sampling-lm-estimate.cc
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// sampling-lm-estimate.cc
// Copyright 2017 Johns Hopkins University (author: Daniel Povey)
// See ../COPYING for clarification regarding multiple authors
//
// 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABILITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include <iomanip>
#include <numeric>
#include "rnnlm/sampling-lm-estimate.h"
namespace kaldi {
namespace rnnlm {
void SamplingLmEstimatorOptions::Check() const {
KALDI_ASSERT(vocab_size > 2);
KALDI_ASSERT(bos_symbol > 0 && bos_symbol < vocab_size);
KALDI_ASSERT(eos_symbol > 0 && eos_symbol < vocab_size);
KALDI_ASSERT(eos_symbol != bos_symbol);
KALDI_ASSERT(ngram_order >= 1 &&
discounting_constant > 0 && discounting_constant <= 1.0 &&
unigram_factor > 0.0 && backoff_factor > 0.0 &&
unigram_factor > backoff_factor &&
bos_factor > 0.0 && bos_factor <= unigram_factor);
KALDI_ASSERT(unigram_power > 0.2 && unigram_power <= 1.0);
}
SamplingLmEstimator::SamplingLmEstimator(
const SamplingLmEstimatorOptions &config):
config_(config) {
config_.Check();
history_states_.resize(config.ngram_order);
}
void SamplingLmEstimator::ProcessLine(BaseFloat corpus_weight,
const std::vector<int32> &sentence) {
KALDI_ASSERT(corpus_weight >= 0.0);
int32 ngram_order = config_.ngram_order,
sentence_length = sentence.size(),
vocab_size = config_.vocab_size;
std::vector<int32> history;
history.push_back(config_.bos_symbol);
int32 i;
for (i = 0; i < sentence_length && i + 1 < ngram_order; i++) {
int32 this_word = sentence[i];
// note: 0 is reserved for <eps>.
KALDI_ASSERT(this_word > 0 && this_word < vocab_size);
AddCount(history, this_word, corpus_weight);
history.push_back(this_word);
}
for (; i < sentence_length; i++) {
history.erase(history.begin());
int32 this_word = sentence[i];
AddCount(history, this_word, corpus_weight);
history.push_back(this_word);
}
if (history.size() >= static_cast<size_t>(ngram_order))
history.erase(history.begin());
AddCount(history, config_.eos_symbol, corpus_weight);
// TODO: remove the following.
KALDI_ASSERT(history.size() == std::min(ngram_order - 1,
sentence_length + 1));
}
void SamplingLmEstimator::Process(std::istream &is) {
int32 num_lines = 0;
std::vector<int32> words;
std::string line;
while (getline(is, line)) {
num_lines++;
std::istringstream line_is(line);
BaseFloat weight;
line_is >> weight;
words.clear();
int32 word;
while (line_is >> word) {
words.push_back(word);
}
if (!line_is.eof()) {
KALDI_ERR << "Could not interpret input: " << line;
}
this->ProcessLine(weight, words);
}
KALDI_LOG << "Processed " << num_lines << " lines of input.";
}
void SamplingLmEstimator::HistoryState::AddCount(int32 word,
BaseFloat corpus_weight) {
new_counts.push_back(std::pair<int32, BaseFloat>(word, corpus_weight));
if (new_counts.size() == new_counts.capacity() &&
new_counts.size() >= counts.size()) {
bool release_memory = false;
ProcessNewCounts(release_memory);
}
}
void SamplingLmEstimator::HistoryState::ComputeTotalCount() {
double tmp_total_count = 0.0;
std::vector<Count>::const_iterator iter = counts.begin(),
end = counts.end();
for (; iter != end; ++iter)
tmp_total_count += iter->count;
total_count = tmp_total_count;
}
// static
void SamplingLmEstimator::SortAndUniqCounts(std::vector<Count> *counts) {
// sort the vector<Count> in counts; they get sorted on 'word'
// so that Counts with the same word are next to each other.
std::sort(counts->begin(), counts->end());
{
// This block merges counts in '*counts' that have the same word.
