mirror of
https://github.com/Ai-hack-MAGNUM-OPUS/AI.git
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1179 lines
122 KiB
Plaintext
1179 lines
122 KiB
Plaintext
{
|
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"cells": [
|
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{
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||
"cell_type": "code",
|
||
"execution_count": 1,
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||
"id": "10e636e2-5f38-43cb-adc6-b2f928d61136",
|
||
"metadata": {},
|
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/ubuntu/venv/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
|
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"import torch\n",
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"import numpy as np\n",
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"from transformers import AutoTokenizer, AutoModel\n",
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"from tqdm import tqdm\n",
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"tqdm.pandas()\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"sns.set()\n",
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"import numpy as np\n",
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"\n",
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"import pandas as pd\n",
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"import docx\n",
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"import os\n",
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"import re\n",
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"\n",
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"device = 'cuda'"
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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": 2,
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||
"id": "19c1e5b6-30d7-4684-87be-2777cc14006c",
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"metadata": {},
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"outputs": [],
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"source": [
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"#word preprocessing functions\n",
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"\n",
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"import nltk\n",
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"from nltk.tokenize import word_tokenize\n",
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"\n",
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"from nltk.corpus import stopwords\n",
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"from nltk.stem import SnowballStemmer\n",
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"from nltk.tokenize import word_tokenize\n",
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"\n",
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"russian_stopwords = stopwords.words(\"russian\")\n",
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"\n",
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"russian_stopwords.append('российской')\n",
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"russian_stopwords.append('федерации')\n",
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"russian_stopwords.append('федерального')\n",
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"russian_stopwords.append('настоящих')\n",
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||
"russian_stopwords.append('соответствии')\n",
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"russian_stopwords.append('также')\n",
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||
"russian_stopwords.append('рф')\n",
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"russian_stopwords.append('ред')\n",
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"\n",
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"russian_stopwords.append('субсидии')\n",
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"russian_stopwords.append('предоставления')\n",
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"\n",
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"\n",
|
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"def lowercase(text):\n",
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" return str(text).lower()\n",
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"\n",
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"def clean_symb(text):\n",
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" return re.sub(r'[^\\w]', ' ', text)\n",
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"\n",
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"def clear_token(text):\n",
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" return word_tokenize(text)\n",
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"\n",
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"def clean_stopwords(token):\n",
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" return ' '.join([i for i in token.split(' ') if i not in russian_stopwords])\n",
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"\n",
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"def clean_stem(token):\n",
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" return [st.stem(i) for i in token]\n",
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"\n",
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"### combo 3 prev\n",
