Models¶
Different base obj choices for the Model
are shown below
The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”)
The avaliable models are further broken down by which can workwith 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 Documenation:
sklearn.tree.DecisionTreeClassifier
Param Distributions
“default”
defaults only“dt classifier dist”
max_depth: ng.p.Scalar(lower=1, upper=30).set_integer_casting() min_samples_split: ng.p.Scalar(lower=2, upper=50).set_integer_casting() class_weight: ng.p.TransitionChoice([None, 'balanced'])
“elastic net logistic”¶
Base Class Documenation:
sklearn.linear_model.LogisticRegression
Param Distributions
“base elastic”
max_iter: 1000 multi_class: 'auto' penalty: 'elasticnet' class_weight: None solver: 'saga' l1_ratio: .5“elastic classifier”
max_iter: 1000 multi_class: 'auto' penalty: 'elasticnet' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: ng.p.Scalar(lower=.01, upper=1) C: ng.p.Log(lower=1e-5, upper=1e5)“elastic clf v2”
max_iter: 1000 multi_class: 'auto' penalty: 'elasticnet' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: ng.p.Scalar(lower=.01, upper=1) C: ng.p.Log(lower=1e-2, upper=1e5)“elastic classifier extra”
max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting() multi_class: 'auto' penalty: 'elasticnet' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: ng.p.Scalar(lower=.01, upper=1) C: ng.p.Log(lower=1e-5, upper=1e5) tol: ng.p.Log(lower=1e-6, upper=.01)
“et classifier”¶
Base Class Documenation:
sklearn.ensemble.ExtraTreesClassifier
Param Distributions
“default”
defaults only
“gaussian nb”¶
Base Class Documenation:
sklearn.naive_bayes.GaussianNB
Param Distributions
“base gnb”
var_smoothing: 1e-9
“gb classifier”¶
Base Class Documenation:
sklearn.ensemble.GradientBoostingClassifier
Param Distributions
“default”
defaults only
“gp classifier”¶
Base Class Documenation:
sklearn.gaussian_process.GaussianProcessClassifier
Param Distributions
“base gp classifier”
n_restarts_optimizer: 5
“hgb classifier”¶
Base Class Documenation:
sklearn.ensemble.gradient_boosting.HistGradientBoostingClassifier
Param Distributions
“default”
defaults only
“knn classifier”¶
Base Class Documenation:
sklearn.neighbors.KNeighborsClassifier
Param Distributions
“base knn”
n_neighbors: 5“knn dist”
weights: ng.p.TransitionChoice(['uniform', 'distance']) n_neighbors: ng.p.Scalar(lower=2, upper=25).set_integer_casting()
“lasso logistic”¶
Base Class Documenation:
sklearn.linear_model.LogisticRegression
Param Distributions
“base lasso”
max_iter: 1000 multi_class: 'auto' penalty: 'l1' class_weight: None solver: 'liblinear'“lasso C”
max_iter: 1000 multi_class: 'auto' penalty: 'l1' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'liblinear' C: ng.p.Log(lower=1e-5, upper=1e3)“lasso C extra”
max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting() multi_class: 'auto' penalty: 'l1' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'liblinear' C: ng.p.Log(lower=1e-5, upper=1e3) tol: ng.p.Log(lower=1e-6, upper=.01)
“light gbm classifier”¶
Base Class Documenation:
lightgbm.LGBMClassifier
Param Distributions
“base lgbm”
silent: True“lgbm classifier dist1”
silent: True boosting_type: ng.p.TransitionChoice(['gbdt', 'dart', 'goss']) n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting() num_leaves: ng.p.Scalar(init=20, lower=6, upper=80).set_integer_casting() min_child_samples: ng.p.Scalar(lower=10, upper=500).set_integer_casting() min_child_weight: ng.p.Log(lower=1e-5, upper=1e4) subsample: ng.p.Scalar(lower=.3, upper=.95) colsample_bytree: ng.p.Scalar(lower=.3, upper=.95) reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)]) reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)]) class_weight: ng.p.TransitionChoice([None, 'balanced'])“lgbm classifier dist2”
silent: True lambda_l2: 0.001 boosting_type: ng.p.TransitionChoice(['gbdt', 'dart']) min_child_samples: ng.p.TransitionChoice([1, 5, 7, 10, 15, 20, 35, 50, 100, 200, 500, 1000]) num_leaves: ng.p.TransitionChoice([2, 4, 7, 10, 15, 20, 25, 30, 35, 40, 50, 65, 80, 100, 125, 150, 200, 250]) colsample_bytree: ng.p.TransitionChoice([0.7, 0.9, 1.0]) subsample: ng.p.Scalar(lower=.3, upper=1) learning_rate: ng.p.TransitionChoice([0.01, 0.05, 0.1]) n_estimators: ng.p.TransitionChoice([5, 20, 35, 50, 75, 100, 150, 200, 350, 500, 750, 1000]) class_weight: ng.p.TransitionChoice([None, 'balanced'])
“linear svm classifier”¶
Base Class Documenation:
sklearn.svm.LinearSVC
Param Distributions
“base linear svc”
max_iter: 1000“linear svc dist”
max_iter: 1000 C: ng.p.Log(lower=1e-4, upper=1e4) class_weight: ng.p.TransitionChoice([None, 'balanced'])
“logistic”¶
Base Class Documenation:
sklearn.linear_model.LogisticRegression
Param Distributions
“base logistic”
max_iter: 1000 multi_class: 'auto' penalty: 'none' class_weight: None solver: 'lbfgs'
“mlp classifier”¶
Base Class Documenation:
BPt.extensions.MLP.MLPClassifier_Wrapper
Param Distributions
“default”
defaults only“mlp dist 3 layer”
hidden_layer_sizes: ng.p.Array(init=(100, 100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)“mlp dist es 3 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999) early_stopping: True n_iter_no_change: ng.p.Scalar(lower=5, upper=50)“mlp dist 2 layer”