// It is adapted from MergePairVectorSumming(). This code is quite
// optimized and not easy to read but MergePairVectorSumming() is well
// tested and the changes in the code are small.
std::vector<Count>::iterator out_iter = counts->begin(),
in_iter = counts->begin(), end_iter = counts->end();
// special case: while there is nothing to be changed, skip over
// initial input (avoids unnecessary copying).
while (in_iter + 1 < end_iter && in_iter[0].word != in_iter[1].word) {
in_iter++;
out_iter++;
}
while (in_iter < end_iter) {
// We reach this point only at the first element of
// each stretch of identical words
*out_iter = *in_iter;
++in_iter;
while (in_iter < end_iter && in_iter->word == out_iter->word) {
if (in_iter->highest_count > out_iter->highest_count)
out_iter->highest_count = in_iter->highest_count;
out_iter->count += in_iter->count;
++in_iter;
}
out_iter++;
}
counts->erase(out_iter, end_iter);
}
}
// see header for what this does.
void SamplingLmEstimator::HistoryState::ProcessNewCounts(bool release_memory) {
if (!new_counts.empty()) {
// Merge all counts in 'new_counts' into 'counts'.
// We could do what we're doing with 'merged_counts' below, with 'counts'
// directly, but that might increase the memory held in this HistoryState so
// we use a temporary vector in some cases.
std::vector<Count> tmp;
size_t orig_counts_size = counts.size(),
merge_size = orig_counts_size + new_counts.size();
// 'merge_location' is the vector we use to merge and sort the Counts, it
// either points to 'tmp' or to 'this->counts'.
std::vector<Count> *merge_location;
if (merge_size > counts.capacity()) {
merge_location = &tmp;
tmp.reserve(merge_size);
tmp.insert(tmp.end(), counts.begin(), counts.end());
} else {
merge_location = &counts;
}
{ // this block converts each member of 'new_counts' into a single Count,
// appending it to the vector pointed to by 'merge_location'.
merge_location->resize(merge_size);
std::vector<std::pair<int32, BaseFloat> >::const_iterator
in_iter = new_counts.begin();
std::vector<Count>::iterator out_iter =
merge_location->begin() + orig_counts_size,
out_end = merge_location->end();
for (; out_iter != out_end; ++in_iter, ++out_iter) {
int32 word = in_iter->first;
BaseFloat count = in_iter->second;
out_iter->word = word;
out_iter->highest_count = count;
out_iter->count = count;
}
}
SortAndUniqCounts(merge_location);
if (merge_location != &counts) // copy to 'counts' if we were using a temporary.
counts = *merge_location;
}
if (release_memory) {
// shallow swapping with a new temporary is a trick to release memory from a
// std::vector.
std::vector<std::pair<int32, BaseFloat> > new_counts_temp;
new_counts.swap(new_counts_temp);
} else {
new_counts.clear();
}
}
void SamplingLmEstimator::ComputeRawCountsForOrder(int32 o) {
KALDI_ASSERT(o >= 1 && o < config_.ngram_order);
// We first make a map from the backed-off history to a list of the
// history-states that back off to it. This will help us to do the backoff in
// a relatively memory-efficient way.
unordered_map<std::vector<int32>,
std::vector<const HistoryState*>, VectorHasher<int32> > lower_to_higher_order;
// Normally we'd iterate over history_states_[o-1] to access counts of order
// o, but we are iterating over history-states for the order one higher than
// o.
unordered_map<std::vector<int32>,
HistoryState*, VectorHasher<int32> >::iterator
iter = history_states_[o].begin(),
end = history_states_[o].end();
for (; iter != end; ++iter) {
const std::vector<int32> &history = iter->first;
// remove the left-most (most distant) word from the history to back off.
std::vector<int32> backed_off_history(history.begin() + 1,
history.end());
const HistoryState *higher_order_state = iter->second;
lower_to_higher_order[backed_off_history].push_back(higher_order_state);
}
unordered_map<std::vector<int32>, std::vector<const HistoryState*>,
VectorHasher<int32> >::const_iterator
state_list_iter = lower_to_higher_order.begin(),
state_list_end= lower_to_higher_order.end();
for (; state_list_iter != state_list_end; ++state_list_iter) {
const std::vector<int32> &history = state_list_iter->first;
const std::vector<const HistoryState*> &higher_order_states =
state_list_iter->second;
HistoryState *this_state = GetHistoryState(history, true);
std::vector<Count> merged_counts;
size_t merged_counts_size = 0;
for (size_t i = 0; i < higher_order_states.size(); i++)
merged_counts_size += higher_order_states[i]->counts.size();
merged_counts.reserve(merged_counts_size);
for (size_t i = 0; i < higher_order_states.size(); i++)
merged_counts.insert(merged_counts.end(),
higher_order_states[i]->counts.begin(),
higher_order_states[i]->counts.end());
SortAndUniqCounts(&merged_counts);
this_state->counts = merged_counts;
}
}
SamplingLmEstimator::HistoryState*
SamplingLmEstimator::GetHistoryState(const std::vector<int32> &history,
bool add_if_absent) {
KALDI_ASSERT(static_cast<int32>(history.size()) < config_.ngram_order);
// 'values' is a reference to a pointer to a HistoryState.
