/// <summary>
/// Save a zero-row dataview that will be used to infer schema information, used in the case
/// where the tranpsose loader is instantiated with no input streams.
/// </summary>
private static void SaveSchema(IHostEnvironment env, ModelSaveContext ctx, Schema schema)
{
Contracts.AssertValue(env);
env.AssertValue(ctx);
env.AssertValue(schema);
var noRows = new EmptyDataView(env, schema);
env.Assert(noRows.GetRowCount() == 0);
var saverArgs = new BinarySaver.Arguments();
saverArgs.Silent = true;
var saver = new BinarySaver(env, saverArgs);
// We load our schema from what amounts to a binary loader, so all columns should likewise be savable.
env.Assert(Enumerable.Range(0, schema.Count).All(c => saver.IsColumnSavable(schema[c].Type)));
ctx.SaveBinaryStream("Schema.idv", w => saver.SaveData(w.BaseStream, noRows, Utils.GetIdentityPermutation(schema.Count)));
}
/// <summary>
/// Load an object from the repository directory.
/// </summary>
public static void LoadModel <TRes, TSig>(IHostEnvironment env, out TRes result, RepositoryReader rep, string dir, params object[] extra)
where TRes : class
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(rep, nameof(rep));
if (!LoadModelOrNull <TRes, TSig>(env, out result, rep, dir, extra))
{
throw env.ExceptDecode("Corrupt model file");
}
env.AssertValue(result);
}
private static TRes CreateCore <TRes, TSig>(IHostEnvironment env, string settings, out string loadName, params object[] extraArgs)
where TRes : class
{
Contracts.AssertValue(env);
env.AssertValue(settings, "settings");
var sc = SubComponent.Parse <TRes, TSig>(settings);
loadName = sc.Kind;
return(sc.CreateInstance(env, extraArgs));
}
private static ISchemaBoundMapper WrapCore <T>(IHostEnvironment env, ISchemaBoundMapper mapper, RoleMappedSchema trainSchema)
{
Contracts.AssertValue(env);
env.AssertValue(mapper);
env.AssertValue(trainSchema);
env.Assert(mapper is ISchemaBoundRowMapper);
env.Assert(trainSchema.Label.HasValue);
var labelColumn = trainSchema.Label.Value;
// Key values from the training schema label, will map to slot names of the score output.
var type = labelColumn.Annotations.Schema.GetColumnOrNull(AnnotationUtils.Kinds.KeyValues)?.Type as VectorDataViewType;
env.AssertValue(type);
// Wrap the fetching of the metadata as a simple getter.
ValueGetter <VBuffer <T> > getter = (ref VBuffer <T> value) =>
labelColumn.GetKeyValues(ref value);
return(MulticlassClassificationScorer.LabelNameBindableMapper.CreateBound <T>(env, (ISchemaBoundRowMapper)mapper, type, getter, AnnotationUtils.Kinds.TrainingLabelValues, CanWrap));
}
internal static List <ConfusionMatrix> Create(IHostEnvironment env, IDataView confusionMatrix)
{
Contracts.AssertValue(env);
env.AssertValue(confusionMatrix);
if (!confusionMatrix.Schema.TryGetColumnIndex(MetricKinds.ColumnNames.Count, out int countColumn))
{
throw env.Except($"ConfusionMatrix data view did not contain a {nameof(MetricKinds.ColumnNames.Count)} column.");
}
IRowCursor cursor = confusionMatrix.GetRowCursor(col => col == countColumn);
var slots = default(VBuffer <DvText>);
confusionMatrix.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, countColumn, ref slots);
string[] classNames = new string[slots.Count];
for (int i = 0; i < slots.Count; i++)
{
classNames[i] = slots.Values[i].ToString();
}
ColumnType type = confusionMatrix.Schema.GetColumnType(countColumn);
env.Assert(type.IsVector);
