public override void Train(List <DataSet> data, List <float> labels = null)
{
if (TrainedModel != null)
{
throw new InvalidOperationException("May only train/load a model once");
}
#if ML_LEGACY
var pipeline = new LearningPipeline();
// add data
pipeline.Add(CollectionDataSource.Create(data));
// choose what to predict
pipeline.Add(new ColumnCopier(("Score", "Label")));
// add columns as features
// do not include the features which should be predicted
pipeline.Add(new ColumnConcatenator("Features", DataSet.ColumnNames()));
// add a regression prediction
pipeline.Add(new FastTreeRegressor());
// train the model
TrainedModel = pipeline.Train <DataSet, DataSetPrediction>();
#else
// add data
var textLoader = GetTextLoader(Context);
// spill to disk !?!?! since there is no way to load from a collection
var pathToData = "";
try
{
// write data to disk
pathToData = WriteToDisk(data);
// read in data
IDataView dataView = textLoader.Load(pathToData);
InputSchema = dataView.Schema;
// configurations
var dataPipeline = Context.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: nameof(DataSet.Score))
.Append(Context.Transforms.Concatenate("Features", DataSet.ColumnNames()));
// set the training algorithm
var trainer = Context.Regression.Trainers.Sdca(labelColumnName: "Label", featureColumnName: "Features");
var trainingPipeline = dataPipeline.Append(trainer);
TrainedModel = trainingPipeline.Fit(dataView);
}
finally
{
// cleanup
if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
{
File.Delete(pathToData);
}
}
#endif
}
public static void Evaluate(PredictionModel <SentimentData, SentimentPrediction> model)
{
var testData = new List <SentimentData>()
{
new SentimentData {
Sentiment = 6f,
SentimentText = "such good thing"
},
new SentimentData {
Sentiment = -9.3f,
SentimentText = "f*****g article"
}
};
var collection = CollectionDataSource.Create(testData);
var evaluator = new BinaryClassificationEvaluator();
BinaryClassificationMetrics metrics = evaluator.Evaluate(model, collection);
Console.WriteLine();
Console.WriteLine("PredictionModel quality metrics evaluation");
Console.WriteLine("------------------------------------------");
Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"Auc: {metrics.Auc:P2}");
Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
}
internal static async Task <PredictionModel <IrisData, ClusterPrediction> > TrainAsync()
{
// LearningPipeline holds all steps of the learning process: data, transforms, learners.
var pipeline = new LearningPipeline
{
// The TextLoader loads a dataset. The schema of the dataset is specified by passing a class containing
// all the column names and their types.
CollectionDataSource.Create <IrisData>(GetIrisDataSet()),
//new TextLoader(DataPath).CreateFrom<IrisData>(useHeader: true),
// ColumnConcatenator concatenates all columns into Features column
new ColumnConcatenator("Features",
"SepalLength",
"SepalWidth",
"PetalLength",
"PetalWidth"),
// KMeansPlusPlusClusterer is an algorithm that will be used to build clusters. We set the number of clusters to 3.
new KMeansPlusPlusClusterer()
{
K = 3
}
};
Console.WriteLine("=============== Training model ===============");
var model = pipeline.Train <IrisData, ClusterPrediction>();
Console.WriteLine("=============== End training ===============");
// Saving the model as a .zip file.
