def test_tfidf_weighted(self):
(n, given_users, expected) = [
5, [4045, 144, 3855, 1637, 2919],
'recommendations for user 4045:\n 807: 0.1932\n 63: 0.1438\n 187: 0.0947\n 11: 0.0900\n 641: 0.0471\nrecommendations for user 144:\n 11: 0.1394\n 585: 0.1229\n 671: 0.1130\n 672: 0.0878\n 141: 0.0436\nrecommendations for user 3855:\n 1892: 0.2243\n 1894: 0.1465\n 604: 0.1258\n 462: 0.1050\n 10020: 0.0898\nrecommendations for user 1637:\n 393: 0.1976\n 24: 0.1900\n 2164: 0.1522\n 601: 0.1334\n 5503: 0.0992\nrecommendations for user 2919:\n 180: 0.1454\n 11: 0.1238\n 1891: 0.1172\n 424: 0.1074\n 2501: 0.0973'
]
self.data.load('testdata/ratings.csv', 'testdata/movie-tags.csv')
self.rs = Recommender(self.data,
model.TFIDFModel(verbose=False),
sc.tfidf_weighted,
sg.top_ns,
verbose=False)
self.rs.build()
given_users = self.data.translate_users(given_users)
recs = self.rs.recommend(given_users, n)
self.assertTrue(
self.data.print_recs(recs,
given_users=given_users,
printer=pr.coursera_pa2_printer) == expected)
def test_tfidf_unweighted(self):
(n, given_users, expected) = [
5, [4045, 144, 3855, 1637, 2919],
'recommendations for user 4045:\n 11: 0.3596\n 63: 0.2612\n 807: 0.2363\n 187: 0.2059\n 2164: 0.1899\nrecommendations for user 144:\n 11: 0.3715\n 585: 0.2512\n 38: 0.1908\n 141: 0.1861\n 807: 0.1748\nrecommendations for user 3855:\n 1892: 0.4303\n 1894: 0.2958\n 63: 0.2226\n 2164: 0.2119\n 604: 0.1941\nrecommendations for user 1637:\n 2164: 0.2272\n 141: 0.2225\n 745: 0.2067\n 601: 0.1995\n 807: 0.1846\nrecommendations for user 2919:\n 11: 0.3659\n 1891: 0.3278\n 640: 0.1958\n 424: 0.1840\n 180: 0.1527'
]
self.data.load('testdata/ratings.csv', 'testdata/movie-tags.csv')
self.rs = Recommender(self.data,
model.TFIDFModel(verbose=False),
sc.tfidf_unweighted,
sg.top_ns,
verbose=False)
self.rs.build()
given_users = self.data.translate_users(given_users)
recs = self.rs.recommend(given_users, n)
self.assertTrue(
self.data.print_recs(recs,
given_users=given_users,
printer=pr.coursera_pa2_printer) == expected)
def test_advanced(self):
(n, given_items, expected) = [
5, [11, 121, 8587],
'11,1891,5.69,1892,5.65,243,5.00,1894,4.72,2164,4.11\n121,122,4.74,120,3.82,2164,3.40,243,3.26,1894,3.22\n8587,10020,4.18,812,4.03,7443,2.63,9331,2.46,786,2.39'
]
self.data.load('testdata/ratings.csv')
self.rs = Recommender(self.data,
model.PreferenceModel(verbose=False),
sc.cooc_advanced,
sg.top_ns,
verbose=False)
self.rs.build()
given_items = self.data.translate_items(given_items)
recs = self.rs.recommend(given_items, n)
self.assertTrue(
self.data.print_recs(recs,
given_items=given_items,
printer=pr.coursera_pa1_printer) == expected)
def test_simple(self):
(n, given_items, expected) = [
5, [11, 121, 8587],
'11,603,0.96,1892,0.94,1891,0.94,120,0.93,1894,0.93\n121,120,0.95,122,0.95,603,0.94,597,0.89,604,0.88\n8587,603,0.92,597,0.90,607,0.87,120,0.86,13,0.86'
]
self.data.load('testdata/ratings.csv')
self.rs = Recommender(self.data,
model.PreferenceModel(verbose=False),
sc.cooc_simple,
sg.top_ns,
verbose=False)
self.rs.build()
given_items = self.data.translate_items(given_items)
recs = self.rs.recommend(given_items, n)
self.assertTrue(
self.data.print_recs(recs,
given_items=given_items,
printer=pr.coursera_pa1_printer) == expected)
def test_tfidf_weighted(self):
