Hugging Face's logo

Datasets:
windchimeran
/
pusl

Modalities:
Tabular
Text
Formats:
csv
Size:
1M - 10M
Libraries:
Datasets
pandas
Dataset card Data Studio Files
xet
Community
2
windchimeran commited on
Commit
76c8cac
·
verified ·
1 Parent(s): 017a2de

ipynbs

Browse files
Files changed (3) hide show
  1. README.md +0 -0
  2. dataloader.ipynb +1095 -0
  3. evaluation.ipynb +91 -0
README.md ADDED
File without changes
dataloader.ipynb ADDED
@@ -0,0 +1,1095 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 3,
6
+ "id": "56c5bf21-53d3-4403-89b3-4cd0a5b0777b",
7
+ "metadata": {},
8
+ "outputs": [
9
+ {
10
+ "name": "stdout",
11
+ "output_type": "stream",
12
+ "text": [
13
+ "/data/ebay/notebooks/haorzhang/examples/pusl_github\n"
14
+ ]
15
+ }
16
+ ],
17
+ "source": [
18
+ "from datasets import load_dataset, concatenate_datasets\n",
19
+ "import os"
20
+ ]
21
+ },
22
+ {
23
+ "cell_type": "code",
24
+ "execution_count": 11,
25
+ "id": "b6948f5d-bf4b-4704-8249-0bfe965bcccc",
26
+ "metadata": {},
27
+ "outputs": [],
28
+ "source": [
29
+ "\n",
30
+ "data_dir = \"data\"\n",
31
+ "\n",
32
+ "def load_data_by_name(data_name, split):\n",
33
+ " postfix = \"csv\"\n",
34
+ " assert data_name in [\"Eurlex-4.3K\", \"AmazonCat-13K\"]\n",
35
+ " data_path = os.path.join(data_dir, data_name)\n",
36
+ " num_data_files = {}\n",
37
+ " num_path_list = []\n",
38
+ "\n",
39
+ " for file_name in os.listdir(data_path):\n",
40
+ " if file_name.startswith(\"num_\") and (split+\".\") in file_name:\n",
41
+ " file_path = os.path.join(data_path, file_name)\n",
42
+ " num_data_files[file_name] = file_path\n",
43
+ " num_path_list.append(file_path)\n",
44
+ " print(\"data list\", num_path_list)\n",
45
+ " \n",
46
+ " num_dataset_list = [\n",
47
+ " load_dataset(postfix, data_files=num_data, split=\"train\")\n",
48
+ " for num_data in num_path_list\n",
49
+ " ]\n",
50
+ " concat_dataset = concatenate_datasets(num_dataset_list)\n",
51
+ " return concat_dataset\n",
52
+ "\n"
53
+ ]
54
+ },
55
+ {
56
+ "cell_type": "code",
57
+ "execution_count": 5,
58
+ "id": "8f268925-b786-42bf-9192-5148fda9d75a",
59
+ "metadata": {},
60
+ "outputs": [],
61
+ "source": []
62
+ },
63
+ {
64
+ "cell_type": "code",
65
+ "execution_count": 6,
66
+ "id": "ec8e19a0-8acd-4a8a-8d0e-ae53fad37443",
67
+ "metadata": {},
68
+ "outputs": [
69
+ {
70
+ "name": "stderr",
71
+ "output_type": "stream",
72
+ "text": [
73
+ "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4\n",
74
+ " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
75
+ ]
76
+ }
77
+ ],
78
+ "source": [
79
+ "eurlex_train = load_data_by_name(\"Eurlex-4.3K\", split=\"train\")\n",
80
+ "amazoncat_train = load_data_by_name(\"AmazonCat-13K\", split=\"train\")"
81
+ ]
82
+ },
83
+ {
84
+ "cell_type": "code",
85
+ "execution_count": 7,
86
+ "id": "310494b0-bdd6-40be-8ff5-30cffa4442ed",
87
+ "metadata": {},
88
+ "outputs": [],
89
+ "source": [
90
+ "# amazoncat_train[:10]"
91
+ ]
92
+ },
93
+ {
94
+ "cell_type": "code",
95
+ "execution_count": 12,
96
+ "id": "761906b8-6c1d-4d48-adcf-20589d9a0385",
97
+ "metadata": {},
98
+ "outputs": [
99
+ {
100
+ "name": "stdout",
101
+ "output_type": "stream",
102
+ "text": [
103
+ "data list ['data/AmazonCat-13K/num_1_test.csv', 'data/AmazonCat-13K/num_15_test.csv', 'data/AmazonCat-13K/num_3_test.csv', 'data/AmazonCat-13K/num_5_test.csv', 'data/AmazonCat-13K/num_7_test.csv', 'data/AmazonCat-13K/num_8_test.csv', 'data/AmazonCat-13K/num_14_test.csv', 'data/AmazonCat-13K/num_17_test.csv', 'data/AmazonCat-13K/num_18_test.csv', 'data/AmazonCat-13K/num_16_test.csv', 'data/AmazonCat-13K/num_4_test.csv', 'data/AmazonCat-13K/num_11_test.csv', 'data/AmazonCat-13K/num_19_test.csv', 'data/AmazonCat-13K/num_6_test.csv', 'data/AmazonCat-13K/num_9_test.csv', 'data/AmazonCat-13K/num_10_test.csv', 'data/AmazonCat-13K/num_13_test.csv', 'data/AmazonCat-13K/num_2_test.csv', 'data/AmazonCat-13K/num_12_test.csv']\n"
104
+ ]
105
+ },
106
+ {
107
+ "data": {
108
+ "application/vnd.jupyter.widget-view+json": {
109
+ "model_id": "9b92cd88a7884f1b9d9195b7c93044f3",
110
+ "version_major": 2,
111
+ "version_minor": 0
112
+ },
113
+ "text/plain": [
114
+ "Generating train split: 0 examples [00:00, ? examples/s]"
115
+ ]
116
+ },
117
+ "metadata": {},
118
+ "output_type": "display_data"
119
+ },
120
+ {
121
+ "data": {
122
+ "application/vnd.jupyter.widget-view+json": {
