1 write to Context
Microsoft.ML.AutoML (1)
API\ExperimentBase.cs (1)
38Context = context;
51 references to Context
Microsoft.ML.AutoML (51)
API\BinaryClassificationExperiment.cs (14)
183var splitData = Context.Data.TrainTestSplit(trainData); 198var detail = BestResultUtil.ToRunDetail(Context, e, _pipeline); 207var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToRunDetail(Context, e, _pipeline)); 208var bestRun = BestResultUtil.ToRunDetail(Context, monitor.BestRun, _pipeline); 231var detail = BestResultUtil.ToRunDetail(Context, e, _pipeline); 240var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToRunDetail(Context, e, _pipeline)); 241var bestRun = BestResultUtil.ToRunDetail(Context, monitor.BestRun, _pipeline); 285var detail = BestResultUtil.ToCrossValidationRunDetail(Context, e, _pipeline); 295var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToCrossValidationRunDetail(Context, e, _pipeline)); 296var bestResult = BestResultUtil.ToCrossValidationRunDetail(Context, monitor.BestRun, _pipeline); 334return preFeaturizer.Append(Context.Auto().Featurizer(trainData, columnInformation, Features)) 335.Append(Context.Auto().BinaryClassification(labelColumnName: columnInformation.LabelColumnName, useSdcaLogisticRegression: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsLogisticRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features)); 339return Context.Auto().Featurizer(trainData, columnInformation, Features) 340.Append(Context.Auto().BinaryClassification(labelColumnName: columnInformation.LabelColumnName, useSdcaLogisticRegression: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsLogisticRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
API\ExperimentBase.cs (11)
122var splitResult = SplitUtil.CrossValSplit(Context, trainData, numCrossValFolds, samplingKeyColumnName); 127var splitResult = SplitUtil.TrainValidateSplit(Context, trainData, samplingKeyColumnName); 223var splitResult = SplitUtil.CrossValSplit(Context, trainData, numberOfCVFolds, samplingKeyColumnName); 290var runner = new TrainValidateRunner<TMetrics>(Context, trainData, validationData, columnInfo.GroupIdColumnName, columnInfo.LabelColumnName, MetricsAgent, 292var columns = DatasetColumnInfoUtil.GetDatasetColumnInfo(Context, trainData, columnInfo); 309var runner = new CrossValRunner<TMetrics>(Context, trainDatasets, validationDatasets, MetricsAgent, preFeaturizer, 311var columns = DatasetColumnInfoUtil.GetDatasetColumnInfo(Context, trainDatasets[0], columnInfo); 314var experiment = new Experiment<CrossValidationRunDetail<TMetrics>, TMetrics>(Context, _task, OptimizingMetricInfo, progressHandler, 336var runner = new CrossValSummaryRunner<TMetrics>(Context, trainDatasets, validationDatasets, MetricsAgent, preFeaturizer, 338var columns = DatasetColumnInfoUtil.GetDatasetColumnInfo(Context, trainDatasets[0], columnInfo); 349var experiment = new Experiment<RunDetail<TMetrics>, TMetrics>(Context, _task, OptimizingMetricInfo, progressHandler,
API\MulticlassClassificationExperiment.cs (14)
167var splitData = Context.Data.TrainTestSplit(trainData); 183var detail = BestResultUtil.ToRunDetail(Context, e, _pipeline); 193var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToRunDetail(Context, e, _pipeline)); 194var bestRun = BestResultUtil.ToRunDetail(Context, monitor.BestRun, _pipeline); 218var detail = BestResultUtil.ToRunDetail(Context, e, _pipeline); 228var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToRunDetail(Context, e, _pipeline)); 229var bestRun = BestResultUtil.ToRunDetail(Context, monitor.BestRun, _pipeline); 275var detail = BestResultUtil.ToCrossValidationRunDetail(Context, e, _pipeline); 285var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToCrossValidationRunDetail(Context, e, _pipeline)); 286var bestResult = BestResultUtil.ToCrossValidationRunDetail(Context, monitor.BestRun, _pipeline); 331pipeline = pipeline.Append(Context.Auto().Featurizer(trainData, columnInformation, Features)); 332pipeline = pipeline.Append(Context.Transforms.Conversion.MapValueToKey(label, label)); 333pipeline = pipeline.Append(Context.Auto().MultiClassification(label, useSdcaMaximumEntrophy: useSdcaMaximumEntrophy, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsMaximumEntrophy: uselbfgsME, useLbfgsLogisticRegression: uselbfgsLR, useFastForest: useFastForest, featureColumnName: Features)); 334pipeline = pipeline.Append(Context.Transforms.Conversion.MapKeyToValue(DefaultColumnNames.PredictedLabel, DefaultColumnNames.PredictedLabel));
API\RegressionExperiment.cs (12)
171var detail = BestResultUtil.ToRunDetail(Context, e, _pipeline); 181var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToRunDetail(Context, e, _pipeline)); 182var bestRun = BestResultUtil.ToRunDetail(Context, monitor.BestRun, _pipeline); 189var splitData = Context.Data.TrainTestSplit(trainData); 212var detail = BestResultUtil.ToRunDetail(Context, e, _pipeline); 222var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToRunDetail(Context, e, _pipeline)); 223var bestRun = BestResultUtil.ToRunDetail(Context, monitor.BestRun, _pipeline); 268var detail = BestResultUtil.ToCrossValidationRunDetail(Context, e, _pipeline); 278var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToCrossValidationRunDetail(Context, e, _pipeline)); 279var bestResult = BestResultUtil.ToCrossValidationRunDetail(Context, monitor.BestRun, _pipeline); 312pipeline = pipeline.Append(Context.Auto().Featurizer(trainData, columnInformation, Features)); 313pipeline = pipeline.Append(Context.Auto().Regression(label, useSdca: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsPoissonRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));