(n, given_users, expected) = [5, [4045,144,3855,1637,2919], 'recommendations for user 4045:\n 807: 0.1932\n 63: 0.1438\n 187: 0.0947\n 11: 0.0900\n 641: 0.0471\nrecommendations for user 144:\n 11: 0.1394\n 585: 0.1229\n 671: 0.1130\n 672: 0.0878\n 141: 0.0436\nrecommendations for user 3855:\n 1892: 0.2243\n 1894: 0.1465\n 604: 0.1258\n 462: 0.1050\n 10020: 0.0898\nrecommendations for user 1637:\n 393: 0.1976\n 24: 0.1900\n 2164: 0.1522\n 601: 0.1334\n 5503: 0.0992\nrecommendations for user 2919:\n 180: 0.1454\n 11: 0.1238\n 1891: 0.1172\n 424: 0.1074\n 2501: 0.0973']
self.data.load('testdata/ratings.csv', 'testdata/movie-tags.csv')
self.rs = Recommender(self.data, model.TFIDFModel(verbose = False), sc.tfidf_weighted,
sg.top_ns, verbose = False)
self.rs.build()
given_users = self.data.translate_users(given_users)
recs = self.rs.recommend(given_users,n)
self.assertTrue(self.data.print_recs(recs,
given_users = given_users,
printer = pr.coursera_pa2_printer) == expected)
def test_tfidf_unweighted(self):
(n, given_users, expected) = [5, [4045,144,3855,1637,2919], 'recommendations for user 4045:\n 11: 0.3596\n 63: 0.2612\n 807: 0.2363\n 187: 0.2059\n 2164: 0.1899\nrecommendations for user 144:\n 11: 0.3715\n 585: 0.2512\n 38: 0.1908\n 141: 0.1861\n 807: 0.1748\nrecommendations for user 3855:\n 1892: 0.4303\n 1894: 0.2958\n 63: 0.2226\n 2164: 0.2119\n 604: 0.1941\nrecommendations for user 1637:\n 2164: 0.2272\n 141: 0.2225\n 745: 0.2067\n 601: 0.1995\n 807: 0.1846\nrecommendations for user 2919:\n 11: 0.3659\n 1891: 0.3278\n 640: 0.1958\n 424: 0.1840\n 180: 0.1527']
self.data.load('testdata/ratings.csv', 'testdata/movie-tags.csv')
self.rs = Recommender(self.data, model.TFIDFModel(verbose = False), sc.tfidf_unweighted,
sg.top_ns, verbose = False)
self.rs.build()
given_users = self.data.translate_users(given_users)
recs = self.rs.recommend(given_users,n)
self.assertTrue(self.data.print_recs(recs,
given_users = given_users,
printer = pr.coursera_pa2_printer) == expected)
def test_advanced(self):
(n, given_items, expected) = [5, [11,121,8587], '11,1891,5.69,1892,5.65,243,5.00,1894,4.72,2164,4.11\n121,122,4.74,120,3.82,2164,3.40,243,3.26,1894,3.22\n8587,10020,4.18,812,4.03,7443,2.63,9331,2.46,786,2.39']
self.data.load('testdata/ratings.csv')
self.rs = Recommender(self.data, model.PreferenceModel(verbose = False), sc.cooc_advanced,
sg.top_ns, verbose = False)
self.rs.build()
given_items = self.data.translate_items(given_items)
recs = self.rs.recommend(given_items,n)
self.assertTrue(self.data.print_recs(recs,
given_items = given_items,
printer = pr.coursera_pa1_printer) == expected)
def test_simple(self):
(n, given_items, expected) = [5, [11,121,8587], '11,603,0.96,1892,0.94,1891,0.94,120,0.93,1894,0.93\n121,120,0.95,122,0.95,603,0.94,597,0.89,604,0.88\n8587,603,0.92,597,0.90,607,0.87,120,0.86,13,0.86']
self.data.load('testdata/ratings.csv')
self.rs = Recommender(self.data, model.PreferenceModel(verbose = False), sc.cooc_simple,
sg.top_ns, verbose = False)
self.rs.build()
given_items = self.data.translate_items(given_items)
recs = self.rs.recommend(given_items,n)
self.assertTrue(self.data.print_recs(recs,
given_items = given_items,
printer = pr.coursera_pa1_printer) == expected)
class RecommenderTest(unittest.TestCase):
def setUp(self):
self.data = ds.DataIO(verbose = False)
