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Upload Preprocessing.ipynb
Browse files- Preprocessing.ipynb +934 -0
Preprocessing.ipynb
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@@ -0,0 +1,934 @@
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1 |
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 329,
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"id": "6cf95722",
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"metadata": {
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"cellId": "eziodlb8kics09v3tpfeks"
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},
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"outputs": [],
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"source": [
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"#!g1.1\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"import transformers\n",
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"import torch\n",
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"import nltk\n",
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"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 330,
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"id": "c692d165",
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"metadata": {
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"cellId": "lvwy4cb1dnfnk3n391yiq"
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},
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"outputs": [],
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"source": [
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"#!g1.1\n",
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"df = pd.read_json('arxiv-metadata-oai-snapshot.json', lines = True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 331,
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"id": "9b51b145",
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"metadata": {
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"cellId": "l71bdxo21obg5fforh5ppi"
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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62 |
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" <th></th>\n",
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63 |
+
" <th>id</th>\n",
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" <th>submitter</th>\n",
|
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" <th>authors</th>\n",
|
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+
" <th>title</th>\n",
|
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+
" <th>comments</th>\n",
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" <th>journal-ref</th>\n",
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" <th>doi</th>\n",
|
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" <th>report-no</th>\n",
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" <th>categories</th>\n",
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" <th>license</th>\n",
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" <th>abstract</th>\n",
|
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" <th>versions</th>\n",
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" <th>update_date</th>\n",
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" <th>authors_parsed</th>\n",
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" </tr>\n",
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+
" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
