ML.NET速览

什么是ml.net？

ml.net是由微软创建，为.net开发者准备的开源机器学习框架。它是跨平台的，可以在macos，linux及windows上运行。

机器学习管道

ml.net通过管道(pipeline)方式组合机器学习过程。整个管道分为以下四个部分：

• load data 加载数据
• transform data 转换数据
• choose algorithm 选择算法
• train model 训练模型

示例

建立一个控制台项目。

dotnet new console -o myapp
cd myapp

添加ml.net类库包。

dotnet add package microsoft.ml

在工程文件夹下创建一个名为iris-data.txt的文本文件，内容如下：

5.1,3.5,1.4,0.2,iris-setosa
4.9,3.0,1.4,0.2,iris-setosa
4.7,3.2,1.3,0.2,iris-setosa
4.6,3.1,1.5,0.2,iris-setosa
5.0,3.6,1.4,0.2,iris-setosa
5.4,3.9,1.7,0.4,iris-setosa
4.6,3.4,1.4,0.3,iris-setosa
5.0,3.4,1.5,0.2,iris-setosa
4.4,2.9,1.4,0.2,iris-setosa
4.9,3.1,1.5,0.1,iris-setosa
5.4,3.7,1.5,0.2,iris-setosa
4.8,3.4,1.6,0.2,iris-setosa
4.8,3.0,1.4,0.1,iris-setosa
4.3,3.0,1.1,0.1,iris-setosa
5.8,4.0,1.2,0.2,iris-setosa
5.7,4.4,1.5,0.4,iris-setosa
5.4,3.9,1.3,0.4,iris-setosa
5.1,3.5,1.4,0.3,iris-setosa
5.7,3.8,1.7,0.3,iris-setosa
5.1,3.8,1.5,0.3,iris-setosa
5.4,3.4,1.7,0.2,iris-setosa
5.1,3.7,1.5,0.4,iris-setosa
4.6,3.6,1.0,0.2,iris-setosa
5.1,3.3,1.7,0.5,iris-setosa
4.8,3.4,1.9,0.2,iris-setosa
5.0,3.0,1.6,0.2,iris-setosa
5.0,3.4,1.6,0.4,iris-setosa
5.2,3.5,1.5,0.2,iris-setosa
5.2,3.4,1.4,0.2,iris-setosa
4.7,3.2,1.6,0.2,iris-setosa
4.8,3.1,1.6,0.2,iris-setosa
5.4,3.4,1.5,0.4,iris-setosa
5.2,4.1,1.5,0.1,iris-setosa
5.5,4.2,1.4,0.2,iris-setosa
4.9,3.1,1.5,0.1,iris-setosa
5.0,3.2,1.2,0.2,iris-setosa
5.5,3.5,1.3,0.2,iris-setosa
4.9,3.1,1.5,0.1,iris-setosa
4.4,3.0,1.3,0.2,iris-setosa
5.1,3.4,1.5,0.2,iris-setosa
5.0,3.5,1.3,0.3,iris-setosa
4.5,2.3,1.3,0.3,iris-setosa
4.4,3.2,1.3,0.2,iris-setosa
5.0,3.5,1.6,0.6,iris-setosa
5.1,3.8,1.9,0.4,iris-setosa
4.8,3.0,1.4,0.3,iris-setosa
5.1,3.8,1.6,0.2,iris-setosa
4.6,3.2,1.4,0.2,iris-setosa
5.3,3.7,1.5,0.2,iris-setosa
5.0,3.3,1.4,0.2,iris-setosa
7.0,3.2,4.7,1.4,iris-versicolor
6.4,3.2,4.5,1.5,iris-versicolor
6.9,3.1,4.9,1.5,iris-versicolor
5.5,2.3,4.0,1.3,iris-versicolor
6.5,2.8,4.6,1.5,iris-versicolor
5.7,2.8,4.5,1.3,iris-versicolor
6.3,3.3,4.7,1.6,iris-versicolor
4.9,2.4,3.3,1.0,iris-versicolor
6.6,2.9,4.6,1.3,iris-versicolor
5.2,2.7,3.9,1.4,iris-versicolor
5.0,2.0,3.5,1.0,iris-versicolor
5.9,3.0,4.2,1.5,iris-versicolor
6.0,2.2,4.0,1.0,iris-versicolor
6.1,2.9,4.7,1.4,iris-versicolor
5.6,2.9,3.6,1.3,iris-versicolor
6.7,3.1,4.4,1.4,iris-versicolor
5.6,3.0,4.5,1.5,iris-versicolor
5.8,2.7,4.1,1.0,iris-versicolor
6.2,2.2,4.5,1.5,iris-versicolor
5.6,2.5,3.9,1.1,iris-versicolor
5.9,3.2,4.8,1.8,iris-versicolor
6.1,2.8,4.0,1.3,iris-versicolor