123
+ "model_id": "be5bb6a3eaa2406eb863fa0db9591f6a",
124
+ "version_major": 2,
125
+ "version_minor": 0
126
+ },
127
+ "text/plain": [
128
+ "Generating train split: 0 examples [00:00, ? examples/s]"
129
+ ]
130
+ },
131
+ "metadata": {},
132
+ "output_type": "display_data"
133
+ },
134
+ {
135
+ "data": {
136
+ "application/vnd.jupyter.widget-view+json": {
137
+ "model_id": "40d1d323222b438296f6ca20469ad357",
138
+ "version_major": 2,
139
+ "version_minor": 0
140
+ },
141
+ "text/plain": [
142
+ "Generating train split: 0 examples [00:00, ? examples/s]"
143
+ ]
144
+ },
145
+ "metadata": {},
146
+ "output_type": "display_data"
147
+ },
148
+ {
149
+ "data": {
150
+ "application/vnd.jupyter.widget-view+json": {
151
+ "model_id": "dc27865a734745d092f56404c073e775",
152
+ "version_major": 2,
153
+ "version_minor": 0
154
+ },
155
+ "text/plain": [
156
+ "Generating train split: 0 examples [00:00, ? examples/s]"
157
+ ]
158
+ },
159
+ "metadata": {},
160
+ "output_type": "display_data"
161
+ },
162
+ {
163
+ "data": {
164
+ "application/vnd.jupyter.widget-view+json": {
165
+ "model_id": "ed08df72911f47d4934d883ca07e7075",
166
+ "version_major": 2,
167
+ "version_minor": 0
168
+ },
169
+ "text/plain": [
170
+ "Generating train split: 0 examples [00:00, ? examples/s]"
171
+ ]
172
+ },
173
+ "metadata": {},
174
+ "output_type": "display_data"
175
+ },
176
+ {
177
+ "data": {
178
+ "application/vnd.jupyter.widget-view+json": {
179
+ "model_id": "34df673160704fdea4ba0160b5322813",
180
+ "version_major": 2,
181
+ "version_minor": 0
182
+ },
183
+ "text/plain": [
184
+ "Generating train split: 0 examples [00:00, ? examples/s]"
185
+ ]
186
+ },
187
+ "metadata": {},
188
+ "output_type": "display_data"
189
+ },
190
+ {
191
+ "data": {
192
+ "application/vnd.jupyter.widget-view+json": {
193
+ "model_id": "9e9612f9473c4aefbfb5aeaa8275068f",
194
+ "version_major": 2,
195
+ "version_minor": 0
196
+ },
197
+ "text/plain": [
198
+ "Generating train split: 0 examples [00:00, ? examples/s]"
199
+ ]
200
+ },
201
+ "metadata": {},
202
+ "output_type": "display_data"
203
+ },
204
+ {
205
+ "data": {
206
+ "application/vnd.jupyter.widget-view+json": {
207
+ "model_id": "3658e985c20e469086a326ab42fb62cb",
208
+ "version_major": 2,
209
+ "version_minor": 0
210
+ },
211
+ "text/plain": [
212
+ "Generating train split: 0 examples [00:00, ? examples/s]"
213
+ ]
214
+ },
215
+ "metadata": {},
216
+ "output_type": "display_data"
217
+ },
218
+ {
219
+ "data": {
220
+ "application/vnd.jupyter.widget-view+json": {
221
+ "model_id": "9d220cdc95cb4b448098a6abbdbf7bf7",
222
+ "version_major": 2,
223
+ "version_minor": 0
224
+ },
225
+ "text/plain": [
226
+ "Generating train split: 0 examples [00:00, ? examples/s]"
227
+ ]
228
+ },
229
+ "metadata": {},
230
+ "output_type": "display_data"
231
+ },
232
+ {
233
+ "data": {
234
+ "application/vnd.jupyter.widget-view+json": {
235
+ "model_id": "173ec3de957b40aaaf02c70fca267108",
236
+ "version_major": 2,
237
+ "version_minor": 0
238
+ },
239
+ "text/plain": [
240
+ "Generating train split: 0 examples [00:00, ? examples/s]"
241
+ ]
242
+ },
243
+ "metadata": {},
244
+ "output_type": "display_data"
245
+ },
246
+ {
247
+ "data": {
248
+ "application/vnd.jupyter.widget-view+json": {
249
+ "model_id": "71eb62f31e4e4f4fab84d5168ac8c36a",
250
+ "version_major": 2,
251
+ "version_minor": 0
252
+ },
253
+ "text/plain": [
254
+ "Generating train split: 0 examples [00:00, ? examples/s]"
255
+ ]
256
+ },
257
+ "metadata": {},
258
+ "output_type": "display_data"
259
+ },
260
+ {
261
+ "data": {
262
+ "application/vnd.jupyter.widget-view+json": {
263
+ "model_id": "be4d2ec7d3054d75bf80cee8d477f480",
264
+ "version_major": 2,
265
+ "version_minor": 0
266
+ },
267
+ "text/plain": [
268
+ "Generating train split: 0 examples [00:00, ? examples/s]"
269
+ ]
270
+ },
271
+ "metadata": {},
272
+ "output_type": "display_data"
273
+ },
274
+ {
275
+ "data": {
276
+ "application/vnd.jupyter.widget-view+json": {
277
+ "model_id": "a87c1ab290264b459eb1840b49e671c9",
278
+ "version_major": 2,
279
+ "version_minor": 0
280
+ },
281
+ "text/plain": [
282
+ "Generating train split: 0 examples [00:00, ? examples/s]"
283
+ ]
284
+ },
285
+ "metadata": {},
286
+ "output_type": "display_data"
287
+ },
288
+ {
289
+ "data": {
290
+ "application/vnd.jupyter.widget-view+json": {
291
+ "model_id": "a5ab312c89104d1f94f84ef7d08afcb4",