// If 'history' did not previously exist as a key in the map, this will
// be NULL. This is a feature of the stl that's valid at least as of C++11,
// whereby POD types that are the values in maps will be set to zero
// if newly created.
/// https://stackoverflow.com/questions/8943261/stdunordered-map-initialization
// or search for unordered_map value-initialized
HistoryState *&value = history_states_[history.size()][history];
if (value == NULL) {
if (add_if_absent) {
value = new HistoryState();
} else {
KALDI_ERR << "Expected history-state to exist (code error).";
}
}
return value;
}
void SamplingLmEstimator::FinalizeRawCountsForOrder(int32 o) {
KALDI_ASSERT(o >= 1 && o <= config_.ngram_order &&
static_cast<int32>(history_states_.size()) ==
config_.ngram_order);
unordered_map<std::vector<int32>,
HistoryState*, VectorHasher<int32> >::iterator
iter = history_states_[o - 1].begin(),
end = history_states_[o - 1].end();
for (; iter != end; ++iter) {
if (o == config_.ngram_order) {
bool release_memory = true;
iter->second->ProcessNewCounts(release_memory);
}
iter->second->ComputeTotalCount();
}
}
void SamplingLmEstimator::Estimate(bool will_write_arpa) {
for (int32 o = config_.ngram_order; o >= 1; o--) {
if (o < config_.ngram_order)
ComputeRawCountsForOrder(o);
FinalizeRawCountsForOrder(o);
}
// Now we have the raw counts of orders but we have not yet done backoff or
// pruning.
ComputeUnigramDistribution();
for (int32 o = 2; o <= config_.ngram_order; o++) {
SmoothDistributionForOrder(o);
PruneNgramsForOrder(o);
}
for (int32 o = config_.ngram_order; o >= 2; o--)
PruneStatesForOrder(o, will_write_arpa);
TakeUnigramCountsToPower(config_.unigram_power);
}
void SamplingLmEstimator::SmoothDistributionForOrder(int32 o) {
KALDI_ASSERT(o >= 2 && o <= config_.ngram_order);
unordered_map<std::vector<int32>, HistoryState*,
VectorHasher<int32> >::iterator
iter = history_states_[o-1].begin(), end = history_states_[o-1].end();
BaseFloat D = config_.discounting_constant; // 0 < D < 1.
for (; iter != end; ++iter) {
HistoryState *state = iter->second;
KALDI_ASSERT(state->total_count > 0.0 && state->backoff_count == 0.0);
std::vector<Count>::iterator counts_iter = state->counts.begin(),
counts_end = state->counts.end();
double backoff_count_tot = 0.0;
for (; counts_iter != counts_end; ++counts_iter) {
// note: in the case without data weightings, 'highest_count' will always be
// 1 and so removed_count will equal D.
