def cs_impl(cls): hps = {'kernel': CatHP(choice,a=['linear','poly','rbf','sigmoid']), 'degree': IntHP(uniform,low=1,high=5), #ignored if kernel != poly 'gamma': RealHP(uniform,low=1e-3,high=1.0), #ignored if kernel != rbf or sigmoid 'C': RealHP(uniform,low=1e-1,high=1e4), 'epsilon': RealHP(uniform,low=1e-1,high=1e-4), 'max_iter': IntHP(uniform,low=100,high=1000)} return ConfigSpace(name='SVR', hps=hps)
def cs_impl(cls): hps = { 'alpha': RealHP(uniform, low=1e-5, high=10.0), 'kernel': CatHP(choice, a=['linear', 'polynomial', 'rbf', 'sigmoid']), 'gamma': RealHP(uniform, low=1e-5, high=10.0), #ignored if kernel = linear 'degree': IntHP(uniform, low=1, high=5) } #ignored if kernel != polynomial return ConfigSpace(name='KRR', hps=hps)
def cs_impl(cls): hps = { 'n_estimators': IntHP(uniform, low=2, high=1000), 'criterion': CatHP(choice, a=['mse', 'mae']), 'max_features': CatHP(choice, a=['auto', 'sqrt', 'log2', None]), 'max_depth': IntHP(uniform, low=2, high=1000), 'min_samples_split': RealHP(uniform, low=1e-6, high=0.5), 'min_samples_leaf': RealHP(uniform, low=1e-6, high=0.5), 'min_weight_fraction_leaf': RealHP(uniform, low=0.0, high=0.5), 'min_impurity_decrease': RealHP(uniform, low=0.0, high=0.2) } return ConfigSpace(name='RF', hps=hps)
def cs_impl(cls): hps = { 'C': RealHP(uniform, low=0.0, high=10000.0), 'kernel': CatHP(choice, a=['linear', 'poly', 'rbf', 'sigmoid']), 'degree': IntHP(uniform, low=0, high=1000), 'gamma': CatHP(choice, a=['scale', 'auto']), 'coef0': RealHP(uniform, low=0.0, high=1000.0), 'shrinking': CatHP(choice, a=[True, False]), 'probability': CatHP(choice, a=[True, False]), 'tol': RealHP(uniform, low=1e-6, high=0.5), 'max_iter': IntHP(uniform, low=-1, max=10000), 'decision_function_shape': CatHP(choice, a=['ovo', 'ovr']) } return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls): hps = { 'solver': CatHP(choice, a=['svd', 'lsqr', 'eigen']), 'shrinkage': CatHP(choice, a=[None, 'auto']), 'tol': RealHP(uniform, low=1e-6, high=0.5) } return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls): hps = { 'penalty': CatHP(choice, a=['l1', 'l2', 'elasticnet', 'none']), 'dual': CatHP(uniform, a=[True, False]), 'tol': RealHP(uniform, low=1e-6, high=0.5), 'C': RealHP(uniform, low=0.0, high=10000.0), 'fit_intercept': CatHP(uniform, a=[True, False]), 'solver': CatHP(choice, a=['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']), 'max_iter': IntHP(uniform, low=1, max=10000) } return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls): # Sw # cs = ConfigSpace('Switch') # st = cs.start() # st.add_children([a.start, b.start, c.start]) # cs.finish([a.end,b.end,c.end]) hps = { 'criterion': CatHP(choice, a=['gini', 'entropy']), 'splitter': CatHP(choice, a=['best']), 'class_weight': CatHP(choice, a=[None, 'balanced']), 'max_features': CatHP(choice, a=['auto', 'sqrt', 'log2', None]), 'max_depth': IntHP(uniform, low=2, high=1000), 'min_samples_split': RealHP(uniform, low=1e-6, high=0.3), 'min_samples_leaf': RealHP(uniform, low=1e-6, high=0.3), 'min_weight_fraction_leaf': RealHP(uniform, low=0.0, high=0.3), 'min_impurity_decrease': RealHP(uniform, low=0.0, high=0.2) } return ConfigSpace(name='DT', hps=hps)
def cs_impl(cls): hps = { 'n_estimators': IntHP(uniform, low=2, high=1000), 'criterion': CatHP(choice, a=['mse', 'mae', 'friedman_mse']), 'max_features': CatHP(choice, a=['auto', 'sqrt', 'log2', None]), 'max_depth': IntHP(uniform, low=2, high=100), 'min_samples_split': RealHP(uniform, low=1e-6, high=0.5), 'min_samples_leaf': RealHP(uniform, low=1e-6, high=0.5), 'min_weight_fraction_leaf': RealHP(uniform, low=0.0, high=0.5), 'min_impurity_decrease': RealHP(uniform, low=0.0, high=0.2), 'loss': CatHP(choice, a=['ls', 'lad', 'huber', 'quantile']), 'learning_rate': RealHP(uniform, low=1e-4, high=1e-1), 'subsample': RealHP(uniform, low=0.1, high=0.9), 'alpha': RealHP(uniform, low=0.1, high=0.9), #ignored if loss != huber or quantile 'validation_fraction': RealHP(uniform, low=0.1, high=0.9), # Using Early Stopping # 'n_iter_no_change': IntHP(uniform, low=5, high=20) # # } return ConfigSpace(name='GB', hps=hps)
def cs_impl(cls): hps = {'alpha': RealHP(uniform, low=1e-5, high=10.0)} return ConfigSpace(name='LASSO', hps=hps)