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https://github.com/wassname/Castor.git
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Achyudh Ram
1b817d3e24
* Add ReutersTrainer, ReutersEvaluator options in Factory classes * Add Reuters to Kim-CNN command line arguments * Fix SST dataset path according to changes in Kim-CNN args The dataset path in args.py was made to point at the dataset folder rather than dataset/SST folder. Hence SST folder was added to paths in the SST dataset class * Add Reuters dataset class, and support in __main__ * Add Reuters dataset trainers and evaluators * Remove debug print statement in reuters_evaluator * Fix rounding bug in reuters_trainer and reuters_evaluator
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
from .evaluators.sick_evaluator import SICKEvaluator
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from .evaluators.msrvid_evaluator import MSRVIDEvaluator
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from .evaluators.sst_evaluator import SSTEvaluator
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from .evaluators.trecqa_evaluator import TRECQAEvaluator
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from .evaluators.wikiqa_evaluator import WikiQAEvaluator
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from .evaluators.pit2015_evaluator import PIT2015Evaluator
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from .evaluators.reuters_evaluator import ReutersEvaluator
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from nce.nce_pairwise_mp.evaluators.trecqa_evaluator import TRECQAEvaluatorNCE
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from nce.nce_pairwise_mp.evaluators.wikiqa_evaluator import WikiQAEvaluatorNCE
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class EvaluatorFactory(object):
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"""
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Get the corresponding Evaluator class for a particular dataset.
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"""
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evaluator_map = {
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'sick': SICKEvaluator,
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'msrvid': MSRVIDEvaluator,
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'SST-1': SSTEvaluator,
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'SST-2': SSTEvaluator,
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'trecqa': TRECQAEvaluator,
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'wikiqa': WikiQAEvaluator,
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'pit2015': PIT2015Evaluator,
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'twitterurl': PIT2015Evaluator,
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'Reuters': ReutersEvaluator
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}
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evaluator_map_nce = {
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'trecqa': TRECQAEvaluatorNCE,
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'wikiqa': WikiQAEvaluatorNCE
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}
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@staticmethod
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def get_evaluator(dataset_cls, model, embedding, data_loader, batch_size, device, nce=False, keep_results=False):
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if data_loader is None:
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return None
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if nce:
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evaluator_map = EvaluatorFactory.evaluator_map_nce
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else:
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evaluator_map = EvaluatorFactory.evaluator_map
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if not hasattr(dataset_cls, 'NAME'):
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raise ValueError('Invalid dataset. Dataset should have NAME attribute.')
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if dataset_cls.NAME not in evaluator_map:
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raise ValueError('{} is not implemented.'.format(dataset_cls))
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return evaluator_map[dataset_cls.NAME](
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dataset_cls, model, embedding, data_loader, batch_size, device, keep_results
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)
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