hidden_layer_sizes: ng.p.Array(init=(100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)“mlp dist es 2 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999) early_stopping: True n_iter_no_change: ng.p.Scalar(lower=5, upper=50)“mlp dist 1 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)“mlp dist es 1 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999) early_stopping: True n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
“pa classifier”¶
Base Class Documenation:
sklearn.linear_model.PassiveAggressiveClassifier
Param Distributions
“default”
defaults only
“random forest classifier”¶
Base Class Documenation:
sklearn.ensemble.RandomForestClassifier
Param Distributions
“base rf regressor”
n_estimators: 100“rf classifier dist”
n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting() max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()]) max_features: ng.p.Scalar(lower=.1, upper=1.0) min_samples_split: ng.p.Scalar(lower=.1, upper=1.0) bootstrap: True class_weight: ng.p.TransitionChoice([None, 'balanced'])
“ridge logistic”¶
Base Class Documenation:
sklearn.linear_model.LogisticRegression
Param Distributions
“base ridge”
max_iter: 1000 penalty: 'l2' solver: 'saga'“ridge C”
max_iter: 1000 solver: 'saga' C: ng.p.Log(lower=1e-5, upper=1e3) class_weight: ng.p.TransitionChoice([None, 'balanced'])“ridge C extra”
max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting() solver: 'saga' C: ng.p.Log(lower=1e-5, upper=1e3) class_weight: ng.p.TransitionChoice([None, 'balanced']) tol: ng.p.Log(lower=1e-6, upper=.01)
“sgd classifier”¶
Base Class Documenation:
sklearn.linear_model.SGDClassifier
Param Distributions
“base sgd”
loss: 'hinge'“sgd classifier”
loss: ng.p.TransitionChoice(['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']) penalty: ng.p.TransitionChoice(['l2', 'l1', 'elasticnet']) alpha: ng.p.Log(lower=1e-5, upper=1e2) l1_ratio: ng.p.Scalar(lower=0, upper=1) max_iter: 1000 learning_rate: ng.p.TransitionChoice(['optimal', 'invscaling', 'adaptive', 'constant']) eta0: ng.p.Log(lower=1e-6, upper=1e3) power_t: ng.p.Scalar(lower=.1, upper=.9) early_stopping: ng.p.TransitionChoice([False, True]) validation_fraction: ng.p.Scalar(lower=.05, upper=.5) n_iter_no_change: ng.p.TransitionChoice(np.arange(2, 20)) class_weight: ng.p.TransitionChoice([None, 'balanced'])
“svm classifier”¶
Base Class Documenation:
sklearn.svm.SVC
Param Distributions
“base svm classifier”
kernel: 'rbf' gamma: 'scale' probability: True“svm classifier dist”
kernel: 'rbf' gamma: ng.p.Log(lower=1e-6, upper=1) C: ng.p.Log(lower=1e-4, upper=1e4) probability: True class_weight: ng.p.TransitionChoice([None, 'balanced'])
“xgb classifier”¶
Base Class Documenation:
xgboost.XGBClassifier
Param Distributions
“base xgb classifier”
verbosity: 0 objective: 'binary:logistic'“xgb classifier dist1”
verbosity: 0 objective: 'binary:logistic' n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting() min_child_weight: ng.p.Log(lower=1e-5, upper=1e4) subsample: ng.p.Scalar(lower=.3, upper=.95) colsample_bytree: ng.p.Scalar(lower=.3, upper=.95) reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)]) reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])“xgb classifier dist2”
verbosity: 0 objective: 'binary:logistic' max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()]) learning_rate: ng.p.Scalar(lower=.01, upper=.5) n_estimators: ng.p.Scalar(lower=3, upper=500).set_integer_casting() min_child_weight: ng.p.TransitionChoice([1, 5, 10, 50]) subsample: ng.p.Scalar(lower=.5, upper=1) colsample_bytree: ng.p.Scalar(lower=.4, upper=.95)“xgb classifier dist3”
verbosity: 0 objective: 'binary:logistic' learning_rare: ng.p.Scalar(lower=.005, upper=.3) min_child_weight: ng.p.Scalar(lower=.5, upper=10) max_depth: ng.p.TransitionChoice(np.arange(3, 10)) subsample: ng.p.Scalar(lower=.5, upper=1) colsample_bytree: ng.p.Scalar(lower=.5, upper=1) reg_alpha: ng.p.Log(lower=.00001, upper=1)
regression¶
“ard regressor”¶
Base Class Documenation:
sklearn.linear_model.ARDRegression
Param Distributions
“default”
defaults only
“bayesian ridge regressor”¶
Base Class Documenation:
sklearn.linear_model.BayesianRidge
Param Distributions
“default”
defaults only
“dt regressor”¶
Base Class Documenation:
sklearn.tree.DecisionTreeRegressor
Param Distributions
“default”
defaults only“dt dist”
max_depth: ng.p.Scalar(lower=1, upper=30).set_integer_casting() min_samples_split: ng.p.Scalar(lower=2, upper=50).set_integer_casting()
“elastic net regressor”¶
Base Class Documenation:
sklearn.linear_model.ElasticNet
Param Distributions
“base elastic net”
max_iter: 1000“elastic regression”
max_iter: 1000 alpha: ng.p.Log(lower=1e-5, upper=1e5) l1_ratio: ng.p.Scalar(lower=.01, upper=1)“elastic regression extra”
max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting() alpha: ng.p.Log(lower=1e-5, upper=1e5) l1_ratio: ng.p.Scalar(lower=.01, upper=1) tol: ng.p.Log(lower=1e-6, upper=.01)
“et regressor”¶
Base Class Documenation:
sklearn.ensemble.ExtraTreesRegressor
Param Distributions
“default”
defaults only
“gb regressor”¶
Base Class Documenation:
sklearn.ensemble.GradientBoostingRegressor
Param Distributions
