text: # Copyright 2018 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 specific to Feature Columns integration."""
  
  import numpy as np
  
  from tensorflow.python import keras
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.feature_column import feature_column_lib as fc
  from tensorflow.python.keras import keras_parameterized
  from tensorflow.python.keras import metrics as metrics_module
  from tensorflow.python.keras import testing_utils
  from tensorflow.python.keras.feature_column import dense_features as df
  from tensorflow.python.keras.utils import np_utils
  from tensorflow.python.platform import test
  
  
  class TestDNNModel(keras.models.Model):
  
    def __init__(self, feature_columns, units, name=None, **kwargs):
      super(TestDNNModel, self).__init__(name=name, **kwargs)
      self._input_layer = df.DenseFeatures(feature_columns, name='input_layer')
      self._dense_layer = keras.layers.Dense(units, name='dense_layer')
  
    def call(self, features):
      net = self._input_layer(features)
      net = self._dense_layer(net)
      return net
  
  
  class FeatureColumnsIntegrationTest(keras_parameterized.TestCase):
    """Most Sequential model API tests are covered in `training_test.py`.
  
    """
  
    @keras_parameterized.run_all_keras_modes
    def test_sequential_model(self):
      columns = [fc.numeric_column('a')]
      model = keras.models.Sequential([
          df.DenseFeatures(columns),
          keras.layers.Dense(64, activation='relu'),
          keras.layers.Dense(20, activation='softmax')
      ])
      model.compile(
          optimizer='rmsprop',
          loss='categorical_crossentropy',
          metrics=['accuracy'],
          run_eagerly=testing_utils.should_run_eagerly())
  
      x = {'a': np.random.random((10, 1))}
      y = np.random.randint(20, size=(10, 1))
      y = np_utils.to_categorical(y, num_classes=20)
      model.fit(x, y, epochs=1, batch_size=5)
      model.fit(x, y, epochs=1, batch_size=5)
      model.evaluate(x, y, batch_size=5)
      model.predict(x, batch_size=5)
  
    @keras_parameterized.run_all_keras_modes
    def test_sequential_model_with_ds_input(self):
      columns = [fc.numeric_column('a')]
      model = keras.models.Sequential([
          df.DenseFeatures(columns),
          keras.layers.Dense(64, activation='relu'),
          keras.layers.Dense(20, activation='softmax')
      ])
      model.compile(
          optimizer='rmsprop',
          loss='categorical_crossentropy',
          metrics=['accuracy'],
          run_eagerly=testing_utils.should_run_eagerly())
  
      y = np.random.randint(20, size=(100, 1))
      y = np_utils.to_categorical(y, num_classes=20)
      x = {'a': np.random.random((100, 1))}
      ds1 = dataset_ops.Dataset.from_tensor_slices(x)
      ds2 = dataset_ops.Dataset.from_tensor_slices(y)
      ds = dataset_ops.Dataset.zip((ds1, ds2)).batch(5)
      model.fit(ds, steps_per_epoch=1)
      model.fit(ds, steps_per_epoch=1)
      model.evaluate(ds, steps=1)
      model.predict(ds, steps=1)
  
    @keras_parameterized.run_all_keras_modes(always_skip_v1=True)
    def test_sequential_model_with_crossed_column(self):
      feature_columns = []
      age_buckets = fc.bucketized_column(
          fc.numeric_column('age'),
          boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
      feature_columns.append(age_buckets)
  
      # indicator cols
      thal = fc.categorical_column_with_vocabulary_list(
          'thal', ['fixed', 'normal', 'reversible'])
  
      crossed_feature = fc.crossed_column([age_buckets, thal],
                                          hash_bucket_size=1000)
      crossed_feature = fc.indicator_column(crossed_feature)
      feature_columns.append(crossed_feature)
  
      feature_layer = df.DenseFeatures(feature_columns)
  
      model = keras.models.Sequential([
          feature_layer,
          keras.layers.Dense(128, activation='relu'),
          keras.layers.Dense(128, activation='relu'),
          keras.layers.Dense(1, activation='sigmoid')
      ])
  
      age_data = np.random.randint(10, 100, size=100)
      thal_data = np.random.choice(['fixed', 'normal', 'reversible'], size=100)
      inp_x = {'age': age_data, 'thal': thal_data}
      inp_y = np.random.randint(0, 1, size=100)
      ds = dataset_ops.Dataset.from_tensor_slices((inp_x, inp_y)).batch(5)
      model.compile(optimizer='adam',
                    loss='binary_crossentropy',
                    metrics=['accuracy'],)
      model.fit(ds, epochs=1)
      model.fit(ds, epochs=1)
      model.evaluate(ds)
      model.predict(ds)
  
    @keras_parameterized.run_all_keras_modes
    def test_subclassed_model_with_feature_columns(self):
      col_a = fc.numeric_column('a')
      col_b = fc.numeric_column('b')
  
      dnn_model = TestDNNModel([col_a, col_b], 20)
  
      dnn_model.compile(
          optimizer='rmsprop',
          loss='categorical_crossentropy',
          metrics=['accuracy'],
          run_eagerly=testing_utils.should_run_eagerly())
  
      x = {'a': np.random.random((10, 1)), 'b': np.random.random((10, 1))}
      y = np.random.randint(20, size=(10, 1))
      y = np_utils.to_categorical(y, num_classes=20)
      dnn_model.fit(x=x, y=y, epochs=1, batch_size=5)
      dnn_model.fit(x=x, y=y, epochs=1, batch_size=5)
      dnn_model.evaluate(x=x, y=y, batch_size=5)
      dnn_model.predict(x=x, batch_size=5)
  