ValueGetter <VBuffer <double> > countGetter = cursor.GetGetter <VBuffer <double> >(countColumn);
VBuffer <double> countValues = default;
List <ConfusionMatrix> confusionMatrices = new List <ConfusionMatrix>();
int valuesRowIndex = 0;
double[,] elements = null;
while (cursor.MoveNext())
{
if (valuesRowIndex == 0)
{
elements = new double[type.VectorSize, type.VectorSize];
}
countGetter(ref countValues);
for (int i = 0; i < countValues.Length; i++)
{
elements[valuesRowIndex, i] = countValues.Values[i];
}
valuesRowIndex++;
if (valuesRowIndex == type.VectorSize)
{
valuesRowIndex = 0;
confusionMatrices.Add(new ConfusionMatrix(elements, classNames));
}
}
return(confusionMatrices);
}
internal static List <ClassificationMetrics> FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix,
int confusionMatriceStartIndex = 0)
{
Contracts.AssertValue(env);
env.AssertValue(overallMetrics);
env.AssertValue(confusionMatrix);
var metricsEnumerable = overallMetrics.AsEnumerable <SerializationClass>(env, true, ignoreMissingColumns: true);
if (!metricsEnumerable.GetEnumerator().MoveNext())
{
throw env.Except("The overall RegressionMetrics didn't have any rows.");
}
List <ClassificationMetrics> metrics = new List <ClassificationMetrics>();
var confusionMatrices = ConfusionMatrix.Create(env, confusionMatrix).GetEnumerator();
int index = 0;
foreach (var metric in metricsEnumerable)
{
if (index++ >= confusionMatriceStartIndex && !confusionMatrices.MoveNext())
{
throw env.Except("Confusion matrices didn't have enough matrices.");
}
metrics.Add(
new ClassificationMetrics()
{
AccuracyMicro = metric.AccuracyMicro,
AccuracyMacro = metric.AccuracyMacro,
LogLoss = metric.LogLoss,
LogLossReduction = metric.LogLossReduction,
TopKAccuracy = metric.TopKAccuracy,
PerClassLogLoss = metric.PerClassLogLoss,
ConfusionMatrix = confusionMatrices.Current
});
}
return(metrics);
}
public BindingsImpl ApplyToSchema(DataViewSchema input, ISchemaBindableMapper bindable, IHostEnvironment env)
{
Contracts.AssertValue(env);
env.AssertValue(input);
env.AssertValue(bindable);
string scoreCol = RowMapper.OutputSchema[ScoreColumnIndex].Name;
var schema = new RoleMappedSchema(input, RowMapper.GetInputColumnRoles());
// Checks compatibility of the predictor input types.
var mapper = bindable.Bind(env, schema);
var rowMapper = mapper as ISchemaBoundRowMapper;
env.CheckParam(rowMapper != null, nameof(bindable), "Mapper must implement ISchemaBoundRowMapper");
int mapperScoreColumn;
bool tmp = rowMapper.OutputSchema.TryGetColumnIndex(scoreCol, out mapperScoreColumn);
env.Check(tmp, "Mapper doesn't have expected score column");
return(new BindingsImpl(input, rowMapper, Suffix, ScoreColumnKind, true, mapperScoreColumn, PredColType));
}
private static bool TryCreateBindableFromScorer(IHostEnvironment env, IPredictor predictor,
SubComponent <IDataScorerTransform, SignatureDataScorer> scorerSettings, out ISchemaBindableMapper bindable)
{
Contracts.AssertValue(env);
env.AssertValue(predictor);
env.Assert(scorerSettings.IsGood());
// Try to find a mapper factory method with the same loadname as the scorer settings.
var mapperComponent = new SubComponent <ISchemaBindableMapper, SignatureBindableMapper>(scorerSettings.Kind, scorerSettings.Settings);
return(ComponentCatalog.TryCreateInstance(env, out bindable, mapperComponent, predictor));
}
/// <summary>
/// Create a Cursorable object on a given data view.
