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[classification] Pre-allocating memory in logistic regression trainer, storing last updated timestamps for sparse stochastic gradient descent and using the new gradient API

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Al
2016-01-09 01:39:24 -05:00
parent 562cc06eaf
commit 023c04d78f
2 changed files with 53 additions and 5 deletions
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53
src/logistic_regression_trainer.c
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@@ -1,4 +1,5 @@
#include "logistic_regression_trainer.h"
#include "sparse_matrix_utils.h"
void logistic_regression_trainer_destroy(logistic_regression_trainer_t *self) {
if (self == NULL) return;
@@ -15,6 +16,22 @@ void logistic_regression_trainer_destroy(logistic_regression_trainer_t *self) {
matrix_destroy(self->weights);
}
if (self->last_updated != NULL) {
uint32_array_destroy(self->last_updated);
}
if (self->unique_columns != NULL) {
kh_destroy(int_set, self->unique_columns);
}
if (self->batch_columns != NULL) {
uint32_array_destroy(self->batch_columns);
}
if (self->gradient != NULL) {
matrix_destroy(self->gradient);
}
free(self);
}
@@ -33,6 +50,13 @@ logistic_regression_trainer_t *logistic_regression_trainer_init(trie_t *feature_
trainer->weights = matrix_new_zeros(trainer->num_features, trainer->num_labels);
trainer->gradient = matrix_new_zeros(trainer->num_features, trainer->num_labels);
trainer->unique_columns = kh_init(int_set);
trainer->batch_columns = uint32_array_new_size(trainer->num_features);
trainer->last_updated = uint32_array_new_zeros(trainer->num_features);
trainer->lambda = DEFAULT_LAMBDA;
trainer->iters = 0;
trainer->epochs = 0;
@@ -75,12 +99,12 @@ double logistic_regression_trainer_batch_cost(logistic_regression_trainer_t *sel
return cost;
}
bool logistic_regression_trainer_train_batch(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels) {
size_t m = self->weights->m;
size_t n = self->weights->n;
matrix_t *gradient = matrix_new_zeros(m, n);
// Optimize
matrix_t *gradient = self->gradient;
sparse_matrix_t *x = feature_matrix(self->feature_ids, features);
uint32_array *y = label_vector(self->label_ids, labels);
@@ -89,21 +113,40 @@ bool logistic_regression_trainer_train_batch(logistic_regression_trainer_t *self
bool ret = false;
if (!logistic_regression_gradient(self->weights, gradient, x, y, p_y, self->lambda)) {
if (!sparse_matrix_add_unique_columns(x, self->unique_columns, self->batch_columns)) {
log_error("Unique columns failed\n");
goto exit_matrices_created;
}
if (self->lambda > 0.0 && !stochastic_gradient_descent_sparse_regularize_weights(self->weights, self->batch_columns, self->last_updated, self->iters, self->lambda)) {
log_error("Error regularizing weights\n");
goto exit_matrices_created;
}
if (!logistic_regression_gradient_sparse(self->weights, gradient, x, y, p_y, self->batch_columns, self->lambda)) {
log_error("Gradient failed\n");
goto exit_matrices_created;
}
size_t data_len = m * n;
ret = stochastic_gradient_descent(self->weights, gradient, self->gamma);
ret = stochastic_gradient_descent_sparse(self->weights, gradient, self->batch_columns, self->gamma);
self->iters++;
exit_matrices_created:
matrix_destroy(gradient);
matrix_destroy(p_y);
uint32_array_destroy(y);
sparse_matrix_destroy(x);
return ret;
}
bool logistic_regression_trainer_finalize(logistic_regression_trainer_t *self) {
if (self == NULL) return false;
if (self->lambda > 0.0) {
return stochastic_gradient_descent_sparse_finalize_weights(self->weights, self->last_updated, self->iters, self->lambda);
}
return true;
}