internal static BinaryClassificationResult AutoFit(this BinaryClassificationContext context,
IDataView trainData,
string label,
IDataView validationData = null,
InferredColumn[] inferredColumns = null,
AutoFitSettings settings = null,
CancellationToken cancellationToken = default,
IProgress <BinaryClassificationItertionResult> iterationCallback = null,
IDebugLogger debugLogger = null)
{
// run autofit & get all pipelines run in that process
var(allPipelines, bestPipeline) = AutoFitApi.Fit(trainData, validationData, label, inferredColumns,
settings, TaskKind.BinaryClassification, OptimizingMetric.Accuracy,
debugLogger);
var results = new BinaryClassificationItertionResult[allPipelines.Length];
for (var i = 0; i < results.Length; i++)
{
var iterationResult = allPipelines[i];
var result = new BinaryClassificationItertionResult(iterationResult.Model, (BinaryClassificationMetrics)iterationResult.EvaluatedMetrics, iterationResult.ScoredValidationData);
results[i] = result;
}
var bestResult = new BinaryClassificationItertionResult(bestPipeline.Model, (BinaryClassificationMetrics)bestPipeline.EvaluatedMetrics, bestPipeline.ScoredValidationData);
return(new BinaryClassificationResult(bestResult, results));
}
public static BinaryClassificationResult AutoFit(this BinaryClassificationContext context,
IDataView trainData,
string label,
IDataView validationData = null,
InferredColumn[] inferredColumns = null,
AutoFitSettings settings = null,
CancellationToken cancellationToken = default,
IProgress <BinaryClassificationItertionResult> iterationCallback = null)
{
return(AutoFit(context, trainData, label, validationData, inferredColumns, settings,
cancellationToken, iterationCallback, null));
}
public AutoFitter(MLContext mlContext, OptimizingMetricInfo metricInfo, AutoFitSettings settings,
TaskKind task, string label, PurposeInference.Column[] puproseOverrides,
IDataView trainData, IDataView validationData, IDebugLogger debugLogger)
{
_debugLogger = debugLogger;
_history = new List <PipelineRunResult>();
_label = label;
_mlContext = mlContext;
_optimizingMetricInfo = metricInfo;
_settings = settings ?? new AutoFitSettings();
_puproseOverrides = puproseOverrides;
_trainData = trainData;
_task = task;
_validationData = validationData;
}
public static void ValidateAutoFitArgs(IDataView trainData, string label, IDataView validationData,
AutoFitSettings settings, IEnumerable <(string, ColumnPurpose)> purposeOverrides)
public static (PipelineRunResult[] allPipelines, PipelineRunResult bestPipeline) Fit(IDataView trainData,
IDataView validationData, string label, AutoFitSettings settings, TaskKind task, OptimizingMetric metric,
IEnumerable <(string, ColumnPurpose)> purposeOverrides, IDebugLogger debugLogger)
public static (PipelineRunResult[] allPipelines, PipelineRunResult bestPipeline) Fit(IDataView trainData,
IDataView validationData, string label, InferredColumn[] inferredColumns, AutoFitSettings settings,
TaskKind task, OptimizingMetric metric, IDebugLogger debugLogger)
{
// hack: init new MLContext
var mlContext = new MLContext();
// infer pipelines
var optimizingMetricfInfo = new OptimizingMetricInfo(metric);
var autoFitter = new AutoFitter(mlContext, optimizingMetricfInfo, settings, task,
label, ToInternalColumnPurposes(inferredColumns),
trainData, validationData, debugLogger);
var allPipelines = autoFitter.Fit(1);
var bestScore = allPipelines.Max(p => p.Score);
var bestPipeline = allPipelines.First(p => p.Score == bestScore);
return(allPipelines, bestPipeline);
}
public static RegressionResult AutoFit(this RegressionContext context,
IDataView trainData,
string label,
IDataView validationData = null,
AutoFitSettings settings = null,
IEnumerable <(string, ColumnPurpose)> purposeOverrides = null,