/// </summary>
/// <param name="env">Host environment.</param>
/// <param name="data">The underlying data view.</param>
/// <param name="ignoreMissingColumns">Whether to ignore missing columns in the data view.</param>
/// <param name="schemaDefinition">The optional user-provided schema.</param>
/// <returns>The constructed Cursorable.</returns>
public static TypedCursorable <TRow> Create(IHostEnvironment env, IDataView data, bool ignoreMissingColumns, SchemaDefinition schemaDefinition)
{
Contracts.AssertValue(env);
env.AssertValue(data);
env.AssertValueOrNull(schemaDefinition);
var outSchema = schemaDefinition == null
? InternalSchemaDefinition.Create(typeof(TRow), SchemaDefinition.Direction.Write)
: InternalSchemaDefinition.Create(typeof(TRow), schemaDefinition);
return(new TypedCursorable <TRow>(env, data, ignoreMissingColumns, outSchema));
}
private static IDataTransform Create(IHostEnvironment env, Arguments args, ITrainer trainer, IDataView input, IComponentFactory <IPredictor, ISchemaBindableMapper> mapperFactory)
{
Contracts.AssertValue(env, nameof(env));
env.AssertValue(args, nameof(args));
env.AssertValue(trainer, nameof(trainer));
env.AssertValue(input, nameof(input));
var host = env.Register("TrainAndScoreTransform");
using (var ch = host.Start("Train"))
{
ch.Trace("Constructing trainer");
var customCols = TrainUtils.CheckAndGenerateCustomColumns(env, args.CustomColumn);
string feat;
string group;
var data = CreateDataFromArgs(ch, input, args, out feat, out group);
var predictor = TrainUtils.Train(host, ch, data, trainer, null,
args.Calibrator, args.MaxCalibrationExamples, null);
return(ScoreUtils.GetScorer(args.Scorer, predictor, input, feat, group, customCols, env, data.Schema, mapperFactory));
}
}
internal static ISchemaBoundMapper WrapCore <T>(IHostEnvironment env, ISchemaBoundMapper mapper, RoleMappedSchema trainSchema)
{
Contracts.AssertValue(env);
env.AssertValue(mapper);
env.AssertValue(trainSchema);
env.Assert(mapper is ISchemaBoundRowMapper);
// Key values from the training schema label, will map to slot names of the score output.
var type = trainSchema.Label.Value.Metadata.Schema.GetColumnOrNull(MetadataUtils.Kinds.KeyValues)?.Type;
env.AssertValue(type);
env.Assert(type is VectorType);
// Wrap the fetching of the metadata as a simple getter.
ValueGetter <VBuffer <T> > getter =
(ref VBuffer <T> value) =>
{
trainSchema.Label.Value.GetKeyValues(ref value);
};
return(LabelNameBindableMapper.CreateBound <T>(env, (ISchemaBoundRowMapper)mapper, type as VectorType, getter, MetadataUtils.Kinds.SlotNames, CanWrap));
}
public static IDataView CreateFromList <TRow>(IHostEnvironment env, IList <TRow> data,
SchemaDefinition schemaDefinition = null)
where TRow : class
{
Contracts.AssertValue(env);
env.AssertValue(data);
env.AssertValueOrNull(schemaDefinition);
var internalSchemaDefn = schemaDefinition == null
? InternalSchemaDefinition.Create(typeof(TRow))
: InternalSchemaDefinition.Create(typeof(TRow), schemaDefinition);
return(new ListDataView <TRow>(env, data, internalSchemaDefn));
}
public static StreamingDataView <TRow> CreateFromEnumerable <TRow>(IHostEnvironment env, IEnumerable <TRow> data,
SchemaDefinition schemaDefinition = null)
where TRow : class
{
Contracts.AssertValue(env);
env.AssertValue(data);
env.AssertValueOrNull(schemaDefinition);
var internalSchemaDefn = schemaDefinition == null
? InternalSchemaDefinition.Create(typeof(TRow), SchemaDefinition.Direction.Read)
