def test_load_sparse(self):
bunch = load_lastfm(cache=True, as_sparse=True)
X = bunch.ratings
assert sparse.issparse(X)
# Assert on artists
artists = bunch.artists
assert isinstance(artists, np.ndarray)
# Show that the next time we load this after caching, it's the same ref
assert load_lastfm(cache=True, as_sparse=True) is bunch
def test_load_bunch(self):
u, i, r = unpack_bunch(load_lastfm(cache=True))
# Show the users have NOT been encoded
for array in (u, i):
unq = np.sort(np.unique(array))
with pytest.raises(AssertionError):
assert_array_equal(unq, np.arange(unq.shape[0]))
from reclab.datasets import load_lastfm
from reclab.model_selection import train_test_split
from reclab.model_deployment import RecommenderDeployment
from reclab.collab import AlternatingLeastSquares
from scipy import sparse
from sklearn.preprocessing import LabelEncoder
from sklearn.externals import joblib
from numpy.testing import assert_array_equal
import pytest
import warnings
import os
# Load data and split into train/test
lastfm = load_lastfm(cache=True, as_sparse=True)
train, test = train_test_split(lastfm.ratings, random_state=42)
class TestRecommenderDeployment(object):
def test_simple_deployment(self):
als = AlternatingLeastSquares(factors=10, use_cg=False, iterations=3)
als.fit(train)
recs1 = als.recommend_for_user(0, test)
deployment = RecommenderDeployment(estimator=als)
recs2 = deployment.recommend_for_user(0, test[0, :].toarray()[0])
assert_array_equal(recs1, recs2)
def test_encoded_deployment(self):
users = ['adam', 'betty', 'betty', 'frank', 'frank']
