TrainingOptions.fromJson constructor

TrainingOptions.fromJson(Map json_)

Implementation

TrainingOptions.fromJson(core.Map json_)
  : this(
      activationFn: json_['activationFn'] as core.String?,
      adjustStepChanges: json_['adjustStepChanges'] as core.bool?,
      approxGlobalFeatureContrib:
          json_['approxGlobalFeatureContrib'] as core.bool?,
      autoArima: json_['autoArima'] as core.bool?,
      autoArimaMaxOrder: json_['autoArimaMaxOrder'] as core.String?,
      autoArimaMinOrder: json_['autoArimaMinOrder'] as core.String?,
      autoClassWeights: json_['autoClassWeights'] as core.bool?,
      batchSize: json_['batchSize'] as core.String?,
      boosterType: json_['boosterType'] as core.String?,
      budgetHours: (json_['budgetHours'] as core.num?)?.toDouble(),
      calculatePValues: json_['calculatePValues'] as core.bool?,
      categoryEncodingMethod: json_['categoryEncodingMethod'] as core.String?,
      cleanSpikesAndDips: json_['cleanSpikesAndDips'] as core.bool?,
      colorSpace: json_['colorSpace'] as core.String?,
      colsampleBylevel: (json_['colsampleBylevel'] as core.num?)?.toDouble(),
      colsampleBynode: (json_['colsampleBynode'] as core.num?)?.toDouble(),
      colsampleBytree: (json_['colsampleBytree'] as core.num?)?.toDouble(),
      contributionMetric: json_['contributionMetric'] as core.String?,
      dartNormalizeType: json_['dartNormalizeType'] as core.String?,
      dataFrequency: json_['dataFrequency'] as core.String?,
      dataSplitColumn: json_['dataSplitColumn'] as core.String?,
      dataSplitEvalFraction:
          (json_['dataSplitEvalFraction'] as core.num?)?.toDouble(),
      dataSplitMethod: json_['dataSplitMethod'] as core.String?,
      decomposeTimeSeries: json_['decomposeTimeSeries'] as core.bool?,
      dimensionIdColumns:
          (json_['dimensionIdColumns'] as core.List?)
              ?.map((value) => value as core.String)
              .toList(),
      distanceType: json_['distanceType'] as core.String?,
      dropout: (json_['dropout'] as core.num?)?.toDouble(),
      earlyStop: json_['earlyStop'] as core.bool?,
      enableGlobalExplain: json_['enableGlobalExplain'] as core.bool?,
      endpointIdleTtl: json_['endpointIdleTtl'] as core.String?,
      feedbackType: json_['feedbackType'] as core.String?,
      fitIntercept: json_['fitIntercept'] as core.bool?,
      forecastLimitLowerBound:
          (json_['forecastLimitLowerBound'] as core.num?)?.toDouble(),
      forecastLimitUpperBound:
          (json_['forecastLimitUpperBound'] as core.num?)?.toDouble(),
      hiddenUnits:
          (json_['hiddenUnits'] as core.List?)
              ?.map((value) => value as core.String)
              .toList(),
      holidayRegion: json_['holidayRegion'] as core.String?,
      holidayRegions:
          (json_['holidayRegions'] as core.List?)
              ?.map((value) => value as core.String)
              .toList(),
      horizon: json_['horizon'] as core.String?,
      hparamTuningObjectives:
          (json_['hparamTuningObjectives'] as core.List?)
              ?.map((value) => value as core.String)
              .toList(),
      huggingFaceModelId: json_['huggingFaceModelId'] as core.String?,
      includeDrift: json_['includeDrift'] as core.bool?,
      initialLearnRate: (json_['initialLearnRate'] as core.num?)?.toDouble(),
      inputLabelColumns:
          (json_['inputLabelColumns'] as core.List?)
              ?.map((value) => value as core.String)
              .toList(),
      instanceWeightColumn: json_['instanceWeightColumn'] as core.String?,
      integratedGradientsNumSteps:
          json_['integratedGradientsNumSteps'] as core.String?,
      isTestColumn: json_['isTestColumn'] as core.String?,
      itemColumn: json_['itemColumn'] as core.String?,
      kmeansInitializationColumn:
          json_['kmeansInitializationColumn'] as core.String?,
      kmeansInitializationMethod:
          json_['kmeansInitializationMethod'] as core.String?,
      l1RegActivation: (json_['l1RegActivation'] as core.num?)?.toDouble(),
      l1Regularization: (json_['l1Regularization'] as core.num?)?.toDouble(),