292
+ "version_major": 2,
293
+ "version_minor": 0
294
+ },
295
+ "text/plain": [
296
+ "Generating train split: 0 examples [00:00, ? examples/s]"
297
+ ]
298
+ },
299
+ "metadata": {},
300
+ "output_type": "display_data"
301
+ },
302
+ {
303
+ "data": {
304
+ "application/vnd.jupyter.widget-view+json": {
305
+ "model_id": "96394096c6b54b918083eb39ecba76dd",
306
+ "version_major": 2,
307
+ "version_minor": 0
308
+ },
309
+ "text/plain": [
310
+ "Generating train split: 0 examples [00:00, ? examples/s]"
311
+ ]
312
+ },
313
+ "metadata": {},
314
+ "output_type": "display_data"
315
+ },
316
+ {
317
+ "data": {
318
+ "application/vnd.jupyter.widget-view+json": {
319
+ "model_id": "9a25a2da1c30496db4679c422d7c05b3",
320
+ "version_major": 2,
321
+ "version_minor": 0
322
+ },
323
+ "text/plain": [
324
+ "Generating train split: 0 examples [00:00, ? examples/s]"
325
+ ]
326
+ },
327
+ "metadata": {},
328
+ "output_type": "display_data"
329
+ },
330
+ {
331
+ "data": {
332
+ "application/vnd.jupyter.widget-view+json": {
333
+ "model_id": "83193442191a48f4bd5811863e4cfb7e",
334
+ "version_major": 2,
335
+ "version_minor": 0
336
+ },
337
+ "text/plain": [
338
+ "Generating train split: 0 examples [00:00, ? examples/s]"
339
+ ]
340
+ },
341
+ "metadata": {},
342
+ "output_type": "display_data"
343
+ },
344
+ {
345
+ "data": {
346
+ "application/vnd.jupyter.widget-view+json": {
347
+ "model_id": "e2427fbeb4bc4fd48deb461459d7aaec",
348
+ "version_major": 2,
349
+ "version_minor": 0
350
+ },
351
+ "text/plain": [
352
+ "Generating train split: 0 examples [00:00, ? examples/s]"
353
+ ]
354
+ },
355
+ "metadata": {},
356
+ "output_type": "display_data"
357
+ }
358
+ ],
359
+ "source": [
360
+ "amazoncat_test = load_data_by_name(\"AmazonCat-13K\", split=\"test\")\n",
361
+ "df_amazoncat_test = amazoncat_test.to_pandas()\n",
362
+ "df_amazoncat_test_narrow = df_amazoncat_test[df_amazoncat_test[\"num_keyphrases\"] <= 2*5]\n",
363
+ "df_amazoncat_test_diverse = df_amazoncat_test[df_amazoncat_test[\"num_keyphrases\"] > 2*5]"
364
+ ]
365
+ },
366
+ {
367
+ "cell_type": "code",
368
+ "execution_count": 19,
369
+ "id": "49f925bf-8bb3-492e-8fed-dd7cad5649f5",
370
+ "metadata": {},
371
+ "outputs": [
372
+ {
373
+ "name": "stdout",
374
+ "output_type": "stream",
375
+ "text": [
376
+ "data list ['data/Eurlex-4.3K/num_1_test.csv', 'data/Eurlex-4.3K/num_15_test.csv', 'data/Eurlex-4.3K/num_21_test.csv', 'data/Eurlex-4.3K/num_23_test.csv', 'data/Eurlex-4.3K/num_28_test.csv', 'data/Eurlex-4.3K/num_30_test.csv', 'data/Eurlex-4.3K/num_35_test.csv', 'data/Eurlex-4.3K/num_3_test.csv', 'data/Eurlex-4.3K/num_5_test.csv', 'data/Eurlex-4.3K/num_7_test.csv', 'data/Eurlex-4.3K/num_8_test.csv', 'data/Eurlex-4.3K/num_14_test.csv', 'data/Eurlex-4.3K/num_17_test.csv', 'data/Eurlex-4.3K/num_18_test.csv', 'data/Eurlex-4.3K/num_20_test.csv', 'data/Eurlex-4.3K/num_26_test.csv', 'data/Eurlex-4.3K/num_16_test.csv', 'data/Eurlex-4.3K/num_37_test.csv', 'data/Eurlex-4.3K/num_40_test.csv', 'data/Eurlex-4.3K/num_42_test.csv', 'data/Eurlex-4.3K/num_4_test.csv', 'data/Eurlex-4.3K/num_11_test.csv', 'data/Eurlex-4.3K/num_19_test.csv', 'data/Eurlex-4.3K/num_22_test.csv', 'data/Eurlex-4.3K/num_27_test.csv', 'data/Eurlex-4.3K/num_34_test.csv', 'data/Eurlex-4.3K/num_39_test.csv', 'data/Eurlex-4.3K/num_6_test.csv', 'data/Eurlex-4.3K/num_9_test.csv', 'data/Eurlex-4.3K/num_10_test.csv', 'data/Eurlex-4.3K/num_13_test.csv', 'data/Eurlex-4.3K/num_32_test.csv', 'data/Eurlex-4.3K/num_33_test.csv', 'data/Eurlex-4.3K/num_41_test.csv', 'data/Eurlex-4.3K/num_2_test.csv', 'data/Eurlex-4.3K/num_12_test.csv', 'data/Eurlex-4.3K/num_24_test.csv', 'data/Eurlex-4.3K/num_25_test.csv', 'data/Eurlex-4.3K/num_29_test.csv', 'data/Eurlex-4.3K/num_31_test.csv', 'data/Eurlex-4.3K/num_36_test.csv', 'data/Eurlex-4.3K/num_38_test.csv']\n"
377
+ ]
378
+ },
379
+ {
380
+ "data": {
381
+ "application/vnd.jupyter.widget-view+json": {
382
+ "model_id": "ca6d22bba9be4a189429b351ea6ca6f4",
383
+ "version_major": 2,
384
+ "version_minor": 0
385
+ },
386
+ "text/plain": [
387
+ "Generating train split: 0 examples [00:00, ? examples/s]"
388
+ ]
389
+ },
390
+ "metadata": {},
391
+ "output_type": "display_data"
392
+ },
393
+ {
394
+ "data": {
395
+ "application/vnd.jupyter.widget-view+json": {
396
+ "model_id": "8649ffd4855b4f64a9aa1b4e35ec0a9d",
397
+ "version_major": 2,
398
+ "version_minor": 0
399
+ },
400
+ "text/plain": [
401
+ "Generating train split: 0 examples [00:00, ? examples/s]"
402
+ ]
403
+ },
404
+ "metadata": {},
405
+ "output_type": "display_data"
406
+ },
407
+ {
408
+ "data": {
409
+ "application/vnd.jupyter.widget-view+json": {
410
+ "model_id": "29d94f1990e04a4b999a9342fbe1c04e",
411
+ "version_major": 2,
412
+ "version_minor": 0
413
+ },
414
+ "text/plain": [
415
+ "Generating train split: 0 examples [00:00, ? examples/s]"
416
+ ]
417
+ },
418
+ "metadata": {},
419
+ "output_type": "display_data"
420
+ },
421
+ {
422
+ "data": {
423
+ "application/vnd.jupyter.widget-view+json": {
424
+ "model_id": "ac71e14d40da4e34ab41e9990b4a1f67",
425
+ "version_major": 2,
426
+ "version_minor": 0
427
+ },
428
+ "text/plain": [
429
+ "Generating train split: 0 examples [00:00, ? examples/s]"
430
+ ]
431
+ },
432
+ "metadata": {},
433
+ "output_type": "display_data"
434
+ },
435
+ {
436
+ "data": {
437
+ "application/vnd.jupyter.widget-view+json": {
438
+ "model_id": "011ab1cecdcb4641bb21319e20360a17",
439
+ "version_major": 2,
440
+ "version_minor": 0
441
+ },
442
+ "text/plain": [
443
+ "Generating train split: 0 examples [00:00, ? examples/s]"
444
+ ]
445
+ },
446
+ "metadata": {},
447
+ "output_type": "display_data"
448
+ },
449
+ {
450
+ "data": {
451
+ "application/vnd.jupyter.widget-view+json": {
452
+ "model_id": "51bf0f8ed4ed4220891a0ac80122f2fe",
453
+ "version_major": 2,
454
+ "version_minor": 0
455
+ },
456
+ "text/plain": [
457
+ "Generating train split: 0 examples [00:00, ? examples/s]"
458
+ ]
459
+ },
460
+ "metadata": {},
461
+ "output_type": "display_data"
462
+ },
463
+ {
464
+ "data": {
465
+ "application/vnd.jupyter.widget-view+json": {
466
+ "model_id": "47b1c5223cdd499881cf0968f1226854",
467
+ "version_major": 2,
468
+ "version_minor": 0
469
+ },
470
+ "text/plain": [
471
+ "Generating train split: 0 examples [00:00, ? examples/s]"
472
+ ]
473
+ },
474
+ "metadata": {},
475
+ "output_type": "display_data"
476
+ },
477
+ {
478
+ "data": {
479
+ "application/vnd.jupyter.widget-view+json": {
480
+ "model_id": "36eaa65b1e114154b886f3cbae44ba6a",
481
+ "version_major": 2,
482
+ "version_minor": 0
483
+ },
484
+ "text/plain": [
485
+ "Generating train split: 0 examples [00:00, ? examples/s]"
486
+ ]
487
+ },
488
+ "metadata": {},
489
+ "output_type": "display_data"
490
+ },
491
+ {
492
+ "data": {
493
+ "application/vnd.jupyter.widget-view+json": {
494
+ "model_id": "cc06d0aa3a1742e29ecb1ae03719bb28",
495
+ "version_major": 2,
496
+ "version_minor": 0
497
+ },
498
+ "text/plain": [
499
+ "Generating train split: 0 examples [00:00, ? examples/s]"
500
+ ]
501
+ },
502
+ "metadata": {},
503
+ "output_type": "display_data"
504
+ },
505
+ {
506
+ "data": {
507
+ "application/vnd.jupyter.widget-view+json": {
508
+ "model_id": "567be222a4914ce0ac899ea3283695a6",
509
+ "version_major": 2,
510
+ "version_minor": 0
511
+ },
512
+ "text/plain": [
513
+ "Generating train split: 0 examples [00:00, ? examples/s]"
514
+ ]
515
+ },
516
+ "metadata": {},
517
+ "output_type": "display_data"
518
+ },
519
+ {
520
+ "data": {
521
+ "application/vnd.jupyter.widget-view+json": {
522
+ "model_id": "308f4cb1d5974af19c6ed59c51dc11ab",
523
+ "version_major": 2,
524
+ "version_minor": 0
525
+ },
526
+ "text/plain": [
527
+ "Generating train split: 0 examples [00:00, ? examples/s]"
528
+ ]
529
+ },
530
+ "metadata": {},
531
+ "output_type": "display_data"
532
+ },
533
+ {
534
+ "data": {
535
+ "application/vnd.jupyter.widget-view+json": {
536
+ "model_id": "a2bc8d44ad7e45499ce188b47985f46e",
537
+ "version_major": 2,
538
+ "version_minor": 0
539
+ },
540
+ "text/plain": [
541
+ "Generating train split: 0 examples [00:00, ? examples/s]"
542
+ ]
543
+ },
544
+ "metadata": {},
545
+ "output_type": "display_data"
546
+ },
547
+ {
548
+ "data": {
549
+ "application/vnd.jupyter.widget-view+json": {
550
+ "model_id": "e07b5cb0e12e411bbb4aafc517d48a81",
551
+ "version_major": 2,
552
+ "version_minor": 0
553
+ },
554
+ "text/plain": [
555
+ "Generating train split: 0 examples [00:00, ? examples/s]"
556
+ ]
557
+ },
558
+ "metadata": {},
559
+ "output_type": "display_data"
560
+ },
561
+ {
562
+ "data": {
563
+ "application/vnd.jupyter.widget-view+json": {
564
+ "model_id": "11db8dada8544432bc94d0612ff3ff27",
565
+ "version_major": 2,
566
+ "version_minor": 0
