def test_bias_transform():
algo = Bias()
ratings = ml_test.ratings
normed = algo.fit_transform(ratings)
assert all(normed['user'] == ratings['user'])
assert all(normed['item'] == ratings['item'])
denorm = algo.inverse_transform(normed)
assert approx(denorm['rating'] == ratings['rating'], 1.0e-6)
def test_bias_transform_indexes():
algo = Bias()
ratings = ml_test.ratings
normed = algo.fit_transform(ratings, indexes=True)
assert all(normed['user'] == ratings['user'])
assert all(normed['item'] == ratings['item'])
assert all(normed['uidx'] == algo.user_offsets_.index.get_indexer(ratings['user']))
assert all(normed['iidx'] == algo.item_offsets_.index.get_indexer(ratings['item']))
denorm = algo.inverse_transform(normed)
assert approx(denorm['rating'] == ratings['rating'], 1.0e-6)
def test_bias_transform():
algo = Bias()
ratings = ml_test.ratings
normed = algo.fit_transform(ratings)
assert all(normed['user'] == ratings['user'])
assert all(normed['item'] == ratings['item'])
denorm = algo.inverse_transform(normed)
assert denorm['rating'].values == approx(ratings['rating'], 1.0e-6)
n2 = ratings.join(algo.item_offsets_, on='item')
n2 = n2.join(algo.user_offsets_, on='user')
nr = n2.rating - algo.mean_ - n2.i_off - n2.u_off
assert normed['rating'].values == approx(nr.values)
