# 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.
# ==============================================================================
"""Tests for tf.contrib.tensor_forest.ops.sample_inputs_op."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.contrib.tensor_forest.python.ops import data_ops

from tensorflow.contrib.tensor_forest.python.ops import tensor_forest_ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest


class SampleInputsTest(test_util.TensorFlowTestCase):

  def setUp(self):
    self.input_data = [[-1., 10.], [-10., 2.],  # node 1
                       [20., 50.], [1., -2.]]  # node 2
    self.node_map = [-1, 0, 1]
    self.leaves = [1, 1, 2, 2]
    self.split_features = [[-1, -1, -1], [1, 0, -1], [-1, -1, -1]]
    self.split_thresholds = [[0., 0., 0.], [5., -2., 0.], [0., 0., 0.]]

    spec_proto = data_ops.TensorForestDataSpec()
    f1 = spec_proto.dense.add()
    f1.name = 'f1'
    f1.original_type = data_ops.DATA_FLOAT
    f1.size = 1

    f2 = spec_proto.dense.add()
    f2.name = 'f2'
    f2.original_type = data_ops.DATA_FLOAT
    f2.size = 1
    spec_proto.dense_features_size = 2
    self.data_spec = spec_proto.SerializeToString()

  def testSimple(self):
    with self.test_session():
      variables.global_variables_initializer().run()
      (indices, feature_updates,
       threshold_updates) = (tensor_forest_ops.sample_inputs(
           self.input_data, [], [], [], [],
           self.node_map,
           self.leaves,
           self.split_features,
           self.split_thresholds,
           split_initializations_per_input=1,
           input_spec=self.data_spec,
           split_sampling_random_seed=2))
      self.assertAllEqual([1, 0], indices.eval())
      self.assertAllEqual([[1, 0, 1], [1, 1, -1]], feature_updates.eval())
      self.assertAllEqual([[5., -2., 50.], [10., 2., 0.]],
                          threshold_updates.eval())

  def testSparse(self):
    sparse_shape = [4, 10]
    sparse_indices = [[0, 0], [0, 4], [0, 9],
                      [1, 0], [1, 7],
                      [2, 0],
                      [3, 1], [3, 4]]
    sparse_values = [3.0, -1.0, 0.5,
                     1.5, 6.0,
                     -2.0,
                     -0.5, 2.0]

    spec_proto = data_ops.TensorForestDataSpec()
    f1 = spec_proto.sparse.add()
    f1.name = 'f1'
    f1.original_type = data_ops.DATA_FLOAT
    f1.size = -1

    spec_proto.dense_features_size = 0
    data_spec = spec_proto.SerializeToString()

    with self.test_session():
      variables.global_variables_initializer().run()
      (indices, feature_updates,
       threshold_updates) = (tensor_forest_ops.sample_inputs(
           [],
           sparse_indices,
           sparse_values,
           sparse_shape, [],
           self.node_map,
           self.leaves,
           self.split_features,
           self.split_thresholds,
           input_spec=data_spec,
           split_initializations_per_input=1,
           split_sampling_random_seed=3))
      self.assertAllEqual([1, 0], indices.eval())
      self.assertAllEqual([[1, 0, 0], [4, 0, -1]], feature_updates.eval())

      self.assertAllEqual([[5., -2., -2.], [-1., 1.5, 0.]],
                          threshold_updates.eval())

  def testWeights(self):
    with self.test_session():
      variables.global_variables_initializer().run()
      (indices, feature_updates,
       threshold_updates) = (tensor_forest_ops.sample_inputs(
           self.input_data, [], [], [], [0.5, 0.1, 0.8, 0.7],
           self.node_map,
           self.leaves,
           self.split_features,
           self.split_thresholds,
           input_spec=self.data_spec,
           split_initializations_per_input=1,
           split_sampling_random_seed=3))
      self.assertAllEqual([1, 0], indices.eval())
      self.assertAllEqual([[1, 0, 0], [-1, -1, -1]], feature_updates.eval())
      self.assertAllEqual([[5., -2., 20.], [0., 0., 0.]],
                          threshold_updates.eval())

  def testNoAccumulators(self):
    with self.test_session():
      variables.global_variables_initializer().run()
      (indices, feature_updates,
       threshold_updates) = (tensor_forest_ops.sample_inputs(
           self.input_data, [], [], [], [], [-1] * 3,
           self.leaves,
           self.split_features,
           self.split_thresholds,
           input_spec=self.data_spec,
           split_initializations_per_input=1,
           split_sampling_random_seed=3))
      self.assertAllEqual([], indices.eval())
      self.assertAllEqual((0, 3), feature_updates.eval().shape)
      self.assertAllEqual((0, 3), threshold_updates.eval().shape)

  def testBadInput(self):
    del self.split_features[1]
    with self.test_session():
      variables.global_variables_initializer().run()
      with self.assertRaisesOpError(
          'split_features and split_thresholds should be the same shape.'):
        indices, _, _ = tensor_forest_ops.sample_inputs(
            self.input_data, [], [], [], [],
            self.node_map,
            self.leaves,
            self.split_features,
            self.split_thresholds,
            input_spec=self.data_spec,
            split_initializations_per_input=1,
            split_sampling_random_seed=3)
        self.assertAllEqual([], indices.eval())


if __name__ == '__main__':
  googletest.main()
