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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.tensor_forest."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.tensor_forest.python import tensor_forest
from tensorflow.python.framework import test_util
from tensorflow.python.platform import googletest
class TensorForestTest(test_util.TensorFlowTestCase):
def testForestHParams(self):
hparams = tensor_forest.ForestHParams(
num_classes=2, num_trees=100, max_nodes=1000,
split_after_samples=25, num_features=60).fill()
self.assertEquals(2, hparams.num_classes)
self.assertEquals(3, hparams.num_output_columns)
# 2 * ceil(log_2(1000)) = 20
self.assertEquals(20, hparams.max_depth)
# sqrt(num_features) < 10, so num_splits_to_consider should be 10.
self.assertEquals(10, hparams.num_splits_to_consider)
# Don't have more fertile nodes than max # leaves, which is 500.
self.assertEquals(500, hparams.max_fertile_nodes)
# Default value of valid_leaf_threshold
self.assertEquals(1, hparams.valid_leaf_threshold)
# split_after_samples is larger than 10
self.assertEquals(1, hparams.split_initializations_per_input)
self.assertEquals(0, hparams.base_random_seed)
def testForestHParamsBigTree(self):
hparams = tensor_forest.ForestHParams(
num_classes=2, num_trees=100, max_nodes=1000000,
split_after_samples=25,
num_features=1000).fill()
self.assertEquals(40, hparams.max_depth)
# sqrt(1000) = 31.63...
self.assertEquals(32, hparams.num_splits_to_consider)
# 1000000 / 32 = 31250
self.assertEquals(31250, hparams.max_fertile_nodes)
# floor(31.63 / 25) = 1
self.assertEquals(1, hparams.split_initializations_per_input)
def testTrainingConstructionClassification(self):
input_data = [[-1., 0.], [-1., 2.], # node 1
[1., 0.], [1., -2.]] # node 2
input_labels = [0, 1, 2, 3]
params = tensor_forest.ForestHParams(
num_classes=4, num_features=2, num_trees=10, max_nodes=1000,
split_after_samples=25).fill()
graph_builder = tensor_forest.RandomForestGraphs(params)
graph = graph_builder.training_graph(input_data, input_labels)
self.assertTrue(isinstance(graph, tf.Operation))
def testTrainingConstructionRegression(self):
input_data = [[-1., 0.], [-1., 2.], # node 1
[1., 0.], [1., -2.]] # node 2
input_labels = [0, 1, 2, 3]
params = tensor_forest.ForestHParams(
num_classes=4, num_features=2, num_trees=10, max_nodes=1000,
split_after_samples=25, regression=True).fill()
graph_builder = tensor_forest.RandomForestGraphs(params)
graph = graph_builder.training_graph(input_data, input_labels)
self.assertTrue(isinstance(graph, tf.Operation))
def testInferenceConstruction(self):
input_data = [[-1., 0.], [-1., 2.], # node 1
[1., 0.], [1., -2.]] # node 2
params = tensor_forest.ForestHParams(
num_classes=4, num_features=2, num_trees=10, max_nodes=1000,
split_after_samples=25).fill()
graph_builder = tensor_forest.RandomForestGraphs(params)
graph = graph_builder.inference_graph(input_data)
self.assertTrue(isinstance(graph, tf.Tensor))
def testImpurityConstruction(self):
params = tensor_forest.ForestHParams(
num_classes=4, num_features=2, num_trees=10, max_nodes=1000,
split_after_samples=25).fill()
graph_builder = tensor_forest.RandomForestGraphs(params)
graph = graph_builder.average_impurity()
self.assertTrue(isinstance(graph, tf.Tensor))
if __name__ == '__main__':
googletest.main()
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