def test_bias_item_predict():
algo = Bias(users=False)
algo.fit(simple_df)
p = algo.predict_for_user(10, [1, 2, 3])
assert len(p) == 3
assert p.values == approx((algo.item_offsets_ + algo.mean_).values)
def test_bias_predict_unknown_user():
algo = Bias()
algo.fit(simple_df)
p = algo.predict_for_user(15, [1, 3])
assert len(p) == 2
assert p.values == approx((algo.item_offsets_.loc[[1, 3]] + algo.mean_).values)
def test_bias_global_predict():
algo = Bias(items=False, users=False)
algo.fit(simple_df)
p = algo.predict_for_user(10, [1, 2, 3])
assert len(p) == 3
assert (p == algo.mean_).all()
assert p.values == approx(algo.mean_)
def test_bias_no_item():
algo = Bias(items=False)
algo.fit(simple_df)
assert algo.mean_ == approx(3.5)
assert algo.item_offsets_ is None
assert algo.user_offsets_ is not None
assert algo.user_offsets_.index.name == 'user'
assert set(algo.user_offsets_.index) == set([10, 12, 13])
assert algo.user_offsets_.loc[[10, 12, 13]].values == approx(np.array([1.0, -0.5, -1.5]))
def test_bias_predict_unknown_item():
algo = Bias()
algo.fit(simple_df)
p = algo.predict_for_user(10, [1, 3, 4])
assert len(p) == 3
intended = algo.item_offsets_.loc[[1, 3]] + algo.mean_ + 0.25
assert p.loc[[1, 3]].values == approx(intended.values)
assert p.loc[4] == approx(algo.mean_ + 0.25)
def test_bias_user_damp():
algo = Bias(items=False, damping=5)
algo.fit(simple_df)
assert algo.mean_ == approx(3.5)
assert algo.item_offsets_ is None
assert algo.user_offsets_ is not None
assert algo.user_offsets_.index.name == 'user'
assert set(algo.user_offsets_.index) == set([10, 12, 13])
assert algo.user_offsets_.loc[[10, 12, 13]].values == \
approx(np.array([0.2857, -0.08333, -0.25]), abs=1.0e-4)
def test_bias_no_user():
algo = Bias(users=False)
algo.fit(simple_df)
assert algo.mean_ == approx(3.5)
assert algo.item_offsets_ is not None
assert algo.item_offsets_.index.name == 'item'
assert set(algo.item_offsets_.index) == set([1, 2, 3])
assert algo.item_offsets_.loc[1:3].values == approx(np.array([0, 1.5, -1.5]))
assert algo.user_offsets_ is None
def test_transform_user_without_user_bias():
user = 12
algo = Bias()
algo.fit(simple_df)
new_ratings = pd.Series([-0.5, 1.5], index=[2, 3]) # items as index and ratings as values
v = algo.inverse_transform_user(user, new_ratings)
assert v[2] == new_ratings[2] + algo.user_offsets_.loc[user] + algo.item_offsets_.loc[2] + algo.mean_
assert v[3] == new_ratings[3] + algo.user_offsets_.loc[user] + algo.item_offsets_.loc[3] + algo.mean_
def test_transform_user_with_user_bias():
algo = Bias()
algo.fit(simple_df)
new_ratings = pd.Series([4.0, 5.0], index=[1, 2]) # items as index and ratings as values
ratings_with_bias, user_bias = algo.transform_user(new_ratings) # user: 13
result = algo.inverse_transform_user(13, ratings_with_bias, user_bias)
assert new_ratings[1] == result[1]
assert new_ratings[2] == result[2]
def test_bias_clone():
algo = Bias()
algo.fit(simple_df)
params = algo.get_params()
assert sorted(params.keys()) == ['damping', 'items', 'users']
a2 = lku.clone(algo)
assert a2 is not algo
assert getattr(a2, 'mean_', None) is None
assert getattr(a2, 'item_offsets_', None) is None