“default”
defaults only
“gp regressor”¶
Base Class Documenation:
sklearn.gaussian_process.GaussianProcessRegressor
Param Distributions
“base gp regressor”
n_restarts_optimizer: 5 normalize_y: True
“hgb regressor”¶
Base Class Documenation:
sklearn.ensemble.gradient_boosting.HistGradientBoostingRegressor
Param Distributions
“default”
defaults only
“knn regressor”¶
Base Class Documenation:
sklearn.neighbors.KNeighborsRegressor
Param Distributions
“base knn regression”
n_neighbors: 5“knn dist regression”
weights: ng.p.TransitionChoice(['uniform', 'distance']) n_neighbors: ng.p.Scalar(lower=2, upper=25).set_integer_casting()
“lasso regressor”¶
Base Class Documenation:
sklearn.linear_model.Lasso
Param Distributions
“base lasso regressor”
max_iter: 1000“lasso regressor dist”
max_iter: 1000 alpha: ng.p.Log(lower=1e-5, upper=1e5)
“light gbm regressor”¶
Base Class Documenation:
lightgbm.LGBMRegressor
Param Distributions
“base lgbm”
silent: True“lgbm dist1”
silent: True boosting_type: ng.p.TransitionChoice(['gbdt', 'dart', 'goss']) n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting() num_leaves: ng.p.Scalar(init=20, lower=6, upper=80).set_integer_casting() min_child_samples: ng.p.Scalar(lower=10, upper=500).set_integer_casting() min_child_weight: ng.p.Log(lower=1e-5, upper=1e4) subsample: ng.p.Scalar(lower=.3, upper=.95) colsample_bytree: ng.p.Scalar(lower=.3, upper=.95) reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)]) reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])“lgbm dist2”
silent: True lambda_l2: 0.001 boosting_type: ng.p.TransitionChoice(['gbdt', 'dart']) min_child_samples: ng.p.TransitionChoice([1, 5, 7, 10, 15, 20, 35, 50, 100, 200, 500, 1000]) num_leaves: ng.p.TransitionChoice([2, 4, 7, 10, 15, 20, 25, 30, 35, 40, 50, 65, 80, 100, 125, 150, 200, 250]) colsample_bytree: ng.p.TransitionChoice([0.7, 0.9, 1.0]) subsample: ng.p.Scalar(lower=.3, upper=1) learning_rate: ng.p.TransitionChoice([0.01, 0.05, 0.1]) n_estimators: ng.p.TransitionChoice([5, 20, 35, 50, 75, 100, 150, 200, 350, 500, 750, 1000])
“linear regressor”¶
Base Class Documenation:
sklearn.linear_model.LinearRegression
Param Distributions
“base linear”
fit_intercept: True
“linear svm regressor”¶
Base Class Documenation:
sklearn.svm.LinearSVR
Param Distributions
“base linear svr”
loss: 'epsilon_insensitive' max_iter: 1000“linear svr dist”
loss: 'epsilon_insensitive' max_iter: 1000 C: ng.p.Log(lower=1e-4, upper=1e4)
“mlp regressor”¶
Base Class Documenation:
BPt.extensions.MLP.MLPRegressor_Wrapper
Param Distributions
“default”
defaults only“mlp dist 3 layer”
hidden_layer_sizes: ng.p.Array(init=(100, 100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)“mlp dist es 3 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999) early_stopping: True n_iter_no_change: ng.p.Scalar(lower=5, upper=50)“mlp dist 2 layer”
hidden_layer_sizes: ng.p.Array(init=(100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)“mlp dist es 2 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999) early_stopping: True n_iter_no_change: ng.p.Scalar(lower=5, upper=50)“mlp dist 1 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)“mlp dist es 1 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999) early_stopping: True n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
“random forest regressor”¶
Base Class Documenation:
sklearn.ensemble.RandomForestRegressor
Param Distributions
“base rf”
n_estimators: 100“rf dist”
n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting() max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()]) max_features: ng.p.Scalar(lower=.1, upper=1.0) min_samples_split: ng.p.Scalar(lower=.1, upper=1.0) bootstrap: True
“ridge regressor”¶
Base Class Documenation:
sklearn.linear_model.Ridge
Param Distributions
“base ridge regressor”
max_iter: 1000 solver: 'lsqr'“ridge regressor dist”
max_iter: 1000 solver: 'lsqr' alpha: ng.p.Log(lower=1e-3, upper=1e5)
“svm regressor”¶
Base Class Documenation:
sklearn.svm.SVR
Param Distributions
“base svm”
kernel: 'rbf' gamma: 'scale'“svm dist”
kernel: 'rbf' gamma: ng.p.Log(lower=1e-6, upper=1) C: ng.p.Log(lower=1e-4, upper=1e4)
“tweedie regressor”¶
Base Class Documenation:
sklearn.linear_model.glm.TweedieRegressor
Param Distributions
“default”
defaults only
“xgb regressor”¶
Base Class Documenation:
xgboost.XGBRegressor
Param Distributions
“base xgb”
verbosity: 0 objective: 'reg:squarederror'“xgb dist1”
verbosity: 0 objective: 'reg:squarederror' n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting() min_child_weight: ng.p.Log(lower=1e-5, upper=1e4) subsample: ng.p.Scalar(lower=.3, upper=.95) colsample_bytree: ng.p.Scalar(lower=.3, upper=.95) reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)]) reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])“xgb dist2”
verbosity: 0 objective: 'reg:squarederror' max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()]) learning_rate: ng.p.Scalar(lower=.01, upper=.5) n_estimators: ng.p.Scalar(lower=3, upper=500).set_integer_casting() min_child_weight: ng.p.TransitionChoice([1, 5, 10, 50]) subsample: ng.p.Scalar(lower=.5, upper=1) colsample_bytree: ng.p.Scalar(lower=.4, upper=.95)“xgb dist3”
