from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter, Constant from automl.utl import json_utils cs = ConfigurationSpace() n_estimators = UniformIntegerHyperparameter("n_estimators", 50, 500, default_value=100) criterion = CategoricalHyperparameter( "criterion", ["gini", "entropy"], default_value="gini") max_features = UniformFloatHyperparameter( "max_features", 0., 1., default_value=0.5) max_depth = Constant("max_depth", "None") min_samples_split = UniformFloatHyperparameter( "min_samples_split", 0., 1., default_value=0.5) min_samples_leaf = UniformFloatHyperparameter( "min_samples_leaf", 0., 0.5, default_value=0.0001) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.) max_leaf_nodes = Constant("max_leaf_nodes", "None") min_impurity_decrease = Constant('min_impurity_decrease', 0.0) bootstrap = CategoricalHyperparameter( "bootstrap", ["True", "False"], default_value="True") cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, bootstrap, min_impurity_decrease]) json_utils.write_cs_to_json_file(cs, "ExtraTreesRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ UniformIntegerHyperparameter from ConfigSpace.forbidden import ForbiddenInClause, \ ForbiddenAndConjunction, ForbiddenEqualsClause from automl.utl import json_utils cs = ConfigurationSpace() n_clusters = UniformIntegerHyperparameter("n_clusters", 2, 400, 2) affinity = CategoricalHyperparameter( "affinity", ["euclidean", "manhattan", "cosine", "l1", "l2"], "euclidean") linkage = CategoricalHyperparameter("linkage", ["ward", "complete", "average", "single"], "ward") cs.add_hyperparameters([n_clusters, affinity, linkage]) affinity_and_linkage = ForbiddenAndConjunction( ForbiddenAndConjunction(ForbiddenEqualsClause(affinity, "manhattan"), ForbiddenEqualsClause(affinity, "cosine"), ForbiddenEqualsClause(affinity, "l1"), ForbiddenEqualsClause(affinity, "l2")), ForbiddenEqualsClause(linkage, "ward")) cs.add_forbidden_clause(affinity_and_linkage) json_utils.write_cs_to_json_file(cs, "FeatureAgglomeration")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter from automl.utl import json_utils gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, default_value=1.0, log=True) n_components = UniformIntegerHyperparameter("n_components", 50, 10000, default_value=100, log=True) cs = ConfigurationSpace() cs.add_hyperparameters([gamma, n_components]) json_utils.write_cs_to_json_file(cs, "RBFSampler")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter, \ UnParametrizedHyperparameter, Constant from automl.utl import json_utils cs = ConfigurationSpace() alpha = UniformFloatHyperparameter(name="alpha", lower=1e-14, upper=1.0, default_value=1e-10, log=True) cs.add_hyperparameter(alpha) json_utils.write_cs_to_json_file(cs, "GaussianProcessRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter from automl.utl import json_utils cs = ConfigurationSpace() var_smoothing = UniformFloatHyperparameter("var_smoothing", 1e-11, 1e-7, default_value=1e-9, log=True) cs.add_hyperparameter(var_smoothing) json_utils.write_cs_to_json_file(cs, "GaussianNB")
default_value=100) criterion = CategoricalHyperparameter("criterion", ["gini", "entropy"], default_value="gini") max_features = UniformFloatHyperparameter("max_features", 0., 1., default_value=0.5) max_depth = Constant("max_depth", "None") min_samples_split = UniformFloatHyperparameter("min_samples_split", 0., 1., default_value=0.5) min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf", 0., 0.5, default_value=0.0001) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.) max_leaf_nodes = Constant("max_leaf_nodes", "None") min_impurity_decrease = Constant('min_impurity_decrease', 0.0) bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"], default_value="True") cs.add_hyperparameters([ n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, bootstrap, min_impurity_decrease ]) json_utils.write_cs_to_json_file(cs, "ExtraTreesClassifier")
