content: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Tests for dnn.py."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import shutil
  import tempfile
  
  import numpy as np
  import six
  
  from tensorflow.core.example import example_pb2
  from tensorflow.core.example import feature_pb2
  from tensorflow.python.estimator.canned import dnn
  from tensorflow.python.estimator.canned import dnn_testing_utils
  from tensorflow.python.estimator.canned import prediction_keys
  from tensorflow.python.estimator.export import export
  from tensorflow.python.estimator.inputs import numpy_io
  from tensorflow.python.estimator.inputs import pandas_io
  from tensorflow.python.feature_column import feature_column
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import data_flow_ops
  from tensorflow.python.ops import parsing_ops
  from tensorflow.python.platform import gfile
  from tensorflow.python.platform import test
  from tensorflow.python.summary.writer import writer_cache
  from tensorflow.python.training import input as input_lib
  from tensorflow.python.training import queue_runner
  
  try:
    # pylint: disable=g-import-not-at-top
    import pandas as pd
    HAS_PANDAS = True
  except IOError:
    # Pandas writes a temporary file during import. If it fails, don't use pandas.
    HAS_PANDAS = False
  except ImportError:
    HAS_PANDAS = False
  
  
  def _dnn_classifier_fn(*args, **kwargs):
    return dnn.DNNClassifier(*args, **kwargs)
  
  
  class DNNModelFnTest(dnn_testing_utils.BaseDNNModelFnTest, test.TestCase):
  
    def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
      test.TestCase.__init__(self, methodName)
      dnn_testing_utils.BaseDNNModelFnTest.__init__(self, dnn._dnn_model_fn)
  
  
  class DNNLogitFnTest(dnn_testing_utils.BaseDNNLogitFnTest, test.TestCase):
  
    def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
      test.TestCase.__init__(self, methodName)
      dnn_testing_utils.BaseDNNLogitFnTest.__init__(self,
                                                    dnn._dnn_logit_fn_builder)
  
  
  class DNNWarmStartingTest(dnn_testing_utils.BaseDNNWarmStartingTest,
                            test.TestCase):
  
    def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
      test.TestCase.__init__(self, methodName)
      dnn_testing_utils.BaseDNNWarmStartingTest.__init__(self, _dnn_classifier_fn,
                                                         _dnn_regressor_fn)
  
  
  class DNNClassifierEvaluateTest(
      dnn_testing_utils.BaseDNNClassifierEvaluateTest, test.TestCase):
  
    def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
      test.TestCase.__init__(self, methodName)
      dnn_testing_utils.BaseDNNClassifierEvaluateTest.__init__(
          self, _dnn_classifier_fn)
  
  
  class DNNClassifierPredictTest(
      dnn_testing_utils.BaseDNNClassifierPredictTest, test.TestCase):
  
    def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
      test.TestCase.__init__(self, methodName)
      dnn_testing_utils.BaseDNNClassifierPredictTest.__init__(
          self, _dnn_classifier_fn)
  
  
  class DNNClassifierTrainTest(
      dnn_testing_utils.BaseDNNClassifierTrainTest, test.TestCase):
  
    def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
      test.TestCase.__init__(self, methodName)
      dnn_testing_utils.BaseDNNClassifierTrainTest.__init__(
          self, _dnn_classifier_fn)
  
  
  def _dnn_regressor_fn(*args, **kwargs):
    return dnn.DNNRegressor(*args, **kwargs)
  
  
  class DNNRegressorEvaluateTest(
      dnn_testing_utils.BaseDNNRegressorEvaluateTest, test.TestCase):
  
    def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
      test.TestCase.__init__(self, methodName)
      dnn_testing_utils.BaseDNNRegressorEvaluateTest.__init__(
          self, _dnn_regressor_fn)
  
  
  class DNNRegressorPredictTest(
      dnn_testing_utils.BaseDNNRegressorPredictTest, test.TestCase):
  
    def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
      test.TestCase.__init__(self, methodName)
      dnn_testing_utils.BaseDNNRegressorPredictTest.__init__(
          self, _dnn_regressor_fn)
  
