Source code for ibm_watsonx_ai.utils.autoai.enums

#  -----------------------------------------------------------------------------------------
#  (C) Copyright IBM Corp. 2023-2024.
#  https://opensource.org/licenses/BSD-3-Clause
#  -----------------------------------------------------------------------------------------

from enum import Enum

__all__ = [
    "ClassificationAlgorithms",
    "ClassificationAlgorithmsCP4D",
    "RegressionAlgorithms",
    "RegressionAlgorithmsCP4D",
    "ForecastingAlgorithms",
    "ForecastingAlgorithmsCP4D",
    "PredictionType",
    "Metrics",
    "Transformers",
    "DataConnectionTypes",
    "RunStateTypes",
    "PipelineTypes",
    "Directions",
    "TShirtSize",
    "MetricsToDirections",
    "PositiveLabelClass",
    "VisualizationTypes",
    "SamplingTypes",
    "ImputationStrategy",
    "ForecastingPipelineTypes",
    "TimeseriesAnomalyPredictionPipelineTypes",
    "TimeseriesAnomalyPredictionAlgorithms",
]


[docs] class ClassificationAlgorithms(Enum): """Classification algorithms that AutoAI can use for IBM Cloud.""" EX_TREES = "ExtraTreesClassifier" GB = "GradientBoostingClassifier" LGBM = "LGBMClassifier" LR = "LogisticRegression" RF = "RandomForestClassifier" XGB = "XGBClassifier" DT = "DecisionTreeClassifier" SnapDT = "SnapDecisionTreeClassifier" SnapRF = "SnapRandomForestClassifier" SnapSVM = "SnapSVMClassifier" SnapLR = "SnapLogisticRegression" SnapBM = "SnapBoostingMachineClassifier"
[docs] class ClassificationAlgorithmsCP4D(Enum): """ Classification algorithms that AutoAI can use for IBM Cloud Pak® for Data(CP4D). The SnapML estimators (SnapDT, SnapRF, SnapSVM, SnapLR) are supported on IBM Cloud Pak® for Data version 4.0.2 and above. """ EX_TREES = "ExtraTreesClassifierEstimator" GB = "GradientBoostingClassifierEstimator" LGBM = "LGBMClassifierEstimator" LR = "LogisticRegressionEstimator" RF = "RandomForestClassifierEstimator" XGB = "XGBClassifierEstimator" DT = "DecisionTreeClassifierEstimator" SnapDT = "SnapDecisionTreeClassifier" SnapRF = "SnapRandomForestClassifier" SnapSVM = "SnapSVMClassifier" SnapLR = "SnapLogisticRegression" SnapBM = "SnapBoostingMachineClassifier"
class BatchedClassificationAlgorithms(Enum): """Batched tree ensemble classification algorithms that AutoAI can use for IBM Cloud.""" RF = "BatchedTreeEnsembleClassifier(RandomForestClassifier)" EX_TREES = "BatchedTreeEnsembleClassifier(ExtraTreesClassifier)" LGBM = "BatchedTreeEnsembleClassifier(LGBMClassifier)" XGB = "BatchedTreeEnsembleClassifier(XGBClassifier)" SnapRF = "BatchedTreeEnsembleClassifier(SnapRandomForestClassifier)" SnapBM = "BatchedTreeEnsembleClassifier(SnapBoostingMachineClassifier)"
[docs] class RegressionAlgorithms(Enum): """Regression algorithms that AutoAI can use for IBM Cloud.""" RF = "RandomForestRegressor" RIDGE = "Ridge" EX_TREES = "ExtraTreesRegressor" GB = "GradientBoostingRegressor" LR = "LinearRegression" XGB = "XGBRegressor" LGBM = "LGBMRegressor" DT = "DecisionTreeRegressor" SnapDT = "SnapDecisionTreeRegressor" SnapRF = "SnapRandomForestRegressor" SnapBM = "SnapBoostingMachineRegressor"