0., 1., default_value=0.5) max_depth = Constant("max_depth_none", "None") min_samples_split = UniformFloatHyperparameter("min_samples_split", 0., 1., default_value=0.5) min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf", 0., 0.5, default_value=0.0001) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.) max_leaf_nodes = Constant("max_leaf_nodes", "None") min_impurity_decrease = Constant('min_impurity_decrease', 0.0) bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"], default_value="True") subsample = UniformFloatHyperparameter(name="subsample", lower=0.01, upper=1.0, default_value=1.0) cs.add_hyperparameters([ loss, learning_rate, n_estimators, max_depth, criterion, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, subsample, max_features, max_leaf_nodes, min_impurity_decrease ]) json_utils.write_cs_to_json_file(cs, "GradientBoostingClassifier")
upper=10**-3, default_value=10**-6) alpha_2 = UniformFloatHyperparameter(name="alpha_2", log=True, lower=10**-10, upper=10**-3, default_value=10**-6) lambda_1 = UniformFloatHyperparameter(name="lambda_1", log=True, lower=10**-10, upper=10**-3, default_value=10**-6) lambda_2 = UniformFloatHyperparameter(name="lambda_2", log=True, lower=10**-10, upper=10**-3, default_value=10**-6) threshold_lambda = UniformFloatHyperparameter(name="threshold_lambda", log=True, lower=10**3, upper=10**5, default_value=10**4) fit_intercept = Constant("fit_intercept", "True") cs.add_hyperparameters([ n_iter, tol, alpha_1, alpha_2, lambda_1, lambda_2, threshold_lambda, fit_intercept ]) json_utils.write_cs_to_json_file(cs, "ARDRegression")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter from automl.utl import json_utils cs = ConfigurationSpace() norm = CategoricalHyperparameter("norm", ["l1", "l2", "max"], "l2") cs.add_hyperparameter(norm) json_utils.write_cs_to_json_file(cs, "Normalizer")
0.03125, 32768, log=True, default_value=1.0) loss = CategoricalHyperparameter( "loss", ["epsilon_insensitive", "squared_epsilon_insensitive"], default_value="squared_epsilon_insensitive") epsilon = UniformFloatHyperparameter(name="epsilon", lower=0.001, upper=1, default_value=0.1, log=True) dual = Constant("dual", "False") tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) fit_intercept = Constant("fit_intercept", "True") intercept_scaling = Constant("intercept_scaling", 1) cs.add_hyperparameters( [C, loss, epsilon, dual, tol, fit_intercept, intercept_scaling]) dual_and_loss = ForbiddenAndConjunction( ForbiddenEqualsClause(dual, "False"), ForbiddenEqualsClause(loss, "epsilon_insensitive")) cs.add_forbidden_clause(dual_and_loss) json_utils.write_cs_to_json_file(cs, "LinearSVR")
default_value=100) criterion = CategoricalHyperparameter("criterion", ["gini", "entropy"], default_value="gini") max_features = UniformFloatHyperparameter("max_features", 0., 1., default_value=1.) max_depth = Constant("max_depth", "None") min_samples_split = UniformFloatHyperparameter("min_samples_split", 0., 1., default_value=0.5) min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf", 0., 0.5, default_value=0.0001) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) max_leaf_nodes = Constant("max_leaf_nodes", "None") min_impurity_decrease = Constant('min_impurity_decrease', 0.0) bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"], default_value="True") cs.add_hyperparameters([ n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, bootstrap, min_impurity_decrease ]) json_utils.write_cs_to_json_file(cs, "RandomForestClassifier")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \ CategoricalHyperparameter from automl.utl import json_utils cs = ConfigurationSpace() n_quantiles = UniformIntegerHyperparameter('n_quantiles', lower=10, upper=2000, default_value=1000) output_distribution = CategoricalHyperparameter('output_distribution', ['uniform', 