567
+ },
568
+ "text/plain": [
569
+ "Generating train split: 0 examples [00:00, ? examples/s]"
570
+ ]
571
+ },
572
+ "metadata": {},
573
+ "output_type": "display_data"
574
+ },
575
+ {
576
+ "data": {
577
+ "application/vnd.jupyter.widget-view+json": {
578
+ "model_id": "e4397ae0e4e14ebfaab0cc8bc0ee6e51",
579
+ "version_major": 2,
580
+ "version_minor": 0
581
+ },
582
+ "text/plain": [
583
+ "Generating train split: 0 examples [00:00, ? examples/s]"
584
+ ]
585
+ },
586
+ "metadata": {},
587
+ "output_type": "display_data"
588
+ },
589
+ {
590
+ "data": {
591
+ "application/vnd.jupyter.widget-view+json": {
592
+ "model_id": "fae8cbce454d42a3a31d0d3661d9e4ed",
593
+ "version_major": 2,
594
+ "version_minor": 0
595
+ },
596
+ "text/plain": [
597
+ "Generating train split: 0 examples [00:00, ? examples/s]"
598
+ ]
599
+ },
600
+ "metadata": {},
601
+ "output_type": "display_data"
602
+ },
603
+ {
604
+ "data": {
605
+ "application/vnd.jupyter.widget-view+json": {
606
+ "model_id": "b32626755caf4a28a21eb5fb9fa72e7f",
607
+ "version_major": 2,
608
+ "version_minor": 0
609
+ },
610
+ "text/plain": [
611
+ "Generating train split: 0 examples [00:00, ? examples/s]"
612
+ ]
613
+ },
614
+ "metadata": {},
615
+ "output_type": "display_data"
616
+ },
617
+ {
618
+ "data": {
619
+ "application/vnd.jupyter.widget-view+json": {
620
+ "model_id": "dadfe9eb07004d85a86223911c05c353",
621
+ "version_major": 2,
622
+ "version_minor": 0
623
+ },
624
+ "text/plain": [
625
+ "Generating train split: 0 examples [00:00, ? examples/s]"
626
+ ]
627
+ },
628
+ "metadata": {},
629
+ "output_type": "display_data"
630
+ },
631
+ {
632
+ "data": {
633
+ "application/vnd.jupyter.widget-view+json": {
634
+ "model_id": "16e0b00a6dcd4f748db4ceaba6ce0f61",
635
+ "version_major": 2,
636
+ "version_minor": 0
637
+ },
638
+ "text/plain": [
639
+ "Generating train split: 0 examples [00:00, ? examples/s]"
640
+ ]
641
+ },
642
+ "metadata": {},
643
+ "output_type": "display_data"
644
+ },
645
+ {
646
+ "data": {
647
+ "application/vnd.jupyter.widget-view+json": {
648
+ "model_id": "f9efa8cef0524848ad40bf91ff819338",
649
+ "version_major": 2,
650
+ "version_minor": 0
651
+ },
652
+ "text/plain": [
653
+ "Generating train split: 0 examples [00:00, ? examples/s]"
654
+ ]
655
+ },
656
+ "metadata": {},
657
+ "output_type": "display_data"
658
+ },
659
+ {
660
+ "data": {
661
+ "application/vnd.jupyter.widget-view+json": {
662
+ "model_id": "d9a8f0b997ee49fb93d920bdca102c6e",
663
+ "version_major": 2,
664
+ "version_minor": 0
665
+ },
666
+ "text/plain": [
667
+ "Generating train split: 0 examples [00:00, ? examples/s]"
668
+ ]
669
+ },
670
+ "metadata": {},
671
+ "output_type": "display_data"
672
+ },
673
+ {
674
+ "data": {
675
+ "application/vnd.jupyter.widget-view+json": {
676
+ "model_id": "dfc32b7f1b6c43ad87df5cb93d74509e",
677
+ "version_major": 2,
678
+ "version_minor": 0
679
+ },
680
+ "text/plain": [
681
+ "Generating train split: 0 examples [00:00, ? examples/s]"
682
+ ]
683
+ },
684
+ "metadata": {},
685
+ "output_type": "display_data"
686
+ },
687
+ {
688
+ "data": {
689
+ "application/vnd.jupyter.widget-view+json": {
690
+ "model_id": "8032caba97b0401594899337b348dc6f",
691
+ "version_major": 2,
692
+ "version_minor": 0
693
+ },
694
+ "text/plain": [
695
+ "Generating train split: 0 examples [00:00, ? examples/s]"
696
+ ]
697
+ },
698
+ "metadata": {},
699
+ "output_type": "display_data"
700
+ },
701
+ {
702
+ "data": {
703
+ "application/vnd.jupyter.widget-view+json": {
704
+ "model_id": "e4849fd7c62941719928969fa67cfac5",
705
+ "version_major": 2,
706
+ "version_minor": 0
707
+ },
708
+ "text/plain": [
709
+ "Generating train split: 0 examples [00:00, ? examples/s]"
710
+ ]
711
+ },
712
+ "metadata": {},
713
+ "output_type": "display_data"
714
+ },
715
+ {
716
+ "data": {
717
+ "application/vnd.jupyter.widget-view+json": {
718
+ "model_id": "fbae7fbc47724893a3dc85aa4788f529",
719
+ "version_major": 2,
720
+ "version_minor": 0
721
+ },
722
+ "text/plain": [
723
+ "Generating train split: 0 examples [00:00, ? examples/s]"
724
+ ]
725
+ },
726
+ "metadata": {},
727
+ "output_type": "display_data"
728
+ },
729
+ {
730
+ "data": {
731
+ "application/vnd.jupyter.widget-view+json": {
732
+ "model_id": "8143b83308284ef98de8fdaa13cdbafb",
733
+ "version_major": 2,
734
+ "version_minor": 0
735
+ },
736
+ "text/plain": [
737