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"def make_clean(s):\n",
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" return ' '.join(clean_stem(clean_stopwords(clear_token(s))))"
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||
]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "193b640f-d312-440c-9c89-5cb868132e2c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
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"162"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
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||
"output_type": "execute_result"
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}
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],
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"source": [
|
||
"#find all docs\n",
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"\n",
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"data_path = './data/Документы/'\n",
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"documents = os.listdir(data_path)\n",
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"documents = [data_path+d for d in documents]\n",
|
||
"len(documents)"
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]
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},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "f8dfdf00-ef42-411c-b629-a1fb349c5c94",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"#load and parse doc functions\n",
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"\n",
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"from nltk.corpus import stopwords\n",
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||
"from nltk.stem import SnowballStemmer\n",
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"from nltk.tokenize import word_tokenize\n",
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"\n",
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"\n",
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"def get_text(filename):\n",
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" doc = docx.Document(filename)\n",
|
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" fullText = ''\n",
|
||
" for para in doc.paragraphs:\n",
|
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" for run in para.runs:\n",
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" fullText+=run.text\n",
|
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" fullText+='\\n'\n",
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" return fullText\n",
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"\n",
|
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"def split_text(text):\n",
|
||
" texts, groups = [],[]\n",
|
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" regt = re.findall(r\"{(.*?)}(.*?){(.*?)}\",text.replace('\\n',''))\n",
|
||
" for t in regt:\n",
|
||
" if t[0]==t[-1]:\n",
|
||
" texts.append(t[1])\n",
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||
" groups.append(int(t[0]))\n",
|
||
" else:\n",
|
||
" print(t)\n",
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" \n",
|
||
" return texts, groups"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "8fdc799a-6ee7-4188-9c70-39b0c11a5833",
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"jupyter": {
|
||
"outputs_hidden": true
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||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
|
||
"text": [
|
||
"('36', '42. ', '37')\n",
|
||
"('37', ' (в ред. Правительства РФ от 04.05.2021 N 699) 43. ', '38')\n",
|
||
"('38', ' 48.', '7')\n",
|
||
"('10', '6. ', '24')\n",
|
||
"('24', ' 7. ', '11')\n",
|
||
"('11', '8. ', '10')\n",
|
||
"('13', 'м) ', '10')\n",
|
||
"('10', '(пп. \"о\" в ред. Правительства РФ от 16.08.2021 N 1337)9. ', '13')\n",
|
||
"('13', '(в ред. Правительства РФ от 16.08.2021 N 1337)12. ', '11')\n",
|
||
"('11', ' ', '24')\n",
|
||
"('24', ' ', '17')\n",
|
||
"('17', '14. ', '21')\n",
|
||
"('21', '15. ', '15')\n",
|
||
"('15', '(в ред. Правительства РФ от 16.08.2021 N 1337)16. ', '22')\n",
|
||
"('22', ' 17. ', '21')\n",
|
||
"('21', '18. ', '28')\n",
|
||
"('28', ' ', '12')\n",
|
||
"('12', ' ', '30')\n",
|
||
"('28', '', '30')\n",
|
||
"('30', 'ж) утратил силу. - Правительства РФ от 16.08.2021 N 1337.19. ', '23')\n",
|
||
"('23', ' 20. ', '13')\n",
|
||
"('12', ' 21. ', '13')\n",
|
||
"('13', ' 22. ', '22')\n",
|
||
"('22', '23. ', '11')\n",
|
||
"(' 11', ' 24. ', '11')\n",
|
||
"('11', '25. ', '21')\n",
|
||
"('21', 'ж) утратил силу. - Правительства РФ от 16.08.2021 N 1337.26. ', '24')\n",
|
||
"('24', ' 28. ', '27')\n",
|
||
"('27', ' ', '22')\n",
|
||
"('22', '29. ', '24')\n",
|
||
"('24', ' 31. ', '37')\n",
|
||
"('37', ' (в ред. Правительства РФ от 16.08.2021 N 1337)32. ', '38')\n",
|
||
"('38', ' (в ред. Правительства РФ от 16.08.2021 N 1337)33. ', '35')\n",
|
||
"('2', ' сведения об организационной структуре реализации исследовательской программы, планы по изменению организационно-штатной структуры организации, на базе которой реализуется исследовательская программа, в целях обеспечения эффективной и независимой реализации исследовательской программы; ', '12')\n",
|
||
"('11', ' отказ от участия в конкурсе. ', '15')\n",
|
||
"('21', ' недостоверность информации, содержащейся в представленных организацией документах, указанных в настоящих Правил; ', '2')\n",
|
||
