31b60db18f5d5b7b5723bc8e76f3ae38bedca335,deepchem/models/multitask.py,SingletaskToMultitask,predict_proba,#SingletaskToMultitask#Any#Any#Any#,148

Before Change

    n_samples = len(dataset) 
    y_pred = np.zeros((n_samples, n_tasks, n_classes))
    for ind, task in enumerate(self.tasks):
      if self.store_in_memory:
        task_model = self.task_models[task]
      else:
        task_model = self.model_builder(
            [task], {task: self.task_types[task]}, self.model_params,
            self.task_model_dirs[task],
            verbosity=self.verbosity)
        task_model.reload()

      y_pred[:, ind] = np.squeeze(task_model.predict_proba(
          dataset, transformers, n_classes))
    return y_pred

After Change

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

      y_pred[:, ind] = np.squeeze(task_model.predict_proba(
          dataset, transformers, n_classes))
    return y_pred

Instances