mirror of
https://github.com/wassname/Castor.git
synced 2026-10-05 12:20:28 +08:00
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
78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
import re
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import os
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import torch
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from torchtext.data import Field, TabularDataset
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from torchtext.data.iterator import BucketIterator
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from torchtext.vocab import Vectors
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def clean_string(string):
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"""
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Performs tokenization and string cleaning for the Reuters dataset
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"""
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string = re.sub(r"[^A-Za-z0-9(),!?\'`]", " ", string)
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string = re.sub(r"\s{2,}", " ", string)
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return string.lower().strip().split()
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def clean_string_fl(string):
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"""
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Returns only the title and first line (excluding the title) for every Reuters article, then calls clean_string
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"""
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split_string = string.split('.')
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if len(split_string) > 1:
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return clean_string(split_string[0] + ". " + split_string[1])
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else:
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return clean_string(string)
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def process_labels(string):
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"""
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Returns the label string as a list of integers
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:param string:
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:return:
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"""
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return [float(x) for x in string]
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class Reuters(TabularDataset):
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NAME = 'Reuters'
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NUM_CLASSES = 90
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TEXT_FIELD = Field(batch_first=True, tokenize=clean_string_fl)
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LABEL_FIELD = Field(sequential=False, use_vocab=False, batch_first=True, preprocessing=process_labels)
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@staticmethod
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def sort_key(ex):
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return len(ex.text)
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@classmethod
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def splits(cls, path, train=os.path.join('Reuters-21578', 'data', 'reuters_train.tsv'),
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validation=os.path.join('Reuters-21578', 'data', 'reuters_validation.tsv'),
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test=os.path.join('Reuters-21578', 'data','reuters_test.tsv'), **kwargs):
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return super(Reuters, cls).splits(
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path, train=train, validation=validation, test=test,
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format='tsv', fields=[('label', cls.LABEL_FIELD), ('text', cls.TEXT_FIELD)]
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)
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@classmethod
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def iters(cls, path, vectors_name, vectors_cache, batch_size=64, shuffle=True, device=0, vectors=None,
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unk_init=torch.Tensor.zero_):
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"""
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:param path: directory containing train, test, dev files
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:param vectors_name: name of word vectors file
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:param vectors_cache: path to directory containing word vectors file
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:param batch_size: batch size
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:param device: GPU device
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:param vectors: custom vectors - either predefined torchtext vectors or your own custom Vector classes
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:param unk_init: function used to generate vector for OOV words
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:return:
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"""
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if vectors is None:
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vectors = Vectors(name=vectors_name, cache=vectors_cache, unk_init=unk_init)
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train, val, test = cls.splits(path)
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cls.TEXT_FIELD.build_vocab(train, val, test, vectors=vectors)
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return BucketIterator.splits((train, val, test), batch_size=batch_size, repeat=False, shuffle=shuffle,
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sort_within_batch=True, device=device) |