diff --git "a/PrepareData.ipynb" "b/PrepareData.ipynb"
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+++ "b/PrepareData.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Ноутбук с подготовкой данных "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 216,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import pandas as pd\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 217,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f = open('arxivData.json')\n",
+ "df = pd.read_json(f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 218,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Данные загрузились нормально, теперь выберем нужные нам колонки."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 219,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = df.drop(['author', 'day', 'id', 'link', 'month', 'year'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 220,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Теперь данные нужно привести к удобному виду:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 221,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data['summary'] = data['summary'].apply(lambda x : x.lower())\n",
+ "data['title'] = data['title'].apply(lambda x : x.lower())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 222,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def extract(term_line):\n",
+ " line = eval(term_line)\n",
+ " for elem in line:\n",
+ " try:\n",
+ " return elem['term'].split('.')[0]\n",
+ " except Exception:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 223,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data['tag'] = data['tag'].apply(lambda x : extract(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 224,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " summary | \n",
+ " tag | \n",
+ " title | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " we propose an architecture for vqa which utili... | \n",
+ " cs | \n",
+ " dual recurrent attention units for visual ques... | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " recent approaches based on artificial neural n... | \n",
+ " cs | \n",
+ " sequential short-text classification with recu... | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " we introduce the multiresolution recurrent neu... | \n",
+ " cs | \n",
+ " multiresolution recurrent neural networks: an ... | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " multi-task learning is motivated by the observ... | \n",
+ " stat | \n",
+ " learning what to share between loosely related... | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " we present milabot: a deep reinforcement learn... | \n",
+ " cs | \n",
+ " a deep reinforcement learning chatbot | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " summary tag \\\n",
+ "0 we propose an architecture for vqa which utili... cs \n",
+ "1 recent approaches based on artificial neural n... cs \n",
+ "2 we introduce the multiresolution recurrent neu... cs \n",
+ "3 multi-task learning is motivated by the observ... stat \n",
+ "4 we present milabot: a deep reinforcement learn... cs \n",
+ "\n",
+ " title \n",
+ "0 dual recurrent attention units for visual ques... \n",
+ "1 sequential short-text classification with recu... \n",
+ "2 multiresolution recurrent neural networks: an ... \n",
+ "3 learning what to share between loosely related... \n",
+ "4 a deep reinforcement learning chatbot "
+ ]
+ },
+ "execution_count": 224,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 225,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from collections import Counter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 226,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Counter({'cs': 34597,\n",
+ " 'stat': 4782,\n",
+ " 'astro-ph': 59,\n",
+ " 'q-bio': 320,\n",
+ " 'eess': 75,\n",
+ " 'cond-mat': 65,\n",
+ " 'math': 612,\n",
+ " 'physics': 216,\n",
+ " 'quant-ph': 66,\n",
+ " 'q-fin': 30,\n",
+ " 'gr-qc': 4,\n",
+ " 'nlin': 47,\n",
+ " 'cmp-lg': 110,\n",
+ " 'econ': 5,\n",
+ " 'hep-ex': 4,\n",
+ " 'hep-th': 1,\n",
+ " 'nucl-th': 1,\n",
+ " 'hep-ph': 2,\n",
+ " 'hep-lat': 2,\n",
+ " 'adap-org': 2})"
+ ]
+ },
+ "execution_count": 226,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Counter(data['tag'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Мы столкнулись с проблемой: классы сильно несбалансированы, на computer scince приходится более 85% от всех статей, половина от оставшихся значений приходится на statistics. Так что с большой долей вероятности, обученная модель будет возвращать один из этих классов. По-хорошему стоило бы уравновесить классы, но мы попробуем добавить статьи из архива в малочисленные категории, а из cs часть данных удалим, потому что перевес слишком большой."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 227,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import arxiv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 228,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "gr-qc\n",
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+ "math.OA\n",