"('38', ' В случае установления в ходе проверок, проведенных Министерством науки и высшего образования Российской Федерации и (или) органом государственного финансового контроля, фактов нарушения целей, условий и порядка предоставления гранта средства гранта в размере, определенном на основании выявленных нарушений, подлежат возврату в доход федерального бюджета: ', '238')\n",
|
||
"('27', 'Результатом предоставления грантов является количество завершенных проектов открытых библиотек, размещенных в открытом доступе в информационно-телекоммуникационной сети \"Интернет\" и предоставляемых любым лицам на условиях безвозмездной бессрочной открытой лицензии, завершенных технологических проектов и проектов по акселерации, по которым завершена акселерационная программа. ', '22')\n",
|
||
"('36', ' при получении кредита по льготной процентной ставке направлять ежеквартально в Министерство строительства и жилищно-коммунального хозяйства Российской Федерации актуальную информацию об имеющихся депозитах и иных финансовых документах. ', '136')\n",
|
||
"('10', 'даты размещения результатов отбора на едином портале и на сайте http://favt.ru/ в сети \"Интернет\", которая не может быть позднее 14-го календарного дня, следующего за днем определения победителя отбора (принятия решения о заключении договора о предоставлении субсидии или об отказе в заключении договора). ', '7')\n",
|
||
"('10', ' наименование производителей, с которыми заключается соглашение, и размер предоставляемой субсидии. ', '11')\n",
|
||
"('37', ' Министерство промышленности и торговли Российской Федерации и органы государственного финансового контроля проводят проверки соблюдения производителем целей, условий и порядка предоставления субсидии, которые предусмотрены настоящими Правилами. ', '35')\n",
|
||
"('30', ' оплата услуг по переводам документов, аудиторских, бухгалтерских, юридических и консультационных услуг, а также патентных и нотариальных услуг, услуг по таможенному оформлению, по проведению экспертиз и получению заключений, связанных с производственной деятельностью и эксплуатацией технического оборудования; ', '33')\n",
|
||
"('11', 'соответствовать следующим требованиям:', '18')\n",
|
||
"('26', 'порядок согласования новых условий соглашения о предоставлении гранта или расторжения соглашения о предоставлении гранта при недостижении согласия по новым условиям в случае уменьшения ранее доведенных до Министерства промышленности и торговли Российской Федерации лимитов бюджетных обязательств, приводящих к невозможности предоставления гранта в размере, определенном в соглашении о предоставлении гранта.', '2')\n",
|
||
"('1', '3. ', '24')\n",
|
||
"('24', '4. ', '10')\n",
|
||
"('10', '5. ', '23')\n",
|
||
"('23', '6. ', '24')\n",
|
||
"('24', '7. ', '19')\n",
|
||
"('19', '8. ', '26')\n",
|
||
"('26', '9. ', '19')\n",
|
||
"('19', '10. ', '38')\n",
|
||
"('38', '11. ', '35')\n",
|
||
"('35', '(п. 11 в ред. Правительства РФ от 21.04.2022 N 723) 12.', '10')\n",
|
||
"('10', '13. ', '26')\n",
|
||
"('26', '14. ', '37')\n",
|
||
"('37', '15. ', '35')\n",
|
||
"('35', '16. ', '19')\n",
|
||
"('19', '17. ', '26')\n",
|
||
"('26', '18. ', '20')\n",
|
||
"('20', '19. ', '16')\n",
|
||
"('16', '20. ', '20')\n",
|
||
"('20', '21. ', '35')\n",
|
||
"('35', '23. ', '24')\n",
|
||
"('24', '24. ', '35')\n",
|
||
"('35', '26. ', '22')\n",
|
||
"('22', '27. ', '19')\n",
|
||
"('19', '28. ', '38')\n",
|
||
"('38', '29. ', '3')\n",
|
||
"('3', '30. ', '38')\n",
|
||
"('38', '31. ', '37')\n",
|
||
"('37', '', '27')\n",
|
||
"('27', '35. ', '37')\n",
|
||
"('37', '37. ', '38')\n",
|
||
"('3', ' формирование и ведение которого осуществляется Министерством финансов Российской Федерации в установленном им порядке при формировании проекта федерального закона о федеральном бюджете (проекта федерального закона о внесении изменений в федеральный закон о федеральном бюджете). ', ' 3')\n",
|
||
"('38', 'Возврат указанной суммы денежных средств осуществляется в порядке, предусмотренном бюджетным законодательством Российской Федерации. ', '35')\n",
|
||
"('24', 'Федеральное агентство по туризму не позднее 10-го рабочего дня со дня принятия решения о заключении соглашения о предоставлении субсидии заключает с организацией указанное соглашение по форме, установленной Министерством финансов Российской Федерации. ', '36')\n",
|
||
"('30', ' оплата услуг по переводам документов, аудиторских, бухгалтерских, юридических и консультационных услуг, а также патентных и нотариальных услуг, услуг по таможенному оформлению, по проведению экспертиз и получению заключений, связанных с производственной деятельностью и эксплуатацией технического оборудования; ', '33')\n",
|
||
"('38', 'на основании требования Федерального агентства по делам молодежи - не позднее 10-го рабочего дня со дня получения получателем гранта указанного требования;', '37')\n",
|
||
"('22', 'предельный размер гранта; ', '24')\n",
|
||
"('38', 'III. Особенности предоставления субсидии в части поддержкипроектов, предусматривающих разработку стандартных образцов(введен Правительства РФ от 14.04.2022 N 653)27. ', '33')\n",
|
||
"('33', '28. ', '5')\n",
|
||
"('5', ' ', '24')\n",
|
||
"('24', 'б) ', '36')\n",
|
||
"('36', 'в) ', '27')\n",
|
||
"('27', 'г) ', '32')\n",
|
||
"('32', 'д) ', '24')\n",
|
||
"('24', 'е) ', '37')\n",
|
||
"('37', ';ж) ', '38')\n",
|
||
"('38', 'з) ', '31')\n",
|
||
"('31', 'и) ', '38')\n",
|
||
"('38', 'к) ', '24')\n",
|
||
