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/*++
Revision History:
Date: Jun 28, 2024.
Author: Rajas Chavadekar.
Desc: Created.
--*/
#include "NaiveBayesClassifier.h"
#include <fstream>
#include <iostream>
#include <cmath>
NaiveBayesClassifier::NaiveBayesClassifier(BaseVectorizer* pvec)
{
pVec = pvec;
}
NaiveBayesClassifier::~NaiveBayesClassifier()
{
delete pVec;
}
void NaiveBayesClassifier::setHyperparameters(std::string hyperparameters)
{
std::string token;
std::istringstream tokenStream(hyperparameters);
// "smoothing_param_m=1.0,smoothing_param_p=0.5"
smoothing_param_m = 1.0;
smoothing_param_p = 0.5;
pVec->ngrams = 1;
while (std::getline(tokenStream, token, ',')) {
std::istringstream pairStream(token);
std::string key;
double value;
if (std::getline(pairStream, key, '=') && pairStream >> value) {
cout << key << " = " << value << endl;
if (key == "ngrams") {
pVec->ngrams = value;
}
else if (key == "minfrequency") {
minfrequency = value;
}
else if (key == "smoothing_param_m") {
smoothing_param_m = value;
}
else if (key == "smoothing_param_p") {
smoothing_param_p = value;
}
}
}
}
void NaiveBayesClassifier::fit(std::string abs_filepath_to_features, std::string abs_filepath_to_labels)
{
if (minfrequency > 0)
{
pVec->scanForSparseHistogram(abs_filepath_to_features, minfrequency);
}
pVec->fit(abs_filepath_to_features, abs_filepath_to_labels);
std::vector<std::shared_ptr<Sentence>> sentences = pVec->sentences;
int num_sentences = sentences.size();
int num_pos = 0;
int num_neg = 0;
std::unordered_map<int, double> word_count_pos;
std::unordered_map<int, double> word_count_neg;
std::vector<double> tfidf_features_pos;
std::vector<double> tfidf_features_neg;
int total_words_pos = 0;
int total_words_neg = 0;
for (const auto& sentence : sentences)
{
if (sentence->label)
{
num_pos++;
for (const auto& entry : sentence->sentence_map)
{
word_count_pos[entry.first] += entry.second;
total_words_pos += entry.second;
}
}
else
{
num_neg++;
for (const auto& entry : sentence->sentence_map)
{
word_count_neg[entry.first] += entry.second;
total_words_neg += entry.second;
}
}
}
log_prior_pos = std::log(static_cast<double>(num_pos) / num_sentences);
log_prior_neg = std::log(static_cast<double>(num_neg) / num_sentences);
double mp = smoothing_param_m * smoothing_param_p;
double tfidf_sum_pos = 0.0;
double tfidf_sum_neg = 0.0;
if (ID_VECTORIZER_TFIDF == pVec->this_vectorizer_id) {
tfidf_features_pos = pVec->getFrequencies(word_count_pos);
tfidf_features_neg = pVec->getFrequencies(word_count_neg);
for (const auto& v : tfidf_features_pos)
{
tfidf_sum_pos += v;
}
for (const auto& v : tfidf_features_neg)
{
tfidf_sum_neg += v;
}
}
for (const auto& word : pVec->word_array)
{
int idx = pVec->word_to_idx[word];
if (ID_VECTORIZER_TFIDF == pVec->this_vectorizer_id) {
log_prob_pos[idx] = std::log((tfidf_features_pos[idx] + mp) / (tfidf_sum_pos + smoothing_param_m + pVec->word_array.size()));
log_prob_neg[idx] = std::log((tfidf_features_neg[idx] + mp) / (tfidf_sum_neg + smoothing_param_m + pVec->word_array.size()));
}
else
{
log_prob_pos[idx] = std::log((word_count_pos[idx] + mp) / (total_words_pos + smoothing_param_m + pVec->word_array.size()));
log_prob_neg[idx] = std::log((word_count_neg[idx] + mp) / (total_words_neg + smoothing_param_m + pVec->word_array.size()));
}
}
}
double NaiveBayesClassifier::calculate_log_probability(const std::vector<double>& features, bool is_positive) const
{
double log_prob = is_positive ? log_prior_pos : log_prior_neg;
const auto& log_prob_map = is_positive ? log_prob_pos : log_prob_neg;
for (ml_size_t i = 0; i < features.size(); ++i)
{
if (features[i] > 0)
{
log_prob += std::abs(features[i]) * log_prob_map.at(i);
}
}
return log_prob;
}
Prediction NaiveBayesClassifier::predict(std::string sentence, bool preprocess)
{
GlobalData vars;
Prediction result;
std::vector<std::string> processed_input = pVec->buildSentenceVector(sentence, preprocess);
