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using CsvHelper;
using Microsoft.ML;
using Microsoft.ML.AutoML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.LightGbm;
using System.Globalization;
using static Microsoft.ML.DataOperationsCatalog;
var list= new List();
for (int i = 1; i < 1000; i++)
{
list.Add(new TaxiTrip()
{
A = (float)i,
Label ="A"
});
}
for (int i = 1000; i < 2000; i++)
{
list.Add(new TaxiTrip()
{
A = (float)i,
Label = "B"
});
}
using (var writer = new StreamWriter("data.csv"))
using (var csv = new CsvWriter(writer, CultureInfo.InvariantCulture))
{
csv.WriteRecords(list);
}
var predictionEngine = ctx.Model.CreatePredictionEngine<TaxiTrip, TaxiTripFarePrediction>(result.Model);
var testTaxiTrip = new TaxiTrip
{
A=888,
};
var prediction = predictionEngine.Predict(testTaxiTrip);
var testTaxiTrip2 = new TaxiTrip
{
A = 1888,
};
var prediction2 = predictionEngine.Predict(testTaxiTrip2);
var testTaxiTrip3 = new TaxiTrip
{
A = 555,
};
var prediction3 = predictionEngine.Predict(testTaxiTrip3);
//Console.WriteLine(prediction.FareAmount);
Console.WriteLine();
public class TaxiTrip
{
[ColumnName("A")]
public float A { get; set; }
[ColumnName("Label")]
public string Label { get; set; }
}
public class TaxiTripFarePrediction
{
[ColumnName(@"Label")]
public uint Label { get; set; }
[ColumnName(@"PredictedLabel")]
public string PredictedLabel { get; set; }
}
The text was updated successfully, but these errors were encountered:
mycode
using CsvHelper;
using Microsoft.ML;
using Microsoft.ML.AutoML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.LightGbm;
using System.Globalization;
using static Microsoft.ML.DataOperationsCatalog;
var list= new List();
for (int i = 1; i < 1000; i++)
{
list.Add(new TaxiTrip()
{
A = (float)i,
Label ="A"
});
}
for (int i = 1000; i < 2000; i++)
{
list.Add(new TaxiTrip()
{
A = (float)i,
Label = "B"
});
}
using (var writer = new StreamWriter("data.csv"))
using (var csv = new CsvWriter(writer, CultureInfo.InvariantCulture))
{
csv.WriteRecords(list);
}
MLContext ctx = new MLContext();
var dataPath = TrainDataPath;
// Infer column information
ColumnInferenceResults columnInference =
ctx.Auto().InferColumns(dataPath, labelColumnName: "Label", groupColumns: false);
// Create text loader
TextLoader loader = ctx.Data.CreateTextLoader(columnInference.TextLoaderOptions);
// Load data into IDataView
IDataView data = loader.Load(dataPath);
// Split into train (80%), validation (20%) sets
TrainTestData trainValidationData = ctx.Data.TrainTestSplit(data, testFraction: 0.2);
var context = new MLContext(1);
var experiment = context.Auto().CreateExperiment();
var pipeline = context.Auto().Featurizer(data, columnInformation: columnInference.ColumnInformation)
//.Append(context.Transforms.Conversion.MapKeyToValue(label, label))
.Append(context.Transforms.Conversion.MapValueToKey(outputColumnName: @"Label", inputColumnName: @"Label"))
.Append(context.Transforms.Conversion.MapKeyToValue(outputColumnName: @"PredictedLabel", inputColumnName: @"PredictedLabel"))
.Append(context.Auto().MultiClassification());
experiment.SetDataset(data, 5)
.SetMulticlassClassificationMetric(MulticlassClassificationMetric.MacroAccuracy, @"Label")
.SetPipeline(pipeline)
.SetTrainingTimeInSeconds(60);
var result = await experiment.RunAsync();
var predictionEngine = ctx.Model.CreatePredictionEngine<TaxiTrip, TaxiTripFarePrediction>(result.Model);
var testTaxiTrip = new TaxiTrip
{
A=888,
};
var prediction = predictionEngine.Predict(testTaxiTrip);
var testTaxiTrip2 = new TaxiTrip
{
A = 1888,
};
var prediction2 = predictionEngine.Predict(testTaxiTrip2);
var testTaxiTrip3 = new TaxiTrip
{
A = 555,
};
var prediction3 = predictionEngine.Predict(testTaxiTrip3);
//Console.WriteLine(prediction.FareAmount);
Console.WriteLine();
public class TaxiTrip
{
[ColumnName("A")]
public float A { get; set; }
}
public class TaxiTripFarePrediction
{
}
The text was updated successfully, but these errors were encountered: