Add WikiQA Dataset (#79)

datasets/wikiqa.py

@@ -0,0 +1,54 @@
+import os
+
+import torch
+from torchtext.data.example import Example
+from torchtext.data.field import Field
+from torchtext.data.iterator import BucketIterator
+from torchtext.vocab import Vectors
+
+from datasets.castor_dataset import CastorPairDataset
+from datasets.idf_utils import get_pairwise_word_to_doc_freq, get_pairwise_overlap_features
+
+
+class WIKIQA(CastorPairDataset):
+    NAME = 'wikiqa'
+    NUM_CLASSES = 2
+    ID_FIELD = Field(sequential=False, tensor_type=torch.FloatTensor, 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)
+    LABEL_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
+
+    @staticmethod
+    def sort_key(ex):
+        return len(ex.sentence_1)
+
+    def __init__(self, path):
+        """
+        Create a WIKIQA dataset instance
+        """
+        super(WIKIQA, self).__init__(path)
+
+    @classmethod
+    def splits(cls, path, train='train', validation='dev', test='test', **kwargs):
+        return super(WIKIQA, cls).splits(path, train=train, validation=validation, test=test, **kwargs)
+
+    @classmethod
+    def iters(cls, path, vectors_name, vectors_cache, batch_size=64, shuffle=True, device=0, vectors=None, unk_init=torch.Tensor.zero_):
+        """
+        :param path: directory containing train, test, dev files
+        :param vectors_name: name of word vectors file
+        :param vectors_cache: 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:
+        """
+        if vectors is None:
+            vectors = Vectors(name=vectors_name, cache=vectors_cache, unk_init=unk_init)
+
+        train, validation, test = cls.splits(path)
+
+        cls.TEXT_FIELD.build_vocab(train, validation, test, vectors=vectors)
+
+        return BucketIterator.splits((train, validation, test), batch_size=batch_size, repeat=False, shuffle=shuffle, device=device)