BaseFloat removed_count = D * counts_iter->highest_count;
counts_iter->count -= removed_count;
backoff_count_tot += removed_count;
}
state->backoff_count = backoff_count_tot;
}
}
void SamplingLmEstimator::PruneNgramsForOrder(int32 o) {
KALDI_ASSERT(o >= 2 && o <= config_.ngram_order);
unordered_map<std::vector<int32>, HistoryState*,
VectorHasher<int32> >::iterator
iter = history_states_[o-1].begin(), end = history_states_[o-1].end();
size_t orig_num_ngrams = 0, cur_num_ngrams = 0;
for (; iter != end; ++iter) {
HistoryState *state = iter->second;
orig_num_ngrams += state->counts.size();
const std::vector<int32> &history = iter->first;
KALDI_ASSERT(history.size() == o - 1);
if (o > 2) {
std::vector<int32> backoff_history(history);
std::vector<const HistoryState*> backoff_states;
while (backoff_history.size() > 1) {
backoff_history.erase(backoff_history.begin());
const HistoryState *backoff_state = GetHistoryState(backoff_history,
false);
backoff_states.push_back(backoff_state);
}
PruneHistoryStateAboveBigram(history, backoff_states, state);
} else { // o == 2
PruneHistoryStateBigram(history, state);
}
cur_num_ngrams += state->counts.size();
}
KALDI_LOG << "For n-gram order " << o << ", pruned from "
<< orig_num_ngrams << " to " << cur_num_ngrams << " ngrams.";
}
void SamplingLmEstimator::PruneStatesForOrder(int32 o, bool will_write_arpa) {
KALDI_ASSERT(o >= 2 && o <= config_.ngram_order);
unordered_map<std::vector<int32>, HistoryState*,
VectorHasher<int32> >::iterator
iter = history_states_[o-1].begin(), end = history_states_[o-1].end();
size_t orig_num_states = history_states_[o-1].size(),
num_restored_ngrams = 0;
// 'states_to_delete' will contain histories whose states we want to delete
// from history_states_ because all their words have been pruned away (and they
// are not protected.
std::unordered_set<std::vector<int32>, VectorHasher<int32> > states_to_delete;
for (; iter != end; ++iter) {
const std::vector<int32> &history = iter->first;
HistoryState *state = iter->second;
if (state->counts.empty() && !state->is_protected) {
// we'll delete this history state.
states_to_delete.insert(history);
} else {
// we'll keep this history state; mark any state that it backs off
// to as protected from being deleted, or we'll have problems later on.
if (history.size() > 1) {
std::vector<int32> backoff_history(history.begin() + 1, history.end());
// mark the state this state backs off to, as protected.
GetHistoryState(backoff_history, false)->is_protected = true;
if (will_write_arpa) {
// Make sure that the n-gram corresponding to this history state
// was not deleted; if it was, we have to put it back into the
// model because it's required to exist in the ARPA model.
std::vector<int32> prev_history(history.begin(), history.end() - 1);
int32 last_word = history.back();
// we're checking that the n-gram 'prev_history -> last_word' exists.
HistoryState *prev_state = GetHistoryState(prev_history, false);
Count c;
c.word = last_word;
std::vector<Count>::iterator
iter = std::lower_bound(prev_state->counts.begin(),
prev_state->counts.end(),
c);
if (iter == prev_state->counts.end() || iter->word != last_word) {
// The n-gram leading to this state had been pruned away, which will
// give us problems when writing the ARPA model (there would be
// nowhere to write the backoff probability for this state). We
// insert it back.
//
// To compute how much of the total count would have been backed off
// in the deleted ngram, We guess at the highest_count, assuming
// it's probably close to 1.0. This will make almost no difference
// to anything, these ngrams will be quite rare.
BaseFloat backoff_count =
std::min<BaseFloat>(config_.discounting_constant,
0.5 * state->total_count);
// because of how we store the stats, the count for that (now-lost)
// ngram would have been equal to state->total_count before pruning.
// Assuming we pruned 'backoff_count' from it, its value after
// pruning would have been roughly as follows:
c.count = state->total_count - backoff_count;
c.highest_count = -123.4; // convenient for future debugging.
prev_state->backoff_count -= c.count; // claw it back.
KALDI_ASSERT(prev_state->backoff_count > 0.0);
prev_state->counts.insert(iter, c);
num_restored_ngrams++;
}
}
}
}
}
{ // remove from the map history states that we marked for deletion.
std::unordered_set<std::vector<int32>, VectorHasher<int32> >::const_iterator
iter = states_to_delete.begin(), end = states_to_delete.end();
for (; iter != end; ++iter) {
const std::vector<int32> &history = *iter;
delete history_states_[o-1][history];
history_states_[o-1].erase(history);
}
}
size_t cur_num_states = history_states_[o-1].size();
std::ostringstream message;
message << "For n-gram order " << o << ", pruned from "
<< orig_num_states << " to " << cur_num_states << " states";
if (num_restored_ngrams > 0) {
message << ", and restored " << num_restored_ngrams << " required n-grams.";
}
KALDI_LOG << message.str();
}
// Prunes a history-state; this version is for the bigram state
// whose left-context is the BOS symbol.
void SamplingLmEstimator::PruneHistoryStateBigram(
const std::vector<int32> &history, HistoryState *state) {
KALDI_ASSERT(history.size() == 1);
BaseFloat total_count = state->total_count;
bool is_bos_state = (history[0] == config_.bos_symbol);
// e.g. factor = is_bos ? 5.0 : 50.0 by default, meaning we keep
// more n-grams for the BOS state than for a typical bigram state;
// this is because the BOS state tends to be seen non-independently
// within the minibatch.