+ "cs.RO\n",
+ "math.AP\n",
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+ "cs.CR\n",
+ "econ.TH\n",
+ "quant-ph\n",
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+ "math.AP\n",
+ "quant-ph\n",
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+ "econ.TH\n",
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+ "quant-ph\n",
+ "math.PR\n",
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+ "math.NT\n",
+ "cs.IR\n",
+ "hep-ph\n",
+ "quant-ph\n",
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+ "hep-ph\n",
+ "astro-ph.GA\n",
+ "astro-ph.CO\n",
+ "astro-ph.HE\n",
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+ "physics.optics\n",
+ "cs.CV\n",
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+ "cs.IT\n",
+ "cs.PL\n",
+ "math.CO\n",
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+ "math.NA\n",
+ "math.NA\n",
+ "q-bio.NC\n",
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+ "cs.PL\n",
+ "gr-qc\n",
+ "math.MG\n",
+ "astro-ph.GA\n",
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+ "math.NT\n",
+ "cs.CV\n",
+ "math.GT\n",
+ "math.ST\n",
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+ "cs.CL\n",
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+ "math.AP\n",
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+ "quant-ph\n",
+ "math.GR\n",
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+ "cs.GT\n",
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+ "cs.CV\n",
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+ "cs.IR\n",
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+ "astro-ph.GA\n",
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+ "cs.CC\n",
+ "cs.CL\n",
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+ "cs.LG\n",
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+ "cond-mat.mtrl-sci\n",
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+ "quant-ph\n",
+ "math.SP\n",
+ "cs.RO\n",
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+ "cs.CL\n",
+ "math.NT\n",
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+ "cond-mat.stat-mech\n",
+ "nucl-ex\n",
+ "astro-ph.GA\n",
+ "astro-ph.SR\n",
+ "cs.DS\n",
+ "q-bio.GN\n",
+ "econ.TH\n",
+ "cond-mat.mtrl-sci\n",
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+ "math.CT\n",
+ "physics.ao-ph\n",
+ "eess.SP\n",
+ "cs.IR\n",
+ "physics.optics\n",
+ "cond-mat.mes-hall\n",
+ "cs.CL\n",
+ "math.RT\n",
+ "physics.app-ph\n",
+ "math.CO\n",
+ "cs.CL\n",
+ "stat.ME\n",
+ "astro-ph.CO\n",
+ "cs.IT\n",
+ "cs.IT\n",
+ "quant-ph\n",
+ "q-bio.PE\n",
+ "cs.CV\n",
+ "gr-qc\n",
+ "math.CO\n",
+ "math.CO\n",
+ "cs.CL\n",
+ "cs.NI\n",
+ "math.AC\n",
+ "cs.DM\n",
+ "math.DG\n",
+ "cs.AI\n",
+ "cond-mat.stat-mech\n",
+ "cs.NI\n",
+ "math.CO\n",
+ "hep-ph\n",
+ "stat.CO\n",
+ "hep-th\n",
+ "math.NA\n",
+ "astro-ph.IM\n",
+ "quant-ph\n",
+ "cs.CL\n",
+ "physics.flu-dyn\n",
+ "cs.CG\n",
+ "hep-th\n",
+ "math.DS\n",
+ "cs.DS\n",
+ "eess.IV\n",
+ "math.CO\n",
+ "cs.CV\n",
+ "math.FA\n",
+ "cs.CL\n",
+ "stat.AP\n",
+ "cond-mat.mtrl-sci\n",
+ "econ.TH\n",
+ "cs.LG\n",
+ "stat.CO\n",
+ "stat.CO\n",
+ "cs.CL\n",
+ "cond-mat.soft\n",
+ "cs.CR\n",
+ "cs.CV\n",
+ "eess.SY\n",
+ "cs.AI\n",
+ "astro-ph.GA\n",
+ "hep-ph\n",
+ "eess.IV\n",
+ "cs.CV\n",
+ "astro-ph.SR\n",
+ "hep-th\n",
+ "astro-ph.SR\n",
+ "hep-ph\n",
+ "astro-ph.HE\n",
+ "astro-ph.CO\n",
+ "math.CO\n",
+ "cs.IT\n",
+ "astro-ph.CO\n",
+ "econ.GN\n",
+ "math.DG\n",
+ "cond-mat.str-el\n",
+ "math.ST\n",
+ "cs.CV\n",
+ "math.OC\n",
+ "hep-th\n",
+ "physics.chem-ph\n",
+ "astro-ph.IM\n",
+ "cond-mat.stat-mech\n",
+ "stat.OT\n",
+ "physics.chem-ph\n",
+ "cs.CY\n",
+ "cond-mat.mes-hall\n",
+ "cs.CR\n",
+ "stat.ML\n",
+ "cs.CV\n",
+ "stat.ME\n",
+ "cs.LG\n",
+ "econ.EM\n",
+ "cs.CL\n"
+ ]
+ },
+ {
+ "ename": "UnexpectedEmptyPageError",
+ "evalue": "Page of results was unexpectedly empty (http://export.arxiv.org/api/query?search_query=all&id_list=&sortBy=submittedDate&sortOrder=descending&start=3200&max_results=100)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mUnexpectedEmptyPageError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/85/hyc80kf10k7b2q8d_m56jbyh0000gn/T/ipykernel_51171/4192306019.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0msort_by\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marxiv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSortCriterion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSubmittedDate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m )\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_category\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_category\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'cs'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.9/site-packages/arxiv/arxiv.py\u001b[0m in \u001b[0;36mresults\u001b[0;34m(self, search)\u001b[0m\n\u001b[1;32m 583\u001b[0m ))\n\u001b[1;32m 584\u001b[0m \u001b[0mpage_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpage_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 585\u001b[0;31m \u001b[0mfeed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_feed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpage_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst_page\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 586\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfirst_page\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[0;31m# NOTE: this is an ugly fix for a known bug. The totalresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.9/site-packages/arxiv/arxiv.py\u001b[0m in \u001b[0;36m_parse_feed\u001b[0;34m(self, url, first_page)\u001b[0m\n\u001b[1;32m 637\u001b[0m \"\"\"\n\u001b[1;32m 638\u001b[0m \u001b[0;31m# Invoke the recursive helper with initial available retries.