doccano/backend/data_import/tests/test_tasks.py

import pathlib

from django.test import TestCase

from data_import.celery_tasks import import_dataset
from api.models import (DOCUMENT_CLASSIFICATION,
                        INTENT_DETECTION_AND_SLOT_FILLING, SEQ2SEQ,
                        SEQUENCE_LABELING, Category, CategoryType, Example, Span,
                        SpanType)
from api.tests.api.utils import prepare_project


class TestImportData(TestCase):
    task = 'Any'
    annotation_class = Category

    def setUp(self):
        self.project = prepare_project(self.task)
        self.user = self.project.users[0]
        self.data_path = pathlib.Path(__file__).parent / 'data'

    def import_dataset(self, filename, file_format, kwargs=None):
        filenames = [str(self.data_path / filename)]
        kwargs = kwargs or {}
        return import_dataset(self.user.id, self.project.item.id, filenames, file_format, **kwargs)


class TestImportClassificationData(TestImportData):
    task = DOCUMENT_CLASSIFICATION

    def assert_examples(self, dataset):
        self.assertEqual(Example.objects.count(), len(dataset))
        for text, expected_labels in dataset:
            example = Example.objects.get(text=text)
            labels = set(cat.label.text for cat in example.categories.all())
            self.assertEqual(labels, set(expected_labels))

    def assert_parse_error(self, response):
        self.assertGreaterEqual(len(response['error']), 1)
        self.assertEqual(Example.objects.count(), 0)
        self.assertEqual(CategoryType.objects.count(), 0)
        self.assertEqual(Category.objects.count(), 0)

    def test_jsonl(self):
        filename = 'text_classification/example.jsonl'
        file_format = 'JSONL'
        kwargs = {'column_label': 'labels'}
        dataset = [
            ('exampleA', ['positive']),
            ('exampleB', ['positive', 'negative']),
            ('exampleC', [])
        ]
        self.import_dataset(filename, file_format, kwargs)
        self.assert_examples(dataset)

    def test_csv(self):
        filename = 'text_classification/example.csv'
        file_format = 'CSV'
        dataset = [
            ('exampleA', ['positive']),
            ('exampleB', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_csv_out_of_order_columns(self):
        filename = 'text_classification/example_out_of_order_columns.csv'
        file_format = 'CSV'
        dataset = [
            ('exampleA', ['positive']),
            ('exampleB', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_fasttext(self):
        filename = 'text_classification/example_fasttext.txt'
        file_format = 'fastText'
        dataset = [
            ('exampleA', ['positive']),
            ('exampleB', ['positive', 'negative']),
            ('exampleC', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_excel(self):
        filename = 'text_classification/example.xlsx'
        file_format = 'Excel'
        dataset = [
            ('exampleA', ['positive']),
            ('exampleB', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_json(self):
        filename = 'text_classification/example.json'
        file_format = 'JSON'
        dataset = [
            ('exampleA', ['positive']),
            ('exampleB', ['positive', 'negative']),
            ('exampleC', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_textfile(self):
        filename = 'example.txt'
        file_format = 'TextFile'
        dataset = [
            ('exampleA\nexampleB\n\nexampleC\n', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_textline(self):
        filename = 'example.txt'
        file_format = 'TextLine'
        dataset = [
            ('exampleA', []),
            ('exampleB', []),
            ('exampleC', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_wrong_jsonl(self):
        filename = 'text_classification/example.json'
        file_format = 'JSONL'
        response = self.import_dataset(filename, file_format)
        self.assert_parse_error(response)

    def test_wrong_json(self):
        filename = 'text_classification/example.jsonl'
        file_format = 'JSON'
        response = self.import_dataset(filename, file_format)
        self.assert_parse_error(response)

    def test_wrong_excel(self):
        filename = 'text_classification/example.jsonl'
        file_format = 'Excel'
        response = self.import_dataset(filename, file_format)
        self.assert_parse_error(response)