[docs] class RegressionAlgorithmsCP4D(Enum): """ Regression algorithms that AutoAI can use for IBM Cloud Pak® for Data(CP4D). The SnapML estimators (SnapDT, SnapRF, SnapBM) are supported on IBM Cloud Pak® for Data version 4.0.2 and above. """ RF = "RandomForestRegressorEstimator" RIDGE = "RidgeEstimator" EX_TREES = "ExtraTreesRegressorEstimator" GB = "GradientBoostingRegressorEstimator" LR = "LinearRegressionEstimator" XGB = "XGBRegressorEstimator" LGBM = "LGBMRegressorEstimator" DT = "DecisionTreeRegressorEstimator" SnapDT = "SnapDecisionTreeRegressor" SnapRF = "SnapRandomForestRegressor" SnapBM = "SnapBoostingMachineRegressor"
class BatchedRegressionAlgorithms(Enum): """Batched tree ensemble regression algorithms that AutoAI can use for IBM Cloud.""" RF = "BatchedTreeEnsembleRegressor(RandomForestRegressor)" EX_TREES = "BatchedTreeEnsembleRegressor(ExtraTreesRegressor)" LGBM = "BatchedTreeEnsembleRegressor(LGBMRegressor)" SnapRF = "BatchedTreeEnsembleRegressor(SnapRandomForestRegressor)" SnapBM = "BatchedTreeEnsembleRegressor(SnapBoostingMachineRegressor)" XGB = "BatchedTreeEnsembleRegressor(XGBRegressor)"
[docs] class ForecastingAlgorithmsCP4D(Enum): """Forecasting algorithms that AutoAI can use for IBM Cloud.""" LR = "LinearRegression" ENSEMBLER = "Ensembler" ARIMA = "ARIMA" HW = "HoltWinters" BATS = "BATS" RF = "RandomForest" SVM = "SVM"
[docs] class ForecastingAlgorithms(Enum): """Forecasting algorithms that AutoAI can use for IBM watsonx.ai software with IBM Cloud Pak for Data.""" LR = "LinearRegression" ENSEMBLER = "Ensembler" ARIMA = "ARIMA" HW = "HoltWinters" BATS = "BATS" RF = "RandomForest" SVM = "SVM"
[docs] class ForecastingPipelineTypes(Enum): """Forecasting pipeline types that AutoAI can use for IBM Cloud Pak® for Data(CP4D).""" RandomForestRegressor = "RandomForestRegressor" ExogenousRandomForestRegressor = "ExogenousRandomForestRegressor" SVM = "SVM" ExogenousSVM = "ExogenousSVM" LocalizedFlattenEnsembler = "LocalizedFlattenEnsembler" DifferenceFlattenEnsembler = "DifferenceFlattenEnsembler" FlattenEnsembler = "FlattenEnsembler" ExogenousLocalizedFlattenEnsembler = "ExogenousLocalizedFlattenEnsembler" ExogenousDifferenceFlattenEnsembler = "ExogenousDifferenceFlattenEnsembler" ExogenousFlattenEnsembler = "ExogenousFlattenEnsembler" MT2RForecaster = "MT2RForecaster" ExogenousMT2RForecaster = "ExogenousMT2RForecaster" HoltWinterAdditive = "HoltWinterAdditive" HoltWinterMultiplicative = "HoltWinterMultiplicative" Bats = "Bats" ARIMA = "ARIMA" ARIMAX = "ARIMAX" ARIMAX_RSAR = "ARIMAX_RSAR" ARIMAX_PALR = "ARIMAX_PALR" ARIMAX_RAR = "ARIMAX_RAR" ARIMAX_DMLR = "ARIMAX_DMLR"
[docs] @staticmethod def get_exogenous(): """Get a list of pipelines that use supporting features (exogenous pipelines). :return: list of pipelines using supporting features :rtype: list[ForecastingPipelineTypes] """ return [ pipeline for pipeline in ForecastingPipelineTypes if pipeline.value.startswith("Exogenous") ]
