Add twitter dataset

datasets/twitter.py

@@ -0,0 +1,99 @@
+import os
+
+import torch
+
+from torchtext.data.dataset import Dataset
+from torchtext.data.example import Example
+from torchtext.data.field import Field
+from torchtext.data.iterator import BucketIterator
+from torchtext.data.iterator import Iterator
+from torchtext.vocab import Vectors
+from torchtext.data import Pipeline
+
+from datasets.castor_dataset import CastorPairDataset
+from datasets.idf_utils import get_pairwise_word_to_doc_freq, get_pairwise_overlap_features
+
+
+class TWITTER(Dataset):
+    NAME = 'twitter'
+    NUM_CLASSES = 2
+    ID_FIELD = Field(sequential=False, tensor_type=torch.FloatTensor, use_vocab=False, batch_first=True)
+    AID_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
+    TEXT_FIELD = Field(batch_first=True, tokenize=lambda x: x)  # tokenizer is identity since we already tokenized it to compute external features
+    EXT_FEATS_FIELD = Field(tensor_type=torch.FloatTensor, use_vocab=False, batch_first=True, tokenize=lambda x: x,
+                            postprocessing=Pipeline(lambda arr, _, train: [float(y) for y in arr]))
+    LABEL_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
+    VOCAB_SIZE = 0
+
+    @staticmethod
+    def sort_key(ex):
+        return len(ex.sentence_1)
+
+    def __init__(self, pardir, subdirs):
+        """
+        Create a Twitter dataset instance.
+        """
+        fields = [('id', self.ID_FIELD), ('sentence_1', self.TEXT_FIELD), ('sentence_2', self.TEXT_FIELD), ('ext_feats',
+                self.EXT_FEATS_FIELD), ('label', self.LABEL_FIELD), ('aid', self.AID_FIELD)]
+
+        examples = []
+        for subdir in subdirs:
+            path = os.path.join(pardir, subdir)
+            with open(os.path.join(path, 'a.toks'), 'r') as f1, open(os.path.join(path, 'b.toks'), 'r') as f2:
+                sent_list_1 = [l.rstrip('.\n').split(' ') for l in f1]
+                sent_list_2 = [l.rstrip('.\n').split(' ') for l in f2]
+
+        word_to_doc_cnt = get_pairwise_word_to_doc_freq(sent_list_1, sent_list_2)
+        overlap_feats = get_pairwise_overlap_features(sent_list_1, sent_list_2, word_to_doc_cnt)
+
+        for subdir_i, subdir in enumerate(subdirs):
+            path = os.path.join(pardir, subdir)
+            with open(os.path.join(path, 'id.txt'), 'r') as id_file, open(os.path.join(path, 'sim.txt'), 'r') as label_file:
+                for i, (pair_id, l1, l2, ext_feats, label) in enumerate(zip(id_file, sent_list_1, sent_list_2, overlap_feats, label_file)):
+                    pair_id = pair_id.rstrip('.\n')
+                    label = label.rstrip('.\n')
+                    example_list = [pair_id, l1, l2, ext_feats, label, (subdir_i) * 100000 + (i + 1)]
+                    example = Example.fromlist(example_list, fields)
+                    examples.append(example)
+
+        super(TWITTER, self).__init__(examples, fields)
+
+    @classmethod
+    def splits(cls, path, train_paths, test_paths, **kwargs):
+        train_data = cls(path, train_paths, **kwargs)
+        test_data = cls(path, test_paths, **kwargs)
+        return train_data, test_data
+
+    @classmethod
+    def set_vectors(cls, field, vector_path):
+        return CastorPairDataset.set_vectors(field, vector_path)
+
+    @classmethod
+    def iters(cls, path, train_dirs, test_dirs, vectors_name, vectors_dir, batch_size=64, shuffle=True, device=0, pt_file=False, vectors=None, unk_init=torch.Tensor.zero_):
+        """
+        :param path: directory containing train, test, dev files
+        :param train_dirs: list of directory names used for training
+        :param test_dirs: list of directory name used for testing
+        :param vectors_name: name of word vectors file
+        :param vectors_dir: directory containing word vectors file
+        :param batch_size: batch size
+        :param device: GPU device
+        :param vectors: custom vectors - either predefined torchtext vectors or your own custom Vector classes
+        :param unk_init: function used to generate vector for OOV words
+        :return:
+        """
+
+        train, test = cls.splits(path, train_dirs, test_dirs)
+        if not pt_file:
+            if vectors is None:
+                vectors = Vectors(name=vectors_name, cache=vectors_dir, unk_init=unk_init)
+            cls.TEXT_FIELD.build_vocab(train, test, vectors=vectors)
+        else:
+            cls.TEXT_FIELD.build_vocab(train, test)
+            cls.TEXT_FIELD = cls.set_vectors(cls.TEXT_FIELD, os.path.join(vectors_dir, vectors_name))
+
+        cls.LABEL_FIELD.build_vocab(train, test)
+
+        cls.VOCAB_SIZE = len(cls.TEXT_FIELD.vocab)
+
+        return BucketIterator.splits((train, test), batch_size=batch_size, repeat=False, shuffle=shuffle, device=device)