public static CommonOutputs.TransformOutput Generate(IHostEnvironment env, GenerateNumberTransform.Arguments input)
{
var h = EntryPointUtils.CheckArgsAndCreateHost(env, "GenerateNumber", input);
var xf = new GenerateNumberTransform(h, input, input.Data);
return(new CommonOutputs.TransformOutput()
{
Model = new TransformModel(h, xf, input.Data),
OutputData = xf
});
}
private IDataView WrapPerInstance(RoleMappedData perInst)
{
var idv = perInst.Data;
// Make a list of column names that Maml outputs as part of the per-instance data view, and then wrap
// the per-instance data computed by the evaluator in a SelectColumnsTransform.
var cols = new List <(string Source, string Name)>();
var colsToKeep = new List <string>();
// If perInst is the result of cross-validation and contains a fold Id column, include it.
int foldCol;
if (perInst.Schema.Schema.TryGetColumnIndex(MetricKinds.ColumnNames.FoldIndex, out foldCol))
{
colsToKeep.Add(MetricKinds.ColumnNames.FoldIndex);
}
// Maml always outputs a name column, if it doesn't exist add a GenerateNumberTransform.
if (perInst.Schema.Name == null)
{
var args = new GenerateNumberTransform.Arguments();
args.Column = new[] { new GenerateNumberTransform.Column()
{
Name = "Instance"
} };
args.UseCounter = true;
idv = new GenerateNumberTransform(Host, args, idv);
colsToKeep.Add("Instance");
}
else
{
cols.Add((perInst.Schema.Name.Name, "Instance"));
colsToKeep.Add("Instance");
}
// Maml outputs the weight column if it exists.
if (perInst.Schema.Weight != null)
{
colsToKeep.Add(perInst.Schema.Weight.Name);
}
// Get the other columns from the evaluator.
foreach (var col in GetPerInstanceColumnsToSave(perInst.Schema))
{
colsToKeep.Add(col);
}
idv = new ColumnsCopyingTransformer(Host, cols.ToArray()).Transform(idv);
idv = ColumnSelectingTransformer.CreateKeep(Host, idv, colsToKeep.ToArray());
return(GetPerInstanceMetricsCore(idv, perInst.Schema));
}
private string GetSplitColumn(IChannel ch, IDataView input, ref IDataView output)
{
// The stratification column and/or group column, if they exist at all, must be present at this point.
var schema = input.Schema;
output = input;
// If no stratification column was specified, but we have a group column of type Single, Double or
// Key (contiguous) use it.
string stratificationColumn = null;
if (!string.IsNullOrWhiteSpace(Args.StratificationColumn))
{
stratificationColumn = Args.StratificationColumn;
}
else
{
string group = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Args.GroupColumn), Args.GroupColumn, DefaultColumnNames.GroupId);
int index;
if (group != null && schema.TryGetColumnIndex(group, out index))
{
// Check if group column key type with known cardinality.
var type = schema.GetColumnType(index);
if (type.KeyCount > 0)
{
stratificationColumn = group;
}
}
}
if (string.IsNullOrEmpty(stratificationColumn))
{
stratificationColumn = "StratificationColumn";
int tmp;
int inc = 0;
while (input.Schema.TryGetColumnIndex(stratificationColumn, out tmp))
{
stratificationColumn = string.Format("StratificationColumn_{0:000}", ++inc);
}
var keyGenArgs = new GenerateNumberTransform.Arguments();
var col = new GenerateNumberTransform.Column();
col.Name = stratificationColumn;
keyGenArgs.Column = new[] { col };
output = new GenerateNumberTransform(Host, keyGenArgs, input);
}
else
{
int col;
if (!input.Schema.TryGetColumnIndex(stratificationColumn, out col))
{
throw ch.ExceptUserArg(nameof(Arguments.StratificationColumn), "Column '{0}' does not exist", stratificationColumn);
}
var type = input.Schema.GetColumnType(col);
if (!RangeFilter.IsValidRangeFilterColumnType(ch, type))
{
ch.Info("Hashing the stratification column");
var origStratCol = stratificationColumn;
int tmp;
int inc = 0;
while (input.Schema.TryGetColumnIndex(stratificationColumn, out tmp))
{
stratificationColumn = string.Format("{0}_{1:000}", origStratCol, ++inc);
}
var hashargs = new HashTransform.Arguments();
hashargs.Column = new[] { new HashTransform.Column {
Source = origStratCol, Name = stratificationColumn
} };
hashargs.HashBits = 30;
output = new HashTransform(Host, hashargs, input);
}
}
return(stratificationColumn);
}
private void RunCore(IChannel ch, string cmd)
{
Host.AssertValue(ch);
IPredictor inputPredictor = null;
if (Args.ContinueTrain && !TrainUtils.TryLoadPredictor(ch, Host, Args.InputModelFile, out inputPredictor))
{
ch.Warning("No input model file specified or model file did not contain a predictor. The model state cannot be initialized.");
}
ch.Trace("Constructing data pipeline");
IDataLoader loader = CreateRawLoader();
// If the per-instance results are requested and there is no name column, add a GenerateNumberTransform.
