c3168479b5d352d45f2225c627154222b18af7bf,deepchem/models/multitask.py,SingletaskToMultitask,predict,#SingletaskToMultitask#Any#Any#,116

Before Change

    
    n_tasks = len(self.tasks)
    n_samples = len(dataset)
    y_pred = np.zeros((n_samples, n_tasks))
    for ind, task in enumerate(self.tasks):
      task_model = self.model_builder(self.task_model_dirs[task])
      task_model.reload()

      y_pred[:, ind] = task_model.predict(dataset, [])
    y_pred = undo_transforms(y_pred, transformers)
    return y_pred

  def predict_proba_on_batch(self, X, n_classes=2):

After Change

    
    n_tasks = len(self.tasks)
    n_samples = len(dataset)
    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(dataset, []))
    y_pred = np.stack(y_preds, axis=1)
    y_pred = undo_transforms(y_pred, transformers)
    return y_pred

  def save(self):

Instances