Datasets:

Languages:
English
ArXiv:
garrethlee commited on
Commit
d780dd1
·
verified ·
1 Parent(s): 4b7790f

Delete loading script

Browse files
Files changed (1) hide show
  1. math_dataset.py +0 -275
math_dataset.py DELETED
@@ -1,275 +0,0 @@
1
- # coding=utf-8
2
- # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
3
- #
4
- # Licensed under the Apache License, Version 2.0 (the "License");
5
- # you may not use this file except in compliance with the License.
6
- # You may obtain a copy of the License at
7
- #
8
- # http://www.apache.org/licenses/LICENSE-2.0
9
- #
10
- # Unless required by applicable law or agreed to in writing, software
11
- # distributed under the License is distributed on an "AS IS" BASIS,
12
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
- # See the License for the specific language governing permissions and
14
- # limitations under the License.
15
-
16
- # Lint as: python3
17
- """Mathematics database."""
18
-
19
- import datasets
20
-
21
-
22
- logger = datasets.logging.get_logger(__name__)
23
-
24
-
25
- _CITATION = """
26
- @article{2019arXiv,
27
- author = {Saxton, Grefenstette, Hill, Kohli},
28
- title = {Analysing Mathematical Reasoning Abilities of Neural Models},
29
- year = {2019},
30
- journal = {arXiv:1904.01557}
31
- }
32
- """
33
-
34
- _DESCRIPTION = """
35
- Mathematics database.
36
-
37
- This dataset code generates mathematical question and answer pairs,
38
- from a range of question types at roughly school-level difficulty.
39
- This is designed to test the mathematical learning and algebraic
40
- reasoning skills of learning models.
41
-
42
- Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
43
- (Saxton, Grefenstette, Hill, Kohli).
44
-
45
- Example usage:
46
- train_examples, val_examples = datasets.load_dataset(
47
- 'math_dataset/arithmetic__mul',
48
- split=['train', 'test'],
49
- as_supervised=True)
50
- """
51
-
52
- _DATA_URL = "https://storage.googleapis.com/mathematics-dataset/mathematics_dataset-v1.0.tar.gz"
53
-
54
- _TRAIN_CATEGORY = [
55
- "train-easy",
56
- "train-medium",
57
- "train-hard",
58
- ]
59
-
60
- _INTERPOLATE_CATEGORY = [
61
- "interpolate",
62
- ]
63
-
64
- _MODULES = [
65
- # extrapolate
66
- "measurement__conversion",
67
- # interpolate
68
- "algebra__linear_1d",
69
- "algebra__linear_1d_composed",
70
- "algebra__linear_2d",
71
- "algebra__linear_2d_composed",
72
- "algebra__polynomial_roots",
73
- "algebra__polynomial_roots_composed",
74
- "algebra__sequence_next_term",
75
- "algebra__sequence_nth_term",
76
- "arithmetic__add_or_sub",
77
- "arithmetic__add_or_sub_in_base",
78
- "arithmetic__add_sub_multiple",
79
- "arithmetic__div",
80
- "arithmetic__mixed",
81
- "arithmetic__mul",
82
- "arithmetic__mul_div_multiple",
83
- "arithmetic__nearest_integer_root",
84
- "arithmetic__simplify_surd",
85
- "calculus__differentiate",
86
- "calculus__differentiate_composed",
87
- "comparison__closest",
88
- "comparison__closest_composed",
89
- "comparison__kth_biggest",
90
- "comparison__kth_biggest_composed",
91
- "comparison__pair",
92
- "comparison__pair_composed",
93
- "comparison__sort",
94
- "comparison__sort_composed",
95
- "measurement__conversion",
96
- "measurement__time",
97
- "numbers__base_conversion",
98
- "numbers__div_remainder",
99
- "numbers__div_remainder_composed",
100
- "numbers__gcd",
101
- "numbers__gcd_composed",
102
- "numbers__is_factor",
103
- "numbers__is_factor_composed",
104
- "numbers__is_prime",
105
- "numbers__is_prime_composed",
106
- "numbers__lcm",
107
- "numbers__lcm_composed",
108
- "numbers__list_prime_factors",
109
- "numbers__list_prime_factors_composed",
110
- "numbers__place_value",
111
- "numbers__place_value_composed",
112
- "numbers__round_number",
113
- "numbers__round_number_composed",
114
- "polynomials__add",
115
- "polynomials__coefficient_named",
116
- "polynomials__collect",
117
- "polynomials__compose",
118
- "polynomials__evaluate",
119
- "polynomials__evaluate_composed",
120
- "polynomials__expand",
121
- "polynomials__simplify_power",
122
- "probability__swr_p_level_set",
123
- "probability__swr_p_sequence",
124
- # train-easy train-medium train-hard
125
- "algebra__linear_1d",
126
- "algebra__linear_1d_composed",
127
- "algebra__linear_2d",
128
- "algebra__linear_2d_composed",