: InternalSchemaDefinition.Create(typeof(TRow), schemaDefinition);
return(new StreamingDataView <TRow>(env, data, internalSchemaDefn));
}
public RowMapper(IHostEnvironment env, BindableMapper parent, RoleMappedSchema schema)
{
Contracts.AssertValue(env);
_env = env;
_env.AssertValue(schema);
_env.AssertValue(parent);
_env.AssertValue(schema.Feature);
_parent = parent;
InputRoleMappedSchema = schema;
var genericMapper = parent.GenericMapper.Bind(_env, schema);
_genericRowMapper = genericMapper as ISchemaBoundRowMapper;
if (parent.Stringify)
{
var builder = new SchemaBuilder();
builder.AddColumn(DefaultColumnNames.FeatureContributions, TextType.Instance, null);
_outputSchema = builder.GetSchema();
if (InputSchema.HasSlotNames(InputRoleMappedSchema.Feature.Index, InputRoleMappedSchema.Feature.Type.VectorSize))
{
InputSchema.GetMetadata(MetadataUtils.Kinds.SlotNames, InputRoleMappedSchema.Feature.Index,
ref _slotNames);
}
else
{
_slotNames = VBufferUtils.CreateEmpty <ReadOnlyMemory <char> >(InputRoleMappedSchema.Feature.Type.VectorSize);
}
}
else
{
_outputSchema = Schema.Create(new FeatureContributionSchema(_env, DefaultColumnNames.FeatureContributions,
new VectorType(NumberType.R4, schema.Feature.Type as VectorType),
InputSchema, InputRoleMappedSchema.Feature.Index));
}
_outputGenericSchema = _genericRowMapper.OutputSchema;
OutputSchema = new CompositeSchema(new Schema[] { _outputGenericSchema, _outputSchema, }).AsSchema;
}
private PredictionEngine(IHostEnvironment env, Func <ISchema, IRowToRowMapper> makeMapper, bool ignoreMissingColumns,
SchemaDefinition inputSchemaDefinition, SchemaDefinition outputSchemaDefinition)
{
Contracts.CheckValue(env, nameof(env));
env.AssertValue(makeMapper);
_inputRow = DataViewConstructionUtils.CreateInputRow <TSrc>(env, inputSchemaDefinition);
var mapper = makeMapper(_inputRow.Schema);
var cursorable = TypedCursorable <TDst> .Create(env, new EmptyDataView(env, mapper.Schema), ignoreMissingColumns, outputSchemaDefinition);
var outputRow = mapper.GetRow(_inputRow, col => true, out _disposer);
_outputRow = cursorable.GetRow(outputRow);
}
private static ISchemaBoundMapper WrapCore <T>(IHostEnvironment env, ISchemaBoundMapper mapper, RoleMappedSchema trainSchema)
{
Contracts.AssertValue(env);
env.AssertValue(mapper);
env.AssertValue(trainSchema);
env.Assert(mapper is ISchemaBoundRowMapper);
// Key values from the training schema label, will map to slot names of the score output.
var type = trainSchema.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.KeyValues, trainSchema.Label.Index);
env.AssertValue(type);
env.Assert(type.IsVector);
// Wrap the fetching of the metadata as a simple getter.
ValueGetter <VBuffer <T> > getter =
(ref VBuffer <T> value) =>
{
trainSchema.Schema.GetMetadata(MetadataUtils.Kinds.KeyValues,
trainSchema.Label.Index, ref value);
};
return(MultiClassClassifierScorer.LabelNameBindableMapper.CreateBound <T>(env, (ISchemaBoundRowMapper)mapper, type.AsVector, getter, MetadataUtils.Kinds.TrainingLabelValues, CanWrap));
}
public PipelinePattern InferPipelines(int numTransformLevels, int batchSize, int numOfTrainingRows)
{
_env.AssertValue(_trainData, nameof(_trainData), "Must set training data prior to calling method.");
_env.AssertValue(_testData, nameof(_testData), "Must set test data prior to calling method.");
var h = _env.Register("InferPipelines");
using (var ch = h.Start("InferPipelines"))
{
// Check if search space has not been initialized. If not,
// run method to define it usign inference.
