机器学习框架ML.NET学习笔记【9】自动学习
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2022-03-20 08:28:26
一、概述 本篇我们首先通过回归算法实现一个葡萄酒品质预测的程序，然后通过AutoML的方法再重新实现，通过对比两种实现方式来学习AutoML的应用。 首先数据集来自于竞赛网站kaggle.com的UCI Wine Quality Dataset数据集，访问地址：https://www.kaggle. ......
一、概述
本篇我们首先通过回归算法实现一个葡萄酒品质预测的程序，然后通过automl的方法再重新实现，通过对比两种实现方式来学习automl的应用。
首先数据集来自于竞赛网站kaggle.com的uci wine quality dataset数据集，访问地址：https://www.kaggle.com/c/uci-wine-quality-dataset/data
该数据集，输入为一些葡萄酒的化学检测数据，比如酒精度等，输出为品酒师的打分，具体字段描述如下：
data fields
input variables (based on physicochemical tests):
1 - fixed acidity
2 - volatile acidity
3 - citric acid
4 - residual sugar
5 - chlorides
6 - free sulfur dioxide
7 - total sulfur dioxide
8 - density
9 - ph
10 - sulphates
11 - alcohol
output variable (based on sensory data):
12 - quality (score between 0 and 10)
other:
13 - id (unique id for each sample, needed for submission)
二、代码
namespace regression_winequality
{
public class winedata
{
[loadcolumn(0)]
public float fixedacidity;
[loadcolumn(1)]
public float volatileacidity;
[loadcolumn(2)]
public float citricacid;
[loadcolumn(3)]
public float residualsugar;
[loadcolumn(4)]
public float chlorides;
[loadcolumn(5)]
public float freesulfurdioxide;
[loadcolumn(6)]
public float totalsulfurdioxide;
[loadcolumn(7)]
public float density;
[loadcolumn(8)]
public float ph;
[loadcolumn(9)]
public float sulphates;
[loadcolumn(10)]
public float alcohol;
[loadcolumn(11)]
[columnname("label")]
public float quality;
[loadcolumn(12)]
public float id;
}
public class wineprediction
{
[columnname("score")]
public float predictionquality;
}
class program
{
static readonly string modelfilepath = path.combine(environment.currentdirectory, "mlmodel", "model.zip");
static void main(string[] args)
{
train();
prediction();
console.writeline("hit any key to finish the app");
console.readkey();
}
public static void train()
{
mlcontext mlcontext = new mlcontext(seed: 1);
// 准备数据
string traindatapath = path.combine(environment.currentdirectory, "data", "winequality-data-full.csv");
var fulldata = mlcontext.data.loadfromtextfile<winedata>(path: traindatapath, separatorchar: ',', hasheader: true);
var traintestdata = mlcontext.data.traintestsplit(fulldata, testfraction: 0.2);
var traindata = traintestdata.trainset;
var testdata = traintestdata.testset;
// 创建学习管道并通过训练数据调整模型
var dataprocesspipeline = mlcontext.transforms.dropcolumns("id")
.append(mlcontext.transforms.normalizemeanvariance(nameof(winedata.freesulfurdioxide)))
.append(mlcontext.transforms.normalizemeanvariance(nameof(winedata.totalsulfurdioxide)))
.append(mlcontext.transforms.concatenate("features", new string[] { nameof(winedata.fixedacidity),
nameof(winedata.volatileacidity),
nameof(winedata.citricacid),
nameof(winedata.residualsugar),
nameof(winedata.chlorides),
nameof(winedata.freesulfurdioxide),
nameof(winedata.totalsulfurdioxide),
nameof(winedata.density),
nameof(winedata.ph),
nameof(winedata.sulphates),
nameof(winedata.alcohol)}));
var trainer = mlcontext.regression.trainers.lbfgspoissonregression(labelcolumnname: "label", featurecolumnname: "features");
var trainingpipeline = dataprocesspipeline.append(trainer);
var trainedmodel = trainingpipeline.fit(traindata);
// 评估
var predictions = trainedmodel.transform(testdata);
var metrics = mlcontext.regression.evaluate(predictions, labelcolumnname: "label", scorecolumnname: "score");
printregressionmetrics(trainer.tostring(), metrics);
// 保存模型
console.writeline("====== save model to local file =========");
mlcontext.model.save(trainedmodel, traindata.schema, modelfilepath);
}
static void prediction()
{
mlcontext mlcontext = new mlcontext(seed: 1);
itransformer loadedmodel = mlcontext.model.load(modelfilepath, out var modelinputschema);
var predictor = mlcontext.model.createpredictionengine<winedata, wineprediction>(loadedmodel);
winedata winedata = new winedata
{
fixedacidity = 7.6f,
volatileacidity = 0.33f,
citricacid = 0.36f,
residualsugar = 2.1f,
chlorides = 0.034f,
freesulfurdioxide = 26f,
totalsulfurdioxide = 172f,
density = 0.9944f,
ph = 3.42f,