BaseFloat factor = is_bos_state ? config_.bos_factor : config_.unigram_factor;
KALDI_ASSERT(factor > 0.0);
// 'factor' is the factor by which the probability given this
// history state must be greater than the probability given the
// unigram state.
std::vector<Count>::iterator iter = state->counts.begin(),
end = state->counts.end();
double backoff_count = state->backoff_count; // accumulate in double
for (; iter != end; ++iter) {
Count &count = *iter;
BaseFloat unigram_prob = unigram_probs_[count.word],
bigram_prob_no_backoff = count.count / total_count;
// note: when computing bigram_prob_no_backoff when deciding which thing to
// prune, we ignore the backoff term 'state->backoff_count * unigram_prob /
// total_count'. This prevents the need for iteration and keeps things
// simple.
if (bigram_prob_no_backoff <= unigram_prob * factor) {
// Completely prune this count away.
backoff_count += count.count;
count.count = 0.0;
}
}
state->backoff_count = backoff_count;
RemoveZeroCounts(&(state->counts));
}
void SamplingLmEstimator::PruneHistoryStateAboveBigram(
const std::vector<int32> &history,
const std::vector<const HistoryState*> &backoff_states,
HistoryState *state) {
BaseFloat unigram_factor = config_.unigram_factor,
backoff_factor = config_.backoff_factor;
BaseFloat total_count = state->total_count;
KALDI_ASSERT(unigram_factor > 0.0 && backoff_factor > 0.0 &&
unigram_factor > backoff_factor);
std::vector<Count>::iterator iter = state->counts.begin(),
end = state->counts.end();
double backoff_count = state->backoff_count; // accumulate in double
for (; iter != end; ++iter) {
Count &count = *iter;
BaseFloat current_prob_no_backoff = count.count / total_count,
prob_given_backoff_state = GetProbForWord(count.word,
backoff_states),
unigram_prob = unigram_probs_[count.word];
if (!(current_prob_no_backoff > unigram_factor * unigram_prob &&
current_prob_no_backoff > backoff_factor * prob_given_backoff_state)) {
// Remove this word. It's not probable enough to keep according to our
// rules.
backoff_count += count.count;
count.count = 0.0;
}
}
state->backoff_count = backoff_count;
RemoveZeroCounts(&(state->counts));
}
BaseFloat SamplingLmEstimator::GetProbForWord(
int32 word, const std::vector<const HistoryState*> &states) const {
// compute the probability from lowest to highest order.
KALDI_ASSERT(word > 0 && word < static_cast<int32>(unigram_probs_.size()));
BaseFloat ans = unigram_probs_[word];
for (size_t i = 0; i < states.size(); i++) {
const HistoryState *state = states[i];
ans *= state->backoff_count / state->total_count;
Count c;
c.word = word;
// look up word in the state's counts.
std::vector<Count>::const_iterator
iter = std::lower_bound(state->counts.begin(),
state->counts.end(),
c);
if (iter != state->counts.end() && iter->word == word) {
// we found the word
ans += iter->count / state->total_count;
}
}
return ans;
}
bool SamplingLmEstimator::IsProtected(const std::vector<int32> &history,
int32 word) const {
// n-grams of the highest order can't be protected because there
// would be no higher-order state.
if (static_cast<int32>(history.size()) + 1 == config_.ngram_order)
return false;
std::vector<int32> new_history;
new_history.reserve(history.size() + 1);
new_history.insert(new_history.end(), history.begin(), history.end());
new_history.push_back(word);
return (history_states_[new_history.size()].count(new_history) != 0);
}
BaseFloat SamplingLmEstimator::BackoffProb(
const std::vector<int32> &history, int32 word) const {
// n-grams of the highest order won't have their own history state.
if (static_cast<int32>(history.size()) + 1 == config_.ngram_order)
return 0.0;
std::vector<int32> new_history;
new_history.reserve(history.size() + 1);
new_history.insert(new_history.end(), history.begin(), history.end());
new_history.push_back(word);
unordered_map<std::vector<int32>, HistoryState*,
VectorHasher<int32>>::const_iterator iter =
history_states_[new_history.size()].find(new_history);
if (iter != history_states_[new_history.size()].end()) {
HistoryState *state = iter->second;
return state->backoff_count / state->total_count;
} else {
return 0.0;
}
}
// static
void SamplingLmEstimator::RemoveZeroCounts(
std::vector<SamplingLmEstimator::Count> *counts) {
std::vector<Count>::const_iterator input_iter = counts->begin(),
end = counts->end();
std::vector<Count>::iterator output_iter = counts->begin();
// this while loop is an optimization to avoid copying where
// source and destination are the same; it could be removed.
while (input_iter != end && input_iter->count != 0.0) {
++input_iter;
++output_iter;
}
for (; input_iter != end; ++input_iter) {
if (input_iter->count != 0.0) {
*output_iter = *input_iter;
++output_iter;
}
}
counts->resize(output_iter - counts->begin());
}
void SamplingLmEstimator::ComputeUnigramDistribution() {
int32 vocab_size = config_.vocab_size;
if (history_states_[0].size() != 1) {
KALDI_ERR << "There are no counts (no data processed?)";
}
HistoryState *unigram_state = history_states_[0].begin()->second;
KALDI_ASSERT(unigram_state->backoff_count == 0.0);
double discounted_count = 0.0;
{ // this block works out 'unigram_state->backoff_count' which is the same as
// 'discounted_count', and discounts the counts in 'unigram_state->counts'.
BaseFloat D = config_.discounting_constant;
std::vector<Count>::iterator iter = unigram_state->counts.begin(),
end = unigram_state->counts.end();
for(; iter != end; ++iter) {
BaseFloat count_to_discount = D * iter->highest_count;
iter->count -= count_to_discount;
discounted_count += count_to_discount;
}
unigram_state->backoff_count = discounted_count;
}
BaseFloat total_count = unigram_state->total_count;
// 'uniform_prob' is the probability that we add to each word in the vocabulary
// even if it was not seen. We divide discounted_count equally among all
// words; the - 2 is to exclude <eps> and <s>, which are never predicted.
BaseFloat uniform_prob = (discounted_count / total_count) / (vocab_size - 2);
KALDI_ASSERT(total_count > 0.0 && uniform_prob > 0.0);
unigram_probs_.clear();
unigram_probs_.resize(vocab_size, uniform_prob);
unigram_probs_[0] = 0.0;
unigram_probs_[config_.bos_symbol] = 0.0;
std::vector<Count>::iterator iter = unigram_state->counts.begin(),
end = unigram_state->counts.end();
for(; iter != end; ++iter) {
BaseFloat this_prob = iter->count / total_count;
// 'this_prob' is the non-smoothed part of the unigram probability.
unigram_probs_[iter->word] += this_prob;
}
double sum = std::accumulate(unigram_probs_.begin(),
unigram_probs_.end(), 0.0);
KALDI_ASSERT(fabs(sum - 1.0) < 0.01);
}
SamplingLmEstimator::~SamplingLmEstimator() {
for (size_t i = 0; i < history_states_.size(); i++) {
for (auto iter = history_states_[i].begin(), end = history_states_[i].end();
iter != end; ++iter)
delete iter->second;
}
}
void SamplingLmEstimator::TakeUnigramCountsToPower(BaseFloat power) {
if (power == 1.0) return;
double sum = 0.0;
for (std::vector<BaseFloat>::iterator iter = unigram_probs_.begin(),
end = unigram_probs_.end(); iter != end; ++iter) {
*iter = std::pow(*iter, power);
sum += *iter;
}
BaseFloat scale = 1.0 / sum;
for (std::vector<BaseFloat>::iterator iter = unigram_probs_.begin(),
end = unigram_probs_.end(); iter != end; ++iter)
*iter = *iter * scale;
}
int32 SamplingLmEstimator::NumNgrams(int32 o) const {
KALDI_ASSERT(o >= 1 && o <= config_.ngram_order);
if (o == 1) {
// - 1 is for <eps>. <s> does get printed in the ARPA file, with a
// probability of -99.
