139 lines
5.1 KiB
Python
139 lines
5.1 KiB
Python
from argparse import ArgumentParser
|
|
from time import time
|
|
|
|
from dataManager.utils import get_dataset
|
|
from evaluation.evaluate import evaluate, log_eval
|
|
from gfun.generalizedFunnelling import GeneralizedFunnelling
|
|
|
|
"""
|
|
TODO:
|
|
- [!] add support for Binary Datasets (e.g. cls) - NB: CLS dataset is loading only "books" domain data
|
|
- [!] documents should be trimmed to the same length (?)
|
|
- [!] logging
|
|
- add documentations sphinx
|
|
- [!] zero-shot setup
|
|
- FFNN posterior-probabilities' dependent
|
|
- re-init langs when loading VGFs?
|
|
- [!] loss of Attention-aggregator seems to be uncorrelated with Macro-F1 on the validation set!
|
|
- [!] experiment with weight init of Attention-aggregator
|
|
"""
|
|
|
|
|
|
def main(args):
|
|
dataset = get_dataset(args.dataset, args)
|
|
lX, lY = dataset.training()
|
|
lX_te, lY_te = dataset.test()
|
|
|
|
tinit = time()
|
|
|
|
if args.load_trained is None:
|
|
assert any(
|
|
[
|
|
args.posteriors,
|
|
args.wce,
|
|
args.multilingual,
|
|
args.multilingual,
|
|
args.textual_transformer,
|
|
args.visual_transformer,
|
|
]
|
|
), "At least one of VGF must be True"
|
|
|
|
gfun = GeneralizedFunnelling(
|
|
# dataset params ----------------------
|
|
dataset_name=args.dataset,
|
|
langs=dataset.langs(),
|
|
num_labels=dataset.num_labels(),
|
|
# Posterior VGF params ----------------
|
|
posterior=args.posteriors,
|
|
# Multilingual VGF params -------------
|
|
multilingual=args.multilingual,
|
|
embed_dir="~/resources/muse_embeddings",
|
|
# WCE VGF params ----------------------
|
|
wce=args.wce,
|
|
# Transformer VGF params --------------
|
|
textual_transformer=args.textual_transformer,
|
|
textual_transformer_name=args.transformer_name,
|
|
batch_size=args.batch_size,
|
|
epochs=args.epochs,
|
|
lr=args.lr,
|
|
max_length=args.max_length,
|
|
patience=args.patience,
|
|
evaluate_step=args.evaluate_step,
|
|
device=args.device,
|
|
# Visual Transformer VGF params --------------
|
|
visual_transformer=args.visual_transformer,
|
|
visual_transformer_name=args.visual_transformer_name,
|
|
# batch_size=args.batch_size,
|
|
# epochs=args.epochs,
|
|
# lr=args.lr,
|
|
# patience=args.patience,
|
|
# evaluate_step=args.evaluate_step,
|
|
# device="cuda",
|
|
# General params ----------------------
|
|
probabilistic=args.features,
|
|
aggfunc=args.aggfunc,
|
|
optimc=args.optimc,
|
|
load_trained=args.load_trained,
|
|
load_meta=args.meta,
|
|
n_jobs=args.n_jobs,
|
|
)
|
|
|
|
# gfun.get_config()
|
|
gfun.fit(lX, lY)
|
|
|
|
if args.load_trained is None and not args.nosave:
|
|
gfun.save(save_first_tier=True, save_meta=True)
|
|
|
|
# print("- Computing evaluation on training set")
|
|
# preds = gfun.transform(lX)
|
|
# train_eval = evaluate(lY, preds)
|
|
# log_eval(train_eval, phase="train")
|
|
|
|
timetr = time()
|
|
print(f"- training completed in {timetr - tinit:.2f} seconds")
|
|
|
|
gfun_preds = gfun.transform(lX_te)
|
|
test_eval = evaluate(lY_te, gfun_preds)
|
|
log_eval(test_eval, phase="test")
|
|
|
|
timeval = time()
|
|
print(f"- testing completed in {timeval - timetr:.2f} seconds")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = ArgumentParser()
|
|
parser.add_argument("-l", "--load_trained", type=str, default=None)
|
|
parser.add_argument("--meta", action="store_true")
|
|
parser.add_argument("--nosave", action="store_true")
|
|
parser.add_argument("--device", type=str, default="cuda")
|
|
# Dataset parameters -------------------
|
|
parser.add_argument("-d", "--dataset", type=str, default="rcv1-2")
|
|
parser.add_argument("--domains", type=str, default="all")
|
|
parser.add_argument("--nrows", type=int, default=None)
|
|
parser.add_argument("--min_count", type=int, default=10)
|
|
parser.add_argument("--max_labels", type=int, default=50)
|
|
# gFUN parameters ----------------------
|
|
parser.add_argument("-p", "--posteriors", action="store_true")
|
|
parser.add_argument("-m", "--multilingual", action="store_true")
|
|
parser.add_argument("-w", "--wce", action="store_true")
|
|
parser.add_argument("-t", "--textual_transformer", action="store_true")
|
|
parser.add_argument("-v", "--visual_transformer", action="store_true")
|
|
parser.add_argument("--n_jobs", type=int, default=-1)
|
|
parser.add_argument("--optimc", action="store_true")
|
|
parser.add_argument("--features", action="store_false")
|
|
parser.add_argument("--aggfunc", type=str, default="mean")
|
|
# transformer parameters ---------------
|
|
parser.add_argument("--transformer_name", type=str, default="mbert")
|
|
parser.add_argument("--batch_size", type=int, default=32)
|
|
parser.add_argument("--epochs", type=int, default=100)
|
|
parser.add_argument("--lr", type=float, default=1e-5)
|
|
parser.add_argument("--max_length", type=int, default=128)
|
|
parser.add_argument("--patience", type=int, default=5)
|
|
parser.add_argument("--evaluate_step", type=int, default=10)
|
|
# Visual Transformer parameters --------------
|
|
parser.add_argument("--visual_transformer_name", type=str, default="vit")
|
|
|
|
args = parser.parse_args()
|
|
|
|
main(args)
|