    def test_wrong_csv(self):
        filename = 'text_classification/example.jsonl'
        file_format = 'CSV'
        response = self.import_dataset(filename, file_format)
        self.assert_parse_error(response)


class TestImportSequenceLabelingData(TestImportData):
    task = SEQUENCE_LABELING

    def assert_examples(self, dataset):
        self.assertEqual(Example.objects.count(), len(dataset))
        for text, expected_labels in dataset:
            example = Example.objects.get(text=text)
            labels = [[span.start_offset, span.end_offset, span.label.text] for span in example.spans.all()]
            self.assertEqual(labels, expected_labels)

    def assert_parse_error(self, response):
        self.assertGreaterEqual(len(response['error']), 1)
        self.assertEqual(Example.objects.count(), 0)
        self.assertEqual(SpanType.objects.count(), 0)
        self.assertEqual(Span.objects.count(), 0)

    def test_jsonl(self):
        filename = 'sequence_labeling/example.jsonl'
        file_format = 'JSONL'
        dataset = [
            ('exampleA', [[0, 1, 'LOC']]),
            ('exampleB', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_conll(self):
        filename = 'sequence_labeling/example.conll'
        file_format = 'CoNLL'
        dataset = [
            ('JAPAN GET', [[0, 5, 'LOC']]),
            ('Nadim Ladki', [[0, 11, 'PER']])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_wrong_conll(self):
        filename = 'sequence_labeling/example.jsonl'
        file_format = 'CoNLL'
        response = self.import_dataset(filename, file_format)
        self.assert_parse_error(response)

    def test_jsonl_with_overlapping(self):
        filename = 'sequence_labeling/example_overlapping.jsonl'
        file_format = 'JSONL'
        response = self.import_dataset(filename, file_format)
        self.assertEqual(len(response['error']), 1)


class TestImportSeq2seqData(TestImportData):
    task = SEQ2SEQ

    def assert_examples(self, dataset):
        self.assertEqual(Example.objects.count(), len(dataset))
        for text, expected_labels in dataset:
            example = Example.objects.get(text=text)
            labels = set(text_label.text for text_label in example.texts.all())
            self.assertEqual(labels, set(expected_labels))

    def test_jsonl(self):
        filename = 'seq2seq/example.jsonl'
        file_format = 'JSONL'
        dataset = [
            ('exampleA', ['label1']),
            ('exampleB', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_json(self):
        filename = 'seq2seq/example.json'
        file_format = 'JSON'
        dataset = [
            ('exampleA', ['label1']),
            ('exampleB', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)

    def test_csv(self):
        filename = 'seq2seq/example.csv'
        file_format = 'CSV'
        dataset = [
            ('exampleA', ['label1']),
            ('exampleB', [])
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)


class TextImportIntentDetectionAndSlotFillingData(TestImportData):
    task = INTENT_DETECTION_AND_SLOT_FILLING

    def assert_examples(self, dataset):
        self.assertEqual(Example.objects.count(), len(dataset))
        for text, expected_labels in dataset:
            example = Example.objects.get(text=text)
            cats = set(cat.label.text for cat in example.categories.all())
            entities = [(span.start_offset, span.end_offset, span.label.text) for span in example.spans.all()]
            self.assertEqual(cats, set(expected_labels['cats']))
            self.assertEqual(entities, expected_labels['entities'])

    def test_entities_and_cats(self):
        filename = 'intent/example.jsonl'
        file_format = 'JSONL'
        dataset = [
            ('exampleA', {'cats': ['positive'], 'entities': [(0, 1, 'LOC')]}),
            ('exampleB', {'cats': ['positive'], 'entities': []}),
            ('exampleC', {'cats': [], 'entities': [(0, 1, 'LOC')]}),
            ('exampleD', {'cats': [], 'entities': []}),
        ]
        self.import_dataset(filename, file_format)
        self.assert_examples(dataset)