moved dataloader function get_dataset
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77227bbe13
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f274ec7615
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@ -0,0 +1,78 @@
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from os.path import expanduser
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from dataManager.gFunDataset import gFunDataset
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from dataManager.multiNewsDataset import MultiNewsDataset
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from dataManager.amazonDataset import AmazonDataset
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def get_dataset(dataset_name, args):
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assert dataset_name in [
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"multinews",
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"amazon",
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"rcv1-2",
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"glami",
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"cls",
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], "dataset not supported"
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RCV_DATAPATH = expanduser(
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"~/datasets/rcv1-2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle"
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)
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JRC_DATAPATH = expanduser(
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"~/datasets/jrc/jrc_doclist_1958-2005vs2006_all_top300_noparallel_processed_run0.pickle"
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)
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CLS_DATAPATH = expanduser("~/datasets/cls-acl10-processed/cls-acl10-processed.pkl")
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MULTINEWS_DATAPATH = expanduser("~/datasets/MultiNews/20110730/")
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GLAMI_DATAPATH = expanduser("~/datasets/GLAMI-1M-dataset")
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if dataset_name == "multinews":
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# TODO: convert to gFunDataset
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raise NotImplementedError
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dataset = MultiNewsDataset(
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expanduser(MULTINEWS_DATAPATH),
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excluded_langs=["ar", "pe", "pl", "tr", "ua"],
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)
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elif dataset_name == "amazon":
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# TODO: convert to gFunDataset
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raise NotImplementedError
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dataset = AmazonDataset(
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domains=args.domains,
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nrows=args.nrows,
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min_count=args.min_count,
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max_labels=args.max_labels,
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)
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elif dataset_name == "jrc":
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dataset = gFunDataset(
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dataset_dir=JRC_DATAPATH,
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is_textual=True,
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is_visual=False,
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is_multilabel=True,
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nrows=args.nrows,
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)
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elif dataset_name == "rcv1-2":
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dataset = gFunDataset(
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dataset_dir=RCV_DATAPATH,
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is_textual=True,
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is_visual=False,
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is_multilabel=True,
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nrows=args.nrows,
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)
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elif dataset_name == "glami":
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dataset = gFunDataset(
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dataset_dir=GLAMI_DATAPATH,
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is_textual=True,
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is_visual=True,
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is_multilabel=False,
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nrows=args.nrows,
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)
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elif dataset_name == "cls":
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dataset = gFunDataset(
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dataset_dir=CLS_DATAPATH,
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is_textual=True,
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is_visual=False,
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is_multilabel=False,
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nrows=args.nrows,
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)
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else:
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raise NotImplementedError
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return dataset
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@ -5,10 +5,15 @@ from evaluation.metrics import *
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def evaluation_metrics(y, y_):
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if len(y.shape) == len(y_.shape) == 1 and len(np.unique(y)) > 2: # single-label
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raise NotImplementedError() # return f1_score(y,y_,average='macro'), f1_score(y,y_,average='micro')
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else: # the metrics I implemented assume multiclass multilabel classification as binary classifiers
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return macroF1(y, y_), microF1(y, y_), macroK(y, y_), microK(y, y_)
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# return macroF1(y, y_), microF1(y, y_), macroK(y, y_), macroAcc(y, y_)
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raise NotImplementedError()
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else:
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return (
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macroF1(y, y_),
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microF1(y, y_),
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macroK(y, y_),
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microK(y, y_),
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# macroAcc(y, y_),
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)
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def evaluate(ly_true, ly_pred, metrics=evaluation_metrics, n_jobs=-1):
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@ -334,7 +334,7 @@ class GeneralizedFunnelling:
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pickle.dump(self.metaclassifier, f)
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return
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def save_first_tier_learners(self, model_id):
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def save_first_tier_learners(self):
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for vgf in self.first_tier_learners:
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vgf.save_vgf(model_id=self._model_id)
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return self
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86
main.py
86
main.py
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@ -1,13 +1,7 @@
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import pickle
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from argparse import ArgumentParser
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from os.path import expanduser
