More bugfixes

    batch_size = self.model_params["batch_size"]

    # Have to include ys/ws since we might pad batches

    ys, w_preds, y_preds = [], [], []

    y_preds = []

    print("predict()")

    print("len(dataset)")

    print(len(dataset))

      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)

      #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 = np.vstack(ys)

    y_pred = np.vstack(y_preds)

    w_pred = np.vstack(w_preds)

    #w_pred = np.vstack(w_preds)

    #X = X[w.flatten() != 0, :]

    #print("Model.predict()")

    #  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

    # 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]

    model.save()

    # Eval model on train

    evaluator = Evaluator(model, train_dataset, transformers, verbose=True)

    evaluator = Evaluator(model, train_dataset, transformers, verbosity=True)

    with tempfile.NamedTemporaryFile() as train_csv_out:

      with tempfile.NamedTemporaryFile() as train_stats_out:

        _, _, _ = evaluator.compute_model_performance(

        _ = evaluator.compute_model_performance(

            metrics, train_csv_out, train_stats_out)

    # Eval model on test

    evaluator = Evaluator(model, test_dataset, transformers, verbose=True)

    evaluator = Evaluator(model, test_dataset, transformers, verbosity=True)

    with tempfile.NamedTemporaryFile() as test_csv_out:

      with tempfile.NamedTemporaryFile() as test_stats_out:

        _, _, _ = evaluator.compute_model_performance(

        _ = evaluator.compute_model_performance(

            metrics, test_csv_out, test_stats_out)

  def _featurize_train_test_split(self, splittype, compound_featurizers, 

    Computes statistics of model on test data and saves results to csv.

    """

    #pred_y_df = self.model.predict(self.dataset, self.transformers)

    #y = self.dataset.get_labels()

    #w = self.dataset.get_weights()

    y, y_pred, w_pred = self.model.predict(self.dataset, self.transformers)

    y = self.dataset.get_labels()

    w = self.dataset.get_weights()

    y_pred = self.model.predict(self.dataset, self.transformers)

    multitask_scores = {}

    colnames = ["task_name"] + [metric.name for metric in metrics]

    # Compute multitask metrics

    for metric in metrics:

      multitask_scores[metric.name] = metric.compute_metric(y, y_pred, w_pred)

      multitask_scores[metric.name] = metric.compute_metric(y, y_pred, w)

    print("multitask_scores")

    print(multitask_scores)