"('24', 'л) ', '25')\n",
|
||
"('25', 'м) ', '24')\n",
|
||
"('24', 'н) ', '26')\n",
|
||
"('26', 'о) ', '24')\n",
|
||
"('24', '29. ', '22')\n",
|
||
"('22', '30. ', '30')\n",
|
||
"('30', '31. ', '19')\n",
|
||
"('19', '32. ', '20')\n",
|
||
"('20', '', '21')\n",
|
||
"('21', '', '14')\n",
|
||
"('14', '', '24')\n",
|
||
"('24', '34. ', '27')\n",
|
||
"('27', '35. ', '36')\n",
|
||
"('36', '37. ', '35')\n",
|
||
"('35', 'ж) ', '7')\n",
|
||
"('7', 'з) ', '35')\n",
|
||
"('35', '38. ', '8')\n",
|
||
"('8', '', '10')\n",
|
||
"('10', 'р) ', '17')\n",
|
||
"('17', '39. ', '11')\n",
|
||
"('11', '', '12')\n",
|
||
"('12', '40. ', '19')\n",
|
||
"('19', '41. ', '24')\n",
|
||
"('24', 'б) ', '22')\n",
|
||
"('22', 'г) ', '30')\n",
|
||
"('30', 'д) ', '24')\n",
|
||
"('24', 'е) ', '36')\n",
|
||
"('36', 'з) ', '24')\n",
|
||
"('24', 'и) ', '38')\n",
|
||
"('38', 'к) ', '31')\n",
|
||
"('31', 'л) ', '24')\n",
|
||
"('24', 'о) ', '27')\n",
|
||
"('27', ' ', '38')\n",
|
||
"('38', 'п) ', '24')\n",
|
||
"('24', 'р) ', '26')\n",
|
||
"('26', 'с) ', '24')\n",
|
||
"('24', 'у) ', '38')\n",
|
||
"('38', '42. ', '35')\n",
|
||
"('35', '43. ', '27')\n",
|
||
"('27', '44. ', '29')\n",
|
||
"('29', '', '28')\n",
|
||
"('28', '45. ', '37')\n",
|
||
"('37', '46. ', '38')\n",
|
||
"('20', 'Министерство науки и высшего образования Российской Федерации рассматривает документы, указанные в настоящих Правил, и в 20-дневный срок со дня их поступления принимает решение о заключении соглашения или об отказе в заключении соглашения, о чем уведомляет оператора в течение 5 рабочих дней со дня принятия решения', '19')\n",
|
||
"('19', '(пп. \"ж\" введен Правительства РФ от 30.12.2020 N 2373)7. ', '20')\n",
|
||
"('20', '', '21')\n",
|
||
"('21', '(в ред. Правительства РФ от 30.12.2020 N 2373)8. ', '27')\n",
|
||
"('27', '9. ', '29')\n",
|
||
"('29', ' ', '28')\n",
|
||
"('28', '(п. 9 в ред. Правительства РФ от 30.12.2020 N 2373)10. Утратил силу. - Правительства РФ от 30.12.2020 N 2373.11. ', '36')\n",
|
||
"('36', '12. ', '37')\n",
|
||
"('37', '13. ', '38')\n",
|
||
"('11', ' организация не является иностранным юридическим лицом, а также российским юридическим лицом, в уставном (складочном) капитале которого доля участия иностранных юридических лиц, местом регистрации которых является государство или территория, ', '24')\n",
|
||
"('24', 'включенные в утвержденный Министерством финансов Российской Федерации государств и территорий, предоставляющих льготный налоговый режим налогообложения и (или) не предусматривающих раскрытия и предоставления информации при проведении финансовых операций (офшорные зоны), в совокупности превышает 50 процентов; ', '11')\n",
|
||
"('11) за исключением случаев, если указанные иностранные юридические лица находятся под контролем российских юридических лиц или физических лиц (конечными бенефициарами являются Российская Федерация, российские юридические лица или физические лица), в том числе в случае, если этот контроль осуществляется через иные иностранные юридические лица; {11', 'з) ', '11')\n",
|
||
"('11', '9. ', '35')\n",
|
||
"('35', '11. ', '15')\n",
|
||
"('15', '12. ', '35')\n",
|
||
"('35', '15. ', '19')\n",
|
||
"('19', '', '11')\n",
|
||
"('11', '16. ', '19')\n",
|
||
"('19', '17. ', '20')\n",
|
||
"('20', '', '21')\n",
|
||
"('21', '19. ', '35')\n",
|
||
"('35', '', '22')\n",
|
||
"('22', '20. ', '35')\n",
|
||
"('35', '21. ', '24')\n",
|
||
"('24', '22. ', '26')\n",
|
||
"('26', 'д) ', '25')\n",
|
||
"('25', 'е) ', '26')\n",
|
||
"('26', '23. ', '35')\n",
|
||
"('35', '24. ', '6')\n",
|
||
"('6', '25. ', '22')\n",
|
||
"('22', '26. ', '19')\n",
|
||
"('19', '', '20')\n",
|
||
"('20', '27. ', '35')\n",
|
||
"('35', '28. ', '3')\n",
|
||
"('3', 'а) ', '20')\n",
|
||
"('20', 'б) ', '29')\n",
|
||
"('29', ' ', '28')\n",
|
||
"('28', '29. ', '21')\n",
|
||
"('21', '30. ', '35')\n",
|
||
"('35', '31. ', '36')\n",
|
||
"('36', '34. ', '27')\n",
|
||
"('27', '35. ', '35')\n",
|
||
"('35', '36. ', '37')\n",
|
||
"('37', '37. ', '38')\n",
|
||
"('25', ' Министерство экономического развития Российской Федерации:', '35')\n",
|
||
"('10', 'доменное имя, и (или) сетевой адрес, и (или) указатели страниц сайта в информационно-телекоммуникационной сети \"Интернет\", на котором обеспечивается проведение отбора; ', '24')\n",
|
||
"('15', ' обязательство Фонда о проведении отбора в соответствии с порядком, установленным настоящих Правил; ', '24')\n",
|
||
"('27', 'значения показателей, необходимых для достижения результата предоставления субсидии, указанные в настоящих Правил; ', '24')\n",
|
||
"('10', ' кредитная организация по состоянию на дату не ранее чем за 30 календарных дней до даты подачи заявки на получение субсидии не имеет просроченной задолженности по возврату в федеральный бюджет субсидий, бюджетных инвестиций, предоставленных из федерального бюджета в том числе в соответствии с иными правовыми актами, и иной просроченной (неурегулированной) задолженности по денежным обязательствам перед Российской Федерацией; ', '12')\n",
|
||
"('35', ' включения заемщика, содержащегося в реестре потенциальных заемщиков, в реестр заемщиков устанавливается ', '3')\n",
|
||
"('3', '', '35')\n",
|
||
"('22', ' Размер субсидии (S) по направлениям ее использования рассчитывается по формуле: ', '21')\n",
|
||
