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stable

Install

  • From Scratch
  • Pip Installation
  • Github / Pip Installation
  • Extra Libraries

User Guide

  • New Users
  • Why BPt?
  • Core Concepts
    • Pipeline Objects
    • Params
    • Scopes
    • Extra Params
    • Custom Input Objects
    • Subjects

Change Logs

  • In Dev
  • Release 1.3.6
  • Release 1.3.5
  • Release 1.3.4
  • Release 1.3.3
  • Release 1.3.1 and 1.3.2
  • Release 1.3

Modeling Params

  • Model_Pipeline
  • Problem_Spec
  • Pieces
    • Loader
    • Imputer
    • Scaler
    • Transformer
    • Feat_Selector
    • Model
    • Ensemble
    • Param_Search
    • Feat_Importance
    • CV
    • CV_Splits
  • Input Types
    • Select
    • Duplicate
    • Pipe
    • Value_Subset
    • Values_Subset

BPt Class Docs

  • Init Phase
    • Import
    • Load
    • Init
  • Loading Phase
    • Set_Default_Load_Params
    • Load_Name_Map
    • Load_Exclusions
    • Load_Inclusions
    • Load_Data
    • Load_Data_Files
    • Drop_Data_Cols
    • Filter_Data_Cols
    • Proc_Data_Unique_Cols
    • Drop_Data_Duplicates
    • Show_Data_Dist
    • Load_Targets
    • Binarize_Target
    • Show_Targets_Dist
    • Load_Covars
    • Show_Covars_Dist
    • Load_Strat
    • Show_Strat_Dist
    • Get_Overlapping_Subjects
    • Clear_Name_Map
    • Clear_Exclusions
    • Clear_Data
    • Clear_Targets
    • Clear_Covars
    • Clear_Strat
    • Get_Nan_Subjects
  • Validation Phase
    • Define_Validation_Strategy
    • Train_Test_Split
  • Modeling Phase
    • Set_Default_ML_Verbosity
    • Evaluate
    • Plot_Global_Feat_Importances
    • Plot_Local_Feat_Importances
  • Testing Phase
    • Test
    • Plot_Global_Feat_Importances
    • Plot_Local_Feat_Importances
  • Extras
    • Save
    • Save_Table