|
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" <th>0</th>\n",
|
82 |
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" <td>0704.0001</td>\n",
|
83 |
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" <td>Pavel Nadolsky</td>\n",
|
84 |
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" <td>C. Bal\\'azs, E. L. Berger, P. M. Nadolsky, C.-...</td>\n",
|
85 |
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" <td>Calculation of prompt diphoton production cros...</td>\n",
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86 |
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" <td>37 pages, 15 figures; published version</td>\n",
|
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+
" <td>Phys.Rev.D76:013009,2007</td>\n",
|
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" <td>10.1103/PhysRevD.76.013009</td>\n",
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" <td>ANL-HEP-PR-07-12</td>\n",
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" <td>hep-ph</td>\n",
|
91 |
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" <td>None</td>\n",
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92 |
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" <td>A fully differential calculation in perturba...</td>\n",
|
93 |
+
" <td>[{'version': 'v1', 'created': 'Mon, 2 Apr 2007...</td>\n",
|
94 |
+
" <td>2008-11-26</td>\n",
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" <td>[[Balázs, C., ], [Berger, E. L., ], [Nadolsky,...</td>\n",
|
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+
" </tr>\n",
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+
" <tr>\n",
|
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" <th>1</th>\n",
|
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+
" <td>0704.0002</td>\n",
|
100 |
+
" <td>Louis Theran</td>\n",
|
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" <td>Ileana Streinu and Louis Theran</td>\n",
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" <td>Sparsity-certifying Graph Decompositions</td>\n",
|
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" <td>To appear in Graphs and Combinatorics</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
|
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" <td>math.CO cs.CG</td>\n",
|
108 |
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" <td>http://arxiv.org/licenses/nonexclusive-distrib...</td>\n",
|
109 |
+
" <td>We describe a new algorithm, the $(k,\\ell)$-...</td>\n",
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110 |
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" <td>[{'version': 'v1', 'created': 'Sat, 31 Mar 200...</td>\n",
|
111 |
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" <td>2008-12-13</td>\n",
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+
" <td>[[Streinu, Ileana, ], [Theran, Louis, ]]</td>\n",
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+
" </tr>\n",
|
114 |
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120 |
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127 |
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|
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137 |
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|
138 |
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|
139 |
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|
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333 |
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334 |
