"""Linear Estimators."""
#  Copyright 2015 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.contrib import layers
from tensorflow.contrib.linear_optimizer.python.ops import sdca_ops
from tensorflow.contrib.linear_optimizer.python.ops.sparse_feature_column import SparseFeatureColumn
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops


# TODO(sibyl-vie3Poto, sibyl-Aix6ihai): Add proper testing to this wrapper once the API is
# stable.
class SDCAOptimizer(object):
  """Wrapper class for SDCA optimizer.

  The wrapper is currently meant for use as an optimizer within a tf.learn
  Estimator.

  Example usage:
    real_feature_column = real_valued_column(...)
    sparse_feature_column = sparse_column_with_hash_bucket(...)
    sdca_optimizer = linear.SDCAOptimizer(example_id_column='example_id',
                                          num_loss_partitions=1,
                                          num_table_shards=1,
                                          symmetric_l2_regularization=2.0)
    classifier = tf.contrib.learn.LinearClassifier(
        feature_columns=[real_feature_column, sparse_feature_column],
        weight_column_name=...,
        optimizer=sdca_optimizer)
    classifier.fit(input_fn_train, steps=50)
    classifier.evaluate(input_fn=input_fn_eval)

  Here the expectation is that the input_fn_* functions passed to train and
  evaluate return a pair (dict, label_tensor) where dict has `example_id_column`
  as `key` whose value is a `Tensor` of shape [batch_size] and dtype string.
  num_loss_partitions defines the number of partitions of the global loss
  function and should be set to (#concurrent train ops/per worker) x (#workers).
  Convergence of (global) loss is guaranteed if num_loss_partitions is larger or
  equal to the above product. Larger values for num_loss_partitions lead to
  slower convergence. The recommended value for num_loss_partitions in tf.learn
  (where currently there is one process per worker) is the number of workers
  running the train steps. It defaults to 1 (single machine). num_table_shards
  defines the number of shards for the internal state table, typically set to
  match the number of parameter servers for large data sets.
  """

  def __init__(self,
               example_id_column,
               num_loss_partitions=1,
               num_table_shards=None,
               symmetric_l1_regularization=0.0,
               symmetric_l2_regularization=1.0):
    self._example_id_column = example_id_column
    self._num_loss_partitions = num_loss_partitions
    self._num_table_shards = num_table_shards
    self._symmetric_l1_regularization = symmetric_l1_regularization
    self._symmetric_l2_regularization = symmetric_l2_regularization

  def get_name(self):
    return 'SDCAOptimizer'

  @property
  def example_id_column(self):
    return self._example_id_column

  @property
  def num_loss_partitions(self):
    return self._num_loss_partitions

  @property
  def num_table_shards(self):
    return self._num_table_shards

  @property
  def symmetric_l1_regularization(self):
    return self._symmetric_l1_regularization

  @property
  def symmetric_l2_regularization(self):
    return self._symmetric_l2_regularization

  def get_train_step(self, columns_to_variables,
                     weight_column_name, loss_type, features, targets,
                     global_step):
    """Returns the training operation of an SdcaModel optimizer."""

    def _tensor_to_sparse_feature_column(dense_tensor):
      """Returns SparseFeatureColumn for the input dense_tensor."""
      ignore_value = 0.0
      sparse_indices = array_ops.where(math_ops.not_equal(
          dense_tensor, math_ops.cast(ignore_value, dense_tensor.dtype)))
      sparse_values = array_ops.gather_nd(dense_tensor, sparse_indices)
      # TODO(sibyl-Aix6ihai, sibyl-vie3Poto): Makes this efficient, as now SDCA supports
      # very sparse features with weights and not weights.
      return SparseFeatureColumn(
          array_ops.reshape(
              array_ops.split(
                  value=sparse_indices, num_or_size_splits=2, axis=1)[0], [-1]),
          array_ops.reshape(
              array_ops.split(
                  value=sparse_indices, num_or_size_splits=2, axis=1)[1], [-1]),
          array_ops.reshape(math_ops.to_float(sparse_values), [-1]))

    def _training_examples_and_variables():
      """Returns dictionaries for training examples and variables."""
      batch_size = targets.get_shape()[0]

      # Iterate over all feature columns and create appropriate lists for dense
      # and sparse features as well as dense and sparse weights (variables) for
      # SDCA.
      # TODO(sibyl-vie3Poto): Reshape variables stored as values in column_to_variables
      # dict as 1-dimensional tensors.
      dense_features, sparse_features, sparse_feature_with_values = [], [], []
      dense_feature_weights = []
      sparse_feature_weights, sparse_feature_with_values_weights = [], []
      # pylint: disable=protected-access
      for column in sorted(columns_to_variables.keys(), key=lambda x: x.key):
        transformed_tensor = features[column]
        if isinstance(column, layers.feature_column._RealValuedColumn):
          # A real-valued column corresponds to a dense feature in SDCA. A
          # transformed tensor corresponding to a RealValuedColumn has rank 2
          # (its shape is typically [batch_size, column.dimension]) and so it
          # can be passed to SDCA as is.
          dense_features.append(transformed_tensor)
          # For real valued columns, the variables list contains exactly one
          # element.
          dense_feature_weights.append(columns_to_variables[column][0])
        elif isinstance(column, layers.feature_column._BucketizedColumn):
          # A bucketized column corresponds to a sparse feature in SDCA. The
          # bucketized feature is "sparsified" for SDCA by converting it to a
          # SparseFeatureColumn respresenting the one-hot encoding of the
          # bucketized feature.
          dense_bucket_tensor = layers.input_from_feature_columns(
              {column: transformed_tensor}, [column])
          sparse_feature_column = _tensor_to_sparse_feature_column(
              dense_bucket_tensor)
          sparse_feature_with_values.append(sparse_feature_column)
          # For bucketized columns, the variables list contains exactly one
          # element.
          sparse_feature_with_values_weights.append(
              columns_to_variables[column][0])
        elif isinstance(column, (layers.feature_column._CrossedColumn,
                                 layers.feature_column._SparseColumn)):
          sparse_features.append(
              SparseFeatureColumn(
                  array_ops.reshape(
                      array_ops.split(
                          value=transformed_tensor.indices,
                          num_or_size_splits=2,
                          axis=1)[0], [-1]),
                  array_ops.reshape(transformed_tensor.values, [-1]),
                  None))
          sparse_feature_weights.append(columns_to_variables[column][0])
        elif isinstance(column, layers.feature_column._WeightedSparseColumn):
          id_tensor = column.id_tensor(transformed_tensor)
          weight_tensor = column.weight_tensor(transformed_tensor)
          sparse_feature_with_values.append(
              SparseFeatureColumn(
                  array_ops.reshape(
                      array_ops.split(
                          value=id_tensor.indices, num_or_size_splits=2, axis=1)
                      [0], [-1]),
                  array_ops.reshape(id_tensor.values, [-1]),
                  array_ops.reshape(weight_tensor.values, [-1])))
          sparse_feature_with_values_weights.append(
            columns_to_variables[column][0])
        else:
          raise ValueError('SDCAOptimizer does not support column type %s.' %
                           type(column).__name__)
      # pylint: enable=protected-access

      example_weights = array_ops.reshape(
          features[weight_column_name],
          shape=[-1]) if weight_column_name else array_ops.ones([batch_size])
      example_ids = features[self._example_id_column]
      sparse_feature_with_values.extend(sparse_features)
      sparse_feature_with_values_weights.extend(sparse_feature_weights)
      examples = dict(sparse_features=sparse_feature_with_values,
                      dense_features=dense_features,
                      example_labels=math_ops.to_float(array_ops.reshape(
                          targets, shape=[-1])),
                      example_weights=example_weights,
                      example_ids=example_ids)
      sdca_variables = dict(
          sparse_features_weights=sparse_feature_with_values_weights,
          dense_features_weights=dense_feature_weights)
      return examples, sdca_variables

    training_examples, training_variables = _training_examples_and_variables()
    sdca_model = sdca_ops.SdcaModel(
        examples=training_examples,
        variables=training_variables,
        options=dict(
            symmetric_l1_regularization=self._symmetric_l1_regularization,
            symmetric_l2_regularization=self._symmetric_l2_regularization,
            num_loss_partitions=self._num_loss_partitions,
            num_table_shards=self._num_table_shards,
            loss_type=loss_type))
    train_op = sdca_model.minimize(global_step=global_step)
    return sdca_model, train_op
