def test_ffm_vector_kendall_tau():
order = np.array([1, 2, 3, 4, 5])
order_wrong = np.array([5, 3, 4, 2, 1])
order_inv = np.array([5, 4, 3, 2, 1])
assert kendall_tau(order, order) == 1
assert kendall_tau(order, order_inv) == -1
assert kendall_tau(order, order_wrong) != -1
def test_fm_sgr_ranking():
w0, w, V, y, X = get_test_problem()
X_test = X.copy()
X_train = X.copy()
import itertools
pairs = [p for p in itertools.combinations(range(len(y)), 2)]
compares = np.zeros((len(pairs), 2), dtype=np.float64)
for i, p in enumerate(pairs):
if y[p[0]] > y[p[1]]:
compares[i, 0] = p[0]
compares[i, 1] = p[1]
else:
compares[i, 0] = p[1]
compares[i, 1] = p[0]
print(compares)
fm = bpr.FMRecommender(n_iter=2000,
init_stdev=0.01, l2_reg_w=.5, l2_reg_V=.5, rank=2,
step_size=.002, random_state=11)
fm.fit(X_train, compares)
y_pred = fm.predict(X_test)
y_pred = np.argsort(y_pred)
print(y)
print(y_pred)
print(np.argsort(y))
assert utils.kendall_tau(np.argsort(y), y_pred) == 1
def test_fm_sgr_ranking():
w0, w, V, y, X = get_test_problem()
X_test = X.copy()
X_train = X.copy()
import itertools
pairs = [p for p in itertools.combinations(range(len(y)), 2)]
compares = np.zeros((len(pairs), 2), dtype=np.float64)
for i, p in enumerate(pairs):
if y[p[0]] > y[p[1]]:
compares[i, 0] = p[0]
compares[i, 1] = p[1]
else:
compares[i, 0] = p[1]
compares[i, 1] = p[0]
print(compares)
fm = bpr.FMRecommender(n_iter=2000,
init_stdev=0.01, l2_reg_w=.5, l2_reg_V=.5, rank=2,
step_size=.002, random_state=11)
fm.fit(X_train, compares)
y_pred = fm.predict(X_test)
y_pred = np.argsort(y_pred)
print(y)
print(y_pred)
print(np.argsort(y))
assert utils.kendall_tau(np.argsort(y), y_pred) == 1
