533 lines
17 KiB
C
533 lines
17 KiB
C
#include <stdio.h>
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#include <stdlib.h>
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#include <stdint.h>
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#include <stdbool.h>
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#include "log/log.h"
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#include "address_dictionary.h"
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#include "language_classifier.h"
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#include "language_classifier_io.h"
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#include "logistic_regression.h"
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#include "logistic_regression_trainer.h"
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#include "shuffle.h"
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#include "sparse_matrix.h"
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#include "sparse_matrix_utils.h"
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#define LANGUAGE_CLASSIFIER_FEATURE_COUNT_THRESHOLD 5.0
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#define LANGUAGE_CLASSIFIER_LABEL_COUNT_THRESHOLD 100
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#define LOG_BATCH_INTERVAL 10
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#define COMPUTE_COST_INTERVAL 100
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#define LANGUAGE_CLASSIFIER_HYPERPARAMETER_BATCHES 100
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static double GAMMA_SCHEDULE[] = {0.01, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0};
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static const size_t GAMMA_SCHEDULE_SIZE = sizeof(GAMMA_SCHEDULE) / sizeof(double);
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#define DEFAULT_GAMMA_0 10.0
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static double LAMBDA_SCHEDULE[] = {0.0, 1e-5, 1e-4, 0.001, 0.01, 0.1, \
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0.2, 0.5, 1.0};
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static const size_t LAMBDA_SCHEDULE_SIZE = sizeof(LAMBDA_SCHEDULE) / sizeof(double);
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#define DEFAULT_LAMBDA 0.0
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#define TRAIN_EPOCHS 10
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#define HYPERPARAMETER_EPOCHS 30
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logistic_regression_trainer_t *language_classifier_init_thresholds(char *filename, double feature_count_threshold, uint32_t label_count_threshold) {
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if (filename == NULL) {
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log_error("Filename was NULL\n");
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return NULL;
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}
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language_classifier_data_set_t *data_set = language_classifier_data_set_init(filename);
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language_classifier_minibatch_t *minibatch;
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khash_t(str_double) *feature_counts = kh_init(str_double);
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khash_t(str_uint32) *label_counts = kh_init(str_uint32);
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size_t num_batches = 0;
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// Count features and labels
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while ((minibatch = language_classifier_data_set_get_minibatch(data_set, NULL)) != NULL) {
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if (!count_labels_minibatch(label_counts, minibatch->labels)) {
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log_error("Counting minibatch labeles failed\n");
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exit(EXIT_FAILURE);
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}
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if (num_batches % LOG_BATCH_INTERVAL == 0) {
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log_info("Counting labels, did %zu batches\n", num_batches);
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}
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num_batches++;
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language_classifier_minibatch_destroy(minibatch);
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}
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log_info("Done counting labels\n");
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language_classifier_data_set_destroy(data_set);
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data_set = language_classifier_data_set_init(filename);
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num_batches = 0;
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khash_t(str_uint32) *label_ids = select_labels_threshold(label_counts, label_count_threshold);
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if (label_ids == NULL) {
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log_error("Error creating labels\n");
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exit(EXIT_FAILURE);
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}
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size_t num_labels = kh_size(label_ids);
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log_info("num_labels=%zu\n", num_labels);
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// Don't free the label strings as the pointers are reused in select_labels_threshold
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kh_destroy(str_uint32, label_counts);
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// Run through the training set again, counting only features which co-occur with valid classes
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while ((minibatch = language_classifier_data_set_get_minibatch(data_set, label_ids)) != NULL) {
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if (!count_features_minibatch(feature_counts, minibatch->features, true)){
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log_error("Counting minibatch features failed\n");
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exit(EXIT_FAILURE);
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}
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if (num_batches % LOG_BATCH_INTERVAL == 0) {
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log_info("Counting features, did %zu batches\n", num_batches);
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}
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num_batches++;
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language_classifier_minibatch_destroy(minibatch);
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}
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log_info("Done counting features, finalizing\n");
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language_classifier_data_set_destroy(data_set);
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// Discard rare features using a count threshold (can be 1) and convert them to trie
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trie_t *feature_ids = select_features_threshold(feature_counts, feature_count_threshold);
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if (feature_ids == NULL) {
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log_error("Error creating features trie\n");
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exit(EXIT_FAILURE);
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}
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// Need to free the keys here as trie uses its own memory
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const char *key;
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kh_foreach_key(feature_counts, key, {
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free((char *)key);
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})
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kh_destroy(str_double, feature_counts);
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return logistic_regression_trainer_init(feature_ids, label_ids, DEFAULT_GAMMA_0, DEFAULT_LAMBDA);
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}
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logistic_regression_trainer_t *language_classifier_init(char *filename) {