await model.WriteAsync(ModelPath);
Console.WriteLine("The model is saved to {0}", ModelPath);
return(model);
}
public async Task <PredictionModel <UsageOfLightBulbModel, UsageOfLightBulbPredictionModel> > Train()
{
var dataFromDb = await _lightBulbRepository.GetAllLightBulbs();
var dataToTrain = new List <UsageOfLightBulbModel>(dataFromDb);
var collection = CollectionDataSource.Create(dataToTrain);
var pipeline = new LearningPipeline
{
collection,
new ColumnCopier(("IsOn", "Label")),
new ColumnConcatenator(
"Features",
"LightBulbID",
"Month",
"Day",
"Time"),
new FastTreeRegressor(),
};
model = pipeline.Train <UsageOfLightBulbModel, UsageOfLightBulbPredictionModel>();
// await model.WriteAsync(_modelpath);
return(model);
}
public static string CreateNNetworkAndLearn(List <NnRow> rows)
{
// Prepare data
double trainingSplitRatio = 0.7;
int trainCount = (int)(rows.Count * trainingSplitRatio);
var trainData = new MLNetData[trainCount];
var testData = new MLNetData[rows.Count - trainCount];
MLNetData[] allData = Convert(rows);
// Split into Training and Testing sets
Array.Copy(allData, 0, trainData, 0, trainCount);
Array.Copy(allData, trainCount, testData, 0, rows.Count - trainCount);
var allCollection = CollectionDataSource.Create(allData);
var trainCollection = CollectionDataSource.Create(trainData);
var testCollection = CollectionDataSource.Create(testData);
double acc, auc, f1;
PredictionModel <MLNetData, MLNetPredict> modelAll, modelTrain, modelBest;
//(acc, auc, f1, modelAll) = TrainAndGetMetrics(allCollection, allCollection, new AveragedPerceptronBinaryClassifier ()); // acc 0.83, auc 0.86, f1 0.45
//(acc, auc, f1, modelAll) = TrainAndGetMetrics(allCollection, allCollection, new FastForestBinaryClassifier ()); // acc 0.85, auc 0.89, f1 0.46
(acc, auc, f1, modelBest) = TrainAndGetMetrics(trainCollection, testCollection, new FastTreeBinaryClassifier()); // acc 0.95, auc 0.97, f1 0.85
//(acc, auc, f1, modelAll) = TrainAndGetMetrics(allCollection, allCollection, new FieldAwareFactorizationMachineBinaryClassifier()); // acc 0.85, auc 0.88, f1 0.56
//(acc, auc, f1, modelAll) = TrainAndGetMetrics(allCollection, allCollection, new GeneralizedAdditiveModelBinaryClassifier ()); // acc 0.81, auc 0.80, f1 NaN
//(acc, auc, f1, modelAll) = TrainAndGetMetrics(allCollection, allCollection, new LinearSvmBinaryClassifier ()); // acc 0.82, auc 0.86, f1 0.16
//(acc, auc, f1, modelAll) = TrainAndGetMetrics(allCollection, allCollection, new LogisticRegressionBinaryClassifier ()); // acc 0.84, auc 0.86, f1 0.40
//(acc, auc, f1, modelAll) = TrainAndGetMetrics(allCollection, allCollection, new StochasticDualCoordinateAscentBinaryClassifier()); // acc 0.84, auc 0.86, f1 0.40
//(acc, auc, f1, modelAll) = TrainAndGetMetrics(allCollection, allCollection, new StochasticGradientDescentBinaryClassifier ()); // acc 0.83, auc 0.86, f1 0.29
///(acc, auc, f1, modelAll) = TrainAndGetMetrics(allCollection, allCollection, new EnsembleBinaryClassifier ());
//(acc, auc, f1, modelTrain) = TrainAndGetMetrics(trainCollection, testCollection, new AveragedPerceptronBinaryClassifier ()); // acc 0.82, auc 0.84, f1 0.45
//(acc, auc, f1, modelTrain) = TrainAndGetMetrics(trainCollection, testCollection, new FastForestBinaryClassifier ()); // acc 0.82, auc 0.83, f1 0.23
//(acc, auc, f1, modelTrain) = TrainAndGetMetrics(trainCollection, testCollection, new FastTreeBinaryClassifier ()); // acc 0.82, auc 0.84, f1 0.46
//(acc, auc, f1, modelTrain) = TrainAndGetMetrics(trainCollection, testCollection, new FieldAwareFactorizationMachineBinaryClassifier()); // acc 0.83, auc 0.85, f1 0.37
//(acc, auc, f1, modelTrain) = TrainAndGetMetrics(trainCollection, testCollection, new GeneralizedAdditiveModelBinaryClassifier ()); // acc 0.81, auc 0.75, f1 NaN
//(acc, auc, f1, modelTrain) = TrainAndGetMetrics(trainCollection, testCollection, new LinearSvmBinaryClassifier ()); // acc 0.81, auc 0.83, f1 0.14
//(acc, auc, f1, modelTrain) = TrainAndGetMetrics(trainCollection, testCollection, new LogisticRegressionBinaryClassifier ()); // acc 0.83, auc 0.84, f1 0.39
