from tensorflow.contrib.learn.python.learn.estimators import estimator_test_utils

                                         metrics)
    estimator_test_utils.assert_in_range(
        0.9, 1.0, 'precision/positive_threshold_0.500000_mean', metrics)
    estimator_test_utils.assert_in_range(
        0.9, 1.0, 'recall/positive_threshold_0.500000_mean', metrics)
    self._assertCommonMetrics(metrics)

  def _assertCommonMetrics(self, metrics):
    estimator_test_utils.assert_in_range(_ITERS, _ITERS + 5, 'global_step',
                                         metrics)
    estimator_test_utils.assert_in_range(0.9, 1.0, 'accuracy', metrics)
    estimator_test_utils.assert_in_range(0.0, 0.2, 'loss', metrics)
    self.report_benchmark(
        iters=metrics['global_step'],
        extras={k: v
                for k, v in metrics.items() if k in _METRIC_KEYS})

  def benchmarkMatrixData(self):
    iris = test_data.prepare_iris_data_for_logistic_regression()
    cont_feature = feature_column.real_valued_column('feature', dimension=4)