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return language_classifier_init_thresholds(filename, LANGUAGE_CLASSIFIER_FEATURE_COUNT_THRESHOLD, LANGUAGE_CLASSIFIER_LABEL_COUNT_THRESHOLD);
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}
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double compute_cv_accuracy(logistic_regression_trainer_t *trainer, char *filename) {
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language_classifier_data_set_t *data_set = language_classifier_data_set_init(filename);
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language_classifier_minibatch_t *minibatch;
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uint32_t correct = 0;
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uint32_t total = 0;
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double_matrix_t *p_y = double_matrix_new_zeros(LANGUAGE_CLASSIFIER_DEFAULT_BATCH_SIZE, trainer->num_labels);
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while ((minibatch = language_classifier_data_set_get_minibatch(data_set, trainer->label_ids)) != NULL) {
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sparse_matrix_t *x = feature_matrix(trainer->feature_ids, minibatch->features);
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uint32_array *y = label_vector(trainer->label_ids, minibatch->labels);
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double_matrix_resize(p_y, x->m, trainer->num_labels);
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if (!logistic_regression_model_expectation(trainer->weights, x, p_y)) {
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log_error("Predict cv batch failed\n");
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exit(EXIT_FAILURE);
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}
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double *row;
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for (size_t i = 0; i < p_y->m; i++) {
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row = double_matrix_get_row(p_y, i);
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int64_t predicted = double_array_argmax(row, p_y->n);
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if (predicted < 0) {
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log_error("Error in argmax\n");
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exit(EXIT_FAILURE);
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}
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uint32_t y_i = y->a[i];
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if (y_i == (uint32_t)predicted) {
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correct++;
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}
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total++;
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}
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sparse_matrix_destroy(x);
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uint32_array_destroy(y);
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language_classifier_minibatch_destroy(minibatch);
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}
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language_classifier_data_set_destroy(data_set);
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double_matrix_destroy(p_y);
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double accuracy = (double)correct / total;
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return accuracy;
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}
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double compute_total_cost(logistic_regression_trainer_t *trainer, char *filename, ssize_t compute_batches) {
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language_classifier_data_set_t *data_set = language_classifier_data_set_init(filename);
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language_classifier_minibatch_t *minibatch;
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double total_cost = 0.0;
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size_t num_batches = 0;
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while ((minibatch = language_classifier_data_set_get_minibatch(data_set, trainer->label_ids)) != NULL) {
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double batch_cost = logistic_regression_trainer_batch_cost(trainer, minibatch->features, minibatch->labels);
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total_cost += batch_cost;
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language_classifier_minibatch_destroy(minibatch);
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num_batches++;
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if (compute_batches > 0 && num_batches == (size_t)compute_batches) {
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break;
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}
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}
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language_classifier_data_set_destroy(data_set);
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return total_cost;
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}
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bool language_classifier_train_epoch(logistic_regression_trainer_t *trainer, char *filename, char *cv_filename, ssize_t train_batches) {
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if (filename == NULL) {
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log_error("Filename was NULL\n");
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return false;
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}
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#if defined(HAVE_SHUF)
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log_info("Shuffling\n");
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if (!shuffle_file(filename)) {
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log_error("Error in shuffle\n");
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logistic_regression_trainer_destroy(trainer);
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return NULL;
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}
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log_info("Shuffle complete\n");
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#endif
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language_classifier_data_set_t *data_set = language_classifier_data_set_init(filename);
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language_classifier_minibatch_t *minibatch;
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size_t num_batches = 0;
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double batch_cost = 0.0;
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double total_cost = 0.0;
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double last_cost = 0.0;
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double train_cost = 0.0;
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double cv_accuracy = 0.0;
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while ((minibatch = language_classifier_data_set_get_minibatch(data_set, trainer->label_ids)) != NULL) {
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bool compute_cost = num_batches % COMPUTE_COST_INTERVAL == 0 && num_batches > 0;
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if (num_batches % LOG_BATCH_INTERVAL == 0 && num_batches > 0) {
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log_info("Epoch %u, doing batch %zu\n", trainer->epochs, num_batches);
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}
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if (compute_cost) {
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train_cost = logistic_regression_trainer_batch_cost(trainer, minibatch->features, minibatch->labels);
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log_info("cost = %f\n", train_cost);
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}
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if (!logistic_regression_trainer_train_batch(trainer, minibatch->features, minibatch->labels)){
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log_error("Train batch failed\n");
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exit(EXIT_FAILURE);
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}
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if (compute_cost && cv_filename != NULL) {