  
  class DNNRegressorTrainTest(
      dnn_testing_utils.BaseDNNRegressorTrainTest, test.TestCase):
  
    def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
      test.TestCase.__init__(self, methodName)
      dnn_testing_utils.BaseDNNRegressorTrainTest.__init__(
          self, _dnn_regressor_fn)
  
  
  def _queue_parsed_features(feature_map):
    tensors_to_enqueue = []
    keys = []
    for key, tensor in six.iteritems(feature_map):
      keys.append(key)
      tensors_to_enqueue.append(tensor)
    queue_dtypes = [x.dtype for x in tensors_to_enqueue]
    input_queue = data_flow_ops.FIFOQueue(capacity=100, dtypes=queue_dtypes)
    queue_runner.add_queue_runner(
        queue_runner.QueueRunner(
            input_queue,
            [input_queue.enqueue(tensors_to_enqueue)]))
    dequeued_tensors = input_queue.dequeue()
    return {keys[i]: dequeued_tensors[i] for i in range(len(dequeued_tensors))}
  
  
  class DNNRegressorIntegrationTest(test.TestCase):
  
    def setUp(self):
      self._model_dir = tempfile.mkdtemp()
  
    def tearDown(self):
      if self._model_dir:
        writer_cache.FileWriterCache.clear()
        shutil.rmtree(self._model_dir)
  
    def _test_complete_flow(
        self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
        label_dimension, batch_size):
      feature_columns = [
          feature_column.numeric_column('x', shape=(input_dimension,))]
      est = dnn.DNNRegressor(
          hidden_units=(2, 2),
          feature_columns=feature_columns,
          label_dimension=label_dimension,
          model_dir=self._model_dir)
  
      # TRAIN
      num_steps = 10
      est.train(train_input_fn, steps=num_steps)
  
      # EVALUTE
      scores = est.evaluate(eval_input_fn)
      self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
      self.assertIn('loss', six.iterkeys(scores))
  
      # PREDICT
      predictions = np.array([
          x[prediction_keys.PredictionKeys.PREDICTIONS]
          for x in est.predict(predict_input_fn)
      ])
      self.assertAllEqual((batch_size, label_dimension), predictions.shape)
  
      # EXPORT
      feature_spec = feature_column.make_parse_example_spec(feature_columns)
      serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
          feature_spec)
      export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                         serving_input_receiver_fn)
      self.assertTrue(gfile.Exists(export_dir))
  
    def test_numpy_input_fn(self):
      """Tests complete flow with numpy_input_fn."""
      label_dimension = 2
      batch_size = 10
      data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
      data = data.reshape(batch_size, label_dimension)
      # learn y = x
      train_input_fn = numpy_io.numpy_input_fn(
          x={'x': data},
          y=data,
          batch_size=batch_size,
          num_epochs=None,
          shuffle=True)
      eval_input_fn = numpy_io.numpy_input_fn(
          x={'x': data},
          y=data,
          batch_size=batch_size,
          shuffle=False)
      predict_input_fn = numpy_io.numpy_input_fn(
          x={'x': data},
          batch_size=batch_size,
          shuffle=False)
  
      self._test_complete_flow(
          train_input_fn=train_input_fn,
          eval_input_fn=eval_input_fn,
          predict_input_fn=predict_input_fn,
          input_dimension=label_dimension,
          label_dimension=label_dimension,
          batch_size=batch_size)
  
    def test_pandas_input_fn(self):
      """Tests complete flow with pandas_input_fn."""
      if not HAS_PANDAS:
        return
      label_dimension = 1
      batch_size = 10
      data = np.linspace(0., 2., batch_size, dtype=np.float32)
      x = pd.DataFrame({'x': data})
      y = pd.Series(data)
      train_input_fn = pandas_io.pandas_input_fn(
          x=x,
          y=y,
          batch_size=batch_size,
          num_epochs=None,
          shuffle=True)
      eval_input_fn = pandas_io.pandas_input_fn(
          x=x,
          y=y,
          batch_size=batch_size,
          shuffle=False)
      predict_input_fn = pandas_io.pandas_input_fn(
          x=x,
          batch_size=batch_size,
          shuffle=False)
  
