public IEnumerable <DemandPrediction> PredictBatch(IDataView inputDataView)
{
CheckTrained();
var predictions = TrainedModel.Transform(inputDataView);
return(predictions.AsEnumerable <DemandPrediction>(_mlcontext, reuseRowObject: false));
}
public RegressionEvaluator.Result Evaluate(IDataView testData)
{
CheckTrained();
var predictions = TrainedModel.Transform(testData);
var metrics = _mlcontext.Regression.Evaluate(predictions, "Count", "Score");
return(metrics);
}
public override ModelFitness Evaluate(List <ModelDataSet> data, ModelValue prediction)
{
if (TrainedModel == null)
{
throw new Exception("Must initialize the model before calling");
}
lock (TrainedModel)
{
#if ML_LEGACY
var testData = CollectionDataSource.Create(data);
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(TrainedModel, testData);
return(new ModelFitness()
{
RMS = metrics.Rms,
RSquared = metrics.RSquared
});
#else
var textLoader = GetTextLoader(Context, prediction);
var pathToData = "";
try
{
// ugh have to spill data to disk for it to work!
pathToData = WriteToDisk(data, prediction);
IDataView dataView = textLoader.Read(pathToData);
var predictions = TrainedModel.Transform(dataView);
var metrics = Context.Regression.Evaluate(predictions, label: "Label", score: "Score");
return(new ModelFitness()
{
RMS = metrics.Rms,
RSquared = metrics.RSquared
});
}
finally
{
// cleanup
if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
{
File.Delete(pathToData);
}
}
#endif
}
}
// return r^2
public override double Evaluate(List <DataSet> data, List <float> labels = null)
{
if (TrainedModel == null)
{
throw new InvalidOperationException("Must train/load a model before evaluating");
}
lock (TrainedModel)
{
#if ML_LEGACY
var testData = CollectionDataSource.Create(data);
//var evaluator = new RegressionEvaluator();
//var metrics = evaluator.Evaluate(TrainedModel, testData);
return(0);
//return metrics.RSquared;
#else
var textLoader = GetTextLoader(Context);
var pathToData = "";
try
{
// ugh have to spill data to disk for it to work!
pathToData = WriteToDisk(data);
IDataView dataView = textLoader.Load(pathToData);
var predictions = TrainedModel.Transform(dataView);
var metrics = Context.Regression.Evaluate(predictions, labelColumnName: "Label", scoreColumnName: "Score");
return(metrics.RSquared);
}
finally
{
// cleanup
if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
{
File.Delete(pathToData);
}
}
#endif
}
}
public override ModelFitness Evaluate(List <ModelDataSet> data, ModelValue prediction)
{
if (TrainedModel == null)
{
throw new Exception("Must initialize the model before calling");
}
lock (TrainedModel)
{
var textLoader = GetTextLoader(Context, prediction);
var pathToData = "";
try
{
// ugh have to spill data to disk for it to work!
pathToData = WriteToDisk(data, prediction);
IDataView dataView = textLoader.Load(pathToData);
var predictions = TrainedModel.Transform(dataView);
var metrics = Context.Regression.Evaluate(predictions, labelColumnName: "Label", scoreColumnName: "Score");
return(new ModelFitness()
{
RMS = metrics.RootMeanSquaredError,
RSquared = metrics.RSquared
});
}
finally
{
// cleanup
if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
{
File.Delete(pathToData);
}
}
}
}