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// Copyright 2017 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.
// =============================================================================
#include "tensorflow/contrib/boosted_trees/lib/learner/stochastic/handlers/sparse-quantized-feature-column-handler.h"
namespace tensorflow {
namespace boosted_trees {
namespace learner {
namespace stochastic {
namespace {
// Creates a sparse default right split node without assigning children.
boosted_trees::trees::TreeNode CreateSparseSplitNodeDefaultRight(
int32 feature_column, float threshold) {
boosted_trees::trees::TreeNode split_node;
auto* split = split_node.mutable_sparse_float_binary_split_default_right()
->mutable_split();
split->set_feature_column(feature_column);
split->set_threshold(threshold);
return split_node;
}
// Creates a sparse default left split node without assigning children.
boosted_trees::trees::TreeNode CreateSparseSplitNodeDefaultLeft(
int32 feature_column, float threshold) {
boosted_trees::trees::TreeNode split_node;
auto* split = split_node.mutable_sparse_float_binary_split_default_left()
->mutable_split();
split->set_feature_column(feature_column);
split->set_threshold(threshold);
return split_node;
}
} // namespace
void SparseQuantizedFeatureColumnHandler::AggregateGradientStats(
const std::vector<int32>& example_partition_ids,
const Tensor& example_first_order_gradients,
const Tensor& example_second_order_gradients,
FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>*
gradient_stats_accumulator) const {
// Pass over all rows and aggregate gradient stats for each partition
// and quantized feature bucket.
const int64 num_rows = sparse_indices_.dimension(0);
for (int64 row_idx = 0; row_idx < num_rows; ++row_idx) {
auto example_idx = sparse_indices_(row_idx, 0);
auto partition_id = example_partition_ids[example_idx];
auto feature_id = sparse_quantized_values_(row_idx);
gradient_stats_accumulator->AddStats(
slot_id_, class_id_, partition_id, feature_id,
GradientStats(example_first_order_gradients,
example_second_order_gradients, example_idx));
}
}
void SparseQuantizedFeatureColumnHandler::GenerateFeatureSplitCandidates(
const LearnerConfig& learner_config, const std::vector<int32>& roots,
const std::vector<NodeStats>& root_stats,
const FeatureStatsAccumulator<GradientStats, GradientStatsAccumulator>&
gradient_stats_accumulator,
std::vector<FeatureSplitCandidate>* split_candidates) const {
// Evaluate split candidates for every root as each is a separate
// logical partition over the examples.
// Then for each root, we do both a forward left to right pass and a backward
// right to left pass over the quantized feature buckets accumulating
// gradients on one side and using the root aggregate gradients to get the
// gradients for the other side. Split gains are evaluated for each pass at
// every threshold and the best split is picked.
split_candidates->clear();
split_candidates->reserve(roots.size());
for (size_t root_idx = 0; root_idx < roots.size(); ++root_idx) {
// Get partition Id and root node stats.
const int32 partition_id = roots[root_idx];
const NodeStats& root_node_stats = root_stats[root_idx];
// Forward pass with right default direction.
GradientStats left_gradient_stats;
GradientStats right_gradient_stats(root_node_stats.gradient_stats);
FeatureSplitCandidate best_split_candidate(
root_node_stats.weight_contribution.size());
best_split_candidate.split_stats.gain =
std::numeric_limits<float>::lowest();
for (int bucket_id = 0; bucket_id < sparse_quantiles_.size(); ++bucket_id) {
// Get gradient stats.
auto gradient_stats = gradient_stats_accumulator.GetStats(
slot_id_, class_id_, partition_id, bucket_id);
if (gradient_stats.IsZero()) {
continue;
}
// Update gradient stats.
left_gradient_stats += gradient_stats;
right_gradient_stats =
root_node_stats.gradient_stats - left_gradient_stats;
// Get node stats
NodeStats left_node_stats(learner_config, left_gradient_stats);
NodeStats right_node_stats(learner_config, right_gradient_stats);
// Generate split candidate.
const float threshold = sparse_quantiles_(bucket_id);
FeatureSplitCandidate split_candidate(
slot_id_,
CreateSparseSplitNodeDefaultRight(sparse_feature_column_, threshold),
SplitStats(learner_config, root_node_stats, left_node_stats,
right_node_stats));
if (split_candidate.split_stats.gain >
best_split_candidate.split_stats.gain) {
best_split_candidate = std::move(split_candidate);
}
}
// Determine if we need a backward pass by checking if the residual gradient
// after forward aggregation is almost the same as the aggregated gradient.
// for the current root. This helps avoid unnecessary computation as well
// as consistency due to floating point precision.
if (!right_gradient_stats.IsAlmostZero()) {
// Backward pass with left default direction.
right_gradient_stats = GradientStats();
left_gradient_stats = root_node_stats.gradient_stats;
for (int bucket_id = sparse_quantiles_.size() - 1; bucket_id > 0;
--bucket_id) {
// Get gradient stats.
auto gradient_stats = gradient_stats_accumulator.GetStats(
slot_id_, class_id_, partition_id, bucket_id);
if (gradient_stats.IsZero()) {
continue;
}
// Update gradient stats.
right_gradient_stats += gradient_stats;
left_gradient_stats = root_node_stats.gradient_stats - gradient_stats;
// Get node stats
NodeStats left_node_stats(learner_config, left_gradient_stats);
NodeStats right_node_stats(learner_config, right_gradient_stats);
// Generate split candidate.
const float threshold = sparse_quantiles_(bucket_id - 1);
FeatureSplitCandidate split_candidate(
slot_id_,
CreateSparseSplitNodeDefaultLeft(sparse_feature_column_, threshold),
SplitStats(learner_config, root_node_stats, left_node_stats,
right_node_stats));
if (split_candidate.split_stats.gain >
best_split_candidate.split_stats.gain) {
best_split_candidate = std::move(split_candidate);
}
}
}
// Add best candidate for partition.
split_candidates->push_back(std::move(best_split_candidate));
}
}
} // namespace stochastic
} // namespace learner
} // namespace boosted_trees
} // namespace tensorflow
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