有了上一篇《.NET Core玩转机器学习》打基础，这一次我们以纽约出租车费的预测做为新的场景案例，来体验一下回归模型。
场景概述
我们的目标是预测纽约的出租车费，乍一看似乎仅仅取决于行程的距离和时长，然而纽约的出租车供应商对其他因素，如额外的乘客数、信用卡而不是现金支付等，会综合考虑而收取不同数额的费用。纽约市官方给出了一份样本数据。
确定策略
为了能够预测出租车费，我们选择通过机器学习建立一个回归模型。使用官方提供的真实数据进行拟合，在训练模型的过程中确定真正能影响出租车费的决定性特征。在获得模型后，对模型进行评估验证，如果偏差在接受的范围内，就以这个模型来对新的数据进行预测。
解决方案
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创建项目
看过上一篇文章的读者，就比较轻车熟路了，推荐使用Visual Studio 2017创建一个.NET Core的控制台应用程序项目，命名为TaxiFarePrediction。使用NuGet包管理工具添加对Microsoft.ML的引用。
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准备数据集
下载训练数据集taxi-fare-train.csv和验证数据集taxi-fare-test.csv，数据集的内容类似为：
vendor_id,rate_code,passenger_count,trip_time_in_secs,trip_distance,payment_type,fare_amount
VTS,1,1,1140,3.75,CRD,15.5
VTS,1,1,480,2.72,CRD,10.0
VTS,1,1,1680,7.8,CSH,26.5
VTS,1,1,600,4.73,CSH,14.5
VTS,1,1,600,2.18,CRD,9.5
...对字段简单说明一下：
字段名 含义 说明 vendor_id 供应商编号 特征值 rate_code 比率码 特征值 passenger_count 乘客人数 特征值 trip_time_in_secs 行程时长 特征值 trip_distance 行程距离 特征值 payment_type 支付类型 特征值 fare_amount 费用 目标值 在项目中添加一个Data目录，将两份数据集复制到该目录下，对文件属性设置“复制到输出目录”。
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定义数据类型和路径
首先声明相关的包引用。
using System;
using Microsoft.ML.Models;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;在Main函数的上方定义一些使用到的常量。
const string DataPath = @".\Data\taxi-fare-train.csv";
const string TestDataPath = @".\Data\taxi-fare-test.csv";
const string ModelPath = @".\Models\Model.zip";
const string ModelDirectory = @".\Models";接下来定义一些使用到的数据类型，以及和数据集中每一行的位置对应关系。
public class TaxiTrip
{
[Column(ordinal: "")]
public string vendor_id;
[Column(ordinal: "")]
public string rate_code;
[Column(ordinal: "")]
public float passenger_count;
[Column(ordinal: "")]
public float trip_time_in_secs;
[Column(ordinal: "")]
public float trip_distance;
[Column(ordinal: "")]
public string payment_type;
[Column(ordinal: "")]
public float fare_amount;
} public class TaxiTripFarePrediction
{
[ColumnName("Score")]
public float fare_amount;
} static class TestTrips
{
internal static readonly TaxiTrip Trip1 = new TaxiTrip
{
vendor_id = "VTS",
rate_code = "",
passenger_count = ,
trip_distance = 10.33f,
payment_type = "CSH",
fare_amount = // predict it. actual = 29.5
};
} -
创建处理过程
创建一个Train方法，定义对数据集的处理过程，随后声明一个模型接收训练后的结果，在返回前把模型保存到指定的位置，以便以后直接取出来使用不需要再重新训练。
public static async Task<PredictionModel<TaxiTrip, TaxiTripFarePrediction>> Train()
{
var pipeline = new LearningPipeline(); pipeline.Add(new TextLoader<TaxiTrip>(DataPath, useHeader: true, separator: ","));
pipeline.Add(new ColumnCopier(("fare_amount", "Label")));
pipeline.Add(new CategoricalOneHotVectorizer("vendor_id",
"rate_code",
"payment_type"));
pipeline.Add(new ColumnConcatenator("Features",
"vendor_id",
"rate_code",
"passenger_count",
"trip_distance",
"payment_type"));
pipeline.Add(new FastTreeRegressor());
PredictionModel<TaxiTrip, TaxiTripFarePrediction> model = pipeline.Train<TaxiTrip, TaxiTripFarePrediction>();
if (!Directory.Exists(ModelDirectory))
{
Directory.CreateDirectory(ModelDirectory);
}
await model.WriteAsync(ModelPath);
return model;
} -
评估验证模型
创建一个Evaluate方法，对训练后的模型进行验证评估。
public static void Evaluate(PredictionModel<TaxiTrip, TaxiTripFarePrediction> model)
{
var testData = new TextLoader<TaxiTrip>(TestDataPath, useHeader: true, separator: ",");
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
