Added nearest_node example.

Author: Mark Hale

examples/nearest_node.py

@@ -0,0 +1,32 @@
+"""
+nearest_nodes example based on breast cancer data.
+"""
+
+from plot_breast_cancer import *
+from sklearn import neighbors, preprocessing
+
+# new patient data incoming
+i = np.random.randint(len(X))
+new_patient_data = 1.05*X[i]
+new_patient_data = new_patient_data.reshape(1, -1)
+
+# re-use lens1 model
+newlens1 = model.decision_function(new_patient_data)
+
+# re-construct lens2 model
+X_norm = np.linalg.norm(X, axis=1)
+scaler = preprocessing.MinMaxScaler()
+scaler.fit(X_norm.reshape(-1, 1))
+
+newlens2 = scaler.transform(np.linalg.norm(new_patient_data, axis=1).reshape(1, -1))
+
+newlens = np.c_[newlens1, newlens2]
+
+# find nearest nodes
+nn = neighbors.NearestNeighbors(n_neighbors=3)
+node_ids = mapper.nearest_nodes(newlens, new_patient_data, graph, mapper.cover, lens, X, nn)
+
+print("Nearest nodes:")
+for node_id in node_ids:
+    diags = y[graph['nodes'][node_id]]
+    print("  {}: diagnosis {:.1f}%".format(node_id, np.sum(diags)*100.0/len(diags)))

examples/output/breast-cancer.html

@@ -68,8 +68,13 @@
 
 # Visualization
 mapper.visualize(graph,
+                 X=X,
+                 X_names=feature_names,
+                 lens=lens,
+                 lens_names=["Isolation forest", "l2-norm"],
                  path_html="output/breast-cancer.html",
                  title="Wisconsin Breast Cancer Dataset",
+                 color_function=y,
                  custom_tooltips=y)