def test_clf_single():
est = FMClassifier(verbose=0, seed=13, num_iter=2).fit(X, y)
out = est.predict(X[:1])
assert isinstance(out, np.ndarray)
out = est.predict_proba(X[:1])
assert isinstance(out, np.ndarray)
assert out.shape == (1, 2)
def test_pickle():
est = FMClassifier(verbose=0, seed=13, num_iter=2).fit(X, y)
out = est.predict(X)
ser = pickle.dumps(est)
est2 = pickle.loads(ser)
out2 = est2.predict(X)
np.testing.assert_array_equal(out, out2)
def test_smoke_classification():
for X_ in (X, sp.csc_matrix(X), sp.csr_matrix(X), sp.coo_matrix(X)):
for lr in ['optimal', 'invscaling', 'constant']:
est = FMClassifier(verbose=0, seed=13, num_iter=2, learning_rate_schedule=lr).fit(X_, y)
proba = est.predict_proba(X_)
assert proba.shape == (n, 2)
np.testing.assert_array_almost_equal(proba[:3], np.array([[0.45637266, 0.54362734],
[0.47387578, 0.52612422],
[0.48227419, 0.51772581]]))
pred = est.predict(X_)
assert pred.shape == (n, )
np.testing.assert_array_almost_equal(pred[:5], np.ones(5))
