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/*
* Copyright (C) 2018 The Android Open Source Project
*
* 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 "actions/feature-processor.h"
namespace libtextclassifier3 {
namespace {
TokenFeatureExtractorOptions BuildTokenFeatureExtractorOptions(
const ActionsTokenFeatureProcessorOptions* const options) {
TokenFeatureExtractorOptions extractor_options;
extractor_options.num_buckets = options->num_buckets();
if (options->chargram_orders() != nullptr) {
for (int order : *options->chargram_orders()) {
extractor_options.chargram_orders.push_back(order);
}
}
extractor_options.max_word_length = options->max_token_length();
extractor_options.extract_case_feature = options->extract_case_feature();
extractor_options.unicode_aware_features = options->unicode_aware_features();
extractor_options.extract_selection_mask_feature = false;
if (options->regexp_features() != nullptr) {
for (const auto regexp_feature : *options->regexp_features()) {
extractor_options.regexp_features.push_back(regexp_feature->str());
}
}
extractor_options.remap_digits = options->remap_digits();
extractor_options.lowercase_tokens = options->lowercase_tokens();
return extractor_options;
}
} // namespace
std::unique_ptr<Tokenizer> CreateTokenizer(
const ActionsTokenizerOptions* options, const UniLib* unilib) {
std::vector<const TokenizationCodepointRange*> codepoint_config;
if (options->tokenization_codepoint_config() != nullptr) {
codepoint_config.insert(codepoint_config.end(),
options->tokenization_codepoint_config()->begin(),
options->tokenization_codepoint_config()->end());
}
std::vector<const CodepointRange*> internal_codepoint_config;
if (options->internal_tokenizer_codepoint_ranges() != nullptr) {
internal_codepoint_config.insert(
internal_codepoint_config.end(),
options->internal_tokenizer_codepoint_ranges()->begin(),
options->internal_tokenizer_codepoint_ranges()->end());
}
const bool tokenize_on_script_change =
options->tokenization_codepoint_config() != nullptr &&
options->tokenize_on_script_change();
return std::unique_ptr<Tokenizer>(new Tokenizer(
options->type(), unilib, codepoint_config, internal_codepoint_config,
tokenize_on_script_change, options->icu_preserve_whitespace_tokens()));
}
ActionsFeatureProcessor::ActionsFeatureProcessor(
const ActionsTokenFeatureProcessorOptions* options, const UniLib* unilib)
: options_(options),
tokenizer_(CreateTokenizer(options->tokenizer_options(), unilib)),
token_feature_extractor_(BuildTokenFeatureExtractorOptions(options),
unilib) {}
int ActionsFeatureProcessor::GetTokenEmbeddingSize() const {
return options_->embedding_size() +
token_feature_extractor_.DenseFeaturesCount();
}
bool ActionsFeatureProcessor::AppendFeatures(
const std::vector<int>& sparse_features,
const std::vector<float>& dense_features,
const EmbeddingExecutor* embedding_executor,
std::vector<float>* output_features) const {
// Embed the sparse features, appending them directly to the output.
const int embedding_size = options_->embedding_size();
output_features->resize(output_features->size() + embedding_size);
float* output_features_end =
output_features->data() + output_features->size();
if (!embedding_executor->AddEmbedding(
TensorView<int>(sparse_features.data(),
{static_cast<int>(sparse_features.size())}),
/*dest=*/output_features_end - embedding_size,
/*dest_size=*/embedding_size)) {
TC3_LOG(ERROR) << "Could not embed token's sparse features.";
return false;
}
// Append the dense features to the output.
output_features->insert(output_features->end(), dense_features.begin(),
dense_features.end());
return true;
}
bool ActionsFeatureProcessor::AppendTokenFeatures(
const Token& token, const EmbeddingExecutor* embedding_executor,
std::vector<float>* output_features) const {
// Extract the sparse and dense features.
std::vector<int> sparse_features;
std::vector<float> dense_features;
if (!token_feature_extractor_.Extract(token, /*(unused) is_in_span=*/false,
&sparse_features, &dense_features)) {
TC3_LOG(ERROR) << "Could not extract token's features.";
return false;
}
return AppendFeatures(sparse_features, dense_features, embedding_executor,
output_features);
}
bool ActionsFeatureProcessor::AppendTokenFeatures(
const std::vector<Token>& tokens,
const EmbeddingExecutor* embedding_executor,
std::vector<float>* output_features) const {
for (const Token& token : tokens) {
if (!AppendTokenFeatures(token, embedding_executor, output_features)) {
return false;
}
}
return true;
}
} // namespace libtextclassifier3