    @keras_parameterized.run_all_keras_modes
    def test_subclassed_model_with_feature_columns_with_ds_input(self):
      col_a = fc.numeric_column('a')
      col_b = fc.numeric_column('b')
  
      dnn_model = TestDNNModel([col_a, col_b], 20)
  
      dnn_model.compile(
          optimizer='rmsprop',
          loss='categorical_crossentropy',
          metrics=['accuracy'],
          run_eagerly=testing_utils.should_run_eagerly())
  
      y = np.random.randint(20, size=(100, 1))
      y = np_utils.to_categorical(y, num_classes=20)
      x = {'a': np.random.random((100, 1)), 'b': np.random.random((100, 1))}
      ds1 = dataset_ops.Dataset.from_tensor_slices(x)
      ds2 = dataset_ops.Dataset.from_tensor_slices(y)
      ds = dataset_ops.Dataset.zip((ds1, ds2)).batch(5)
      dnn_model.fit(ds, steps_per_epoch=1)
      dnn_model.fit(ds, steps_per_epoch=1)
      dnn_model.evaluate(ds, steps=1)
      dnn_model.predict(ds, steps=1)
  
    # TODO(kaftan) seems to throw an error when enabled.
    @keras_parameterized.run_all_keras_modes
    def DISABLED_test_function_model_feature_layer_input(self):
      col_a = fc.numeric_column('a')
      col_b = fc.numeric_column('b')
  
      feature_layer = df.DenseFeatures([col_a, col_b], name='fc')
      dense = keras.layers.Dense(4)
  
      # This seems problematic.... We probably need something for DenseFeatures
      # the way Input is for InputLayer.
      output = dense(feature_layer)
  
      model = keras.models.Model([feature_layer], [output])
  
      optimizer = 'rmsprop'
      loss = 'mse'
      loss_weights = [1., 0.5]
      model.compile(
          optimizer,
          loss,
          metrics=[metrics_module.CategoricalAccuracy(), 'mae'],
          loss_weights=loss_weights)
  
      data = ({'a': np.arange(10), 'b': np.arange(10)}, np.arange(10, 20))
      model.fit(*data, epochs=1)
  
    # TODO(kaftan) seems to throw an error when enabled.
    @keras_parameterized.run_all_keras_modes
    def DISABLED_test_function_model_multiple_feature_layer_inputs(self):
      col_a = fc.numeric_column('a')
      col_b = fc.numeric_column('b')
      col_c = fc.numeric_column('c')
  
      fc1 = df.DenseFeatures([col_a, col_b], name='fc1')
      fc2 = df.DenseFeatures([col_b, col_c], name='fc2')
      dense = keras.layers.Dense(4)
  
      # This seems problematic.... We probably need something for DenseFeatures
      # the way Input is for InputLayer.
      output = dense(fc1) + dense(fc2)
  
      model = keras.models.Model([fc1, fc2], [output])
  
      optimizer = 'rmsprop'
      loss = 'mse'
      loss_weights = [1., 0.5]
      model.compile(
          optimizer,
          loss,
          metrics=[metrics_module.CategoricalAccuracy(), 'mae'],
          loss_weights=loss_weights)
  
      data_list = ([{
          'a': np.arange(10),
          'b': np.arange(10)
      }, {
          'b': np.arange(10),
          'c': np.arange(10)
      }], np.arange(10, 100))
      model.fit(*data_list, epochs=1)
  
      data_bloated_list = ([{
          'a': np.arange(10),
          'b': np.arange(10),
          'c': np.arange(10)
      }, {
          'a': np.arange(10),
          'b': np.arange(10),
          'c': np.arange(10)
      }], np.arange(10, 100))
      model.fit(*data_bloated_list, epochs=1)
  
      data_dict = ({
          'fc1': {
              'a': np.arange(10),
              'b': np.arange(10)
          },
          'fc2': {
              'b': np.arange(10),
              'c': np.arange(10)
          }
      }, np.arange(10, 100))
      model.fit(*data_dict, epochs=1)
  
      data_bloated_dict = ({
          'fc1': {
              'a': np.arange(10),
              'b': np.arange(10),
              'c': np.arange(10)
          },
          'fc2': {
              'a': np.arange(10),
              'b': np.arange(10),
              'c': np.arange(10)
          }
      }, np.arange(10, 100))
      model.fit(*data_bloated_dict, epochs=1)
  
    @keras_parameterized.run_all_keras_modes
    def test_string_input(self):
      x = {'age': np.random.random((1024, 1)),
           'cabin': np.array(['a'] * 1024)}
      y = np.random.randint(2, size=(1024, 1))
      ds1 = dataset_ops.Dataset.from_tensor_slices(x)
      ds2 = dataset_ops.Dataset.from_tensor_slices(y)
      dataset = dataset_ops.Dataset.zip((ds1, ds2)).batch(4)
      categorical_cols = [fc.categorical_column_with_hash_bucket('cabin', 10)]
      feature_cols = ([fc.numeric_column('age')]
                      + [fc.indicator_column(cc) for cc in categorical_cols])
      layers = [df.DenseFeatures(feature_cols),
                keras.layers.Dense(128),
                keras.layers.Dense(1)]
  
      model = keras.models.Sequential(layers)
      model.compile(optimizer='sgd',
                    loss=keras.losses.BinaryCrossentropy())
      model.fit(dataset)
  
  
  if __name__ == '__main__':
    test.main()
  