config_space.py

import ConfigSpace as cs
import ConfigSpace.hyperparameters as csh

from copy import deepcopy


BASE_PARAM_GRID = cs.ConfigurationSpace(seed=42)
BASE_PARAM_GRID.add_hyperparameter(csh.CategoricalHyperparameter(
    'preprocessor__numeric_transformer__log_creator__take_log', ['yes', 'no']))
BASE_PARAM_GRID.add_hyperparameter(csh.CategoricalHyperparameter(
    'preprocessor__categorical_transformer__category_combiner__combine_categories', ['yes', 'no']))
BASE_PARAM_GRID.add_hyperparameter(csh.UniformIntegerHyperparameter(
    'feature_selector__percentile', 1, 100))
FOREST_PARAM_GRID = deepcopy(BASE_PARAM_GRID)
FOREST_PARAM_GRID.add_hyperparameter(csh.UniformIntegerHyperparameter('model__base_estimator__max_depth', 3, 30))
FOREST_PARAM_GRID.add_hyperparameter(csh.UniformFloatHyperparameter('model__base_estimator__min_samples_leaf',
                                                                    0.0001, 0.01))
FOREST_PARAM_GRID.add_hyperparameter(csh.CategoricalHyperparameter('model__base_estimator__max_features',
                                                                   ['log2', 'sqrt']))


MODEL_TRAINING_DICT = {
    'random_forest': [RandomForestClassifier(n_estimators=500), FOREST_PARAM_GRID, 10]
}