File size: 2,830 Bytes
7e3e85d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
import unittest

from dataset import SUPPORTED_SUMM_DATASETS, list_all_datasets
from dataset.st_dataset import SummDataset, SummInstance
from dataset.dataset_loaders import ArxivDataset

from helpers import print_with_color


class TestDatasets(unittest.TestCase):
    def _test_instance(
        self,
        ins: SummInstance,
        is_query: bool = False,
        is_multi_document: bool = False,
        is_dialogue: bool = False,
    ):
        if is_multi_document or is_dialogue:
            self.assertTrue(isinstance(ins.source, list))
        else:
            self.assertTrue(isinstance(ins.source, list) or isinstance(ins.source, str))
        if is_query:
            self.assertTrue(isinstance(ins.query, str))

    def test_all_datasets(self):
        print_with_color(f"{'#' * 10} Testing all datasets... {'#' * 10}\n\n", "35")

        print(list_all_datasets())

        num_datasets = 0

        for ds_cls in SUPPORTED_SUMM_DATASETS:

            # TODO: Temporarily skipping Arxiv (size/time), > 30min download time for Travis-CI
            if ds_cls in [ArxivDataset]:
                continue

            print_with_color(f"Testing {ds_cls} dataset...", "35")
            ds: SummDataset = ds_cls()

            ds.show_description()

            # must have at least one of train/dev/test set
            assert ds.train_set or ds.validation_set or ds.test_set

            if ds.train_set is not None:
                train_set = list(ds.train_set)
                print(f"{ds_cls} has a training set of {len(train_set)} examples")
                self._test_instance(
                    train_set[0],
                    is_multi_document=ds.is_multi_document,
                    is_dialogue=ds.is_dialogue_based,
                )

            if ds.validation_set is not None:
                val_set = list(ds.validation_set)
                print(f"{ds_cls} has a validation set of {len(val_set)} examples")
                self._test_instance(
                    val_set[0],
                    is_multi_document=ds.is_multi_document,
                    is_dialogue=ds.is_dialogue_based,
                )

            if ds.test_set is not None:
                test_set = list(ds.test_set)
                print(f"{ds_cls} has a test set of {len(test_set)} examples")
                self._test_instance(
                    test_set[0],
                    is_multi_document=ds.is_multi_document,
                    is_dialogue=ds.is_dialogue_based,
                )

            print_with_color(f"{ds.dataset_name} dataset test complete\n", "32")
            num_datasets += 1

        print_with_color(
            f"{'#' * 10} test_all_datasets {__name__} complete ({num_datasets} datasets) {'#' * 10}",
            "32",
        )


if __name__ == "__main__":
    unittest.main()