1 write to TrainSet
Microsoft.ML.Data (1)
DataLoadSave\DataOperationsCatalog.cs (1)
42
TrainSet
= trainSet;
74 references to TrainSet
Microsoft.ML.AutoML (10)
API\AutoMLExperimentExtension.cs (2)
48
/// Set train and validation dataset for <see cref="AutoMLExperiment"/>. This will make <see cref="AutoMLExperiment"/> uses <see cref="TrainTestData.
TrainSet
"/> from <paramref name="trainValidationSplit"/>
56
return experiment.SetDataset(trainValidationSplit.
TrainSet
, trainValidationSplit.TestSet);
API\BinaryClassificationExperiment.cs (1)
184
_experiment.SetDataset(splitData.
TrainSet
, splitData.TestSet);
API\MulticlassClassificationExperiment.cs (1)
168
return Execute(splitData.
TrainSet
, splitData.TestSet, columnInformation, preFeaturizer, progressHandler);
API\RegressionExperiment.cs (1)
190
return Execute(splitData.
TrainSet
, splitData.TestSet, columnInformation, preFeaturizer, progressHandler);
AutoMLExperiment\Runner\SweepablePipelineRunner.cs (1)
52
var model = mlnetPipeline.Fit(split.
TrainSet
);
Tuner\SmacTuner.cs (1)
148
var model = trainer.Fit(trainTestSplit.
TrainSet
);
Utils\SplitUtil.cs (3)
23
if (DatasetDimensionsUtil.IsDataViewEmpty(split.
TrainSet
) ||
29
var trainDataset = DropAllColumnsExcept(context, split.
TrainSet
, originalColumnNames);
54
trainData = DropAllColumnsExcept(context, splitData.
TrainSet
, originalColumnNames);
Microsoft.ML.AutoML.Samples (2)
AutoMLExperiment.cs (1)
46
.SetDataset(trainTestSplit.
TrainSet
, fold: 5) // use 5-fold cross validation to evaluate each trial
Sweepable\SweepableLightGBMBinaryExperiment.cs (1)
72
.SetDataset(trainTestSplit.
TrainSet
, fold: 5) // use 5-fold cross validation to evaluate each trial
Microsoft.ML.AutoML.Tests (7)
AutoFitTests.cs (7)
114
.Execute(dataTrainTest.
TrainSet
, dataTrainTest.TestSet, DatasetUtil.UciAdultLabel);
173
.Execute(trainTestSplit.
TrainSet
, trainTestSplit.TestSet, label);
339
IDataView trainDataset = SplitUtil.DropAllColumnsExcept(context, trainTestData.
TrainSet
, originalColumnNames);
609
.Execute(dataTrainTest.
TrainSet
,
616
.Execute(dataCV.First().
TrainSet
,
626
var resTrainTest = model.Transform(dataTrainTest.
TrainSet
);
627
var resCV = model.Transform(dataCV.First().
TrainSet
);
Microsoft.ML.Data (1)
TrainCatalog.cs (1)
105
var model = estimator.Fit(split.
TrainSet
);
Microsoft.ML.IntegrationTests (2)
Training.cs (1)
37
var trainData = trainTestSplit.
TrainSet
;
Validation.cs (1)
108
var trainData = dataSplit.
TrainSet
;
Microsoft.ML.PerformanceTests (1)
ImageClassificationBench.cs (1)
72
_trainDataset = trainTestData.
TrainSet
;
Microsoft.ML.Samples (25)
Dynamic\DataOperations\CrossValidationSplit.cs (6)
35
.CreateEnumerable<DataPoint>(folds[0].
TrainSet
,
59
.CreateEnumerable<DataPoint>(folds[1].
TrainSet
,
82
.CreateEnumerable<DataPoint>(folds[2].
TrainSet
,
107
.CreateEnumerable<DataPoint>(folds[0].
TrainSet
,
130
.CreateEnumerable<DataPoint>(folds[1].
TrainSet
,
153
.CreateEnumerable<DataPoint>(folds[2].
TrainSet
,
Dynamic\DataOperations\TrainTestSplit.cs (2)
35
.CreateEnumerable<DataPoint>(split.
TrainSet
, reuseRowObject: false);
59
.CreateEnumerable<DataPoint>(split.
TrainSet
, reuseRowObject: false);
Dynamic\ModelOperations\OnnxConversion.cs (2)
67
var transformer = wholePipeline.Fit(trainTestOriginalData.
TrainSet
);
86
using var onnxTransformer = onnxEstimator.Fit(trainTestOriginalData.
TrainSet
);
Dynamic\Trainers\BinaryClassification\Calibrators\FixedPlatt.cs (1)
33
var transformer = pipeline.Fit(trainTestData.
TrainSet
);
Dynamic\Trainers\BinaryClassification\Calibrators\Isotonic.cs (1)
33
var transformer = pipeline.Fit(trainTestData.
TrainSet
);
Dynamic\Trainers\BinaryClassification\Calibrators\Naive.cs (1)
33
var transformer = pipeline.Fit(trainTestData.