assert getattr(a2, 'user_offsets_', None) is None
def test_bias_user_predict():
algo = Bias(items=False)
algo.fit(simple_df)
p = algo.predict_for_user(10, [1, 2, 3])
assert len(p) == 3
assert p.values == approx(algo.mean_ + 1.0)
p = algo.predict_for_user(12, [1, 3])
assert len(p) == 2
assert p.values == approx(algo.mean_ - 0.5)
def test_bias_full():
algo = Bias()
algo.fit(simple_df)
assert algo.mean_ == approx(3.5)
assert algo.item_offsets_ is not None
assert algo.item_offsets_.index.name == 'item'
assert set(algo.item_offsets_.index) == set([1, 2, 3])
assert algo.item_offsets_.loc[1:3].values == approx(np.array([0, 1.5, -1.5]))
assert algo.user_offsets_ is not None
assert algo.user_offsets_.index.name == 'user'
assert set(algo.user_offsets_.index) == set([10, 12, 13])
assert algo.user_offsets_.loc[[10, 12, 13]].values == approx(np.array([0.25, -0.5, 0]))
def test_bias_new_user_predict():
algo = Bias()
algo.fit(simple_df)
ratings = pd.DataFrame({'item': [1, 2, 3], 'rating': [1.5, 2.5, 3.5]})
ratings = ratings.set_index('item').rating
p = algo.predict_for_user(None, [1, 3], ratings=ratings)
offs = ratings - algo.mean_ - algo.item_offsets_
umean = offs.mean()
_log.info('user mean is %f', umean)
assert len(p) == 2
assert p.values == approx((algo.mean_ + algo.item_offsets_ + umean).loc[[1, 3]].values)
def test_bias_damped():
algo = Bias(damping=5)
algo.fit(simple_df)
assert algo.mean_ == approx(3.5)
assert algo.item_offsets_ is not None
assert algo.item_offsets_.index.name == 'item'
assert set(algo.item_offsets_.index) == set([1, 2, 3])
assert algo.item_offsets_.loc[1:3].values == approx(np.array([0, 0.25, -0.25]))
assert algo.user_offsets_ is not None
assert algo.user_offsets_.index.name == 'user'
assert set(algo.user_offsets_.index) == set([10, 12, 13])
assert algo.user_offsets_.loc[[10, 12, 13]].values == \
approx(np.array([0.25, -00.08333, -0.20833]), abs=1.0e-4)
def test_bias_separate_damping():
algo = Bias(damping=(5, 10))
algo.fit(simple_df)
assert algo.mean_ == approx(3.5)
assert algo.item_offsets_ is not None
assert algo.item_offsets_.index.name == 'item'
assert set(algo.item_offsets_.index) == set([1, 2, 3])
assert algo.item_offsets_.loc[1:3].values == \
approx(np.array([0, 0.136364, -0.13636]), abs=1.0e-4)
assert algo.user_offsets_ is not None
assert algo.user_offsets_.index.name == 'user'
assert set(algo.user_offsets_.index) == set([10, 12, 13])
assert algo.user_offsets_.loc[[10, 12, 13]].values == \
approx(np.array([0.266234, -0.08333, -0.22727]), abs=1.0e-4)
def test_global_metric():
import lenskit.crossfold as xf
import lenskit.batch as batch
from lenskit.algorithms.bias import Bias
train, test = next(
xf.sample_users(lktu.ml_test.ratings, 1, 200, xf.SampleFrac(0.5)))
algo = Bias()
algo.fit(train)
preds = batch.predict(algo, test)
rmse = pm.global_metric(preds)
assert rmse == pm.rmse(preds.prediction, preds.rating)
mae = pm.global_metric(preds, metric=pm.mae)
assert mae == pm.mae(preds.prediction, preds.rating)
def test_bias_train_ml_ratings():
algo = Bias()
ratings = ml_test.ratings
algo.fit(ratings)
assert algo.mean_ == approx(ratings.rating.mean())