"('30', 'Расходы на оплату труда работников, на начисления на выплаты по оплате труда работников в соответствии с законодательством Российской Федерации, а также на иные указанные в настоящих Правил направления расходов не могут превышать 15 процентов размера субсидии.', '3')\n",
|
||
"('22', ' Si_март - размер субсидии i-му российскому аэропорту за март, рублей; ', '221')\n",
|
||
"('где:{22', ' Рмарт - размер расходной ставки аэропорта за март, равный 772 рублям на 1 \"потерянного\" пассажира, руб./пасс.; ', '22')\n",
|
||
"('22', ' Si_март - размер субсидии i-му российскому аэропорту за март, рублей; ', '221')\n",
|
||
"('где:{22', ' Рмарт - размер расходной ставки аэропорта за март, равный 772 рублям на 1 \"потерянного\" пассажира, руб./пасс.; ', '22')\n",
|
||
"('38', 'где: ', '22')\n",
|
||
"('22', '22. ', '38')\n",
|
||
"('36', ' сроки и формы представления отчета оператором о ходе реализации программ по развитию отдельных направлений Национальной технологической инициативы и отчета о расходах, источником финансового обеспечения которых является субсидия, а также отчета о достижении значения результата предоставления субсидии в соответствии с настоящих Правил;', '46')\n",
|
||
"('4', ' цели, условия и порядок предоставления и использования гранта;', '2')\n",
|
||
"('16', ' Отбор проводится образуемой Министерством промышленности и торговли Российской Федерации комиссией по проведению отбора (далее - комиссия). ', '316')\n",
|
||
"('12', ' выписка из Единого государственного реестра юридических лиц, заверенная в установленном порядке (в случае непредставления российской компанией такого документа Министерство промышленности и торговли Российской Федерации запрашивает его самостоятельно); ', '13')\n",
|
||
"('24', ' Основаниями для расторжения соглашения, в том числе расторжения соглашения Министерством промышленности и торговли Российской Федерации в одностороннем порядке, являются: ', '21')\n",
|
||
"('', '15указанных в настоящих Правил, полноту содержащихся в них сведений и направляет их на рассмотрение Комиссии. ', '15')\n",
|
||
"('15', ' заключает с производителями соглашения о предоставлении субсидии в течение 15 рабочих дней со дня окончания проверки, предусмотренной настоящего пункта. ', '35')\n",
|
||
"('35', 'уведомляет об этом производителя и возвращает заявление о включении в реестр получателей субсидии по договорам о реализации, заключенным в 2018 - 2019 годах, и документы, представленные производителем в соответствии с настоящих Правил; ', '15')\n",
|
||
"('7', 'Субсидии в целях осуществления мероприятий по приобретению объектов особо ценного движимого имущества в части транспортных средств. ', '2')\n",
|
||
"('35', 'отсутствие бюджетных ассигнований федерального бюджета, предусмотренных в целях предоставления субсидии.', '38')\n",
|
||
"('28', 'В случае если совокупный размер выплаты за период оплаты не превышает объем лимитов бюджетных обязательств, доведенных до ', '3')\n",
|
||
"('3', ' в соответствии с настоящих Правил, Федеральное агентство по туризму вправе установить очередной период оплаты.', '28')\n",
|
||
"('11', ' основные направления деятельности некоммерческой организации в соответствии с учредительными документами должны соответствовать тематике конкурса, в котором участвует некоммерческая организация; ', '25')\n",
|
||
"('26', ' Соглашение о предоставлении субсидии (дополнительное соглашение к соглашению о предоставлении субсидии, в том числе дополнительное соглашение о расторжении соглашения о предоставлении субсидии) с соблюдением требований о защите государственной тайны заключается', '24')\n",
|
||
"('38', 'В случае если требование, указанное в настоящих Правил, не выполнено в установленный срок, организация возвращает в федеральный бюджет полученные средства на основании: ', '36')\n",
|
||
"('21', 'Основаниями для отказа в предоставлении обществу субсидии являются: ', '23')\n",
|
||
"('27', 'Результатом предоставления субсидии является обеспечение страховой и гарантийной поддержки в отношении проектов, включенных в перечень. ', '28')\n",
|
||
"('11', 'соответствовать следующим требованиям:', '18')\n",
|
||
"('20', 'Перечисление субсидии получателю субсидии осуществляется на казначейский счет для осуществления и отражения операций с денежными средствами юридических лиц, не являющихся участниками бюджетного процесса, бюджетными и автономными учреждениями, открытый в территориальном органе Федерального казначейства, ', '29')\n",
|
||
"('37', 'Федеральное агентство воздушного транспорта осуществляет проверку соблюдения авиакомпанией условий и порядка предоставления субсидий, в том числе в части достижения результата предоставления субсидии, а органы государственного финансового контроля осуществляют проверки в соответствии со и Бюджетного кодекса Российской Федерации.', '7')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#load data\n",
|
||
"\n",
|
||
"all_text, all_groups, doc_paths, doc_names = [],[],[],[]\n",
|
||
"for d in documents:\n",
|
||
" if 'ipynb' not in d:\n",
|
||
" text = get_text(d)\n",
|
||
" texts,groups = split_text(text)\n",
|
||
" all_text.extend(texts)\n",
|
||
" all_groups.extend(groups)\n",
|
||
" doc_paths.extend([d for a in range(len(texts))])\n",
|
||
" doc_names.extend([d.split('/')[-1] for a in range(len(texts))])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "1413bdb0-ed8d-453d-9b67-fde0cfacd4e5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#load documents\n",
|
||
"#apply preprocessing to documents\n",
|
||
"\n",
|
