805106ddabbe02b57718a4132190d00c98e94fa4,deepchem/models/__init__.py,Model,predict,#Model#Any#Any#,144

Before Change

      y_pred_batch = undo_transforms(y_pred_batch, transformers)
      y_preds.append(y_pred_batch)
      ys.append(y_batch)
      w_preds.append(w_batch)
      print("X_batch.shape, y_batch.shape, y_pred_batch.shape")
      print(X_batch.shape, y_batch.shape, y_pred_batch.shape)
    y = np.vstack(ys)
    y_pred = np.vstack(y_preds)
    w_pred = np.vstack(w_preds)
  
    //X = X[w.flatten() != 0, :]
    //print("Model.predict()")
    //print("y.shape, w.shape, y_pred.shape")
    //print(y.shape, w.shape, y_pred.shape)
    //for task in xrange(num_tasks):
    //  y_task, w_task, y_pred_task = y[:, task], w[:, task], y_pred[:, task]
    //  y_task = y_task[w_task.flatten() != 0]
    //  y_task = to_one_hot(y_task)
    //  y_pred_task = y_pred_task[w_task.flatten() != 0][:, np.newaxis]

    return y, y_pred, w_pred

    //task_names = dataset.get_task_names()
    //pred_task_names = ["%s_pred" % task_name for task_name in task_names]
    //w_task_names = ["%s_weight" % task_name for task_name in task_names]

After Change

    batch_size = self.model_params["batch_size"]
    // Have to include ys/ws since we might pad batches
    y_preds = []
    print("predict()")
    print("len(dataset)")
    print(len(dataset))
    for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(batch_size):
      y_pred_batch = np.reshape(self.predict_on_batch(X_batch), y_batch.shape)
      y_pred_batch = undo_transforms(y_pred_batch, transformers)
      y_preds.append(y_pred_batch)
      //ys.append(y_batch)
      //w_preds.append(w_batch)
      print("X_batch.shape, y_batch.shape, y_pred_batch.shape")
      print(X_batch.shape, y_batch.shape, y_pred_batch.shape)
    //y = np.vstack(ys)
    y_pred = np.vstack(y_preds)
    //w_pred = np.vstack(w_preds)
  
    //X = X[w.flatten() != 0, :]
    //print("Model.predict()")
    //print("y.shape, w.shape, y_pred.shape")
    //print(y.shape, w.shape, y_pred.shape)
    //for task in xrange(num_tasks):
    //  y_task, w_task, y_pred_task = y[:, task], w[:, task], y_pred[:, task]
    //  y_task = y_task[w_task.flatten() != 0]
    //  y_task = to_one_hot(y_task)
    //  y_pred_task = y_pred_task[w_task.flatten() != 0][:, np.newaxis]

    // The iterbatches does padding with zero-weight examples on the last batch.
    // Remove padded examples.
    y_pred = y_pred[:len(dataset)]

    return y_pred

    //task_names = dataset.get_task_names()
    //pred_task_names = ["%s_pred" % task_name for task_name in task_names]
    //w_task_names = ["%s_weight" % task_name for task_name in task_names]

Instances