# predict, and evaluate
pred = model.predict(Xs)
acc = np.mean(pred==ytrue)
print "accuracy:", round(acc, 3)
# plot probabilities
[minx, maxx] = [np.min(Xs[:, 0]), np.max(Xs[:, 0])]
[miny, maxy] = [np.min(Xs[:, 1]), np.max(Xs[:, 1])]
gridsize = 100
xx = np.linspace(minx, maxx, gridsize)
yy = np.linspace(miny, maxy, gridsize).T
xx, yy = np.meshgrid(xx, yy)
Xfull = np.c_[xx.ravel(), yy.ravel()]
probas = model.predict_proba(Xfull)
plt.imshow(probas[:, 1].reshape((gridsize, gridsize)), extent=(minx, maxx, miny, maxy), origin='lower')
# plot decision boundary
try:
plt.contour(model.predict(Xfull).reshape((gridsize, gridsize)), extent=(minx, maxx, miny, maxy), origin='lower')
except:
print "contour failed"
# plot data points
P = np.max(model.predict_proba(Xs), axis=1)
plt.scatter(Xs[:, 0], Xs[:,1], c=ytrue, s=(ys>-1)*300+100, linewidth=1, edgecolor=[cols[p]*P[p] for p in model.predict(Xs).astype(int)], cmap='hot')
plt.scatter(Xs[ys>-1, 0], Xs[ys>-1,1], c=ytrue[ys>-1], s=300, linewidth=1, edgecolor=[cols[p]*P[p] for p in model.predict(Xs).astype(int)], cmap='hot')
plt.title(lbl + str(round(acc, 2)))
plt.show(block=True)
