| from copy import deepcopy |
| from unittest.case import TestCase |
|
|
| import pytest |
|
|
| from datasets.arrow_dataset import Dataset |
| from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value |
| from datasets.info import DatasetInfo |
| from datasets.tasks import ( |
| AudioClassification, |
| AutomaticSpeechRecognition, |
| ImageClassification, |
| LanguageModeling, |
| QuestionAnsweringExtractive, |
| Summarization, |
| TextClassification, |
| task_template_from_dict, |
| ) |
| from datasets.utils.py_utils import asdict |
|
|
|
|
| SAMPLE_QUESTION_ANSWERING_EXTRACTIVE = { |
| "id": "5733be284776f41900661182", |
| "title": "University_of_Notre_Dame", |
| "context": 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.', |
| "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?", |
| "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [515]}, |
| } |
|
|
|
|
| @pytest.mark.parametrize( |
| "task_cls", |
| [ |
| AudioClassification, |
| AutomaticSpeechRecognition, |
| ImageClassification, |
| LanguageModeling, |
| QuestionAnsweringExtractive, |
| Summarization, |
| TextClassification, |
| ], |
| ) |
| def test_reload_task_from_dict(task_cls): |
| task = task_cls() |
| task_dict = asdict(task) |
| reloaded = task_template_from_dict(task_dict) |
| assert task == reloaded |
|
|
|
|
| class TestLanguageModeling: |
| def test_column_mapping(self): |
| task = LanguageModeling(text_column="input_text") |
| assert {"input_text": "text"} == task.column_mapping |
|
|
| def test_from_dict(self): |
| input_schema = Features({"text": Value("string")}) |
| template_dict = {"text_column": "input_text"} |
| task = LanguageModeling.from_dict(template_dict) |
| assert "language-modeling" == task.task |
| assert input_schema == task.input_schema |
|
|
|
|
| class TextClassificationTest(TestCase): |
| def setUp(self): |
| self.labels = sorted(["pos", "neg"]) |
|
|
| def test_column_mapping(self): |
| task = TextClassification(text_column="input_text", label_column="input_label") |
| self.assertDictEqual({"input_text": "text", "input_label": "labels"}, task.column_mapping) |
|
|
| def test_from_dict(self): |
| input_schema = Features({"text": Value("string")}) |
| |
| label_schema = Features({"labels": ClassLabel}) |
| template_dict = {"text_column": "input_text", "label_column": "input_labels"} |
| task = TextClassification.from_dict(template_dict) |
| self.assertEqual("text-classification", task.task) |
| self.assertEqual(input_schema, task.input_schema) |
| self.assertEqual(label_schema, task.label_schema) |
|
|
| def test_align_with_features(self): |
| task = TextClassification(text_column="input_text", label_column="input_label") |
| self.assertEqual(task.label_schema["labels"], ClassLabel) |
| task = task.align_with_features(Features({"input_label": ClassLabel(names=self.labels)})) |
| self.assertEqual(task.label_schema["labels"], ClassLabel(names=self.labels)) |
|
|
|
|
| class QuestionAnsweringTest(TestCase): |
| def test_column_mapping(self): |
| task = QuestionAnsweringExtractive( |
| context_column="input_context", question_column="input_question", answers_column="input_answers" |
| ) |
| self.assertDictEqual( |
| {"input_context": "context", "input_question": "question", "input_answers": "answers"}, task.column_mapping |
| ) |
|
|
| def test_from_dict(self): |
| input_schema = Features({"question": Value("string"), "context": Value("string")}) |
| label_schema = Features( |
| { |
| "answers": Sequence( |
| { |
| "text": Value("string"), |
| "answer_start": Value("int32"), |
| } |
| ) |
| } |
| ) |
| template_dict = { |
| "context_column": "input_input_context", |
| "question_column": "input_question", |
| "answers_column": "input_answers", |
| } |
| task = QuestionAnsweringExtractive.from_dict(template_dict) |
| self.assertEqual("question-answering-extractive", task.task) |
| self.assertEqual(input_schema, task.input_schema) |
| self.assertEqual(label_schema, task.label_schema) |
|
|
|
|
| class SummarizationTest(TestCase): |
| def test_column_mapping(self): |
| task = Summarization(text_column="input_text", summary_column="input_summary") |
| self.assertDictEqual({"input_text": "text", "input_summary": "summary"}, task.column_mapping) |
|
|
| def test_from_dict(self): |
| input_schema = Features({"text": Value("string")}) |
| label_schema = Features({"summary": Value("string")}) |
| template_dict = {"text_column": "input_text", "summary_column": "input_summary"} |
| task = Summarization.from_dict(template_dict) |
| self.assertEqual("summarization", task.task) |
| self.assertEqual(input_schema, task.input_schema) |
| self.assertEqual(label_schema, task.label_schema) |
|
|
|