imeans_data = ratings.groupby('item').rating.mean()
imeans_algo = algo.item_offsets_ + algo.mean_
ares, data = imeans_algo.align(imeans_data)
assert ares.values == approx(data.values)
urates = ratings.set_index('user').loc[2].set_index('item').rating
umean = (urates - imeans_data[urates.index]).mean()
p = algo.predict_for_user(2, [10, 11, -1])
assert len(p) == 3
assert p.iloc[0] == approx(imeans_data.loc[10] + umean)
assert p.iloc[1] == approx(imeans_data.loc[11] + umean)
assert p.iloc[2] == approx(ratings.rating.mean() + umean)
def test_user_metric():
import lenskit.crossfold as xf
import lenskit.batch as batch
from lenskit.algorithms.bias import Bias
train, test = next(
xf.sample_users(lktu.ml_test.ratings, 1, 200, xf.SampleFrac(0.5)))
algo = Bias()
algo.fit(train)
preds = batch.predict(algo, test)
rmse = pm.user_metric(preds)
u_rmse = preds.groupby('user').apply(
lambda df: pm.rmse(df.prediction, df.rating))
assert rmse == approx(u_rmse.mean())
mae = pm.user_metric(preds, metric=pm.mae)
u_mae = preds.groupby('user').apply(
lambda df: pm.mae(df.prediction, df.rating))
assert mae == approx(u_mae.mean())
def test_bias_binpickle(tmp_path):
original = Bias(damping=5)
original.fit(simple_df)
assert original.mean_ == approx(3.5)
_log.info('saving baseline model')
fn = tmp_path / 'bias.bpk'
binpickle.dump(original, fn)
algo = binpickle.load(fn)
assert algo.mean_ == original.mean_
assert algo.item_offsets_ is not None
assert algo.item_offsets_.index.name == 'item'
assert set(algo.item_offsets_.index) == set([1, 2, 3])
assert algo.item_offsets_.loc[1:3].values == approx(np.array([0, 0.25, -0.25]))
assert algo.user_offsets_ is not None
assert algo.user_offsets_.index.name == 'user'
assert set(algo.user_offsets_.index) == set([10, 12, 13])
assert algo.user_offsets_.loc[[10, 12, 13]].values == \
approx(np.array([0.25, -00.08333, -0.20833]), abs=1.0e-4)
def test_bias_save():
original = Bias(damping=5)
original.fit(simple_df)
assert original.mean_ == approx(3.5)
_log.info('saving baseline model')
mod = pickle.dumps(original)
_log.info('serialized to %d bytes', len(mod))
algo = pickle.loads(mod)
assert algo.mean_ == original.mean_
assert algo.item_offsets_ is not None
assert algo.item_offsets_.index.name == 'item'
assert set(algo.item_offsets_.index) == set([1, 2, 3])
assert algo.item_offsets_.loc[1:3].values == approx(np.array([0, 0.25, -0.25]))
assert algo.user_offsets_ is not None
assert algo.user_offsets_.index.name == 'user'
assert set(algo.user_offsets_.index) == set([10, 12, 13])
assert algo.user_offsets_.loc[[10, 12, 13]].values == \
approx(np.array([0.25, -00.08333, -0.20833]), abs=1.0e-4)
def test_bias_global_only():
algo = Bias(users=False, items=False)
algo.fit(simple_df)
assert algo.mean_ == approx(3.5)
assert algo.item_offsets_ is None
assert algo.user_offsets_ is None
def test_bias_index_props():
algo = Bias()
algo.fit(simple_df)
assert all(np.sort(algo.user_index) == np.unique(simple_df['user']))
assert all(np.sort(algo.item_index) == np.unique(simple_df['item']))
def mlb():
ratings = lktu.ml_test.ratings
algo = Bias()
algo.fit(ratings)
return MLB(ratings, algo)
