Learning directed interactions on a 2d grid¶

import numpy as np
import matplotlib.pyplot as plt

from pystruct.models import DirectionalGridCRF
import pystruct.learners as ssvm
from pystruct.datasets import generate_blocks_multinomial


X, Y = generate_blocks_multinomial(noise=2, n_samples=20, seed=1)
crf = DirectionalGridCRF(inference_method="qpbo", neighborhood=4)
clf = ssvm.OneSlackSSVM(model=crf, n_jobs=-1, 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 = clf.w.reshape(3, 3, 3)
fig, plots = plt.subplots(1, 3, figsize=(12, 4))
plots[0].matshow(w[0], cmap='gray', vmin=-5, vmax=5)
plots[0].set_title("Unary weights")
plots[1].matshow(w[1], cmap='gray', vmin=-5, vmax=5)
plots[1].set_title("Horizontal weights")
plots[2].matshow(w[2], cmap='gray', vmin=-5, vmax=5)
plots[2].set_title("Vertical weights")
for p in plots:
    p.set_xticks(())
    p.set_yticks(())
plt.show()