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348 |
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+
"0 None \n",
|
461 |
+
"1 http://arxiv.org/licenses/nonexclusive-distrib... \n",
|
462 |
+
"2 None \n",
|
463 |
+
"3 None \n",
|
464 |
+
"4 None \n",
|
465 |
+
"\n",
|
466 |
+
" abstract \\\n",
|
467 |
+
"0 A fully differential calculation in perturba... \n",
|
468 |
+
"1 We describe a new algorithm, the $(k,\\ell)$-... \n",
|
469 |
+
"2 The evolution of Earth-Moon system is descri... \n",
|
470 |
+
"3 We show that a determinant of Stirling cycle... \n",
|
471 |
+
"4 In this paper we show how to compute the $\\L... \n",
|
472 |
+
"\n",
|
473 |
+
" versions update_date \\\n",
|
474 |
+
"0 [{'version': 'v1', 'created': 'Mon, 2 Apr 2007... 2008-11-26 \n",
|
475 |
+
"1 [{'version': 'v1', 'created': 'Sat, 31 Mar 200... 2008-12-13 \n",
|
476 |
+
"2 [{'version': 'v1', 'created': 'Sun, 1 Apr 2007... 2008-01-13 \n",
|
477 |
+
"3 [{'version': 'v1', 'created': 'Sat, 31 Mar 200... 2007-05-23 \n",
|
478 |
+
"4 [{'version': 'v1', 'created': 'Mon, 2 Apr 2007... 2013-10-15 \n",
|
479 |
+
"\n",
|
480 |
+
" authors_parsed tag \n",
|
481 |
+
"0 [[Balázs, C., ], [Berger, E. L., ], [Nadolsky,... hep-ph \n",
|
482 |
+
"1 [[Streinu, Ileana, ], [Theran, Louis, ]] math \n",
|
483 |
+
"2 [[Pan, Hongjun, ]] physics \n",
|
484 |
+
"3 [[Callan, David, ]] math \n",
|
485 |
+
"4 [[Abu-Shammala, Wael, ], [Torchinsky, Alberto, ]] math "
|
486 |
+
]
|
487 |
+
},
|
488 |
+
"execution_count": 333,
|
489 |
+
"metadata": {},
|
490 |
+
"output_type": "execute_result"
|
491 |
+
},
|
492 |
+
{
|
493 |
+
"name": "stderr",
|
494 |
+
"output_type": "stream",
|
495 |
+
"text": [
|
496 |
+
"/kernel/lib/python3.8/site-packages/ml_kernel/ignored_keyboard_interrupt.py:16: UserWarning: State committing stage cannot be interrupted. Please wait.\n",
|
497 |
+
" warnings.warn(self._warn_message)\n"
|
498 |
+
]
|
499 |
+
}
|
500 |
+
],
|
501 |
+
"source": [
|
502 |
+
"#!g1.1\n",
|
503 |
+
"def get_tag(row):\n",
|
504 |
+
" try:\n",
|
505 |
+
" return str(row).split()[0].split('.')[0]\n",
|
506 |
+
" except Exception:\n",
|
507 |
+
" pass\n",
|
508 |
+
" \n",
|
509 |
+
"\n",
|
510 |
+
"df['tag'] = df['categories'].apply(lambda x: get_tag(x))\n",
|
511 |
+
"df.head(5)"
|
512 |
+
]
|
513 |
+
},
|
514 |
+
{
|
515 |
+
"cell_type": "code",
|
516 |
+
"execution_count": 390,
|
517 |
+
"id": "18bc627a",
|
518 |
+
"metadata": {
|
519 |
+
"cellId": "yo10sp842yn569jdjhn34c"
|
520 |
+
},
|
521 |
+
"outputs": [],
|
522 |
+
"source": [
|
523 |
+
"#!g1.1\n",
|
524 |
+
"labels = []\n",
|
525 |
+
"for index, row in df.iterrows():\n",
|
526 |
+
" labels.append(row['tag'])\n",
|
527 |
+
" "
|
528 |
+
]
|
529 |
+
},
|
530 |
+
{
|
531 |
+
"cell_type": "code",
|
532 |
+
"execution_count": 391,
|
533 |
+
"id": "6c522ad9",
|
534 |
+
"metadata": {
|
535 |
+
"cellId": "hpc6uu4tlccmq4o0dsx38c"
|
536 |
+
},
|
537 |
+
"outputs": [],
|
538 |
+
"source": [
|
539 |
+
"#!g1.1\n",
|
540 |
+
"map_labels = dict()\n",
|
541 |
+
"for label in labels:\n",
|
542 |
+
" if label in map_labels:\n",
|
543 |
+
" map_labels[label] += 1\n",
|
544 |
+
" else:\n",
|
545 |
+
" map_labels[label] = 1 "
|
546 |
+
]
|
547 |
+
},
|
548 |
+
{
|
549 |
+
"cell_type": "code",
|
550 |
+
"execution_count": 397,
|
551 |
+
"id": "43b9f3cb",
|
552 |
+
"metadata": {
|
553 |
+
"cellId": "lbwz1g67k60opqhyjm3v6b"
|
554 |
+
},
|
555 |
+
"outputs": [
|
556 |
+
{
|
557 |
+
"data": {
|
558 |
+
"text/plain": [