'normal']) cs.add_hyperparameters((n_quantiles, output_distribution)) json_utils.write_cs_to_json_file(cs, "QuantileTransformer")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ UniformIntegerHyperparameter, Constant from ConfigSpace.conditions import EqualsCondition from automl.utl import json_utils cs = ConfigurationSpace() n_components = Constant( "n_components", "None") algorithm = CategoricalHyperparameter('algorithm', ['parallel', 'deflation'], 'parallel') whiten = CategoricalHyperparameter('whiten', ['False', 'True'], 'False') fun = CategoricalHyperparameter( 'fun', ['logcosh', 'exp', 'cube'], 'logcosh') cs.add_hyperparameters([n_components, algorithm, whiten, fun]) cs.add_condition(EqualsCondition(n_components, whiten, "True")) json_utils.write_cs_to_json_file(cs, "FastICA")
default_value=100) criterion = CategoricalHyperparameter("criterion", ['mse', 'friedman_mse', 'mae']) max_features = UniformFloatHyperparameter("max_features", 0., 1., default_value=0.5) max_depth = Constant("max_depth", "None") min_samples_split = UniformFloatHyperparameter("min_samples_split", 0., 1., default_value=0.5) min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf", 0., 0.5, default_value=0.0001) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.) max_leaf_nodes = Constant("max_leaf_nodes", "None") min_impurity_decrease = Constant('min_impurity_decrease', 0.0) bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"], default_value="True") cs.add_hyperparameters([ n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, bootstrap, min_impurity_decrease ]) json_utils.write_cs_to_json_file(cs, "RandomForestRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter from ConfigSpace.conditions import InCondition, EqualsCondition from automl.utl import json_utils cs = ConfigurationSpace() kernel = CategoricalHyperparameter('kernel', ['poly', 'rbf', 'sigmoid', 'cosine'], 'rbf') n_components = UniformIntegerHyperparameter( "n_components", 50, 10000, default_value=100, log=True) gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8, log=True, default_value=0.1) degree = UniformIntegerHyperparameter('degree', 2, 5, 3) coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0) cs.add_hyperparameters([kernel, degree, gamma, coef0, n_components]) degree_depends_on_poly = EqualsCondition(degree, kernel, "poly") coef0_condition = InCondition(coef0, kernel, ["poly", "sigmoid"]) gamma_kernels = ["poly", "rbf", "sigmoid"] gamma_condition = InCondition(gamma, kernel, gamma_kernels) cs.add_conditions([degree_depends_on_poly, coef0_condition, gamma_condition]) json_utils.write_cs_to_json_file(cs, "Nystroem")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter, \ UnParametrizedHyperparameter, Constant from automl.utl import json_utils cs = ConfigurationSpace() # the smoothing parameter is a non-negative float # I will limit it to 100 and put it on a logarithmic scale. (SF) # Please adjust that, if you know a proper range, this is just a guess. alpha = UniformFloatHyperparameter(name="alpha", lower=1e-2, upper=100, default_value=1, log=True) fit_prior = CategoricalHyperparameter(name="fit_prior", choices=["True", "False"], default_value="True") cs.add_hyperparameters([alpha, fit_prior]) json_utils.write_cs_to_json_file(cs, "MultinomialNB")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter from automl.utl import json_utils cs = ConfigurationSpace() n_estimators = UniformIntegerHyperparameter(name="n_estimators", lower=50, upper=500, default_value=100) learning_rate = UniformFloatHyperparameter(name="learning_rate", lower=0., upper=1., default_value=1.) algorithm = CategoricalHyperparameter(name="algorithm", choices=["SAMME.R", "SAMME"], default_value="SAMME.R") cs.add_hyperparameters([n_estimators, learning_rate, algorithm]) json_utils.write_cs_to_json_file(cs, "AdaBoostClassifier")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter from automl.utl import json_utils cs = ConfigurationSpace() shrinkage = UniformFloatHyperparameter("shrinkage", 