Latent Variable Hierarchical CRFΒΆ

Solving a 2d grid toy problem by introducing an additional layer of latent variables.

Script output:

Training score binary grid CRF: 0.903125
Training score with latent nodes: 1.000000
import numpy as np
import itertools

from pystruct.models import GraphCRF, LatentNodeCRF
from pystruct.learners import NSlackSSVM, OneSlackSSVM, LatentSSVM
from pystruct.datasets import make_simple_2x2
from pystruct.utils import make_grid_edges, plot_grid
import matplotlib.pyplot as plt

def plot_boxes(boxes, size=4, title=""):
    cmap =
    if boxes[0].size == size * size:
        fig, ax = plt.subplots(1, len(boxes), figsize=(8, 0.7))
        for a, x in zip(ax, boxes):
            plot_grid(x[:size * size].reshape(size, size), cmap=cmap, axes=a,
        # have hidden states
        fig, ax = plt.subplots(2, len(boxes), figsize=(8, 1))
        for a, x in zip(ax[0], boxes):
            plot_grid(x[size * size:].reshape(size / 2, size / 2), cmap=cmap,
                      axes=a, border_color="green")
        for a, x in zip(ax[1], boxes):
            plot_grid(x[:size * size].reshape(size, size), cmap=cmap, axes=a,
    fig.subplots_adjust(.01, .03, .98, .75, .2, .05)

# learn the "easy" 2x2 boxes dataset.
# a 2x2 box is placed randomly in a 4x4 grid
# we add a latent variable for each 2x2 patch
# that should make the model fairly simple

X, Y = make_simple_2x2(seed=1)

# flatten X and Y
X_flat = [x.reshape(-1, 1).astype(np.float) for x in X]
Y_flat = [y.ravel() for y in Y]

# first, use standard graph CRF. Can't do much, high loss.
crf = GraphCRF()
svm = NSlackSSVM(model=crf, max_iter=200, C=1, n_jobs=1)

G = [make_grid_edges(x) for x in X]

X_grid_edges = list(zip(X_flat, G)), Y_flat)
plot_boxes(svm.predict(X_grid_edges), title="Non-latent SSVM predictions")
print("Training score binary grid CRF: %f" % svm.score(X_grid_edges, Y_flat))

# using one latent variable for each 2x2 rectangle
latent_crf = LatentNodeCRF(n_labels=2, n_features=1, n_hidden_states=2,

ssvm = OneSlackSSVM(model=latent_crf, max_iter=200, C=100,
                    n_jobs=-1, show_loss_every=10, inference_cache=50)
latent_svm = LatentSSVM(ssvm)

# make edges for hidden states:
edges = []
node_indices = np.arange(4 * 4).reshape(4, 4)
for i, (x, y) in enumerate(itertools.product([0, 2], repeat=2)):
    for j in range(x, x + 2):
        for k in range(y, y + 2):
            edges.append([i + 4 * 4, node_indices[j, k]])

G = [np.vstack([make_grid_edges(x), edges]) for x in X]

# Random initialization
H_init = [np.hstack([y.ravel(), np.random.randint(2, 4, size=2 * 2)])
          for y in Y]
plot_boxes(H_init, title="Top: Random initial hidden states. Bottom: Ground"
           "truth labeling.")

X_ = list(zip(X_flat, G, [2 * 2 for x in X_flat])), Y_flat, H_init)

print("Training score with latent nodes: %f " % latent_svm.score(X_, Y_flat))
H = latent_svm.predict_latent(X_)
plot_boxes(H, title="Top: Hidden states after training. Bottom: Prediction.")

Total running time of the script: (2 minutes 7.478 seconds)

Download Python source code: