      logger.info("Processing shard %d" % shard_num)

      basename = "dataset-%d" % shard_num

      for task_num, task in enumerate(tasks):

        log("\tTask %s" % task, dataset.verbose)

        logger.info("\tTask %s" % task)

        if len(w.shape) == 1:

          w_task = w

        elif w.shape[1] == 1:

    """

    if not isinstance(dataset, DiskDataset):

      raise ValueError('SingletaskToMultitask only works with DiskDatasets')

    log("About to create task-specific datasets", self.verbose)

    logger.info("About to create task-specific datasets")

    task_datasets = self._create_task_datasets(dataset)

    for ind, task in enumerate(self.tasks):

      log("Fitting model for task %s" % task, self.verbose)

      logger.info("Fitting model for task %s" % task)

      task_model = self.model_builder(self.task_model_dirs[task])

      task_model.fit(task_datasets[ind], **kwargs)

      task_model.save()