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 639\u001b[0;31m return self.__try_parse_feed(\n\u001b[0m\u001b[1;32m 640\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 641\u001b[0m \u001b[0mfirst_page\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfirst_page\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.9/site-packages/arxiv/arxiv.py\u001b[0m in \u001b[0;36m__try_parse_feed\u001b[0;34m(self, url, first_page, retries_left, last_err)\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mretries_left\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m return self.__try_parse_feed(\n\u001b[0m\u001b[1;32m 682\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 683\u001b[0m \u001b[0mfirst_page\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfirst_page\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.9/site-packages/arxiv/arxiv.py\u001b[0m in \u001b[0;36m__try_parse_feed\u001b[0;34m(self, url, first_page, retries_left, last_err)\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mretries_left\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m return self.__try_parse_feed(\n\u001b[0m\u001b[1;32m 682\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 683\u001b[0m \u001b[0mfirst_page\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfirst_page\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.9/site-packages/arxiv/arxiv.py\u001b[0m in \u001b[0;36m__try_parse_feed\u001b[0;34m(self, url, first_page, retries_left, last_err)\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mretries_left\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m return self.__try_parse_feed(\n\u001b[0m\u001b[1;32m 682\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 683\u001b[0m \u001b[0mfirst_page\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfirst_page\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.9/site-packages/arxiv/arxiv.py\u001b[0m in \u001b[0;36m__try_parse_feed\u001b[0;34m(self, url, first_page, retries_left, last_err)\u001b[0m\n\u001b[1;32m 687\u001b[0m \u001b[0;31m# Feed was never returned in self.num_retries tries. Raise the last\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 688\u001b[0m \u001b[0;31m# exception encountered.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 689\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 690\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfeed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mUnexpectedEmptyPageError\u001b[0m: Page of results was unexpectedly empty (http://export.arxiv.org/api/query?search_query=all&id_list=&sortBy=submittedDate&sortOrder=descending&start=3200&max_results=100)"
+ ]
+ }
+ ],
+ "source": [
+ "additional_cat = [] \n",
+ "additional_title = []\n",
+ "additional_summary = []\n",
+ "search = arxiv.Search(\n",
+ " query = 'all',\n",
+ " max_results = 7000,\n",
+ " sort_by = arxiv.SortCriterion.SubmittedDate\n",
+ ")\n",
+ "for result in search.results():\n",
+ " print(result.primary_category)\n",
+ " if result.primary_category[:2] != 'cs':\n",
+ " additional_cat.append(result.primary_category)\n",
+ " additional_title.append(result.title)\n",
+ " additional_summary.append(result.summary)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 229,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_additional = pd.DataFrame(list(zip(additional_summary, additional_cat, additional_title)),\n",
+ " columns =['summary', 'category', 'title']) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 230,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ " Modeling, robust control synthesis and worst-c... | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " We show that associativity of the tree-level O... | \n",
+ " hep-th | \n",
+ " On Effective Field Theories with Celestial Duals | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
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+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Carbon-enhanced metal-poor (CEMP) stars make-u... | \n",
+ " astro-ph.GA | \n",
+ " Over 2.7 Million Carbon-Enhanced Metal-Poor st... | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " This paper studies the features of a homopolym... | \n",
+ " cond-mat.soft | \n",
+ " End-pulled polymer translocation through a man... | \n",
+ "
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+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