|
559 |
+
"{'hep-ph': 120410,\n",
|
560 |
+
" 'math': 414977,\n",
|
561 |
+
" 'physics': 146186,\n",
|
562 |
+
" 'cond-mat': 276428,\n",
|
563 |
+
" 'gr-qc': 54599,\n",
|
564 |
+
" 'astro-ph': 266321,\n",
|
565 |
+
" 'hep-th': 96207,\n",
|
566 |
+
" 'hep-ex': 20735,\n",
|
567 |
+
" 'nlin': 17315,\n",
|
568 |
+
" 'q-bio': 23287,\n",
|
569 |
+
" 'quant-ph': 88923,\n",
|
570 |
+
" 'cs': 338681,\n",
|
571 |
+
" 'nucl-th': 30599,\n",
|
572 |
+
" 'math-ph': 28805,\n",
|
573 |
+
" 'hep-lat': 16623,\n",
|
574 |
+
" 'nucl-ex': 10500,\n",
|
575 |
+
" 'q-fin': 9131,\n",
|
576 |
+
" 'stat': 37132,\n",
|
577 |
+
" 'eess': 27531,\n",
|
578 |
+
" 'econ': 3904,\n",
|
579 |
+
" 'acc-phys': 46,\n",
|
580 |
+
" 'adap-org': 306,\n",
|
581 |
+
" 'alg-geom': 1209,\n",
|
582 |
+
" 'ao-sci': 13,\n",
|
583 |
+
" 'atom-ph': 68,\n",
|
584 |
+
" 'bayes-an': 11,\n",
|
585 |
+
" 'chao-dyn': 1770,\n",
|
586 |
+
" 'chem-ph': 129,\n",
|
587 |
+
" 'cmp-lg': 894,\n",
|
588 |
+
" 'comp-gas': 140,\n",
|
589 |
+
" 'dg-ga': 562,\n",
|
590 |
+
" 'funct-an': 320,\n",
|
591 |
+
" 'mtrl-th': 165,\n",
|
592 |
+
" 'patt-sol': 452,\n",
|
593 |
+
" 'plasm-ph': 28,\n",
|
594 |
+
" 'q-alg': 1177,\n",
|
595 |
+
" 'solv-int': 844,\n",
|
596 |
+
" 'supr-con': 69}"
|
597 |
+
]
|
598 |
+
},
|
599 |
+
"execution_count": 397,
|
600 |
+
"metadata": {},
|
601 |
+
"output_type": "execute_result"
|
602 |
+
}
|
603 |
+
],
|
604 |
+
"source": [
|
605 |
+
"#!g1.1\n",
|
606 |
+
"map_labels"
|
607 |
+
]
|
608 |
+
},
|
609 |
+
{
|
610 |
+
"cell_type": "code",
|
611 |
+
"execution_count": 398,
|
612 |
+
"id": "7e8b9e14",
|
613 |
+
"metadata": {
|
614 |
+
"cellId": "0xg7kt36566l12t6gavfeoq"
|
615 |
+
},
|
616 |
+
"outputs": [],
|
617 |
+
"source": [
|
618 |
+
"#!g1.1\n",
|
619 |
+
"dict_label = {'math': 4,\n",
|
620 |
+
" 'physics': 5,\n",
|
621 |
+
" 'q-bio': 6,\n",
|
622 |
+
" 'cs': 1,\n",
|
623 |
+
" 'q-fin': 7,\n",
|
624 |
+
" 'stat': 8,\n",
|
625 |
+
" 'eess': 3,\n",
|
626 |
+
" 'econ': 2}"
|
627 |
+
]
|
628 |
+
},
|
629 |
+
{
|
630 |
+
"cell_type": "code",
|
631 |
+
"execution_count": 399,
|
632 |
+
"id": "39bfd5dc",
|
633 |
+
"metadata": {
|
634 |
+
"cellId": "twjsbrjsiloyjbah2fbtm"
|
635 |
+
},
|
636 |
+
"outputs": [],
|
637 |
+
"source": [
|
638 |
+
"#!g1.1\n",
|
639 |
+
"new_map_labels = dict()\n",
|
640 |
+
"for key, value in map_labels.items():\n",
|
641 |
+
" if key in dict_label:\n",
|
642 |
+
" new_map_labels[key] = value "
|
643 |
+
]
|
644 |
+
},
|
645 |
+
{
|
646 |
+
"cell_type": "code",
|
647 |
+
"execution_count": 400,
|
648 |
+
"id": "3eca80b5",
|
649 |
+
"metadata": {
|
650 |
+
"cellId": "9qzelfdhdtgizy330a4jzi"
|
651 |
+
},
|
652 |
+
"outputs": [
|
653 |
+
{
|
654 |
+
"data": {
|
655 |
+
"text/plain": [
|
656 |
+
"{'math': 414977,\n",
|
657 |
+
" 'physics': 146186,\n",
|
658 |
+
" 'q-bio': 23287,\n",
|
659 |
+
" 'cs': 338681,\n",
|
660 |
+
" 'q-fin': 9131,\n",
|
661 |
+
" 'stat': 37132,\n",
|
662 |
+
" 'eess': 27531,\n",
|
663 |
+
" 'econ': 3904}"
|
664 |
+
]
|
665 |
+
},
|
666 |
+
"execution_count": 400,
|
667 |
+
"metadata": {},
|
668 |
+
"output_type": "execute_result"
|
669 |
+
}
|
670 |
+
],
|
671 |
+
"source": [
|
672 |
+
"#!g1.1\n",
|
673 |
+
"new_map_labels"
|
674 |
+
]
|
675 |
+
},
|
676 |
+
{
|
677 |
+
"cell_type": "code",
|
678 |
+
"execution_count": 401,
|
679 |
+
"id": "eaa44619",
|
680 |
+
"metadata": {
|
681 |
+
"cellId": "9o0bhawr3id19hxhy7m4xt"
|
682 |
+
},
|
683 |
+
"outputs": [
|
684 |
+
{
|
685 |
+
"data": {
|
686 |
+