+ "Generating train split: 0 examples [00:00, ? examples/s]"
738
+ ]
739
+ },
740
+ "metadata": {},
741
+ "output_type": "display_data"
742
+ },
743
+ {
744
+ "data": {
745
+ "application/vnd.jupyter.widget-view+json": {
746
+ "model_id": "3165953202584c3db9c5b41a8bbc1707",
747
+ "version_major": 2,
748
+ "version_minor": 0
749
+ },
750
+ "text/plain": [
751
+ "Generating train split: 0 examples [00:00, ? examples/s]"
752
+ ]
753
+ },
754
+ "metadata": {},
755
+ "output_type": "display_data"
756
+ },
757
+ {
758
+ "data": {
759
+ "application/vnd.jupyter.widget-view+json": {
760
+ "model_id": "4823e131bbf4436895a04c577123998c",
761
+ "version_major": 2,
762
+ "version_minor": 0
763
+ },
764
+ "text/plain": [
765
+ "Generating train split: 0 examples [00:00, ? examples/s]"
766
+ ]
767
+ },
768
+ "metadata": {},
769
+ "output_type": "display_data"
770
+ },
771
+ {
772
+ "data": {
773
+ "application/vnd.jupyter.widget-view+json": {
774
+ "model_id": "89e32dd82687487d9391d7d938378cb3",
775
+ "version_major": 2,
776
+ "version_minor": 0
777
+ },
778
+ "text/plain": [
779
+ "Generating train split: 0 examples [00:00, ? examples/s]"
780
+ ]
781
+ },
782
+ "metadata": {},
783
+ "output_type": "display_data"
784
+ },
785
+ {
786
+ "data": {
787
+ "application/vnd.jupyter.widget-view+json": {
788
+ "model_id": "aa3199550dfd46eb81b610275dc2c077",
789
+ "version_major": 2,
790
+ "version_minor": 0
791
+ },
792
+ "text/plain": [
793
+ "Generating train split: 0 examples [00:00, ? examples/s]"
794
+ ]
795
+ },
796
+ "metadata": {},
797
+ "output_type": "display_data"
798
+ },
799
+ {
800
+ "data": {
801
+ "application/vnd.jupyter.widget-view+json": {
802
+ "model_id": "3df62eccd0ea4093ab641aafa55ffd89",
803
+ "version_major": 2,
804
+ "version_minor": 0
805
+ },
806
+ "text/plain": [
807
+ "Generating train split: 0 examples [00:00, ? examples/s]"
808
+ ]
809
+ },
810
+ "metadata": {},
811
+ "output_type": "display_data"
812
+ },
813
+ {
814
+ "data": {
815
+ "application/vnd.jupyter.widget-view+json": {
816
+ "model_id": "235c176234a6464a8d5a88edf397dde4",
817
+ "version_major": 2,
818
+ "version_minor": 0
819
+ },
820
+ "text/plain": [
821
+ "Generating train split: 0 examples [00:00, ? examples/s]"
822
+ ]
823
+ },
824
+ "metadata": {},
825
+ "output_type": "display_data"
826
+ },
827
+ {
828
+ "data": {
829
+ "application/vnd.jupyter.widget-view+json": {
830
+ "model_id": "3426003bb9fd4631a87f4802435b3ac6",
831
+ "version_major": 2,
832
+ "version_minor": 0
833
+ },
834
+ "text/plain": [
835
+ "Generating train split: 0 examples [00:00, ? examples/s]"
836
+ ]
837
+ },
838
+ "metadata": {},
839
+ "output_type": "display_data"
840
+ },
841
+ {
842
+ "data": {
843
+ "application/vnd.jupyter.widget-view+json": {
844
+ "model_id": "b68e30bbce624b20a1f83c037e9b8681",
845
+ "version_major": 2,
846
+ "version_minor": 0
847
+ },
848
+ "text/plain": [
849
+ "Generating train split: 0 examples [00:00, ? examples/s]"
850
+ ]
851
+ },
852
+ "metadata": {},
853
+ "output_type": "display_data"
854
+ },
855
+ {
856
+ "data": {
857
+ "application/vnd.jupyter.widget-view+json": {
858
+ "model_id": "4ec3e04a59d94d54b34517a3bd5de1dc",
859
+ "version_major": 2,
860
+ "version_minor": 0
861
+ },
862
+ "text/plain": [
863
+ "Generating train split: 0 examples [00:00, ? examples/s]"
864
+ ]
865
+ },
866
+ "metadata": {},
867
+ "output_type": "display_data"
868
+ },
869
+ {
870
+ "data": {
871
+ "application/vnd.jupyter.widget-view+json": {
872
+ "model_id": "8fc2181ab29e41b8b49e5e527f75b09e",
873
+ "version_major": 2,
874
+ "version_minor": 0
875
+ },
876
+ "text/plain": [
877
+ "Generating train split: 0 examples [00:00, ? examples/s]"
878
+ ]
879
+ },
880
+ "metadata": {},
881
+ "output_type": "display_data"
882
+ },
883
+ {
884
+ "data": {
885
+ "application/vnd.jupyter.widget-view+json": {
886
+ "model_id": "566f002d194246a082f2216fc4ba53d6",
887
+ "version_major": 2,
888
+ "version_minor": 0
889
+ },
890
+ "text/plain": [
891
+ "Generating train split: 0 examples [00:00, ? examples/s]"
892
+ ]
893
+ },
894
+ "metadata": {},
895
+ "output_type": "display_data"
896
+ },
897
+ {
898
+ "data": {
899
+ "application/vnd.jupyter.widget-view+json": {
900
+ "model_id": "4e283eaffd8d428c860d6264bcbd3cc9",
901
+ "version_major": 2,
902
+ "version_minor": 0
903
+ },
904
+ "text/plain": [
905
+ "Generating train split: 0 examples [00:00, ? examples/s]"