//(acc, auc, f1, modelTrain) = TrainAndGetMetrics(trainCollection, testCollection, new StochasticDualCoordinateAscentBinaryClassifier()); // acc 0.82, auc 0.84, f1 0.43
//(acc, auc, f1, modelTrain) = TrainAndGetMetrics(trainCollection, testCollection, new StochasticGradientDescentBinaryClassifier ()); // acc 0.83, auc 0.83, f1 0.34
// Evaluate a training model
//Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
//Console.WriteLine($"Auc: {metrics.Auc:P2}");
//Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
//var cv = new CrossValidator();
//CrossValidationOutput<MLNetData, MLNetPredict> cvRes = cv.CrossValidate<MLNetData, MLNetPredict>(pipelineAll);
//Console.WriteLine($"Rms = {metrics.Rms}");
//Console.WriteLine($"RSquared = {metrics.RSquared}");
// Train the overall model
string NnModelPath = @"NnInputs\mlDotNet_Datacup.model";
modelBest.WriteAsync(NnModelPath);
return(NnModelPath);
}
public async Task TrainModelAsync(string csvPath, string modelPath)
{
var pipeline = new LearningPipeline();
pipeline.Add(CollectionDataSource.Create(new CsvReader().GetData(csvPath)));
pipeline.Add(new Dictionarizer(("Categories", "Label")));
pipeline.Add(new TextFeaturizer("Name", "Name"));
pipeline.Add(new TextFeaturizer("GenericName", "GenericName"));
pipeline.Add(new ColumnConcatenator("Features", "Name", "GenericName"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier()
{
NumThreads = Math.Max(2, Environment.ProcessorCount - 1)
});
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
Console.WriteLine("=============== Training model ===============");
var model = pipeline.Train <Product, ProductCategoryPrediction>();
await model.WriteAsync(modelPath).ConfigureAwait(false);
Console.WriteLine("=============== End training ===============");
Console.WriteLine("The model is saved to {0}", modelPath);
}
static async Task <PredictionModel <Open311Data, Open311DataPrediction> > TrainOpen311(string dataPath)
{
var pipeline = new LearningPipeline();
var dataSource = CollectionDataSource.Create(OpenFile(dataPath, 3, 0, 1, 2));
pipeline.Add(dataSource);
pipeline.Add(new Dictionarizer(@"Label"));
pipeline.Add(new TextFeaturizer(@"Features", @"Request")
{
KeepDiacritics = false,
KeepPunctuations = false,
TextCase = TextNormalizerTransformCaseNormalizationMode.Lower,
OutputTokens = true,
Language = TextTransformLanguage.German,
StopWordsRemover = new PredefinedStopWordsRemover(),
VectorNormalizer = TextTransformTextNormKind.L2,
CharFeatureExtractor = new NGramNgramExtractor()
{
NgramLength = 3, AllLengths = false
},
WordFeatureExtractor = new NGramNgramExtractor()
{
NgramLength = 3, AllLengths = true
}
});
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
pipeline.Add(new PredictedLabelColumnOriginalValueConverter {
PredictedLabelColumn = @"PredictedLabel"
});
var model = pipeline.Train <Open311Data, Open311DataPrediction>();
await model.WriteAsync(_modelPath);
return(model);
}
public void PredictClusters()
{
int n = 1000;
int k = 4;
var rand = new Random(1);
var clusters = new ClusteringData[k];
var data = new ClusteringData[n];
for (int i = 0; i < k; i++)
{
//pick clusters as points on circle with angle to axis X equal to 360*i/k
clusters[i] = new ClusteringData {
Points = new float[2] {
(float)Math.Cos(Math.PI * i * 2 / k), (float)Math.Sin(Math.PI * i * 2 / k)
}
};
}
// create data points by randomly picking cluster and shifting point slightly away from it.
for (int i = 0; i < n; i++)
{
var index = rand.Next(0, k);
var shift = (rand.NextDouble() - 0.5) / 10;
data[i] = new ClusteringData
{
Points = new float[2]
{
(float)(clusters[index].Points[0] + shift),
(float)(clusters[index].Points[1] + shift)
}
};
}
var pipeline = new LearningPipeline(seed: 1, conc: 1);
pipeline.Add(CollectionDataSource.Create(data));
pipeline.Add(new KMeansPlusPlusClusterer()
{
K = k
});
var model = pipeline.Train <ClusteringData, ClusteringPrediction>();
//validate that initial points we pick up as centers of cluster during data generation belong to different clusters.