Avaliable Modeling Options

  • Models
    • binary
      • “dt classifier”
      • “elastic net logistic”
      • “et classifier”
      • “gaussian nb”
      • “gb classifier”
      • “gp classifier”
      • “hgb classifier”
      • “knn classifier”
      • “lasso logistic”
      • “light gbm classifier”
      • “linear svm classifier”
      • “logistic”
      • “mlp classifier”
      • “pa classifier”
      • “random forest classifier”
      • “ridge logistic”
      • “sgd classifier”
      • “svm classifier”
      • “xgb classifier”
    • regression
      • “ard regressor”
      • “bayesian ridge regressor”
      • “dt regressor”
      • “elastic net regressor”
      • “et regressor”
      • “gb regressor”
      • “gp regressor”
      • “hgb regressor”
      • “knn regressor”
      • “lasso regressor”
      • “light gbm regressor”
      • “linear regressor”
      • “linear svm regressor”
      • “mlp regressor”
      • “random forest regressor”
      • “ridge regressor”
      • “svm regressor”
      • “tweedie regressor”
      • “xgb regressor”
    • categorical
      • “dt classifier”
      • “elastic net logistic”
      • “et classifier”
      • “gaussian nb”
      • “gb classifier”
      • “gp classifier”
      • “hgb classifier”
      • “knn classifier”
      • “lasso logistic”
      • “light gbm classifier”
      • “linear svm classifier”
      • “logistic”
      • “mlp classifier”
      • “pa classifier”
      • “random forest classifier”
      • “ridge logistic”
      • “sgd classifier”
      • “svm classifier”
      • “xgb classifier”
  • Scorers
    • binary
      • “accuracy”
      • “roc_auc”
      • “roc_auc_ovr”
      • “roc_auc_ovo”
      • “roc_auc_ovr_weighted”
      • “roc_auc_ovo_weighted”
      • “balanced_accuracy”
      • “average_precision”
      • “neg_log_loss”
      • “neg_brier_score”
      • “precision”
      • “precision_macro”
      • “precision_micro”
      • “precision_samples”
      • “precision_weighted”
      • “recall”
      • “recall_macro”
      • “recall_micro”
      • “recall_samples”
      • “recall_weighted”
      • “f1”
      • “f1_macro”
      • “f1_micro”
      • “f1_samples”
      • “f1_weighted”
      • “jaccard”
      • “jaccard_macro”
      • “jaccard_micro”
      • “jaccard_samples”
      • “jaccard_weighted”
      • “neg_hamming”
      • “matthews”
      • “default”
    • regression
      • “explained_variance”
      • “explained_variance score”
      • “r2”
      • “max_error”
      • “neg_median_absolute_error”
      • “median_absolute_error”
      • “neg_mean_absolute_error”
      • “mean_absolute_error”
      • “neg_mean_squared_error”
      • “mean_squared_error”
      • “neg_mean_squared_log_error”
      • “mean_squared_log_error”
      • “neg_root_mean_squared_error”
      • “root_mean_squared_error”
      • “neg_mean_poisson_deviance”
      • “mean_poisson_deviance”
      • “neg_mean_gamma_deviance”
      • “mean_gamma_deviance”
      • “default”
    • categorical
      • “accuracy”
      • “roc_auc”
      • “roc_auc_ovr”
      • “roc_auc_ovo”
      • “roc_auc_ovr_weighted”
      • “roc_auc_ovo_weighted”
      • “balanced_accuracy”
      • “average_precision”
      • “neg_log_loss”
      • “neg_brier_score”
      • “precision”
      • “precision_macro”
      • “precision_micro”
      • “precision_samples”
      • “precision_weighted”
      • “recall”
      • “recall_macro”
      • “recall_micro”
      • “recall_samples”
      • “recall_weighted”
      • “f1”
      • “f1_macro”
      • “f1_micro”
      • “f1_samples”
      • “f1_weighted”
      • “jaccard”
      • “jaccard_macro”
      • “jaccard_micro”
      • “jaccard_samples”
      • “jaccard_weighted”
      • “neg_hamming”
      • “matthews”
      • “default”
  • Loaders
    • All Problem Types
      • “identity”
      • “surface rois”
      • “volume rois”
      • “connectivity”
  • Imputers
    • All Problem Types
      • “mean”
      • “median”
      • “most frequent”
      • “constant”
      • “iterative”
  • Scalers
    • All Problem Types
      • “standard”
      • “minmax”
      • “maxabs”
      • “robust”
      • “yeo”
      • “boxcox”
      • “winsorize”
      • “quantile norm”
      • “quantile uniform”
      • “normalize”
  • Transformers
    • All Problem Types
      • “pca”
      • “sparse pca”
      • “mini batch sparse pca”
      • “factor analysis”
      • “dictionary learning”
      • “mini batch dictionary learning”
      • “fast ica”
      • “incremental pca”
      • “kernel pca”
      • “nmf”
      • “truncated svd”
      • “one hot encoder”
      • “backward difference encoder”
      • “binary encoder”
      • “cat boost encoder”
      • “helmert encoder”
      • “james stein encoder”
      • “leave one out encoder”
      • “m estimate encoder”
      • “polynomial encoder”
      • “sum encoder”
      • “target encoder”
      • “woe encoder”
  • Feat Selectors
    • binary
      • “rfe”
      • “selector”
      • “univariate selection c”
      • “variance threshold”
    • regression
      • “rfe”
      • “selector”
      • “univariate selection r”
      • “variance threshold”
    • categorical
      • “rfe”
      • “selector”
      • “univariate selection c”
      • “variance threshold”
  • Ensemble Types
    • binary
      • “adaboost classifier”
      • “aposteriori”
      • “apriori”
      • “bagging classifier”
      • “balanced bagging classifier”
      • “des clustering”
      • “des knn”
      • “deskl”
      • “desmi”
      • “desp”
      • “exponential”
      • “knop”
      • “knorae”
      • “knrau”
      • “lca”
      • “logarithmic”
      • “mcb”
      • “metades”
      • “min dif”
      • “mla”
      • “ola”
      • “rank”
      • “rrc”
      • “single best”
      • “stacked”
      • “stacking classifier”
      • “voting classifier”
    • regression
      • “adaboost regressor”
      • “bagging regressor”
      • “stacking regressor”
      • “voting regressor”
    • categorical
      • “adaboost classifier”
      • “aposteriori”
      • “apriori”
      • “bagging classifier”
      • “balanced bagging classifier”
      • “des clustering”
      • “des knn”
      • “deskl”
      • “desmi”
      • “desp”
      • “exponential”
      • “knop”
      • “knorae”
      • “knrau”
      • “lca”
      • “logarithmic”
      • “mcb”
      • “metades”
      • “min dif”
      • “mla”
      • “ola”
      • “rank”
      • “rrc”
      • “single best”
      • “stacked”
      • “stacking classifier”
      • “voting classifier”