verbosity: 0 objective: 'reg:squarederror' learning_rare: ng.p.Scalar(lower=.005, upper=.3) min_child_weight: ng.p.Scalar(lower=.5, upper=10) max_depth: ng.p.TransitionChoice(np.arange(3, 10)) subsample: ng.p.Scalar(lower=.5, upper=1) colsample_bytree: ng.p.Scalar(lower=.5, upper=1) reg_alpha: ng.p.Log(lower=.00001, upper=1)
categorical¶
“dt classifier”¶
Base Class Documenation:
sklearn.tree.DecisionTreeClassifier
Param Distributions
“default”
defaults only“dt classifier dist”
max_depth: ng.p.Scalar(lower=1, upper=30).set_integer_casting() min_samples_split: ng.p.Scalar(lower=2, upper=50).set_integer_casting() class_weight: ng.p.TransitionChoice([None, 'balanced'])
“elastic net logistic”¶
Base Class Documenation:
sklearn.linear_model.LogisticRegression
Param Distributions
“base elastic”
max_iter: 1000 multi_class: 'auto' penalty: 'elasticnet' class_weight: None solver: 'saga' l1_ratio: .5“elastic classifier”
max_iter: 1000 multi_class: 'auto' penalty: 'elasticnet' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: ng.p.Scalar(lower=.01, upper=1) C: ng.p.Log(lower=1e-5, upper=1e5)“elastic clf v2”
max_iter: 1000 multi_class: 'auto' penalty: 'elasticnet' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: ng.p.Scalar(lower=.01, upper=1) C: ng.p.Log(lower=1e-2, upper=1e5)“elastic classifier extra”
max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting() multi_class: 'auto' penalty: 'elasticnet' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'saga' l1_ratio: ng.p.Scalar(lower=.01, upper=1) C: ng.p.Log(lower=1e-5, upper=1e5) tol: ng.p.Log(lower=1e-6, upper=.01)
“et classifier”¶
Base Class Documenation:
sklearn.ensemble.ExtraTreesClassifier
Param Distributions
“default”
defaults only
“gaussian nb”¶
Base Class Documenation:
sklearn.naive_bayes.GaussianNB
Param Distributions
“base gnb”
var_smoothing: 1e-9
“gb classifier”¶
Base Class Documenation:
sklearn.ensemble.GradientBoostingClassifier
Param Distributions
“default”
defaults only
“gp classifier”¶
Base Class Documenation:
sklearn.gaussian_process.GaussianProcessClassifier
Param Distributions
“base gp classifier”
n_restarts_optimizer: 5
“hgb classifier”¶
Base Class Documenation:
sklearn.ensemble.gradient_boosting.HistGradientBoostingClassifier
Param Distributions
“default”
defaults only
“knn classifier”¶
Base Class Documenation:
sklearn.neighbors.KNeighborsClassifier
Param Distributions
“base knn”
n_neighbors: 5“knn dist”
weights: ng.p.TransitionChoice(['uniform', 'distance']) n_neighbors: ng.p.Scalar(lower=2, upper=25).set_integer_casting()
“lasso logistic”¶
Base Class Documenation:
sklearn.linear_model.LogisticRegression
Param Distributions
“base lasso”
max_iter: 1000 multi_class: 'auto' penalty: 'l1' class_weight: None solver: 'liblinear'“lasso C”
max_iter: 1000 multi_class: 'auto' penalty: 'l1' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'liblinear' C: ng.p.Log(lower=1e-5, upper=1e3)“lasso C extra”
max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting() multi_class: 'auto' penalty: 'l1' class_weight: ng.p.TransitionChoice([None, 'balanced']) solver: 'liblinear' C: ng.p.Log(lower=1e-5, upper=1e3) tol: ng.p.Log(lower=1e-6, upper=.01)
“light gbm classifier”¶
Base Class Documenation:
lightgbm.LGBMClassifier
Param Distributions
“base lgbm”
silent: True“lgbm classifier dist1”
silent: True boosting_type: ng.p.TransitionChoice(['gbdt', 'dart', 'goss']) n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting() num_leaves: ng.p.Scalar(init=20, lower=6, upper=80).set_integer_casting() min_child_samples: ng.p.Scalar(lower=10, upper=500).set_integer_casting() min_child_weight: ng.p.Log(lower=1e-5, upper=1e4) subsample: ng.p.Scalar(lower=.3, upper=.95) colsample_bytree: ng.p.Scalar(lower=.3, upper=.95) reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)]) reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)]) class_weight: ng.p.TransitionChoice([None, 'balanced'])“lgbm classifier dist2”
silent: True lambda_l2: 0.001 boosting_type: ng.p.TransitionChoice(['gbdt', 'dart']) min_child_samples: ng.p.TransitionChoice([1, 5, 7, 10, 15, 20, 35, 50, 100, 200, 500, 1000]) num_leaves: ng.p.TransitionChoice([2, 4, 7, 10, 15, 20, 25, 30, 35, 40, 50, 65, 80, 100, 125, 150, 200, 250]) colsample_bytree: ng.p.TransitionChoice([0.7, 0.9, 1.0]) subsample: ng.p.Scalar(lower=.3, upper=1) learning_rate: ng.p.TransitionChoice([0.01, 0.05, 0.1]) n_estimators: ng.p.TransitionChoice([5, 20, 35, 50, 75, 100, 150, 200, 350, 500, 750, 1000]) class_weight: ng.p.TransitionChoice([None, 'balanced'])
“linear svm classifier”¶
Base Class Documenation:
sklearn.svm.LinearSVC
Param Distributions
“base linear svc”
max_iter: 1000“linear svc dist”
max_iter: 1000 C: ng.p.Log(lower=1e-4, upper=1e4) class_weight: ng.p.TransitionChoice([None, 'balanced'])
“logistic”¶
Base Class Documenation:
sklearn.linear_model.LogisticRegression
Param Distributions
“base logistic”
max_iter: 1000 multi_class: 'auto' penalty: 'none' class_weight: None solver: 'lbfgs'
“mlp classifier”¶
Base Class Documenation:
BPt.extensions.MLP.MLPClassifier_Wrapper
Param Distributions
“default”
defaults only“mlp dist 3 layer”