var labels = new HashSet <uint>();
for (int i = 0; i < k; i++)
{
var scores = model.Predict(clusters[i]);
Assert.True(!labels.Contains(scores.SelectedClusterId));
labels.Add(scores.SelectedClusterId);
}
var evaluator = new ClusterEvaluator();
var testData = CollectionDataSource.Create(clusters);
ClusterMetrics metrics = evaluator.Evaluate(model, testData);
//Label is not specified, so NMI would be equal to NaN
Assert.Equal(metrics.Nmi, double.NaN);
//Calculate dbi is false by default so Dbi would be 0
Assert.Equal(metrics.Dbi, (double)0.0);
Assert.Equal(metrics.AvgMinScore, (double)0.0, 5);
}
/// <summary>
/// 训练并生成模型
/// </summary>
/// <returns></returns>
public static async Task <PredictionModel <JiaMiTu, JiaMiTuPrediction> > Train(IEnumerable <JiaMiTu> trainData, string modelFileName, string labelColumn, string[] oneHotColumns, string[] features, string[] drops)
{
//创建学习管道
var pipeline = new LearningPipeline();
//加载和转换您的数据
//var textLoader = new TextLoader<JiaMiTu>(DataPath, useHeader: true, separator: ",");
//pipeline.Add(textLoader);
pipeline.Add(CollectionDataSource.Create(trainData));
//使用该ColumnCopier()功能将“票价_帐户”列复制到名为“标签”的新列中。此列是标签。
pipeline.Add(new ColumnCopier((labelColumn, "Label")));
//一个对象叫ColumnDropper，可以用来在训练开始前舍弃掉不需要的字段，比如id，对结果没有任何影响，因此可以去掉
if (drops.Count() > 0)
{
pipeline.Add(new ColumnDropper()
{
Column = drops
});
}
//进行一些特征工程来转换数据，以便它可以有效地用于机器学习。该训练模型需要算法的数字功能，
//您变换中的分类数据（vendor_id，rate_code，和payment_type）为数字。
//该CategoricalOneHotVectorizer()
//函数为每个列中的值分配一个数字键。通过添加以下代码来转换您的数据：
if (oneHotColumns.Count() > 0)
{
pipeline.Add(new CategoricalOneHotVectorizer(oneHotColumns));
}
//数据准备的最后一步是使用该功能将所有功能组合到一个向量中ColumnConcatenator()。这一必要步骤
//有助于算法轻松处理您的功能。按照您在最后一步中编写的内容添加以下代码：
//请注意，“trip_time_in_secs”列不包括在内。你已经确定它不是一个有用的预测功能。
pipeline.Add(new ColumnConcatenator("Features",
features
));
//在将数据添加到流水线并将其转换为正确的输入格式之后，您可以选择一种学习算法（学习者）。学习算
//法训练模型。你为这个问题选择了一个回归任务，所以你增加了一个学习者调用FastTreeRegressor()到
//使用梯度提升的管道。
//渐变增强是回归问题的机器学习技术。它以逐步的方式构建每个回归树。它使用预定义的损失函数来测
//量每个步骤中的错误，并在下一步中对其进行修正。结果是预测模型实际上是较弱预测模型的集合。
pipeline.Add(new FastTreeRegressor());
//泊松回归
//pipeline.Add(new PoissonRegressor());
//训练模型
//最后一步是训练模型。在此之前，管道中没有任何东西被执行。该pipeline.Train<T_Input, T_Output>()
//函数接受预定义的JiaMiTu类类型并输出一个JiaMiTuPrediction类型。将这最后一段代码添加到Train()
//函数中：
PredictionModel <JiaMiTu, JiaMiTuPrediction> model = pipeline.Train <JiaMiTu, JiaMiTuPrediction>();
//改性Train（）方法为异步方法public static async Task<PredictionModel<JiaMiTu, JiaMiTuPrediction>> Train()
///通过生么预测什么
if (!string.IsNullOrEmpty(modelFileName))
{
await model.WriteAsync(modelFileName);
}
return(model);
}
public static async Task <PredictionModel <SentimentData, SentimentPrediction> > Train(IMongoDatabase db)
{
// LearningPipeline allows you to add steps in order to keep everything together
// during the learning process.
// <Snippet5>
var pipeline = new LearningPipeline();
// </Snippet5>
// <Snippet6>
var collection = db.GetCollection <SentimentData>("review_train");
var documents = collection.Find <SentimentData>(new BsonDocument()).ToEnumerable();
pipeline.Add(CollectionDataSource.Create(documents));
// </Snippet6>
// TextFeaturizer is a transform that is used to featurize an input column.
// This is used to format and clean the data.
// <Snippet7>
pipeline.Add(new TextFeaturizer("Features", "text")
{
KeepDiacritics = false,
KeepPunctuations = false,
TextCase = TextNormalizerTransformCaseNormalizationMode.Lower,
});
//</Snippet7>
// Adds a FastTreeBinaryClassifier, the decision tree learner for this project, and
// three hyperparameters to be used for tuning decision tree performance.