def test_simple(self):
(n, given_items, expected) = [5, [11,121,8587], '11,603,0.96,1892,0.94,1891,0.94,120,0.93,1894,0.93\n121,120,0.95,122,0.95,603,0.94,597,0.89,604,0.88\n8587,603,0.92,597,0.90,607,0.87,120,0.86,13,0.86']
self.data.load('testdata/ratings.csv')
self.rs = Recommender(self.data, model.PreferenceModel(verbose = False), sc.cooc_simple,
sg.top_ns, verbose = False)
self.rs.build()
given_items = self.data.translate_items(given_items)
recs = self.rs.recommend(given_items,n)
self.assertTrue(self.data.print_recs(recs,
given_items = given_items,
printer = pr.coursera_pa1_printer) == expected)
def test_advanced(self):
(n, given_items, expected) = [5, [11,121,8587], '11,1891,5.69,1892,5.65,243,5.00,1894,4.72,2164,4.11\n121,122,4.74,120,3.82,2164,3.40,243,3.26,1894,3.22\n8587,10020,4.18,812,4.03,7443,2.63,9331,2.46,786,2.39']
self.data.load('testdata/ratings.csv')
self.rs = Recommender(self.data, model.PreferenceModel(verbose = False), sc.cooc_advanced,
sg.top_ns, verbose = False)
self.rs.build()
given_items = self.data.translate_items(given_items)
recs = self.rs.recommend(given_items,n)
self.assertTrue(self.data.print_recs(recs,
given_items = given_items,
printer = pr.coursera_pa1_printer) == expected)
def test_tfidf_unweighted(self):
(n, given_users, expected) = [5, [4045,144,3855,1637,2919], 'recommendations for user 4045:\n 11: 0.3596\n 63: 0.2612\n 807: 0.2363\n 187: 0.2059\n 2164: 0.1899\nrecommendations for user 144:\n 11: 0.3715\n 585: 0.2512\n 38: 0.1908\n 141: 0.1861\n 807: 0.1748\nrecommendations for user 3855:\n 1892: 0.4303\n 1894: 0.2958\n 63: 0.2226\n 2164: 0.2119\n 604: 0.1941\nrecommendations for user 1637:\n 2164: 0.2272\n 141: 0.2225\n 745: 0.2067\n 601: 0.1995\n 807: 0.1846\nrecommendations for user 2919:\n 11: 0.3659\n 1891: 0.3278\n 640: 0.1958\n 424: 0.1840\n 180: 0.1527']
self.data.load('testdata/ratings.csv', 'testdata/movie-tags.csv')
self.rs = Recommender(self.data, model.TFIDFModel(verbose = False), sc.tfidf_unweighted,
sg.top_ns, verbose = False)
self.rs.build()
given_users = self.data.translate_users(given_users)
recs = self.rs.recommend(given_users,n)
self.assertTrue(self.data.print_recs(recs,
given_users = given_users,
printer = pr.coursera_pa2_printer) == expected)
def test_tfidf_weighted(self):
(n, given_users, expected) = [5, [4045,144,3855,1637,2919], 'recommendations for user 4045:\n 807: 0.1932\n 63: 0.1438\n 187: 0.0947\n 11: 0.0900\n 641: 0.0471\nrecommendations for user 144:\n 11: 0.1394\n 585: 0.1229\n 671: 0.1130\n 672: 0.0878\n 141: 0.0436\nrecommendations for user 3855:\n 1892: 0.2243\n 1894: 0.1465\n 604: 0.1258\n 462: 0.1050\n 10020: 0.0898\nrecommendations for user 1637:\n 393: 0.1976\n 24: 0.1900\n 2164: 0.1522\n 601: 0.1334\n 5503: 0.0992\nrecommendations for user 2919:\n 180: 0.1454\n 11: 0.1238\n 1891: 0.1172\n 424: 0.1074\n 2501: 0.0973']
self.data.load('testdata/ratings.csv', 'testdata/movie-tags.csv')
self.rs = Recommender(self.data, model.TFIDFModel(verbose = False), sc.tfidf_weighted,
sg.top_ns, verbose = False)
self.rs.build()
given_users = self.data.translate_users(given_users)
recs = self.rs.recommend(given_users,n)
self.assertTrue(self.data.print_recs(recs,
given_users = given_users,
printer = pr.coursera_pa2_printer) == expected)
class RecommenderTest(unittest.TestCase):
