.

animals.py

@@ -17,7 +17,6 @@ def animals(k, n_iter, alpha, beta):
     #from sgll.sgl import LearnGraphTopolgy
     X = np.array(X_animals)
 
-    #SCM = np.dot((X - np.mean(X, axis = 0).reshape(1, -1)), (X - np.mean(X, axis = 0).reshape(1, -1)).T)
     SCM = np.dot(X, X.T) / X.shape[0]
     SCM = 1/3 * np.eye(SCM.shape[0]) + SCM
 

cancer.py

@@ -0,0 +1,39 @@
+import os
+import numpy as np
+import networkx as nx
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_moons, make_blobs
+import pandas as pd
+from SGL import LearnGraphTopology
+plots_dir = './plots'
+if not os.path.exists(plots_dir):
+    os.makedirs(plots_dir)
+
+def load_data_cancer(df_cancer):
+    X = np.array(df_cancer)
+    SCM = np.dot((X - np.mean(X, axis = 0).reshape(1, -1)), (X - np.mean(X, axis = 0).reshape(1, -1)).T) / X.shape[0]
+    return SCM
+
+def Cancer(df_cancer, y_cancer, alpha, beta, k, n_iter):
+    if not os.path.exists(os.path.join(plots_dir, 'cancer')):
+        os.makedirs(os.path.join(plots_dir, 'cancer'))
+    SCM = load_data_cancer(df_cancer)
+    # estimate underlying graph
+    sgl = LearnGraphTopology(SCM, n_iter=10, beta=beta, alpha=alpha)
+    graph = sgl.learn_graph(k=k)
+
+    # Build graph
+    A = graph['adjacency']
+    G = nx.from_numpy_matrix(A)
+    pos = nx.spring_layout(G)
+    fig = plt.figure(figsize=(12,12))
+    # Color labels
+    color_map = []
+    color_dict = {'PRAD' : 'blue', 'LUAD' : 'red', 'BRCA' : 'green', 'KIRC' : 'orange', 'COAD' : 'purple'}
+    for i in range(y_cancer.shape[0]):
+        color_map.append(color_dict[y_cancer['Class'][i]])
+    # Plot graph
+    nx.draw(G, node_color=color_map, with_labels=True, pos = pos, font_weight='bold')
+    plt.title("Learned graph for the cancer dataset k=%s n_iter=%s alpha=%.3f beta=%.3f" % (k , n_iter, alpha, beta))
+    filename = os.path.join(plots_dir, 'cancer', 'graph')
+    fig.savefig(filename)