Merge pull request #1425 from lilleswing/fix-classification-metrics

    transformers = []

    metric = dc.metrics.Metric(

        dc.metrics.matthews_corrcoef, np.mean, mode="classification")

        dc.metrics.roc_auc_score, np.mean, mode="classification")

    params_dict = {"layer_sizes": [(10,), (100,)]}

    def model_builder(model_params, model_dir):

      else:

        raise ValueError("Must specify mode for new metric.")

    assert mode in ["classification", "regression"]

    if self.metric.__name__ in [

        "accuracy_score", "balanced_accuracy_score", "recall_score",

        "matthews_corrcoef"

    ] and threshold is None:

      self.threshold = 0.5

    self.mode = mode

    # The convention used is that the first task is the metric.

    # TODO(rbharath, joegomes): This doesn't seem like it should be hard-coded as

    Raises:

      NotImplementedError: If metric_str is not in METRICS.

    """

    y_true = np.array(np.squeeze(y_true[w != 0]))

    y_pred = np.array(np.squeeze(y_pred[w != 0]))

    if not y_true.size:

      return np.nan

    if self.threshold is not None:

      y_pred = np.greater(y_pred, threshold)

      y_pred = y_pred[:, 1]

      y_pred = np.greater(y_pred, self.threshold)

    if len(y_true.shape) == 0:

      y_true = np.expand_dims(y_true, 0)

    if len(y_pred.shape) == 0: