include mfvi and fcvi into vi visualized demo

demos/variational_inference_visualized.py

@@ -74,65 +74,91 @@ def main():
     plt.pause(2.0)
     plt.close()
 
-    pos = ift.from_random(ham.domain, 'normal')
-    MAP = ift.EnergyAdapter(pos, ham, want_metric=True)
-    minimizer = ift.NewtonCG(
-        ift.GradientNormController(iteration_limit=20, name='Mini'))
-    MAP, _ = minimizer(MAP)
-    map_xs, map_ys = [], []
-    for ii in range(10):
-        samp = (MAP.metric.draw_sample(from_inverse=True) + MAP.position).val
-        map_xs.append(samp['a'])
-        map_ys.append(samp['b'])
+    mapx = xx[z==np.max(z)]
+    mapy = yy[z==np.max(z)]
+    meanx = (xx*z).sum()/z.sum()
+    meany = (yy*z).sum()/z.sum()
 
+    n_samples = 100
     minimizer = ift.NewtonCG(
         ift.GradientNormController(iteration_limit=2, name='Mini'))
-    pos = pos1 = ift.from_random(ham.domain, 'normal')
-    fig, axs = plt.subplots(2, 1, figsize=[12, 8])
-    for ii in range(15):
-        if ii % 3 == 0:
-            # Resample
-            mgkl = ift.MetricGaussianKL(pos, ham, 100, False)
-            mini_samp = ift.NewtonCG(ift.GradientNormController(iteration_limit=5))
-            geokl = ift.GeoMetricKL(pos1, ham, 100, mini_samp, False)
-
-        for axx in axs:
+    IC = ift.StochasticAbsDeltaEnergyController(0.1, iteration_limit=20,
+                                                name='advi')
+    stochastic_minimizer_mf = ift.ADVIOptimizer(IC, eta = 0.5)
+    stochastic_minimizer_fc = ift.ADVIOptimizer(IC, eta = 0.5)
+    posmg = posgeo = posmf = posfc = ift.from_random(ham.domain, 'normal')
+    fc = ift.FullCovarianceVI(posfc, ham, 10, False, initial_sig=0.01)
+    mf = ift.MeanFieldVI(posmf, ham, 10, False, initial_sig=0.01)
+
+    fig, axs = plt.subplots(2, 2, figsize=[12, 8])
+    axs = axs.flatten()
+
+    def update_plot(runs):
+        for axx, (nn, kl, pp, sam) in zip(axs,runs):
             axx.clear()
-            im = axx.imshow(z.T, origin='lower', norm=LogNorm(vmin=1e-3, vmax=np.max(z)),
-                               cmap='gist_earth_r', extent=x_limits_scaled + y_limits)
-            if ii == 0:
-                cbar = plt.colorbar(im, ax=axx)
-                cbar.ax.set_ylabel('pdf')
-
-        for jj, nn, kl, pp in ((0, "MGVI", mgkl, pos), (1, "GeoVI", geokl, pos1)):
+            axx.imshow(z.T, origin='lower', norm=LogNorm(vmin=1e-3, vmax=np.max(z)),
+                       cmap='gist_earth_r', extent=x_limits_scaled + y_limits)
             xs, ys = [], []
-            for samp in kl.samples:
-                samp = (samp + pp).val
-                xs.append(samp['a'])
-                ys.append(samp['b'])
-            axs[jj].scatter(np.array(xs)*scale, np.array(ys), label=f'{nn} samples')
-            axs[jj].scatter(pp.val['a']*scale, pp.val['b'], label=f'{nn} latent mean')
-            axs[jj].set_title(nn)
-
-        for axx in axs:
-            axx.scatter(np.array(map_xs)*scale, np.array(map_ys),
-                        label='Laplace samples')
-            axx.scatter(MAP.position.val['a']*scale, MAP.position.val['b'],
-                        label='Maximum a posterior solution')
+            if sam:
+                samples = (samp + pp for samp in kl.samples)
+            else:
+                samples = (kl.draw_sample() for _ in range(n_samples))
+            mx, my = 0., 0.
+            for samp in samples:
+                a = samp.val['a']
+                xs.append(a)
+                mx += a
+                b = samp.val['b']
+                ys.append(b)
+                my += b
+            mx /= n_samples
+            my /= n_samples
+            axx.scatter(np.array(xs)*scale, np.array(ys), label=f'{nn} samples')
+            axx.scatter(mx*scale, my, label=f'{nn} mean')
+            axx.scatter(mapx*scale, mapy, label = 'MAP')
+            axx.scatter(meanx*scale, meany, label = 'Posterior mean')
+            axx.set_title(nn)
             axx.set_xlim(x_limits_scaled)
             axx.set_ylim(y_limits)
-            axx.set_ylabel('y')
             axx.legend(loc='lower right')
         axs[0].xaxis.set_visible(False)
-        axs[1].set_xlabel('x')
+        axs[1].xaxis.set_visible(False)
+        axs[1].yaxis.set_visible(False)
+        axs[2].set_xlabel('x')
+        axs[2].set_ylabel('y')
+        axs[3].yaxis.set_visible(False)
+        axs[3].set_xlabel('x')
         plt.tight_layout()
         plt.draw()
-        plt.pause(1.0)
+        plt.pause(2.0)
+    
+    for ii in range(15):
+        if ii % 2 == 0:
+            # Resample
+            mgkl = ift.MetricGaussianKL(posmg, ham, n_samples, False)
+            mini_samp = ift.NewtonCG(ift.AbsDeltaEnergyController(1E-8,
+                                                                  iteration_limit=5))
+            geokl = ift.GeoMetricKL(posgeo, ham, n_samples, mini_samp, False)
+
+            runs = (("MGVI", mgkl, posmg, True),
+                    ("GeoVI", geokl, posgeo, True),
+                    ("MeanfieldVI", mf, posmf, False),
+                    ("FullCovarianceVI", fc, posfc, False))
+            update_plot(runs)
 
         mgkl, _ = minimizer(mgkl)
         geokl, _ = minimizer(geokl)
-        pos = mgkl.position
-        pos1 = geokl.position
+        mf.minimize(stochastic_minimizer_mf)
+        fc.minimize(stochastic_minimizer_fc)
+        posmg = mgkl.position
+        posgeo = geokl.position
+        posmf = mf.mean
+        posfc = fc.mean
+        runs = (("MGVI", mgkl, posmg, True),
+                ("GeoVI", geokl, posgeo, True),
+                ("MeanfieldVI", mf, posmf, False),
+                ("FullCovarianceVI", fc, posfc, False))
+        update_plot(runs)
     ift.logger.info('Finished')
     # Uncomment the following line in order to leave the plots open
     # plt.show()