private MatrixFactorizationPredictor TrainCore(IChannel ch, RoleMappedData data, RoleMappedData validData = null)
{
Host.AssertValue(ch);
ch.AssertValue(data);
ch.AssertValueOrNull(validData);
ColumnInfo matrixColumnIndexColInfo;
ColumnInfo matrixRowIndexColInfo;
ColumnInfo validMatrixColumnIndexColInfo = null;
ColumnInfo validMatrixRowIndexColInfo = null;
ch.CheckValue(data.Schema.Label, nameof(data), "Input data did not have a unique label");
RecommenderUtils.CheckAndGetMatrixIndexColumns(data, out matrixColumnIndexColInfo, out matrixRowIndexColInfo, isDecode: false);
if (data.Schema.Label.Type != NumberType.R4 && data.Schema.Label.Type != NumberType.R8)
{
throw ch.Except("Column '{0}' for label should be floating point, but is instead {1}", data.Schema.Label.Name, data.Schema.Label.Type);
}
MatrixFactorizationPredictor predictor;
if (validData != null)
{
ch.CheckValue(validData, nameof(validData));
ch.CheckValue(validData.Schema.Label, nameof(validData), "Input validation data did not have a unique label");
RecommenderUtils.CheckAndGetMatrixIndexColumns(validData, out validMatrixColumnIndexColInfo, out validMatrixRowIndexColInfo, isDecode: false);
if (validData.Schema.Label.Type != NumberType.R4 && validData.Schema.Label.Type != NumberType.R8)
{
throw ch.Except("Column '{0}' for validation label should be floating point, but is instead {1}", data.Schema.Label.Name, data.Schema.Label.Type);
}
if (!matrixColumnIndexColInfo.Type.Equals(validMatrixColumnIndexColInfo.Type))
{
throw ch.ExceptParam(nameof(validData), "Train and validation sets' matrix-column types differed, {0} vs. {1}",
matrixColumnIndexColInfo.Type, validMatrixColumnIndexColInfo.Type);
}
if (!matrixRowIndexColInfo.Type.Equals(validMatrixRowIndexColInfo.Type))
{
throw ch.ExceptParam(nameof(validData), "Train and validation sets' matrix-row types differed, {0} vs. {1}",
matrixRowIndexColInfo.Type, validMatrixRowIndexColInfo.Type);
}
}
int colCount = matrixColumnIndexColInfo.Type.KeyCount;
int rowCount = matrixRowIndexColInfo.Type.KeyCount;
ch.Assert(rowCount > 0);
ch.Assert(colCount > 0);
// Checks for equality on the validation set ensure it is correct here.
using (var cursor = data.Data.GetRowCursor(c => c == matrixColumnIndexColInfo.Index || c == matrixRowIndexColInfo.Index || c == data.Schema.Label.Index))
{
// LibMF works only over single precision floats, but we want to be able to consume either.
var labGetter = RowCursorUtils.GetGetterAs <float>(NumberType.R4, cursor, data.Schema.Label.Index);
var matrixColumnIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberType.U4, cursor, matrixColumnIndexColInfo.Index);
var matrixRowIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberType.U4, cursor, matrixRowIndexColInfo.Index);
if (validData == null)
{
// Have the trainer do its work.
using (var buffer = PrepareBuffer())
{
buffer.Train(ch, rowCount, colCount, cursor, labGetter, matrixRowIndexGetter, matrixColumnIndexGetter);
predictor = new MatrixFactorizationPredictor(Host, buffer, matrixColumnIndexColInfo.Type.AsKey, matrixRowIndexColInfo.Type.AsKey);
}
}
else
{
using (var validCursor = validData.Data.GetRowCursor(
c => c == validMatrixColumnIndexColInfo.Index || c == validMatrixRowIndexColInfo.Index || c == validData.Schema.Label.Index))
{
ValueGetter <float> validLabelGetter = RowCursorUtils.GetGetterAs <float>(NumberType.R4, validCursor, validData.Schema.Label.Index);
var validMatrixColumnIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberType.U4, validCursor, validMatrixColumnIndexColInfo.Index);
var validMatrixRowIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberType.U4, validCursor, validMatrixRowIndexColInfo.Index);
// Have the trainer do its work.
