/// <summary>
/// Fits the scored <see cref="IDataView"/> creating a <see cref="CalibratorTransformer{TICalibrator}"/> that can transform the data by adding a
/// <see cref="DefaultColumnNames.Probability"/> column containing the calibrated <see cref="DefaultColumnNames.Score"/>.
/// </summary>
/// <param name="input"></param>
/// <returns>A trained <see cref="CalibratorTransformer{TICalibrator}"/> that will transform the data by adding the
/// <see cref="DefaultColumnNames.Probability"/> column.</returns>
public CalibratorTransformer <TICalibrator> Fit(IDataView input)
{
TICalibrator calibrator = null;
var roles = new List <KeyValuePair <RoleMappedSchema.ColumnRole, string> >();
roles.Add(RoleMappedSchema.CreatePair(MetadataUtils.Const.ScoreValueKind.Score, DefaultColumnNames.Score));
roles.Add(RoleMappedSchema.ColumnRole.Label.Bind(LabelColumn.Name));
roles.Add(RoleMappedSchema.ColumnRole.Feature.Bind(FeatureColumn.Name));
if (WeightColumn.IsValid)
{
roles.Add(RoleMappedSchema.ColumnRole.Weight.Bind(WeightColumn.Name));
}
var roleMappedData = new RoleMappedData(input, opt: false, roles.ToArray());
using (var ch = Host.Start("Creating calibrator."))
calibrator = (TICalibrator)CalibratorUtils.TrainCalibrator(Host, ch, CalibratorTrainer, Predictor, roleMappedData);
return(Create(Host, calibrator));
}
public static CombinedOutput CombineMetrics(IHostEnvironment env, CombineMetricsInput input)
{
var eval = GetEvaluator(env, input.Kind);
var perInst = EvaluateUtils.ConcatenatePerInstanceDataViews(env, eval, true, true, input.PerInstanceMetrics.Select(
idv => RoleMappedData.CreateOpt(idv, new[]
{
RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, input.LabelColumn),
RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Weight, input.WeightColumn.Value),
RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Group, input.GroupColumn.Value)
})).ToArray(),
out var variableSizeVectorColumnNames);
var warnings = input.Warnings != null ? new List <IDataView>(input.Warnings) : new List <IDataView>();
if (variableSizeVectorColumnNames.Length > 0)
{
var dvBldr = new ArrayDataViewBuilder(env);
var warn = $"Detected columns of variable length: {string.Join(", ", variableSizeVectorColumnNames)}." +
$" Consider setting collateMetrics- for meaningful per-Folds results.";
dvBldr.AddColumn(MetricKinds.ColumnNames.WarningText, TextType.Instance, new DvText(warn));
warnings.Add(dvBldr.GetDataView());
}
env.Assert(Utils.Size(perInst) == 1);
var overall = eval.GetOverallResults(input.OverallMetrics);
overall = EvaluateUtils.CombineFoldMetricsDataViews(env, overall, input.OverallMetrics.Length);
IDataView conf = null;
if (Utils.Size(input.ConfusionMatrix) > 0)
{
EvaluateUtils.ReconcileSlotNames <double>(env, input.ConfusionMatrix, MetricKinds.ColumnNames.Count, NumberType.R8);
for (int i = 0; i < input.ConfusionMatrix.Length; i++)
{
var idv = input.ConfusionMatrix[i];
// Find the old Count column and drop it.
for (int col = 0; col < idv.Schema.ColumnCount; col++)
{
if (idv.Schema.IsHidden(col) &&
idv.Schema.GetColumnName(col).Equals(MetricKinds.ColumnNames.Count))
{
input.ConfusionMatrix[i] = new ChooseColumnsByIndexTransform(env,
new ChooseColumnsByIndexTransform.Arguments()
{
Drop = true, Index = new[] { col }
}, idv);
break;
}
}
}
conf = EvaluateUtils.ConcatenateOverallMetrics(env, input.ConfusionMatrix);
}
var warningsIdv = warnings.Count > 0 ? AppendRowsDataView.Create(env, warnings[0].Schema, warnings.ToArray()) : null;
return(new CombinedOutput()
{
PerInstanceMetrics = perInst[0],
OverallMetrics = overall,
ConfusionMatrix = conf,
Warnings = warningsIdv
});
}
public CountTableTransformer Fit(IDataView input)
{
var labelCol = input.Schema.GetColumnOrNull(_labelColumnName);
if (labelCol == null)
{
throw _host.ExceptUserArg(nameof(_labelColumnName), "Label column '{0}' not found", _labelColumnName);
}
CheckLabelType(new RoleMappedData(input, roles: RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, _labelColumnName)), out var labelCardinality);
var labelColumnType = labelCol.GetValueOrDefault().Type;
var labelClassNames = InitLabelClassNames(_host, labelCol.GetValueOrDefault(), labelCardinality);
var n = _columns.Length;
var inputColumns = new DataViewSchema.Column[_columns.Length];
for (int i = 0; i < inputColumns.Length; i++)
{
var col = input.Schema.GetColumnOrNull(_columns[i].InputColumnName);
if (col == null)
{
throw _host.Except($"Could not find column {_columns[i].InputColumnName} in input schema");
}
inputColumns[i] = col.GetValueOrDefault();
}
_host.Assert(_initialCounts != null || _sharedBuilder != null || _builders != null);
MultiCountTableBuilderBase multiBuilder;
if (_initialCounts != null)
{
multiBuilder = _initialCounts.Featurizer.MultiCountTable.ToBuilder(_host, inputColumns, labelCardinality);
}
else if (_builders != null)
{
multiBuilder = new ParallelMultiCountTableBuilder(_host, inputColumns, _builders, labelCardinality);
}
else
{
multiBuilder = new BagMultiCountTableBuilder(_host, inputColumns, _sharedBuilder, labelCardinality);
}
var cols = new List <DataViewSchema.Column>();
foreach (var c in _columns)
{
var col = input.Schema.GetColumnOrNull(c.InputColumnName);
_host.Assert(col.HasValue);
cols.Add(col.Value);
}
TrainTables(input, cols, multiBuilder, labelCol.GetValueOrDefault());
var multiCountTable = multiBuilder.CreateMultiCountTable();
var featurizer = new CountTargetEncodingFeaturizer(_host, _columns.Select(col => col.PriorCoefficient).ToArray(), _columns.Select(col => col.LaplaceScale).ToArray(), labelCardinality, multiCountTable);
return(new CountTableTransformer(_host, featurizer, labelClassNames,
_columns.Select(col => col.Seed).ToArray(), _columns.Select(col => (col.Name, col.InputColumnName)).ToArray()));
}
