def test_augment():
# The following linear separable dataset cannot be modeled with just an FM
X_evil = np.array([[-1, -1], [1, 1]])
y_evil = np.array([-1, 1])
clf = FactorizationMachineClassifier(fit_linear=False, fit_lower=None,
random_state=0)
clf.fit(X_evil, y_evil)
assert_equal(0.5, clf.score(X_evil, y_evil))
# However, by adding a dummy feature (a column of all ones), the linear
# effect can be captured.
clf = FactorizationMachineClassifier(fit_linear=False, fit_lower='augment',
random_state=0)
clf.fit(X_evil, y_evil)
assert_equal(1.0, clf.score(X_evil, y_evil))
def check_classification_losses(loss, degree):
y = np.sign(_poly_predict(X, P, lams, kernel="anova", degree=degree))
clf = FactorizationMachineClassifier(degree=degree, loss=loss, beta=1e-3,
fit_lower=None, fit_linear=False,
tol=1e-3, random_state=0)
clf.fit(X, y)
assert_equal(1.0, clf.score(X, y))
def test_augment():
# The following linear separable dataset cannot be modeled with just an FM
X_evil = np.array([[-1, -1], [1, 1]])
y_evil = np.array([-1, 1])
clf = FactorizationMachineClassifier(fit_linear=False,
fit_lower=None,
random_state=0)
clf.fit(X_evil, y_evil)
assert_equal(0.5, clf.score(X_evil, y_evil))
# However, by adding a dummy feature (a column of all ones), the linear
# effect can be captured.
clf = FactorizationMachineClassifier(fit_linear=False,
fit_lower='augment',
random_state=0)
clf.fit(X_evil, y_evil)
assert_equal(1.0, clf.score(X_evil, y_evil))