hidden_layer_sizes: ng.p.Array(init=(100, 100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)“mlp dist es 3 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999) early_stopping: True n_iter_no_change: ng.p.Scalar(lower=5, upper=50)“mlp dist 2 layer”
hidden_layer_sizes: ng.p.Array(init=(100, 100)).set_mutation(sigma=50).set_bounds(lower=1, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)“mlp dist es 2 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999) early_stopping: True n_iter_no_change: ng.p.Scalar(lower=5, upper=50)“mlp dist 1 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999)“mlp dist es 1 layer”
hidden_layer_sizes: ng.p.Scalar(init=100, lower=2, upper=300).set_integer_casting() activation: ng.p.TransitionChoice(['identity', 'logistic', 'tanh', 'relu']) alpha: ng.p.Log(lower=1e-5, upper=1e2) batch_size: ng.p.TransitionChoice(['auto', ng.p.Scalar(init=200, lower=50, upper=400).set_integer_casting()]) learning_rate: ng.p.TransitionChoice(['constant', 'invscaling', 'adaptive']) learning_rate_init: ng.p.Log(lower=1e-5, upper=1e2) max_iter: ng.p.Scalar(init=200, lower=100, upper=1000).set_integer_casting() beta_1: ng.p.Scalar(init=.9, lower=.1, upper=.99) beta_2: ng.p.Scalar(init=.999, lower=.1, upper=.9999) early_stopping: True n_iter_no_change: ng.p.Scalar(lower=5, upper=50)
“pa classifier”¶
Base Class Documenation:
sklearn.linear_model.PassiveAggressiveClassifier
Param Distributions
“default”
defaults only
“random forest classifier”¶
Base Class Documenation:
sklearn.ensemble.RandomForestClassifier
Param Distributions
“base rf regressor”
n_estimators: 100“rf classifier dist”
n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting() max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()]) max_features: ng.p.Scalar(lower=.1, upper=1.0) min_samples_split: ng.p.Scalar(lower=.1, upper=1.0) bootstrap: True class_weight: ng.p.TransitionChoice([None, 'balanced'])
“ridge logistic”¶
Base Class Documenation:
sklearn.linear_model.LogisticRegression
Param Distributions
“base ridge”
max_iter: 1000 penalty: 'l2' solver: 'saga'“ridge C”
max_iter: 1000 solver: 'saga' C: ng.p.Log(lower=1e-5, upper=1e3) class_weight: ng.p.TransitionChoice([None, 'balanced'])“ridge C extra”
max_iter: ng.p.Scalar(lower=1000, upper=10000).set_integer_casting() solver: 'saga' C: ng.p.Log(lower=1e-5, upper=1e3) class_weight: ng.p.TransitionChoice([None, 'balanced']) tol: ng.p.Log(lower=1e-6, upper=.01)
“sgd classifier”¶
Base Class Documenation:
sklearn.linear_model.SGDClassifier
Param Distributions
“base sgd”
loss: 'hinge'“sgd classifier”
loss: ng.p.TransitionChoice(['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']) penalty: ng.p.TransitionChoice(['l2', 'l1', 'elasticnet']) alpha: ng.p.Log(lower=1e-5, upper=1e2) l1_ratio: ng.p.Scalar(lower=0, upper=1) max_iter: 1000 learning_rate: ng.p.TransitionChoice(['optimal', 'invscaling', 'adaptive', 'constant']) eta0: ng.p.Log(lower=1e-6, upper=1e3) power_t: ng.p.Scalar(lower=.1, upper=.9) early_stopping: ng.p.TransitionChoice([False, True]) validation_fraction: ng.p.Scalar(lower=.05, upper=.5) n_iter_no_change: ng.p.TransitionChoice(np.arange(2, 20)) class_weight: ng.p.TransitionChoice([None, 'balanced'])
“svm classifier”¶
Base Class Documenation:
sklearn.svm.SVC
Param Distributions
“base svm classifier”
kernel: 'rbf' gamma: 'scale' probability: True“svm classifier dist”
kernel: 'rbf' gamma: ng.p.Log(lower=1e-6, upper=1) C: ng.p.Log(lower=1e-4, upper=1e4) probability: True class_weight: ng.p.TransitionChoice([None, 'balanced'])
“xgb classifier”¶
Base Class Documenation:
xgboost.XGBClassifier
Param Distributions
“base xgb classifier”
verbosity: 0 objective: 'binary:logistic'“xgb classifier dist1”
verbosity: 0 objective: 'binary:logistic' n_estimators: ng.p.Scalar(init=100, lower=3, upper=500).set_integer_casting() min_child_weight: ng.p.Log(lower=1e-5, upper=1e4) subsample: ng.p.Scalar(lower=.3, upper=.95) colsample_bytree: ng.p.Scalar(lower=.3, upper=.95) reg_alpha: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)]) reg_lambda: ng.p.TransitionChoice([0, ng.p.Log(lower=1e-5, upper=1)])“xgb classifier dist2”
verbosity: 0 objective: 'binary:logistic' max_depth: ng.p.TransitionChoice([None, ng.p.Scalar(init=25, lower=2, upper=200).set_integer_casting()]) learning_rate: ng.p.Scalar(lower=.01, upper=.5) n_estimators: ng.p.Scalar(lower=3, upper=500).set_integer_casting() min_child_weight: ng.p.TransitionChoice([1, 5, 10, 50]) subsample: ng.p.Scalar(lower=.5, upper=1) colsample_bytree: ng.p.Scalar(lower=.4, upper=.95)“xgb classifier dist3”
verbosity: 0 objective: 'binary:logistic' learning_rare: ng.p.Scalar(lower=.005, upper=.3) min_child_weight: ng.p.Scalar(lower=.5, upper=10) max_depth: ng.p.TransitionChoice(np.arange(3, 10)) subsample: ng.p.Scalar(lower=.5, upper=1) colsample_bytree: ng.p.Scalar(lower=.5, upper=1) reg_alpha: ng.p.Log(lower=.00001, upper=1)
Scorers¶
Different availible choices for the scorer parameter are shown below.
scorer is accepted by Problem_Spec
, Param_Search
and Feat_Importance
The str indicator for each scorer is represented bythe sub-heading (within “”)
The avaliable scorers are further broken down by which can work with different problem_types.
Additionally, a link to the original models documentation is shown.
