forked from huggingface/datasets
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_tasks.py
160 lines (134 loc) · 7.2 KB
/
test_tasks.py
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
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
from copy import deepcopy
from unittest.case import TestCase
from datasets.arrow_dataset import Dataset
from datasets.features import ClassLabel, Features, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
AutomaticSpeechRecognition,
ImageClassification,
QuestionAnsweringExtractive,
Summarization,
TextClassification,
)
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]},
}
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", labels=self.labels)
self.assertDictEqual({"input_text": "text", "input_label": "labels"}, task.column_mapping)
def test_from_dict(self):
input_schema = Features({"text": Value("string")})
# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
label_schema = Features({"labels": ClassLabel(names=tuple(self.labels))})
template_dict = {"text_column": "input_text", "label_column": "input_labels", "labels": self.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)
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_file_path_column="input_audio_file_path", transcription_column="input_transcription"
)
self.assertDictEqual(
{"input_audio_file_path": "audio_file_path", "input_transcription": "transcription"}, task.column_mapping
)
def test_from_dict(self):
input_schema = Features({"audio_file_path": Value("string")})
label_schema = Features({"transcription": Value("string")})
template_dict = {
"audio_file_path_column": "input_audio_file_path",
"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 ImageClassificationTest(TestCase):
def setUp(self):
self.labels = sorted(["pos", "neg"])
def test_column_mapping(self):
task = ImageClassification(image_file_path_column="file_paths", label_column="input_label")
self.assertDictEqual({"file_paths": "image_file_path", "input_label": "labels"}, task.column_mapping)
def test_from_dict(self):
input_schema = Features({"image_file_path": Value("string")})
label_schema = Features({"labels": ClassLabel(names=tuple(self.labels))})
template_dict = {
"image_file_path_column": "input_image_file_path",
"label_column": "input_label",
"labels": self.labels,
}
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)
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
# reload from cache
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 == []
# reload from cache
mapped_dataset = dataset.map(dont_keep_task)
assert mapped_dataset.info.task_templates == []