def setUp(self):
self.lsi = LSIModel("title")
self.df = pd.read_csv(
resource_filename(testfm.__name__, "data/movielenshead.dat"),
sep="::",
header=None,
names=["user", "item", "rating", "date", "title"])
class TestLSI(unittest.TestCase):
def setUp(self):
self.lsi = LSIModel("title")
self.df = pd.read_csv(
resource_filename(testfm.__name__, "data/movielenshead.dat"),
sep="::",
header=None,
names=["user", "item", "rating", "date", "title"])
def test_fit(self):
self.lsi.fit(self.df)
self.assertEqual(len(self.lsi._user_representation),
len(self.df.user.unique()))
self.assertEqual(len(self.lsi._item_representation),
len(self.df.item.unique()))
def test_score(self):
self.lsi.fit(self.df)
#item in the user profile (Booberang) should have higher prediction than movie not in the profile Rob Roy
self.assertTrue(
self.lsi.get_score(1, 122) > self.lsi.get_score(1, 151))
def test_user_model(self):
um = self.lsi._get_user_models(self.df)
self.assertEqual(um[93], ["collateral", "man", "fire"])
def test_item_model(self):
im = self.lsi._get_item_models(self.df)
self.assertEqual(im[122], ["boomerang"])
self.assertEqual(im[329], ["star", "trek", "generations"])
class TestLSI(unittest.TestCase):
def setUp(self):
self.lsi = LSIModel("title")
self.df = pd.read_csv(resource_filename(testfm.__name__,"data/movielenshead.dat"), sep="::", header=None,
names=["user", "item", "rating", "date", "title"])
def test_fit(self):
self.lsi.fit(self.df)
self.assertEqual(len(self.lsi._user_representation), len(self.df.user.unique()))
self.assertEqual(len(self.lsi._item_representation), len(self.df.item.unique()))
def test_score(self):
self.lsi.fit(self.df)
#item in the user profile (Booberang) should have higher prediction than movie not in the profile Rob Roy
self.assertTrue(self.lsi.get_score(1, 122) > self.lsi.get_score(1, 151))
def test_user_model(self):
um = self.lsi._get_user_models(self.df)
self.assertEqual(um[93], ["collateral", "man", "fire"])
def test_item_model(self):
im = self.lsi._get_item_models(self.df)
self.assertEqual(im[122], ["boomerang"])
self.assertEqual(im[329], ["star", "trek", "generations"])
class TestLSI(unittest.TestCase):
def setUp(self):
self.lsi = LSIModel("title")
self.df = pd.read_csv(resource_filename(testfm.__name__,'data/movielenshead.dat'), sep="::", header=None, names=['user', 'item', 'rating', 'date', 'title'])
def test_fit(self):
self.lsi.fit(self.df)
self.assertEqual(len(self.lsi._user_representation), len(self.df.user.unique()))
self.assertEqual(len(self.lsi._item_representation), len(self.df.item.unique()))
def test_score(self):
self.lsi.fit(self.df)
#item in the user profile (Booberang) should have higher prediction than movie not in the profile Rob Roy
self.assertTrue(self.lsi.getScore(1, 122) > self.lsi.getScore(1, 151))
def test_user_model(self):
um = self.lsi._get_user_models(self.df)
self.assertEqual(um[93], ['collateral', 'man', 'fire'])
def test_item_model(self):
im = self.lsi._get_item_models(self.df)
self.assertEqual(im[122], ['boomerang'])
self.assertEqual(im[329], ['star', 'trek', 'generations'])
def setUp(self):
self.lsi = LSIModel("title")
self.df = pd.read_csv(resource_filename(testfm.__name__,"data/movielenshead.dat"), sep="::", header=None,
names=["user", "item", "rating", "date", "title"])
def setUp(self):
self.lsi = LSIModel("title")
self.df = pd.read_csv(resource_filename(testfm.__name__,'data/movielenshead.dat'), sep="::", header=None, names=['user', 'item', 'rating', 'date', 'title'])