def test__recommend__good__train():
"""test the recommend() method on the training users"""
model = RankFM(factors=2)
model.fit(intx_train_pd_int)
recs = model.recommend(train_users, n_items=3)
klass = isinstance(recs, pd.DataFrame)
shape = recs.shape == (3, 3)
index = np.array_equal(recs.index.values, train_users)
items = recs.isin(intx_train_pd_int['item_id'].values).all().all()
assert klass and shape and index and items
def test__recommend__good__valid__nan():
"""test the recommend() method on a disjoint set of validation users"""
model = RankFM(factors=2)
model.fit(intx_train_pd_int)
recs = model.recommend(valid_users, n_items=3, cold_start='nan')
klass = isinstance(recs, pd.DataFrame)
shape = recs.shape == (4, 3)
index = np.array_equal(sorted(recs.index.values), sorted(valid_users))
items = recs.dropna().isin(intx_train_pd_int['item_id'].values).all().all()
new_users = list(set(valid_users) - set(train_users))
nmiss = recs.loc[new_users].isnull().all().all()
assert klass and shape and index and items and nmiss
def test__recommend__good__valid__drop():
"""test the recommend() method on a disjoint set of validation users"""
model = RankFM(factors=2)
model.fit(intx_train_pd_int)
recs = model.recommend(valid_users, n_items=3, cold_start='drop')
klass = isinstance(recs, pd.DataFrame)
shape = recs.shape == (2, 3)
index = np.isin(recs.index.values, valid_users).all()
items = recs.dropna().isin(intx_train_pd_int['item_id'].values).all().all()
same_users = list(set(valid_users) & set(train_users))
match_users = np.array_equal(sorted(same_users), sorted(recs.index.values))
assert klass and shape and index and items and match_users
def test__recommend__good__train__filter():
"""test the recommend() method on the training users but filter previous items"""
model = RankFM(factors=2)
model.fit(intx_train_pd_int)
recs = model.recommend(train_users, n_items=3, filter_previous=True)
klass = isinstance(recs, pd.DataFrame)
shape = recs.shape == (3, 3)
index = np.array_equal(recs.index.values, train_users)
items = recs.isin(intx_train_pd_int['item_id'].values).all().all()
recs_long = recs.stack().reset_index().drop('level_1', axis=1)
recs_long.columns = ['user_id', 'item_id']
intersect = pd.merge(intx_train_pd_int,
recs_long,
on=['user_id', 'item_id'],
how='inner').empty
assert klass and shape and index and items and intersect