def test_model_1d():
# 10 1d datapoints between -1 and 1
np.random.seed(0)
X = np.random.uniform(size=(10, 1))
# linearly separable labels
Y = 1 - 2 * (X.ravel() < .5)
pbl = BinarySVMModel(n_features=2)
# we have to add a constant 1 feature by hand :-/
X = np.hstack([X, np.ones((X.shape[0], 1))])
w = [1, -.5]
Y_pred = np.hstack([pbl.inference(x, w) for x in X])
assert_array_equal(Y, Y_pred)
# check that sign of psi and inference agree
for x, y in zip(X, Y):
assert_true(np.dot(w, pbl.psi(x, y)) > np.dot(w, pbl.psi(x, -y)))
# check that sign of psi and the sign of y correspond
for x, y in zip(X, Y):
assert_true(np.dot(w, pbl.psi(x, y)) == -np.dot(w, pbl.psi(x, -y)))
def test_blobs_batch():
# make two gaussian blobs
X, Y = make_blobs(n_samples=80, centers=2, random_state=1)
Y = 2 * Y - 1
pbl = BinarySVMModel(n_features=2)
# test psi
psi_mean = pbl.batch_psi(X, Y)
psi_mean2 = np.sum([pbl.psi(x, y) for x, y in zip(X, Y)], axis=0)
assert_array_equal(psi_mean, psi_mean2)
# test inference
w = np.random.uniform(-1, 1, size=pbl.size_psi)
Y_hat = pbl.batch_inference(X, w)
for i, (x, y_hat) in enumerate(zip(X, Y_hat)):
assert_array_equal(Y_hat[i], pbl.inference(x, w))
# test inference
Y_hat = pbl.batch_loss_augmented_inference(X, Y, w)
for i, (x, y, y_hat) in enumerate(zip(X, Y, Y_hat)):
assert_array_equal(Y_hat[i], pbl.loss_augmented_inference(x, y, w))