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[classification] flexible logistic regression trainer that can handle either SGD (with either L1 or L2) or FTRL as optimiers

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Al
2017-04-02 14:30:14 -04:00
parent cf88bc7f65
commit 64c049730a
2 changed files with 241 additions and 75 deletions
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48
src/logistic_regression_trainer.h
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@@ -9,6 +9,7 @@
#include "averaged_perceptron_tagger.h"
#include "collections.h"
#include "features.h"
#include "ftrl.h"
#include "logistic_regression.h"
#include "minibatch.h"
#include "sparse_matrix.h"
@@ -21,28 +22,39 @@
* Helper struct for training logistic regression model
*/
typedef enum {
LOGISTIC_REGRESSION_OPTIMIZER_SGD,
LOGISTIC_REGRESSION_OPTIMIZER_FTRL
} logistic_regression_optimizer_type;
typedef struct logistic_regression_trainer {
trie_t *feature_ids; // Trie mapping features to array indices
size_t num_features; // Number of features
khash_t(str_uint32) *label_ids; // Hashtable mapping labels to array indices
size_t num_labels; // Number of labels
double_matrix_t *weights; // Matrix of logistic regression weights
double_matrix_t *gradient; // Gradient matrix to be reused
khash_t(int_set) *unique_columns; // Unique columns set
uint32_array *batch_columns; // Unique columns as array
uint32_array *last_updated; // Array of length N indicating the last time each feature was updated
double lambda; // Regularization weight
uint32_t iters; // Number of iterations, used to decay learning rate
uint32_t epochs; // Number of epochs
double gamma_0; // Initial learning rate
trie_t *feature_ids; // Trie mapping features to array indices
size_t num_features; // Number of features
khash_t(str_uint32) *label_ids; // Hashtable mapping labels to array indices
size_t num_labels; // Number of labels
double_matrix_t *gradient; // Gradient matrix to be reused
khash_t(int_uint32) *unique_columns; // Unique columns set
uint32_array *batch_columns; // Unique columns as array
double_matrix_t *batch_weights; // Weights updated in this batch
uint32_t epochs; // Number of epochs
logistic_regression_optimizer_type optimizer_type; // Trainer type
union {
sgd_trainer_t *sgd; // Stochastic (ok, minibatch) gradient descent
ftrl_trainer_t *ftrl; // Follow-the-regularized-leader (FTRL) Proximal
} optimizer;
} logistic_regression_trainer_t;
logistic_regression_trainer_t *logistic_regression_trainer_init_sgd(trie_t *feature_ids, khash_t(str_uint32) *label_ids, bool fit_intercept, regularization_type_t reg_type, double lambda, double gamma_0);
logistic_regression_trainer_t *logistic_regression_trainer_init_ftrl(trie_t *feature_ids, khash_t(str_uint32) *label_ids, double lambda1, double lambda2, double alpha, double beta);
bool logistic_regression_trainer_reset_params_sgd(logistic_regression_trainer_t *self, double lambda, double gamma_0);
bool logistic_regression_trainer_reset_params_ftrl(logistic_regression_trainer_t *self, double alpha, double beta, double lambda1, double lambda2);
bool logistic_regression_trainer_train_minibatch(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
double logistic_regression_trainer_minibatch_cost(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
logistic_regression_trainer_t *logistic_regression_trainer_init(trie_t *feature_ids, khash_t(str_uint32) *label_ids, double gamma_0, double lambda);
bool logistic_regression_trainer_train_batch(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
double logistic_regression_trainer_batch_cost(logistic_regression_trainer_t *self, feature_count_array *features, cstring_array *labels);
bool logistic_regression_trainer_finalize(logistic_regression_trainer_t *self);
double_matrix_t *logistic_regression_trainer_get_weights(logistic_regression_trainer_t *self);
double_matrix_t *logistic_regression_trainer_get_regularized_weights(logistic_regression_trainer_t *self);
double_matrix_t *logistic_regression_trainer_final_weights(logistic_regression_trainer_t *self);
sparse_matrix_t *logistic_regression_trainer_final_weights_sparse(logistic_regression_trainer_t *self);
void logistic_regression_trainer_destroy(logistic_regression_trainer_t *self);