public static SuggestedPipeline GetNextInferredPipeline(MLContext context,
IEnumerable <SuggestedPipelineRunDetail> history,
DatasetColumnInfo[] columns,
TaskKind task,
bool isMaximizingMetric,
CacheBeforeTrainer cacheBeforeTrainer,
IChannel logger,
IEnumerable <TrainerName> trainerAllowList = null)
{
var availableTrainers = RecipeInference.AllowedTrainers(context, task,
ColumnInformationUtil.BuildColumnInfo(columns), trainerAllowList);
var transforms = TransformInferenceApi.InferTransforms(context, task, columns).ToList();
var transformsPostTrainer = TransformInferenceApi.InferTransformsPostTrainer(context, task, columns).ToList();
// if we haven't run all pipelines once
if (history.Count() < availableTrainers.Count())
{
return(GetNextFirstStagePipeline(context, history, availableTrainers, transforms, transformsPostTrainer, cacheBeforeTrainer));
}
// get top trainers from stage 1 runs
var topTrainers = GetTopTrainers(history, availableTrainers, isMaximizingMetric);
// sort top trainers by # of times they've been run, from lowest to highest
var orderedTopTrainers = OrderTrainersByNumTrials(history, topTrainers);
// keep as hash set of previously visited pipelines
var visitedPipelines = new HashSet <SuggestedPipeline>(history.Select(h => h.Pipeline));
// iterate over top trainers (from least run to most run),
// to find next pipeline
foreach (var trainer in orderedTopTrainers)
{
var newTrainer = trainer.Clone();
// repeat until passes or runs out of chances
const int maxNumberAttempts = 10;
var count = 0;
do
{
// sample new hyperparameters for the learner
if (!SampleHyperparameters(context, newTrainer, history, isMaximizingMetric, logger))
{
// if unable to sample new hyperparameters for the learner
// (ie SMAC returned 0 suggestions), break
break;
}
var suggestedPipeline = SuggestedPipelineBuilder.Build(context, transforms, transformsPostTrainer, newTrainer, cacheBeforeTrainer);
// make sure we have not seen pipeline before
if (!visitedPipelines.Contains(suggestedPipeline))
{
return(suggestedPipeline);
}
} while (++count <= maxNumberAttempts);
}
return(null);
}
public static ColumnInferenceResults InferColumns(MLContext context, string path, ColumnInformation columnInfo, bool hasHeader,
TextFileContents.ColumnSplitResult splitInference, ColumnTypeInference.InferenceResult typeInference,
bool trimWhitespace, bool groupColumns)
{
var loaderColumns = ColumnTypeInference.GenerateLoaderColumns(typeInference.Columns);
var typedLoaderOptions = new TextLoader.Options
{
Columns = loaderColumns,
Separators = new[] { splitInference.Separator.Value },
AllowSparse = splitInference.AllowSparse,
AllowQuoting = splitInference.AllowQuote,
ReadMultilines = splitInference.ReadMultilines,
HasHeader = hasHeader,
TrimWhitespace = trimWhitespace
};
var textLoader = context.Data.CreateTextLoader(typedLoaderOptions);
var dataView = textLoader.Load(path);
// Validate all columns specified in column info exist in inferred data view
ColumnInferenceValidationUtil.ValidateSpecifiedColumnsExist(columnInfo, dataView);
var purposeInferenceResult = PurposeInference.InferPurposes(context, dataView, columnInfo);
// start building result objects
IEnumerable <TextLoader.Column> columnResults = null;
IEnumerable <(string, ColumnPurpose)> purposeResults = null;
// infer column grouping and generate column names
if (groupColumns)
{
var groupingResult = ColumnGroupingInference.InferGroupingAndNames(context, hasHeader,
typeInference.Columns, purposeInferenceResult);
columnResults = groupingResult.Select(c => c.GenerateTextLoaderColumn());
purposeResults = groupingResult.Select(c => (c.SuggestedName, c.Purpose));
}
else
{
columnResults = loaderColumns;
purposeResults = purposeInferenceResult.Select(p => (dataView.Schema[p.ColumnIndex].Name, p.Purpose));
}
var textLoaderOptions = new TextLoader.Options()
{
Columns = columnResults.ToArray(),
AllowQuoting = splitInference.AllowQuote,
AllowSparse = splitInference.AllowSparse,
Separators = new char[] { splitInference.Separator.Value },
ReadMultilines = splitInference.ReadMultilines,
HasHeader = hasHeader,
TrimWhitespace = trimWhitespace
};
return(new ColumnInferenceResults()
{
TextLoaderOptions = textLoaderOptions,
ColumnInformation = ColumnInformationUtil.BuildColumnInfo(purposeResults)
});
}
/// <summary>
/// Validate all columns specified in column info exist in inferred data view.
/// </summary>
public static void ValidateSpecifiedColumnsExist(ColumnInformation columnInfo,
IDataView dataView)
{
var columnNames = ColumnInformationUtil.GetColumnNames(columnInfo);
foreach (var columnName in columnNames)
{
if (dataView.Schema.GetColumnOrNull(columnName) == null)
{
throw new ArgumentException($"Specified column {columnName} " +
$"is not found in the dataset.");
}
}
}