class TestSVDFilter(unittest.TestCase):
def setUp(self):
self.data = {
'Lisa Rose': {
'Lady in the Water': 2.5,
'Snakes on a Plane': 3.5,
'Just My Luck': 3.0,
'Superman Returns': 3.5,
'You, Me and Dupree': 2.5,
'The Night Listener': 3.0
},
'Gene Seymour': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 3.5,
'Just My Luck': 1.5,
'Superman Returns': 5.0,
'The Night Listener': 3.0,
'You, Me and Dupree': 3.5
},
'Michael Phillips': {
'Lady in the Water': 2.5,
'Snakes on a Plane': 3.0,
'Superman Returns': 3.5,
'The Night Listener': 4.0
},
'Claudia Puig': {
'Snakes on a Plane': 3.5,
'Just My Luck': 3.0,
'The Night Listener': 4.5,
'Superman Returns': 4.0,
'You, Me and Dupree': 2.5
},
'Mick LaSalle': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 4.0,
'Just My Luck': 2.0,
'Superman Returns': 3.0,
'The Night Listener': 3.0,
'You, Me and Dupree': 2.0
},
'Jack Matthews': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 4.0,
'The Night Listener': 3.0,
'Superman Returns': 5.0,
'You, Me and Dupree': 3.5
},
'Toby': {
'Snakes on a Plane': 4.5,
'You, Me and Dupree': 1.0,
'Superman Returns': 4.0
}
}
self.instance = 'Gene Seymour'
self.feature = 'Snakes on a Plane'
self.testInstance = {'Gene Seymour': {'Snakes on a Plane': 10}}
self.recTester = {'Gene Seymour': ['Snakes on a Plane']}
self.fltr = CollaborativeFilter(self.data, 1)
self.test2Instance = {'Gene Seymour': {'Snakes on a Plane': 20}}
def test_update_value(self):
self.fltr.updateValues(self.testInstance)
self.assertEqual(10,
self.fltr.getDataDict()[self.instance][self.feature])
def test_get_recommendation(self):
recom = self.fltr.getRecommendation(self.recTester)
self.assertIsNot(recom, None)
def test_force_model_update(self):
model = self.fltr.getModel()
self.fltr.updateValues(self.testInstance)
self.fltr.forceModelUpdate()
self.assertTrue(self.listNotEqual(model, self.fltr.getModel()))
def test_get_data_dict(self):
self.assertEqual(self.data, self.fltr.getDataDict())
def test_get_data(self):
self.assertIsNot(
self.fltr.getData("Gene Seymour", "Snakes on a Plane"), None)
self.assertIsInstance(self.fltr.getData(), type([]))
def test_get_model(self):
temp = np.array([1])
self.assertIsInstance(self.fltr.getModel(), type(temp))
def test_sparseify_data(self):
temp = sparse.dok_matrix([[1, 0], [1, 0]])
self.assertIsInstance(self.fltr.getSparseData(), type(temp))
temp2 = self.fltr.getSparseData()
self.fltr.updateValues(self.testInstance)
self.fltr.getRecommendation(self.recTester)
self.assertIsInstance(self.fltr.getSparseData(), type(temp))
self.assertTrue(self.npListNotEqual(self.fltr.getSparseData(), temp2))
temp3 = self.fltr.getSparseData()
self.fltr.updateValues(self.test2Instance)
self.fltr.forceModelUpdate()
self.assertIsInstance(self.fltr.getSparseData(), type(temp))
self.assertTrue(self.npListNotEqual(self.fltr.getSparseData(), temp2))
self.assertTrue(self.npListNotEqual(self.fltr.getSparseData(), temp3))
def test_is_updated(self):
self.assertFalse(self.fltr.getUpdated())
self.fltr.updateValues(self.testInstance)
self.assertTrue(self.fltr.getUpdated())
self.fltr.getRecommendation(self.recTester)
self.assertFalse(self.fltr.getUpdated())
self.fltr.updateValues(self.test2Instance)
self.assertTrue(self.fltr.getUpdated())
self.fltr.forceModelUpdate()
self.assertFalse(self.fltr.getUpdated())
def npListNotEqual(self, list1, list2):
if list1.get_shape()[0] != list2.get_shape()[0] or list1.get_shape(
)[1] != list2.get_shape()[1]:
return True
for i in range(list1.get_shape()[0]):
for j in range(list1.get_shape()[1]):
if list1[i, j] != list2[i, j]:
return True
return False
def listNotEqual(self, list1, list2):
if len(list1) != len(list2):
return True
for i in range(len(list1)):
if len(list1[i]) != len(list2[i]):
return True
for j in range(len(list1[i])):
if list1[i][j] != list2[i][j]:
return True
return False