using (var buffer = PrepareBuffer())
{
buffer.TrainWithValidation(ch, rowCount, colCount,
cursor, labGetter, matrixRowIndexGetter, matrixColumnIndexGetter,
validCursor, validLabelGetter, validMatrixRowIndexGetter, validMatrixColumnIndexGetter);
predictor = new MatrixFactorizationPredictor(Host, buffer, matrixColumnIndexColInfo.Type.AsKey, matrixRowIndexColInfo.Type.AsKey);
}
}
}
}
return(predictor);
}
private MatrixFactorizationModelParameters TrainCore(IChannel ch, RoleMappedData data, RoleMappedData validData = null)
{
_host.AssertValue(ch);
ch.AssertValue(data);
ch.AssertValueOrNull(validData);
ch.CheckParam(data.Schema.Label.HasValue, nameof(data), "Input data did not have a unique label");
RecommenderUtils.CheckAndGetMatrixIndexColumns(data, out var matrixColumnIndexColInfo, out var matrixRowIndexColInfo, isDecode: false);
var labelCol = data.Schema.Label.Value;
if (labelCol.Type != NumberDataViewType.Single && labelCol.Type != NumberDataViewType.Double)
{
throw ch.Except("Column '{0}' for label should be floating point, but is instead {1}", labelCol.Name, labelCol.Type);
}
MatrixFactorizationModelParameters predictor;
if (validData != null)
{
ch.CheckValue(validData, nameof(validData));
ch.CheckParam(validData.Schema.Label.HasValue, nameof(validData), "Input validation data did not have a unique label");
RecommenderUtils.CheckAndGetMatrixIndexColumns(validData, out var validMatrixColumnIndexColInfo, out var validMatrixRowIndexColInfo, isDecode: false);
var validLabelCol = validData.Schema.Label.Value;
if (validLabelCol.Type != NumberDataViewType.Single && validLabelCol.Type != NumberDataViewType.Double)
{
throw ch.Except("Column '{0}' for validation label should be floating point, but is instead {1}", validLabelCol.Name, validLabelCol.Type);
}
if (!matrixColumnIndexColInfo.Type.Equals(validMatrixColumnIndexColInfo.Type))
{
throw ch.ExceptParam(nameof(validData), "Train and validation sets' matrix-column types differed, {0} vs. {1}",
matrixColumnIndexColInfo.Type, validMatrixColumnIndexColInfo.Type);
}
if (!matrixRowIndexColInfo.Type.Equals(validMatrixRowIndexColInfo.Type))
{
throw ch.ExceptParam(nameof(validData), "Train and validation sets' matrix-row types differed, {0} vs. {1}",
matrixRowIndexColInfo.Type, validMatrixRowIndexColInfo.Type);
}
}
int colCount = matrixColumnIndexColInfo.Type.GetKeyCountAsInt32(_host);
int rowCount = matrixRowIndexColInfo.Type.GetKeyCountAsInt32(_host);
ch.Assert(rowCount > 0);
ch.Assert(colCount > 0);
// Checks for equality on the validation set ensure it is correct here.
using (var cursor = data.Data.GetRowCursor(matrixColumnIndexColInfo, matrixRowIndexColInfo, data.Schema.Label.Value))
{
// LibMF works only over single precision floats, but we want to be able to consume either.
var labGetter = RowCursorUtils.GetGetterAs <float>(NumberDataViewType.Single, cursor, data.Schema.Label.Value.Index);
var matrixColumnIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberDataViewType.UInt32, cursor, matrixColumnIndexColInfo.Index);
var matrixRowIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberDataViewType.UInt32, cursor, matrixRowIndexColInfo.Index);
if (validData == null)
{
// Have the trainer do its work.
using (var buffer = PrepareBuffer())
{
buffer.Train(ch, rowCount, colCount, cursor, labGetter, matrixRowIndexGetter, matrixColumnIndexGetter);
predictor = new MatrixFactorizationModelParameters(_host, buffer, (KeyDataViewType)matrixColumnIndexColInfo.Type, (KeyDataViewType)matrixRowIndexColInfo.Type);
}
}
else
{
RecommenderUtils.CheckAndGetMatrixIndexColumns(validData, out var validMatrixColumnIndexColInfo, out var validMatrixRowIndexColInfo, isDecode: false);
using (var validCursor = validData.Data.GetRowCursor(matrixColumnIndexColInfo, matrixRowIndexColInfo, data.Schema.Label.Value))
{
ValueGetter <float> validLabelGetter = RowCursorUtils.GetGetterAs <float>(NumberDataViewType.Single, validCursor, validData.Schema.Label.Value.Index);
var validMatrixColumnIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberDataViewType.UInt32, validCursor, validMatrixColumnIndexColInfo.Index);
var validMatrixRowIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberDataViewType.UInt32, validCursor, validMatrixRowIndexColInfo.Index);
// Have the trainer do its work.
using (var buffer = PrepareBuffer())
{
buffer.TrainWithValidation(ch, rowCount, colCount,
cursor, labGetter, matrixRowIndexGetter, matrixColumnIndexGetter,
validCursor, validLabelGetter, validMatrixRowIndexGetter, validMatrixColumnIndexGetter);
predictor = new MatrixFactorizationModelParameters(_host, buffer, (KeyDataViewType)matrixColumnIndexColInfo.Type, (KeyDataViewType)matrixRowIndexColInfo.Type);
}
}
}
}
return(predictor);
}