906
+ ]
907
+ },
908
+ "metadata": {},
909
+ "output_type": "display_data"
910
+ },
911
+ {
912
+ "data": {
913
+ "application/vnd.jupyter.widget-view+json": {
914
+ "model_id": "2050cda0af144794a2dc417c648a23bd",
915
+ "version_major": 2,
916
+ "version_minor": 0
917
+ },
918
+ "text/plain": [
919
+ "Generating train split: 0 examples [00:00, ? examples/s]"
920
+ ]
921
+ },
922
+ "metadata": {},
923
+ "output_type": "display_data"
924
+ },
925
+ {
926
+ "data": {
927
+ "application/vnd.jupyter.widget-view+json": {
928
+ "model_id": "6ad38712f3fd44ee9245d5869cf45c0e",
929
+ "version_major": 2,
930
+ "version_minor": 0
931
+ },
932
+ "text/plain": [
933
+ "Generating train split: 0 examples [00:00, ? examples/s]"
934
+ ]
935
+ },
936
+ "metadata": {},
937
+ "output_type": "display_data"
938
+ },
939
+ {
940
+ "data": {
941
+ "application/vnd.jupyter.widget-view+json": {
942
+ "model_id": "e23b74600bf3483da0ee0739f2eb4243",
943
+ "version_major": 2,
944
+ "version_minor": 0
945
+ },
946
+ "text/plain": [
947
+ "Generating train split: 0 examples [00:00, ? examples/s]"
948
+ ]
949
+ },
950
+ "metadata": {},
951
+ "output_type": "display_data"
952
+ },
953
+ {
954
+ "data": {
955
+ "application/vnd.jupyter.widget-view+json": {
956
+ "model_id": "c08764520d8b46f1a4a253717ab08807",
957
+ "version_major": 2,
958
+ "version_minor": 0
959
+ },
960
+ "text/plain": [
961
+ "Generating train split: 0 examples [00:00, ? examples/s]"
962
+ ]
963
+ },
964
+ "metadata": {},
965
+ "output_type": "display_data"
966
+ }
967
+ ],
968
+ "source": [
969
+ "eurlex_test = load_data_by_name(\"Eurlex-4.3K\", split=\"test\")\n",
970
+ "df_eurlex_test = eurlex_test.to_pandas()\n",
971
+ "df_eurlex_test_narrow = df_eurlex_test[df_eurlex_test[\"num_keyphrases\"] <= 2*15]\n",
972
+ "df_eurlex_test_diverse = df_eurlex_test[df_eurlex_test[\"num_keyphrases\"] > 2*15]"
973
+ ]
974
+ },
975
+ {
976
+ "cell_type": "code",
977
+ "execution_count": 17,
978
+ "id": "16165803-a38b-434b-baf4-ac4b7d5a0eb5",
979
+ "metadata": {},
980
+ "outputs": [
981
+ {
982
+ "data": {
983
+ "text/plain": [
984
+ "10000"
985
+ ]
986
+ },
987
+ "execution_count": 17,
988
+ "metadata": {},
989
+ "output_type": "execute_result"
990
+ }
991
+ ],
992
+ "source": [
993
+ "def return_eval(pred2score, target2score, mean):\n",
994
+ " mean2 = 2 * mean\n",
995
+ " pred = [p.lower() for p in pred2score]\n",
996
+ " target = [p.lower() for p in target2score]\n",
997
+ " o = len(set(target))\n",
998
+ "\n",
999
+ " intersect = len(set(pred[:o]).intersection(set(target)))\n",
1000
+ " budgetaccone = len(set(pred[:mean]).intersection(set(target)))/mean\n",
1001
+ " budgetacctwo = len(set(pred[:mean2]).intersection(set(target)))/mean2\n",
1002
+ " prec = intersect/len(set(pred[:o])) if len(pred) > 0 else 0.0\n",
1003
+ " rec = intersect/len(target)\n",
1004
+ "\n",
1005
+ " \n",
1006
+ " kmean = len(set(pred[:mean]))\n",
1007
+ " k2mean = len(set(pred[:mean2]))\n",
1008
+ "\n",
1009
+ " if prec==0 and rec==0:\n",
1010
+ " f1=0\n",
1011
+ " else:\n",
1012
+ " f1 = 2*prec*rec/(prec+rec)\n",
1013
+ " \n",
1014
+ " return {\"P@O\":100*prec, \"R@O\": 100*rec, \"F1@O\":100*f1, \"B@mean\": budgetaccone, \"B@2mean\": budgetacctwo, \"#k@mean\": kmean, \"#k@2mean\": k2mean}\n",
1015
+ "\n",
1016
+ "def final_metric_results(preds_keyphrases, labels_keyphrases, mean):\n",
1017
+ " avg_scores = defaultdict(list)\n",
1018
+ " for pred, target in zip(preds_keyphrases, labels_keyphrases):\n",
1019
+ "\n",
1020
+ " all_exact_results = return_eval(pred, target, mean)\n",
1021
+ " \n",
1022
+ " for m_name, value in all_exact_results.items():\n",
1023
+ " avg_scores[m_name].append(value)\n",
1024
+ "\n",
1025
+ " avg_scores[\"pred_kpnum\"].append(len(set(pred)))\n",
1026
+ " avg_scores[\"gt_kpnum\"].append(len(set(target)))\n",
1027
+ " \n",
1028
+ " avg_scores = {m_name: round(np.mean(values),2) for m_name, values in avg_scores.items()}\n",
1029
+ "\n",
1030
+ " return avg_scores\n",
1031
+ " \n",
1032
+ "def generate_results(df, mean):\n",
1033
+ " \n",
1034
+ " labels_keyphrases = [p.lower().split(\";\") for p in df[\"target\"]]\n",
1035
+ " preds_keyphrases = []\n",
1036
+ " for i in range(len(df)):\n",
1037
+ " # preds_keyphrases.append(post_process(df.iloc[i][\"keyword\"])[:k])\n",
1038
+ " preds_keyphrases.append(post_process(df.iloc[i][\"keyword\"]))\n",
