/
tpot_configs.py
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/
tpot_configs.py
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"""
This file is modified from the default config files of the TPOT library.
It contains a customed dict of operators that we want to optimize using
genetic algorithm.
We can add/remove Regressors/Preprocessors/Selectors-related operators
to custom the operators to be optimized by tpot in the future. For instance,
the Preprocessors/Selectors-related procedures are currently taken care of
by the Preprocess class in automatminer, so we may consider to comment out the
related operators in the config_dicts (or use tpot instead of Preprocess
to optimize some procedures).
Check the TPOT documentation for information on the structure of config_dicts
"""
import numpy as np
tree_estimators = [20, 100, 200, 500, 1000]
tree_max_features = np.arange(0.05, 1.01, 0.1)
tree_learning_rates = [1e-2, 1e-1, 0.5, 1.0]
tree_max_depths = range(1, 11, 2)
tree_min_samples_split = range(2, 21, 3)
tree_min_samples_leaf = range(1, 21, 3)
tree_ensemble_subsample = np.arange(0.05, 1.01, 0.1)
TPOT_REGRESSOR_CONFIG = {
# Regressors
"sklearn.linear_model.ElasticNetCV": {
"l1_ratio": np.arange(0.0, 1.01, 0.05),
"tol": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1],
},
"sklearn.ensemble.ExtraTreesRegressor": {
"n_estimators": tree_estimators,
"max_features": tree_max_features,
"min_samples_split": tree_min_samples_split,
"min_samples_leaf": tree_min_samples_leaf,
"bootstrap": [True, False],
},
"sklearn.ensemble.GradientBoostingRegressor": {
"n_estimators": tree_estimators,
"loss": ["ls", "lad", "huber", "quantile"],
"learning_rate": tree_learning_rates,
"max_depth": tree_max_depths,
"min_samples_split": tree_min_samples_split,
"min_samples_leaf": tree_min_samples_leaf,
"subsample": np.arange(0.05, 1.01, 0.05),
"max_features": np.arange(0.05, 1.01, 0.05),
"alpha": [0.75, 0.8, 0.85, 0.9, 0.95, 0.99],
},
# 'sklearn.ensemble.AdaBoostRegressor': {
# 'n_estimators': tree_estimators,
# 'learning_rate': tree_learning_rates,
# 'loss': ["linear", "square", "exponential"],
# 'max_depth': tree_max_depths
# },
"sklearn.tree.DecisionTreeRegressor": {
"max_depth": tree_max_depths,
"min_samples_split": tree_min_samples_split,
"min_samples_leaf": tree_min_samples_leaf,
},
"sklearn.neighbors.KNeighborsRegressor": {
"n_neighbors": range(1, 101),
"weights": ["uniform", "distance"],
"p": [1, 2],
},
"sklearn.linear_model.LassoLarsCV": {"normalize": [True, False]},
"sklearn.svm.LinearSVR": {
"loss": ["epsilon_insensitive", "squared_epsilon_insensitive"],
"dual": [True, False],
"tol": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1],
"C": [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0],
"epsilon": [1e-4, 1e-3, 1e-2, 1e-1, 1.0],
},
"sklearn.ensemble.RandomForestRegressor": {
"n_estimators": tree_estimators,
"max_features": np.arange(0.05, 1.01, 0.1),
"min_samples_split": tree_min_samples_split,
"min_samples_leaf": tree_min_samples_leaf,
"bootstrap": [True, False],
},
"sklearn.linear_model.RidgeCV": {},
# "xgboost.XGBRegressor": {
# "n_estimators": tree_estimators,
# "max_depth": tree_max_depths,
# "learning_rate": tree_learning_rates,
# "subsample": tree_ensemble_subsample,
# "min_child_weight": range(1, 21, 4),
# "nthread": [1],
# },
# Preprocesssors
"sklearn.preprocessing.Binarizer": {"threshold": np.arange(0.0, 1.01, 0.05)},
"sklearn.decomposition.FastICA": {"tol": np.arange(0.0, 1.01, 0.05)},
"sklearn.cluster.FeatureAgglomeration": {
"linkage": ["ward", "complete", "average"],
"affinity": ["euclidean", "l1", "l2", "manhattan", "cosine"],
},
"sklearn.preprocessing.MaxAbsScaler": {},
"sklearn.preprocessing.MinMaxScaler": {},
"sklearn.preprocessing.Normalizer": {"norm": ["l1", "l2", "max"]},
"sklearn.kernel_approximation.Nystroem": {
"kernel": [
"rbf",
"cosine",
"chi2",
"laplacian",
"polynomial",
"poly",
"linear",
"additive_chi2",
"sigmoid",
],
"gamma": np.arange(0.0, 1.01, 0.05),
"n_components": range(1, 11),
},
"sklearn.decomposition.PCA": {
"svd_solver": ["randomized"],
"iterated_power": range(1, 11),
},
"sklearn.preprocessing.PolynomialFeatures": {
"degree": [2],
"include_bias": [False],
"interaction_only": [False],
},
"sklearn.kernel_approximation.RBFSampler": {"gamma": np.arange(0.0, 1.01, 0.05)},
"sklearn.preprocessing.RobustScaler": {},
"sklearn.preprocessing.StandardScaler": {},
"tpot.builtins.ZeroCount": {},
"tpot.builtins.OneHotEncoder": {
"minimum_fraction": [0.05, 0.1, 0.15, 0.2, 0.25],
"sparse": [False],
"threshold": [10],
},
# Selectors
"sklearn.feature_selection.SelectFwe": {
"alpha": np.arange(0, 0.05, 0.001),
"score_func": {"sklearn.feature_selection.f_regression": None},
},
"sklearn.feature_selection.SelectPercentile": {
"percentile": range(1, 100),
"score_func": {"sklearn.feature_selection.f_regression": None},
},
"sklearn.feature_selection.VarianceThreshold": {
"threshold": [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]
},
"sklearn.feature_selection.SelectFromModel": {
"threshold": np.arange(0, 1.01, 0.05),
"estimator": {
"sklearn.ensemble.ExtraTreesRegressor": {
"n_estimators": [100],
"max_features": tree_max_features,
}
},
},
}
TPOT_CLASSIFIER_CONFIG = {
# Classifiers
"sklearn.naive_bayes.GaussianNB": {},
"sklearn.naive_bayes.BernoulliNB": {
"alpha": [1e-3, 1e-2, 1e-1, 1.0, 10.0, 100.0],
"fit_prior": [True, False],
},
"sklearn.naive_bayes.MultinomialNB": {
"alpha": [1e-3, 1e-2, 1e-1, 1.0, 10.0, 100.0],
"fit_prior": [True, False],
},
"sklearn.tree.DecisionTreeClassifier": {
"criterion": ["gini", "entropy"],
"max_depth": tree_max_depths,
"min_samples_split": tree_min_samples_split,
"min_samples_leaf": tree_min_samples_leaf,
},
"sklearn.ensemble.ExtraTreesClassifier": {
"n_estimators": tree_estimators,
"criterion": ["gini", "entropy"],
"max_features": tree_max_features,
"min_samples_split": tree_min_samples_split,
"min_samples_leaf": tree_min_samples_leaf,
"bootstrap": [True, False],
},
"sklearn.ensemble.RandomForestClassifier": {
"n_estimators": tree_estimators,
"criterion": ["gini", "entropy"],
"max_features": tree_max_features,
"min_samples_split": tree_min_samples_split,
"min_samples_leaf": tree_min_samples_leaf,
"bootstrap": [True, False],
},
"sklearn.ensemble.GradientBoostingClassifier": {
"n_estimators": tree_estimators,
"learning_rate": tree_learning_rates,
"max_depth": tree_max_depths,
"min_samples_split": tree_min_samples_split,
"min_samples_leaf": tree_min_samples_leaf,
"subsample": tree_ensemble_subsample,
"max_features": tree_max_features,
},
"sklearn.neighbors.KNeighborsClassifier": {
"n_neighbors": range(1, 101),
"weights": ["uniform", "distance"],
"p": [1, 2],
},
"sklearn.svm.LinearSVC": {
"penalty": ["l1", "l2"],
"loss": ["hinge", "squared_hinge"],
"dual": [True, False],
"tol": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1],
"C": [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0],
},
"sklearn.linear_model.LogisticRegression": {
"penalty": ["l1", "l2"],
"C": [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0],
"dual": [True, False],
},
# "xgboost.XGBClassifier": {
# "n_estimators": tree_estimators,
# "max_depth": tree_max_depths,
# "learning_rate": tree_learning_rates,
# "subsample": tree_ensemble_subsample,
# "min_child_weight": range(1, 21),
# "nthread": [1],
# },
# Preprocesssors
"sklearn.preprocessing.Binarizer": {"threshold": np.arange(0.0, 1.01, 0.05)},
"sklearn.decomposition.FastICA": {"tol": np.arange(0.0, 1.01, 0.05)},
"sklearn.cluster.FeatureAgglomeration": {
"linkage": ["ward", "complete", "average"],
"affinity": ["euclidean", "l1", "l2", "manhattan", "cosine"],
},
"sklearn.preprocessing.MaxAbsScaler": {},
"sklearn.preprocessing.MinMaxScaler": {},
"sklearn.preprocessing.Normalizer": {"norm": ["l1", "l2", "max"]},
"sklearn.kernel_approximation.Nystroem": {
"kernel": [
"rbf",
"cosine",
"chi2",
"laplacian",
"polynomial",
"poly",
"linear",
"additive_chi2",
"sigmoid",
],
"gamma": np.arange(0.0, 1.01, 0.05),
"n_components": range(1, 11),
},
"sklearn.decomposition.PCA": {
"svd_solver": ["randomized"],
"iterated_power": range(1, 11),
},
"sklearn.preprocessing.PolynomialFeatures": {
"degree": [2],
"include_bias": [False],
"interaction_only": [False],
},
"sklearn.kernel_approximation.RBFSampler": {"gamma": np.arange(0.0, 1.01, 0.05)},
"sklearn.preprocessing.RobustScaler": {},
"sklearn.preprocessing.StandardScaler": {},
"tpot.builtins.ZeroCount": {},
"tpot.builtins.OneHotEncoder": {
"minimum_fraction": [0.05, 0.1, 0.15, 0.2, 0.25],
"sparse": [False],
"threshold": [10],
},
# Selectors
"sklearn.feature_selection.SelectFwe": {
"alpha": np.arange(0, 0.05, 0.001),
"score_func": {"sklearn.feature_selection.f_classif": None},
},
"sklearn.feature_selection.SelectPercentile": {
"percentile": range(1, 100),
"score_func": {"sklearn.feature_selection.f_classif": None},
},
"sklearn.feature_selection.VarianceThreshold": {
"threshold": [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]
},
"sklearn.feature_selection.RFE": {
"step": np.arange(0.05, 1.01, 0.05),
"estimator": {
"sklearn.ensemble.ExtraTreesClassifier": {
"n_estimators": [100],
"criterion": ["gini", "entropy"],
"max_features": tree_max_features,
}
},
},
"sklearn.feature_selection.SelectFromModel": {
"threshold": np.arange(0, 1.01, 0.05),
"estimator": {
"sklearn.ensemble.ExtraTreesClassifier": {
"n_estimators": [100],
"criterion": ["gini", "entropy"],
"max_features": tree_max_features,
}
},
},
}