6.3,2.5,4.9,1.5,iris-versicolor
6.1,2.8,4.7,1.2,iris-versicolor
6.4,2.9,4.3,1.3,iris-versicolor
6.6,3.0,4.4,1.4,iris-versicolor
6.8,2.8,4.8,1.4,iris-versicolor
6.7,3.0,5.0,1.7,iris-versicolor
6.0,2.9,4.5,1.5,iris-versicolor
5.7,2.6,3.5,1.0,iris-versicolor
5.5,2.4,3.8,1.1,iris-versicolor
5.5,2.4,3.7,1.0,iris-versicolor
5.8,2.7,3.9,1.2,iris-versicolor
6.0,2.7,5.1,1.6,iris-versicolor
5.4,3.0,4.5,1.5,iris-versicolor
6.0,3.4,4.5,1.6,iris-versicolor
6.7,3.1,4.7,1.5,iris-versicolor
6.3,2.3,4.4,1.3,iris-versicolor
5.6,3.0,4.1,1.3,iris-versicolor
5.5,2.5,4.0,1.3,iris-versicolor
5.5,2.6,4.4,1.2,iris-versicolor
6.1,3.0,4.6,1.4,iris-versicolor
5.8,2.6,4.0,1.2,iris-versicolor
5.0,2.3,3.3,1.0,iris-versicolor
5.6,2.7,4.2,1.3,iris-versicolor
5.7,3.0,4.2,1.2,iris-versicolor
5.7,2.9,4.2,1.3,iris-versicolor
6.2,2.9,4.3,1.3,iris-versicolor
5.1,2.5,3.0,1.1,iris-versicolor
5.7,2.8,4.1,1.3,iris-versicolor
6.3,3.3,6.0,2.5,iris-virginica
5.8,2.7,5.1,1.9,iris-virginica
7.1,3.0,5.9,2.1,iris-virginica
6.3,2.9,5.6,1.8,iris-virginica
6.5,3.0,5.8,2.2,iris-virginica
7.6,3.0,6.6,2.1,iris-virginica
4.9,2.5,4.5,1.7,iris-virginica
7.3,2.9,6.3,1.8,iris-virginica
6.7,2.5,5.8,1.8,iris-virginica
7.2,3.6,6.1,2.5,iris-virginica
6.5,3.2,5.1,2.0,iris-virginica
6.4,2.7,5.3,1.9,iris-virginica
6.8,3.0,5.5,2.1,iris-virginica
5.7,2.5,5.0,2.0,iris-virginica
5.8,2.8,5.1,2.4,iris-virginica
6.4,3.2,5.3,2.3,iris-virginica
6.5,3.0,5.5,1.8,iris-virginica
7.7,3.8,6.7,2.2,iris-virginica
7.7,2.6,6.9,2.3,iris-virginica
6.0,2.2,5.0,1.5,iris-virginica
6.9,3.2,5.7,2.3,iris-virginica
5.6,2.8,4.9,2.0,iris-virginica
7.7,2.8,6.7,2.0,iris-virginica
6.3,2.7,4.9,1.8,iris-virginica
6.7,3.3,5.7,2.1,iris-virginica
7.2,3.2,6.0,1.8,iris-virginica
6.2,2.8,4.8,1.8,iris-virginica
6.1,3.0,4.9,1.8,iris-virginica
6.4,2.8,5.6,2.1,iris-virginica
7.2,3.0,5.8,1.6,iris-virginica
7.4,2.8,6.1,1.9,iris-virginica
7.9,3.8,6.4,2.0,iris-virginica
6.4,2.8,5.6,2.2,iris-virginica
6.3,2.8,5.1,1.5,iris-virginica
6.1,2.6,5.6,1.4,iris-virginica
7.7,3.0,6.1,2.3,iris-virginica
6.3,3.4,5.6,2.4,iris-virginica
6.4,3.1,5.5,1.8,iris-virginica
6.0,3.0,4.8,1.8,iris-virginica
6.9,3.1,5.4,2.1,iris-virginica
6.7,3.1,5.6,2.4,iris-virginica
6.9,3.1,5.1,2.3,iris-virginica
5.8,2.7,5.1,1.9,iris-virginica
6.8,3.2,5.9,2.3,iris-virginica
6.7,3.3,5.7,2.5,iris-virginica
6.7,3.0,5.2,2.3,iris-virginica
6.3,2.5,5.0,1.9,iris-virginica
6.5,3.0,5.2,2.0,iris-virginica
6.2,3.4,5.4,2.3,iris-virginica
5.9,3.0,5.1,1.8,iris-virginica