if (!IsSearchSpaceDefined())
{
InferSearchSpace(numTransformLevels);
}
// Learn for a given number of iterations
MainLearningLoop(batchSize, numOfTrainingRows);
// Return best pipeline seen
return(_sortedSampledElements.Count > 0 ? _sortedSampledElements.First().Value : null);
}
}
internal static BinaryClassificationMetrics FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix)
{
Contracts.AssertValue(env);
env.AssertValue(overallMetrics);
env.AssertValue(confusionMatrix);
var metricsEnumerable = overallMetrics.AsEnumerable <SerializationClass>(env, true, ignoreMissingColumns: true);
var enumerator = metricsEnumerable.GetEnumerator();
if (!enumerator.MoveNext())
{
throw env.Except("The overall RegressionMetrics didn't have any rows.");
}
SerializationClass metrics = enumerator.Current;
if (enumerator.MoveNext())
{
throw env.Except("The overall RegressionMetrics contained more than 1 row.");
}
return(new BinaryClassificationMetrics()
{
Auc = metrics.Auc,
Accuracy = metrics.Accuracy,
PositivePrecision = metrics.PositivePrecision,
PositiveRecall = metrics.PositiveRecall,
NegativePrecision = metrics.NegativePrecision,
NegativeRecall = metrics.NegativeRecall,
LogLoss = metrics.LogLoss,
LogLossReduction = metrics.LogLossReduction,
Entropy = metrics.Entropy,
F1Score = metrics.F1Score,
Auprc = metrics.Auprc,
ConfusionMatrix = ConfusionMatrix.Create(env, confusionMatrix),
});
}
private RandomNumberGenerator(IHostEnvironment env, ModelLoadContext ctx)
{
Contracts.AssertValue(env);
env.AssertValue(ctx);
// *** Binary format ***
// int: sizeof(Float)
// Float: gamma
int cbFloat = ctx.Reader.ReadInt32();
env.CheckDecode(cbFloat == sizeof(float));
_gamma = ctx.Reader.ReadFloat();
env.CheckDecode(FloatUtils.IsFinite(_gamma));
}
public XGBoostScalarRowMapperBase(RoleMappedSchema schema, XGBoostPredictorBase <TOutput> parent, IHostEnvironment env, ISchema outputSchema)
{
Contracts.AssertValue(env, "env");
env.AssertValue(schema, "schema");
env.AssertValue(parent, "parent");
env.AssertValue(schema.Feature, "schema");
// REVIEW xadupre: only one feature columns is allowed.
// This should be revisited in the future.
// XGBoost has plans for others types.
// Look at https://github.com/dmlc/xgboost/issues/874.
env.Check(schema.Feature != null, "Unexpected number of feature columns, 1 expected.");
_parent = parent;
var columns = new[] { schema.Feature };
var fc = new[] { new KeyValuePair <RoleMappedSchema.ColumnRole, string>(RoleMappedSchema.ColumnRole.Feature, columns[0].Name) };
_inputSchema = new RoleMappedSchema(schema.Schema, fc);
_outputSchema = outputSchema;
_inputCols = new List <int>();
foreach (var kvp in columns)
{
int index;
if (schema.Schema.TryGetColumnIndex(kvp.Name, out index))
{
_inputCols.Add(index);
}
else
{
Contracts.Assert(false);
}
}
_booster = _parent.GetBooster();
}
/// <summary>
/// Determines the scorer subcomponent (if the given one is null or empty), and creates the schema bound mapper.
/// </summary>
private static SubComponent <IDataScorerTransform, SignatureDataScorer> GetScorerComponentAndMapper(
IPredictor predictor, SubComponent <IDataScorerTransform, SignatureDataScorer> scorer,
RoleMappedSchema schema, IHostEnvironment env, out ISchemaBoundMapper mapper)
{
Contracts.AssertValue(env);
var bindable = GetSchemaBindableMapper(env, predictor, scorer);
env.AssertValue(bindable);
mapper = bindable.Bind(env, schema);
if (scorer.IsGood())
{
return(scorer);
}
return(GetScorerComponent(mapper));
}
public LinearModelStatistics(IHostEnvironment env, ModelLoadContext ctx)
{
Contracts.CheckValue(env, nameof(env));
_env = env;
_env.AssertValue(ctx);
// *** Binary Format ***
// int: count of parameters
// long: count of training examples
// Single: deviance
// Single: null deviance
// bool: whether standard error is included
// (Conditional) Single[_paramCount]: values of std errors of coefficients
// (Conditional) int: length of std errors of coefficients
// (Conditional) int[_paramCount]: indices of std errors of coefficients
_paramCount = ctx.Reader.ReadInt32();
_env.CheckDecode(_paramCount > 0);
_trainingExampleCount = ctx.Reader.ReadInt64();
_env.CheckDecode(_trainingExampleCount > 0);
_deviance = ctx.Reader.ReadFloat();
_nullDeviance = ctx.Reader.ReadFloat();
var hasStdErrors = ctx.Reader.ReadBoolean();
if (!hasStdErrors)
{
_env.Assert(_coeffStdError == null);
return;
}
Single[] stdErrorValues = ctx.Reader.ReadFloatArray(_paramCount);
int length = ctx.Reader.ReadInt32();
_env.CheckDecode(length >= _paramCount);
if (length == _paramCount)
{
_coeffStdError = new VBuffer <Single>(length, stdErrorValues);
return;
}
_env.Assert(length > _paramCount);
int[] stdErrorIndices = ctx.Reader.ReadIntArray(_paramCount);
_coeffStdError = new VBuffer <Single>(length, _paramCount, stdErrorValues, stdErrorIndices);
}
/// <summary>
/// Determines the scorer component factory (if the given one is null or empty), and creates the schema bound mapper.
