/// <summary>
/// Prepares the specified context.
/// </summary>
/// <param name="context">The context.</param>
/// <param name="log">The log.</param>
/// <exception cref="ArgumentException">context</exception>
public override void Prepare(DocumentSelectResult context, ILogBuilder log)
{
String p_m = FeatureWeightModel.GetModelDefinitionFilename(modelDefinitionFile, context.folder);
String p_d = FeatureWeightModel.GetModelDataFilename(modelDefinitionFile, context.folder);
TermWeightModel = FeatureWeightModel.LoadModel(p_m, log);
//if (File.Exists(p_m))
//{
// //TermWeightModel = objectSerialization.loadObjectFromXML<FeatureWeightModel>(p_m, log);
//}
TermWeightModel.Deploy(log);
if (context.spaceModel == null)
{
String msg = "Error: TermWeight factor requires SpaceModel declared in the context for operation";
throw new ArgumentException(msg, nameof(context));
}
if (File.Exists(p_d) && useStoredData)
{
WeightingModelDataSet data = objectSerialization.loadObjectFromXML <WeightingModelDataSet>(p_d, log);
TermWeightModel.LoadModelDataSet(data, log);
if (useSelectedFeatures)
{
SelectedTerms = WeightDictionary.LoadFile(WeightDictionary.GetDictionaryFilename(modelDefinitionFile + "_sf", context.folder), log);
}
}
else
{
TermWeightModel.PrepareTheModel(context.spaceModel, log);
}
if (context.query.isNullOrEmpty())
{
context.query.QueryTerms = context.query.QueryTerms.Trim();
List <String> tkns = context.query.QueryTerms.getTokens(true, true, true, false, 4);
foreach (String tkn in tkns)
{
queryTerms.Add(context.stemmingContext.Stem(tkn));
}
}
}
public override void Prepare(DocumentSelectResult context, ILogBuilder log)
{
String p_m = "";
String p_d = "";
modelDefinitionFile = modelDefinitionFile.Replace("*", "");
if (!modelDefinitionFile.isNullOrEmpty())
{
p_m = FeatureWeightModel.GetModelDefinitionFilename(modelDefinitionFile, context.folder);
p_d = FeatureWeightModel.GetModelDataFilename(modelDefinitionFile, context.folder);
}
if (TermWeightModel == null)
{
log.log("Loading model from [" + p_m + "]");
if (File.Exists(p_m))
{
TermWeightModel = FeatureWeightModel.LoadModel(p_m, log);
}
}
TermWeightModel.Deploy(log);
if (File.Exists(p_d) && UseModelData)
{
log.log("Loading model data from [" + p_d + "]");
var dataset = objectSerialization.loadObjectFromXML <WeightingModelDataSet>(p_d, log);
// WeightingModelDataSet
TermWeightModel.LoadModelDataSet(dataset, log);
}
else
{
log.log("Preparing model ...");
TermWeightModel.PrepareTheModel(context.spaceModel, log);
}
if (computation.HasFlag(ScoreComputationModeEnum.category))
{
vectorDictionary = context.TransformToFVDictionaryAsCategorySimilarity(TermWeightModel, function, log);
}
else if (computation.HasFlag(ScoreComputationModeEnum.site))
{
vectorDictionary = context.TransformToFVDictionaryAsSiteSimilarity(TermWeightModel, function, log);
}
else if (computation.HasFlag(ScoreComputationModeEnum.pageDivergence))
{
vectorDictionary = context.TransformToFVDictionaryAsPageSimilarity(TermWeightModel, function, ScoreComputationModeEnum.site, log);
}
else if (computation.HasFlag(ScoreComputationModeEnum.pagesOfCategory))
{
vectorDictionary = context.TransformToFVDictionaryAsPageSimilarity(TermWeightModel, function, ScoreComputationModeEnum.category, log);
}
else if (computation.HasFlag(ScoreComputationModeEnum.pagesOfDataset))
{
vectorDictionary = context.TransformToFVDictionaryAsPageSimilarity(TermWeightModel, function, ScoreComputationModeEnum.dataset, log);
}
log.log("Category similarity ready ... [" + computation.ToString() + "]");
}
/// <summary>
/// Prepares the specified context.
/// </summary>
/// <param name="context">The context.</param>
/// <param name="log">The log.</param>
/// <exception cref="ArgumentException">context</exception>
public override void Prepare(DocumentSelectResult context, ILogBuilder log)
{
//context.folder.GetOrFindFiles("*", dictionaryFile + "*.xml");
scoreDictionary = FeatureVectorDictionaryWithDimensions.LoadFile(context.folder, dictionaryFile, log); // WeightDictionary.LoadFile(WeightDictionary.GetDictionaryFilename(dictionaryFile, context.folder), log);
if (scoreDictionary == null)
{
String msg = "Error: Failed to find score dictionary [" + dictionaryFile + "] in " + context.folder.path;
throw new ArgumentException(msg, nameof(context));
}
if (useMachineLearning)
{
#region --------------- PREPARING TERM WEIGHT MODEL
String p_m = FeatureWeightModel.GetModelDefinitionFilename(modelDefinitionFile, context.folder);
String p_d = FeatureWeightModel.GetModelDataFilename(modelDefinitionFile, context.folder);
if (TermWeightModel == null)
{
TermWeightModel = FeatureWeightModel.LoadModel(p_m, log);
}
TermWeightModel.Deploy(log);
if (context.spaceModel == null)
{
String msg = "Error: TermWeight factor requires SpaceModel declared in the context for operation";
throw new ArgumentException(msg, nameof(context));
}
if (File.Exists(p_d) && useStoredData)
{
WeightingModelDataSet data = objectSerialization.loadObjectFromXML <WeightingModelDataSet>(p_d, log);
TermWeightModel.LoadModelDataSet(data, log);
if (useSelectedFeatures)
{
SelectedTerms = WeightDictionary.LoadFile(WeightDictionary.GetDictionaryFilename(modelDefinitionFile + "_sf", context.folder), log);
}
}
else
{
TermWeightModel.PrepareTheModel(context.spaceModel, log);
}
if (SelectedTerms.Count == 0)
{
SelectedTerms = context.selectedFeatures;
}
List <String> sel_tkns = new List <String>();
sel_tkns.AddRange(SelectedTerms.index.Values.Select(x => x.name));
if (!sel_tkns.Any())
{
sel_tkns.AddRange(context.spaceModel.terms_known_label.GetTokens());
}
#endregion
fvConstructor.Deploy(featureMethod.constructor, sel_tkns);
classifier = featureMethod.classifierSettings.GetClassifier();
sc_id = scoreDictionary.GetVectorsWithLabelID(null, criterion).ToNameVsLabelID();
List <FeatureVectorWithLabelID> trainingSet = new List <FeatureVectorWithLabelID>();
foreach (var item in context.items)
{
if (sc_id.ContainsKey(item.AssignedID))
{
WeightDictionary dc_vec = TermWeightModel.GetWeights(sel_tkns, item.spaceDocument, context.spaceModel);
var n_vec = fvConstructor.ConstructFeatureVector(dc_vec, item.AssignedID);
FeatureVectorWithLabelID id_vec = new FeatureVectorWithLabelID(n_vec, sc_id[item.AssignedID]);
trainingSet.Add(id_vec);
}
}
log.log("Training [" + classifier.name + "] with [" + sc_id.Count + "] feature vectors.");
classifier.DoTraining(trainingSet, log);
}
}