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",
    "RAGMetrics",
    "Metrics",
    "Transformers",
    "DataConnectionTypes",
    "RunStateTypes",
    "PipelineTypes",
    "Directions",
    "TShirtSize",
    "MetricsToDirections",
    "PositiveLabelClass",
    "VisualizationTypes",
    "SamplingTypes",
    "ImputationStrategy",
    "ForecastingPipelineTypes",
    "TimeseriesAnomalyPredictionPipelineTypes",
    "TimeseriesAnomalyPredictionAlgorithms",
    "DocumentsSamplingTypes",
]



[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 later.
    """

    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 later.
    """

    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 RAGMetrics:
    """Supported types of AutoAI RAG metrics"""

    ANSWER_CORRECTNESS = "answer_correctness"
    CONTEXT_CORRECTNESS = "context_correctness"
    FAITHFULNESS = "faithfulness"




[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 DocumentsSamplingTypes:
    """Types of training data sampling."""

    RANDOM = "random"
    BENCHMARK_DRIVEN = "benchmark_driven"




[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"