add plotting for mps #7

VotingAnalysis.py

@@ -135,6 +135,42 @@ def compute_heatmap(weight, grid_height, grid_width):
 
     return heatmap
 
+def plot_mps(fig, ax, x, y, labels, colors, cmap = plt.cm.RdYlGn):
+    ANNOTATION_DISTANCE = 5
+    TRANSPARENCY = 0.8
+    scatterplot = plt.scatter(x,y,c=colors, s=5, cmap=cmap)
+
+    annot = ax.annotate("", xy=(0,0), 
+                        xytext=(ANNOTATION_DISTANCE, ANNOTATION_DISTANCE),
+                        textcoords="offset points",
+                        bbox=dict(boxstyle="Square"))
+    annot.set_visible(False)
+
+    def update_annot(ind):
+        index = ind["ind"][0]
+        pos = scatterplot.get_offsets()[index]
+        annot.xy = pos
+        text = f'{labels[index]}'
+        annot.set_text(text)
+        annot.get_bbox_patch().set_facecolor(cmap(colors[index]))
+        annot.get_bbox_patch().set_alpha(TRANSPARENCY)
+
+    def hover(event):
+        vis = annot.get_visible()
+        if event.inaxes == ax:
+            cont, ind = scatterplot.contains(event)
+            if cont:
+                update_annot(ind)
+                annot.set_visible(True)
+                fig.canvas.draw_idle()
+            else:
+                if vis:
+                    annot.set_visible(False)
+                    fig.canvas.draw_idle()
+
+    fig.canvas.mpl_connect("motion_notify_event", hover)
+    #plt.show()
+
 #Simple SOFM for German
 plt.style.use('ggplot')
 
@@ -163,12 +199,23 @@ sofmnet = algorithms.SOFM(
 sofmnet.train(X, epochs=ep)
 
 #Visualizing Output
-plt.figure()
+fig,ax = plt.subplots()
+
 weight = sofmnet.weight.reshape((sofmnet.n_inputs, h, w))
 heatmap = compute_heatmap(weight, h, w)
 plt.imshow(heatmap, cmap='Greys_r', interpolation='nearest')
-plt.axis('off')
+#plt.axis('off')
 plt.colorbar()
+
+prediction = sofmnet.predict(X)
+print(f'prediction: {prediction}')
+# converting to x and y coordinates
+# TODO: verify that actually correct
+xs = np.argmax(np.argmax(prediction.reshape(X.shape[0], h, w), axis=1), axis=1)
+ys = np.argmax(np.argmax(prediction.reshape(X.shape[0], h, w), axis=2), axis=1)
+
+# TODO: fix color
+plot_mps(fig, ax, xs, ys, data[:,0], np.random.rand(X.shape[0]))
 plt.show()
 
 #Simple SOFM for UK
@@ -208,3 +255,5 @@ ax.set_xlabel('vote_0')
 ax.set_ylabel('vote_1')
 ax.set_zlabel('vote_2')
 ax.legend()
+
+plt.show()