// <Snippet8>
pipeline.Add(new FastTreeBinaryClassifier()
{
NumLeaves = 100,
NumTrees = 50,
MinDocumentsInLeafs = 2,
LearningRates = 0.4f,
});
// </Snippet8>
// Train the pipeline based on the dataset that has been loaded, transformed.
// <Snippet9>
PredictionModel <SentimentData, SentimentPrediction> model =
pipeline.Train <SentimentData, SentimentPrediction>();
// </Snippet9>
// Saves the model we trained to a zip file.
// <Snippet10>
await model.WriteAsync(_modelpath);
// </Snippet10>
// Returns the model we trained to use for evaluation.
// <Snippet11>
return(model);
// </Snippet11>
}
static void Main(string[] args)
{
var trainData = GeneratePData(2000);
Print(trainData.Take(20));
var testData = GeneratePData(50, test: true);
// ML görevi için obje oluşturur
var learningPipe = new LearningPipeline();
var trainCollection = CollectionDataSource.Create(trainData);
learningPipe.Add(trainCollection);
// Verilerin kolon isimleri olan labelları numeric indexe çevirir.
learningPipe.Add(new Dictionarizer("Label"));
learningPipe.Add(
new ColumnConcatenator("Features", "UnitA", "UnitS", "Volume"));
// Algoritma sınıflandırması
learningPipe.Add(new StochasticDualCoordinateAscentClassifier());
// Tahmin edilen kolon değerini çevir
learningPipe.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
// Modeli eğitme
var model = learningPipe.Train <ProcessData, ProcessPrediction>();
// Model değerlendirilmesi ve kesin kontrol
var evaluator = new ClassificationEvaluator();
var metrics = evaluator.Evaluate(model, trainCollection);
Console.WriteLine("AccuracyMicro: " + metrics.AccuracyMicro);
Console.WriteLine("LogLoss: " + metrics.LogLoss);
// Test datayı tahmin et
var predicted = model.Predict(testData);
// Testdata ve tahmin edilen labelı string içinde topla
var results = testData.Zip(predicted, (t, p) => new ProcessData
{
UnitA = t.UnitA,
UnitS = t.UnitS,
Volume = t.Volume,
Label = p.PredictedLabels
}).ToList();
// Sonucu yazdır
Print(results);
Console.ReadLine();
}
public void EvaluateModel(IEnumerable <PivotData> testData, PredictionModel <PivotData, ClusteringPrediction> model)
{
ConsoleWriteHeader("Metrics for Customer Segmentation");
var testDataSource = CollectionDataSource.Create(testData);
var evaluator = new ClusterEvaluator();
ClusterMetrics metrics = evaluator.Evaluate(model, testDataSource);
Console.WriteLine($"Average mean score: {metrics.AvgMinScore:0.##}");
//Console.WriteLine($"* Davies-Bouldin Index: {metrics.Dbi:#.##}");
//Console.WriteLine($"* Normalized mutual information: {metrics.Nmi:#.##}");
}
public static void Evaluate(PredictionModel <PokerHandData, PokerHandPrediction> model, IEnumerable <PokerHandData> data)
{
var evaluator = new ClassificationEvaluator();
var collection = CollectionDataSource.Create(data);
var metrics = evaluator.Evaluate(model, collection);
Console.WriteLine();
Console.WriteLine("PredictionModel quality metrics evaluation");
Console.WriteLine("------------------------------------------");
Console.WriteLine($"LogLossReduction: {metrics.LogLossReduction }");
Console.WriteLine($"LogLoss: {metrics.LogLoss }");
}
public static PredictionModel <PokerHandData, PokerHandPrediction> Train(IEnumerable <PokerHandData> data)
{
var pipeline = new LearningPipeline();
var collection = CollectionDataSource.Create(data);
pipeline.Add(collection);
pipeline.Add(new ColumnConcatenator("Features", "IsSameSuit", "IsStraight", "FourOfKind", "ThreeOfKind", "PairsCount"));
pipeline.Add(new LogisticRegressionClassifier());
var model = pipeline.Train <PokerHandData, PokerHandPrediction>();
return(model);
}
public void BooleanLabelPipeline()
{
var data = new BooleanLabelData[1];
data[0] = new BooleanLabelData();
data[0].Features = new float[] { 0.0f, 1.0f };
data[0].Label = false;
var pipeline = new LearningPipeline();
pipeline.Add(CollectionDataSource.Create(data));
pipeline.Add(new FastForestBinaryClassifier());
var model = pipeline.Train <Data, Prediction>();
}
protected LearningPipeline BuildModel(IEnumerable <SalesRecommendationData> salesData)
{
ConsoleWriteHeader("Build model pipeline");