[docs] @staticmethod def get_non_exogenous(): """Get a list of pipelines that are not using supporting features (non-exogenous pipelines). :return: list of pipelines that do not use supporting features :rtype: list[ForecastingPipelineTypes] """ return [ pipeline for pipeline in ForecastingPipelineTypes if not pipeline.value.startswith("Exogenous") ]
[docs] class TimeseriesAnomalyPredictionAlgorithms(Enum): """Timeseries Anomaly Prediction algorithms that AutoAI can use for IBM Cloud.""" Forecasting = "Forecasting" Window = "Window" Relationship = "Relationship"
[docs] class TimeseriesAnomalyPredictionPipelineTypes(Enum): """Timeseries Anomaly Prediction pipeline types that AutoAI can use for IBM Cloud.""" PointwiseBoundedHoltWintersAdditive = "PointwiseBoundedHoltWintersAdditive" PointwiseBoundedBATS = "PointwiseBoundedBATS" PointwiseBoundedBATSForceUpdate = "PointwiseBoundedBATSForceUpdate" WindowNN = "WindowNN" WindowPCA = "WindowPCA" WindowLOF = "WindowLOF"
[docs] class PredictionType: """Supported types of learning.""" CLASSIFICATION = "classification" BINARY = "binary" MULTICLASS = "multiclass" REGRESSION = "regression" FORECASTING = "forecasting" TIMESERIES_ANOMALY_PREDICTION = "timeseries_anomaly_prediction"
[docs] class PositiveLabelClass: """Metrics that need positive label definition for binary classification.""" AVERAGE_PRECISION_SCORE = "average_precision" F1_SCORE = "f1" PRECISION_SCORE = "precision" RECALL_SCORE = "recall" F1_SCORE_MICRO = "f1_micro" F1_SCORE_MACRO = "f1_macro" F1_SCORE_WEIGHTED = "f1_weighted" PRECISION_SCORE_MICRO = "precision_micro" PRECISION_SCORE_MACRO = "precision_macro" PRECISION_SCORE_WEIGHTED = "precision_weighted" RECALL_SCORE_MICRO = "recall_micro" RECALL_SCORE_MACRO = "recall_macro" RECALL_SCORE_WEIGHTED = "recall_weighted"
[docs] class Metrics: """Supported types of classification and regression metrics in AutoAI.""" ACCURACY_SCORE = "accuracy" ACCURACY_AND_DISPARATE_IMPACT_SCORE = "accuracy_and_disparate_impact" AVERAGE_PRECISION_SCORE = "average_precision" F1_SCORE = "f1" LOG_LOSS = "neg_log_loss" PRECISION_SCORE = "precision" RECALL_SCORE = "recall" ROC_AUC_SCORE = "roc_auc" F1_SCORE_MICRO = "f1_micro" F1_SCORE_MACRO = "f1_macro" F1_SCORE_WEIGHTED = "f1_weighted" PRECISION_SCORE_MICRO = "precision_micro" PRECISION_SCORE_MACRO = "precision_macro" PRECISION_SCORE_WEIGHTED = "precision_weighted" RECALL_SCORE_MICRO = "recall_micro" RECALL_SCORE_MACRO = "recall_macro" RECALL_SCORE_WEIGHTED = "recall_weighted" EXPLAINED_VARIANCE_SCORE = "explained_variance" MEAN_ABSOLUTE_ERROR = "neg_mean_absolute_error" MEAN_SQUARED_ERROR = "neg_mean_squared_error" MEAN_SQUARED_LOG_ERROR = "neg_mean_squared_log_error" MEDIAN_ABSOLUTE_ERROR = "neg_median_absolute_error" ROOT_MEAN_SQUARED_ERROR = "neg_root_mean_squared_error" ROOT_MEAN_SQUARED_LOG_ERROR = "neg_root_mean_squared_log_error" R2_SCORE = "r2" R2_AND_DISPARATE_IMPACT_SCORE = "r2_and_disparate_impact"