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+ "
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+ " \n",
+ " 1958 | \n",
+ " Predicting structural and energetic properties... | \n",
+ " physics.chem-ph | \n",
+ " Transferring Chemical and Energetic Knowledge ... | \n",
+ "
\n",
+ " \n",
+ " 1959 | \n",
+ " When normal metals (NMs) are attached to topol... | \n",
+ " cond-mat.mes-hall | \n",
+ " Proximity-induced zero-energy states indisting... | \n",
+ "
\n",
+ " \n",
+ " 1960 | \n",
+ " Nowcasting can play a key role in giving polic... | \n",
+ " stat.ML | \n",
+ " Benchmarking Econometric and Machine Learning ... | \n",
+ "
\n",
+ " \n",
+ " 1961 | \n",
+ " There is an increasing trend of research in me... | \n",
+ " stat.ME | \n",
+ " Comparison of Effect Size Measures for Mediati... | \n",
+ "
\n",
+ " \n",
+ " 1962 | \n",
+ " Cluster-robust inference is widely used in mod... | \n",
+ " econ.EM | \n",
+ " Leverage, Influence, and the Jackknife in Clus... | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
1963 rows × 3 columns
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+ ],
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+ " summary category \\\n",
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+ "2 Let dimensions $d\\ge 3$, and codimensions $c\\g... math.DG \n",
+ "3 Carbon-enhanced metal-poor (CEMP) stars make-u... astro-ph.GA \n",
+ "4 This paper studies the features of a homopolym... cond-mat.soft \n",
+ "... ... ... \n",
+ "1958 Predicting structural and energetic properties... physics.chem-ph \n",
+ "1959 When normal metals (NMs) are attached to topol... cond-mat.mes-hall \n",
+ "1960 Nowcasting can play a key role in giving polic... stat.ML \n",
+ "1961 There is an increasing trend of research in me... stat.ME \n",
+ "1962 Cluster-robust inference is widely used in mod... econ.EM \n",
+ "\n",
+ " title \n",
+ "0 Modeling, robust control synthesis and worst-c... \n",
+ "1 On Effective Field Theories with Celestial Duals \n",
+ "2 Homologically area minimizing surfaces with no... \n",
+ "3 Over 2.7 Million Carbon-Enhanced Metal-Poor st... \n",
+ "4 End-pulled polymer translocation through a man... \n",
+ "... ... \n",
+ "1958 Transferring Chemical and Energetic Knowledge ... \n",
+ "1959 Proximity-induced zero-energy states indisting... \n",
+ "1960 Benchmarking Econometric and Machine Learning ... \n",
+ "1961 Comparison of Effect Size Measures for Mediati... \n",
+ "1962 Leverage, Influence, and the Jackknife in Clus... \n",
+ "\n",
+ "[1963 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 230,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_additional"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 231,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_additional['summary'] = df_additional['summary'].apply(lambda x : x.lower())\n",
+ "df_additional['title'] = df_additional['title'].apply(lambda x : x.lower())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 232,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def extract_cat(term_line):\n",
+ " try:\n",
+ " return term_line.split('.')[0]\n",
+ " except Exception:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 233,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_additional['tag'] = df_additional['category'].apply(lambda x : extract_cat(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 234,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_additional = df_additional.drop(['category'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 235,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Counter({'eess': 102,\n",
+ " 'hep-th': 66,\n",
+ " 'math': 573,\n",
+ " 'astro-ph': 292,\n",
+ " 'cond-mat': 181,\n",
+ " 'gr-qc': 73,\n",
+ " 'stat': 96,\n",
+ " 'hep-ex': 29,\n",
+ " 'physics': 197,\n",
+ " 'quant-ph': 139,\n",
+ " 'hep-ph': 84,\n",
+ " 'q-bio': 34,\n",
+ " 'econ': 33,\n",
+ " 'hep-lat': 7,\n",
+ " 'nlin': 8,\n",
+ " 'nucl-th': 11,\n",
+ " 'math-ph': 25,\n",
+ " 'nucl-ex': 7,\n",
+ " 'q-fin': 6})"
+ ]
+ },
+ "execution_count": 235,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Counter(df_additional['tag'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 236,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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\n",
+ " \n",
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+ "4 this paper studies the features of a homopolym... \n",
+ "\n",
+ " title tag \n",
+ "0 modeling, robust control synthesis and worst-c... eess \n",
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+ "4 end-pulled polymer translocation through a man... cond-mat "
+ ]
+ },
+ "execution_count": 236,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_additional.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 237,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_additional = df_additional[['summary','tag','title']].copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 238,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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\n",
