.. _Models: ****** Models ****** Different base obj choices for the :class:`Model` are shown below The exact str indicator, as passed to the `obj` param is represented by the sub-heading (within "") The available models are further broken down by which can work with different problem_types. Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown. binary ====== "dt classifier" *************** Base Class Documentation: :class:`sklearn.tree.DecisionTreeClassifier` Param Distributions 0. "default" :: defaults only 1. "dt classifier dist" :: max_depth: Scalar(lower=1, upper=30).set_mutation(sigma=4.833333333333333).set_bounds(full_range_sampling=True, lower=1, upper=30).set_integer_casting() min_samples_split: Scalar(lower=2, upper=50).set_mutation(sigma=8.0).set_bounds(full_range_sampling=True, lower=2, upper=50).set_integer_casting() class_weight: TransitionChoice([None, 'balanced']) "elastic net logistic" ********************** Base Class Documentation: :class:`sklearn.linear_model.LogisticRegression` Param Distributions 0. "base elastic" :: max_iter: 100 multi_class: 'auto' penalty: 'elasticnet' class_weight: None solver: 'saga' l1_ratio: 0.5 1. "elastic classifier" :: max_iter: 100 multi_class: 'auto' penalty: 'elasticnet' class_weight: TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) C: Log(lower=1e-05, upper=100000.0) 2. "elastic clf v2" :: max_iter: 100 multi_class: 'auto' penalty: 'elasticnet' class_weight: TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) C: Log(lower=0.01, upper=100000.0) 3. "elastic classifier extra" :: max_iter: Scalar(lower=100, upper=1000).set_mutation(sigma=150.0).set_bounds(full_range_sampling=True, lower=100, upper=1000).set_integer_casting() multi_class: 'auto' penalty: 'elasticnet' class_weight: TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) C: Log(lower=1e-05, upper=100000.0) tol: Log(lower=1e-06, upper=0.01) "et classifier" *************** Base Class Documentation: :class:`sklearn.ensemble.ExtraTreesClassifier` Param Distributions 0. "default" :: defaults only "gaussian nb" ************* Base Class Documentation: :class:`sklearn.naive_bayes.GaussianNB` Param Distributions 0. "base gnb" :: var_smoothing: 1e-09 "gb classifier" *************** Base Class Documentation: :class:`sklearn.ensemble.GradientBoostingClassifier` Param Distributions 0. "default" :: defaults only "gp classifier" *************** Base Class Documentation: :class:`sklearn.gaussian_process.GaussianProcessClassifier` Param Distributions 0. "base gp classifier" :: n_restarts_optimizer: 5 "hgb classifier" **************** Base Class Documentation: :class:`sklearn.ensemble.gradient_boosting.HistGradientBoostingClassifier` Param Distributions 0. "default" :: defaults only 1. "hgb dist1" :: max_iter: Scalar(init=100, lower=3, upper=200).set_mutation(sigma=32.833333333333336).set_bounds(full_range_sampling=False, lower=3, upper=200).set_integer_casting() 2. "hgb dist2" :: max_iter: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() min_samples_leaf: Scalar(lower=10, upper=100).set_mutation(sigma=15.0).set_bounds(full_range_sampling=True, lower=10, upper=100).set_integer_casting() max_leaf_nodes: Scalar(init=20, lower=6, upper=80).set_mutation(sigma=12.333333333333334).set_bounds(full_range_sampling=False, lower=6, upper=80).set_integer_casting() l2_regularization: TransitionChoice([0, Log(lower=1e-05, upper=1)]) "knn classifier" **************** Base Class Documentation: :class:`sklearn.neighbors.KNeighborsClassifier` Param Distributions 0. "base knn" :: n_neighbors: 5 1. "knn dist" :: weights: TransitionChoice(['uniform', 'distance']) n_neighbors: Scalar(lower=2, upper=25).set_mutation(sigma=3.8333333333333335).set_bounds(full_range_sampling=True, lower=2, upper=25).set_integer_casting() "lasso logistic" **************** Base Class Documentation: :class:`sklearn.linear_model.LogisticRegression` Param Distributions 0. "base lasso" :: max_iter: 100 multi_class: 'auto' penalty: 'l1' class_weight: None solver: 'liblinear' 1. "lasso C" :: max_iter: 100 multi_class: 'auto' penalty: 'l1' class_weight: TransitionChoice([None, 'balanced']) solver: 'liblinear' C: Log(lower=1e-05, upper=1000.0) 2. "lasso C extra" :: max_iter: Scalar(lower=100, upper=1000).set_mutation(sigma=150.0).set_bounds(full_range_sampling=True, lower=100, upper=1000).set_integer_casting() multi_class: 'auto' penalty: 'l1' class_weight: TransitionChoice([None, 'balanced']) solver: 'liblinear' C: Log(lower=1e-05, upper=1000.0) tol: Log(lower=1e-06, upper=0.01) "light