Modifying Hyperparameter Settings

Offering: openGaussDev

More detail: Modifying Hyperparameter Settings

Match-id-c7650485108bc6fe7b1f63cd7ff9bf23bbc3325f

doc/src/sgml/ref/create_model.sgmlin

@@ -80,6 +80,7 @@ For example:
   * verbose:  0 (no output), 1 (less output), or 2 (full output)
   
 # Hyperparameter list for 'xgboost_regression_logistic', 'xgboost_binary_logistic', 'xgboost_regression_gamma' and 'xgboost_regression_squarederror':
+  * n_iter: Maximum iterations until convergence
   * batch_size: Number of tuples in each processing batch
   * booster: Which booster to use, e.g., gbtree, gblinear or dart (default: gbtree)
   * tree_method: The tree construction algorithm used in XGBoost. Choices: auto, exact, approx, hist, gpu_hist (gpu_hist only supported with GPU)

src/gausskernel/dbmind/db4ai/executor/gd/pca.cpp

@@ -257,7 +257,7 @@ static Datum pca_predict(const Matrix *features, const Matrix *weights,
 static HyperparameterDefinition pca_hyperparameter_definitions[] = {
     HYPERPARAMETER_INT4("number_components", 1, 1, true, INT32_MAX, true, HyperparametersGD, number_dimensions,
                         HP_NO_AUTOML()),
-    HYPERPARAMETER_INT4("batch_size", 1000, 1, true, INT32_MAX, true, HyperparametersGD, batch_size, HP_NO_AUTOML()),
+    HYPERPARAMETER_INT4("batch_size", 1000, 1, true, MAX_BATCH_SIZE, true, HyperparametersGD, batch_size, HP_NO_AUTOML()),
     HYPERPARAMETER_INT4("max_iterations", 100, 1, true, ITER_MAX, true, HyperparametersGD, max_iterations,
                         HP_NO_AUTOML()),
     HYPERPARAMETER_INT4("max_seconds", 0, 0, true, INT32_MAX, true, HyperparametersGD, max_seconds, HP_NO_AUTOML()),

src/gausskernel/dbmind/db4ai/executor/kmeans/kmeans.cpp

@@ -1044,7 +1044,7 @@ HyperparameterDefinition kmeans_hyperparameter_definitions[] = {
     HYPERPARAMETER_INT4("max_iterations", 10, 1, true, ITER_MAX, true, HyperparametersKMeans, num_iterations,
                         HP_NO_AUTOML()),
     HYPERPARAMETER_INT4("num_features", 0, 1, true, INT32_MAX, true, HyperparametersKMeans, n_features, HP_NO_AUTOML()),
-    HYPERPARAMETER_INT4("batch_size", 1000, 1, true, 1000000, true, HyperparametersKMeans, batch_size, HP_NO_AUTOML()),
+    HYPERPARAMETER_INT4("batch_size", 1000, 1, true, MAX_BATCH_SIZE, true, HyperparametersKMeans, batch_size, HP_NO_AUTOML()),
     HYPERPARAMETER_INT4("seed", 0, 0, true, INT32_MAX, true, HyperparametersKMeans, external_seed,
                         HP_AUTOML_INT(1, INT32_MAX, 1, ProbabilityDistribution::UNIFORM_RANGE)),
     HYPERPARAMETER_FLOAT8("tolerance", 0.00001, 0.0, false, 1.0, true, HyperparametersKMeans, tolerance,

src/gausskernel/dbmind/db4ai/executor/xgboost/xgboost.cpp

@@ -226,13 +226,13 @@ const char *xgboost_tree_method_str[] = {"auto", "exact", "approx", "hist", "gpu
 const char *xgboost_eval_metric_str[] = {"rmse", "rmsle", "map", "mae", "auc", "aucpr" };
 static HyperparameterDefinition xgboost_hyperparameter_definitions[] = {
     HYPERPARAMETER_INT4("n_iter", 10, 1, true, ITER_MAX, true, HyperparamsXGBoost, n_iterations, HP_NO_AUTOML()),
-    HYPERPARAMETER_INT4("batch_size", 10000, 1, true, INT32_MAX, true, HyperparamsXGBoost, batch_size, HP_NO_AUTOML()),
+    HYPERPARAMETER_INT4("batch_size", 10000, 1, true, MAX_BATCH_SIZE, true, HyperparamsXGBoost, batch_size, HP_NO_AUTOML()),
     HYPERPARAMETER_INT4("max_depth", 5, 0, true, INT32_MAX, true, HyperparamsXGBoost, max_depth, HP_NO_AUTOML()),
     HYPERPARAMETER_INT4("min_child_weight", 1, 0, true, INT32_MAX, true, HyperparamsXGBoost, min_child_weight,
                         HP_NO_AUTOML()),
-    HYPERPARAMETER_FLOAT8("gamma", 0.0, 0.0, true, 1, true, HyperparamsXGBoost, gamma, HP_NO_AUTOML()),
+    HYPERPARAMETER_FLOAT8("gamma", 0.0, 0.0, true, DBL_MAX, true, HyperparamsXGBoost, gamma, HP_NO_AUTOML()),
     HYPERPARAMETER_FLOAT8("eta", 0.3, 0.0, true, 1, true, HyperparamsXGBoost, eta, HP_NO_AUTOML()),
-    HYPERPARAMETER_INT4("nthread", 1, 0, true, INT32_MAX, true, HyperparamsXGBoost, nthread, HP_NO_AUTOML()),
+    HYPERPARAMETER_INT4("nthread", 1, 0, true, 100, true, HyperparamsXGBoost, nthread, HP_NO_AUTOML()),
     HYPERPARAMETER_INT4("verbosity", 1, 0, true, 3, true, HyperparamsXGBoost, verbosity, HP_NO_AUTOML()),
     HYPERPARAMETER_INT4("seed", 0, 0, true, INT32_MAX, true, HyperparamsXGBoost, seed,
                         HP_AUTOML_INT(1, INT32_MAX, 1, ProbabilityDistribution::UNIFORM_RANGE)),

src/include/db4ai/db4ai_common.h

@@ -26,6 +26,7 @@
 #include "utils/timestamp.h"
 
 #define ITER_MAX 10000
+#define MAX_BATCH_SIZE  0x0fffff
 
 uint64_t time_diff(struct timespec *time_p1, struct timespec *time_p2);
 double interval_to_sec(double time_interval);

src/include/db4ai/gd.h

@@ -261,7 +261,7 @@ typedef struct HyperparametersGD {
 } HyperparametersGD;
 
 #define GD_HYPERPARAMETERS_SUPERVISED \
-    HYPERPARAMETER_INT4("batch_size", 1000, 1, true, INT32_MAX, true, \
+    HYPERPARAMETER_INT4("batch_size", 1000, 1, true, MAX_BATCH_SIZE, true, \
                         HyperparametersGD, batch_size, \
                         HP_AUTOML_INT(1, 10000, 4, ProbabilityDistribution::LOG_RANGE)), \
     HYPERPARAMETER_FLOAT8("decay", 0.95, 0.0, false, DBL_MAX, true, \

src/test/regress/input/db4ai_kmeans_train_predict.source

@@ -1201,7 +1201,7 @@ CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivar
 
 -- Batch size
 CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 50, num_centroids = 10, tolerance = 0.00001, batch_size = 0, num_features = 7, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;
-CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 50, num_centroids = 10, tolerance = 0.00001, batch_size = 1000001, num_features = 7, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;
+CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 50, num_centroids = 10, tolerance = 0.00001, batch_size = 1048576, num_features = 7, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;
 
 -- Num of features (not matching the data)
 CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 50, num_centroids = 10, tolerance = 0.00001, batch_size = 1000, num_features = 9, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;

src/test/regress/output/db4ai_gd_train_predict.source

@@ -14,13 +14,13 @@ CREATE MODEL m using logistic_regression FEATURES size,lot FROM db4ai_houses;
 ERROR:  Supervised ML algorithms require TARGET clause
 -- Errors with semantic validation of hyperparameters
 CREATE MODEL m USING logistic_regression FEATURES size, lot TARGET price <100000 FROM db4ai_houses with batch_size = 0, seed=1;
-ERROR:  Hyperparameter batch_size must be in the range [1,2147483647]
+ERROR:  Hyperparameter batch_size must be in the range [1,1048575]
 CREATE MODEL m USING logistic_regression FEATURES size, lot TARGET price <100000 FROM db4ai_houses with decay = 0.0, seed=1;
 ERROR:  Hyperparameter decay must be in the range (0,1.7976931e+308]
 CREATE MODEL m USING logistic_regression FEATURES size, lot TARGET price <100000 FROM db4ai_houses with learning_rate = 0.0, seed=1;
 ERROR:  Hyperparameter learning_rate must be in the range (0,1.7976931e+308]
 CREATE MODEL m USING logistic_regression FEATURES size, lot TARGET price <100000 FROM db4ai_houses with max_iterations = 0, seed=1;
-ERROR:  Hyperparameter max_iterations must be in the range [1,2147483647]
+ERROR:  Hyperparameter max_iterations must be in the range [1,10000]
 CREATE MODEL m USING logistic_regression FEATURES size, lot TARGET price <100000 FROM db4ai_houses with max_seconds = -1, seed=1;
 ERROR:  Hyperparameter max_seconds must be in the range [0,2147483647]
 CREATE MODEL m USING logistic_regression FEATURES size, lot TARGET price <100000 FROM db4ai_houses with optimizer = nogd, seed=1;