||
"df = pd.DataFrame([doc_paths, doc_names,all_text, all_groups]).T\n",
|
||
"df.columns = ['path','doc','text','type']\n",
|
||
"df['r_text']='r'\n",
|
||
"df['r_text'] = df.text.apply(lowercase)\n",
|
||
"df['r_text'] = df.r_text.apply(clean_symb)\n",
|
||
"df['r_text'] = df.r_text.apply(lambda x:''.join([a for a in x if not a.isdigit()]))\n",
|
||
"df['r_text'] = df.r_text.apply(lambda x:' '.join([a for a in x.split(' ') if len(a)>1]))\n",
|
||
"df['r_text'] = df.r_text.apply(clean_stopwords)\n",
|
||
"df['text_size'] = df['text'].apply(lambda x: len(x.strip()))\n",
|
||
"df['is_text'] = df.r_text.apply(lambda x:x.strip().isdigit())\n",
|
||
"df = df[~df.is_text]\n",
|
||
"df = df[df['text_size']>5]\n",
|
||
"df = df.reset_index(drop=True)\n",
|
||
"df.shape\n",
|
||
"df.type = df.type.astype(int)-1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "1f4d8205-7f3f-430b-ac84-9e19ee913f81",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"правил 3588\n",
|
||
"организации 2263\n",
|
||
"отбора 2042\n",
|
||
"соглашения 1940\n",
|
||
"предоставлении 1747\n",
|
||
"результата 1578\n",
|
||
"гранта 1556\n",
|
||
"министерством 1544\n",
|
||
"дня 1360\n",
|
||
"заявок 1344\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"#show most popular words in datset\n",
|
||
"\n",
|
||
"all_words = np.hstack([np.array(a) for a in df.r_text.apply(lambda x:[a for a in x.split(' ') if len(a)>0]).values])\n",
|
||
"pd.Series(all_words).value_counts()[:10]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "4c22b4c2-9155-4655-bc95-6e3387b28ac4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#split data to train/test\n",
|
||
"\n",
|
||
"from sklearn.utils import resample\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"X_data, X_val, y_data, y_val = train_test_split(list(df['r_text']), list(df['type']), test_size=0.1, random_state=42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "eaac3775-7552-49c7-a891-c8ef5cccf6d9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import torch\n",
|
||
"from transformers import BertTokenizer, BertForSequenceClassification\n",
|
||
"from torch.utils.data import Dataset, DataLoader\n",
|
||
"from transformers import AdamW, get_linear_schedule_with_warmup\n",
|
||
"\n",
|
||
"from bert_dataset import CustomDataset"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "c75236ca-e832-4748-a312-9cc7f7935f51",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#Bert Classifier class\n",
|
||
"\n",
|
||
"class BertClassifier:\n",
|
||
"\n",
|
||
" def __init__(self, model_path, tokenizer_path, n_classes=2, epochs=1, model_save_path='/content/bert.pt'):\n",
|
||
" self.model = BertForSequenceClassification.from_pretrained(model_path)\n",
|
||
" self.tokenizer = BertTokenizer.from_pretrained(tokenizer_path)\n",
|
||
" self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
|
||
" self.model_save_path=model_save_path\n",
|
||
" self.max_len = 512\n",
|
||
" self.epochs = epochs\n",
|
||
" self.out_features = self.model.bert.encoder.layer[1].output.dense.out_features\n",
|
||
" self.model.classifier = torch.nn.Linear(self.out_features, n_classes)\n",
|
||
" self.model.to(self.device)\n",
|
||
" \n",
|
||
" def preparation(self, X_train, y_train, X_valid, y_valid):\n",
|
||
" # create datasets\n",
|
||
" self.train_set = CustomDataset(X_train, y_train, self.tokenizer)\n",
|
||
" self.valid_set = CustomDataset(X_valid, y_valid, self.tokenizer)\n",
|
||
"\n",
|
||
" # create data loaders\n",
|
||
" self.train_loader = DataLoader(self.train_set, batch_size=8, shuffle=True)\n",
|
||
" self.valid_loader = DataLoader(self.valid_set, batch_size=8, shuffle=True)\n",
|
||
"\n",
|
||
" # helpers initialization\n",
|
||
" self.optimizer = AdamW(self.model.parameters(), lr=2e-5, correct_bias=False)\n",
|
||
" self.scheduler = get_linear_schedule_with_warmup(\n",
|
||
" self.optimizer,\n",
|
||
" num_warmup_steps=0,\n",
|
||
" num_training_steps=len(self.train_loader) * self.epochs\n",
|
||
" )\n",
|
||
" self.loss_fn = torch.nn.CrossEntropyLoss().to(self.device)\n",
|
||
" \n",
|
||
" def fit(self):\n",
|
||
" self.model = self.model.train()\n",
|
||
" losses = []\n",
|
||
" correct_predictions = 0\n",
|
||
"\n",
|
||
" for data in tqdm(self.train_loader):\n",
|
||
" input_ids = data[\"input_ids\"].to(self.device)\n",
|
||
" attention_mask = data[\"attention_mask\"].to(self.device)\n",
|
||
" targets = data[\"targets\"].to(self.device)\n",
|
||
"\n",
|
||
" outputs = self.model(\n",
|
||
" input_ids=input_ids,\n",
|
||
" attention_mask=attention_mask\n",
|
||
" )\n",
|
||
"\n",
|
||
" preds = torch.argmax(outputs.logits, dim=1)\n",
|
||
" loss = self.loss_fn(outputs.logits, targets)\n",
|
||
"\n",
|
||
" correct_predictions += torch.sum(preds == targets)\n",
|
||
"\n",
|
||
" losses.append(loss.item())\n",
|
||
" \n",
|
||
" loss.backward()\n",
|
||
" torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)\n",
|
||
" self.optimizer.step()\n",
|
||
" self.scheduler.step()\n",
|
||
" self.optimizer.zero_grad()\n",
|
||
"\n",
|
||
" train_acc = correct_predictions.double() / len(self.train_set)\n",
|
||
" train_loss = np.mean(losses)\n",
|
||
" return train_acc, train_loss\n",
|
||
" \n",
|
||
" def eval(self):\n",
|
||
" self.model = self.model.eval()\n",
|
||
" losses = []\n",
|
||
" correct_predictions = 0\n",
|
||
"\n",
|
||
" with torch.no_grad():\n",
|
||