      self._test_complete_flow(
          train_input_fn=train_input_fn,
          eval_input_fn=eval_input_fn,
          predict_input_fn=predict_input_fn,
          input_dimension=label_dimension,
          label_dimension=label_dimension,
          batch_size=batch_size)
  
    def test_input_fn_from_parse_example(self):
      """Tests complete flow with input_fn constructed from parse_example."""
      label_dimension = 2
      batch_size = 10
      data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
      data = data.reshape(batch_size, label_dimension)
  
      serialized_examples = []
      for datum in data:
        example = example_pb2.Example(features=feature_pb2.Features(
            feature={
                'x': feature_pb2.Feature(
                    float_list=feature_pb2.FloatList(value=datum)),
                'y': feature_pb2.Feature(
                    float_list=feature_pb2.FloatList(value=datum)),
            }))
        serialized_examples.append(example.SerializeToString())
  
      feature_spec = {
          'x': parsing_ops.FixedLenFeature([label_dimension], dtypes.float32),
          'y': parsing_ops.FixedLenFeature([label_dimension], dtypes.float32),
      }
      def _train_input_fn():
        feature_map = parsing_ops.parse_example(serialized_examples, feature_spec)
        features = _queue_parsed_features(feature_map)
        labels = features.pop('y')
        return features, labels
      def _eval_input_fn():
        feature_map = parsing_ops.parse_example(
            input_lib.limit_epochs(serialized_examples, num_epochs=1),
            feature_spec)
        features = _queue_parsed_features(feature_map)
        labels = features.pop('y')
        return features, labels
      def _predict_input_fn():
        feature_map = parsing_ops.parse_example(
            input_lib.limit_epochs(serialized_examples, num_epochs=1),
            feature_spec)
        features = _queue_parsed_features(feature_map)
        features.pop('y')
        return features, None
  
      self._test_complete_flow(
          train_input_fn=_train_input_fn,
          eval_input_fn=_eval_input_fn,
          predict_input_fn=_predict_input_fn,
          input_dimension=label_dimension,
          label_dimension=label_dimension,
          batch_size=batch_size)
  
  
  class DNNClassifierIntegrationTest(test.TestCase):
  
    def setUp(self):
      self._model_dir = tempfile.mkdtemp()
  
    def tearDown(self):
      if self._model_dir:
        writer_cache.FileWriterCache.clear()
        shutil.rmtree(self._model_dir)
  
    def _as_label(self, data_in_float):
      return np.rint(data_in_float).astype(np.int64)
  
    def _test_complete_flow(
        self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
        n_classes, batch_size):
      feature_columns = [
          feature_column.numeric_column('x', shape=(input_dimension,))]
      est = dnn.DNNClassifier(
          hidden_units=(2, 2),
          feature_columns=feature_columns,
          n_classes=n_classes,
          model_dir=self._model_dir)
  
      # TRAIN
      num_steps = 10
      est.train(train_input_fn, steps=num_steps)
  
      # EVALUTE
      scores = est.evaluate(eval_input_fn)
      self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
      self.assertIn('loss', six.iterkeys(scores))
  
      # PREDICT
      predicted_proba = np.array([
          x[prediction_keys.PredictionKeys.PROBABILITIES]
          for x in est.predict(predict_input_fn)
      ])
      self.assertAllEqual((batch_size, n_classes), predicted_proba.shape)
  