|
| class AutomaticSpeechRecognitionTest(TestCase): |
| def test_column_mapping(self): |
| task = AutomaticSpeechRecognition(audio_column="input_audio", transcription_column="input_transcription") |
| self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}, task.column_mapping) |
|
|
| def test_from_dict(self): |
| input_schema = Features({"audio": Audio()}) |
| label_schema = Features({"transcription": Value("string")}) |
| template_dict = { |
| "audio_column": "input_audio", |
| "transcription_column": "input_transcription", |
| } |
| task = AutomaticSpeechRecognition.from_dict(template_dict) |
| self.assertEqual("automatic-speech-recognition", task.task) |
| self.assertEqual(input_schema, task.input_schema) |
| self.assertEqual(label_schema, task.label_schema) |
|
|
|
|
| class AudioClassificationTest(TestCase): |
| def setUp(self): |
| self.labels = sorted(["pos", "neg"]) |
|
|
| def test_column_mapping(self): |
| task = AudioClassification(audio_column="input_audio", label_column="input_label") |
| self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}, task.column_mapping) |
|
|
| def test_from_dict(self): |
| input_schema = Features({"audio": Audio()}) |
| label_schema = Features({"labels": ClassLabel}) |
| template_dict = { |
| "audio_column": "input_image", |
| "label_column": "input_label", |
| } |
| task = AudioClassification.from_dict(template_dict) |
| self.assertEqual("audio-classification", task.task) |
| self.assertEqual(input_schema, task.input_schema) |
| self.assertEqual(label_schema, task.label_schema) |
|
|
| def test_align_with_features(self): |
| task = AudioClassification(audio_column="input_audio", label_column="input_label") |
| self.assertEqual(task.label_schema["labels"], ClassLabel) |
| task = task.align_with_features(Features({"input_label": ClassLabel(names=self.labels)})) |
| self.assertEqual(task.label_schema["labels"], ClassLabel(names=self.labels)) |
|
|
|
|
| class ImageClassificationTest(TestCase): |
| def setUp(self): |
| self.labels = sorted(["pos", "neg"]) |
|
|
| def test_column_mapping(self): |
| task = ImageClassification(image_column="input_image", label_column="input_label") |
| self.assertDictEqual({"input_image": "image", "input_label": "labels"}, task.column_mapping) |
|
|
| def test_from_dict(self): |
| input_schema = Features({"image": Image()}) |
| label_schema = Features({"labels": ClassLabel}) |
| template_dict = { |
| "image_column": "input_image", |
| "label_column": "input_label", |
| } |
| task = ImageClassification.from_dict(template_dict) |
| self.assertEqual("image-classification", task.task) |
| self.assertEqual(input_schema, task.input_schema) |
| self.assertEqual(label_schema, task.label_schema) |
|
|
| def test_align_with_features(self): |
| task = ImageClassification(image_column="input_image", label_column="input_label") |
| self.assertEqual(task.label_schema["labels"], ClassLabel) |
| task = task.align_with_features(Features({"input_label": ClassLabel(names=self.labels)})) |
| self.assertEqual(task.label_schema["labels"], ClassLabel(names=self.labels)) |
|
|
|
|
| class DatasetWithTaskProcessingTest(TestCase): |
| def test_map_on_task_template(self): |
| info = DatasetInfo(task_templates=QuestionAnsweringExtractive()) |
| dataset = Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}, info=info) |
| assert isinstance(dataset.info.task_templates, list) |
| assert len(dataset.info.task_templates) == 1 |
|
|
| def keep_task(x): |
| return x |
|
|
| def dont_keep_task(x): |
| out = deepcopy(SAMPLE_QUESTION_ANSWERING_EXTRACTIVE) |
| out["answers"]["foobar"] = 0 |
| return out |
|
|
| mapped_dataset = dataset.map(keep_task) |
| assert mapped_dataset.info.task_templates == dataset.info.task_templates |
| |
| mapped_dataset = dataset.map(keep_task) |
| assert mapped_dataset.info.task_templates == dataset.info.task_templates |
|
|
| mapped_dataset = dataset.map(dont_keep_task) |
| assert mapped_dataset.info.task_templates == [] |
| |
| mapped_dataset = dataset.map(dont_keep_task) |
| assert mapped_dataset.info.task_templates == [] |
|
|
| def test_remove_and_map_on_task_template(self): |
| features = Features({"text": Value("string"), "label": ClassLabel(names=("pos", "neg"))}) |
| task_templates = TextClassification(text_column="text", label_column="label") |
| info = DatasetInfo(features=features, task_templates=task_templates) |
| dataset = Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}, info=info) |
|
|
| def process(example): |
| return example |
|
|
| modified_dataset = dataset.remove_columns("label") |
| mapped_dataset = modified_dataset.map(process) |
| assert mapped_dataset.info.task_templates == [] |
|
|
