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# 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.count_extremely_random_stats."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.tensor_forest.python.ops import training_ops
from tensorflow.python.framework import test_util
from tensorflow.python.platform import googletest
class CountExtremelyRandomStatsClassificationTest(test_util.TensorFlowTestCase):
def setUp(self):
self.input_data = [[-1., 0.], [-1., 2.], # node 1
[1., 0.], [1., -2.]] # node 2
self.input_labels = [0, 1, 2, 3]
self.tree = [[1, 0], [-1, 0], [-1, 0]]
self.tree_thresholds = [0., 0., 0.]
self.node_map = [-1, 0, -1]
self.split_features = [[1], [-1]]
self.split_thresholds = [[1.], [0.]]
self.ops = training_ops.Load()
def testSimple(self):
with self.test_session():
(pcw_node_sums, _, pcw_splits_indices, pcw_splits_sums, _,
pcw_totals_indices, pcw_totals_sums, _, leaves) = (
self.ops.count_extremely_random_stats(
self.input_data, self.input_labels, self.tree,
self.tree_thresholds, self.node_map,
self.split_features, self.split_thresholds, num_classes=5,
regression=False))
self.assertAllEqual(
[[4., 1., 1., 1., 1.], [2., 1., 1., 0., 0.], [2., 0., 0., 1., 1.]],
pcw_node_sums.eval())
self.assertAllEqual([[0, 0, 0], [0, 0, 1]], pcw_splits_indices.eval())
self.assertAllEqual([1., 1.], pcw_splits_sums.eval())
self.assertAllEqual([[0, 2], [0, 0], [0, 1]], pcw_totals_indices.eval())
self.assertAllEqual([1., 2., 1.], pcw_totals_sums.eval())
self.assertAllEqual([1, 1, 2, 2], leaves.eval())
def testThreaded(self):
with self.test_session(
config=tf.ConfigProto(intra_op_parallelism_threads=2)):
(pcw_node_sums, _, pcw_splits_indices, pcw_splits_sums, _,
pcw_totals_indices, pcw_totals_sums, _, leaves) = (
self.ops.count_extremely_random_stats(
self.input_data, self.input_labels, self.tree,
self.tree_thresholds, self.node_map, self.split_features,
self.split_thresholds, num_classes=5, regression=False))
self.assertAllEqual([[4., 1., 1., 1., 1.], [2., 1., 1., 0., 0.],
[2., 0., 0., 1., 1.]],
pcw_node_sums.eval())
self.assertAllEqual([[0, 0, 0], [0, 0, 1]], pcw_splits_indices.eval())
self.assertAllEqual([1., 1.], pcw_splits_sums.eval())
self.assertAllEqual([[0, 2], [0, 0], [0, 1]], pcw_totals_indices.eval())
self.assertAllEqual([1., 2., 1.], pcw_totals_sums.eval())
self.assertAllEqual([1, 1, 2, 2], leaves.eval())
def testNoAccumulators(self):
with self.test_session():
(pcw_node_sums, _, pcw_splits_indices, pcw_splits_sums, _,
pcw_totals_indices, pcw_totals_sums, _, leaves) = (
self.ops.count_extremely_random_stats(
self.input_data, self.input_labels, self.tree,
self.tree_thresholds, [-1] * 3,
self.split_features, self.split_thresholds, num_classes=5,
regression=False))
self.assertAllEqual([[4., 1., 1., 1., 1.], [2., 1., 1., 0., 0.],
[2., 0., 0., 1., 1.]],
pcw_node_sums.eval())
self.assertEquals((0, 3), pcw_splits_indices.eval().shape)
self.assertAllEqual([], pcw_splits_sums.eval())
self.assertEquals((0, 2), pcw_totals_indices.eval().shape)
self.assertAllEqual([], pcw_totals_sums.eval())
self.assertAllEqual([1, 1, 2, 2], leaves.eval())
def testBadInput(self):
del self.node_map[-1]
with self.test_session():
with self.assertRaisesOpError(
'Number of nodes should be the same in '
'tree, tree_thresholds, and node_to_accumulator'):
pcw_node, _, _, _, _, _, _, _, _ = (
self.ops.count_extremely_random_stats(
self.input_data, self.input_labels, self.tree,
self.tree_thresholds, self.node_map,
self.split_features, self.split_thresholds, num_classes=5,
regression=False))
self.assertAllEqual([], pcw_node.eval())
class CountExtremelyRandomStatsRegressionTest(test_util.TensorFlowTestCase):
def setUp(self):
self.input_data = [[-1., 0.], [-1., 2.], # node 1
[1., 0.], [1., -2.]] # node 2
self.input_labels = [[3.], [6.], [2.], [3.]]
self.tree = [[1, 0], [-1, 0], [-1, 0]]
self.tree_thresholds = [0., 0., 0.]
self.node_map = [-1, 0, -1]
self.split_features = [[1], [-1]]
self.split_thresholds = [[1.], [0.]]
self.ops = training_ops.Load()
def testSimple(self):
with self.test_session():
(pcw_node_sums, pcw_node_squares, pcw_splits_indices, pcw_splits_sums,
pcw_splits_squares, pcw_totals_indices,
pcw_totals_sums, pcw_totals_squares, leaves) = (
self.ops.count_extremely_random_stats(
self.input_data, self.input_labels, self.tree,
self.tree_thresholds, self.node_map,
self.split_features, self.split_thresholds, num_classes=2,
regression=True))
self.assertAllEqual(
[[4., 14.], [2., 9.], [2., 5.]], pcw_node_sums.eval())
self.assertAllEqual(
[[4., 58.], [2., 45.], [2., 13.]], pcw_node_squares.eval())
self.assertAllEqual([[0, 0]], pcw_splits_indices.eval())
self.assertAllEqual([[1., 3.]], pcw_splits_sums.eval())
self.assertAllEqual([[1., 9.]], pcw_splits_squares.eval())
self.assertAllEqual([[0]], pcw_totals_indices.eval())
self.assertAllEqual([[2., 9.]], pcw_totals_sums.eval())
self.assertAllEqual([[2., 45.]], pcw_totals_squares.eval())
self.assertAllEqual([1, 1, 2, 2], leaves.eval())
if __name__ == '__main__':
googletest.main()
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