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cv_accuracy = compute_cv_accuracy(trainer, cv_filename);
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log_info("cv accuracy=%f\n", cv_accuracy);
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}
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num_batches++;
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if (train_batches > 0 && num_batches == (size_t)train_batches) {
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log_info("Epoch %u, trained %zu batches\n", trainer->epochs, num_batches);
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train_cost = logistic_regression_trainer_batch_cost(trainer, minibatch->features, minibatch->labels);
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log_info("cost = %f\n", train_cost);
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break;
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}
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language_classifier_minibatch_destroy(minibatch);
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}
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language_classifier_data_set_destroy(data_set);
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return true;
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}
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typedef struct language_classifier_params {
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double lambda;
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double gamma_0;
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} language_classifier_params_t;
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language_classifier_params_t language_classifier_parameter_sweep(char *filename, char *cv_filename) {
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// Select features using the full data set
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logistic_regression_trainer_t *trainer = language_classifier_init(filename);
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double best_cost = 0.0;
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language_classifier_params_t best_params = (language_classifier_params_t){0.0, 0.0};
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for (size_t i = 0; i < LAMBDA_SCHEDULE_SIZE; i++) {
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for (size_t j = 0; j < GAMMA_SCHEDULE_SIZE; j++) {
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trainer->lambda = LAMBDA_SCHEDULE[i];
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trainer->gamma_0 = GAMMA_SCHEDULE[j];
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log_info("Optimizing hyperparameters. Trying lambda=%f, gamma_0=%f\n", trainer->lambda, trainer->gamma_0);
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for (int k = 0; k < HYPERPARAMETER_EPOCHS; k++) {
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trainer->epochs = k;
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if (!language_classifier_train_epoch(trainer, filename, NULL, LANGUAGE_CLASSIFIER_HYPERPARAMETER_BATCHES)) {
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log_error("Error in epoch\n");
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logistic_regression_trainer_destroy(trainer);
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exit(EXIT_FAILURE);
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}
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}
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ssize_t cost_batches;
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char *cost_file;
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if (cv_filename == NULL) {
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cost_file = filename;
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cost_batches = LANGUAGE_CLASSIFIER_HYPERPARAMETER_BATCHES;
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} else {
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cost_file = cv_filename;
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cost_batches = -1;
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}
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double cost = compute_total_cost(trainer, cost_file, cost_batches);
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log_info("Total cost = %f\n", cost);
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if ((i == 0 && j == 0) || cost < best_cost) {
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log_info("Better than current best parameters: lambda=%f, gamma_0=%f\n", trainer->lambda, trainer->gamma_0);
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best_cost = cost;
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best_params.lambda = trainer->lambda;
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best_params.gamma_0 = trainer->gamma_0;
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}
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}
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}
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return best_params;
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}
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language_classifier_t *language_classifier_train(char *filename, char *subset_filename, bool cross_validation_set, char *cv_filename, char *test_filename, uint32_t num_iterations) {
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language_classifier_params_t params = language_classifier_parameter_sweep(subset_filename, cv_filename);
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log_info("Best params: lambda=%f, gamma_0=%f\n", params.lambda, params.gamma_0);
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logistic_regression_trainer_t *trainer = language_classifier_init(filename);
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trainer->lambda = params.lambda;
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trainer->gamma_0 = params.gamma_0;
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/* If there's not a distinct cross-validation set, e.g.
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when training the production model, then the cross validation
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file is just a subset of the training data and only used
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for setting the hyperparameters, so ignore it after we're
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done with the parameter sweep.
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*/
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if (!cross_validation_set) {
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cv_filename = NULL;
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}
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for (uint32_t epoch = 0; epoch < num_iterations; epoch++) {
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log_info("Doing epoch %d\n", epoch);
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trainer->epochs = epoch;
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if (!language_classifier_train_epoch(trainer, filename, cv_filename, -1)) {
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log_error("Error in epoch\n");
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logistic_regression_trainer_destroy(trainer);
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return NULL;
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}
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}
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log_info("Done training\n");
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if (!logistic_regression_trainer_finalize(trainer)) {
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log_error("Error in finalization\n");
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logistic_regression_trainer_destroy(trainer);
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return NULL;
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}
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if (test_filename != NULL) {
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double test_accuracy = compute_cv_accuracy(trainer, test_filename);
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log_info("Test accuracy = %f\n", test_accuracy);
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}
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language_classifier_t *classifier = language_classifier_new();