      # EXPORT
      feature_spec = feature_column.make_parse_example_spec(feature_columns)
      serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
          feature_spec)
      export_dir = est.export_savedmodel(tempfile.mkdtemp(),
                                         serving_input_receiver_fn)
      self.assertTrue(gfile.Exists(export_dir))
  
    def test_numpy_input_fn(self):
      """Tests complete flow with numpy_input_fn."""
      n_classes = 3
      input_dimension = 2
      batch_size = 10
      data = np.linspace(
          0., n_classes - 1., batch_size * input_dimension, dtype=np.float32)
      x_data = data.reshape(batch_size, input_dimension)
      y_data = np.reshape(self._as_label(data[:batch_size]), (batch_size, 1))
      # learn y = x
      train_input_fn = numpy_io.numpy_input_fn(
          x={'x': x_data},
          y=y_data,
          batch_size=batch_size,
          num_epochs=None,
          shuffle=True)
      eval_input_fn = numpy_io.numpy_input_fn(
          x={'x': x_data},
          y=y_data,
          batch_size=batch_size,
          shuffle=False)
      predict_input_fn = numpy_io.numpy_input_fn(
          x={'x': x_data},
          batch_size=batch_size,
          shuffle=False)
  
      self._test_complete_flow(
          train_input_fn=train_input_fn,
          eval_input_fn=eval_input_fn,
          predict_input_fn=predict_input_fn,
          input_dimension=input_dimension,
          n_classes=n_classes,
          batch_size=batch_size)
  
    def test_pandas_input_fn(self):
      """Tests complete flow with pandas_input_fn."""
      if not HAS_PANDAS:
        return
      input_dimension = 1
      n_classes = 3
      batch_size = 10
      data = np.linspace(0., n_classes - 1., batch_size, dtype=np.float32)
      x = pd.DataFrame({'x': data})
      y = pd.Series(self._as_label(data))
      train_input_fn = pandas_io.pandas_input_fn(
          x=x,
          y=y,
          batch_size=batch_size,
          num_epochs=None,
          shuffle=True)
      eval_input_fn = pandas_io.pandas_input_fn(
          x=x,
          y=y,
          batch_size=batch_size,
          shuffle=False)
      predict_input_fn = pandas_io.pandas_input_fn(
          x=x,
          batch_size=batch_size,
          shuffle=False)
  
      self._test_complete_flow(
          train_input_fn=train_input_fn,
          eval_input_fn=eval_input_fn,
          predict_input_fn=predict_input_fn,
          input_dimension=input_dimension,
          n_classes=n_classes,
          batch_size=batch_size)
  
    def test_input_fn_from_parse_example(self):
      """Tests complete flow with input_fn constructed from parse_example."""
      input_dimension = 2
      n_classes = 3
      batch_size = 10
      data = np.linspace(
          0., n_classes - 1., batch_size * input_dimension, dtype=np.float32)
      data = data.reshape(batch_size, input_dimension)
  
      serialized_examples = []
      for datum in data:
        example = example_pb2.Example(features=feature_pb2.Features(
            feature={
                'x':
                    feature_pb2.Feature(float_list=feature_pb2.FloatList(
                        value=datum)),
                'y':
                    feature_pb2.Feature(int64_list=feature_pb2.Int64List(
                        value=self._as_label(datum[:1]))),
            }))
        serialized_examples.append(example.SerializeToString())
  
      feature_spec = {
          'x': parsing_ops.FixedLenFeature([input_dimension], dtypes.float32),
          'y': parsing_ops.FixedLenFeature([1], dtypes.int64),
      }
      def _train_input_fn():
        feature_map = parsing_ops.parse_example(serialized_examples, feature_spec)
        features = _queue_parsed_features(feature_map)
        labels = features.pop('y')
        return features, labels
      def _eval_input_fn():
        feature_map = parsing_ops.parse_example(
            input_lib.limit_epochs(serialized_examples, num_epochs=1),
            feature_spec)
        features = _queue_parsed_features(feature_map)
        labels = features.pop('y')
        return features, labels
      def _predict_input_fn():
        feature_map = parsing_ops.parse_example(
            input_lib.limit_epochs(serialized_examples, num_epochs=1),
            feature_spec)
        features = _queue_parsed_features(feature_map)
        features.pop('y')
        return features, None
  
      self._test_complete_flow(
          train_input_fn=_train_input_fn,
          eval_input_fn=_eval_input_fn,
          predict_input_fn=_predict_input_fn,
          input_dimension=input_dimension,
          n_classes=n_classes,
          batch_size=batch_size)
  
  
  if __name__ == '__main__':
    test.main()
  