var pipeline = new LearningPipeline();
pipeline.Add(CollectionDataSource.Create(salesData));
// One Hot Encoding using Hash Vector. The new columns are named as the original ones, but adding the suffix "_OH"
pipeline.Add(new CategoricalHashOneHotVectorizer((nameof(SalesRecommendationData.ProductId), nameof(SalesRecommendationData.ProductId) + "_OH"))
{
HashBits = 18
});
public PowerballPrediction PredictPowerball()
{
var pipeline = new LearningPipeline();
var allPicks = _ctx.Powerballs;
var data = new List <PowerballData>();
foreach (var powerball in allPicks)
{
var ts = powerball.draw_date - DateTime.Now;
var newPick = new PowerballData()
{
Ball1 = Convert.ToInt32(powerball.ball1),
Ball2 = Convert.ToInt32(powerball.ball2),
Ball3 = Convert.ToInt32(powerball.ball3),
Ball4 = Convert.ToInt32(powerball.ball4),
Ball5 = Convert.ToInt32(powerball.ball5),
PowerBall = Convert.ToInt32(powerball.powerball),
daysAgo = (float)ts.TotalDays
};
data.Add(newPick);
}
var collection = CollectionDataSource.Create(data);
pipeline.Add(collection);
pipeline.Add(new ColumnCopier(("daysAgo", "Label")));
pipeline.Add(new CategoricalOneHotVectorizer("id"));
pipeline.Add(new ColumnConcatenator("Features", "id", "daysAgo"));
pipeline.Add(new FastTreeRegressor());
var model = pipeline.Train <PowerballData, PowerballPrediction>();
var eval = new RegressionEvaluator();
RegressionMetrics metrics = eval.Evaluate(model, collection);
Console.WriteLine($"Rms = {metrics.Rms}");
Console.WriteLine($"RSquared = {metrics.RSquared}");
var nextPowerball = _ctx.NextPowerball.FirstOrDefault();
var predictedDays = (nextPowerball.next_jackpot_date.AddDays(1)) - DateTime.Now;
var prediction = model.Predict(new PowerballData()
{
Ball1 = 0, Ball2 = 0, Ball3 = 0, Ball4 = 0, Ball5 = 0, PowerBall = 0, daysAgo = predictedDays.Days
});
return(prediction);
}
/// <summary>
/// 评估模型
/// </summary>
/// <param name="model"></param>
public static void Evaluate(IEnumerable <JiaMiTu> testData, PredictionModel <JiaMiTu, JiaMiTuPrediction> model)
{
//var testData = new TextLoader<JiaMiTu>(TestDataPath, useHeader: true, separator: ",");
var test = CollectionDataSource.Create(testData);
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, test);
// Rms should be around 2.795276
//RMS是评估回归问题的一个指标。它越低，你的模型就越好。将以下代码添加到该Evaluate()函数中以打印模型的RMS。
Console.WriteLine("Rms=" + metrics.Rms);
Console.WriteLine("LossFn=" + metrics.LossFn);
//Squared是评估回归问题的另一个指标。RSquared将是介于0和1之间的值。越接近1，模型越好。将下面的代码添加到该Evaluate()函数中以打印模型的RSquared值。
Console.WriteLine("RSquared = " + metrics.RSquared);
}
public override ModelFitness Evaluate(List <ModelDataSet> data, ModelValue prediction)
{
if (TrainedModel == null)
{
throw new Exception("Must initialize the model before calling");
}
lock (TrainedModel)
{
#if ML_LEGACY
var testData = CollectionDataSource.Create(data);
var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(TrainedModel, testData);
return(new ModelFitness()
{
RMS = metrics.Rms,
RSquared = metrics.RSquared
});
#else
var textLoader = GetTextLoader(Context, prediction);
var pathToData = "";
try
{
// ugh have to spill data to disk for it to work!
pathToData = WriteToDisk(data, prediction);
IDataView dataView = textLoader.Read(pathToData);
var predictions = TrainedModel.Transform(dataView);
var metrics = Context.Regression.Evaluate(predictions, label: "Label", score: "Score");
return(new ModelFitness()
{
RMS = metrics.Rms,
RSquared = metrics.RSquared
});
}
finally
{
// cleanup
if (!string.IsNullOrWhiteSpace(pathToData) && File.Exists(pathToData))
{
File.Delete(pathToData);
}
}
#endif
}
}
public void PredictClusters()
{
int n = 1000;
int k = 5;
var rand = new Random();
var clusters = new ClusteringData[k];
var data = new ClusteringData[n];
for (int i = 0; i < k; i++)
{
//pick clusters as points on circle with angle to axis X equal to 360*i/k
clusters[i] = new ClusteringData {
Points = new float[2] {
(float)Math.Cos(Math.PI * i * 2 / k), (float)Math.Sin(Math.PI * i * 2 / k)
}
};
}
// create data points by randomly picking cluster and shifting point slightly away from it.