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+ " we show that associativity of the tree-level o... | \n",
+ " hep-th | \n",
+ " on effective field theories with celestial duals | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " let dimensions $d\\ge 3$, and codimensions $c\\g... | \n",
+ " math | \n",
+ " homologically area minimizing surfaces with no... | \n",
+ "
\n",
+ " \n",
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+ " astro-ph | \n",
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+ "3 carbon-enhanced metal-poor (cemp) stars make-u... astro-ph \n",
+ "4 this paper studies the features of a homopolym... cond-mat \n",
+ "\n",
+ " title \n",
+ "0 modeling, robust control synthesis and worst-c... \n",
+ "1 on effective field theories with celestial duals \n",
+ "2 homologically area minimizing surfaces with no... \n",
+ "3 over 2.7 million carbon-enhanced metal-poor st... \n",
+ "4 end-pulled polymer translocation through a man... "
+ ]
+ },
+ "execution_count": 238,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_additional.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 239,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "frames = [data, new_additional]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 240,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "merged_data = pd.concat(frames)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 241,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
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+ " title | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " we propose an architecture for vqa which utili... | \n",
+ " cs | \n",
+ " dual recurrent attention units for visual ques... | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " recent approaches based on artificial neural n... | \n",
+ " cs | \n",
+ " sequential short-text classification with recu... | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " we introduce the multiresolution recurrent neu... | \n",
+ " cs | \n",
+ " multiresolution recurrent neural networks: an ... | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " multi-task learning is motivated by the observ... | \n",
+ " stat | \n",
+ " learning what to share between loosely related... | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " we present milabot: a deep reinforcement learn... | \n",
+ " cs | \n",
+ " a deep reinforcement learning chatbot | \n",
+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " summary tag \\\n",
+ "0 we propose an architecture for vqa which utili... cs \n",
+ "1 recent approaches based on artificial neural n... cs \n",
+ "2 we introduce the multiresolution recurrent neu... cs \n",
+ "3 multi-task learning is motivated by the observ... stat \n",
+ "4 we present milabot: a deep reinforcement learn... cs \n",
+ "\n",
+ " title \n",
+ "0 dual recurrent attention units for visual ques... \n",
+ "1 sequential short-text classification with recu... \n",
+ "2 multiresolution recurrent neural networks: an ... \n",
+ "3 learning what to share between loosely related... \n",
+ "4 a deep reinforcement learning chatbot "
+ ]
+ },
+ "execution_count": 241,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "merged_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 242,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Counter({'cs': 34597,\n",
+ " 'stat': 4878,\n",
+ " 'astro-ph': 351,\n",
+ " 'q-bio': 354,\n",
+ " 'eess': 177,\n",
+ " 'cond-mat': 246,\n",
+ " 'math': 1185,\n",
+ " 'physics': 413,\n",
+ " 'quant-ph': 205,\n",
+ " 'q-fin': 36,\n",
+ " 'gr-qc': 77,\n",
+ " 'nlin': 55,\n",
+ " 'cmp-lg': 110,\n",
+ " 'econ': 38,\n",
+ " 'hep-ex': 33,\n",
+ " 'hep-th': 67,\n",
+ " 'nucl-th': 12,\n",
+ " 'hep-ph': 86,\n",
+ " 'hep-lat': 9,\n",
+ " 'adap-org': 2,\n",
+ " 'math-ph': 25,\n",
+ " 'nucl-ex': 7})"
+ ]
+ },
+ "execution_count": 242,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Counter(merged_data['tag'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Как видим ситуация стала лучше. Теперь данные можно почистить от лишнего"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 243,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[nltk_data] Downloading package stopwords to /Users/seal/nltk_data...\n",
+ "[nltk_data] Package stopwords is already up-to-date!\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 243,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import nltk\n",
+ "nltk.download('stopwords')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 244,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trash = nltk.corpus.stopwords.words('english')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 245,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def clean_data(line):\n",
+ " output= [i for i in line.split() if i not in trash]\n",
+ " return ' '.join(output)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 246,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "merged_data['summary'] = merged_data['summary'].apply(lambda x : clean_data(x))\n",