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if (classifier == NULL) {
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log_error("Error creating classifier\n");
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logistic_regression_trainer_destroy(trainer);
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return NULL;
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}
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// Reassign weights and features to the classifier model
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classifier->weights = trainer->weights;
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trainer->weights = NULL;
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classifier->num_features = trainer->num_features;
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classifier->features = trainer->feature_ids;
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// Set trainer feature_ids to NULL so it doesn't get destroyed
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trainer->feature_ids = NULL;
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size_t num_labels = trainer->num_labels;
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classifier->num_labels = num_labels;
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char **strings = malloc(sizeof(char *) * num_labels);
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const char *label;
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uint32_t label_id;
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kh_foreach(trainer->label_ids, label, label_id, {
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if (label_id >= num_labels) {
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log_error("label_id %d >= num_labels %zu\n", label_id, num_labels);
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exit(EXIT_FAILURE);
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}
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strings[label_id] = (char *)label;
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})
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classifier->labels = cstring_array_from_strings(strings, num_labels);
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for (size_t i = 0; i < num_labels; i++) {
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free(strings[i]);
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}
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free(strings);
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logistic_regression_trainer_destroy(trainer);
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return classifier;
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}
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#define LANGUAGE_CLASSIFIER_TRAIN_USAGE "Usage: ./language_classifier_train [train|cv] filename [cv_filename] [test_filename] [output_dir]\n"
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int main(int argc, char **argv) {
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if (argc < 3) {
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printf(LANGUAGE_CLASSIFIER_TRAIN_USAGE);
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exit(EXIT_FAILURE);
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}
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char *command = argv[1];
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bool cross_validation_set = false;
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if (string_equals(command, "cv")) {
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cross_validation_set = true;
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} else if (!string_equals(command, "train")) {
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printf(LANGUAGE_CLASSIFIER_TRAIN_USAGE);
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exit(EXIT_FAILURE);
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}
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char *filename = argv[2];
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char *cv_filename = NULL;
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char *test_filename = NULL;
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if (cross_validation_set && argc < 5) {
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printf(LANGUAGE_CLASSIFIER_TRAIN_USAGE);
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exit(EXIT_FAILURE);
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} else if (cross_validation_set) {
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cv_filename = argv[3];
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test_filename = argv[4];
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}
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char *output_dir = LIBPOSTAL_LANGUAGE_CLASSIFIER_DIR;
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int output_dir_arg = cross_validation_set ? 5 : 3;
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if (argc > output_dir_arg) {
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output_dir = argv[output_dir_arg];
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}
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#if !defined(HAVE_SHUF)
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log_warn("shuf must be installed to train address parser effectively. If this is a production machine, please install shuf. No shuffling will be performed.\n");
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#endif
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if (!address_dictionary_module_setup(NULL)) {
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log_error("Could not load address dictionaries\n");
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exit(EXIT_FAILURE);
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}
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char_array *temp_file = char_array_new();
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char_array_cat_printf(temp_file, "%s.tmp", filename);
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char *temp_filename = char_array_get_string(temp_file);
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char_array *head_command = char_array_new();
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size_t subset_examples = LANGUAGE_CLASSIFIER_HYPERPARAMETER_BATCHES * LANGUAGE_CLASSIFIER_DEFAULT_BATCH_SIZE;
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char_array_cat_printf(head_command, "head -n %d %s > %s", subset_examples, filename, temp_filename);
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int ret = system(char_array_get_string(head_command));
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if (ret != 0) {
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exit(EXIT_FAILURE);
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}
|
|
|
|
char_array *temp_cv_file = NULL;
|
|
if (!cross_validation_set) {
|
|
char_array_clear(head_command);
|
|
|
|
temp_cv_file = char_array_new();
|
|
char_array_cat_printf(temp_cv_file, "%s.cv.tmp", filename);
|
|
|
|
char *temp_cv_filename = char_array_get_string(temp_cv_file);
|
|
|
|
char_array_cat_printf(head_command, "head -n %d %s | tail -n %d > %s", subset_examples * 2, filename, subset_examples, temp_cv_filename);
|
|
int ret = system(char_array_get_string(head_command));
|
|
|
|
cv_filename = temp_cv_filename;
|
|
}
|
|
|
|
if (ret != 0) {
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
char_array_destroy(head_command);
|
|
|
|
language_classifier_t *language_classifier = language_classifier_train(filename, temp_filename, cross_validation_set, cv_filename, test_filename, TRAIN_EPOCHS);
|
|
|
|
remove(temp_filename);
|
|
char_array_destroy(temp_file);
|
|
if (temp_cv_file != NULL) {
|
|
char_array_destroy(temp_cv_file);
|
|
}
|
|
|
|
log_info("Done with classifier\n");
|
|
char_array *path = char_array_new_size(strlen(output_dir) + PATH_SEPARATOR_LEN + strlen(LANGUAGE_CLASSIFIER_COUNTRY_FILENAME));
|
|
|
|
char *classifier_path;
|
|
if (language_classifier != NULL) {
|
|
char_array_cat_joined(path, PATH_SEPARATOR, true, 2, output_dir, LANGUAGE_CLASSIFIER_FILENAME);
|
|
classifier_path = char_array_get_string(path);
|
|
|
|
language_classifier_save(language_classifier, classifier_path);
|
|
language_classifier_destroy(language_classifier);
|
|
}
|
|
|
|
char_array_destroy(path);
|
|
|
|
log_info("Success!\n");
|
|
|
|
address_dictionary_module_teardown();
|
|
|
|
}
|