for (int i = 0; i < n; i++)
{
var index = rand.Next(0, k);
var shift = (rand.NextDouble() - 0.5) / k;
data[i] = new ClusteringData
{
Points = new float[2]
{
(float)(clusters[index].Points[0] + shift),
(float)(clusters[index].Points[1] + shift)
}
};
}
var pipeline = new LearningPipeline();
pipeline.Add(CollectionDataSource.Create(data));
pipeline.Add(new KMeansPlusPlusClusterer()
{
K = k
});
var model = pipeline.Train <ClusteringData, ClusteringPrediction>();
//validate that initial points we pick up as centers of cluster during data generation belong to different clusters.
var labels = new HashSet <uint>();
for (int i = 0; i < k; i++)
{
var scores = model.Predict(clusters[i]);
Assert.True(!labels.Contains(scores.SelectedClusterId));
labels.Add(scores.SelectedClusterId);
}
}
public void NoTransformPipeline()
{
var data = new Data[1];
data[0] = new Data
{
Features = new float[] { 0.0f, 1.0f },
Label = 0f
};
var pipeline = new Legacy.LearningPipeline();
pipeline.Add(CollectionDataSource.Create(data));
pipeline.Add(new FastForestBinaryClassifier());
var model = pipeline.Train <Data, Prediction>();
}
private static PredictionModel <WorkshopData, ClusterPrediction> Train()
{
var pipeline = new LearningPipeline();
//building dataset of WorkshopData
List <WorkshopData> data = new List <WorkshopData>();
string line;
using (var reader = File.OpenText(_dataPath))
{
while ((line = reader.ReadLine()) != null)
{
string convertedData = line;
List <string> WorkshopFeaturesSet = convertedData.Split(',').ToList();
WorkshopData wd = new WorkshopData
{
price = float.Parse(WorkshopFeaturesSet[0]),
duration = float.Parse(WorkshopFeaturesSet[1]),
day = float.Parse(WorkshopFeaturesSet[2]),
time = float.Parse(WorkshopFeaturesSet[3]),
teacher = float.Parse(WorkshopFeaturesSet[4])
};
data.Add(wd);
}
}
var collection = CollectionDataSource.Create(data);
pipeline.Add(collection);
pipeline.Add(new ColumnConcatenator(
"Features",
"price",
"duration",
"day",
"time",
"teacher")
);
pipeline.Add(new KMeansPlusPlusClusterer()
{
K = 3
});
var model = pipeline.Train <WorkshopData, ClusterPrediction>();
return(model);
}
static async Task <PredictionModel <IrisData, IrisPrediction> > TrainAsync()
{
LearningPipeline pipeline = new LearningPipeline();
var collection = CollectionDataSource.Create(await getData(@"./myconsoleapp/traindata.json"));
pipeline.Add(collection);
pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
var model = pipeline.Train <IrisData, IrisPrediction>();
return(model);
}
public void CanTrainProperties()
{
var pipeline = new Legacy.LearningPipeline();
var data = new List <IrisData>()
{
new IrisData {
SepalLength = 1f, SepalWidth = 1f, PetalLength = 0.3f, PetalWidth = 5.1f, Label = 1
},
new IrisData {
SepalLength = 1f, SepalWidth = 1f, PetalLength = 0.3f, PetalWidth = 5.1f, Label = 1
},
new IrisData {
SepalLength = 1.2f, SepalWidth = 0.5f, PetalLength = 0.3f, PetalWidth = 5.1f, Label = 0
}
};
var collection = CollectionDataSource.Create(data);
pipeline.Add(collection);
pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
"SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
var model = pipeline.Train <IrisData, IrisPredictionProperties>();
IrisPredictionProperties prediction = model.Predict(new IrisData
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
});
pipeline = new Legacy.LearningPipeline();
collection = CollectionDataSource.Create(data.AsEnumerable());
pipeline.Add(collection);
pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
"SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
model = pipeline.Train <IrisData, IrisPredictionProperties>();
prediction = model.Predict(new IrisData
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
});
}
public static Task TrainModel()
{
var pipeline = new LearningPipeline();
var data = new List <Artist> {
