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gauravbaruah
a67e2d12c4
+ sm model no external features baseline + sm model with IDF weights + sm model with IDF weights without removing punctuation --> barely better than df/idf (a la Pytorch). + sm model with stemming before computing IDF weights ^ all on the TrecQA dataset
52 lines
2.2 KiB
Python
52 lines
2.2 KiB
Python
import argparse
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import os
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from model import QAModel
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from train import Trainer
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import utils
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if __name__ == "__main__":
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ap = argparse.ArgumentParser(description="Makes a run in trec_eval run format, given a model and a train|dev|test set" )
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ap.add_argument('model')
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ap.add_argument('--word_embeddings_cache',
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default='../../data/word2vec/aquaint+wiki.txt.gz.ndim=50.cache')
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ap.add_argument('--paper-ext-feats', action="store_true", \
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help="external features as per the paper")
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ap.add_argument('dataset_folder', help="the QA dataset folder",
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choices=['../../data/TrecQA', '../../data/WikQA'])
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ap.add_argument('set_split', help="train, dev or test split as the data_folder")
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ap.add_argument("batch_size", help="the number of pairs to compare in each batch.\
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should be same as during training")
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ap.add_argument('out_scorefile', help='output file in trec_eval format')
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args = ap.parse_args()
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vocab_size, vec_dim = utils.load_embedding_dimensions(args.word_embeddings_cache)
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trained_model = QAModel.load(args.model)
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trained_model.no_ext_feats = True
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evaluator = Trainer(trained_model, 0, 0, False, vec_dim) # 0, 0, False are dummy arguments
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evaluator.load_input_data(args.dataset_folder, args.word_embeddings_cache,
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None, None, args.set_split,
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True if args.ext_feats else False)
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test_scores = evaluator.test(args.set_split, args.batch_size)
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questions, sentences, labels, vocab, maxlen_q, maxlen_s, ext_feats = \
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evaluator.data_splits[args.set_split]
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qids = [id.strip() for id in open(os.path.join(args.dataset_folder, args.set_split, 'id.txt'))\
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.readlines()]
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with open(args.out_scorefile, 'w') as outf:
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old_qid = 0
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docid_c = 0
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for i in range(len(qids)):
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if qids[i] != old_qid and args.dataset_folder.endswith('WikiQA'):
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docid_c = 0
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old_qid = qids[i]
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print('{} 0 {} 0 {} {}'.format(qids[i], docid_c, test_scores[i],
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os.path.basename(args.model)),
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file=outf)
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docid_c += 1
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