Simple Python script to plot datapoints based on clusters.

[pingpong.git] / python_ml / plotting.py

diff --git a/python_ml/plotting.py b/python_ml/plotting.py
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+from sklearn.cluster import KMeans
+import matplotlib.cm as cm
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Create a subplot with 1 row and 2 columns
+fig, (ax2) = plt.subplots(1, 1)
+fig.set_size_inches(7, 7)
+
+
+# Read from file
+# TODO: Just change the following path and filename 
+#      when needed to read from a different file
+path = "/scratch/July-2018/Pairs/"
+filename = "dlink-off.txt"
+
+# Read and create an array of pairs
+with open(path + filename, "r") as pairs:
+       pairsArr = []
+       for line in pairs:
+               # We will see a pair and we need to split it into xpoint and ypoint
+               xpoint, ypoint = line.split(", ")
+               pair = [int(xpoint), int(ypoint)]
+               pairsArr.append(pair)
+
+# Formed array of pairs                
+#print(pairsArr)
+X = np.array(pairsArr);
+
+clusters = 9
+
+# Plot the data points based on the clusters
+clusterer = KMeans(n_clusters=clusters, random_state=10)
+cluster_labels = clusterer.fit_predict(X)
+# 2nd Plot showing the actual clusters formed
+colors = cm.nipy_spectral(cluster_labels.astype(float) / clusters)
+ax2.scatter(X[:, 0], X[:, 1], marker='o', s=100, lw=0, alpha=0.3,
+            c=colors, edgecolor='k')
+
+# Labeling the clusters
+centers = clusterer.cluster_centers_
+# Label with cluster centers and frequencies
+for i, c in enumerate(centers):
+       mark = '[' + str(int(c[0])) + ', ' + str(int(c[1])) + ']' + ', ' + str(clusterer.labels_.tolist().count(i))
+       ax2.scatter(c[0], c[1], marker='$%s$' % mark, alpha=1, s=3000, edgecolor='k')
+
+ax2.set_title("The visualization of the clustered data.")
+ax2.set_xlabel("Feature space for the 1st feature")
+ax2.set_ylabel("Feature space for the 2nd feature")
+plt.show()