/// <summary>
/// Gets a cached prediction engine or creates a new one if not cached
/// </summary>
private BatchPredictionEngine <SarUsageEvent, SarScoreResult> GetOrCreateBatchPredictionEngine(
IList <SarUsageEvent> usageItems, SarScoringArguments sarScoringArguments)
{
var arguments = new RecommenderScorerTransform.Arguments
{
// round the recommendations count to optimize engine cache
recommendationCount = GetRoundedNumberOfResults(sarScoringArguments.RecommendationCount),
includeHistory = sarScoringArguments.IncludeHistory
};
// create a data column mapping
var dataColumnMapping = new Dictionary <RoleMappedSchema.ColumnRole, string>
{
{ new RoleMappedSchema.ColumnRole("Item"), "Item" },
{ new RoleMappedSchema.ColumnRole("User"), "user" }
};
string weightColumn = null;
if (sarScoringArguments.ReferenceDate.HasValue)
{
// rounding the reference date to the beginning of next day to optimize engine cache
DateTime referenceDate = sarScoringArguments.ReferenceDate.Value.Date + TimeSpan.FromDays(1);
arguments.referenceDate = referenceDate.ToString("s");
if (sarScoringArguments.Decay.HasValue)
{
arguments.decay = sarScoringArguments.Decay.Value.TotalDays;
}
dataColumnMapping.Add(new RoleMappedSchema.ColumnRole("Date"), "date");
weightColumn = "weight";
}
// create an engine cache key
string cacheKey = $"{arguments.recommendationCount}|{arguments.includeHistory}|{arguments.referenceDate}|{arguments.decay}";
_tracer.TraceVerbose("Trying to find the engine in the cache");
var engine = _enginesCache.Get(cacheKey) as BatchPredictionEngine <SarUsageEvent, SarScoreResult>;
if (engine == null)
{
_tracer.TraceInformation("Engine is not cached - creating a new engine");
IDataView pipeline = _environment.CreateDataView(usageItems, _usageDataSchema);
RoleMappedData usageDataMappedData = _environment.CreateExamples(pipeline, null, weight: weightColumn, custom: dataColumnMapping);
ISchemaBindableMapper mapper = RecommenderScorerTransform.Create(_environment, arguments, _recommender);
ISchemaBoundMapper boundMapper = mapper.Bind(_environment, usageDataMappedData.Schema);
IDataScorerTransform scorer = RecommenderScorerTransform.Create(
_environment, arguments, pipeline, boundMapper, null);
engine = _environment.CreateBatchPredictionEngine <SarUsageEvent, SarScoreResult>(scorer, false, _usageDataSchema);
bool result = _enginesCache.Add(cacheKey, engine, new CacheItemPolicy {
SlidingExpiration = TimeSpan.FromDays(1)
});
_tracer.TraceVerbose($"Addition of engine to the cache resulted with '{result}'");
}
return(engine);
}
/// <summary>
/// Scores the input usage items.
/// </summary>
/// <param name="usageItems">The items to score</param>
/// <param name="scoringArguments">The scoring arguments</param>
/// <returns></returns>
public IEnumerable <SarScoreResult> ScoreUsageItems(IList <SarUsageEvent> usageItems, SarScoringArguments scoringArguments)
{
if (_disposed)
{
throw new ObjectDisposedException(typeof(SarScorer).FullName);
}
if (usageItems == null)
{
throw new ArgumentNullException(nameof(usageItems));
}
if (scoringArguments == null)
{
throw new ArgumentNullException(nameof(scoringArguments));
}
_tracer.TraceVerbose("Getting or creating the prediction engine");
BatchPredictionEngine <SarUsageEvent, SarScoreResult> engine =
GetOrCreateBatchPredictionEngine(usageItems, scoringArguments);
_tracer.TraceInformation($"Getting recommendation for {usageItems.Count} input items");
lock (engine)
{
return(engine.Predict(usageItems, false)
.OrderByDescending(x => x.Score)
.Take(scoringArguments.RecommendationCount)
.ToArray());
}
}