+ "merged_data['title'] = merged_data['title'].apply(lambda x : clean_data(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 247,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " summary | \n",
+ " tag | \n",
+ " title | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " propose architecture vqa utilizes recurrent la... | \n",
+ " cs | \n",
+ " dual recurrent attention units visual question... | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " recent approaches based artificial neural netw... | \n",
+ " cs | \n",
+ " sequential short-text classification recurrent... | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " introduce multiresolution recurrent neural net... | \n",
+ " cs | \n",
+ " multiresolution recurrent neural networks: app... | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " multi-task learning motivated observation huma... | \n",
+ " stat | \n",
+ " learning share loosely related tasks | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " present milabot: deep reinforcement learning c... | \n",
+ " cs | \n",
+ " deep reinforcement learning chatbot | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " summary tag \\\n",
+ "0 propose architecture vqa utilizes recurrent la... cs \n",
+ "1 recent approaches based artificial neural netw... cs \n",
+ "2 introduce multiresolution recurrent neural net... cs \n",
+ "3 multi-task learning motivated observation huma... stat \n",
+ "4 present milabot: deep reinforcement learning c... cs \n",
+ "\n",
+ " title \n",
+ "0 dual recurrent attention units visual question... \n",
+ "1 sequential short-text classification recurrent... \n",
+ "2 multiresolution recurrent neural networks: app... \n",
+ "3 learning share loosely related tasks \n",
+ "4 deep reinforcement learning chatbot "
+ ]
+ },
+ "execution_count": 247,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "merged_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Закодируем таргет для более удобной работы"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 248,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import LabelEncoder"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 249,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "cs 34597\n",
+ "stat 4878\n",
+ "math 1185\n",
+ "physics 413\n",
+ "q-bio 354\n",
+ "astro-ph 351\n",
+ "cond-mat 246\n",
+ "quant-ph 205\n",
+ "eess 177\n",
+ "cmp-lg 110\n",
+ "hep-ph 86\n",
+ "gr-qc 77\n",
+ "hep-th 67\n",
+ "nlin 55\n",
+ "econ 38\n",
+ "q-fin 36\n",
+ "hep-ex 33\n",
+ "math-ph 25\n",
+ "nucl-th 12\n",
+ "hep-lat 9\n",
+ "nucl-ex 7\n",
+ "adap-org 2\n",
+ "Name: tag, dtype: int64"
+ ]
+ },
+ "execution_count": 249,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "merged_data.tag.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 250,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "merged_data = merged_data.drop(merged_data[merged_data.tag == 'adap-org'].index)\n",
+ "merged_data = merged_data.drop(merged_data[merged_data.tag == 'nucl-ex'].index)\n",
+ "merged_data = merged_data.drop(merged_data[merged_data.tag == 'hep-lat'].index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 251,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "label_encoder = LabelEncoder()\n",
+ "merged_data['tags'] = label_encoder.fit_transform(merged_data['tag'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Поскольку модель должна обрабатывать запросы как содержащие summary и title сразу, так и содержащие только одно заполненнное поле, сольём эти данные в одну колонку"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 252,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "merged_data['full_info'] = merged_data['title'] + '. ' + merged_data['summary']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Избавимся от лишнего"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 253,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "merged_data = merged_data.drop(['summary', 'tag', 'title'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 254,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " tags | \n",
+ " full_info | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 3 | \n",
+ " dual recurrent attention units visual question... | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 3 | \n",
+ " sequential short-text classification recurrent... | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 3 | \n",
+ " multiresolution recurrent neural networks: app... | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 18 | \n",
+ " learning share loosely related tasks. multi-ta... | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 3 | \n",
+ " deep reinforcement learning chatbot. present m... | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " tags full_info\n",
+ "0 3 dual recurrent attention units visual question...\n",
+ "1 3 sequential short-text classification recurrent...\n",
+ "2 3 multiresolution recurrent neural networks: app...\n",
+ "3 18 learning share loosely related tasks. multi-ta...\n",
+ "4 3 deep reinforcement learning chatbot. present m..."