return config_.vocab_size - 1;
} else {
int32 ans = 0;
unordered_map<std::vector<int32>, HistoryState*,
VectorHasher<int32> >::const_iterator
iter = history_states_[o-1].begin(),
end = history_states_[o-1].end();
for (; iter != end; ++iter) {
HistoryState *state = iter->second;
ans += static_cast<int32>(state->counts.size());
}
return ans;
}
}
void SamplingLmEstimator::PrintNgramsUnigram(
std::ostream &os, const fst::SymbolTable &symbols) const {
int32 vocab_size = config_.vocab_size,
bos_symbol = config_.bos_symbol;
std::vector<int32> unigram_history;
for (int32 word = 1; word < vocab_size; word++) {
std::string printed_word = symbols.Find(word);
KALDI_ASSERT(!printed_word.empty() && "Mismatching symbol-table?");
BaseFloat word_logprob = (word == bos_symbol ? -99.0 :
log10(unigram_probs_[word]));
BaseFloat backoff_prob = BackoffProb(unigram_history, word);
os << word_logprob << '\t' << printed_word;
if (backoff_prob != 0.0) os << '\t' << log10(backoff_prob) << '\n';
else os << '\n';
}
}
void SamplingLmEstimator::PrintNgramsAboveUnigram(
std::ostream &os, int32 o, const fst::SymbolTable &symbols) {
unordered_map<std::vector<int32>, HistoryState*,
VectorHasher<int32> >::const_iterator
hist_iter = history_states_[o-1].begin(),
hist_end = history_states_[o-1].end();
for (; hist_iter != hist_end; ++hist_iter) {
const std::vector<int32> &history = hist_iter->first;
const HistoryState *state = hist_iter->second;
// 'backoff_states' will list states that 'state' backs off to down to and
// including bigram. it's used when computing probabilities. It will be
// empty if o == 2.
std::vector<const HistoryState*> backoff_states;
{ // this block sets up 'states'
std::vector<int32> backoff_history(history);
while (backoff_history.size() > 1) {
backoff_history.erase(backoff_history.begin());
const HistoryState *backoff_state = GetHistoryState(backoff_history,
false);
backoff_states.push_back(backoff_state);
}
}
std::string history_str;
{ // This block will set history_str to the sequence of history words for
// this history state, separated by space; e.g. "on Tuesdays".
std::ostringstream history_os;
for (size_t i = 0; i < history.size(); i++) {
std::string printed_word = symbols.Find(history[i]);
KALDI_ASSERT(printed_word != "" && "mismatched symbol table?");
history_os << printed_word;
if (i + 1 < history.size()) history_os << ' ';
}
history_str = history_os.str();
}
std::vector<Count>::const_iterator count_iter = state->counts.begin(),
count_end = state->counts.end();
BaseFloat total_count = state->total_count,
backoff_count = state->backoff_count;
for (; count_iter != count_end; ++count_iter) {
const Count &count = *count_iter;
std::string printed_word = symbols.Find(count.word);
KALDI_ASSERT(printed_word != "" && "mismatched symbol table?");
BaseFloat word_prob =
(count.count + backoff_count * GetProbForWord(count.word,
backoff_states)) /
total_count,
backoff_prob = BackoffProb(history, count.word);
os << log10(word_prob) << '\t' << history_str << ' ' << printed_word;
if (backoff_prob != 0.0) os << '\t' << log10(backoff_prob) << '\n';
else os << '\n';
}
}
}
void SamplingLmEstimator::PrintAsArpa(std::ostream &os,
const fst::SymbolTable &symbols) {
os << std::fixed << std::setprecision(3); // print log-probs as -a.bcd
os << "\\data\\\n";
for (int32 o = 1; o <= config_.ngram_order; o++)
os << "ngram " << o << "=" << NumNgrams(o) << "\n";
for (int32 o = 1; o <= config_.ngram_order; o++) {
os << '\n' << '\\' << o << "-grams:\n";
if (o == 1) PrintNgramsUnigram(os, symbols);
else PrintNgramsAboveUnigram(os, o, symbols);
}
os << "\n\\end\\\n";
}
} // namespace rnnlm
} // namespace kaldi