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使用ML.NET预测纽约出租车费

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：

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    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”。


  • 定义数据类型和路径

    首先声明相关的包引用。

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    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。

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    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。

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    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。

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    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。

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    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。

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    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。

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    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：

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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: "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。

 

from:http://www.cnblogs.com/BeanHsiang/p/9017618.html

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