1039
+ " \n",
1040
+ " print(\"@\",mean) \n",
1041
+ " return final_metric_results(preds_keyphrases, labels_keyphrases, mean)"
1042
+ ]
1043
+ },
1044
+ {
1045
+ "cell_type": "code",
1046
+ "execution_count": 18,
1047
+ "id": "2a99636a-3950-495a-bf1b-68344e994088",
1048
+ "metadata": {},
1049
+ "outputs": [
1050
+ {
1051
+ "data": {
1052
+ "text/plain": [
1053
+ "4313"
1054
+ ]
1055
+ },
1056
+ "execution_count": 18,
1057
+ "metadata": {},
1058
+ "output_type": "execute_result"
1059
+ }
1060
+ ],
1061
+ "source": [
1062
+ "return_eval()"
1063
+ ]
1064
+ },
1065
+ {
1066
+ "cell_type": "code",
1067
+ "execution_count": null,
1068
+ "id": "2ac7d00a-da2c-4e23-a384-c197c99b11bb",
1069
+ "metadata": {},
1070
+ "outputs": [],
1071
+ "source": []
1072
+ }
1073
+ ],
1074
+ "metadata": {
1075
+ "kernelspec": {
1076
+ "display_name": "Python 3 (ipykernel)",
1077
+ "language": "python",
1078
+ "name": "python3"
1079
+ },
1080
+ "language_info": {
1081
+ "codemirror_mode": {
1082
+ "name": "ipython",
1083
+ "version": 3
1084
+ },
1085
+ "file_extension": ".py",
1086
+ "mimetype": "text/x-python",
1087
+ "name": "python",
1088
+ "nbconvert_exporter": "python",
1089
+ "pygments_lexer": "ipython3",
1090
+ "version": "3.10.12"
1091
+ }
1092
+ },
1093
+ "nbformat": 4,
1094
+ "nbformat_minor": 5
1095
+ }
evaluation.ipynb ADDED
@@ -0,0 +1,91 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 6,
6
+ "id": "b6948f5d-bf4b-4704-8249-0bfe965bcccc",
7
+ "metadata": {},
8
+ "outputs": [],
9
+ "source": [
10
+ "def return_eval(pred2score, target2score, mean):\n",
11
+ " mean2 = 2 * mean\n",
12
+ " pred = [p.lower() for p in pred2score]\n",
13
+ " target = [p.lower() for p in target2score]\n",
14
+ " o = len(set(target))\n",
15
+ "\n",
16
+ " intersect = len(set(pred[:o]).intersection(set(target)))\n",
17
+ " budgetaccone = len(set(pred[:mean]).intersection(set(target)))/mean\n",
18
+ " budgetacctwo = len(set(pred[:mean2]).intersection(set(target)))/mean2\n",
19
+ " prec = intersect/len(set(pred[:o])) if len(pred) > 0 else 0.0\n",
20
+ " rec = intersect/len(target)\n",
21
+ "\n",
22
+ " \n",
23
+ " kmean = len(set(pred[:mean]))\n",
24
+ " k2mean = len(set(pred[:mean2]))\n",
25
+ "\n",
26
+ " if prec==0 and rec==0:\n",
27
+ " f1=0\n",
28
+ " else:\n",
29
+ " f1 = 2*prec*rec/(prec+rec)\n",
30
+ " \n",
31
+ " return {\"P@O\":100*prec, \"R@O\": 100*rec, \"F1@O\":100*f1, \"B@mean\": budgetaccone, \"B@2mean\": budgetacctwo, \"#k@mean\": kmean, \"#k@2mean\": k2mean}\n",
32
+ "\n",
33
+ "def final_metric_results(preds_keyphrases, labels_keyphrases, mean):\n",
34
+ " avg_scores = defaultdict(list)\n",
35
+ " for pred, target in zip(preds_keyphrases, labels_keyphrases):\n",
36
+ "\n",
37
+ " all_exact_results = return_eval(pred, target, mean)\n",
38
+ " \n",
39
+ " for m_name, value in all_exact_results.items():\n",
40
+ " avg_scores[m_name].append(value)\n",
41
+ "\n",
42
+ " avg_scores[\"pred_kpnum\"].append(len(set(pred)))\n",
43
+ " avg_scores[\"gt_kpnum\"].append(len(set(target)))\n",
44
+ " \n",
45
+ " avg_scores = {m_name: round(np.mean(values),2) for m_name, values in avg_scores.items()}\n",
46
+ "\n",
47
+ " return avg_scores\n",
48
+ " \n",
49
+ "def generate_results(df, mean):\n",
50
+ " \n",
51
+ " labels_keyphrases = [p.lower().split(\";\") for p in df[\"target\"]]\n",
52
+ " preds_keyphrases = []\n",
53
+ " for i in range(len(df)):\n",
54
+ " # preds_keyphrases.append(post_process(df.iloc[i][\"keyword\"])[:k])\n",
55
+ " preds_keyphrases.append(post_process(df.iloc[i][\"keyword\"]))\n",
56
+ " \n",
57
+ " print(\"@\",mean) \n",
58
+ " return final_metric_results(preds_keyphrases, labels_keyphrases, mean)"
59
+ ]
60
+ },
61
+ {
62
+ "cell_type": "code",
63
+ "execution_count": null,
64
+ "id": "761906b8-6c1d-4d48-adcf-20589d9a0385",
65
+ "metadata": {},
66
+ "outputs": [],
67
+ "source": []
68
+ }
69
+ ],
70
+ "metadata": {
71
+ "kernelspec": {
72
+ "display_name": "Python 3 (ipykernel)",
73
+ "language": "python",
74
+ "name": "python3"
75
+ },
76
+ "language_info": {
77
+ "codemirror_mode": {
78
+ "name": "ipython",
79
+ "version": 3
80
+ },
81
+ "file_extension": ".py",
82
+ "mimetype": "text/x-python",
83
+ "name": "python",
84
+ "nbconvert_exporter": "python",
85
+ "pygments_lexer": "ipython3",
86
+ "version": "3.10.12"
87
+ }
88
+ },
89
+ "nbformat": 4,
90
+ "nbformat_minor": 5
91
+ }