// Rms should be around 2.795276
Console.WriteLine("Rms=" + metrics.Rms);
Console.WriteLine("RSquared = " + metrics.RSquared);
} -
预测新数据
定义一个被用于预测的新数据，对于各个特征进行恰当地赋值。
static class TestTrips
{
internal static readonly TaxiTrip Trip1 = new TaxiTrip
{
vendor_id = "VTS",
rate_code = "",
passenger_count = ,
trip_distance = 10.33f,
payment_type = "CSH",
fare_amount = // predict it. actual = 29.5
};
}预测的方法很简单，prediction即预测的结果，从中打印出预测的费用和真实费用。
var prediction = model.Predict(TestTrips.Trip1); Console.WriteLine("Predicted fare: {0}, actual fare: 29.5", prediction.fare_amount); -
运行结果
到此我们完成了所有的步骤，关于这些代码的详细说明，可以参看《Tutorial: Use ML.NET to Predict New York Taxi Fares (Regression)》，只是要注意该文中的部分代码有误，由于使用到了C# 7.1的语法特性，本文的代码是经过了修正的。完整的代码如下：
using System;
using Microsoft.ML.Models;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using System.Threading.Tasks;
using System.IO; namespace TaxiFarePrediction
{
class Program
{
const string DataPath = @".\Data\taxi-fare-train.csv";
const string TestDataPath = @".\Data\taxi-fare-test.csv";
const string ModelPath = @".\Models\Model.zip";
const string ModelDirectory = @".\Models"; public class TaxiTrip
{
[Column(ordinal: "")]
public string vendor_id;
[Column(ordinal: "")]
public string rate_code;
[Column(ordinal: "")]
public float passenger_count;
[Column(ordinal: "")]
public float trip_time_in_secs;
[Column(ordinal: "")]
public float trip_distance;
[Column(ordinal: "")]
public string payment_type;
[Column(ordinal: "")]
public float fare_amount;
} public class TaxiTripFarePrediction
{
[ColumnName("Score")]
public float fare_amount;
} static class TestTrips
{
internal static readonly TaxiTrip Trip1 = new TaxiTrip
{
vendor_id = "VTS",
rate_code = "",
passenger_count = ,
trip_distance = 10.33f,
payment_type = "CSH",
fare_amount = // predict it. actual = 29.5
};
} public static async Task<PredictionModel<TaxiTrip, TaxiTripFarePrediction>> Train()
{
var pipeline = new LearningPipeline(); pipeline.Add(new TextLoader<TaxiTrip>(DataPath, useHeader: true, separator: ","));
pipeline.Add(new ColumnCopier(("fare_amount", "Label")));
pipeline.Add(new CategoricalOneHotVectorizer("vendor_id",
"rate_code",
"payment_type"));
pipeline.Add(new ColumnConcatenator("Features",
"vendor_id",
"rate_code",
"passenger_count",
"trip_distance",
"payment_type"));
pipeline.Add(new FastTreeRegressor());
PredictionModel<TaxiTrip, TaxiTripFarePrediction> model = pipeline.Train<TaxiTrip, TaxiTripFarePrediction>();
if (!Directory.Exists(ModelDirectory))
{
Directory.CreateDirectory(ModelDirectory);
}
await model.WriteAsync(ModelPath);
return model;
} public static void Evaluate(PredictionModel<TaxiTrip, TaxiTripFarePrediction> model)
{
var testData = new TextLoader<TaxiTrip>(TestDataPath, useHeader: true, separator: ",");
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
// Rms should be around 2.795276
Console.WriteLine("Rms=" + metrics.Rms);
Console.WriteLine("RSquared = " + metrics.RSquared);
} static async Task Main(string[] args)
{
PredictionModel<TaxiTrip, TaxiTripFarePrediction> model = await Train();
Evaluate(model); var prediction = model.Predict(TestTrips.Trip1); Console.WriteLine("Predicted fare: {0}, actual fare: 29.5", prediction.fare_amount);
}
}
}
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