def test_svd_batch_accuracy():
from lenskit.algorithms import basic
import lenskit.crossfold as xf
from lenskit import batch
import lenskit.metrics.predict as pm
ratings = lktu.ml100k.ratings
svd_algo = svd.BiasedSVD(25, damping=10)
algo = basic.Fallback(svd_algo, basic.Bias(damping=10))
def eval(train, test):
_log.info('running training')
algo.fit(train)
_log.info('testing %d users', test.user.nunique())
return batch.predict(algo, test)
folds = xf.partition_users(ratings, 5, xf.SampleFrac(0.2))
preds = pd.concat(eval(train, test) for (train, test) in folds)
mae = pm.mae(preds.prediction, preds.rating)
assert mae == approx(0.74, abs=0.025)
user_rmse = preds.groupby('user').apply(
lambda df: pm.rmse(df.prediction, df.rating))
assert user_rmse.mean() == approx(0.92, abs=0.05)
def test_svd_clone():
algo = svd.BiasedSVD(5, damping=10)
a2 = clone(algo)
assert a2.factorization.n_components == algo.factorization.n_components
assert a2.bias.user_damping == algo.bias.user_damping
assert a2.bias.item_damping == algo.bias.item_damping
def test_svd_predict_bad_user():
algo = svd.BiasedSVD(2)
algo.fit(simple_df)
preds = algo.predict_for_user(50, [3])
assert len(preds) == 1
assert preds.index[0] == 3
assert np.isnan(preds.loc[3])
def test_svd_predict_basic():
algo = svd.BiasedSVD(2)
algo.fit(simple_df)
preds = algo.predict_for_user(10, [3])
assert len(preds) == 1
assert preds.index[0] == 3
assert preds.loc[3] >= 0
assert preds.loc[3] <= 5
def test_svd_save_load():
ratings = lktu.ml_test.ratings
original = svd.BiasedSVD(20)
original.fit(ratings)
mod = pickle.dumps(original)
_log.info('serialized to %d bytes', len(mod))
algo = pickle.loads(mod)
assert algo.bias.mean_ == original.bias.mean_
assert np.all(algo.bias.user_offsets_ == original.bias.user_offsets_)
assert np.all(algo.bias.item_offsets_ == original.bias.item_offsets_)
assert np.all(algo.user_components_ == original.user_components_)
def test_svd_basic_build():
algo = svd.BiasedSVD(2)
algo.fit(simple_df)
assert algo.user_components_.shape == (3, 2)
