logistic-regression-eval.cc
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// ivectorbin/logistic-regression-eval.cc
// Copyright 2014 David Snyder
// 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,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "ivector/logistic-regression.h"
using namespace kaldi;
int ComputeLogPosteriors(ParseOptions &po,
const LogisticRegressionConfig &config,
bool apply_log) {
std::string model = po.GetArg(1),
vector_rspecifier = po.GetArg(2),
log_posteriors_wspecifier = po.GetArg(3);
LogisticRegression classifier;
ReadKaldiObject(model, &classifier);
std::vector<Vector<BaseFloat> > vectors;
SequentialBaseFloatVectorReader vector_reader(vector_rspecifier);
BaseFloatVectorWriter posterior_writer(log_posteriors_wspecifier);
std::vector<std::string> utt_list;
int32 num_utt_done = 0;
for (; !vector_reader.Done(); vector_reader.Next()) {
std::string utt = vector_reader.Key();
const Vector<BaseFloat> &vector = vector_reader.Value();
Vector<BaseFloat> log_posteriors;
classifier.GetLogPosteriors(vector, &log_posteriors);
if (!apply_log)
log_posteriors.ApplyExp();
posterior_writer.Write(utt, log_posteriors);
num_utt_done++;
}
KALDI_LOG << "Calculated log posteriors for " << num_utt_done << " vectors.";
return (num_utt_done == 0 ? 1 : 0);
}
int32 ComputeScores(ParseOptions &po, const LogisticRegressionConfig &config,
bool apply_log) {
std::string model_rspecifier = po.GetArg(1),
trials_rspecifier = po.GetArg(2),
vector_rspecifier = po.GetArg(3),
scores_out = po.GetArg(4);
SequentialInt32Reader class_reader(trials_rspecifier);
LogisticRegression classifier = LogisticRegression();
ReadKaldiObject(model_rspecifier, &classifier);
std::vector<Vector<BaseFloat> > vectors;
std::vector<int32> ys;
std::vector<std::string> utt_list;
int32 num_utt_done = 0, num_utt_err = 0;
RandomAccessBaseFloatVectorReader vector_reader(vector_rspecifier);
for (; !class_reader.Done(); class_reader.Next()) {
std::string utt = class_reader.Key();
int32 class_label = class_reader.Value();
if (!vector_reader.HasKey(utt)) {
KALDI_WARN << "No vector for utterance " << utt;
num_utt_err++;
} else {
utt_list.push_back(utt);
ys.push_back(class_label);
const Vector<BaseFloat> &vector = vector_reader.Value(utt);
vectors.push_back(vector);
num_utt_done++;
}
}
if (vectors.empty()) {
KALDI_WARN << "Read no input";
return 1;
}
Matrix<BaseFloat> xs(vectors.size(), vectors[0].Dim());
for (int i = 0; i < vectors.size(); i++) {
xs.Row(i).CopyFromVec(vectors[i]);
}
Matrix<BaseFloat> log_posteriors;
classifier.GetLogPosteriors(xs, &log_posteriors);
bool binary = false;
Output ko(scores_out.c_str(), binary);
if (!apply_log)
log_posteriors.ApplyExp();
for (int i = 0; i < ys.size(); i++) {
ko.Stream() << utt_list[i] << " " << ys[i] << " " << log_posteriors(i, ys[i]) << std::endl;
}
KALDI_LOG << "Calculated scores for " << num_utt_done
<< " vectors with "
<< num_utt_err << " missing. ";
return (num_utt_done == 0 ? 1 : 0);
}
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
try {
const char *usage =
"Evaluates a model on input vectors and outputs either\n"
"log posterior probabilities or scores.\n"
"Usage1: logistic-regression-eval <model> <input-vectors-rspecifier>\n"
" <output-log-posteriors-wspecifier>\n"
"Usage2: logistic-regression-eval <model> <trials-file> <input-vectors-rspecifier>\n"
" <output-scores-file>\n";
ParseOptions po(usage);
bool apply_log = true;
po.Register("apply-log", &apply_log,
"If false, apply Exp to the log posteriors output. This is "
"helpful when combining posteriors from multiple logistic "
"regression models.");
LogisticRegressionConfig config;
config.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() != 3 && po.NumArgs() != 4) {
po.PrintUsage();
exit(1);
}
if (po.NumArgs() == 4) {
return ComputeScores(po, config, apply_log);
} else {
return ComputeLogPosteriors(po, config, apply_log);
}
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}