/// </summary>
private static TScorerFactory GetScorerComponentAndMapper(
IPredictor predictor,
TScorerFactory scorerFactory,
RoleMappedSchema schema,
IHostEnvironment env,
IComponentFactory<IPredictor, ISchemaBindableMapper> mapperFactory,
out ISchemaBoundMapper mapper)
{
Contracts.AssertValue(env);
var bindable = GetSchemaBindableMapper(env, predictor, mapperFactory, scorerFactory as ICommandLineComponentFactory);
env.AssertValue(bindable);
mapper = bindable.Bind(env, schema);
if (scorerFactory != null)
return scorerFactory;
return GetScorerComponent(mapper);
}
public XmlGenerator(IHostEnvironment env, Arguments args, string regenerate)
{
Contracts.CheckValue(env, nameof(env));
env.AssertValue(args, nameof(args));
env.AssertNonEmpty(regenerate, nameof(regenerate));
_xmlFilename = args.XmlFilename;
if (!string.IsNullOrWhiteSpace(_xmlFilename))
{
Utils.CheckOptionalUserDirectory(_xmlFilename, nameof(args.XmlFilename));
}
else
{
_xmlFilename = null;
}
_host = env.Register("XML Generator");
}
private void SaveInputSchema(DataViewSchema inputSchema, RepositoryWriter rep)
{
_env.AssertValueOrNull(inputSchema);
_env.AssertValue(rep);
if (inputSchema == null)
{
return;
}
using (var ch = _env.Start("Saving Schema"))
{
var entry = rep.CreateEntry(SchemaEntryName);
var saver = new BinarySaver(_env, new BinarySaver.Arguments {
Silent = true
});
DataSaverUtils.SaveDataView(ch, saver, new EmptyDataView(_env, inputSchema), entry.Stream, keepHidden: true);
}
}
private static void GetPipeline(IHostEnvironment env, InputBase input, out IDataView startingData, out RoleMappedData transformedData)
{
Contracts.AssertValue(env);
env.AssertValue(input);
env.AssertNonEmpty(input.Models);
ISchema inputSchema = null;
startingData = null;
transformedData = null;
byte[][] transformedDataSerialized = null;
string[] transformedDataZipEntryNames = null;
for (int i = 0; i < input.Models.Length; i++)
{
var model = input.Models[i];
var inputData = new EmptyDataView(env, model.TransformModel.InputSchema);
model.PrepareData(env, inputData, out RoleMappedData transformedDataCur, out IPredictor pred);
if (inputSchema == null)
{
env.Assert(i == 0);
inputSchema = model.TransformModel.InputSchema;
startingData = inputData;
transformedData = transformedDataCur;
}
else if (input.ValidatePipelines)
{
using (var ch = env.Start("Validating pipeline"))
{
if (transformedDataSerialized == null)
{
ch.Assert(transformedDataZipEntryNames == null);
SerializeRoleMappedData(env, ch, transformedData, out transformedDataSerialized,
out transformedDataZipEntryNames);
}
CheckSamePipeline(env, ch, transformedDataCur, transformedDataSerialized, transformedDataZipEntryNames);
ch.Done();
}
}
}
}
/// <summary>
/// Potentially apply a min-max normalizer to the data's feature column, keeping all existing role
/// mappings except for the feature role mapping.
