def setUp(self):
"""
Preload an engine, sim_rows and sim_cols for column based recommendations.
"""
self.engine_nan = SPE(fixtures.DFCRITICS_NAN, SPE.RATING_NAN)
self.engine_nan.preload(verbose=False)
self.engine_binary = SPE(fixtures.DFCRITICS_BINARY, SPE.BINARY)
self.engine_binary.preload(verbose=False)
def setUp(self):
"""
Preload an engine, sim_rows and sim_cols for column based recommendations.
"""
self.engine_nan = SPE(fixtures.DFCRITICS_NAN, SPE.RATING_NAN)
self.engine_nan.preload(verbose=False)
self.engine_binary = SPE(fixtures.DFCRITICS_BINARY, SPE.BINARY)
self.engine_binary.preload(verbose=False)
class TestPyrecommend(unittest.TestCase):
def setUp(self):
"""
Preload an engine, sim_rows and sim_cols for column based recommendations.
"""
self.engine_nan = SPE(fixtures.DFCRITICS_NAN, SPE.RATING_NAN)
self.engine_nan.preload(verbose=False)
self.engine_binary = SPE(fixtures.DFCRITICS_BINARY, SPE.BINARY)
self.engine_binary.preload(verbose=False)
# tests
# helper
def _test_helper(self, ix, func1, func2, binary=False):
# A is provided
A = [sim for sim in func1(ix) if sim[0] > 0.0]
# B is expected
B = [sim for sim in func2(ix, binary=binary) if sim[0] > 0.0]
if binary:
# if engine is binary, scores will not necessarily be the same
# but recommendations will be very similar
if (len(A) == 0) or (len(B) == 0):
# then one of the engines has not returned any recommendations
# it could be the case that our engine did not return any recs
# but in that case we will just assume that it's due to a small DF
return
# else we'll test that least 75% of the items match
_ = set(zip(*A)[1]) and set(zip(*B)[1])
self.assertTrue(len(_)*2.0 >= (len(A) + len(B))*0.75)
return
# rating_nan engine
for a, b in zip(A, B):
self.assertAlmostEqual(a[0], b[0], places=5)
# for rating_nan & binary
# test similarities
def test_similar_users(self):
"""
We can predict similar users using RATING_NAN / BINARY.
"""
user = random.choice(fixtures.DFCRITICS_NAN.index)
self._test_helper(user, self.engine_nan.similar_users, fixtures.similar_users)
self._test_helper(user, self.engine_binary.similar_users, fixtures.similar_users, binary=True)
def test_similar_items_nan(self):
"""
We can predict similar items using RATING_NAN / BINARY.
"""
item = random.choice(fixtures.DFCRITICS_NAN.transpose().index)
self._test_helper(item, self.engine_nan.similar_items, fixtures.similar_items)
self._test_helper(item, self.engine_binary.similar_items, fixtures.similar_items, binary=True)
# test user/item recs by row
def test_recommend_items(self):
"""
We can predict recommended items for user, by row, using RATING_NAN / BINARY.
"""
user = random.choice(fixtures.DFCRITICS_NAN.index)
self._test_helper(user, self.engine_nan.recommend_items, fixtures.recommend_items)
self._test_helper(user, self.engine_binary.recommend_items, fixtures.recommend_items, binary=True)
def test_recommend_users(self):
"""
We can predict recommended users for item, by row, using RATING_NAN / BINARY.
"""
item = random.choice(fixtures.DFCRITICS_NAN.transpose().index)
self._test_helper(item, self.engine_nan.recommend_users, fixtures.recommend_users)
self._test_helper(item, self.engine_binary.recommend_users, fixtures.recommend_users, binary=True)
# test column based recs
def test_recommend_items_bycol(self):
"""
We can predict recommended items for user, by column.
"""
user = random.choice(fixtures.DFCRITICS_NAN.index)
self._test_helper(user,
functools.partial(self.engine_nan.recommend_items, method=self.engine_nan.ITEM_BASED),
fixtures.recommend_items_bycol)
self._test_helper(user,
functools.partial(self.engine_binary.recommend_items, method=self.engine_binary.ITEM_BASED),
fixtures.recommend_items_bycol, binary=True)
def test_recommend_users_bycol(self):
"""
We can predict recommended users for item, by column.
"""
item = random.choice(fixtures.DFCRITICS_NAN.transpose().index)
self._test_helper(item,
functools.partial(self.engine_nan.recommend_users, method=self.engine_nan.USER_BASED),
fixtures.recommend_users_bycol)
self._test_helper(item,
functools.partial(self.engine_binary.recommend_users, method=self.engine_binary.USER_BASED),
fixtures.recommend_users_bycol, binary=True)
def tearDown(self):
pass
class TestPyrecommend(unittest.TestCase):
def setUp(self):
"""
Preload an engine, sim_rows and sim_cols for column based recommendations.
"""
self.engine_nan = SPE(fixtures.DFCRITICS_NAN, SPE.RATING_NAN)
self.engine_nan.preload(verbose=False)
self.engine_binary = SPE(fixtures.DFCRITICS_BINARY, SPE.BINARY)
self.engine_binary.preload(verbose=False)
# tests
# helper
def _test_helper(self, ix, func1, func2, binary=False):
# A is provided
A = [sim for sim in func1(ix) if sim[0] > 0.0]
# B is expected
B = [sim for sim in func2(ix, binary=binary) if sim[0] > 0.0]
if binary:
# if engine is binary, scores will not necessarily be the same
# but recommendations will be very similar
if (len(A) == 0) or (len(B) == 0):
# then one of the engines has not returned any recommendations
# it could be the case that our engine did not return any recs
# but in that case we will just assume that it's due to a small DF
return
# else we'll test that least 75% of the items match
_ = set(zip(*A)[1]) and set(zip(*B)[1])
self.assertTrue(len(_) * 2.0 >= (len(A) + len(B)) * 0.75)
return
# rating_nan engine
for a, b in zip(A, B):
self.assertAlmostEqual(a[0], b[0], places=5)
# for rating_nan & binary
# test similarities
def test_similar_users(self):
"""
We can predict similar users using RATING_NAN / BINARY.
"""
user = random.choice(fixtures.DFCRITICS_NAN.index)
self._test_helper(user, self.engine_nan.similar_users,
fixtures.similar_users)
self._test_helper(user,
self.engine_binary.similar_users,
fixtures.similar_users,
binary=True)
def test_similar_items_nan(self):
"""
We can predict similar items using RATING_NAN / BINARY.
"""
item = random.choice(fixtures.DFCRITICS_NAN.transpose().index)
self._test_helper(item, self.engine_nan.similar_items,
fixtures.similar_items)
self._test_helper(item,
self.engine_binary.similar_items,
fixtures.similar_items,
binary=True)
# test user/item recs by row
def test_recommend_items(self):
"""
We can predict recommended items for user, by row, using RATING_NAN / BINARY.
"""
user = random.choice(fixtures.DFCRITICS_NAN.index)
self._test_helper(user, self.engine_nan.recommend_items,
fixtures.recommend_items)
self._test_helper(user,
self.engine_binary.recommend_items,
fixtures.recommend_items,
binary=True)
def test_recommend_users(self):
"""
We can predict recommended users for item, by row, using RATING_NAN / BINARY.
"""
item = random.choice(fixtures.DFCRITICS_NAN.transpose().index)
self._test_helper(item, self.engine_nan.recommend_users,
fixtures.recommend_users)
self._test_helper(item,
self.engine_binary.recommend_users,
fixtures.recommend_users,
binary=True)
# test column based recs
def test_recommend_items_bycol(self):
"""
We can predict recommended items for user, by column.
"""
user = random.choice(fixtures.DFCRITICS_NAN.index)
self._test_helper(
user,
functools.partial(self.engine_nan.recommend_items,
method=self.engine_nan.ITEM_BASED),
fixtures.recommend_items_bycol)
self._test_helper(user,
functools.partial(
self.engine_binary.recommend_items,
method=self.engine_binary.ITEM_BASED),
fixtures.recommend_items_bycol,
binary=True)
def test_recommend_users_bycol(self):
"""
We can predict recommended users for item, by column.
"""
item = random.choice(fixtures.DFCRITICS_NAN.transpose().index)
self._test_helper(
item,
functools.partial(self.engine_nan.recommend_users,
method=self.engine_nan.USER_BASED),
fixtures.recommend_users_bycol)
self._test_helper(item,
functools.partial(
self.engine_binary.recommend_users,
method=self.engine_binary.USER_BASED),
fixtures.recommend_users_bycol,
binary=True)
def tearDown(self):
pass
