random_forest.py 文件源码

def _get_eval_ops(self, features, targets, metrics):
    features, _, spec = data_ops.ParseDataTensorOrDict(features)
    labels = data_ops.ParseLabelTensorOrDict(targets)
    _assert_float32(features)
    _assert_float32(labels)

    graph_builder = self.graph_builder_class(
        self.params, device_assigner=self.device_assigner, training=False,
        **self.construction_args)

    probabilities = graph_builder.inference_graph(features, data_spec=spec)

    # One-hot the labels.
    if not self.params.regression:
      labels = math_ops.to_int64(array_ops.one_hot(math_ops.to_int64(
          array_ops.squeeze(labels)), self.params.num_classes, 1, 0))

    if metrics is None:
      metrics = {self.accuracy_metric:
                 eval_metrics.get_metric(self.accuracy_metric)}

    result = {}
    for name, metric in six.iteritems(metrics):
      result[name] = metric(probabilities, labels)

    return result