"text/plain": [
|
687 |
+
"59904"
|
688 |
+
]
|
689 |
+
},
|
690 |
+
"execution_count": 401,
|
691 |
+
"metadata": {},
|
692 |
+
"output_type": "execute_result"
|
693 |
+
}
|
694 |
+
],
|
695 |
+
"source": [
|
696 |
+
"#!g1.1\n",
|
697 |
+
"sum_value = 0\n",
|
698 |
+
"for key, value in new_map_labels.items():\n",
|
699 |
+
" sum_value += min(value, 8000) \n",
|
700 |
+
" new_map_labels[key] = min(value, 8000) \n",
|
701 |
+
"sum_value"
|
702 |
+
]
|
703 |
+
},
|
704 |
+
{
|
705 |
+
"cell_type": "code",
|
706 |
+
"execution_count": 405,
|
707 |
+
"id": "8b5d06a1",
|
708 |
+
"metadata": {
|
709 |
+
"cellId": "eyq1ocx4irpx2lplb54qu"
|
710 |
+
},
|
711 |
+
"outputs": [],
|
712 |
+
"source": [
|
713 |
+
"#!g1.1 \n",
|
714 |
+
"small_df = pd.DataFrame()\n",
|
715 |
+
"\n",
|
716 |
+
"for idx, row in df.iterrows():\n",
|
717 |
+
" if row['tag'] in new_map_labels:\n",
|
718 |
+
" \n",
|
719 |
+
" if new_map_labels[row['tag']] > 0:\n",
|
720 |
+
" new_map_labels[row['tag']] -= 1\n",
|
721 |
+
"\n",
|
722 |
+
" small_df = small_df.append(row, ignore_index=True)\n",
|
723 |
+
" "
|
724 |
+
]
|
725 |
+
},
|
726 |
+
{
|
727 |
+
"cell_type": "code",
|
728 |
+
"execution_count": 406,
|
729 |
+
"id": "f0d17685",
|
730 |
+
"metadata": {
|
731 |
+
"cellId": "1xwjz5xpl4hfeu6hcby3sw"
|
732 |
+
},
|
733 |
+
"outputs": [
|
734 |
+
{
|
735 |
+
"data": {
|
736 |
+
"text/plain": [
|
737 |
+
"(59904, 15)"
|
738 |
+
]
|
739 |
+
},
|
740 |
+
"execution_count": 406,
|
741 |
+
"metadata": {},
|
742 |
+
"output_type": "execute_result"
|
743 |
+
}
|
744 |
+
],
|
745 |
+
"source": [
|
746 |
+
"#!g1.1\n",
|
747 |
+
"small_df.shape"
|
748 |
+
]
|
749 |
+
},
|
750 |
+
{
|
751 |
+
"cell_type": "code",
|
752 |
+
"execution_count": 407,
|
753 |
+
"id": "0726c5ad",
|
754 |
+
"metadata": {
|
755 |
+
"cellId": "0hj81qc6cxmjk68l9h30wu"
|
756 |
+
},
|
757 |
+
"outputs": [],
|
758 |
+
"source": [
|
759 |
+
"#!g1.1\n",
|
760 |
+
"small_df[\"text\"] = small_df[\"title\"] + \". \" + small_df[\"abstract\"]\n",
|
761 |
+
"small_df[\"text\"] = small_df[\"text\"].map(lambda x : x.replace(\"\\n\", \" \"))\n",
|
762 |
+
"small_df[\"text\"] = small_df[\"text\"].str.lower()\n"
|
763 |
+
]
|
764 |
+
},
|
765 |
+
{
|
766 |
+
"cell_type": "code",
|
767 |
+
"execution_count": 428,
|
768 |
+
"id": "85a623dd",
|
769 |
+
"metadata": {
|
770 |
+
"cellId": "cy86suh5aidkyhqe8g9l8"
|
771 |
+
},
|
772 |
+
"outputs": [],
|
773 |
+
"source": [
|
774 |
+
"#!g1.1\n",
|
775 |
+
"for idx, row in small_df.iterrows():\n",
|
776 |
+
" row['categories'] = dict_label[row['tag']] - 1\n",
|
777 |
+
" \n",
|
778 |
+
"small_df['label'] = small_df['categories']"
|
779 |
+
]
|
780 |
+
},
|
781 |
+
{
|
782 |
+
"cell_type": "code",
|
783 |
+
"execution_count": 435,
|
784 |
+
"id": "1dbb6b66",
|
785 |
+
"metadata": {
|
786 |
+
"cellId": "j8fduhc4vzcf23ghd01uh"
|
787 |
+
},
|
788 |
+
"outputs": [],
|
789 |
+
"source": [
|
790 |
+
"#!g1.1\n",
|
791 |
+
"data = small_df[['text', 'label']]"
|
792 |
+
]
|
793 |
+
},
|
794 |
+
{
|
795 |
+
"cell_type": "code",
|
796 |
+
"execution_count": 440,
|
797 |
+
"id": "0ec4a8d7",
|
798 |
+
"metadata": {
|
799 |
+
"cellId": "axjkoz0rhguz42agwj0azs"
|
800 |
+
},
|
801 |
+
"outputs": [
|
802 |
+
{
|
803 |
+
"data": {
|
804 |
+
"text/plain": [
|
805 |
+
"0"
|
806 |
+
]
|
807 |
+
},
|
808 |
+
"execution_count": 440,
|
809 |
+
"metadata": {},
|
810 |
+
"output_type": "execute_result"
|
811 |
+
}
|
812 |
+
],
|
813 |
+
"source": [
|
814 |
+
"#!g1.1\n",
|