[docs] class Transformers: """Supported types of congito transformers names in AutoAI.""" SQRT = "sqrt" LOG = "log" ROUND = "round" SQUARE = "square" CBRT = "cbrt" SIN = "sin" COS = "cos" TAN = "tan" ABS = "abs" SIGMOID = "sigmoid" PRODUCT = "product" MAX = "max" DIFF = "diff" SUM = "sum" DIVIDE = "divide" STDSCALER = "stdscaler" MINMAXSCALER = "minmaxscaler" PCA = "pca" NXOR = "nxor" CUBE = "cube" FEATUREAGGLOMERATION = "featureagglomeration" ISOFORESTANOMALY = "isoforestanomaly"
[docs] class DataConnectionTypes: """Supported types of DataConnection.""" S3 = "s3" FS = "fs" DS = "data_asset" CA = "connection_asset" CN = "container"
[docs] class RunStateTypes: """Supported types of AutoAI fit/run.""" COMPLETED = "completed" FAILED = "failed"
[docs] class PipelineTypes: """Supported types of Pipelines.""" LALE = "lale" SKLEARN = "sklearn"
[docs] class Directions: """Possible metrics directions""" ASCENDING = "ascending" DESCENDING = "descending"
[docs] class TShirtSize: """Possible sizes of the AutoAI POD. Depending on the POD size, AutoAI can support different data set sizes. - S - small (2vCPUs and 8GB of RAM) - M - Medium (4vCPUs and 16GB of RAM) - L - Large (8vCPUs and 32GB of RAM)) - XL - Extra Large (16vCPUs and 64GB of RAM) """ S = "s" M = "m" L = "l" XL = "xl"
[docs] class MetricsToDirections(Enum): """Map of metrics directions.""" ROC_AUC = Directions.ASCENDING NORMALIZED_GINI_COEFFICIENT = Directions.ASCENDING PRECISION = Directions.ASCENDING AVERAGE_PRECISION = Directions.ASCENDING NEG_LOG_LOSS = Directions.DESCENDING RECALL = Directions.ASCENDING ACCURACY = Directions.ASCENDING F1 = Directions.ASCENDING PRECISION_MICRO = Directions.ASCENDING PRECISION_MACRO = Directions.ASCENDING PRECISION_WEIGHTED = Directions.ASCENDING F1_MICRO = Directions.ASCENDING F1_MACRO = Directions.ASCENDING F1_WEIGHTED = Directions.ASCENDING RECALL_MICRO = Directions.ASCENDING RECALL_MACRO = Directions.ASCENDING RECALL_WEIGHTED = Directions.ASCENDING NEG_ROOT_MEAN_SQUARED_ERROR = Directions.DESCENDING EXPLAINED_VARIANCE = Directions.ASCENDING NEG_MEAN_ABSOLUTE_ERROR = Directions.DESCENDING NEG_MEAN_SQUARED_ERROR = Directions.DESCENDING NEG_MEAN_SQUARED_LOG_ERROR = Directions.DESCENDING NEG_MEDIAN_ABSOLUTE_ERROR = Directions.DESCENDING NEG_ROOT_MEAN_SQUARED_LOG_ERROR = Directions.DESCENDING R2 = Directions.ASCENDING
[docs] class VisualizationTypes: """Types of visualization options.""" PDF = "pdf" INPLACE = "inplace"
[docs] class SamplingTypes: """Types of training data sampling.""" FIRST_VALUES = "first_n_records" RANDOM = "random" STRATIFIED = "stratified" LAST_VALUES = "truncate"
[docs] class ImputationStrategy(Enum): """Missing values imputation strategies.""" MEAN = "mean" MEDIAN = "median" MOST_FREQUENT = "most_frequent" BEST_OF_DEFAULT_IMPUTERS = "best_of_default_imputers" VALUE = "value" FLATTEN_ITERATIVE = "flatten_iterative" LINEAR = "linear" CUBIC = "cubic" PREVIOUS = "previous" NEXT = "next" NO_IMPUTATION = "no_imputation"