gbm classifier" ********************** Base Class Documentation: :class:`BPt.extensions.BPtLGBM.BPtLGBMClassifier` Param Distributions 0. "base lgbm" :: silent: True 1. "lgbm classifier dist1" :: silent: True boosting_type: TransitionChoice(['gbdt', 'dart', 'goss']) n_estimators: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() num_leaves: Scalar(init=20, lower=6, upper=80).set_mutation(sigma=12.333333333333334).set_bounds(full_range_sampling=False, lower=6, upper=80).set_integer_casting() min_child_samples: Scalar(lower=10, upper=500).set_mutation(sigma=81.66666666666667).set_bounds(full_range_sampling=True, lower=10, upper=500).set_integer_casting() min_child_weight: Log(lower=1e-05, upper=10000.0) subsample: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) colsample_bytree: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) reg_alpha: TransitionChoice([0, Log(lower=1e-05, upper=1)]) reg_lambda: TransitionChoice([0, Log(lower=1e-05, upper=1)]) class_weight: TransitionChoice([None, 'balanced']) 2. "lgbm classifier dist2" :: silent: True lambda_l2: 0.001 boosting_type: TransitionChoice(['gbdt', 'dart']) min_child_samples: TransitionChoice([1, 5, 7, 10, 15, 20, 35, 50, 100, 200, 500, 1000]) num_leaves: TransitionChoice([2, 4, 7, 10, 15, 20, 25, 30, 35, 40, 50, 65, 80, 100, 125, 150, 200, 250]) colsample_bytree: TransitionChoice([0.7, 0.9, 1.0]) subsample: Scalar(lower=0.3, upper=1).set_mutation(sigma=0.11666666666666665).set_bounds(full_range_sampling=True, lower=0.3, upper=1) learning_rate: TransitionChoice([0.01, 0.05, 0.1]) n_estimators: TransitionChoice([5, 20, 35, 50, 75, 100, 150, 200, 350, 500, 750, 1000]) class_weight: TransitionChoice([None, 'balanced']) 3. "lgbm classifier dist3" :: silent: True n_estimators: 1000 early_stopping_rounds: 150 eval_split: 0.2 boosting_type: 'gbdt' learning_rate: Log(init=0.1, lower=0.005, upper=0.2) colsample_bytree: Scalar(init=1, lower=0.75, upper=1).set_mutation(sigma=0.041666666666666664).set_bounds(full_range_sampling=False, lower=0.75, upper=1) min_child_samples: Scalar(init=20, lower=2, upper=30).set_mutation(sigma=4.666666666666667).set_bounds(full_range_sampling=False, lower=2, upper=30).set_integer_casting() num_leaves: Scalar(init=31, lower=16, upper=96).set_mutation(sigma=13.333333333333334).set_bounds(full_range_sampling=False, lower=16, upper=96).set_integer_casting() class_weight: TransitionChoice([None, 'balanced']) "linear svm classifier" *********************** Base Class Documentation: :class:`sklearn.svm.LinearSVC` Param Distributions 0. "base linear svc" :: max_iter: 100 1. "linear svc dist" :: max_iter: 100 C: Log(lower=1, upper=10000.0) class_weight: TransitionChoice([None, 'balanced']) "logistic" ********** Base Class Documentation: :class:`sklearn.linear_model.LogisticRegression` Param Distributions 0. "base logistic" :: max_iter: 100 multi_class: 'auto' penalty: 'none' class_weight: None solver: 'lbfgs' "pa classifier" *************** Base Class Documentation: :class:`sklearn.linear_model.PassiveAggressiveClassifier` Param Distributions 0. "default" :: defaults only "random forest classifier" ************************** Base Class Documentation: :class:`sklearn.ensemble.RandomForestClassifier` Param Distributions 0. "base rf regressor" :: n_estimators: 100 1. "rf classifier dist best" :: n_estimators: Scalar(init=100, lower=10, upper=200).set_mutation(sigma=31.666666666666668).set_bounds(full_range_sampling=False, lower=10, upper=200).set_integer_casting() class_weight: TransitionChoice([None, 'balanced']) 2. "rf classifier dist" :: n_estimators: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() max_depth: TransitionChoice([None, Scalar(init=25, lower=2, upper=200).set_mutation(sigma=33.0).set_bounds(full_range_sampling=False, lower=2, upper=200).set_integer_casting()]) max_features: Scalar(lower=0.1, upper=1.0).set_mutation(sigma=0.15).set_bounds(full_range_sampling=True, lower=0.1, upper=1.0) min_samples_split: Scalar(lower=0.1, upper=1.0).set_mutation(sigma=0.15).set_bounds(full_range_sampling=True, lower=0.1, upper=1.0) bootstrap: True class_weight: TransitionChoice([None, 'balanced']) "ridge logistic" **************** Base Class Documentation: :class:`sklearn.linear_model.LogisticRegression` Param Distributions 0. "base ridge" :: max_iter: 100 penalty: 'l2' solver: 'saga' 1. "ridge C" :: max_iter: 100 solver: 'saga' C: Log(lower=1e-05, upper=1000.0) class_weight: TransitionChoice([None, 'balanced']) 2. "ridge C extra" :: max_iter: Scalar(lower=100, upper=1000).set_mutation(sigma=150.0).set_bounds(full_range_sampling=True, lower=100, upper=1000).set_integer_casting() solver: 'saga' C: Log(lower=1e-05, upper=1000.0) class_weight: TransitionChoice([None, 'balanced']) tol: Log(lower=1e-06, upper=0.01) "sgd classifier" **************** Base Class Documentation: :class:`sklearn.linear_model.SGDClassifier` Param Distributions 0. "default" :: defaults only 1. "sgd elastic classifier" :: loss: 'squared_epsilon_insensitive' penalty: 'elasticnet' alpha: Log(lower=1e-05, upper=100000.0) l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) class_weight: TransitionChoice([None, 'balanced']) 2. "sgd classifier big search" :: loss: TransitionChoice(['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']) penalty: TransitionChoice(['l2', 'l1', 'elasticnet']) alpha: Log(lower=1e-05, upper=100.0) l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) max_iter: 100 learning_rate: TransitionChoice(['optimal', 'invscaling', 'adaptive', 'constant']) eta0: Log(lower=1e-06, upper=1000.0) power_t: Scalar(lower=0.1, upper=0.9).set_mutation(sigma=0.13333333333333333).set_bounds(full_range_sampling=True, lower=0.1, upper=0.9) early_stopping: TransitionChoice([False, True]) validation_fraction: Scalar(lower=0.05, upper=0.5).set_mutation(sigma=0.075).set_bounds(full_range_sampling=True, lower=0.05, upper=0.5) n_iter_no_change: Scalar(lower=5, upper=30).set_mutation(sigma=4.166666666666667).set_bounds(full_range_sampling=True, lower=5, upper=30).set_integer_casting() class_weight: TransitionChoice([None, 'balanced']) "svm classifier" **************** Base Class Documentation: :class:`sklearn.svm.SVC` Param Distributions 0. "base svm classifier" :: kernel: 'rbf' gamma: 'scale' probability: True 1. "svm classifier dist" :: kernel: 'rbf' gamma: Log(lower=1e-06, upper=1) C: Log(lower=0.0001, upper=10000.0) probability: True class_weight: TransitionChoice([None, 'balanced']) "xgb classifier" **************** Base Class Documentation: :class:`xgboost.XGBClassifier` Param Distributions 0. "base xgb classifier" :: verbosity: 0 objective: 'binary:logistic' 1. "xgb classifier dist1" :: verbosity: 0 objective: 'binary:logistic' n_estimators: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() min_child_weight: Log(lower=1e-05, upper=10000.0) subsample: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) colsample_bytree: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) reg_alpha: TransitionChoice([0, Log(lower=1e-05, upper=1)]) reg_lambda: TransitionChoice([0, Log(lower=1e-05, upper=1)]) 2. "xgb classifier dist2" :: verbosity: 0 objective: 'binary:logistic' max_depth: TransitionChoice([None, Scalar(init=25, lower=2, upper=200).set_mutation(sigma=33.0).set_bounds(full_range_sampling=False, lower=2, upper=200).set_integer_casting()]) learning_rate: Scalar(lower=0.01, upper=0.5).set_mutation(sigma=0.08166666666666667).set_bounds(full_range_sampling=True, lower=0.01, upper=0.5) n_estimators: Scalar(lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=True, lower=3, upper=500).set_integer_casting() min_child_weight: TransitionChoice([1, 5, 10, 50]) subsample: Scalar(lower=0.5, upper=1).set_mutation(sigma=0.08333333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=1) colsample_bytree: Scalar(lower=0.4, upper=0.95).set_mutation(sigma=0.09166666666666666).set_bounds(full_range_sampling=True, lower=0.4, upper=0.95) 3. "xgb classifier dist3" :: verbosity: 0 objective: 'binary:logistic' learning_rare: Scalar(lower=0.005, upper=0.3).set_mutation(sigma=0.049166666666666664).set_bounds(full_range_sampling=True, lower=0.005, upper=0.3) min_child_weight: Scalar(lower=0.5, upper=10).set_mutation(sigma=1.5833333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=10) max_depth: TransitionChoice(array([3, 4, 5, 6, 7, 8, 9])) subsample: Scalar(lower=0.5, upper=1).set_mutation(sigma=0.08333333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=1) colsample_bytree: Scalar(lower=0.5, upper=1).set_mutation(sigma=0.08333333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=1) reg_alpha: Log(lower=1e-05, upper=1) regression ========== "ard regressor" *************** Base Class Documentation: :class:`sklearn.linear_model.ARDRegression` Param Distributions 0. "default" :: defaults only "bayesian ridge regressor" ************************** Base Class Documentation: :class:`sklearn.linear_model.BayesianRidge` Param Distributions 0. "default" :: defaults only "dt regressor" ************** Base Class Documentation: :class:`sklearn.tree.DecisionTreeRegressor` Param Distributions 0. "default" :: defaults only 1. "dt dist" :: max_depth: Scalar(lower=1, upper=30).set_mutation(sigma=4.833333333333333).set_bounds(full_range_sampling=True, lower=1, upper=30).set_integer_casting() min_samples_split: Scalar(lower=2, upper=50).set_mutation(sigma=8.0).set_bounds(full_range_sampling=True, lower=2, upper=50).set_integer_casting() "elastic net regressor" *********************** Base Class Documentation: :class:`sklearn.linear_model.ElasticNet` Param Distributions 0. "base elastic net" :: max_iter: 100 1. "elastic regression" :: max_iter: 100 alpha: Log(lower=1e-05, upper=100000.0) l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) 2. "elastic regression extra" :: max_iter: Scalar(lower=100, upper=1000).set_mutation(sigma=150.0).set_bounds(full_range_sampling=True, lower=100, upper=1000).set_integer_casting() alpha: Log(lower=1e-05, upper=100000.0) l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) tol: Log(lower=1e-06, upper=0.01) "et regressor" ************** Base Class Documentation: :class:`sklearn.ensemble.ExtraTreesRegressor` Param Distributions 0. "default" :: defaults only "gb regressor" ************** Base Class Documentation: :class:`sklearn.ensemble.GradientBoostingRegressor` Param Distributions 0. "default" :: defaults only "gp regressor" ************** Base Class Documentation: :class:`sklearn.gaussian_process.GaussianProcessRegressor` Param Distributions 0. "base gp regressor" :: n_restarts_optimizer: 5 normalize_y: True "hgb regressor" *************** Base Class Documentation: :class:`sklearn.ensemble.gradient_boosting.HistGradientBoostingRegressor` Param Distributions 0. "default" :: defaults only 1. "hgb dist1" :: max_iter: Scalar(init=100, lower=3, upper=200).set_mutation(sigma=32.833333333333336).set_bounds(full_range_sampling=False, lower=3, upper=200).set_integer_casting() 2. "hgb dist2" :: max_iter: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() min_samples_leaf: Scalar(lower=10, upper=100).set_mutation(sigma=15.0).set_bounds(full_range_sampling=True, lower=10, upper=100).set_integer_casting() max_leaf_nodes: Scalar(init=20, lower=6, upper=80).set_mutation(sigma=12.333333333333334).set_bounds(full_range_sampling=False, lower=6, upper=80).set_integer_casting() l2_regularization: TransitionChoice([0, Log(lower=1e-05, upper=1)]) "knn regressor" *************** Base Class Documentation: :class:`sklearn.neighbors.KNeighborsRegressor` Param Distributions 0. "base knn regression" :: n_neighbors: 5 1. "knn dist regression" :: weights: TransitionChoice(['uniform', 'distance']) n_neighbors: Scalar(lower=2, upper=25).set_mutation(sigma=3.8333333333333335).set_bounds(full_range_sampling=True, lower=2, upper=25).set_integer_casting() "lasso regressor" ***************** Base Class Documentation: :class:`sklearn.linear_model.Lasso` Param Distributions 0. "base lasso regressor" :: max_iter: 100 1. "lasso regressor dist" :: max_iter: 100 alpha: Log(lower=1e-05, upper=100000.0) "light gbm regressor" ********************* Base Class Documentation: :class:`BPt.extensions.BPtLGBM.BPtLGBMRegressor` Param Distributions 0. "base lgbm" :: silent: True 1. "lgbm dist best" :: silent: True lambda_l2: 0.001 boosting_type: TransitionChoice(['gbdt', 'dart']) min_child_samples: TransitionChoice([1, 5, 7, 10, 15, 20, 35, 50, 100, 200, 500, 1000]) num_leaves: TransitionChoice([2, 4, 7, 10, 15, 20, 25, 30, 35, 40, 50, 65, 80, 100, 125, 150, 200, 250]) colsample_bytree: TransitionChoice([0.7, 0.9, 1.0]) subsample: Scalar(lower=0.3, upper=1).set_mutation(sigma=0.11666666666666665).set_bounds(full_range_sampling=True, lower=0.3, upper=1) learning_rate: TransitionChoice([0.01, 0.05, 0.1]) n_estimators: TransitionChoice([5, 20, 35, 50, 75, 100, 150, 200, 350, 500, 750, 1000]) 2. "lgbm dist1" :: silent: True boosting_type: TransitionChoice(['gbdt', 'dart', 'goss']) n_estimators: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() num_leaves: Scalar(init=20, lower=6, upper=80).set_mutation(sigma=12.333333333333334).set_bounds(full_range_sampling=False, lower=6, upper=80).set_integer_casting() min_child_samples: Scalar(lower=10, upper=500).set_mutation(sigma=81.66666666666667).set_bounds(full_range_sampling=True, lower=10, upper=500).set_integer_casting() min_child_weight: Log(lower=1e-05, upper=10000.0) subsample: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) colsample_bytree: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) reg_alpha: TransitionChoice([0, Log(lower=1e-05, upper=1)]) reg_lambda: TransitionChoice([0, Log(lower=1e-05, upper=1)]) 3. "lgbm dist3" :: silent: True n_estimators: 1000 early_stopping_rounds: 150 eval_split: 0.2 boosting_type: 'gbdt' learning_rate: Log(init=0.1, lower=0.005, upper=0.2) colsample_bytree: Scalar(init=1, lower=0.75, upper=1).set_mutation(sigma=0.041666666666666664).set_bounds(full_range_sampling=False, lower=0.75, upper=1) min_child_samples: Scalar(init=20, lower=2, upper=30).set_mutation(sigma=4.666666666666667).set_bounds(full_range_sampling=False, lower=2, upper=30).set_integer_casting() num_leaves: Scalar(init=31, lower=16, upper=96).set_mutation(sigma=13.333333333333334).set_bounds(full_range_sampling=False, lower=16, upper=96).set_integer_casting() "linear regressor" ****************** Base Class Documentation: :class:`sklearn.linear_model.LinearRegression` Param Distributions 0. "base linear" :: fit_intercept: True "linear svm regressor" ********************** Base Class Documentation: :class:`sklearn.svm.LinearSVR` Param Distributions 0. "base linear svr" :: loss: 'epsilon_insensitive' max_iter: 10000.0 1. "linear svr dist" :: loss: 'epsilon_insensitive' max_iter: 10000.0 C: Log(lower=1, upper=10000.0) "random forest regressor" ************************* Base Class Documentation: :class:`sklearn.ensemble.RandomForestRegressor` Param Distributions 0. "base rf" :: n_estimators: 100 1. "rf dist best" :: n_estimators: Scalar(init=100, lower=10, upper=200).set_mutation(sigma=31.666666666666668).set_bounds(full_range_sampling=False, lower=10, upper=200).set_integer_casting() 2. "rf dist" :: n_estimators: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() max_depth: TransitionChoice([None, Scalar(init=25, lower=2, upper=200).set_mutation(sigma=33.0).set_bounds(full_range_sampling=False, lower=2, upper=200).set_integer_casting()]) max_features: Scalar(lower=0.1, upper=1.0).set_mutation(sigma=0.15).set_bounds(full_range_sampling=True, lower=0.1, upper=1.0) min_samples_split: Scalar(lower=0.1, upper=1.0).set_mutation(sigma=0.15).set_bounds(full_range_sampling=True, lower=0.1, upper=1.0) bootstrap: True "ridge regressor" ***************** Base Class Documentation: :class:`sklearn.linear_model.Ridge` Param Distributions 0. "base ridge regressor" :: max_iter: 100 solver: 'lsqr' 1. "ridge regressor best" :: max_iter: 1000 solver: 'lsqr' alpha: Log(lower=0.001, upper=1000000.0) 2. "ridge regressor dist" :: max_iter: 100 solver: 'lsqr' alpha: Log(lower=0.001, upper=100000.0) "svm regressor" *************** Base Class Documentation: :class:`sklearn.svm.SVR` Param Distributions 0. "base svm" :: kernel: 'rbf' gamma: 'scale' 1. "svm dist" :: kernel: 'rbf' gamma: Log(lower=1e-06, upper=1) C: Log(lower=0.0001, upper=10000.0) "tweedie regressor" ******************* Base Class Documentation: :class:`sklearn.linear_model.glm.TweedieRegressor` Param Distributions 0. "default" :: defaults only "xgb regressor" *************** Base Class Documentation: :class:`xgboost.XGBRegressor` Param Distributions 0. "base xgb" :: verbosity: 0 objective: 'reg:squarederror' 1. "xgb dist1" :: verbosity: 0 objective: 'reg:squarederror' n_estimators: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() min_child_weight: Log(lower=1e-05, upper=10000.0) subsample: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) colsample_bytree: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) reg_alpha: TransitionChoice([0, Log(lower=1e-05, upper=1)]) reg_lambda: TransitionChoice([0, Log(lower=1e-05, upper=1)]) 2. "xgb dist2" :: verbosity: 0 objective: 'reg:squarederror' max_depth: TransitionChoice([None, Scalar(init=25, lower=2, upper=200).set_mutation(sigma=33.0).set_bounds(full_range_sampling=False, lower=2, upper=200).set_integer_casting()]) learning_rate: Scalar(lower=0.01, upper=0.5).set_mutation(sigma=0.08166666666666667).set_bounds(full_range_sampling=True, lower=0.01, upper=0.5) n_estimators: Scalar(lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=True, lower=3, upper=500).set_integer_casting() min_child_weight: TransitionChoice([1, 5, 10, 50]) subsample: Scalar(lower=0.5, upper=1).set_mutation(sigma=0.08333333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=1) colsample_bytree: Scalar(lower=0.4, upper=0.95).set_mutation(sigma=0.09166666666666666).set_bounds(full_range_sampling=True, lower=0.4, upper=0.95) 3. "xgb dist3" :: verbosity: 0 objective: 'reg:squarederror' learning_rare: Scalar(lower=0.005, upper=0.3).set_mutation(sigma=0.049166666666666664).set_bounds(full_range_sampling=True, lower=0.005, upper=0.3) min_child_weight: Scalar(lower=0.5, upper=10).set_mutation(sigma=1.5833333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=10) max_depth: TransitionChoice(array([3, 4, 5, 6, 7, 8, 9])) subsample: Scalar(lower=0.5, upper=1).set_mutation(sigma=0.08333333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=1) colsample_bytree: Scalar(lower=0.5, upper=1).set_mutation(sigma=0.08333333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=1) reg_alpha: Log(lower=1e-05, upper=1) categorical =========== "dt classifier" *************** Base Class Documentation: :class:`sklearn.tree.DecisionTreeClassifier` Param Distributions 0. "default" :: defaults only 1. "dt classifier dist" :: max_depth: Scalar(lower=1, upper=30).set_mutation(sigma=4.833333333333333).set_bounds(full_range_sampling=True, lower=1, upper=30).set_integer_casting() min_samples_split: Scalar(lower=2, upper=50).set_mutation(sigma=8.0).set_bounds(full_range_sampling=True, lower=2, upper=50).set_integer_casting() class_weight: TransitionChoice([None, 'balanced']) "elastic net logistic" ********************** Base Class Documentation: :class:`sklearn.linear_model.LogisticRegression` Param Distributions 0. "base elastic" :: max_iter: 100 multi_class: 'auto' penalty: 'elasticnet' class_weight: None solver: 'saga' l1_ratio: 0.5 1. "elastic classifier" :: max_iter: 100 multi_class: 'auto' penalty: 'elasticnet' class_weight: TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) C: Log(lower=1e-05, upper=100000.0) 2. "elastic clf v2" :: max_iter: 100 multi_class: 'auto' penalty: 'elasticnet' class_weight: TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) C: Log(lower=0.01, upper=100000.0) 3. "elastic classifier extra" :: max_iter: Scalar(lower=100, upper=1000).set_mutation(sigma=150.0).set_bounds(full_range_sampling=True, lower=100, upper=1000).set_integer_casting() multi_class: 'auto' penalty: 'elasticnet' class_weight: TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) C: Log(lower=1e-05, upper=100000.0) tol: Log(lower=1e-06, upper=0.01) "et classifier" *************** Base Class Documentation: :class:`sklearn.ensemble.ExtraTreesClassifier` Param Distributions 0. "default" :: defaults only "gaussian nb" ************* Base Class Documentation: :class:`sklearn.naive_bayes.GaussianNB` Param Distributions 0. "base gnb" :: var_smoothing: 1e-09 "gb classifier" *************** Base Class Documentation: :class:`sklearn.ensemble.GradientBoostingClassifier` Param Distributions 0. "default" :: defaults only "gp classifier" *************** Base Class Documentation: :class:`sklearn.gaussian_process.GaussianProcessClassifier` Param Distributions 0. "base gp classifier" :: n_restarts_optimizer: 5 "hgb classifier" **************** Base Class Documentation: :class:`sklearn.ensemble.gradient_boosting.HistGradientBoostingClassifier` Param Distributions 0. "default" :: defaults only 1. "hgb dist1" :: max_iter: Scalar(init=100, lower=3, upper=200).set_mutation(sigma=32.833333333333336).set_bounds(full_range_sampling=False, lower=3, upper=200).set_integer_casting() 2. "hgb dist2" :: max_iter: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() min_samples_leaf: Scalar(lower=10, upper=100).set_mutation(sigma=15.0).set_bounds(full_range_sampling=True, lower=10, upper=100).set_integer_casting() max_leaf_nodes: Scalar(init=20, lower=6, upper=80).set_mutation(sigma=12.333333333333334).set_bounds(full_range_sampling=False, lower=6, upper=80).set_integer_casting() l2_regularization: TransitionChoice([0, Log(lower=1e-05, upper=1)]) "knn classifier" **************** Base Class Documentation: :class:`sklearn.neighbors.KNeighborsClassifier` Param Distributions 0. "base knn" :: n_neighbors: 5 1. "knn dist" :: weights: TransitionChoice(['uniform', 'distance']) n_neighbors: Scalar(lower=2, upper=25).set_mutation(sigma=3.8333333333333335).set_bounds(full_range_sampling=True, lower=2, upper=25).set_integer_casting() "lasso logistic" **************** Base Class Documentation: :class:`sklearn.linear_model.LogisticRegression` Param Distributions 0. "base lasso" :: max_iter: 100 multi_class: 'auto' penalty: 'l1' class_weight: None solver: 'liblinear' 1. "lasso C" :: max_iter: 100 multi_class: 'auto' penalty: 'l1' class_weight: TransitionChoice([None, 'balanced']) solver: 'liblinear' C: Log(lower=1e-05, upper=1000.0) 2. "lasso C extra" :: max_iter: Scalar(lower=100, upper=1000).set_mutation(sigma=150.0).set_bounds(full_range_sampling=True, lower=100, upper=1000).set_integer_casting() multi_class: 