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);
}
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:#.##}");
}