var preXf = Args.PreTransform;
if (!string.IsNullOrEmpty(Args.OutputDataFile))
{
string name = TrainUtils.MatchNameOrDefaultOrNull(ch, loader.Schema, nameof(Args.NameColumn), Args.NameColumn, DefaultColumnNames.Name);
if (name == null)
{
var args = new GenerateNumberTransform.Arguments();
args.Column = new[] { new GenerateNumberTransform.Column()
{
Name = DefaultColumnNames.Name
}, };
args.UseCounter = true;
var options = CmdParser.GetSettings(ch, args, new GenerateNumberTransform.Arguments());
preXf = preXf.Concat(
new[]
{
new KeyValuePair <string, SubComponent <IDataTransform, SignatureDataTransform> >(
"", new SubComponent <IDataTransform, SignatureDataTransform>(
GenerateNumberTransform.LoadName, options))
}).ToArray();
}
}
loader = CompositeDataLoader.Create(Host, loader, preXf);
ch.Trace("Binding label and features columns");
IDataView pipe = loader;
var stratificationColumn = GetSplitColumn(ch, loader, ref pipe);
var scorer = Args.Scorer;
var evaluator = Args.Evaluator;
Func <IDataView> validDataCreator = null;
if (Args.ValidationFile != null)
{
validDataCreator =
() =>
{
// Fork the command.
var impl = new CrossValidationCommand(this);
return(impl.CreateRawLoader(dataFile: Args.ValidationFile));
};
}
FoldHelper fold = new FoldHelper(Host, RegistrationName, pipe, stratificationColumn,
Args, CreateRoleMappedData, ApplyAllTransformsToData, scorer, evaluator,
validDataCreator, ApplyAllTransformsToData, inputPredictor, cmd, loader, !string.IsNullOrEmpty(Args.OutputDataFile));
var tasks = fold.GetCrossValidationTasks();
if (!evaluator.IsGood())
{
evaluator = EvaluateUtils.GetEvaluatorType(ch, tasks[0].Result.ScoreSchema);
}
var eval = evaluator.CreateInstance(Host);
// Print confusion matrix and fold results for each fold.
for (int i = 0; i < tasks.Length; i++)
{
var dict = tasks[i].Result.Metrics;
MetricWriter.PrintWarnings(ch, dict);
eval.PrintFoldResults(ch, dict);
}
// Print the overall results.
if (!TryGetOverallMetrics(tasks.Select(t => t.Result.Metrics).ToArray(), out var overallList))
{
throw ch.Except("No overall metrics found");
}
var overall = eval.GetOverallResults(overallList.ToArray());
MetricWriter.PrintOverallMetrics(Host, ch, Args.SummaryFilename, overall, Args.NumFolds);
eval.PrintAdditionalMetrics(ch, tasks.Select(t => t.Result.Metrics).ToArray());
Dictionary <string, IDataView>[] metricValues = tasks.Select(t => t.Result.Metrics).ToArray();
SendTelemetryMetric(metricValues);
// Save the per-instance results.
if (!string.IsNullOrWhiteSpace(Args.OutputDataFile))
{
var perInstance = EvaluateUtils.ConcatenatePerInstanceDataViews(Host, eval, Args.CollateMetrics,
Args.OutputExampleFoldIndex, tasks.Select(t => t.Result.PerInstanceResults).ToArray(), out var variableSizeVectorColumnNames);
if (variableSizeVectorColumnNames.Length > 0)
{
ch.Warning("Detected columns of variable length: {0}. Consider setting collateMetrics- for meaningful per-Folds results.",
string.Join(", ", variableSizeVectorColumnNames));
}
if (Args.CollateMetrics)
{
ch.Assert(perInstance.Length == 1);
MetricWriter.SavePerInstance(Host, ch, Args.OutputDataFile, perInstance[0]);
}
else
{
int i = 0;
foreach (var idv in perInstance)
{
MetricWriter.SavePerInstance(Host, ch, ConstructPerFoldName(Args.OutputDataFile, i), idv);
i++;
}
}
}
}
private void RunCore(IChannel ch, string cmd)
{
Host.AssertValue(ch);
IPredictor inputPredictor = null;
if (Args.ContinueTrain && !TrainUtils.TryLoadPredictor(ch, Host, Args.InputModelFile, out inputPredictor))
{
ch.Warning("No input model file specified or model file did not contain a predictor. The model state cannot be initialized.");
}
ch.Trace("Constructing data pipeline");
IDataLoader loader = CreateRawLoader();
// If the per-instance results are requested and there is no name column, add a GenerateNumberTransform.