'auto' penalty: 'l1' class_weight: TransitionChoice([None, 'balanced']) solver: 'liblinear' C: Log(lower=1e-05, upper=1000.0) tol: Log(lower=1e-06, upper=0.01) "light gbm classifier" ********************** Base Class Documentation: :class:`BPt.extensions.BPtLGBM.BPtLGBMClassifier` Param Distributions 0. "base lgbm" :: silent: True 1. "lgbm classifier dist1" :: silent: True boosting_type: TransitionChoice(['gbdt', 'dart', 'goss']) n_estimators: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() num_leaves: Scalar(init=20, lower=6, upper=80).set_mutation(sigma=12.333333333333334).set_bounds(full_range_sampling=False, lower=6, upper=80).set_integer_casting() min_child_samples: Scalar(lower=10, upper=500).set_mutation(sigma=81.66666666666667).set_bounds(full_range_sampling=True, lower=10, upper=500).set_integer_casting() min_child_weight: Log(lower=1e-05, upper=10000.0) subsample: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) colsample_bytree: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) reg_alpha: TransitionChoice([0, Log(lower=1e-05, upper=1)]) reg_lambda: TransitionChoice([0, Log(lower=1e-05, upper=1)]) class_weight: TransitionChoice([None, 'balanced']) 2. "lgbm classifier dist2" :: silent: True lambda_l2: 0.001 boosting_type: TransitionChoice(['gbdt', 'dart']) min_child_samples: TransitionChoice([1, 5, 7, 10, 15, 20, 35, 50, 100, 200, 500, 1000]) num_leaves: TransitionChoice([2, 4, 7, 10, 15, 20, 25, 30, 35, 40, 50, 65, 80, 100, 125, 150, 200, 250]) colsample_bytree: TransitionChoice([0.7, 0.9, 1.0]) subsample: Scalar(lower=0.3, upper=1).set_mutation(sigma=0.11666666666666665).set_bounds(full_range_sampling=True, lower=0.3, upper=1) learning_rate: TransitionChoice([0.01, 0.05, 0.1]) n_estimators: TransitionChoice([5, 20, 35, 50, 75, 100, 150, 200, 350, 500, 750, 1000]) class_weight: TransitionChoice([None, 'balanced']) 3. "lgbm classifier dist3" :: silent: True n_estimators: 1000 early_stopping_rounds: 150 eval_split: 0.2 boosting_type: 'gbdt' learning_rate: Log(init=0.1, lower=0.005, upper=0.2) colsample_bytree: Scalar(init=1, lower=0.75, upper=1).set_mutation(sigma=0.041666666666666664).set_bounds(full_range_sampling=False, lower=0.75, upper=1) min_child_samples: Scalar(init=20, lower=2, upper=30).set_mutation(sigma=4.666666666666667).set_bounds(full_range_sampling=False, lower=2, upper=30).set_integer_casting() num_leaves: Scalar(init=31, lower=16, upper=96).set_mutation(sigma=13.333333333333334).set_bounds(full_range_sampling=False, lower=16, upper=96).set_integer_casting() class_weight: TransitionChoice([None, 'balanced']) "linear svm classifier" *********************** Base Class Documentation: :class:`sklearn.svm.LinearSVC` Param Distributions 0. "base linear svc" :: max_iter: 100 1. "linear svc dist" :: max_iter: 100 C: Log(lower=1, upper=10000.0) class_weight: TransitionChoice([None, 'balanced']) "logistic" ********** Base Class Documentation: :class:`sklearn.linear_model.LogisticRegression` Param Distributions 0. "base logistic" :: max_iter: 100 multi_class: 'auto' penalty: 'none' class_weight: None solver: 'lbfgs' "pa classifier" *************** Base Class Documentation: :class:`sklearn.linear_model.PassiveAggressiveClassifier` Param Distributions 0. "default" :: defaults only "random forest classifier" ************************** Base Class Documentation: :class:`sklearn.ensemble.RandomForestClassifier` Param Distributions 0. "base rf regressor" :: n_estimators: 100 1. "rf classifier dist best" :: n_estimators: Scalar(init=100, lower=10, upper=200).set_mutation(sigma=31.666666666666668).set_bounds(full_range_sampling=False, lower=10, upper=200).set_integer_casting() class_weight: TransitionChoice([None, 'balanced']) 2. "rf classifier dist" :: n_estimators: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() max_depth: TransitionChoice([None, Scalar(init=25, lower=2, upper=200).set_mutation(sigma=33.0).set_bounds(full_range_sampling=False, lower=2, upper=200).set_integer_casting()]) max_features: Scalar(lower=0.1, upper=1.0).set_mutation(sigma=0.15).set_bounds(full_range_sampling=True, lower=0.1, upper=1.0) min_samples_split: Scalar(lower=0.1, upper=1.0).set_mutation(sigma=0.15).set_bounds(full_range_sampling=True, lower=0.1, upper=1.0) bootstrap: True class_weight: TransitionChoice([None, 'balanced']) "ridge logistic" **************** Base Class Documentation: :class:`sklearn.linear_model.LogisticRegression` Param Distributions 0. "base ridge" :: max_iter: 100 penalty: 'l2' solver: 'saga' 1. "ridge