added internal clustering evaluation

internal_clustering_evaluation.go

@@ -0,0 +1,158 @@
+package main
+
+import (
+	"fmt"
+	"github.com/kniren/gota/dataframe"
+	"gonum.org/v1/gonum/floats"
+	"log"
+	"os"
+)
+
+type centroid []float64
+
+func main() {
+
+	// Pull in the CSV file.
+	irisFile, err := os.Open("iris.csv")
+	if err != nil {
+		log.Fatal(err)
+	}
+	defer irisFile.Close()
+
+	// Create a dataframe from the CSV file.
+	irisDF := dataframe.ReadCSV(irisFile)
+
+	// Define the names of the three separate species contained in the CSV file.
+	speciesNames := []string{
+		"Iris-setosa",
+		"Iris-versicolor",
+		"Iris-virginica",
+	}
+
+	// Create a map to hold our centroid information.
+	centroids := make(map[string]centroid)
+
+	// Create a map to hold the filtered dataframe for each cluster.
+	clusters := make(map[string]dataframe.DataFrame)
+
+	// Filter the dataset into three separate dataframes,
+	// each corresponding to one of the Iris species.
+	for _, species := range speciesNames {
+
+		// Filter the original dataset.
+		filter := dataframe.F{
+			Colname:    "species",
+			Comparator: "==",
+			Comparando: species,
+		}
+		filtered := irisDF.Filter(filter)
+
+		// Calculate the mean of features.
+		summaryDF := filtered.Describe()
+
+		// Put each dimension's mean into the corresponding centroid.
+		var c centroid
+		for _, feature := range summaryDF.Names() {
+
+			// Skip the irrelevant columns.
+			if feature == "column" || feature == "species" {
+				continue
+			}
+			c = append(c, summaryDF.Col(feature).Float()[0])
+		}
+
+		// Add this centroid to our map.
+		centroids[species] = c
+
+		// Add the filtered dataframe to the map of clusters.
+		clusters[species] = filtered
+	}
+
+	// As a sanity check, output our centroids.
+	for _, species := range speciesNames {
+		fmt.Printf("%s centroid: %v\n", species, centroids[species])
+	}
+
+	// Convert our labels into a slice of strings and create a slice
+	// of float column names for convenience.
+	labels := irisDF.Col("species").Records()
+	floatColumns := []string{
+		"sepal_length",
+		"sepal_width",
+		"petal_length",
+		"petal_width",
+	}
+
+	// Loop over the records accumulating the average silhouette coefficient.
+	var silhouette float64
+
+	for idx, label := range labels {
+
+		// a will store our accumulated value for a.
+		var a float64
+
+		// Loop over the data points in the same cluster.
+		for i := 0; i < clusters[label].Nrow(); i++ {
+
+			// Get the data point for comparison.
+			current := dfFloatRow(irisDF, floatColumns, idx)
+			other := dfFloatRow(clusters[label], floatColumns, i)
+
+			// Add to a.
+			a += floats.Distance(current, other, 2) / float64(clusters[label].Nrow())
+		}
+
+		// Determine the nearest other cluster.
+		var otherCluster string
+		var distanceToCluster float64
+		for _, species := range speciesNames {
+
+			// Skip the cluster containing the data point.
+			if species == label {
+				continue
+			}
+
+			// Calculate the distance to the cluster from the current cluster.
+			distanceForThisCluster := floats.Distance(centroids[label], centroids[species], 2)
+
+			// Replace the current cluster if relevant.
+			if distanceToCluster == 0.0 || distanceForThisCluster < distanceToCluster {
+				otherCluster = species
+				distanceToCluster = distanceForThisCluster
+			}
+		}
+
+		// b will store our accumulated value for b.
+		var b float64
+
+		// Loop over the data points in the nearest other cluster.
+		for i := 0; i < clusters[otherCluster].Nrow(); i++ {
+
+			// Get the data point for comparison.
+			current := dfFloatRow(irisDF, floatColumns, idx)
+			other := dfFloatRow(clusters[otherCluster], floatColumns, i)
+
+			// Add to b.
+			b += floats.Distance(current, other, 2) / float64(clusters[otherCluster].Nrow())
+		}
+
+		// Add to the average silhouette coefficient.
+		if a > b {
+			silhouette += ((b - a) / a) / float64(len(labels))
+		}
+		silhouette += ((b - a) / b) / float64(len(labels))
+	}
+
+	// Output the final average silhouette coeffcient to stdout.
+	fmt.Printf("\nAverage Silhouette Coefficient: %0.2f\n\n", silhouette)
+}
+
+// dfFloatRow retrieves a slice of float values from a DataFrame
+// at the given index and for the given column names.
+func dfFloatRow(df dataframe.DataFrame, names []string, idx int) []float64 {
+	var row []float64
+	for _, name := range names {
+		row = append(row, df.Col(name).Float()[idx])
+	}
+	return row
+}