def setUp(self):
self.data = ds.DataIO(verbose=False)
def test_simple(self):
(n, given_items, expected) = [
5, [11, 121, 8587],
'11,603,0.96,1892,0.94,1891,0.94,120,0.93,1894,0.93\n121,120,0.95,122,0.95,603,0.94,597,0.89,604,0.88\n8587,603,0.92,597,0.90,607,0.87,120,0.86,13,0.86'
]
self.data.load('testdata/ratings.csv')
self.rs = Recommender(self.data,
model.PreferenceModel(verbose=False),
sc.cooc_simple,
sg.top_ns,
verbose=False)
self.rs.build()
given_items = self.data.translate_items(given_items)
recs = self.rs.recommend(given_items, n)
self.assertTrue(
self.data.print_recs(recs,
given_items=given_items,
printer=pr.coursera_pa1_printer) == expected)
def test_advanced(self):
(n, given_items, expected) = [
5, [11, 121, 8587],
'11,1891,5.69,1892,5.65,243,5.00,1894,4.72,2164,4.11\n121,122,4.74,120,3.82,2164,3.40,243,3.26,1894,3.22\n8587,10020,4.18,812,4.03,7443,2.63,9331,2.46,786,2.39'
]
self.data.load('testdata/ratings.csv')
self.rs = Recommender(self.data,
model.PreferenceModel(verbose=False),
sc.cooc_advanced,
sg.top_ns,
verbose=False)
self.rs.build()
given_items = self.data.translate_items(given_items)
recs = self.rs.recommend(given_items, n)
self.assertTrue(
self.data.print_recs(recs,
given_items=given_items,
printer=pr.coursera_pa1_printer) == expected)
def test_tfidf_unweighted(self):
(n, given_users, expected) = [
5, [4045, 144, 3855, 1637, 2919],
'recommendations for user 4045:\n 11: 0.3596\n 63: 0.2612\n 807: 0.2363\n 187: 0.2059\n 2164: 0.1899\nrecommendations for user 144:\n 11: 0.3715\n 585: 0.2512\n 38: 0.1908\n 141: 0.1861\n 807: 0.1748\nrecommendations for user 3855:\n 1892: 0.4303\n 1894: 0.2958\n 63: 0.2226\n 2164: 0.2119\n 604: 0.1941\nrecommendations for user 1637:\n 2164: 0.2272\n 141: 0.2225\n 745: 0.2067\n 601: 0.1995\n 807: 0.1846\nrecommendations for user 2919:\n 11: 0.3659\n 1891: 0.3278\n 640: 0.1958\n 424: 0.1840\n 180: 0.1527'
]
self.data.load('testdata/ratings.csv', 'testdata/movie-tags.csv')
self.rs = Recommender(self.data,
model.TFIDFModel(verbose=False),
sc.tfidf_unweighted,
sg.top_ns,
verbose=False)
self.rs.build()
given_users = self.data.translate_users(given_users)
recs = self.rs.recommend(given_users, n)
self.assertTrue(
self.data.print_recs(recs,
given_users=given_users,
printer=pr.coursera_pa2_printer) == expected)
def test_tfidf_weighted(self):
(n, given_users, expected) = [
5, [4045, 144, 3855, 1637, 2919],
'recommendations for user 4045:\n 807: 0.1932\n 63: 0.1438\n 187: 0.0947\n 11: 0.0900\n 641: 0.0471\nrecommendations for user 144:\n 11: 0.1394\n 585: 0.1229\n 671: 0.1130\n 672: 0.0878\n 141: 0.0436\nrecommendations for user 3855:\n 1892: 0.2243\n 1894: 0.1465\n 604: 0.1258\n 462: 0.1050\n 10020: 0.0898\nrecommendations for user 1637:\n 393: 0.1976\n 24: 0.1900\n 2164: 0.1522\n 601: 0.1334\n 5503: 0.0992\nrecommendations for user 2919:\n 180: 0.1454\n 11: 0.1238\n 1891: 0.1172\n 424: 0.1074\n 2501: 0.0973'
]
self.data.load('testdata/ratings.csv', 'testdata/movie-tags.csv')
self.rs = Recommender(self.data,
model.TFIDFModel(verbose=False),
sc.tfidf_weighted,
sg.top_ns,
verbose=False)
self.rs.build()
given_users = self.data.translate_users(given_users)
recs = self.rs.recommend(given_users, n)
self.assertTrue(
self.data.print_recs(recs,
given_users=given_users,
printer=pr.coursera_pa2_printer) == expected)