var preXf = Args.PreTransform;
if (!string.IsNullOrEmpty(Args.OutputDataFile))
{
string name = TrainUtils.MatchNameOrDefaultOrNull(ch, loader.Schema, nameof(Args.NameColumn), Args.NameColumn, DefaultColumnNames.Name);
if (name == null)
{
var args = new GenerateNumberTransform.Arguments();
args.Column = new[] { new GenerateNumberTransform.Column()
{
Name = DefaultColumnNames.Name
}, };
args.UseCounter = true;
var options = CmdParser.GetSettings(ch, args, new GenerateNumberTransform.Arguments());
preXf = preXf.Concat(
new[]
{
new KeyValuePair <string, SubComponent <IDataTransform, SignatureDataTransform> >(
"", new SubComponent <IDataTransform, SignatureDataTransform>(
GenerateNumberTransform.LoadName, options))
}).ToArray();
}
}
loader = CompositeDataLoader.Create(Host, loader, preXf);
ch.Trace("Binding label and features columns");
IDataView pipe = loader;
var stratificationColumn = GetSplitColumn(ch, loader, ref pipe);
var scorer = Args.Scorer;
var evaluator = Args.Evaluator;
Func <IDataView> validDataCreator = null;
if (Args.ValidationFile != null)
{
validDataCreator =
() =>
{
// Fork the command.
var impl = new CrossValidationCommand(this);
return(impl.CreateRawLoader(dataFile: Args.ValidationFile));
};
}
FoldHelper fold = new FoldHelper(Host, RegistrationName, pipe, stratificationColumn,
Args, CreateRoleMappedData, ApplyAllTransformsToData, scorer, evaluator,
validDataCreator, ApplyAllTransformsToData, inputPredictor, cmd, loader, !string.IsNullOrEmpty(Args.OutputDataFile));
var tasks = fold.GetCrossValidationTasks();
if (!evaluator.IsGood())
{
evaluator = EvaluateUtils.GetEvaluatorType(ch, tasks[0].Result.ScoreSchema);
}
var eval = evaluator.CreateInstance(Host);
// Print confusion matrix and fold results for each fold.
for (int i = 0; i < tasks.Length; i++)
{
var dict = tasks[i].Result.Metrics;
MetricWriter.PrintWarnings(ch, dict);
eval.PrintFoldResults(ch, dict);
}
// Print the overall results.
eval.PrintOverallResults(ch, Args.SummaryFilename, tasks.Select(t => t.Result.Metrics).ToArray());
Dictionary <string, IDataView>[] metricValues = tasks.Select(t => t.Result.Metrics).ToArray();
SendTelemetryMetric(metricValues);
// Save the per-instance results.
if (!string.IsNullOrWhiteSpace(Args.OutputDataFile))
{
Func <Task <FoldHelper.FoldResult>, int, IDataView> getPerInstance =
(task, i) =>
{
if (!Args.OutputExampleFoldIndex)
{
return(task.Result.PerInstanceResults);
}
// If the fold index is requested, add a column containing it. We use the first column in the data view
// as an input column to the LambdaColumnMapper, because it must have an input.
var inputColName = task.Result.PerInstanceResults.Schema.GetColumnName(0);
var inputColType = task.Result.PerInstanceResults.Schema.GetColumnType(0);
return(Utils.MarshalInvoke(EvaluateUtils.AddKeyColumn <int>, inputColType.RawType, Host,
task.Result.PerInstanceResults, inputColName, MetricKinds.ColumnNames.FoldIndex,
inputColType, Args.NumFolds, i + 1, "FoldIndex", default(ValueGetter <VBuffer <DvText> >)));
};
var foldDataViews = tasks.Select(getPerInstance).ToArray();
if (Args.CollateMetrics)
{
var perInst = AppendPerInstanceDataViews(foldDataViews, ch);
MetricWriter.SavePerInstance(Host, ch, Args.OutputDataFile, perInst);
}
else
{
int i = 0;
foreach (var idv in foldDataViews)
{
MetricWriter.SavePerInstance(Host, ch, ConstructPerFoldName(Args.OutputDataFile, i), idv);
i++;
}
}
}
}