129
- "algebra__polynomial_roots",
130
- "algebra__polynomial_roots_composed",
131
- "algebra__sequence_next_term",
132
- "algebra__sequence_nth_term",
133
- "arithmetic__add_or_sub",
134
- "arithmetic__add_or_sub_in_base",
135
- "arithmetic__add_sub_multiple",
136
- "arithmetic__div",
137
- "arithmetic__mixed",
138
- "arithmetic__mul",
139
- "arithmetic__mul_div_multiple",
140
- "arithmetic__nearest_integer_root",
141
- "arithmetic__simplify_surd",
142
- "calculus__differentiate",
143
- "calculus__differentiate_composed",
144
- "comparison__closest",
145
- "comparison__closest_composed",
146
- "comparison__kth_biggest",
147
- "comparison__kth_biggest_composed",
148
- "comparison__pair",
149
- "comparison__pair_composed",
150
- "comparison__sort",
151
- "comparison__sort_composed",
152
- "measurement__conversion",
153
- "measurement__time",
154
- "numbers__base_conversion",
155
- "numbers__div_remainder",
156
- "numbers__div_remainder_composed",
157
- "numbers__gcd",
158
- "numbers__gcd_composed",
159
- "numbers__is_factor",
160
- "numbers__is_factor_composed",
161
- "numbers__is_prime",
162
- "numbers__is_prime_composed",
163
- "numbers__lcm",
164
- "numbers__lcm_composed",
165
- "numbers__list_prime_factors",
166
- "numbers__list_prime_factors_composed",
167
- "numbers__place_value",
168
- "numbers__place_value_composed",
169
- "numbers__round_number",
170
- "numbers__round_number_composed",
171
- "polynomials__add",
172
- "polynomials__coefficient_named",
173
- "polynomials__collect",
174
- "polynomials__compose",
175
- "polynomials__evaluate",
176
- "polynomials__evaluate_composed",
177
- "polynomials__expand",
178
- "polynomials__simplify_power",
179
- "probability__swr_p_level_set",
180
- "probability__swr_p_sequence",
181
- ]
182
-
183
- _QUESTION = "question"
184
- _ANSWER = "answer"
185
-
186
- _DATASET_VERSION = "mathematics_dataset-v1.0"
187
-
188
-
189
- def _generate_builder_configs():
190
- """Generate configs with different subsets of mathematics dataset."""
191
- configs = []
192
- for module in sorted(set(_MODULES)):
193
- configs.append(
194
- datasets.BuilderConfig(
195
- name=module,
196
- version=datasets.Version("1.0.0"),
197
- description=_DESCRIPTION,
198
- )
199
- )
200
-
201
- return configs
202
-
203
-
204
- class MathDataset(datasets.GeneratorBasedBuilder):
205
- """Math Dataset."""
206
-
207
- BUILDER_CONFIGS = _generate_builder_configs()
208
-
209
- def _info(self):
210
- return datasets.DatasetInfo(
211
- description=_DESCRIPTION,
212
- features=datasets.Features(
213
- {
214
- _QUESTION: datasets.Value("string"),
215
- _ANSWER: datasets.Value("string"),
216
- }
217
- ),
218
- supervised_keys=(_QUESTION, _ANSWER),
219
- homepage="https://github.com/deepmind/mathematics_dataset",
220
- citation=_CITATION,
221
- )
222
-
223
- def _get_filepaths_from_categories(self, config, categories):
224
- filepaths = []
225
- for category in categories:
226
- data_file = "/".join([_DATASET_VERSION, category, config])
227
- filepaths.append(data_file)
228
- return set(filepaths)
229
-
230
- def _split_generators(self, dl_manager):
231
- """Returns SplitGenerators."""
232
-
233
- archive = dl_manager.download(_DATA_URL)
234
- config = self.config.name + ".txt"
235
-
236
- return [
237
- datasets.SplitGenerator(
238
- name=datasets.Split.TRAIN,
239
- gen_kwargs={
240
- "files": dl_manager.iter_archive(archive),
241
- "config": config,
242
- "categories": _TRAIN_CATEGORY,
243
- },
244
- ),
245
- datasets.SplitGenerator(
246
- name=datasets.Split.TEST,
247
- gen_kwargs={
248
- "files": dl_manager.iter_archive(archive),
249
- "config": config,
250
- "categories": _INTERPOLATE_CATEGORY,
251
- },
252
- ),
253
- ]
254
-
255
- def _generate_examples(self, files, config, categories):
256
- """Yields examples based on directory, module file.."""
257
-
258
- idx = 0
259
- filepaths = self._get_filepaths_from_categories(config, categories)
260
- for path, f in files:
261
- if not filepaths:
262
- break
263
- elif path in filepaths:
264
- for question in f:
265
- if not question:
266
- continue
267
- else:
268
- for answer in f:
269
- if not answer:
270
- continue
271
- else:
272
- yield idx, {_QUESTION: question, _ANSWER: answer}
273
- idx += 1
274
- break
275
- filepaths.remove(path)