Search Types

  • Random Search
    • ‘RandomSearch’
    • ‘RandomSearchPlusMiddlePoint’
    • ‘QORandomSearch’
    • ORandomSearch
  • One Shot Optimization
    • ‘HaltonSearch’
    • ‘HaltonSearchPlusMiddlePoint’
    • ‘ScrHaltonSearch’
    • ‘ScrHaltonSearchPlusMiddlePoint’
    • ‘HammersleySearch’
    • ‘HammersleySearchPlusMiddlePoint’
    • ‘ScrHammersleySearchPlusMiddlePoint’
    • ‘ScrHammersleySearch’
    • ‘OScrHammersleySearch’
    • ‘QOScrHammersleySearch’
    • ‘CauchyScrHammersleySearch’
    • ‘LHSSearch’
    • ‘CauchyLHSSearch’
    • ‘MetaRecentering’
    • ‘MetaTuneRecentering’
    • HAvgMetaRecentering
    • AvgMetaRecenteringNoHull
  • One Plus One
    • ‘OnePlusOne’
    • ‘NoisyOnePlusOne’
    • ‘OptimisticNoisyOnePlusOne’
    • ‘DiscreteOnePlusOne’
    • ‘DiscreteLenglerOnePlusOne’
    • ‘AdaptiveDiscreteOnePlusOne’
    • ‘AnisotropicAdaptiveDiscreteOnePlusOne’
    • ‘DiscreteBSOOnePlusOne’
    • ‘DiscreteDoerrOnePlusOne’
    • ‘CauchyOnePlusOne’
    • ‘OptimisticDiscreteOnePlusOne’
    • ‘NoisyDiscreteOnePlusOne’
    • ‘DoubleFastGADiscreteOnePlusOne’
    • ‘FastGADiscreteOnePlusOne’
    • ‘RecombiningPortfolioOptimisticNoisyDiscreteOnePlusOne’
  • CMA
    • ‘CMA’
    • ‘DiagonalCMA’
    • ‘FCMA’
    • ‘EDA’
    • ‘PCEDA’
    • ‘MPCEDA’
    • ‘MEDA’
  • Evolution Strategies
    • ‘ES’
    • ‘RecES’
    • ‘RecMixES’
    • ‘RecMutDE’
    • ‘MixES’
    • ‘MutDE’
    • ‘NSGAIIES’
  • Differential Evolution
    • ‘DE’
    • ‘OnePointDE’
    • ‘TwoPointsDE’
    • ‘LhsDE’
    • ‘QrDE’
    • ‘MiniDE’
    • ‘MiniLhsDE’
    • ‘MiniQrDE’
    • ‘NoisyDE’
    • ‘AlmostRotationInvariantDE’
    • ‘AlmostRotationInvariantDEAndBigPop’
    • ‘RotationInvariantDE’
    • ‘BPRotationInvariantDE’
  • Algorithm Selection
    • ‘ASCMA2PDEthird’
    • ‘ASCMADEQRthird’
    • ‘ASCMADEthird’
    • ‘TripleCMA’
    • ‘MultiCMA’
    • ‘MultiScaleCMA’
    • ‘Portfolio’
    • ‘ParaPortfolio’
    • ‘SQPCMA’
  • Competence Maps
    • ‘NGO’
    • ‘NGOpt’
    • ‘CM’
    • ‘CMandAS’
    • ‘CMandAS2’
    • ‘CMandAS3’
    • ‘Shiva’
  • Misc.
    • ‘NaiveIsoEMNA’
    • ‘TBPSA’
    • ‘NaiveTBPSA’
    • ‘NoisyBandit’
    • ‘PBIL’
    • ‘PSO’
    • ‘SQP’
    • ‘SPSA’
    • ‘SplitOptimizer’
    • ‘cGA’
    • ‘chainCMAPowell’
  • Experimental Variants

Feature Importances

  • Feat Importances
    • “base”
    • “shap”
    • “shap train”
    • “shap all”
    • “perm”
    • “perm train”
    • “perm all”

Extension Classes

  • Extensions
    • Winsorizer
    • FeatureSelector
    • SurfLabels
    • SurfMaps
BPt
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