class RankingEvaluator:
def __init__(self):
self.submission_data = ChallengeData()
self.solution_data = ChallengeData()
def read_data(self, submission_file, solution_file):
self.submission_data.read_from_file(submission_file)
self.solution_data.read_from_file(solution_file)
def run(self):
average_NDCG = 0
days = self.solution_data.get_days()
for day in days:
submission = self.submission_data.get(day)
solution = self.solution_data.get(day)
computer = NDCGComputer(submission, solution, 100)
average_NDCG += computer.run()
average_NDCG /= float(len(days))
return average_NDCG
class RankingEvaluator:
def __init__(self):
self.submission_data = ChallengeData()
self.solution_data = ChallengeData()
def read_data(self,submission_file,solution_file):
self.submission_data.read_from_file(submission_file)
self.solution_data.read_from_file(solution_file)
def run(self):
average_NDCG = 0
days = self.solution_data.get_days()
for day in days:
submission = self.submission_data.get(day)
solution = self.solution_data.get(day)
computer = NDCGComputer(submission,solution,100)
average_NDCG += computer.run()
average_NDCG /= float(len(days))
return average_NDCG
class DecreaseEvaluator:
def __init__(self):
self.submission_data = ChallengeData()
self.solution_data = ChallengeData()
def read_data(self, submission_file, solution_file):
self.submission_data.read_from_file(submission_file)
self.solution_data.read_from_file(solution_file)
def run(self):
MSE = 0
N = 0
days = self.solution_data.get_days()
for day in days:
solution = self.solution_data.get(day)
submission = self.submission_data.get(day)
for station in solution:
pred = 0
if station in submission:
pred = submission[station]
MSE += math.pow(pred - solution[station], 2)
N += 1
return math.pow(MSE / float(N), 0.5)
class DecreaseEvaluator:
def __init__(self):
self.submission_data = ChallengeData()
self.solution_data = ChallengeData()
def read_data(self, submission_file, solution_file):
self.submission_data.read_from_file(submission_file)
self.solution_data.read_from_file(solution_file)
def run(self):
MSE = 0
N = 0
days = self.solution_data.get_days()
for day in days:
solution = self.solution_data.get(day)
submission = self.submission_data.get(day)
for station in solution:
pred = 0
if station in submission:
pred = submission[station]
MSE += math.pow(pred-solution[station], 2)
N += 1
return math.pow(MSE / float(N), 0.5)
def __init__(self): self.submission_data = ChallengeData() self.solution_data = ChallengeData()
def __init__(self):
self.submission_data = ChallengeData()
self.solution_data = ChallengeData()
help='disables CUDA training')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
# Load CIFAR10 using torch data paradigm
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
cifar10_mean_color = [0.49131522, 0.48209435, 0.44646862]
# std dev of color across training images
cifar10_std_color = [0.01897398, 0.03039277, 0.03872553]
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(cifar10_mean_color, cifar10_std_color),
])
test_dataset = ChallengeData(args.test_dir, download=True,
transform=transform)
# Datasets
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.test_batch_size, shuffle=False, **kwargs)
if os.path.exists(args.model):
model = torch.load(args.model)
model.drop_path_prob = 0.0
else:
print('Model path specified does not exst')
sys.exit(1)
# cross-entropy loss function
criterion = F.cross_entropy
if args.cuda:
model.cuda()