0., 1., 0.5) tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) cs.add_hyperparameters([shrinkage, tol]) json_utils.write_cs_to_json_file(cs, "LinearDiscriminantAnalysis")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ CategoricalHyperparameter from automl.utl import json_utils cs = ConfigurationSpace() n_components = UniformFloatHyperparameter( "n_components", 0.5, 0.9999, default_value=0.9999) whiten = CategoricalHyperparameter( "whiten", ["False", "True"], default_value="False") cs.add_hyperparameters([n_components, whiten]) json_utils.write_cs_to_json_file(cs, "PCA")
# minimum loss reduction required to make a further partition on a # leaf node of the tree gamma = Constant(name="gamma", value=0) # absolute regularization (in contrast to eta), comparable to # gradient clipping in deep learning - according to the internet this # is most important for unbalanced data max_delta_step = Constant(name="max_delta_step", value=0) base_score = Constant(name="base_score", value=0.5) scale_pos_weight = Constant(name="scale_pos_weight", value=1) cs.add_hyperparameters([ # Active max_depth, learning_rate, n_estimators, booster, subsample, colsample_bytree, colsample_bylevel, reg_alpha, reg_lambda, # Inactive min_child_weight, max_delta_step, gamma, base_score, scale_pos_weight ]) json_utils.write_cs_to_json_file(cs, "XGBRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter from automl.utl import json_utils strategy = CategoricalHyperparameter("strategy", ["mean", "median", "most_frequent"], default_value="mean") cs = ConfigurationSpace() cs.add_hyperparameter(strategy) json_utils.write_cs_to_json_file(cs, "SimpleImputer")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter, \ UnParametrizedHyperparameter, Constant from automl.utl import json_utils cs = ConfigurationSpace() n_neighbors = UniformIntegerHyperparameter(name="n_neighbors", lower=1, upper=100, log=True, default_value=5) weights = CategoricalHyperparameter(name="weights", choices=["uniform", "distance"], default_value="uniform") p = CategoricalHyperparameter(name="p", choices=[1, 2], default_value=2) cs.add_hyperparameters([n_neighbors, weights, p]) json_utils.write_cs_to_json_file(cs, "KNeighborsRegressor")
default_value=0) # probability is no hyperparameter, but an argument to the SVM algo shrinking = CategoricalHyperparameter(name="shrinking", choices=["True", "False"], default_value="True") tol = UniformFloatHyperparameter(name="tol", lower=1e-5, upper=1e-1, default_value=1e-3, log=True) max_iter = Constant("max_iter", -1) cs = ConfigurationSpace() cs.add_hyperparameters( [C, kernel, degree, gamma, coef0, shrinking, tol, max_iter, epsilon]) degree_depends_on_kernel = InCondition(child=degree, parent=kernel, values=('poly', 'rbf', 'sigmoid')) gamma_depends_on_kernel = InCondition(child=gamma, parent=kernel, values=('poly', 'rbf')) coef0_depends_on_kernel = InCondition(child=coef0, parent=kernel, values=('poly', 'sigmoid')) cs.add_conditions([ degree_depends_on_kernel, gamma_depends_on_kernel, coef0_depends_on_kernel ]) json_utils.write_cs_to_json_file(cs, "SVR")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter from automl.utl import json_utils cs =ConfigurationSpace() reg_param = UniformFloatHyperparameter('reg_param', 0.0, 1.0, default_value=0.0) tol = UniformFloatHyperparameter("tol", 1e-6, 1e-2, default_value=1e-4, log=True) cs.add_hyperparameter(reg_param) json_utils.write_cs_to_json_file(cs, "QuadraticDiscriminantAnalysis")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter, \ UnParametrizedHyperparameter, Constant from automl.utl import json_utils cs = ConfigurationSpace() criterion = CategoricalHyperparameter( "criterion", ["gini", "entropy"], default_value="gini") max_depth = Constant("max_depth", "None") min_samples_split = UniformFloatHyperparameter( "min_samples_split", 0., 1., default_value=0.5) min_samples_leaf = UniformFloatHyperparameter( "min_samples_leaf", 0., 0.5, default_value=0.0001) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) max_features = UniformFloatHyperparameter( "max_features", 0., 1., default_value=0.5) max_leaf_nodes = Constant("max_leaf_nodes", "None") min_impurity_decrease = Constant('min_impurity_decrease', 0.0) cs.add_hyperparameters([criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, min_impurity_decrease]) json_utils.write_cs_to_json_file(cs, "DecisionTreeRegressor")
UnParametrizedHyperparameter, Constant from automl.utl import json_utils cs = ConfigurationSpace() criterion = CategoricalHyperparameter("criterion", ["gini", "entropy"], default_value="gini") max_depth = Constant("max_depth", "None") min_samples_split = UniformFloatHyperparameter("min_samples_split", 0., 1., default_value=0.5) min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf", 0., 0.5, default_value=0.0001) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0) max_features = UniformFloatHyperparameter("max_features", 0., 1., default_value=0.5) max_leaf_nodes = Constant("max_leaf_nodes", "None") min_impurity_decrease = Constant('min_impurity_decrease', 0.0) cs.add_hyperparameters([ criterion, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, min_impurity_decrease ]) json_utils.write_cs_to_json_file(cs, "DecisionTreeClassifier")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter, Constant from automl.utl import json_utils cs = ConfigurationSpace() alpha = UniformFloatHyperparameter("alpha", 10**-5, 10., log=True, default_value=1.) fit_intercept = Constant("fit_intercept", "True") tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-3, log=True) cs.add_hyperparameters([alpha, fit_intercept, tol]) json_utils.write_cs_to_json_file(cs, "Ridge")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import CategoricalHyperparameter, \ UniformIntegerHyperparameter from automl.utl import json_utils cs = ConfigurationSpace() degree = UniformIntegerHyperparameter("degree", 2, 3, 2) interaction_only = CategoricalHyperparameter("interaction_only", ["False", "True"], "False") include_bias = CategoricalHyperparameter("include_bias", ["True", "False"], "True") cs.add_hyperparameters([degree, interaction_only, include_bias]) json_utils.write_cs_to_json_file(cs, "PolynomialFeatures")
min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf", 0., 0.5, default_value=0.0001) min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.) subsample = UniformFloatHyperparameter(name="subsample", lower=0.01, upper=1.0, default_value=1.0, log=False) max_features = UniformFloatHyperparameter("max_features", 0., 1., default_value=0.5) max_leaf_nodes = Constant("max_leaf_nodes", "None") min_impurity_decrease = Constant('min_impurity_decrease', 0.0) alpha = UniformFloatHyperparameter("alpha", lower=0.75, upper=0.99, default_value=0.9) cs.add_hyperparameters([ loss, learning_rate, n_estimators, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, subsample, max_features, max_leaf_nodes, min_impurity_decrease, alpha ]) cs.add_condition(InCondition(alpha, loss, ['huber', 'quantile'])) json_utils.write_cs_to_json_file(cs, "GradientBoostingRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ UniformIntegerHyperparameter, CategoricalHyperparameter, \ UnParametrizedHyperparameter, Constant from automl.utl import json_utils C = UniformFloatHyperparameter("C", 1e-5, 10, 1.0, log=True) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") loss = CategoricalHyperparameter("loss", ["hinge", "squared_hinge"], default_value="hinge") tol = UniformFloatHyperparameter("tol", 1e-5, 1e-1, default_value=1e-4, log=True) # Note: Average could also be an Integer if > 1 average = CategoricalHyperparameter('average', ['False', 'True'], default_value='False') cs = ConfigurationSpace() cs.add_hyperparameters([loss, fit_intercept, tol, C, average]) json_utils.write_cs_to_json_file(cs, "PassiveAggressiveClassifier")