理解问题
出租车的车费不仅与距离有关，还涉及乘客数量，是否使用信用卡等因素(这是的出租车是指纽约市的)。所以并不是一个简单的一元方程问题。
准备数据
建立一控制台应用程序工程，新建Data文件夹，在其目录下添加taxi-fare-train.csv与taxi-fare-test.csv文件，不要忘了把它们的Copy to Output Directory属性改为Copy if newer。之后，添加Microsoft.ML类库包。
加载数据
新建MLContext对象，及创建TextLoader对象。TextLoader对象可用于从文件中读取数据。
MLContext mlContext = new MLContext(seed: 0);
_textLoader = mlContext.Data.TextReader(new TextLoader.Arguments()
{
Separator = ",",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("VendorId", DataKind.Text, 0),
new TextLoader.Column("RateCode", DataKind.Text, 1),
new TextLoader.Column("PassengerCount", DataKind.R4, 2),
new TextLoader.Column("TripTime", DataKind.R4, 3),
new TextLoader.Column("TripDistance", DataKind.R4, 4),
new TextLoader.Column("PaymentType", DataKind.Text, 5),
new TextLoader.Column("FareAmount", DataKind.R4, 6)
}
});
提取特征
数据集文件里共有七列，前六列做为特征数据，最后一列是标记数据。
public class TaxiTrip
{
[Column("0")]
public string VendorId;
[Column("1")]
public string RateCode;
[Column("2")]
public float PassengerCount;
[Column("3")]
public float TripTime;
[Column("4")]
public float TripDistance;
[Column("5")]
public string PaymentType;
[Column("6")]
public float FareAmount;
}
public class TaxiTripFarePrediction
{
[ColumnName("Score")]
public float FareAmount;
}
训练模型
首先读取训练数据集，其次建立管道。管道中第一步是把FareAmount列复制到Label列，做为标记数据。第二步，通过OneHotEncoding方式将VendorId，RateCode，PaymentType三个字符串类型列转换成数值类型列。第三步，合并六个数据列为一个特征数据列。最后一步，选择FastTreeRegressionTrainer算法做为训练方法。
完成管道后，开始训练模型。
IDataView dataView = _textLoader.Read(dataPath);
var pipeline = mlContext.Transforms.CopyColumns("FareAmount", "Label")
.Append(mlContext.Transforms.Categorical.OneHotEncoding("VendorId"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("RateCode"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("PaymentType"))
.Append(mlContext.Transforms.Concatenate("Features", "VendorId", "RateCode", "PassengerCount", "TripTime", "TripDistance", "PaymentType"))
.Append(mlContext.Regression.Trainers.FastTree());
var model = pipeline.Fit(dataView);
评估模型
这里要使用测试数据集，并用回归问题的Evaluate方法进行评估。
IDataView dataView = _textLoader.Read(_testDataPath);
var predictions = model.Transform(dataView);
var metrics = mlContext.Regression.Evaluate(predictions, "Label", "Score");
Console.WriteLine();
Console.WriteLine($"*************************************************");
Console.WriteLine($"* Model quality metrics evaluation ");
Console.WriteLine($"*------------------------------------------------");
Console.WriteLine($"* R2 Score: {metrics.RSquared:0.##}");
Console.WriteLine($"* RMS loss: {metrics.Rms:#.##}");
保存模型
完成训练的模型可以被保存为zip文件以备之后使用。
using (var fileStream = new FileStream(_modelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
mlContext.Model.Save(model, fileStream);
使用模型
首先加载已经保存的模型。接着建立预测函数对象，TaxiTrip为函数的输入类型，TaxiTripFarePrediction为输出类型。之后执行预测方法，传入待测数据。
ITransformer loadedModel;
using (var stream = new FileStream(_modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
loadedModel = mlContext.Model.Load(stream);
}
var predictionFunction = loadedModel.MakePredictionFunction<TaxiTrip, TaxiTripFarePrediction>(mlContext);
var taxiTripSample = new TaxiTrip()
{
VendorId = "VTS",
RateCode = "1",
PassengerCount = 1,
TripTime = 1140,
TripDistance = 3.75f,
PaymentType = "CRD",
FareAmount = 0 // To predict. Actual/Observed = 15.5
};
var prediction = predictionFunction.Predict(taxiTripSample);
Console.WriteLine($"**********************************************************************");
Console.WriteLine($"Predicted fare: {prediction.FareAmount:0.####}, actual fare: 15.5");
Console.WriteLine($"**********************************************************************");
完整示例代码
using Microsoft.ML;
using Microsoft.ML.Core.Data;
using Microsoft.ML.Runtime.Data;
using System;
using System.IO;
namespace TexiFarePredictor
{
class Program
{