TrainSet
);
Dynamic\Trainers\BinaryClassification\Calibrators\Platt.cs (1)
33
var transformer = pipeline.Fit(trainTestData.
TrainSet
);
Dynamic\Trainers\BinaryClassification\Gam.cs (1)
28
var trainSet = dataSets.
TrainSet
;
Dynamic\Trainers\BinaryClassification\GamWithOptions.cs (1)
29
var trainSet = dataSets.
TrainSet
;
Dynamic\Trainers\MulticlassClassification\ImageClassification\ImageClassificationDefault.cs (1)
61
IDataView trainDataset = trainTestData.
TrainSet
;
Dynamic\Trainers\MulticlassClassification\ImageClassification\ResnetV2101TransferLearningEarlyStopping.cs (1)
60
IDataView trainDataset = trainTestData.
TrainSet
;
Dynamic\Trainers\MulticlassClassification\ImageClassification\ResnetV2101TransferLearningTrainTestSplit.cs (1)
60
IDataView trainDataset = trainTestData.
TrainSet
;
Dynamic\Trainers\Regression\GamAdvanced.cs (1)
28
var trainSet = dataSets.
TrainSet
;
Dynamic\Trainers\Regression\GamWithOptionsAdvanced.cs (1)
29
var trainSet = dataSets.
TrainSet
;
Dynamic\Trainers\Regression\LightGbmAdvanced.cs (1)
48
var model = pipeline.Fit(split.
TrainSet
);
Dynamic\Trainers\Regression\LightGbmWithOptionsAdvanced.cs (1)
57
var model = pipeline.Fit(split.
TrainSet
);
Dynamic\Trainers\Regression\OrdinaryLeastSquaresAdvanced.cs (1)
55
var model = pipeline.Fit(split.
TrainSet
);
Dynamic\Trainers\Regression\OrdinaryLeastSquaresWithOptionsAdvanced.cs (1)
59
var model = pipeline.Fit(split.
TrainSet
);
Microsoft.ML.Samples.GPU (3)
docs\samples\Microsoft.ML.Samples\Dynamic\Trainers\MulticlassClassification\ImageClassification\ImageClassificationDefault.cs (1)
61
IDataView trainDataset = trainTestData.
TrainSet
;
docs\samples\Microsoft.ML.Samples\Dynamic\Trainers\MulticlassClassification\ImageClassification\ResnetV2101TransferLearningEarlyStopping.cs (1)
60
IDataView trainDataset = trainTestData.
TrainSet
;
docs\samples\Microsoft.ML.Samples\Dynamic\Trainers\MulticlassClassification\ImageClassification\ResnetV2101TransferLearningTrainTestSplit.cs (1)
60
IDataView trainDataset = trainTestData.
TrainSet
;
Microsoft.ML.TensorFlow.Tests (5)
TensorflowTests.cs (5)
1415
IDataView trainDataset = trainTestData.
TrainSet
;
1490
IDataView trainDataset = trainTestData.
TrainSet
;
1622
IDataView trainDataset = trainTestData.
TrainSet
;
1777
IDataView trainDataset = trainTestData.
TrainSet
;
1867
IDataView trainDataset = trainTestData.
TrainSet
;
Microsoft.ML.Tests (15)
Scenarios\Api\CookbookSamples\CookbookSamplesDynamicApi.cs (1)
658
var model = pipeline.Fit(split.
TrainSet
);
Scenarios\Api\TestApi.cs (12)
318
ttSplit.
TrainSet
,
320
ttSplitWithSeed.
TrainSet
,
322
ttSplitWithSeedAndSamplingKey.
TrainSet
,
324
cvSplit.First().
TrainSet
,
326
cvSplitWithSeed.First().
TrainSet
,
328
cvSplitWithSeedAndSamplingKey.First().
TrainSet
,
378
var stratTrainWorkclass = getWorkclass(stratSplit.
TrainSet
);
390
var stratTrainWithSeedWorkclass = getWorkclass(stratSeed.
TrainSet
);
473
ids = split.
TrainSet
.GetColumn<int>(split.
TrainSet
.Schema[nameof(Input.Id)]);
486
Assert.NotNull(split.
TrainSet
.Schema.GetColumnOrNull("KeyStrat")); // Check that the key column used as SamplingKeyColumn wasn't deleted by the split
508
Assert.NotNull(split.
TrainSet
.Schema.GetColumnOrNull(colname));
Scenarios\RegressionTest.cs (1)
25
IDataView trainingDataView = context.Data.FilterRowsByColumn(splitData.
TrainSet
, "FareAmount", lowerBound: 1, upperBound: 150);
TrainerEstimators\TreeEnsembleFeaturizerTest.cs (1)
800
var trainData = split.
TrainSet
;
Microsoft.ML.TorchSharp.Tests (3)
NerTests.cs (1)
232
var transformer = estimator.Fit(trainTest.
TrainSet
);
TextClassificationTests.cs (2)
180
var model = pipeline.Fit(trainTestSplit.
TrainSet
);
443
var model = estimator.Fit(dataSplit.
TrainSet
);