binary¶
“accuracy”¶
Base Func Documenation:
sklearn.metrics.accuracy_score()
“roc_auc”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“roc_auc_ovr”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“roc_auc_ovo”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“roc_auc_ovr_weighted”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“roc_auc_ovo_weighted”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“balanced_accuracy”¶
Base Func Documenation:
sklearn.metrics.balanced_accuracy_score()
“average_precision”¶
Base Func Documenation:
sklearn.metrics.average_precision_score()
“neg_log_loss”¶
Base Func Documenation:
sklearn.metrics.log_loss()
“neg_brier_score”¶
Base Func Documenation:
sklearn.metrics.brier_score_loss()
“precision”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“precision_macro”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“precision_micro”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“precision_samples”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“precision_weighted”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“recall”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“recall_macro”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“recall_micro”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“recall_samples”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“recall_weighted”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“f1”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“f1_macro”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“f1_micro”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“f1_samples”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“f1_weighted”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“jaccard”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“jaccard_macro”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“jaccard_micro”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“jaccard_samples”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“jaccard_weighted”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“neg_hamming”¶
Base Func Documenation:
sklearn.metrics.hamming_loss()
“matthews”¶
Base Func Documenation:
sklearn.metrics.matthews_corrcoef()
“default”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
regression¶
“explained_variance”¶
Base Func Documenation:
sklearn.metrics.explained_variance_score()
“explained_variance score”¶
Base Func Documenation:
sklearn.metrics.explained_variance_score()
“r2”¶
Base Func Documenation:
sklearn.metrics.r2_score()
“max_error”¶
Base Func Documenation:
sklearn.metrics.max_error()
“neg_median_absolute_error”¶
Base Func Documenation:
sklearn.metrics.median_absolute_error()
“median_absolute_error”¶
Base Func Documenation:
sklearn.metrics.median_absolute_error()
“neg_mean_absolute_error”¶
Base Func Documenation:
sklearn.metrics.mean_absolute_error()
“mean_absolute_error”¶
Base Func Documenation:
sklearn.metrics.mean_absolute_error()
“neg_mean_squared_error”¶
Base Func Documenation:
sklearn.metrics.mean_squared_error()
“mean_squared_error”¶
Base Func Documenation:
sklearn.metrics.mean_squared_error()
“neg_mean_squared_log_error”¶
Base Func Documenation:
sklearn.metrics.mean_squared_log_error()
“mean_squared_log_error”¶
Base Func Documenation:
sklearn.metrics.mean_squared_log_error()
“neg_root_mean_squared_error”¶
Base Func Documenation:
sklearn.metrics.mean_squared_error()
“root_mean_squared_error”¶
Base Func Documenation:
sklearn.metrics.mean_squared_error()
“neg_mean_poisson_deviance”¶
Base Func Documenation:
sklearn.metrics.mean_poisson_deviance()
“mean_poisson_deviance”¶
Base Func Documenation:
sklearn.metrics.mean_poisson_deviance()
“neg_mean_gamma_deviance”¶
Base Func Documenation:
sklearn.metrics.mean_gamma_deviance()
“mean_gamma_deviance”¶
Base Func Documenation:
sklearn.metrics.mean_gamma_deviance()
“default”¶
Base Func Documenation:
sklearn.metrics.r2_score()
categorical¶
“accuracy”¶
Base Func Documenation:
sklearn.metrics.accuracy_score()
“roc_auc”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“roc_auc_ovr”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“roc_auc_ovo”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“roc_auc_ovr_weighted”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“roc_auc_ovo_weighted”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
“balanced_accuracy”¶
Base Func Documenation:
sklearn.metrics.balanced_accuracy_score()
“average_precision”¶
Base Func Documenation:
sklearn.metrics.average_precision_score()
“neg_log_loss”¶
Base Func Documenation:
sklearn.metrics.log_loss()
“neg_brier_score”¶
Base Func Documenation:
sklearn.metrics.brier_score_loss()
“precision”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“precision_macro”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“precision_micro”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“precision_samples”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“precision_weighted”¶
Base Func Documenation:
sklearn.metrics.precision_score()
“recall”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“recall_macro”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“recall_micro”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“recall_samples”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“recall_weighted”¶
Base Func Documenation:
sklearn.metrics.recall_score()
“f1”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“f1_macro”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“f1_micro”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“f1_samples”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“f1_weighted”¶
Base Func Documenation:
sklearn.metrics.f1_score()
“jaccard”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“jaccard_macro”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“jaccard_micro”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“jaccard_samples”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“jaccard_weighted”¶
Base Func Documenation:
sklearn.metrics.jaccard_score()
“neg_hamming”¶
Base Func Documenation:
sklearn.metrics.hamming_loss()
“matthews”¶
Base Func Documenation:
sklearn.metrics.matthews_corrcoef()
“default”¶
Base Func Documenation:
sklearn.metrics.roc_auc_score()
Loaders¶
Different base obj choices for the Loader
are shown below
The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”)
Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.
All Problem Types¶
“identity”¶
Base Class Documenation:
BPt.extensions.Loaders.Identity
Param Distributions
“default”
defaults only
“surface rois”¶
Base Class Documenation:
BPt.extensions.Loaders.SurfLabels
Param Distributions
“default”
defaults only
“volume rois”¶
Base Class Documenation:
nilearn.input_data.nifti_labels_masker.NiftiLabelsMasker
Param Distributions
“default”
defaults only
“connectivity”¶
Base Class Documenation:
BPt.extensions.Loaders.Connectivity
Param Distributions
“default”
defaults only
Imputers¶
Different base obj choices for the Imputer
are shown below
The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”)
Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.
Note that if the iterative imputer is requested, base_model must also be passed.
All Problem Types¶
“mean”¶
Base Class Documenation:
sklearn.impute.SimpleImputer
Param Distributions
“mean imp”
strategy: 'mean'
“median”¶
Base Class Documenation:
sklearn.impute.SimpleImputer
Param Distributions
“median imp”
strategy: 'median'
“most frequent”¶
Base Class Documenation:
sklearn.impute.SimpleImputer
Param Distributions
“most freq imp”
strategy: 'most_frequent'
“constant”¶
Base Class Documenation:
sklearn.impute.SimpleImputer
Param Distributions
“constant imp”
strategy: 'constant'
“iterative”¶
Base Class Documenation:
sklearn.impute.IterativeImputer
Param Distributions
“iterative imp”
initial_strategy: 'mean' skip_complete: True
Scalers¶
Different base obj choices for the Scaler
are shown below
The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”)
Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.