src/test/regress/output/db4ai_kmeans_train_predict.source

@@ -742,7 +742,7 @@ CONTEXT:  referenced column: centroid_id
 -- Wrong parameters
 -- Number of iterations
 CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 0, num_centroids = 10, tolerance = 0.00001, batch_size = 1000, num_features = 7, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;
-ERROR:  Hyperparameter max_iterations must be in the range [1,2147483647]
+ERROR:  Hyperparameter max_iterations must be in the range [1,10000]
 -- Number of centroids
 CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 50, num_centroids = 0, tolerance = 0.00001, batch_size = 1000, num_features = 7, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;
 ERROR:  Hyperparameter num_centroids must be in the range [1,1000000]
@@ -755,9 +755,9 @@ CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivar
 ERROR:  Hyperparameter tolerance must be in the range (0,1]
 -- Batch size
 CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 50, num_centroids = 10, tolerance = 0.00001, batch_size = 0, num_features = 7, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;
-ERROR:  Hyperparameter batch_size must be in the range [1,1000000]
-CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 50, num_centroids = 10, tolerance = 0.00001, batch_size = 1000001, num_features = 7, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;
-ERROR:  Hyperparameter batch_size must be in the range [1,1000000]
+ERROR:  Hyperparameter batch_size must be in the range [1,1048575]
+CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 50, num_centroids = 10, tolerance = 0.00001, batch_size = 1048576, num_features = 7, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;
+ERROR:  Hyperparameter batch_size must be in the range [1,1048575]
 -- Num of features (not matching the data)
 CREATE MODEL my_kmeans_pp_empty USING kmeans FROM (SELECT position FROM multivariate_7_1000_10) WITH max_iterations = 50, num_centroids = 10, tolerance = 0.00001, batch_size = 1000, num_features = 9, distance_function = 'L2_Squared', seeding_function = 'Random++', verbose = 1, seed = 1255025990;
 NOTICE:  *** Initial statistics gathered:

src/test/regress/output/db4ai_xgboost_train_predict.source

@@ -12,9 +12,9 @@ CREATE MODEL m using xgboost_binary_logistic FROM db4ai_rain;
 ERROR:  Supervised ML algorithms require FEATURES clause
 -- Errors with semantic validation of hyperparameters
 CREATE MODEL m USING xgboost_binary_logistic FEATURES rainfall, temp9am TARGET raintoday FROM db4ai_rain WITH n_iter=-1;
-ERROR:  Hyperparameter n_iter must be in the range [1,2147483647]
+ERROR:  Hyperparameter n_iter must be in the range [1,10000]
 CREATE MODEL m USING xgboost_binary_logistic FEATURES rainfall, temp9am TARGET raintoday FROM db4ai_rain WITH batch_size=0;
-ERROR:  Hyperparameter batch_size must be in the range [1,2147483647]
+ERROR:  Hyperparameter batch_size must be in the range [1,1048575]
 CREATE MODEL m USING xgboost_binary_logistic FEATURES rainfall, temp9am TARGET raintoday FROM db4ai_rain WITH max_depth=-1;
 ERROR:  Hyperparameter max_depth must be in the range [0,2147483647]
 CREATE MODEL m USING xgboost_binary_logistic FEATURES rainfall, temp9am TARGET raintoday FROM db4ai_rain WITH min_child_weight=-1;
@@ -24,7 +24,7 @@ ERROR:  Hyperparameter eta must be in the range [0,1]
 CREATE MODEL m USING xgboost_binary_logistic FEATURES rainfall, temp9am TARGET raintoday FROM db4ai_rain WITH seed=-1;
 ERROR:  Hyperparameter seed must be in the range [0,2147483647]
 CREATE MODEL m USING xgboost_binary_logistic FEATURES rainfall, temp9am TARGET raintoday FROM db4ai_rain WITH nthread=-1;
-ERROR:  Hyperparameter nthread must be in the range [0,2147483647]
+ERROR:  Hyperparameter nthread must be in the range [0,100]
 CREATE MODEL m USING xgboost_binary_logistic FEATURES rainfall, temp9am TARGET raintoday FROM db4ai_rain WITH booster=10;
 ERROR:  Hyperparameter booster must be a string
 CREATE MODEL m USING xgboost_binary_logistic FEATURES rainfall, temp9am TARGET raintoday FROM db4ai_rain WITH tree_method=10;