parser.add_argument("--cutoff", type=int,
default=10,
help="Length of displayed rankings.")
parser.add_argument("--ranker_pair", type=int,
default=0,
help="Ranker pair to put in comparison.")
args = parser.parse_args()
click_model = args.click_model
binarize_labels = 'binarized' in click_model
eta = args.eta
cutoff = args.cutoff
data = dataset.get_dataset_from_json_info(
args.dataset,
args.dataset_info_path,
)
fold_id = (args.fold_id-1)%data.num_folds()
data = data.get_data_folds()[fold_id]
start = time.time()
data.read_data()
print('Time past for reading data: %d seconds' % (time.time() - start))
pretrain_models = prtr.read_many_models(args.model_file, data)
n_models = pretrain_models.shape[0]
# chosen_models = np.random.choice(n_models, size=2, replace=False)
chosen_models = np.array([(args.ranker_pair-1)*2, (args.ranker_pair-1)*2+1])
help="Name of dataset to sample from.")
parser.add_argument("--dataset_info_path", type=str,
default="local_dataset_info.txt",
help="Path to dataset info file.")
parser.add_argument("--cutoff", type=int,
help="Maximum number of items that can be displayed.",
default=5)
args = parser.parse_args()
click_model_name = args.click_model
cutoff = args.cutoff
data = dataset.get_dataset_from_json_info(
args.dataset,
args.dataset_info_path,
shared_resource = False,
)
fold_id = (args.fold_id-1)%data.num_folds()
data = data.get_data_folds()[fold_id]
start = time.time()
data.read_data()
print('Time past for reading data: %d seconds' % (time.time() - start))
max_ranking_size = np.min((cutoff, data.max_query_size()))
click_model = clk.get_click_model(click_model_name)
alpha, beta = click_model(np.arange(max_ranking_size))
