Fixing broken tests

  else:

    ids, X, y, w = raw_data

    df_file = ""

    # Some shape sanity checks

    print("write_dataset_single")

    print("X.shape")

    print(X.shape)

    print("y.shape")

    print(y.shape)

    print("w.shape")

    print(w.shape)

    print("ids.shape")

    print(ids.shape)

    print("-------------")

    assert X.shape[0] == y.shape[0]

    assert y.shape == w.shape

    assert len(ids) == X.shape[0]

    input_file = "../../models/test/example.csv"

    train_samples, test_samples = (

        self._featurize_train_valid_test_split(

            splittype, input_file, tasks), frac_train=.8,

            splittype, input_file, tasks, frac_train=.8,

            frac_valid=0, frac_test=.2))

    assert len(train_samples) == 8

    assert len(test_samples) == 2

        os.makedirs(task_model_dir)

      log("Initializing model for task %s" % task,

          self.verbosity, "high")

      self.models[task] = model_builder(task_types, model_params,

      self.models[task] = model_builder([tasks], task_types, model_params,

                                        task_model_dir,

                                        verbosity=verbosity)

      y_pred[:, ind] = self.models[task].predict_on_batch(X)

    return y_pred

#  def predict(self, dataset, transformers):

#    """

#    Generate predictions of models on dataset.

#    """

#    task_names = sorted(dataset.get_task_names())

#    pred_y_df = self._create_prediction_datafram(dataset) 

#

  def save(self):

    """Save all models"""

    for task in self.tasks:

                                  user_specified_features=None,

                                  split_field=None,

                                  shard_size=100):

    print("_featurize_train_test_split")

    print("tasks")

    print(tasks)

    # Featurize input

    featurizers = compound_featurizers + complex_featurizers