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 Legacy.LearningPipeline(seed: 1, conc: 1);
pipeline.Add(Legacy.Data.CollectionDataSource.Create(data));
pipeline.Add(new Legacy.Trainers.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 Legacy.Models.ClusterEvaluator();
var testData = Legacy.Data.CollectionDataSource.Create(clusters);
var 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);
}
public Task <ClusteringPrediction> Predict(ClusteringData clusteringData)
{
return(Task.Run(() =>
{
var predictionFunc = Model.MakePredictionFunction <ClusteringData, ClusteringPrediction>(_mlContext);
return predictionFunc.Predict(clusteringData);
}));
}
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 ClusteringMetrics Evaluate(ITransformer model)
{
var predictions = model.Transform(_testingDataView);
var metrics = _mlContext.Clustering.Evaluate(predictions, score: "Score", features: "Features");
TrainModel = model;
// Run test cases to identify clusters
var predictionFunction = TrainModel.CreatePredictionEngine <ClusteringData, ClusteringPrediction>(_mlContext);
var tests = new List <TestCase>();
for (var r = 1; r <= RfmMaxForTests; r++)
{
for (var f = 1; f <= RfmMaxForTests; f++)
{
for (var m = 1; m <= RfmMaxForTests; m++)
{
var data = new ClusteringData
{
R = r,
M = f,
F = m
};
var prediction = predictionFunction.Predict(data);
tests.Add(new TestCase
{
Data = data,
Cluster = prediction.SelectedClusterId
});
}
}
}
// save RFM cluster matching in csv
var fileService = new FileService();
fileService.ExportToCsv(tests);
return(metrics);
}
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 mlContext = new MLContext(seed: 1, conc: 1);
// Turn the data into the ML.NET data view.
// We can use CreateDataView or CreateStreamingDataView, depending on whether 'churnData' is an IList,
// or merely an IEnumerable.
var trainData = mlContext.CreateStreamingDataView(data);
var testData = mlContext.CreateStreamingDataView(clusters);
// Create Estimator
var pipe = mlContext.Clustering.Trainers.KMeans("Features", clustersCount: k);
// Train the pipeline
var trainedModel = pipe.Fit(trainData);
// Validate that initial points we pick up as centers of cluster during data generation belong to different clusters.
var labels = new HashSet <uint>();
var predictFunction = trainedModel.CreatePredictionEngine <ClusteringData, ClusteringPrediction>(mlContext);
for (int i = 0; i < k; i++)
{
var scores = predictFunction.Predict(clusters[i]);
Assert.True(!labels.Contains(scores.SelectedClusterId));
labels.Add(scores.SelectedClusterId);
}
// Evaluate the trained pipeline
var predicted = trainedModel.Transform(testData);
var metrics = mlContext.Clustering.Evaluate(predicted);
//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);
}