815 |
+
"sum(data['label'] == 8)"
|
816 |
+
]
|
817 |
+
},
|
818 |
+
{
|
819 |
+
"cell_type": "code",
|
820 |
+
"execution_count": 409,
|
821 |
+
"id": "1b0209aa",
|
822 |
+
"metadata": {
|
823 |
+
"cellId": "cr69xpmgajao3i52mfmsw"
|
824 |
+
},
|
825 |
+
"outputs": [],
|
826 |
+
"source": [
|
827 |
+
"#!g1.1\n",
|
828 |
+
"dict_label_map = dict()\n",
|
829 |
+
"for idx, row in small_df.iterrows():\n",
|
830 |
+
" if row['tag'] in dict_label_map:\n",
|
831 |
+
" dict_label_map[row['tag']] += 1\n",
|
832 |
+
" else:\n",
|
833 |
+
" dict_label_map[row['tag']] = 1"
|
834 |
+
]
|
835 |
+
},
|
836 |
+
{
|
837 |
+
"cell_type": "code",
|
838 |
+
"execution_count": 410,
|
839 |
+
"id": "e752a6ab",
|
840 |
+
"metadata": {
|
841 |
+
"cellId": "475aq0jltlwie0y5rce39f"
|
842 |
+
},
|
843 |
+
"outputs": [
|
844 |
+
{
|
845 |
+
"data": {
|
846 |
+
"text/plain": [
|
847 |
+
"{'math': 8000,\n",
|
848 |
+
" 'physics': 8000,\n",
|
849 |
+
" 'q-bio': 8000,\n",
|
850 |
+
" 'cs': 8000,\n",
|
851 |
+
" 'q-fin': 8000,\n",
|
852 |
+
" 'stat': 8000,\n",
|
853 |
+
" 'eess': 8000,\n",
|
854 |
+
" 'econ': 3904}"
|
855 |
+
]
|
856 |
+
},
|
857 |
+
"execution_count": 410,
|
858 |
+
"metadata": {},
|
859 |
+
"output_type": "execute_result"
|
860 |
+
}
|
861 |
+
],
|
862 |
+
"source": [
|
863 |
+
"#!g1.1\n",
|
864 |
+
"dict_label_map"
|
865 |
+
]
|
866 |
+
},
|
867 |
+
{
|
868 |
+
"cell_type": "code",
|
869 |
+
"execution_count": 441,
|
870 |
+
"id": "f01b5e91",
|
871 |
+
"metadata": {
|
872 |
+
"cellId": "zvkn5omccecd93rwwd4v4"
|
873 |
+
},
|
874 |
+
"outputs": [],
|
875 |
+
"source": [
|
876 |
+
"#!g1.1\n",
|
877 |
+
"from sklearn.model_selection import train_test_split\n",
|
878 |
+
"train_data, test_data = train_test_split(data, test_size=0.2)\n",
|
879 |
+
"train_data, val_data = train_test_split(data, test_size=0.25)"
|
880 |
+
]
|
881 |
+
},
|
882 |
+
{
|
883 |
+
"cell_type": "code",
|
884 |
+
"execution_count": 442,
|
885 |
+
"id": "ee989ca4",
|
886 |
+
"metadata": {
|
887 |
+
"cellId": "xlfdta76w5j3xypjxddpl"
|
888 |
+
},
|
889 |
+
"outputs": [],
|
890 |
+
"source": [
|
891 |
+
"#!g1.1\n",
|
892 |
+
"train_data.to_csv('train_data.csv', index=None)\n",
|
893 |
+
"val_data.to_csv('val_data.csv', index=None)\n",
|
894 |
+
"test_data.to_csv('test_data.csv', index=None)"
|
895 |
+
]
|
896 |
+
},
|
897 |
+
{
|
898 |
+
"cell_type": "code",
|
899 |
+
"execution_count": 414,
|
900 |
+
"id": "f998553d",
|
901 |
+
"metadata": {
|
902 |
+
"cellId": "ftho4ti37uey8j3jmeqjk"
|
903 |
+
},
|
904 |
+
"outputs": [],
|
905 |
+
"source": [
|
906 |
+
"#!g1.1\n",
|
907 |
+
"# тут датасфера не дает мне нормальной возможности работать с токенизатором, поэтому перейдем в другой ��оутбук где и продолжим "
|
908 |
+
]
|
909 |
+
}
|
910 |
+
],
|
911 |
+
"metadata": {
|
912 |
+
"kernelspec": {
|
913 |
+
"display_name": "Yandex DataSphere Kernel",
|
914 |
+
"language": "python",
|
915 |
+
"name": "python3"
|
916 |
+
},
|
917 |
+
"language_info": {
|
918 |
+
"codemirror_mode": {
|
919 |
+
"name": "ipython",
|
920 |
+
"version": 3
|
921 |
+
},
|
922 |
+
"file_extension": ".py",
|
923 |
+
"mimetype": "text/x-python",
|
924 |
+
"name": "python",
|
925 |
+
"nbconvert_exporter": "python",
|
926 |
+
"pygments_lexer": "ipython3",
|
927 |
+
"version": "3.7.7"
|
928 |
+
},
|
929 |
+
"notebookId": "8cdde807-2f63-4837-814d-b292cfd142b6",
|
930 |
+
"notebookPath": "Untitled (1).ipynb"
|
931 |
+
},
|
932 |
+
"nbformat": 4,
|
933 |
+
"nbformat_minor": 5
|
934 |
+
}
|