/// <summary>
/// The counter constructor of re-creating <see cref="MatrixFactorizationPredictionTransformer"/> from the context where
/// the original transform is saved.
/// </summary>
public MatrixFactorizationPredictionTransformer(IHostEnvironment host, ModelLoadContext ctx)
: base(Contracts.CheckRef(host, nameof(host)).Register(nameof(MatrixFactorizationPredictionTransformer)), ctx)
{
// *** Binary format ***
// <base info>
// string: the column name of matrix's column ids.
// string: the column name of matrix's row ids.
MatrixColumnIndexColumnName = ctx.LoadString();
MatrixRowIndexColumnName = ctx.LoadString();
if (!TrainSchema.TryGetColumnIndex(MatrixColumnIndexColumnName, out int xCol))
{
throw Host.ExceptSchemaMismatch(nameof(MatrixColumnIndexColumnName), RecommenderUtils.MatrixColumnIndexKind.Value, MatrixColumnIndexColumnName);
}
MatrixColumnIndexColumnType = TrainSchema.GetColumnType(xCol);
if (!TrainSchema.TryGetColumnIndex(MatrixRowIndexColumnName, out int yCol))
{
throw Host.ExceptSchemaMismatch(nameof(MatrixRowIndexColumnName), RecommenderUtils.MatrixRowIndexKind.Value, MatrixRowIndexColumnName);
}
MatrixRowIndexColumnType = TrainSchema.GetColumnType(yCol);
BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, Model);
var schema = GetSchema();
var args = new GenericScorer.Arguments {
Suffix = ""
};
Scorer = new GenericScorer(Host, args, new EmptyDataView(Host, TrainSchema), BindableMapper.Bind(Host, schema), schema);
}
static void Main(string[] args)
{
using (var p = Process.GetCurrentProcess())
p.PriorityClass = ProcessPriorityClass.Idle;
using (var ctx = new Db())
{
var shingles = ctx.Shingles.Where(x => x.UseForML).ToArray();
int NextNetworkID = ctx.TrainSchemas.Select(x => x.NetworkID).Max() + 1;
for (int i = 0; i <= shingles.Length / shinglesPerNetwork; i++)
{
int firstShingle = i * shinglesPerNetwork;
int shinglesCount = shingles.Length > firstShingle + shinglesPerNetwork
? shinglesPerNetwork
: shingles.Length - firstShingle;
for (int inputNr = 0; inputNr < shinglesPerNetwork; inputNr++)
{
var ts = new TrainSchema
{
ForecastDays = daysToForecast,
NetworkID = NextNetworkID + i,
Input = true,
NeuronID = inputNr,
ShingleID = shingles[i * shinglesPerNetwork + inputNr].ID
};
}
}
}
}
private protected SingleFeaturePredictionTransformerBase(IHost host, ModelLoadContext ctx, TModel model)
: base(host, ctx, model)
{
FeatureColumnName = ctx.LoadStringOrNull();
if (FeatureColumnName == null)
FeatureColumnType = null;
else if (!TrainSchema.TryGetColumnIndex(FeatureColumnName, out int col))
throw Host.ExceptSchemaMismatch(nameof(FeatureColumnName), "feature", FeatureColumnName);
else
FeatureColumnType = TrainSchema[col].Type;
BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, ModelAsPredictor);
}
internal SingleFeaturePredictionTransformerBase(IHost host, ModelLoadContext ctx)
: base(host, ctx)
{
FeatureColumn = ctx.LoadStringOrNull();
if (FeatureColumn == null)
{
FeatureColumnType = null;
}
else if (!TrainSchema.TryGetColumnIndex(FeatureColumn, out int col))
{
throw Host.ExceptSchemaMismatch(nameof(FeatureColumn), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn);
}
else
{
FeatureColumnType = TrainSchema[col].Type;
}
BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, Model);
}
internal FieldAwareFactorizationMachinePredictionTransformer(IHostEnvironment host, ModelLoadContext ctx)
: base(Contracts.CheckRef(host, nameof(host)).Register(nameof(FieldAwareFactorizationMachinePredictionTransformer)), ctx)
{
// *** Binary format ***
// <base info>
// ids of strings: feature columns.
// float: scorer threshold
// id of string: scorer threshold column
// count of feature columns. FAFM uses more than one.
int featCount = Model.FieldCount;
var featureColumns = new string[featCount];
var featureColumnTypes = new DataViewType[featCount];
for (int i = 0; i < featCount; i++)
{
featureColumns[i] = ctx.LoadString();
if (!TrainSchema.TryGetColumnIndex(featureColumns[i], out int col))
{
throw Host.ExceptSchemaMismatch(nameof(FeatureColumns), "feature", featureColumns[i]);
}
featureColumnTypes[i] = TrainSchema[col].Type;
}
FeatureColumns = featureColumns;
FeatureColumnTypes = featureColumnTypes;
_threshold = ctx.Reader.ReadSingle();
_thresholdColumn = ctx.LoadString();
BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, Model);
var schema = GetSchema();
var args = new BinaryClassifierScorer.Arguments {
Threshold = _threshold, ThresholdColumn = _thresholdColumn
};
Scorer = new BinaryClassifierScorer(Host, args, new EmptyDataView(Host, TrainSchema), BindableMapper.Bind(Host, schema), schema);
}