public override void Train(Split split)
{
var mmlInstance = (FM)MmlRecommenderInstance;
var featBuilder = new FmFeatureBuilder();
var wFm = MmlRecommenderInstance as WeightedBPRFM;
if (DataType == WrapRec.IO.DataType.Ratings)
{
var mmlFeedback = new Ratings();
foreach (var feedback in split.Train)
{
var rating = (Rating)feedback;
mmlFeedback.Add(UsersMap.ToInternalID(rating.User.Id), ItemsMap.ToInternalID(rating.Item.Id), rating.Value);
// the attributes are translated so that they can be used later for training
foreach (var attr in feedback.GetAllAttributes())
{
attr.Translation = featBuilder.TranslateAttribute(attr);
// hard code attribute group. User is 0, item is 1, others is 2
attr.Group = 2;
if (wFm != null && !wFm.FeatureGroups.ContainsKey(attr.Translation.Item1))
{
wFm.FeatureGroups.Add(attr.Translation.Item1, 2);
}
}
}
((IRatingPredictor)MmlRecommenderInstance).Ratings = mmlFeedback;
}
foreach (var feedback in split.Test)
{
// the attributes are translated so that they can be used later for training
foreach (var attr in feedback.GetAllAttributes())
{
attr.Translation = featBuilder.TranslateAttribute(attr);
// hard code attribute group. User is 0, item is 1, others is 2
attr.Group = 2;
if (wFm != null && !wFm.FeatureGroups.ContainsKey(attr.Translation.Item1))
{
wFm.FeatureGroups.Add(attr.Translation.Item1, 2);
}
}
}
mmlInstance.Split = split;
mmlInstance.Model = this;
mmlInstance.UsersMap = UsersMap;
mmlInstance.ItemsMap = ItemsMap;
mmlInstance.FeatureBuilder = featBuilder;
Logger.Current.Trace("Training with MmlFmRecommender recommender...");
PureTrainTime = (int)Wrap.MeasureTime(delegate() { mmlInstance.Train(); }).TotalMilliseconds;
}
public override void Train(Split split)
{
var mmlInstance = (BPRFM)MmlRecommenderInstance;
var featBuilder = new FmFeatureBuilder();
var mmlFeedback = new PosOnlyFeedback <SparseBooleanMatrix>();
var wBprFm = MmlRecommenderInstance as WeightedBPRFM;
foreach (var feedback in split.Train)
{
mmlFeedback.Add(UsersMap.ToInternalID(feedback.User.Id), ItemsMap.ToInternalID(feedback.Item.Id));
// the attributes are translated so that they can be used later for training
foreach (var attr in feedback.GetAllAttributes())
{
attr.Translation = featBuilder.TranslateAttribute(attr);
// hard code attribute group. User is 0, item is 1, others is 2
attr.Group = 2;
if (wBprFm != null && !wBprFm.FeatureGroups.ContainsKey(attr.Translation.Item1))
{
wBprFm.FeatureGroups.Add(attr.Translation.Item1, 2);
}
}
}
foreach (var feedback in split.Test)
{
// the attributes are translated so that they can be used later for training
foreach (var attr in feedback.GetAllAttributes())
{
attr.Translation = featBuilder.TranslateAttribute(attr);
// hard code attribute group. User is 0, item is 1, others is 2
attr.Group = 2;
if (wBprFm != null && !wBprFm.FeatureGroups.ContainsKey(attr.Translation.Item1))
{
wBprFm?.FeatureGroups.Add(attr.Translation.Item1, 2);
}
}
}
mmlInstance.Feedback = mmlFeedback;
mmlInstance.Split = split;
mmlInstance.Model = this;
mmlInstance.UsersMap = UsersMap;
mmlInstance.ItemsMap = ItemsMap;
mmlInstance.FeatureBuilder = featBuilder;
Logger.Current.Trace("Training with MmlBprfmRecommender recommender...");
PureTrainTime = (int)Wrap.MeasureTime(delegate() { mmlInstance.Train(); }).TotalMilliseconds;
}