class TestSVDFilter(unittest.TestCase):
def setUp(self):
self.data = {
"Lisa Rose": {
"Lady in the Water": 2.5,
"Snakes on a Plane": 3.5,
"Just My Luck": 3.0,
"Superman Returns": 3.5,
"You, Me and Dupree": 2.5,
"The Night Listener": 3.0,
},
"Gene Seymour": {
"Lady in the Water": 3.0,
"Snakes on a Plane": 3.5,
"Just My Luck": 1.5,
"Superman Returns": 5.0,
"The Night Listener": 3.0,
"You, Me and Dupree": 3.5,
},
"Michael Phillips": {
"Lady in the Water": 2.5,
"Snakes on a Plane": 3.0,
"Superman Returns": 3.5,
"The Night Listener": 4.0,
},
"Claudia Puig": {
"Snakes on a Plane": 3.5,
"Just My Luck": 3.0,
"The Night Listener": 4.5,
"Superman Returns": 4.0,
"You, Me and Dupree": 2.5,
},
"Mick LaSalle": {
"Lady in the Water": 3.0,
"Snakes on a Plane": 4.0,
"Just My Luck": 2.0,
"Superman Returns": 3.0,
"The Night Listener": 3.0,
"You, Me and Dupree": 2.0,
},
"Jack Matthews": {
"Lady in the Water": 3.0,
"Snakes on a Plane": 4.0,
"The Night Listener": 3.0,
"Superman Returns": 5.0,
"You, Me and Dupree": 3.5,
},
"Toby": {"Snakes on a Plane": 4.5, "You, Me and Dupree": 1.0, "Superman Returns": 4.0},
}
self.instance = "Gene Seymour"
self.feature = "Snakes on a Plane"
self.testInstance = {"Gene Seymour": {"Snakes on a Plane": 10}}
self.recTester = {"Gene Seymour": ["Snakes on a Plane"]}
self.fltr = CollaborativeFilter(self.data, 1)
self.test2Instance = {"Gene Seymour": {"Snakes on a Plane": 20}}
def test_update_value(self):
self.fltr.updateValues(self.testInstance)
self.assertEqual(10, self.fltr.getDataDict()[self.instance][self.feature])
def test_get_recommendation(self):
recom = self.fltr.getRecommendation(self.recTester)
self.assertIsNot(recom, None)
def test_force_model_update(self):
model = self.fltr.getModel()
self.fltr.updateValues(self.testInstance)
self.fltr.forceModelUpdate()
self.assertTrue(self.listNotEqual(model, self.fltr.getModel()))
def test_get_data_dict(self):
self.assertEqual(self.data, self.fltr.getDataDict())
def test_get_data(self):
self.assertIsNot(self.fltr.getData("Gene Seymour", "Snakes on a Plane"), None)
self.assertIsInstance(self.fltr.getData(), type([]))
def test_get_model(self):
temp = np.array([1])
self.assertIsInstance(self.fltr.getModel(), type(temp))
def test_sparseify_data(self):
temp = sparse.dok_matrix([[1, 0], [1, 0]])
self.assertIsInstance(self.fltr.getSparseData(), type(temp))
temp2 = self.fltr.getSparseData()
self.fltr.updateValues(self.testInstance)
self.fltr.getRecommendation(self.recTester)
self.assertIsInstance(self.fltr.getSparseData(), type(temp))
self.assertTrue(self.npListNotEqual(self.fltr.getSparseData(), temp2))
temp3 = self.fltr.getSparseData()
self.fltr.updateValues(self.test2Instance)
self.fltr.forceModelUpdate()
self.assertIsInstance(self.fltr.getSparseData(), type(temp))
self.assertTrue(self.npListNotEqual(self.fltr.getSparseData(), temp2))
self.assertTrue(self.npListNotEqual(self.fltr.getSparseData(), temp3))
def test_is_updated(self):
self.assertFalse(self.fltr.getUpdated())
self.fltr.updateValues(self.testInstance)
self.assertTrue(self.fltr.getUpdated())
self.fltr.getRecommendation(self.recTester)
self.assertFalse(self.fltr.getUpdated())
self.fltr.updateValues(self.test2Instance)
self.assertTrue(self.fltr.getUpdated())
self.fltr.forceModelUpdate()
self.assertFalse(self.fltr.getUpdated())
def npListNotEqual(self, list1, list2):
if list1.get_shape()[0] != list2.get_shape()[0] or list1.get_shape()[1] != list2.get_shape()[1]:
return True
for i in range(list1.get_shape()[0]):
for j in range(list1.get_shape()[1]):
if list1[i, j] != list2[i, j]:
return True
return False
def listNotEqual(self, list1, list2):
if len(list1) != len(list2):
return True
for i in range(len(list1)):
if len(list1[i]) != len(list2[i]):
return True
for j in range(len(list1[i])):
if list1[i][j] != list2[i][j]:
return True
return False