std::vector<double> feature_vector = pVec->getSentenceFeatures(processed_input);
double log_prob_pos = calculate_log_probability(feature_vector, true);
double log_prob_neg = calculate_log_probability(feature_vector, false);
double max_log_prob = std::max(log_prob_pos, log_prob_neg);
double exp_log_prob_pos = std::exp(log_prob_pos - max_log_prob);
double exp_log_prob_neg = std::exp(log_prob_neg - max_log_prob);
double sum_exp_log_probs = exp_log_prob_pos + exp_log_prob_neg;
result.probability = exp_log_prob_pos / sum_exp_log_probs;
if (log_prob_pos > log_prob_neg)
{
result.label = vars.POS;
}
else
{
result.label = vars.NEG;
}
return result;
}
void NaiveBayesClassifier::predict(std::string abs_filepath_to_features, std::string abs_filepath_to_labels, bool preprocess)
{
std::ifstream in(abs_filepath_to_features);
std::ofstream out(abs_filepath_to_labels);
std::string feature_input;
if (!in)
{
std::cerr << "ERROR: Cannot open features file.\n";
return;
}
if (!out)
{
std::cerr << "ERROR: Cannot open labels file.\n";
return;
}
#ifdef BENCHMARK
double sumduration = 0.0;
double sumstrlen = 0.0;
ml_size_t num_rows = 0;
#endif
while (getline(in, feature_input))
{
#ifdef BENCHMARK
auto start = std::chrono::high_resolution_clock::now();
#endif
Prediction result = predict(feature_input, preprocess);
#ifdef BENCHMARK
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<double, std::milli> duration = end - start;
double milliseconds = duration.count();
sumduration += milliseconds;
sumstrlen += feature_input.length();
num_rows++;
#endif
out << result.label << "," << result.probability << std::endl;
}
#ifdef BENCHMARK
double avgduration = sumduration / num_rows;
cout << "Average Time per Text = " << avgduration << " ms" << endl;
double avgstrlen = sumstrlen / num_rows;
cout << "Average Length of Text (chars) = " << avgstrlen << endl;
#endif
in.close();
out.close();
}
void NaiveBayesClassifier::save(const std::string& filename) const
{
std::ofstream outFile(filename, std::ios::binary);
if (!outFile.is_open())
{
std::cerr << "Failed to open file for writing." << std::endl;
return;
}
pVec->save(outFile);
ml_size_t size;
size = log_prob_pos.size();
outFile.write(reinterpret_cast<const char*>(&size), sizeof(size));
for (const auto& pair : log_prob_pos)
{
outFile.write(reinterpret_cast<const char*>(&pair.first), sizeof(pair.first));
outFile.write(reinterpret_cast<const char*>(&pair.second), sizeof(pair.second));
}
size = log_prob_neg.size();
outFile.write(reinterpret_cast<const char*>(&size), sizeof(size));
for (const auto& pair : log_prob_neg)
{
outFile.write(reinterpret_cast<const char*>(&pair.first), sizeof(pair.first));
outFile.write(reinterpret_cast<const char*>(&pair.second), sizeof(pair.second));
}
outFile.write(reinterpret_cast<const char*>(&log_prior_pos), sizeof(log_prior_pos));
outFile.write(reinterpret_cast<const char*>(&log_prior_neg), sizeof(log_prior_neg));
outFile.close();
}
void NaiveBayesClassifier::load(const std::string& filename)
{
std::ifstream inFile(filename, std::ios::binary);
if (!inFile.is_open())
{
std::cerr << "Failed to open file for reading." << std::endl;
return;
}
pVec->load(inFile);
ml_size_t size;
inFile.read(reinterpret_cast<char*>(&size), sizeof(size));
log_prob_pos.clear();
for (ml_size_t i = 0; i < size; ++i)
{
int key;
double value;
inFile.read(reinterpret_cast<char*>(&key), sizeof(key));
inFile.read(reinterpret_cast<char*>(&value), sizeof(value));
log_prob_pos[key] = value;
}
inFile.read(reinterpret_cast<char*>(&size), sizeof(size));
log_prob_neg.clear();
for (ml_size_t i = 0; i < size; ++i)
{
int key;
double value;
inFile.read(reinterpret_cast<char*>(&key), sizeof(key));
inFile.read(reinterpret_cast<char*>(&value), sizeof(value));
log_prob_neg[key] = value;
}
inFile.read(reinterpret_cast<char*>(&log_prior_pos), sizeof(log_prior_pos));
inFile.read(reinterpret_cast<char*>(&log_prior_neg), sizeof(log_prior_neg));
inFile.close();
}