def test_get_wins_loses(self):
diff_teams_pairs = {(1, 4): [1, 2]}
otpc = DifferentTeamsPairsContainer(diff_teams_pairs)
self.assertEqual(otpc.get_wins_loses(1, 4), (1, 2))
self.assertEqual(otpc.get_wins_loses(4, 1), (2, 1))
self.assertEqual(otpc.get_wins_loses(5, 7), (0, 0))
STAT_FILE_NAME = "stat_data.p"
TEST_DATA_FILE_NAME = "test_data16451.p"
TEAM_OFFSET = 1000
print "load stat data from {0}".format(STAT_FILE_NAME)
stat_data = pickle.load(open(STAT_FILE_NAME, 'rb'))
print "load complete"
print "load test data from {0}".format(TEST_DATA_FILE_NAME)
test_data = pickle.load(open(TEST_DATA_FILE_NAME, 'rb'))
print "load complete"
solo_hr, stpc, dtpc = stat_data
solo_hr = defaultdict(lambda: [0, 0], solo_hr)
stpc = SameTeamsPairsContainer(stpc)
dtpc = DifferentTeamsPairsContainer(dtpc)
tr = FractionTermResolver(TEAM_OFFSET, solo_hr, stpc, dtpc, smoothing=0.5)
#tr = OddsTermResolver(TEAM_OFFSET, solo_hr, stpc, dtpc)
predictor = PredictorNaiveBayesBigram(TEAM_OFFSET, tr)
num_correct = 0.0
num_matches = len(test_data)
for record in test_data:
radiant_win, radiant, dire = record
probability = predictor.predict(radiant, dire)
if probability > 0.5001 and radiant_win or probability < 0.4999 and not radiant_win:
num_correct += 1.0
def setUp(self):
self.otpc = DifferentTeamsPairsContainer()