sulphates = 0.48f,
alcohol = 10.5f
};
var winequality = predictor.predict(winedata);
console.writeline($"wine data quality is:{winequality.predictionquality} ");
}
}
}
关于泊松回归的算法，我们在进行人脸颜值判断的那篇文章已经介绍过了，这个程序没有涉及任何新的知识点，就不重复解释了，主要目的是和下面的automl代码对比用的。
三、自动学习
我们发现机器学习的大致流程基本都差不多，如：准备数据-明确特征-选择算法-训练等，有时我们存在这样一个问题：该选择什么算法？算法的参数该如何配置？等等。而自动学习就解决了这个问题，框架会多次重复数据选择、算法选择、参数调优、评估结果这一过程，通过这个过程找出评估效果最好的模型。
全部代码如下：
namespace regression_winequality
{
public class winedata
{
[loadcolumn(0)]
public float fixedacidity;
[loadcolumn(1)]
public float volatileacidity;
[loadcolumn(2)]
public float citricacid;
[loadcolumn(3)]
public float residualsugar;
[loadcolumn(4)]
public float chlorides;
[loadcolumn(5)]
public float freesulfurdioxide;
[loadcolumn(6)]
public float totalsulfurdioxide;
[loadcolumn(7)]
public float density;
[loadcolumn(8)]
public float ph;
[loadcolumn(9)]
public float sulphates;
[loadcolumn(10)]
public float alcohol;
[loadcolumn(11)]
[columnname("label")]
public float quality;
[loadcolumn(12)]
public float id;
}
public class wineprediction
{
[columnname("score")]
public float predictionquality;
}
class program
{
static readonly string modelfilepath = path.combine(environment.currentdirectory, "mlmodel", "model.zip");
static readonly string traindatapath = path.combine(environment.currentdirectory, "data", "winequality-data-train.csv");
static readonly string testdatapath = path.combine(environment.currentdirectory, "data", "winequality-data-test.csv");
static void main(string[] args)
{
trainandsave();
loadandprediction();
console.writeline("hit any key to finish the app");
console.readkey();
}
public static void trainandsave()
{
mlcontext mlcontext = new mlcontext(seed: 1);
// 准备数据
var traindata = mlcontext.data.loadfromtextfile<winedata>(path: traindatapath, separatorchar: ',', hasheader: true);
var testdata = mlcontext.data.loadfromtextfile<winedata>(path: testdatapath, separatorchar: ',', hasheader: true);
var progresshandler = new regressionexperimentprogresshandler();
uint experimenttime = 200;
experimentresult<regressionmetrics> experimentresult = mlcontext.auto()
.createregressionexperiment(experimenttime)
.execute(traindata, "label", progresshandler: progresshandler);
debugger.printtopmodels(experimentresult);
rundetail<regressionmetrics> best = experimentresult.bestrun;
itransformer trainedmodel = best.model;
// 评估 bestrun
var predictions = trainedmodel.transform(testdata);
var metrics = mlcontext.regression.evaluate(predictions, labelcolumnname: "label", scorecolumnname: "score");
debugger.printregressionmetrics(best.trainername, metrics);
// 保存模型
console.writeline("====== save model to local file =========");
mlcontext.model.save(trainedmodel, traindata.schema, modelfilepath);
}
static void loadandprediction()
{
mlcontext mlcontext = new mlcontext(seed: 1);
itransformer loadedmodel = mlcontext.model.load(modelfilepath, out var modelinputschema);
var predictor = mlcontext.model.createpredictionengine<winedata, wineprediction>(loadedmodel);
winedata winedata = new winedata
{
fixedacidity = 7.6f,
volatileacidity = 0.33f,
citricacid = 0.36f,
residualsugar = 2.1f,
chlorides = 0.034f,
freesulfurdioxide = 26f,
totalsulfurdioxide = 172f,
density = 0.9944f,
ph = 3.42f,
sulphates = 0.48f,
alcohol = 10.5f
};
var winequality = predictor.predict(winedata);
console.writeline($"wine data quality is:{winequality.predictionquality} ");
}
}
}
四、代码分析
1、自动学习过程
var progresshandler = new regressionexperimentprogresshandler();
uint experimenttime = 200;
experimentresult<regressionmetrics> experimentresult = mlcontext.auto()
.createregressionexperiment(experimenttime)
.execute(traindata, "label", progresshandler: progresshandler);
debugger.printtopmodels(experimentresult); //打印所有模型数据
experimenttime 是允许的试验时间，progresshandler是一个报告程序，当每完成一种学习，系统就会调用一次报告事件。
public class regressionexperimentprogresshandler : iprogress<rundetail<regressionmetrics>>
{
private int _iterationindex;
public void report(rundetail<regressionmetrics> iterationresult)
{
_iterationindex++;
console.writeline($"report index:{_iterationindex},trainername:{iterationresult.trainername},runtimeinseconds:{iterationresult.runtimeinseconds}");
}
}
调试结果如下：
report index:1,trainername:sdcaregression,runtimeinseconds:12.5244426
report index:2,trainername:lightgbmregression,runtimeinseconds:11.2034988
report index:3,trainername:fasttreeregression,runtimeinseconds:14.810409
report index:4,trainername:fasttreetweedieregression,runtimeinseconds:14.7338553
report index:5,trainername:fastforestregression,runtimeinseconds:15.6224459
report index:6,trainername:lbfgspoissonregression,runtimeinseconds:11.1668197
report index:7,trainername:onlinegradientdescentregression,runtimeinseconds:10.5353
report index:8,trainername:olsregression,runtimeinseconds:10.8905459
report index:9,trainername:lightgbmregression,runtimeinseconds:10.5703296
report index:10,trainername:fasttreeregression,runtimeinseconds:19.4470509
report index:11,trainername:fasttreetweedieregression,runtimeinseconds:63.638882
report index:12,trainername:lightgbmregression,runtimeinseconds:10.7710518
学习结束后我们通过debugger.printtopmodels打印出所有模型数据：
public class debugger
{
private const int width = 114;
public static void printtopmodels(experimentresult<regressionmetrics> experimentresult)
{
var topruns = experimentresult.rundetails
.where(r => r.validationmetrics != null && !double.isnan(r.validationmetrics.rsquared))
.orderbydescending(r => r.validationmetrics.rsquared);
console.writeline("top models ranked by r-squared --");
printregressionmetricsheader();
for (var i = 0; i < topruns.count(); i++)
{
var run = topruns.elementat(i);
printiterationmetrics(i + 1, run.trainername, run.validationmetrics, run.runtimeinseconds);
}
}
public static void printregressionmetricsheader()
{
createrow($"{"",-4} {"trainer",-35} {"rsquared",8} {"absolute-loss",13} {"squared-loss",12} {"rms-loss",8} {"duration",9}", width);
}
public static void printiterationmetrics(int iteration, string trainername, regressionmetrics metrics, double? runtimeinseconds)
{
createrow($"{iteration,-4} {trainername,-35} {metrics?.rsquared ?? double.nan,8:f4} {metrics?.meanabsoluteerror ?? double.nan,13:f2} {metrics?.meansquarederror ?? double.nan,12:f2} {metrics?.rootmeansquarederror ?? double.nan,8:f2} {runtimeinseconds.value,9:f1}", width);
}
public static void createrow(string message, int width)
{
console.writeline("|" + message.padright(width - 2) + "|");
}
}
其中createrow代码功能用于排版。调试结果如下：
top models ranked by r-squared --
| trainer rsquared absolute-loss squared-loss rms-loss duration |
|1 fasttreetweedieregression 0.4731 0.46 0.41 0.64 63.6 |
|2 fasttreetweedieregression 0.4431 0.49 0.43 0.65 14.7 |
|3 fasttreeregression 0.4386 0.54 0.49 0.70 19.4 |
|4 lightgbmregression 0.4177 0.52 0.45 0.67 10.8 |
|5 fasttreeregression 0.4102 0.51 0.45 0.67 14.8 |
|6 lightgbmregression 0.3944 0.52 0.46 0.68 11.2 |
|7 lightgbmregression 0.3501 0.60 0.57 0.75 10.6 |
|8 fastforestregression 0.3381 0.60 0.58 0.76 15.6 |
|9 olsregression 0.2829 0.56 0.53 0.73 10.9 |
|10 lbfgspoissonregression 0.2760 0.62 0.63 0.80 11.2 |
|11 sdcaregression 0.2746 0.58 0.56 0.75 12.5 |
|12 onlinegradientdescentregression 0.0593 0.69 0.81 0.90 10.5 |
根据结果可以看到，一些算法被重复试验，但在使用同一个算法时其配置参数并不一样，如阙值、深度等。
2、获取最优模型
rundetail<regressionmetrics> best = experimentresult.bestrun;
itransformer trainedmodel = best.model;
获取最佳模型后，其评估和保存的过程和之前代码一致。用测试数据评估结果：
*************************************************
* metrics for fasttreetweedieregression regression model
*------------------------------------------------
* lossfn: 0.67
* r2 score: 0.34
* absolute loss: .63
* squared loss: .67
* rms loss: .82
*************************************************
看结果识别率约70%左右，这种结果是没有办法用于生产的，问题应该是我们没有找到决定葡萄酒品质的关键特征。
五、小结
到这篇文章为止，《ml.net学习笔记系列》就结束了。学习过程中涉及的原始代码主要来源于：https://github.com/dotnet/machinelearning-samples 。
该工程中还有一些其他算法应用的例子，包括：聚类、矩阵分解、异常检测，其大体流程基本都差不多，有了我们这个系列的学习基础有兴趣的朋友可以自己研究一下。
六、资源获取
源码下载地址：https://github.com/seabluescn/study_ml.net
回归工程名称：regression_winequality
automl工程名称：regression_winequality_automl
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