"max_pvalue"
]
bcach = binocache()
wholecsvfilename = "WHOLE"+language + "_" + str(min_sizes[language]) + "_results.csv"
outfn = language + "_" + str(min_sizes[language]) + "_results.csv"
#wholeout = open(wholecsvfilename, "w")
with open(op.join(evaluation_output_directory, outfn), "w") as fout:
fout.write("\t".join(cols_for_output) + "\n")
for mdir in m_dirs:
experiment_dirs = [f.path for f in scandir(mdir) if f.is_dir()]
# experiment_dirs.sort(key=lambda x:x.split("_")[-1])
for exp_dir in experiment_dirs:
param_dict = get_params_from_dirname(exp_dir)
pattern = "k" + str(param_dict["k"]) + "_" + "th" + str(param_dict["th"]) + "_type%d.json"
induced_candidates_ = load_candidates(exp_dir, pattern=pattern)
if len(induced_candidates_) == 0:
logger.error(str(param_dict) + " has an empyt directory: " + str(exp_dir))
break
induced_candidates_ = [can for can in induced_candidates_
if len(can["entities"]) >= min_sizes[language]]
if len(induced_candidates_) < 2:
logger.error("\n\nToo few candidates left for " + str(param_dict))
continue
all_entities = collect_entities(induced_candidates_)
# Generating randoms
randomized_candidates_ = []
if num_random > 0:
randomized_candidates_ = create_random_candidates(induced_candidates_,
num_random + margin)
import utils
recall_cands_file = sys.argv[1]
ce_score_file = sys.argv[2]
outfile = sys.argv[3]
random_seed = 111
rng = random.Random(random_seed)
neg_cnt = 4
ce_threshold_neg = 0.1
ce_threshold_pos = 0.9
q_text, p_text, p_title = utils.load_corpus(corpus='marco', q_type='train')
pos_qp, pos_qp_add = utils.load_pos_examples(p_text)
cand_qp_all, train_qids = utils.load_candidates(recall_cands_file, col=4)
ce_score = utils.load_ce_score(ce_score_file, train_qids)
# neg examples
neg_qp = {}
for qid, pids in cand_qp_all.items():
if qid not in pos_qp:
continue
select_pid = []
pos_cnt = len(pos_qp[qid])
for index in range(50):
_pid = pids[index]
if len(select_pid) == neg_cnt * pos_cnt:
break
if _pid in pos_qp[qid] or _pid in select_pid or _pid in pos_qp_add.get(
qid, []):
msg = (
"path of text file containing embeddings of the queries and candidates"
)
parser.add_argument("path_embeddings", help=msg)
parser.add_argument("path_output", help="path of output (pickle file)")
parser.add_argument("-s", "--seed", type=int, default=91500)
args = parser.parse_args()
# Check dataset name
dataset_name_exp = utils.expand_subtask_name(args.subtask)
# Load candidates
print("Loading candidates...")
path_candidates = "{}/vocabulary/{}.vocabulary.txt".format(
args.dir_datasets, dataset_name_exp)
candidates = utils.load_candidates(path_candidates, normalize=True)
print("Nb candidates: {}".format(len(candidates)))
# Load queries
print("Loading queries...")
path_q_train = "{}/training/data/{}.training.data.txt".format(
args.dir_datasets, dataset_name_exp)
path_q_dev = "{}/trial/data/{}.trial.data.txt".format(
args.dir_datasets, dataset_name_exp)
path_q_test = "{}/test/data/{}.test.data.txt".format(
args.dir_datasets, dataset_name_exp)
q_train, _ = utils.load_queries(path_q_train, normalize=True)
q_dev, _ = utils.load_queries(path_q_dev, normalize=True)
q_test, _ = utils.load_queries(path_q_test, normalize=True)
print("Nb training queries: {}".format(len(q_train)))
print("Nb dev queries: {}".format(len(q_dev)))