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@NMZivkovic

NMZivkovic/Model.cs

Created Jul 12, 2019
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using BikeSharingDemand.BikeSharingDemandData;
using Microsoft.ML;
using Microsoft.ML.Data;
using System.Linq;
namespace BikeSharingDemand.ModelNamespace
{
public sealed class Model
{
private readonly MLContext _mlContext;
private IDataView _trainingDataView;
private IEstimator<ITransformer> _algorythim;
private ITransformer _trainedModel;
private PredictionEngine<BikeSharingDemandSample, BikeSharingDemandPrediction> _predictionEngine;
public string Name { get; private set; }
public Model(MLContext mlContext, IEstimator<ITransformer> algorythm, string trainingDataLocation)
{
_mlContext = mlContext;
_algorythim = algorythm;
_trainingDataView = _mlContext.Data.LoadFromTextFile<BikeSharingDemandSample>(
path: trainingDataLocation,
hasHeader: true,
separatorChar: ',');
Name = algorythm.GetType().ToString().Split('.').Last();
}
public void BuildAndFit()
{
var pipeline = _mlContext.Transforms.CopyColumns(inputColumnName: "Count", outputColumnName: "Label")
.Append(_mlContext.Transforms.Categorical.OneHotEncoding("Season"))
.Append(_mlContext.Transforms.Categorical.OneHotEncoding("Year"))
.Append(_mlContext.Transforms.Categorical.OneHotEncoding("Holiday"))
.Append(_mlContext.Transforms.Categorical.OneHotEncoding("Weather"))
.Append(_mlContext.Transforms.Concatenate("Features",
"Season",
"Year",
"Month",
"Hour",
"Weekday",
"Weather",
"Temperature",
"Humidity",
"Windspeed",
"Casual"))
.Append(_mlContext.Transforms.NormalizeMinMax("Features", "Features"))
.AppendCacheCheckpoint(_mlContext)
.Append(_algorythim);
_trainedModel = pipeline.Fit(_trainingDataView);
_predictionEngine = _mlContext.Model.CreatePredictionEngine<BikeSharingDemandSample, BikeSharingDemandPrediction>(_trainedModel);
}
public BikeSharingDemandPrediction Predict(BikeSharingDemandSample sample)
{
return _predictionEngine.Predict(sample);
}
public RegressionMetrics Evaluate(string testDataLocation)
{
var testDataView = _mlContext.Data.LoadFromTextFile<BikeSharingDemandSample>(
path: testDataLocation,
hasHeader: true,
separatorChar: ',',
allowQuoting: true,
allowSparse: false);
var predictions = _trainedModel.Transform(testDataView);
return _mlContext.Regression.Evaluate(predictions, "Label", "Score");
}
public void SaveModel()
{
_mlContext.Model.Save(_trainedModel, _trainingDataView.Schema, "./BikeSharingDemandsModel.zip");
}
}
}
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