| /* |
| * Copyright (C) 2017 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 "lang_id/language-identifier-features.h" |
| |
| #include <utility> |
| #include <vector> |
| |
| #include "common/feature-extractor.h" |
| #include "common/feature-types.h" |
| #include "common/task-context.h" |
| #include "util/hash/hash.h" |
| #include "util/strings/utf8.h" |
| |
| namespace libtextclassifier { |
| namespace nlp_core { |
| namespace lang_id { |
| |
| bool ContinuousBagOfNgramsFunction::Setup(TaskContext *context) { |
| // Parameters in the feature function descriptor. |
| ngram_id_dimension_ = GetIntParameter("id_dim", 10000); |
| ngram_size_ = GetIntParameter("size", 3); |
| |
| counts_.assign(ngram_id_dimension_, 0); |
| return true; |
| } |
| |
| bool ContinuousBagOfNgramsFunction::Init(TaskContext *context) { |
| set_feature_type(new NumericFeatureType(name(), ngram_id_dimension_)); |
| return true; |
| } |
| |
| int ContinuousBagOfNgramsFunction::ComputeNgramCounts( |
| const LightSentence &sentence) const { |
| // Invariant 1: counts_.size() == ngram_id_dimension_. Holds at the end of |
| // the constructor. After that, no method changes counts_.size(). |
| TC_DCHECK_EQ(counts_.size(), ngram_id_dimension_); |
| |
| // Invariant 2: the vector non_zero_count_indices_ is empty. The vector |
| // non_zero_count_indices_ is empty at construction time and gets emptied at |
| // the end of each call to Evaluate(). Hence, this invariant holds at the |
| // beginning of each run of Evaluate(), where the only call to this code takes |
| // place. |
| TC_DCHECK(non_zero_count_indices_.empty()); |
| |
| int total_count = 0; |
| |
| for (int i = 0; i < sentence.num_words(); ++i) { |
| const std::string &word = sentence.word(i); |
| const char *const word_end = word.data() + word.size(); |
| |
| // Set ngram_start at the start of the current token (word). |
| const char *ngram_start = word.data(); |
| |
| // Set ngram_end ngram_size UTF8 characters after ngram_start. Note: each |
| // UTF8 character contains between 1 and 4 bytes. |
| const char *ngram_end = ngram_start; |
| int num_utf8_chars = 0; |
| do { |
| ngram_end += GetNumBytesForNonZeroUTF8Char(ngram_end); |
| num_utf8_chars++; |
| } while ((num_utf8_chars < ngram_size_) && (ngram_end < word_end)); |
| |
| if (num_utf8_chars < ngram_size_) { |
| // Current token is so small, it does not contain a single ngram of |
| // ngram_size UTF8 characters. Not much we can do in this case ... |
| continue; |
| } |
| |
| // At this point, [ngram_start, ngram_end) is the first ngram of ngram_size |
| // UTF8 characters from current token. |
| while (true) { |
| // Compute ngram_id: hash(ngram) % ngram_id_dimension |
| int ngram_id = |
| (Hash32WithDefaultSeed(ngram_start, ngram_end - ngram_start) % |
| ngram_id_dimension_); |
| |
| // Use a reference to the actual count, such that we can both test whether |
| // the count was 0 and increment it without perfoming two lookups. |
| // |
| // Due to the way we compute ngram_id, 0 <= ngram_id < ngram_id_dimension. |
| // Hence, by Invariant 1 (above), the access counts_[ngram_id] is safe. |
| int &ref_to_count_for_ngram = counts_[ngram_id]; |
| if (ref_to_count_for_ngram == 0) { |
| non_zero_count_indices_.push_back(ngram_id); |
| } |
| ref_to_count_for_ngram++; |
| total_count++; |
| if (ngram_end >= word_end) { |
| break; |
| } |
| |
| // Advance both ngram_start and ngram_end by one UTF8 character. This |
| // way, the number of UTF8 characters between them remains constant |
| // (ngram_size). |
| ngram_start += GetNumBytesForNonZeroUTF8Char(ngram_start); |
| ngram_end += GetNumBytesForNonZeroUTF8Char(ngram_end); |
| } |
| } // end of loop over tokens. |
| |
| return total_count; |
| } |
| |
| void ContinuousBagOfNgramsFunction::Evaluate(const WorkspaceSet &workspaces, |
| const LightSentence &sentence, |
| FeatureVector *result) const { |
| // Find the char ngram counts. |
| int total_count = ComputeNgramCounts(sentence); |
| |
| // Populate the feature vector. |
| const float norm = static_cast<float>(total_count); |
| |
| for (int ngram_id : non_zero_count_indices_) { |
| const float weight = counts_[ngram_id] / norm; |
| FloatFeatureValue value(ngram_id, weight); |
| result->add(feature_type(), value.discrete_value); |
| |
| // Clear up counts_, for the next invocation of Evaluate(). |
| counts_[ngram_id] = 0; |
| } |
| |
| // Clear up non_zero_count_indices_, for the next invocation of Evaluate(). |
| non_zero_count_indices_.clear(); |
| } |
| |
| } // namespace lang_id |
| } // namespace nlp_core |
| } // namespace libtextclassifier |