Learning interactions on a 2d grid¶

import numpy as np
import matplotlib.pyplot as plt

from pystruct.models import GridCRF
import pystruct.learners as ssvm
from pystruct.datasets import generate_crosses_explicit
from pystruct.utils import expand_sym


X, Y = generate_crosses_explicit(n_samples=50, noise=10)
crf = GridCRF(neighborhood=4)
clf = ssvm.OneSlackSSVM(model=crf, C=100, inference_cache=100,
                        tol=.1)
clf.fit(X, Y)
Y_pred = np.array(clf.predict(X))

print("overall accuracy (training set): %f" % clf.score(X, Y))

# plot one example
x, y, y_pred = X[0], Y[0], Y_pred[0]
y_pred = y_pred.reshape(x.shape[:2])
fig, plots = plt.subplots(1, 4, figsize=(12, 4))
plots[0].matshow(y)
plots[0].set_title("ground truth")
plots[1].matshow(np.argmax(x, axis=-1))
plots[1].set_title("input")
plots[2].matshow(y_pred)
plots[2].set_title("prediction")
loss_augmented = clf.model.loss_augmented_inference(x, y, clf.w)
loss_augmented = loss_augmented.reshape(y.shape)
plots[3].matshow(loss_augmented)
plots[3].set_title("loss augmented")
for p in plots:
    p.set_xticks(())
    p.set_yticks(())

# visualize weights
w_un = clf.w[:3 * 3].reshape(3, 3)
# decode the symmetric pairwise potential
w_pw = expand_sym(clf.w[3 * 3:])

fig, plots = plt.subplots(1, 2, figsize=(8, 4))
plots[0].matshow(w_un, cmap='gray', vmin=-5, vmax=5)
plots[0].set_title("Unary weights")
plots[1].matshow(w_pw, cmap='gray', vmin=-5, vmax=5)
plots[1].set_title("Pairwise weights")
for p in plots:
    p.set_xticks(())
    p.set_yticks(())
plt.show()