static async Task RegressionExample(bool train = true)
{
var bestAlg = string.Empty;
double mse = double.MaxValue;
var mlContext = new MLContext();
var sqlConnection = $@"Server = localhost;database = Local;user = sa;password = sa";
var testdata = (await Utilities.SQL.SQLServer.ExecuteReaderAsync <TaxiFare>(sqlConnection, "SELECT TOP(10) * FROM [taxi-fare-test]", parameters: null, objectBuilder: (row) => Utilities.Shared.Data.RowBuilderExplicit <TaxiFare>(row)));
var traindata = (await Utilities.SQL.SQLServer.ExecuteReaderAsync <TaxiFare>(sqlConnection, "SELECT * FROM [taxi-fare-train] ORDER BY NEWID()", parameters: null, objectBuilder: (row) => Utilities.Shared.Data.RowBuilderExplicit <TaxiFare>(row)));
var algorithms = new Dictionary <string, Func <IEnumerable <TaxiFare>, Action <ITransformer>, PredictionEngine <TaxiFare, TaxiFareRegression> > >()
{
{ "SDCA", (data, action) => Regression.StochasticDoubleCoordinateAscent <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
{ "LBFGS", (data, action) => Regression.LbfgsPoisson <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
{ "FastTree", (data, action) => Regression.FastTree <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
{ "FastTreeTweedie", (data, action) => Regression.FastTreeTweedie <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
{ "FastForest", (data, action) => Regression.FastForest <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
};
foreach (var algorithm in algorithms)
{
PredictionEngine <TaxiFare, TaxiFareRegression> engine = default;
ITransformer model = default;
var path = $@"Regression_{algorithm.Key}.zip";
if (File.Exists(path) && !train)
{
model = Global.LoadModel(path);
engine = mlContext.Model.CreatePredictionEngine <TaxiFare, TaxiFareRegression>(model);
}
else
{
engine = algorithm.Value(traindata, (mdl) =>
{
model = mdl;
});
}
MachineLearning.Global.SaveModel(model, $@"Regression_{algorithm.Key}.zip");
var metrics = Metrics.EvaluateRegressionModel(model, mlContext.Data.LoadFromEnumerable(testdata));
MachineLearning.ConsoleHelper.ConsoleWriteHeader($@"Evaluate metrics for {algorithm.Key} algorithm.");
foreach (var prop in metrics.GetType().GetProperties())
{
Console.WriteLine($@"{prop.Name} : {prop.GetValue(metrics)}");
}
if (metrics.MeanSquaredError < mse)
{
mse = metrics.MeanSquaredError;
bestAlg = algorithm.Key;
}
//var predictedList = new List<TaxiFareRegression>();
//foreach (var t in testdata)
//{
// var predict = engine.Predict(t);
// predictedList.Add(predict);
// Console.WriteLine(string.Format(@"Actual : {0,5} / Predict {1,5} ({2,0}%)", Math.Round(t.fare_amount, 2), Math.Round(predict.Predicted_Score, 2), predict.CalculateVariance(t)));
//}
var predictedList = engine.Predict(testdata);
VisualizeRegression(algorithm.Key, testdata, predictedList, metrics, $"{algorithm.Key}_reg.svg");
}
Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine($@"Best algorithm based-on Mean Squared Error : {bestAlg}");
Console.ForegroundColor = ConsoleColor.White;
}
static async Task RegressionExample(bool train = true)
{
var bestAlg = string.Empty;
double mse = double.MaxValue;
var mlContext = new MLContext();
var sqlConnection = $@"Server = localhost;database = Local;user = sa;password = sa";
IEnumerable <TaxiFare> testdata = null;
IEnumerable <TaxiFare> traindata = null;
using (var connection = new SQLServer(sqlConnection))
{
testdata = await connection.QueryAsync <TaxiFareTest>(top : 10);
traindata = await connection.QueryAsync <TaxiFareTrain>();
var context = new MLContext(1);
var dataframe = context.Data.LoadFromEnumerable(traindata);
var crossValidatePreparer = context.Transforms.Concatenate("Features", new[] { "rate_code", "passenger_count", "trip_time_in_secs", "trip_distance" }).
Append(context.Transforms.NormalizeMinMax("Features"));
var cleanedData = crossValidatePreparer.Fit(dataframe);
var transformedData = cleanedData.Transform(dataframe);
var crossValidate = context.Regression.CrossValidate(transformedData, context.Regression.Trainers.FastTreeTweedie(), numberOfFolds: 5);
var rsqrs = crossValidate.Select(x => new
{
model = x.Model,
rsquared = x.Metrics.RSquared
}).ToList();
}
return;
var algorithms = new Dictionary <string, Func <IEnumerable <TaxiFare>, Action <ITransformer>, PredictionEngine <TaxiFare, TaxiFareRegression> > >()
{
{ "SDCA", (data, action) => Regression.StochasticDoubleCoordinateAscent <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
{ "LBFGS", (data, action) => Regression.LbfgsPoisson <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
{ "FastTree", (data, action) => Regression.FastTree <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
{ "FastTreeTweedie", (data, action) => Regression.FastTreeTweedie <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
{ "FastForest", (data, action) => Regression.FastForest <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
{ "GeneralizedAdditiveModel", (data, action) => Regression.GeneralizedAdditiveModel <TaxiFare, TaxiFareRegression>(data, additionModelAction: action) },
};
foreach (var algorithm in algorithms)
{
PredictionEngine <TaxiFare, TaxiFareRegression> engine = default;
ITransformer model = default;
var path = $@"Regression_{algorithm.Key}.zip";
if (File.Exists(path) && !train)
{
model = Global.LoadModel(path);
engine = mlContext.Model.CreatePredictionEngine <TaxiFare, TaxiFareRegression>(model);
}
else
{
engine = algorithm.Value(traindata, (mdl) =>
{
model = mdl;
});
}
MachineLearning.Global.SaveModel(model, $@"Regression_{algorithm.Key}.zip");
var metrics = Metrics.EvaluateRegressionModel(model, mlContext.Data.LoadFromEnumerable(testdata));
MachineLearning.ConsoleHelper.ConsoleWriteHeader($@"Evaluate metrics for {algorithm.Key} algorithm.");
foreach (var prop in metrics.GetType().GetProperties())
{
Console.WriteLine($@"{prop.Name} : {prop.GetValue(metrics)}");
}
if (metrics.MeanSquaredError < mse)
{
mse = metrics.MeanSquaredError;
bestAlg = algorithm.Key;
}
//var predictedList = new List<TaxiFareRegression>();
//foreach (var t in testdata)
//{
// var predict = engine.Predict(t);
// predictedList.Add(predict);
// Console.WriteLine(string.Format(@"Actual : {0,5} / Predict {1,5} ({2,0}%)", Math.Round(t.fare_amount, 2), Math.Round(predict.Predicted_Score, 2), predict.CalculateVariance(t)));
//}
var predictedList = engine.Predict(testdata);
VisualizeRegression(algorithm.Key, testdata, predictedList, metrics, $"{algorithm.Key}_reg.svg");
}
Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine($@"Best algorithm based-on Mean Squared Error : {bestAlg}");
Console.ForegroundColor = ConsoleColor.White;
}