content: # Copyright 2016 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.
  # ==============================================================================
  """Sources for numpy arrays and pandas DataFrames."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.contrib.learn.python.learn.dataframe import transform
  from tensorflow.contrib.learn.python.learn.dataframe.queues import feeding_functions
  
  
  class BaseInMemorySource(transform.TensorFlowTransform):
    """Abstract parent class for NumpySource and PandasSource."""
  
    def __init__(self,
                 data,
                 num_threads=None,
                 enqueue_size=None,
                 batch_size=None,
                 queue_capacity=None,
                 shuffle=False,
                 min_after_dequeue=None,
                 seed=None,
                 data_name="in_memory_data"):
      super(BaseInMemorySource, self).__init__()
      self._data = data
      self._num_threads = 1 if num_threads is None else num_threads
      self._batch_size = (32 if batch_size is None else batch_size)
      self._enqueue_size = max(1, int(self._batch_size / self._num_threads)
                              ) if enqueue_size is None else enqueue_size
      self._queue_capacity = (self._batch_size * 10 if queue_capacity is None else
                              queue_capacity)
      self._shuffle = shuffle
      self._min_after_dequeue = (batch_size if min_after_dequeue is None else
                                 min_after_dequeue)
      self._seed = seed
      self._data_name = data_name
  
    @transform.parameter
    def data(self):
      return self._data
  
    @transform.parameter
    def num_threads(self):
      return self._num_threads
  
    @transform.parameter
    def enqueue_size(self):
      return self._enqueue_size
  
    @transform.parameter
    def batch_size(self):
      return self._batch_size
  
    @transform.parameter
    def queue_capacity(self):
      return self._queue_capacity
  
    @transform.parameter
    def shuffle(self):
      return self._shuffle
  
    @transform.parameter
    def min_after_dequeue(self):
      return self._min_after_dequeue
  
    @transform.parameter
    def seed(self):
      return self._seed
  
    @transform.parameter
    def data_name(self):
      return self._data_name
  
    @property
    def input_valency(self):
      return 0
  
    def _apply_transform(self, transform_input, **kwargs):
      queue = feeding_functions.enqueue_data(self.data,
                                             self.queue_capacity,
                                             self.shuffle,
                                             self.min_after_dequeue,
                                             num_threads=self.num_threads,
                                             seed=self.seed,
                                             name=self.data_name,
                                             enqueue_size=self.enqueue_size,
                                             num_epochs=kwargs.get("num_epochs"))
  
      dequeued = queue.dequeue_many(self.batch_size)
  
      # TODO(jamieas): dequeue and dequeue_many will soon return a list regardless
      # of the number of enqueued tensors. Remove the following once that change
      # is in place.
      if not isinstance(dequeued, (tuple, list)):
        dequeued = (dequeued,)
      # pylint: disable=not-callable
      return self.return_type(*dequeued)
  
  
  class NumpySource(BaseInMemorySource):
    """A zero-input Transform that produces a single column from a numpy array."""
  
    @property
    def name(self):
      return "NumpySource"
  
    @property
    def _output_names(self):
      return ("index", "value")
  
  
  class OrderedDictNumpySource(BaseInMemorySource):
    """A zero-input Transform that produces Series from a dict of numpy arrays."""
  
    def __init__(self,
                 ordered_dict_of_arrays,
                 num_threads=None,
                 enqueue_size=None,
                 batch_size=None,
                 queue_capacity=None,
                 shuffle=False,
                 min_after_dequeue=None,
                 seed=None,
                 data_name="pandas_data"):
      if "index" in ordered_dict_of_arrays.keys():
        raise ValueError("Column name `index` is reserved.")
      super(OrderedDictNumpySource, self).__init__(ordered_dict_of_arrays,
                                                   num_threads, enqueue_size,
                                                   batch_size, queue_capacity,
                                                   shuffle, min_after_dequeue,
                                                   seed, data_name)
  
    @property
    def name(self):
      return "OrderedDictNumpySource"
  
    @property
    def _output_names(self):
      return tuple(["index"] + list(self._data.keys()))
  
  
  class PandasSource(BaseInMemorySource):
    """A zero-input Transform that produces Series from a DataFrame."""
  
    def __init__(self,
                 dataframe,
                 num_threads=None,
                 enqueue_size=None,
                 batch_size=None,
                 queue_capacity=None,
                 shuffle=False,
                 min_after_dequeue=None,
                 seed=None,
                 data_name="pandas_data"):
      if "index" in dataframe.columns:
        raise ValueError("Column name `index` is reserved.")
      super(PandasSource, self).__init__(dataframe, num_threads, enqueue_size,
                                         batch_size, queue_capacity, shuffle,
                                         min_after_dequeue, seed, data_name)
  
    @property
    def name(self):
      return "PandasSource"
  
    @property
    def _output_names(self):
      return tuple(["index"] + self._data.columns.tolist())
  