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// Copyright 2016 Google Inc. 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.
// =============================================================================
#include "tensorflow/contrib/tensor_forest/core/ops/tree_utils.h"
#include "testing/base/public/gunit.h"
#include "tensorflow/core/framework/tensor_testutil.h"
namespace tensorflow {
namespace tensorforest {
TEST(TestSum, Basic) {
Tensor two_dim = test::AsTensor<float>({1, 3, 5, 7, 2, 4, 6, 8}, {2, 4});
EXPECT_EQ(Sum<float>(two_dim), 36.0);
Tensor multi_dim_single_value = test::AsTensor<int>({42}, {1, 1, 1, 1});
EXPECT_EQ(Sum<int>(multi_dim_single_value), 42);
Tensor nothing = test::AsTensor<float>({}, {0});
EXPECT_EQ(Sum<float>(nothing), 0.0);
}
TEST(TestWeightedGiniImpurity, Basic) {
Tensor vals = test::AsTensor<float>({2, 2, 2}, {3});
EXPECT_EQ(WeightedGiniImpurity(vals.unaligned_flat<float>()), 6);
Tensor zero = test::AsTensor<float>({0}, {1});
EXPECT_EQ(WeightedGiniImpurity(zero.unaligned_flat<float>()), 0);
}
TEST(TestWeightedVariance, Basic) {
// Lets say values were: (2, 2), (3, 2), (4, 2)
Tensor sums = test::AsTensor<float>({9, 6}, {2});
Tensor squares = test::AsTensor<float>({29, 12}, {2});
EXPECT_FLOAT_EQ(WeightedVariance(sums.unaligned_flat<float>(),
squares.unaligned_flat<float>(), 3), 2.0);
Tensor zero = test::AsTensor<float>({0}, {1});
EXPECT_FLOAT_EQ(WeightedVariance(zero.unaligned_flat<float>(),
zero.unaligned_flat<float>(), 1), 0);
}
TEST(TestInitialize, Basic) {
Tensor t = test::AsTensor<float>({0, 0, 0, 0, 0, 0, 0, 0}, {4, 2});
Initialize<float>(t, 42.0);
const auto vals = t.tensor<float, 2>();
EXPECT_FLOAT_EQ(vals(0, 0), 42);
EXPECT_FLOAT_EQ(vals(1, 1), 42);
EXPECT_FLOAT_EQ(vals(3, 0), 42);
Tensor nothing = test::AsTensor<float>({}, {0});
// Just make sure this runs.
Initialize<float>(nothing, -1.0);
}
TEST(DecideNode, Basic) {
// Even though we only want a 1-D point, make sure to test a 2-D tensor
// because that's usually what comes off a Slice().
Tensor point = test::AsTensor<float>({10, 10, 10, 10}, {1, 4});
EXPECT_TRUE(DecideNode(point, 0, 9));
EXPECT_TRUE(DecideNode(point, 1, 0));
EXPECT_TRUE(DecideNode(point, 2, -3));
EXPECT_FALSE(DecideNode(point, 3, 11));
}
TEST(IsAllInitialized, Basic) {
// Even though we only want a 1-D feature set, make sure to test a 2-D tensor
// because that's usually what comes off a Slice().
Tensor features = test::AsTensor<int32>({10, 2, -1}, {1, 3});
EXPECT_FALSE(IsAllInitialized(features));
features = test::AsTensor<int32>({10, 2, 0}, {1, 3});
EXPECT_TRUE(IsAllInitialized(features));
}
TEST(BestFeatureClassification, Basic) {
const int32 num_accumulators = 4;
const int32 num_splits = 3;
const int32 num_classes = 4;
Tensor totals = test::AsTensor<float>({1, 5, 6, 7,
0, 0, 0, 0,
30, 10, 10, 10, // this one
-1, -1, -1, -1},
{num_accumulators, num_classes});
Tensor splits = test::AsTensor<float>(
{1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
30, 10, 10, 10, 10, 0, 0, 10, 19, 5, 6, 8, // this one
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{num_accumulators, num_splits, num_classes});
EXPECT_EQ(BestFeatureClassification(totals, splits, 2), 1);
}
TEST(BestFeatureClassification, NoWinner) {
const int32 num_accumulators = 4;
const int32 num_splits = 3;
const int32 num_classes = 4;
// When counts are all the same, the most reasonable thing to do is pick 0.
Tensor totals = test::AsTensor<float>({1, 5, 6, 7,
0, 0, 0, 0,
18, 6, 6, 6, // this one
-1, -1, -1, -1},
{num_accumulators, num_classes});
Tensor splits = test::AsTensor<float>(
{1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
9, 3, 3, 3, 9, 3, 3, 3, 9, 3, 3, 3, // this one
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{num_accumulators, num_splits, num_classes});
EXPECT_EQ(BestFeatureClassification(totals, splits, 2), 0);
}
TEST(BestFeatureRegression, Basic) {
const int32 num_accumulators = 4;
const int32 num_splits = 3;
const int32 num_classes = 4;
Tensor total_sums = test::AsTensor<float>(
{1, 5, 6, 7,
0, 0, 0, 0,
10, 8, 6, 9, // this one
-1, -1, -1, -1},
{num_accumulators, num_classes});
Tensor total_squares = test::AsTensor<float>(
{1, 5, 6, 7,
0, 0, 0, 0,
100, 50, 40, 45, // this one
-1, -1, -1, -1},
{num_accumulators, num_classes});
Tensor split_sums = test::AsTensor<float>(
{1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
10, 8, 6, 9, 9, 8, 5, 9, 0, 0, 0, 0, // this one
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{num_accumulators, num_splits, num_classes});
// lower the variance by lowering one of the squares just a little.
Tensor split_squares = test::AsTensor<float>(
{1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
100, 50, 40, 45, 100, 50, 40, 43, 0, 0, 0, 0, // this one
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{num_accumulators, num_splits, num_classes});
EXPECT_EQ(BestFeatureRegression(total_sums, total_squares, split_sums,
split_squares, 2), 1);
}
TEST(BestFeatureRegression, NoWinner) {
const int32 num_accumulators = 4;
const int32 num_splits = 3;
const int32 num_classes = 4;
// when counts are all the same, the most reasonable thing to do is pick 0.
Tensor total_sums = test::AsTensor<float>(
{1, 5, 6, 7,
0, 0, 0, 0,
10, 8, 6, 9, // this one
-1, -1, -1, -1},
{num_accumulators, num_classes});
Tensor total_squares = test::AsTensor<float>(
{1, 5, 6, 7,
0, 0, 0, 0,
100, 50, 40, 45, // this one
-1, -1, -1, -1},
{num_accumulators, num_classes});
Tensor split_sums = test::AsTensor<float>(
{1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
10, 8, 6, 9, 10, 8, 6, 9, 10, 8, 6, 9, // this one
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{num_accumulators, num_splits, num_classes});
Tensor split_squares = test::AsTensor<float>(
{1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
100, 50, 40, 45, 100, 50, 40, 45, 100, 50, 40, 45, // this one
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
{num_accumulators, num_splits, num_classes});
EXPECT_EQ(BestFeatureRegression(total_sums, total_squares, split_sums,
split_squares, 2), 0);
}
} // namespace tensorforest
} // namespace tensorflow
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