static readonly string _trainDataPath = Path.Combine(Environment.CurrentDirectory, "Data", "taxi-fare-train.csv");
static readonly string _testDataPath = Path.Combine(Environment.CurrentDirectory, "Data", "taxi-fare-test.csv");
static readonly string _modelPath = Path.Combine(Environment.CurrentDirectory, "Data", "Model.zip");
static TextLoader _textLoader;
static void Main(string[] args)
{
MLContext mlContext = new MLContext(seed: 0);
_textLoader = mlContext.Data.TextReader(new TextLoader.Arguments()
{
Separator = ",",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("VendorId", DataKind.Text, 0),
new TextLoader.Column("RateCode", DataKind.Text, 1),
new TextLoader.Column("PassengerCount", DataKind.R4, 2),
new TextLoader.Column("TripTime", DataKind.R4, 3),
new TextLoader.Column("TripDistance", DataKind.R4, 4),
new TextLoader.Column("PaymentType", DataKind.Text, 5),
new TextLoader.Column("FareAmount", DataKind.R4, 6)
}
});
var model = Train(mlContext, _trainDataPath);
Evaluate(mlContext, model);
TestSinglePrediction(mlContext);
Console.Read();
}
public static ITransformer Train(MLContext mlContext, string dataPath)
{
IDataView dataView = _textLoader.Read(dataPath);
var pipeline = mlContext.Transforms.CopyColumns("FareAmount", "Label")
.Append(mlContext.Transforms.Categorical.OneHotEncoding("VendorId"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("RateCode"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("PaymentType"))
.Append(mlContext.Transforms.Concatenate("Features", "VendorId", "RateCode", "PassengerCount", "TripTime", "TripDistance", "PaymentType"))
.Append(mlContext.Regression.Trainers.FastTree());
var model = pipeline.Fit(dataView);
SaveModelAsFile(mlContext, model);
return model;
}
private static void SaveModelAsFile(MLContext mlContext, ITransformer model)
{
using (var fileStream = new FileStream(_modelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
mlContext.Model.Save(model, fileStream);
}
private static void Evaluate(MLContext mlContext, ITransformer model)
{
IDataView dataView = _textLoader.Read(_testDataPath);
var predictions = model.Transform(dataView);
var metrics = mlContext.Regression.Evaluate(predictions, "Label", "Score");
Console.WriteLine();
Console.WriteLine($"*************************************************");
Console.WriteLine($"* Model quality metrics evaluation ");
Console.WriteLine($"*------------------------------------------------");
Console.WriteLine($"* R2 Score: {metrics.RSquared:0.##}");
Console.WriteLine($"* RMS loss: {metrics.Rms:#.##}");
}
private static void TestSinglePrediction(MLContext mlContext)
{
ITransformer loadedModel;
using (var stream = new FileStream(_modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
loadedModel = mlContext.Model.Load(stream);
}
var predictionFunction = loadedModel.MakePredictionFunction<TaxiTrip, TaxiTripFarePrediction>(mlContext);
var taxiTripSample = new TaxiTrip()
{
VendorId = "VTS",
RateCode = "1",
PassengerCount = 1,
TripTime = 1140,
TripDistance = 3.75f,
PaymentType = "CRD",
FareAmount = 0 // To predict. Actual/Observed = 15.5
};
var prediction = predictionFunction.Predict(taxiTripSample);
Console.WriteLine($"**********************************************************************");
Console.WriteLine($"Predicted fare: {prediction.FareAmount:0.####}, actual fare: 15.5");
Console.WriteLine($"**********************************************************************");
}
}
}
程序运行后显示的结果：
*************************************************
* Model quality metrics evaluation
*------------------------------------------------
* R2 Score: 0.92
* RMS loss: 2.81
**********************************************************************
Predicted fare: 15.7855, actual fare: 15.5
**********************************************************************
最后的预测结果还是比较符合实际数值的。