C" :: max_iter: 100 solver: 'saga' C: Log(lower=1e-05, upper=1000.0) class_weight: TransitionChoice([None, 'balanced']) 2. "ridge C extra" :: max_iter: Scalar(lower=100, upper=1000).set_mutation(sigma=150.0).set_bounds(full_range_sampling=True, lower=100, upper=1000).set_integer_casting() solver: 'saga' C: Log(lower=1e-05, upper=1000.0) class_weight: TransitionChoice([None, 'balanced']) tol: Log(lower=1e-06, upper=0.01) "sgd classifier" **************** Base Class Documentation: :class:`sklearn.linear_model.SGDClassifier` Param Distributions 0. "default" :: defaults only 1. "sgd elastic classifier" :: loss: 'squared_epsilon_insensitive' penalty: 'elasticnet' alpha: Log(lower=1e-05, upper=100000.0) l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) class_weight: TransitionChoice([None, 'balanced']) 2. "sgd classifier big search" :: loss: TransitionChoice(['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']) penalty: TransitionChoice(['l2', 'l1', 'elasticnet']) alpha: Log(lower=1e-05, upper=100.0) l1_ratio: Scalar(lower=0.01, upper=1).set_mutation(sigma=0.165).set_bounds(full_range_sampling=True, lower=0.01, upper=1) max_iter: 100 learning_rate: TransitionChoice(['optimal', 'invscaling', 'adaptive', 'constant']) eta0: Log(lower=1e-06, upper=1000.0) power_t: Scalar(lower=0.1, upper=0.9).set_mutation(sigma=0.13333333333333333).set_bounds(full_range_sampling=True, lower=0.1, upper=0.9) early_stopping: TransitionChoice([False, True]) validation_fraction: Scalar(lower=0.05, upper=0.5).set_mutation(sigma=0.075).set_bounds(full_range_sampling=True, lower=0.05, upper=0.5) n_iter_no_change: Scalar(lower=5, upper=30).set_mutation(sigma=4.166666666666667).set_bounds(full_range_sampling=True, lower=5, upper=30).set_integer_casting() class_weight: TransitionChoice([None, 'balanced']) "svm classifier" **************** Base Class Documentation: :class:`sklearn.svm.SVC` Param Distributions 0. "base svm classifier" :: kernel: 'rbf' gamma: 'scale' probability: True 1. "svm classifier dist" :: kernel: 'rbf' gamma: Log(lower=1e-06, upper=1) C: Log(lower=0.0001, upper=10000.0) probability: True class_weight: TransitionChoice([None, 'balanced']) "xgb classifier" **************** Base Class Documentation: :class:`xgboost.XGBClassifier` Param Distributions 0. "base xgb classifier" :: verbosity: 0 objective: 'binary:logistic' 1. "xgb classifier dist1" :: verbosity: 0 objective: 'binary:logistic' n_estimators: Scalar(init=100, lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=False, lower=3, upper=500).set_integer_casting() min_child_weight: Log(lower=1e-05, upper=10000.0) subsample: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) colsample_bytree: Scalar(lower=0.3, upper=0.95).set_mutation(sigma=0.10833333333333332).set_bounds(full_range_sampling=True, lower=0.3, upper=0.95) reg_alpha: TransitionChoice([0, Log(lower=1e-05, upper=1)]) reg_lambda: TransitionChoice([0, Log(lower=1e-05, upper=1)]) 2. "xgb classifier dist2" :: verbosity: 0 objective: 'binary:logistic' max_depth: TransitionChoice([None, Scalar(init=25, lower=2, upper=200).set_mutation(sigma=33.0).set_bounds(full_range_sampling=False, lower=2, upper=200).set_integer_casting()]) learning_rate: Scalar(lower=0.01, upper=0.5).set_mutation(sigma=0.08166666666666667).set_bounds(full_range_sampling=True, lower=0.01, upper=0.5) n_estimators: Scalar(lower=3, upper=500).set_mutation(sigma=82.83333333333333).set_bounds(full_range_sampling=True, lower=3, upper=500).set_integer_casting() min_child_weight: TransitionChoice([1, 5, 10, 50]) subsample: Scalar(lower=0.5, upper=1).set_mutation(sigma=0.08333333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=1) colsample_bytree: Scalar(lower=0.4, upper=0.95).set_mutation(sigma=0.09166666666666666).set_bounds(full_range_sampling=True, lower=0.4, upper=0.95) 3. "xgb classifier dist3" :: verbosity: 0 objective: 'binary:logistic' learning_rare: Scalar(lower=0.005, upper=0.3).set_mutation(sigma=0.049166666666666664).set_bounds(full_range_sampling=True, lower=0.005, upper=0.3) min_child_weight: Scalar(lower=0.5, upper=10).set_mutation(sigma=1.5833333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=10) max_depth: TransitionChoice(array([3, 4, 5, 6, 7, 8, 9])) subsample: Scalar(lower=0.5, upper=1).set_mutation(sigma=0.08333333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=1) colsample_bytree: Scalar(lower=0.5, upper=1).set_mutation(sigma=0.08333333333333333).set_bounds(full_range_sampling=True, lower=0.5, upper=1) reg_alpha: Log(lower=1e-05, upper=1)