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from time import time
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from dataManager.amazonDataset import AmazonDataset
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from dataManager.multilingualDataset import MultilingualDataset
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from dataManager.multiNewsDataset import MultiNewsDataset
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from dataManager.glamiDataset import GlamiDataset
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from dataManager.gFunDataset import gFunDataset
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from dataManager.utils import get_dataset
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from evaluation.evaluate import evaluate, log_eval
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from gfun.generalizedFunnelling import GeneralizedFunnelling
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@ -25,85 +19,10 @@ TODO:
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"""
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def get_dataset(datasetname, args):
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assert datasetname in [
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"multinews",
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"amazon",
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"rcv1-2",
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"glami",
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"cls",
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], "dataset not supported"
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RCV_DATAPATH = expanduser(
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"~/datasets/rcv1-2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle"
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)
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JRC_DATAPATH = expanduser(
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"~/datasets/jrc/jrc_doclist_1958-2005vs2006_all_top300_noparallel_processed_run0.pickle"
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)
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CLS_DATAPATH = expanduser("~/datasets/cls-acl10-processed/cls-acl10-processed.pkl")
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MULTINEWS_DATAPATH = expanduser("~/datasets/MultiNews/20110730/")
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GLAMI_DATAPATH = expanduser("~/datasets/GLAMI-1M-dataset")
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if datasetname == "multinews":
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# TODO: convert to gFunDataset
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raise NotImplementedError
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dataset = MultiNewsDataset(
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expanduser(MULTINEWS_DATAPATH),
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excluded_langs=["ar", "pe", "pl", "tr", "ua"],
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)
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elif datasetname == "amazon":
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# TODO: convert to gFunDataset
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raise NotImplementedError
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dataset = AmazonDataset(
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domains=args.domains,
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nrows=args.nrows,
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min_count=args.min_count,
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max_labels=args.max_labels,
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)
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elif datasetname == "rcv1-2":
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dataset = gFunDataset(
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dataset_dir=RCV_DATAPATH,
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is_textual=True,
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is_visual=False,
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is_multilabel=True,
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nrows=args.nrows,
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)
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elif datasetname == "glami":
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dataset = gFunDataset(
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dataset_dir=GLAMI_DATAPATH,
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is_textual=True,
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is_visual=True,
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is_multilabel=False,
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nrows=args.nrows,
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)
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elif datasetname == "cls":
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dataset = gFunDataset(
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dataset_dir=CLS_DATAPATH,
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is_textual=True,
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is_visual=False,
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is_multilabel=False,
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nrows=args.nrows,
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)
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else:
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raise NotImplementedError
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return dataset
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def main(args):
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dataset = get_dataset(args.dataset, args)
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if (
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isinstance(dataset, MultilingualDataset)
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or isinstance(dataset, MultiNewsDataset)
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or isinstance(dataset, GlamiDataset)
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or isinstance(dataset, gFunDataset)
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):
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lX, lY = dataset.training()
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lX_te, lY_te = dataset.test()
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else:
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lX = dataset.dX
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lY = dataset.dY
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tinit = time()
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@ -140,7 +59,7 @@ def main(args):
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max_length=args.max_length,
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patience=args.patience,
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evaluate_step=args.evaluate_step,
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device="cuda",
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device=args.device,
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# Visual Transformer VGF params --------------
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visual_transformer=args.visual_transformer,
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visual_transformer_name=args.visual_transformer_name,
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@ -186,6 +105,7 @@ if __name__ == "__main__":
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parser.add_argument("-l", "--load_trained", type=str, default=None)
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parser.add_argument("--meta", action="store_true")
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parser.add_argument("--nosave", action="store_true")
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parser.add_argument("--device", type=str, default="cuda")
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# Dataset parameters -------------------
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parser.add_argument("-d", "--dataset", type=str, default="rcv1-2")
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parser.add_argument("--domains", type=str, default="all")
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