/// </summary>
/// <param name="env">The host environment to use to potentially instantiate the transform</param>
/// <param name="data">The role-mapped data that is potentially going to be modified by this method.</param>
/// <param name="trainer">The trainer to query as to whether it wants normalization. If the
/// <see cref="ITrainer.Info"/>'s <see cref="TrainerInfo.NeedNormalization"/> is <c>true</c></param>
/// <returns>True if the normalizer was applied and <paramref name="data"/> was modified</returns>
public static bool CreateIfNeeded(IHostEnvironment env, ref RoleMappedData data, ITrainer trainer)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(data, nameof(data));
env.CheckValue(trainer, nameof(trainer));
// If the trainer does not need normalization, or if the features either don't exist
// or are not normalized, return false.
if (!trainer.Info.NeedNormalization || data.Schema.FeaturesAreNormalized() != false)
{
return(false);
}
var featInfo = data.Schema.Feature;
env.AssertValue(featInfo); // Should be defined, if FeaturesAreNormalized returned a definite value.
var view = CreateMinMaxNormalizer(env, data.Data, name: featInfo.Name);
data = new RoleMappedData(view, data.Schema.GetColumnRoleNames());
return(true);
}
/// <summary>
/// Create a TransformModel containing the given (optional) transforms applied to the
/// given root schema.
/// </summary>
public TransformModel(IHostEnvironment env, Schema schemaRoot, IDataTransform[] xfs)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(schemaRoot, nameof(schemaRoot));
env.CheckValueOrNull(xfs);
IDataView view = new EmptyDataView(env, schemaRoot);
_schemaRoot = view.Schema;
if (Utils.Size(xfs) > 0)
{
foreach (var xf in xfs)
{
env.AssertValue(xf, "xfs", "Transforms should not be null");
view = ApplyTransformUtils.ApplyTransformToData(env, xf, view);
}
}
_chain = view;
}
internal ModelStatisticsBase(IHostEnvironment env, ModelLoadContext ctx)
{
Contracts.CheckValue(env, nameof(env));
Env = env;
Env.AssertValue(ctx);
// *** Binary Format ***
// int: count of parameters
// long: count of training examples
// float: deviance
// float: null deviance
ParametersCount = ctx.Reader.ReadInt32();
Env.CheckDecode(ParametersCount > 0);
TrainingExampleCount = ctx.Reader.ReadInt64();
Env.CheckDecode(TrainingExampleCount > 0);
Deviance = ctx.Reader.ReadFloat();
NullDeviance = ctx.Reader.ReadFloat();
}
/// <summary>
/// Save schema associations of role/column-name in <paramref name="rep"/>.
/// </summary>
internal static void SaveRoleMappings(IHostEnvironment env, IChannel ch, RoleMappedSchema schema, RepositoryWriter rep)
{
// REVIEW: Should we also save this stuff, for instance, in some portion of the
// score command or transform?
Contracts.AssertValue(env);
env.AssertValue(ch);
ch.AssertValue(schema);
ArrayDataViewBuilder builder = new ArrayDataViewBuilder(env);
List <string> rolesList = new List <string>();
List <string> columnNamesList = new List <string>();
// OrderBy is stable, so there is no danger in it "reordering" columns
// when a role is filled by multiple columns.
foreach (var role in schema.GetColumnRoleNames().OrderBy(r => r.Key.Value))
{
rolesList.Add(role.Key.Value);
columnNamesList.Add(role.Value);
}
builder.AddColumn("Role", rolesList.ToArray());
builder.AddColumn("Column", columnNamesList.ToArray());
using (var entry = rep.CreateEntry(DirTrainingInfo, RoleMappingFile))
{
// REVIEW: It seems very important that we have the role mappings
// be easily human interpretable and even manipulable, but relying on the
// text saver/loader means that special characters like '\n' won't be reinterpretable.
// On the other hand, no one is such a big lunatic that they will actually
// ever go ahead and do something so stupid as that.
var saver = new TextSaver(env, new TextSaver.Arguments()
{
Dense = true, Silent = true
});
var view = builder.GetDataView();
saver.SaveData(entry.Stream, view, Utils.GetIdentityPermutation(view.Schema.ColumnCount));
}
}