粘贴下面的代码到program文件中。

using system;
using microsoft.ml;
using microsoft.ml.runtime.api;
using microsoft.ml.runtime.data;

namespace myapp
{
    class program
    {
        public class irisdata
        {
            public float sepallength;
            public float sepalwidth;
            public float petallength;
            public float petalwidth;
            public string label;
        }

        public class irisprediction
        {
            [columnname("predictedlabel")]
            public string predictedlabels;
        }

        static void main(string[] args)
        {
            var mlcontext = new mlcontext();

            string datapath = "iris-data.txt";
            var reader = mlcontext.data.textreader(new textloader.arguments()
            {
                separator = ",",
                hasheader = true,
                column = new[]
                {
                    new textloader.column("sepallength", datakind.r4, 0),
                    new textloader.column("sepalwidth", datakind.r4, 1),
                    new textloader.column("petallength", datakind.r4, 2),
                    new textloader.column("petalwidth", datakind.r4, 3),
                    new textloader.column("label", datakind.text, 4)
                }
            });

            idataview trainingdataview = reader.read(new multifilesource(datapath));

            var pipeline = mlcontext.transforms.categorical.mapvaluetokey("label")
                .append(mlcontext.transforms.concatenate("features", "sepallength", "sepalwidth", "petallength", "petalwidth"))
                .append(mlcontext.multiclassclassification.trainers.stochasticdualcoordinateascent(label: "label", features: "features"))
                .append(mlcontext.transforms.conversion.mapkeytovalue("predictedlabel"));

            var model = pipeline.fit(trainingdataview);

            var prediction = model.makepredictionfunction<irisdata, irisprediction>(mlcontext).predict(
                new irisdata()
                {
                    sepallength = 3.3f,
                    sepalwidth = 1.6f,
                    petallength = 0.2f,
                    petalwidth = 5.1f,
                });

            console.writeline($"predicted flower type is: {prediction.predictedlabels}");
        }
    }
}

通过dotnet run命令运行程序后可得到预测结果。

predicted flower type is: iris-virginica

解例

例子中定义了两个类，irisdata与irisprediction。irisdata类是用于训练的数据结构，而irisprediction则用于预测。

mlcontext类用于定义ml.net的上下文(context)，可以理解为是它的运行时环境。

接着，创建一个textreader，用于读取数据集文件，可以看到其中规定了读取的格式。这里即是机器学习管道的第一步。

第二步，转换irisdata类中label属性的类型，使之成为数值类型，因为只有数值类型的数据才能在模型训练中被使用。再将sepallength，sepalwidth，petallength与petalwidth合并为一，统合为数据集的features。

第三步，为训练选择合适的算法，并传入标签(label)和特征(features)。

第四步，训练模型。

完成模型后，就可以用它进行预测了。因为最后预测的结果是字符串类型，所以在上述第三步的操作后有必要加上转换操作，把结果从数值类型再转回字符串类型。