new Artist {
name = "radiohead"
},
new Artist {
name = "radiohead"
}
};
var collection = CollectionDataSource.Create(data);
pipeline.Add(collection);
return(null);
}
public void Evaluate(PredictionModel <NrlResult, ClusterPrediction> model,
IEnumerable <NrlResult> nrlResults)
{
var testData = CollectionDataSource.Create(nrlResults);
var evaluator = new BinaryClassificationEvaluator();
Console.WriteLine("=============== Evaluating model ===============");
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"Auc: {metrics.Auc:P2}");
Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
Console.WriteLine("=============== End evaluating ===============");
Console.WriteLine();
}
public void CanSuccessfullyApplyATransform()
{
var collection = CollectionDataSource.Create(new List <Input>()
{
new Input {
Number1 = 1, String1 = "1"
}
});
var environment = new MLContext();
Experiment experiment = environment.CreateExperiment();
Legacy.ILearningPipelineDataStep output = (Legacy.ILearningPipelineDataStep)collection.ApplyStep(null, experiment);
Assert.NotNull(output.Data);
Assert.NotNull(output.Data.VarName);
Assert.Null(output.Model);
}
static void EvaluateOpen311(PredictionModel <Open311Data, Open311DataPrediction> model, string testDataPath)
{
var testData = CollectionDataSource.Create(OpenFile(testDataPath, 3, 0, 1, 2));
var evaluator = new ClassificationEvaluator();
var metrics = evaluator.Evaluate(model, testData);
Console.WriteLine();
Console.WriteLine("PredictionModel quality metrics evaluation");
Console.WriteLine("------------------------------------------");
Console.WriteLine($"Accuracy Macro: {metrics.AccuracyMacro:P2}");
Console.WriteLine($"Accuracy Micro: {metrics.AccuracyMicro:P2}");
Console.WriteLine($"Top KAccuracy: {metrics.TopKAccuracy:P2}");
Console.WriteLine($"LogLoss: {metrics.LogLoss:P2}");
for (var classIndex = 0; classIndex < metrics.PerClassLogLoss.Length; classIndex++)
{
Console.WriteLine($"Class: {classIndex} - {metrics.PerClassLogLoss[classIndex]:P2}");
}
}
private static PredictionModel <BookData, ClusterPrediction> Train()
{
var pipeline = new LearningPipeline();
// pipeline.Add(new TextLoader(_dataPath).CreateFrom<BookData>(separator: ','));
//building dataset of BookData
List <BookData> data = new List <BookData>();
string line;
using (var reader = File.OpenText(_dataPath))
{
while ((line = reader.ReadLine()) != null)
{
string convertedData = line;
List <string> BookFeaturesSet = convertedData.Split(',').ToList();
BookData bd = new BookData
{
genre = float.Parse(BookFeaturesSet[0]), //book.Genre,
releaseTime = float.Parse(BookFeaturesSet[1]),
price = float.Parse(BookFeaturesSet[2]) //book.Price
};
data.Add(bd);
}
}
var collection = CollectionDataSource.Create(data);
pipeline.Add(collection);
pipeline.Add(new ColumnConcatenator(
"Features",
"price",
"genre",
"releaseTime")
);
pipeline.Add(new KMeansPlusPlusClusterer()
{
K = 5
});
var model = pipeline.Train <BookData, ClusterPrediction>();
return(model);
}
public PredictionModel <NrlResult, ClusterPrediction> TrainData(IEnumerable <NrlResult> nrlResults)
{
var pipeline = new LearningPipeline
{
CollectionDataSource.Create(nrlResults),
new Dictionarizer("Label"),
new ColumnConcatenator("Features", "PreviousWeeksHomeFor", "PreviousWeeksHomeAgainst",
"PreviousWeeksAwayFor", "PreviousWeeksAwayAgainst", "AwayTeamAwayForm", "AwayTeamHomeForm",
"HomeTeamAwayForm", "HomeTeamHomeForm", "HomeTeamLastWeekScore", "AwayTeamLastWeekScore"),
new StochasticDualCoordinateAscentClassifier(),
//new KMeansPlusPlusClusterer { K = 3 },
//new FastTreeBinaryClassifier {NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2},
//new PredictedLabelColumnOriginalValueConverter {PredictedLabelColumn = "PredictedLabel"}
};
return(pipeline.Train <NrlResult, ClusterPrediction>());
}