      l2Regularization: (json_['l2Regularization'] as core.num?)?.toDouble(),
      labelClassWeights: (json_['labelClassWeights']
              as core.Map<core.String, core.dynamic>?)
          ?.map(
            (key, value) =>
                core.MapEntry(key, (value as core.num).toDouble()),
          ),
      learnRate: (json_['learnRate'] as core.num?)?.toDouble(),
      learnRateStrategy: json_['learnRateStrategy'] as core.String?,
      lossType: json_['lossType'] as core.String?,
      machineType: json_['machineType'] as core.String?,
      maxIterations: json_['maxIterations'] as core.String?,
      maxParallelTrials: json_['maxParallelTrials'] as core.String?,
      maxReplicaCount: json_['maxReplicaCount'] as core.String?,
      maxTimeSeriesLength: json_['maxTimeSeriesLength'] as core.String?,
      maxTreeDepth: json_['maxTreeDepth'] as core.String?,
      minAprioriSupport:
          (json_['minAprioriSupport'] as core.num?)?.toDouble(),
      minRelativeProgress:
          (json_['minRelativeProgress'] as core.num?)?.toDouble(),
      minReplicaCount: json_['minReplicaCount'] as core.String?,
      minSplitLoss: (json_['minSplitLoss'] as core.num?)?.toDouble(),
      minTimeSeriesLength: json_['minTimeSeriesLength'] as core.String?,
      minTreeChildWeight: json_['minTreeChildWeight'] as core.String?,
      modelGardenModelName: json_['modelGardenModelName'] as core.String?,
      modelRegistry: json_['modelRegistry'] as core.String?,
      modelUri: json_['modelUri'] as core.String?,
      nonSeasonalOrder:
          json_.containsKey('nonSeasonalOrder')
              ? ArimaOrder.fromJson(
                json_['nonSeasonalOrder']
                    as core.Map<core.String, core.dynamic>,
              )
              : null,
      numClusters: json_['numClusters'] as core.String?,
      numFactors: json_['numFactors'] as core.String?,
      numParallelTree: json_['numParallelTree'] as core.String?,
      numPrincipalComponents: json_['numPrincipalComponents'] as core.String?,
      numTrials: json_['numTrials'] as core.String?,
      optimizationStrategy: json_['optimizationStrategy'] as core.String?,
      optimizer: json_['optimizer'] as core.String?,
      pcaExplainedVarianceRatio:
          (json_['pcaExplainedVarianceRatio'] as core.num?)?.toDouble(),
      pcaSolver: json_['pcaSolver'] as core.String?,
      reservationAffinityKey: json_['reservationAffinityKey'] as core.String?,
      reservationAffinityType:
          json_['reservationAffinityType'] as core.String?,
      reservationAffinityValues:
          (json_['reservationAffinityValues'] as core.List?)
              ?.map((value) => value as core.String)
              .toList(),
      sampledShapleyNumPaths: json_['sampledShapleyNumPaths'] as core.String?,
      scaleFeatures: json_['scaleFeatures'] as core.bool?,
      standardizeFeatures: json_['standardizeFeatures'] as core.bool?,
      subsample: (json_['subsample'] as core.num?)?.toDouble(),
      tfVersion: json_['tfVersion'] as core.String?,
      timeSeriesDataColumn: json_['timeSeriesDataColumn'] as core.String?,
      timeSeriesIdColumn: json_['timeSeriesIdColumn'] as core.String?,
      timeSeriesIdColumns:
          (json_['timeSeriesIdColumns'] as core.List?)
              ?.map((value) => value as core.String)
              .toList(),
      timeSeriesLengthFraction:
          (json_['timeSeriesLengthFraction'] as core.num?)?.toDouble(),
      timeSeriesTimestampColumn:
          json_['timeSeriesTimestampColumn'] as core.String?,
      treeMethod: json_['treeMethod'] as core.String?,
      trendSmoothingWindowSize:
          json_['trendSmoothingWindowSize'] as core.String?,
      userColumn: json_['userColumn'] as core.String?,
      vertexAiModelVersionAliases:
          (json_['vertexAiModelVersionAliases'] as core.List?)
              ?.map((value) => value as core.String)
              .toList(),
      walsAlpha: (json_['walsAlpha'] as core.num?)?.toDouble(),
      warmStart: json_['warmStart'] as core.bool?,
      xgboostVersion: json_['xgboostVersion'] as core.String?,
    );