All Problem Types¶
“standard”¶
Base Class Documenation:
sklearn.preprocessing.StandardScaler
Param Distributions
“base standard”
with_mean: True with_std: True
“minmax”¶
Base Class Documenation:
sklearn.preprocessing.MinMaxScaler
Param Distributions
“base minmax”
feature_range: (0, 1)
“maxabs”¶
Base Class Documenation:
sklearn.preprocessing.MaxAbsScaler
Param Distributions
“default”
defaults only
“robust”¶
Base Class Documenation:
sklearn.preprocessing.RobustScaler
Param Distributions
“base robust”
quantile_range: (5, 95)“robust gs”
quantile_range: ng.p.TransitionChoice([(x, 100-x) for x in np.arange(1, 40)])
“yeo”¶
Base Class Documenation:
sklearn.preprocessing.PowerTransformer
Param Distributions
“base yeo”
method: 'yeo-johnson' standardize: True
“boxcox”¶
Base Class Documenation:
sklearn.preprocessing.PowerTransformer
Param Distributions
“base boxcox”
method: 'box-cox' standardize: True
“winsorize”¶
Base Class Documenation:
BPt.extensions.Scalers.Winsorizer
Param Distributions
“base winsorize”
quantile_range: (1, 99)“winsorize gs”
quantile_range: ng.p.TransitionChoice([(x, 100-x) for x in np.arange(1, 40)])
“quantile norm”¶
Base Class Documenation:
sklearn.preprocessing.QuantileTransformer
Param Distributions
“base quant norm”
output_distribution: 'normal'
“quantile uniform”¶
Base Class Documenation:
sklearn.preprocessing.QuantileTransformer
Param Distributions
“base quant uniform”
output_distribution: 'uniform'
“normalize”¶
Base Class Documenation:
sklearn.preprocessing.Normalizer
Param Distributions
“default”
defaults only
Transformers¶
Different base obj choices for the Transformer
are shown below
The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”)
Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.
All Problem Types¶
“pca”¶
Base Class Documenation:
sklearn.decomposition.PCA
Param Distributions
“default”
defaults only“pca var search”
n_components: ng.p.Scalar(init=.75, lower=.1, upper=.99) svd_solver: 'full'
“sparse pca”¶
Base Class Documenation:
sklearn.decomposition.SparsePCA
Param Distributions
“default”
defaults only
“mini batch sparse pca”¶
Base Class Documenation:
sklearn.decomposition.MiniBatchSparsePCA
Param Distributions
“default”
defaults only
“factor analysis”¶
Base Class Documenation:
sklearn.decomposition.FactorAnalysis
Param Distributions
“default”
defaults only
“dictionary learning”¶
Base Class Documenation:
sklearn.decomposition.DictionaryLearning
Param Distributions
“default”
defaults only
“mini batch dictionary learning”¶
Base Class Documenation:
sklearn.decomposition.MiniBatchDictionaryLearning
Param Distributions
“default”
defaults only
“fast ica”¶
“incremental pca”¶
Base Class Documenation:
sklearn.decomposition.IncrementalPCA
Param Distributions
“default”
defaults only
“kernel pca”¶
Base Class Documenation:
sklearn.decomposition.KernelPCA
Param Distributions
“default”
defaults only
“nmf”¶
“truncated svd”¶
Base Class Documenation:
sklearn.decomposition.TruncatedSVD
Param Distributions
“default”
defaults only
“one hot encoder”¶
Base Class Documenation:
sklearn.preprocessing.OneHotEncoder
Param Distributions
“ohe”
sparse: False handle_unknown: 'ignore'
“backward difference encoder”¶
Base Class Documenation:
category_encoders.backward_difference.BackwardDifferenceEncoder
Param Distributions
“default”
defaults only
“binary encoder”¶
Base Class Documenation:
category_encoders.binary.BinaryEncoder
Param Distributions
“default”
defaults only
“cat boost encoder”¶
Base Class Documenation:
category_encoders.cat_boost.CatBoostEncoder
Param Distributions
“default”
defaults only
“helmert encoder”¶
Base Class Documenation:
category_encoders.helmert.HelmertEncoder
Param Distributions
“default”
defaults only
“james stein encoder”¶
Base Class Documenation:
category_encoders.james_stein.JamesSteinEncoder
Param Distributions
“default”
defaults only
“leave one out encoder”¶
Base Class Documenation:
category_encoders.leave_one_out.LeaveOneOutEncoder
Param Distributions
“default”
defaults only
“m estimate encoder”¶
Base Class Documenation:
category_encoders.m_estimate.MEstimateEncoder
Param Distributions
“default”
defaults only
“polynomial encoder”¶
Base Class Documenation:
category_encoders.polynomial.PolynomialEncoder
Param Distributions
“default”
defaults only
“sum encoder”¶
Base Class Documenation:
category_encoders.sum_coding.SumEncoder
Param Distributions
“default”
defaults only
“target encoder”¶
Base Class Documenation:
category_encoders.target_encoder.TargetEncoder
Param Distributions
“default”
defaults only
“woe encoder”¶
Base Class Documenation:
category_encoders.woe.WOEEncoder
Param Distributions
“default”
defaults only
Feat Selectors¶
Different base obj choices for the Feat_Selector
are shown below
The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”)
The avaliable feat selectors are further broken down by which can workwith different problem_types.
Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.
binary¶
“rfe”¶
Base Class Documenation:
sklearn.feature_selection.RFE
Param Distributions
“base rfe”
n_features_to_select: None“rfe num feats dist”
n_features_to_select: ng.p.Scalar(init=.5, lower=.1, upper=.99)
“selector”¶
Base Class Documenation:
BPt.extensions.Feat_Selectors.FeatureSelector
Param Distributions
“random”
mask: 'sets as random features'“searchable”
mask: 'sets as hyperparameters'
“univariate selection c”¶
Base Class Documenation:
sklearn.feature_selection.SelectPercentile
Param Distributions
“base univar fs classifier”
score_func: f_classif percentile: 50“univar fs classifier dist”
score_func: f_classif percentile: ng.p.Scalar(init=50, lower=1, upper=99)“univar fs classifier dist2”
score_func: f_classif percentile: ng.p.Scalar(init=75, lower=50, upper=99)
“variance threshold”¶
Base Class Documenation:
sklearn.feature_selection.VarianceThreshold
Param Distributions
“default”
defaults only
regression¶
“rfe”¶
Base Class Documenation:
sklearn.feature_selection.RFE
Param Distributions
“base rfe”
n_features_to_select: None“rfe num feats dist”
n_features_to_select: ng.p.Scalar(init=.5, lower=.1, upper=.99)
“selector”¶
Base Class Documenation:
BPt.extensions.Feat_Selectors.FeatureSelector
Param Distributions
“random”
mask: 'sets as random features'“searchable”
mask: 'sets as hyperparameters'
“univariate selection r”¶
Base Class Documenation:
sklearn.feature_selection.SelectPercentile
Param Distributions
“base univar fs regression”
score_func: f_regression percentile: 50“univar fs regression dist”
score_func: f_regression percentile: ng.p.Scalar(init=50, lower=1, upper=99)“univar fs regression dist2”
score_func: f_regression percentile: ng.p.Scalar(init=75, lower=50, upper=99)
“variance threshold”¶
Base Class Documenation:
sklearn.feature_selection.VarianceThreshold
Param Distributions
“default”
defaults only
categorical¶
“rfe”¶
Base Class Documenation:
sklearn.feature_selection.RFE
Param Distributions
“base rfe”
n_features_to_select: None“rfe num feats dist”
n_features_to_select: ng.p.Scalar(init=.5, lower=.1, upper=.99)
“selector”¶
Base Class Documenation:
BPt.extensions.Feat_Selectors.FeatureSelector
Param Distributions
“random”
mask: 'sets as random features'“searchable”
mask: 'sets as hyperparameters'
“univariate selection c”¶
Base Class Documenation:
sklearn.feature_selection.SelectPercentile
Param Distributions
“base univar fs classifier”
score_func: f_classif percentile: 50“univar fs classifier dist”
score_func: f_classif percentile: ng.p.Scalar(init=50, lower=1, upper=99)“univar fs classifier dist2”
score_func: f_classif percentile: ng.p.Scalar(init=75, lower=50, upper=99)
“variance threshold”¶
Base Class Documenation:
sklearn.feature_selection.VarianceThreshold
Param Distributions
“default”
defaults only
Ensemble Types¶
Different base obj choices for the Ensemble
are shown below
The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”)
The avaliable ensembles are further broken down by which can workwith different problem_types.
Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.
Also note that ensemble require a few extra params! I.e., in general, all DESlib based ensemble need needs_split = True
binary¶
“adaboost classifier”¶
Base Class Documenation:
sklearn.ensemble.AdaBoostClassifier
Param Distributions
“default”
defaults only
“aposteriori”¶
Base Class Documenation:
deslib.dcs.a_posteriori.APosteriori
Param Distributions
“default”
defaults only
“apriori”¶
“bagging classifier”¶
Base Class Documenation:
sklearn.ensemble.BaggingClassifier
Param Distributions
“default”
defaults only
“balanced bagging classifier”¶
Base Class Documenation:
imblearn.ensemble.BalancedBaggingClassifier
Param Distributions
“default”
defaults only
“des clustering”¶
Base Class Documenation:
deslib.des.des_clustering.DESClustering
Param Distributions
“default”
defaults only
“des knn”¶
“deskl”¶
“desmi”¶
“desp”¶
“exponential”¶
Base Class Documenation:
deslib.des.probabilistic.Exponential
Param Distributions
“default”
defaults only
“knop”¶
“knorae”¶
“knrau”¶
“lca”¶
“logarithmic”¶
Base Class Documenation:
deslib.des.probabilistic.Logarithmic
Param Distributions
“default”
defaults only
“mcb”¶
“metades”¶
“min dif”¶
Base Class Documenation:
deslib.des.probabilistic.MinimumDifference
Param Distributions
“default”
defaults only
“mla”¶
“ola”¶
“rank”¶
“rrc”¶
“single best”¶
Base Class Documenation:
deslib.static.single_best.SingleBest
Param Distributions
“default”
defaults only
“stacked”¶
Base Class Documenation:
deslib.static.stacked.StackedClassifier
Param Distributions
“default”
defaults only
“stacking classifier”¶
Base Class Documenation:
BPt.pipeline.Ensembles.BPtStackingClassifier
Param Distributions
“default”
defaults only
“voting classifier”¶
Base Class Documenation:
BPt.pipeline.Ensembles.BPtVotingClassifier
Param Distributions
“voting classifier”
voting: 'soft'
regression¶
“adaboost regressor”¶
Base Class Documenation:
sklearn.ensemble.AdaBoostRegressor
Param Distributions
“default”
defaults only
“bagging regressor”¶
Base Class Documenation:
sklearn.ensemble.BaggingRegressor
Param Distributions
“default”
defaults only
“stacking regressor”¶
Base Class Documenation:
BPt.pipeline.Ensembles.BPtStackingRegressor
Param Distributions
“default”
defaults only
“voting regressor”¶
Base Class Documenation:
BPt.pipeline.Ensembles.BPtVotingRegressor
Param Distributions
“default”
defaults only
categorical¶
“adaboost classifier”¶
Base Class Documenation:
sklearn.ensemble.AdaBoostClassifier
Param Distributions
“default”
defaults only
“aposteriori”¶
Base Class Documenation:
deslib.dcs.a_posteriori.APosteriori
Param Distributions
“default”
defaults only
“apriori”¶
“bagging classifier”¶
Base Class Documenation:
sklearn.ensemble.BaggingClassifier
Param Distributions
“default”
defaults only
“balanced bagging classifier”¶
Base Class Documenation:
imblearn.ensemble.BalancedBaggingClassifier
Param Distributions
“default”
defaults only
“des clustering”¶
Base Class Documenation:
deslib.des.des_clustering.DESClustering
Param Distributions
“default”
defaults only
“des knn”¶
“deskl”¶
“desmi”¶
“desp”¶
“exponential”¶
Base Class Documenation:
deslib.des.probabilistic.Exponential
Param Distributions
“default”
defaults only
“knop”¶
“knorae”¶
“knrau”¶
“lca”¶
“logarithmic”¶
Base Class Documenation:
deslib.des.probabilistic.Logarithmic
Param Distributions
“default”
defaults only
“mcb”¶
“metades”¶
“min dif”¶
Base Class Documenation:
deslib.des.probabilistic.MinimumDifference
Param Distributions
“default”
defaults only
“mla”¶
“ola”¶
“rank”¶
“rrc”¶
“single best”¶
Base Class Documenation:
deslib.static.single_best.SingleBest
Param Distributions
“default”
defaults only
“stacked”¶
Base Class Documenation:
deslib.static.stacked.StackedClassifier
Param Distributions
“default”
defaults only
“stacking classifier”¶
Base Class Documenation:
BPt.pipeline.Ensembles.BPtStackingClassifier
Param Distributions
“default”
defaults only
“voting classifier”¶
Base Class Documenation:
BPt.pipeline.Ensembles.BPtVotingClassifier
Param Distributions
“voting classifier”
voting: 'soft'