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