+ ]
+ },
+ "execution_count": 254,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "merged_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 255,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from imblearn.datasets import make_imbalance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 256,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = merged_data.drop(['tags'], axis=1)\n",
+ "y = merged_data['tags']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 257,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Counter({3: 34584,\n",
+ " 18: 4877,\n",
+ " 0: 351,\n",
+ " 15: 354,\n",
+ " 5: 177,\n",
+ " 2: 246,\n",
+ " 10: 1185,\n",
+ " 14: 413,\n",
+ " 17: 205,\n",
+ " 16: 36,\n",
+ " 6: 77,\n",
+ " 12: 55,\n",
+ " 1: 110,\n",
+ " 4: 38,\n",
+ " 7: 33,\n",
+ " 9: 67,\n",
+ " 13: 12,\n",
+ " 8: 86,\n",
+ " 11: 25})"
+ ]
+ },
+ "execution_count": 257,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Counter(y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Теперь выбросим часть данных cs и stats"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 258,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_balanced, y_balanced = make_imbalance(X, y,\n",
+ " sampling_strategy={0: 351,\n",
+ " 1: 110,\n",
+ " 2: 246,\n",
+ " 3: 7279,\n",
+ " 4: 38,\n",
+ " 5: 177,\n",
+ " 6: 77,\n",
+ " 7: 33,\n",
+ " 8: 86,\n",
+ " 9: 67,\n",
+ " 10: 1185,\n",
+ " 11: 25, \n",
+ " 12: 55, \n",
+ " 13: 12, \n",
+ " 14: 413, \n",
+ " 15: 354,\n",
+ " 16: 36, \n",
+ " 17: 205, \n",
+ " 18: 3231\n",
+ " },\n",
+ " random_state=123)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 259,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Counter({0: 351,\n",
+ " 1: 110,\n",
+ " 2: 246,\n",
+ " 3: 7279,\n",
+ " 4: 38,\n",
+ " 5: 177,\n",
+ " 6: 77,\n",
+ " 7: 33,\n",
+ " 8: 86,\n",
+ " 9: 67,\n",
+ " 10: 1185,\n",
+ " 11: 25,\n",
+ " 12: 55,\n",
+ " 13: 12,\n",
+ " 14: 413,\n",
+ " 15: 354,\n",
+ " 16: 36,\n",
+ " 17: 205,\n",
+ " 18: 3231})"
+ ]
+ },
+ "execution_count": 259,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Counter(y_balanced)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 260,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_balanced['tags'] = y_balanced"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 261,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#X_balanced.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 268,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "# возьмём маленький размер тестовой выборки, чтобы в train попало как можно больше классов\n",
+ "train, test = train_test_split(X_balanced, test_size=0.15, shuffle=True,random_state=123)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 269,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train.to_csv('data_train.csv', index=None)\n",
+ "test.to_csv('data_test.csv', index=None)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Будем считать, что данные подготовлены к тому, чтобы что-то на них обучить."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 171,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "name_dict = {3: 'cs',\n",
+ " 18: 'stat',\n",
+ " 10: 'math',\n",
+ " 14: 'physics',\n",
+ " 15: 'q-bio',\n",
+ " 0: 'astro-ph', \n",
+ " 2: 'cond-mat',\n",
+ " 17: 'quant-ph', \n",
+ " 5: 'eess', \n",
+ " 1: 'cmp-lg',\n",
+ " 8: 'hep-ph', \n",
+ " 6: 'gr-qc',\n",
+ " 9: 'hep-th', \n",
+ " 12: 'nlin', \n",
+ " 4: 'econ' \n",
+ " 16: 'q-fin', \n",
+ " 7: 'hep-ex', \n",
+ " 11: 'math-ph',\n",
+ " 13: 'nucl-th'}"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}