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
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。
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_amountVTS,1,1,1140,3.75,CRD,15.5VTS,1,1,480,2.72,CRD,10.0VTS,1,1,1680,7.8,CSH,26.5VTS,1,1,600,4.73,CSH,14.5VTS,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”。
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-test.csv";const string TestDataPath = @".\Data\taxi-fare-train.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: "0")] ???public string vendor_id; ???[Column(ordinal: "1")] ???public string rate_code; ???[Column(ordinal: "2")] ???public float passenger_count; ???[Column(ordinal: "3")] ???public float trip_time_in_secs; ???[Column(ordinal: "4")] ???public float trip_distance; ???[Column(ordinal: "5")] ???public string payment_type; ???[Column(ordinal: "6")] ???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 = "1", ???????passenger_count = 1, ???????trip_distance = 10.33f, ???????payment_type = "CSH", ???????fare_amount = 0 // 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 = "1", ???????passenger_count = 1, ???????trip_distance = 10.33f, ???????payment_type = "CSH", ???????fare_amount = 0 // 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-test.csv"; ???????const string TestDataPath = @".\Data\taxi-fare-train.csv"; ???????const string ModelPath = @".\Models\Model.zip"; ???????const string ModelDirectory = @".\Models"; ???????public class TaxiTrip ???????{ ???????????[Column(ordinal: "0")] ???????????public string vendor_id; ???????????[Column(ordinal: "1")] ???????????public string rate_code; ???????????[Column(ordinal: "2")] ???????????public float passenger_count; ???????????[Column(ordinal: "3")] ???????????public float trip_time_in_secs; ???????????[Column(ordinal: "4")] ???????????public float trip_distance; ???????????[Column(ordinal: "5")] ???????????public string payment_type; ???????????[Column(ordinal: "6")] ???????????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 = "1", ???????????????passenger_count = 1, ???????????????trip_distance = 10.33f, ???????????????payment_type = "CSH", ???????????????fare_amount = 0 // 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); ???????} ???}}不知不觉我们的ML.NET之旅又向前进了一步，是不是对于使用.NET Core进行机器学习解决现实生活中的问题更有兴趣了？请保持关注吧。
使用ML.NET预测纽约出租车费
原文地址：https://www.cnblogs.com/BeanHsiang/p/9017618.html