" for data in self.valid_loader:\n",
|
||
" input_ids = data[\"input_ids\"].to(self.device)\n",
|
||
" attention_mask = data[\"attention_mask\"].to(self.device)\n",
|
||
" targets = data[\"targets\"].to(self.device)\n",
|
||
"\n",
|
||
" outputs = self.model(\n",
|
||
" input_ids=input_ids,\n",
|
||
" attention_mask=attention_mask\n",
|
||
" )\n",
|
||
"\n",
|
||
" preds = torch.argmax(outputs.logits, dim=1)\n",
|
||
" loss = self.loss_fn(outputs.logits, targets)\n",
|
||
" correct_predictions += torch.sum(preds == targets)\n",
|
||
" losses.append(loss.item())\n",
|
||
" \n",
|
||
" val_acc = correct_predictions.double() / len(self.valid_set)\n",
|
||
" val_loss = np.mean(losses)\n",
|
||
" return val_acc, val_loss\n",
|
||
" \n",
|
||
" def train(self):\n",
|
||
" best_accuracy = 0\n",
|
||
" for epoch in range(self.epochs):\n",
|
||
" print(f'Epoch {epoch + 1}/{self.epochs}')\n",
|
||
" train_acc, train_loss = self.fit()\n",
|
||
" print(f'Train loss {train_loss} accuracy {train_acc}')\n",
|
||
"\n",
|
||
" val_acc, val_loss = self.eval()\n",
|
||
" print(f'Val loss {val_loss} accuracy {val_acc}')\n",
|
||
" print('-' * 10)\n",
|
||
"\n",
|
||
" if val_acc > best_accuracy:\n",
|
||
" torch.save(self.model, self.model_save_path)\n",
|
||
" best_accuracy = val_acc\n",
|
||
"\n",
|
||
" self.model = torch.load(self.model_save_path)\n",
|
||
" \n",
|
||
" def predict(self, text):\n",
|
||
" encoding = self.tokenizer.encode_plus(\n",
|
||
" text,\n",
|
||
" add_special_tokens=True,\n",
|
||
" max_length=self.max_len,\n",
|
||
" return_token_type_ids=False,\n",
|
||
" truncation=True,\n",
|
||
" padding='max_length',\n",
|
||
" return_attention_mask=True,\n",
|
||
" return_tensors='pt',\n",
|
||
" )\n",
|
||
" \n",
|
||
" out = {\n",
|
||
" 'text': text,\n",
|
||
" 'input_ids': encoding['input_ids'].flatten(),\n",
|
||
" 'attention_mask': encoding['attention_mask'].flatten()\n",
|
||
" }\n",
|
||
" \n",
|
||
" input_ids = out[\"input_ids\"].to(self.device)\n",
|
||
" attention_mask = out[\"attention_mask\"].to(self.device)\n",
|
||
" \n",
|
||
" outputs = self.model(\n",
|
||
" input_ids=input_ids.unsqueeze(0),\n",
|
||
" attention_mask=attention_mask.unsqueeze(0)\n",
|
||
" )\n",
|
||
" \n",
|
||
" prediction = torch.argmax(outputs.logits, dim=1).cpu().numpy()[0]\n",
|
||
"\n",
|
||
" return prediction"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "38cf183b-a3ee-4ef2-94b9-051bcb8918c7",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Some weights of the model checkpoint at sberbank-ai/ruBert-base were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
|
||
"- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
|
||
"- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
|
||
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at sberbank-ai/ruBert-base and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
|
||
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#make bert\n",
|
||
"\n",
|
||
"classifier = BertClassifier(\n",
|
||
" model_path='sberbank-ai/ruBert-base',\n",
|
||
" tokenizer_path='sberbank-ai/ruBert-base',\n",
|
||
" n_classes=39,\n",
|
||
" epochs=10,\n",
|
||
" model_save_path='bert_fit_models/bert.pt'\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "2b3a3cb5-1dd5-42aa-9df7-903604ba28c4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#prepare data to classification\n",
|
||
"classifier.preparation(\n",
|
||
" X_train=list(X_data),\n",
|
||
" y_train=list(y_data),\n",
|
||
" X_valid=list(X_val),\n",
|
||
" y_valid=list(y_val),\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "54dacb80-c43f-47b1-83f9-490ad0d6d176",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/10\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|███████████████████████████████████████| 1960/1960 [24:31<00:00, 1.33it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train loss 1.6872646148563648 accuracy 0.5666815485681486\n",
|
||
"Val loss 1.412977649804649 accuracy 0.6115892139988525\n",
|
||
"----------\n",
|
||
"Epoch 2/10\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|███████████████████████████████████████| 1960/1960 [24:32<00:00, 1.33it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train loss 1.1436501777863928 accuracy 0.6886918808597488\n",
|
||
"Val loss 1.2984553687616225 accuracy 0.6374067699368904\n",
|
||
"----------\n",
|
||
"Epoch 3/10\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|███████████████████████████████████████| 1960/1960 [24:32<00:00, 1.33it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train loss 0.9005808831667718 accuracy 0.7508131896166848\n",
|
||
"Val loss 1.2479785930946332 accuracy 0.6666666666666666\n",
|
||
"----------\n",
|
||
"Epoch 4/10\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|███████████████████████████████████████| 1960/1960 [24:32<00:00, 1.33it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train loss 0.7234191348244037 accuracy 0.7962242489954717\n",
|
||
"Val loss 1.2792062138062004 accuracy 0.6580608146873207\n",
|
||
"----------\n",
|
||
"Epoch 5/10\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|███████████████████████████████████████| 1960/1960 [24:30<00:00, 1.33it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train loss 0.5877058356572703 accuracy 0.8332164041074048\n",
|
||
"Val loss 1.3536883816464778 accuracy 0.6678141135972461\n",
|
||
"----------\n",
|
||
"Epoch 6/10\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|███████████████████████████████████████| 1960/1960 [24:30<00:00, 1.33it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train loss 0.49206937486923547 accuracy 0.8616620957969259\n",
|
||
"Val loss 1.4424607625248236 accuracy 0.6752725186460126\n",
|
||
"----------\n",
|
||
"Epoch 7/10\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|███████████████████████████████████████| 1960/1960 [24:25<00:00, 1.34it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train loss 0.40955075885392533 accuracy 0.8821353402640475\n",
|
||
"Val loss 1.5361568299250319 accuracy 0.6620768789443487\n",
|
||
"----------\n",
|
||
"Epoch 8/10\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|███████████████████████████████████████| 1960/1960 [24:25<00:00, 1.34it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train loss 0.3543910782568024 accuracy 0.899292046686651\n",
|
||
"Val loss 1.6124461362428895 accuracy 0.6712564543889844\n",
|
||
"----------\n",
|
||
"Epoch 9/10\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" 58%|██████████████████████▌ | 1132/1960 [14:04<10:16, 1.34it/s]IOPub message rate exceeded.\n",
|
||
"The Jupyter server will temporarily stop sending output\n",
|
||
"to the client in order to avoid crashing it.\n",
|
||
"To change this limit, set the config variable\n",
|
||
"`--ServerApp.iopub_msg_rate_limit`.\n",
|
||
"\n",
|
||
"Current values:\n",
|
||
"ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
|
||
"ServerApp.rate_limit_window=3.0 (secs)\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#train\n",
|
||
"classifier.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "8dece2c7-b3be-4e69-b51b-a5a1b70bfaaf",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#save best model again\n",
|
||
"\n",
|
||
"torch.save(classifier.model,'bert_magnus_opum.pt')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "0e3977a9-ddeb-46e1-9b2a-46fc7b31ea1a",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"#load bert and truncate last layer\n",
|
||
"#get the embeddings layer\n",
|
||
"\n",
|
||
"model = torch.load('bert_opossum_best_0.6753.pt')\n",
|
||
"model = model.bert\n",
|
||
"model.to(device)\n",
|
||
"pass\n",
|
||
"\n",
|
||
"#load tokenizer\n",
|
||
"tokenizer = BertTokenizer.from_pretrained('sberbank-ai/ruBert-base')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "defe53ec-baa1-45f8-8d52-74ef2b425828",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#get token and embedding functions\n",
|
||
"\n",
|
||
"def get_token(text):\n",
|
||
" return tokenizer.encode_plus(\n",
|
||
" text,\n",
|
||
" add_special_tokens=True,\n",
|
||
" max_length=512,\n",
|
||
" return_token_type_ids=False,\n",
|
||
" truncation=True,\n",
|
||
" padding='max_length',\n",
|
||
" return_attention_mask=True,\n",
|
||
" return_tensors='pt')\n",
|
||
"\n",
|
||
"def get_embeddings(text):\n",
|
||
" encoding = get_token(text)\n",
|
||
" out = {\n",
|
||
" 'text': text,\n",
|
||
" 'input_ids': encoding['input_ids'].flatten(),\n",
|
||
" 'attention_mask': encoding['attention_mask'].flatten()\n",
|
||
" }\n",
|
||
"\n",
|
||
" input_ids = out[\"input_ids\"].to(device)\n",
|
||
" attention_mask = out[\"attention_mask\"].to(device)\n",
|
||
"\n",
|
||
" outputs = model(\n",
|
||
" input_ids=input_ids.unsqueeze(0),\n",
|
||
" attention_mask=attention_mask.unsqueeze(0)\n",
|
||
" )\n",
|
||
" return outputs[1].cpu().detach().numpy()[0]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "14627fdd-fa38-4cb8-b4c0-70a83f22c00e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|█████████████████████████████████████| 17422/17422 [10:11<00:00, 28.48it/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#get embeddings from text\n",
|
||
"\n",
|
||
"df['emb'] = df.r_text.progress_apply(get_embeddings)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"id": "b23c2f54-9533-4fac-a28f-db02f97d4ce6",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#split data again\n",
|
||
"\n",
|
||
"X_full = np.asarray([a for a in df['emb'].values])\n",
|
||
"y_full = df['type'].values.astype(int)\n",
|
||
"\n",
|
||
"X_data, X_val, y_data, y_val = train_test_split(X_full, y_full, test_size=0.1, random_state=42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"id": "142b7f1a-4701-48a0-a801-f275efe683a4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"## check upsampling data\n",
|
||
"## permutation data\n",
|
||
"\n",
|
||
"\n",
|
||
"np.random.seed(77112)\n",
|
||
"\n",
|
||
"class_size=833\n",
|
||
"X_data_r, y_data_r = [],[]\n",
|
||
"\n",
|
||
"\n",
|
||
"for d in np.unique(y_data):\n",
|
||
" X_data_r.append(resample(X_data[y_data==d],\n",
|
||
" replace=True,\n",
|
||
" n_samples=833,\n",
|
||
" random_state=42))\n",
|
||
" y_data_r.append(np.full([class_size],d))\n",
|
||
"\n",
|
||
"X_data, y_data = np.vstack(X_data_r), np.hstack(y_data_r)\n",
|
||
"\n",
|
||
"permutations = np.random.permutation(len(X_data))\n",
|
||
"\n",
|
||
"X_data, y_data = X_data[permutations], y_data[permutations]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"id": "dc89ab58-527d-4691-8ae0-37281426f3e9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(32487, 768)"
|
||
]
|
||
},
|
||
"execution_count": 47,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X_data.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"id": "565440c4-0f36-4b20-804b-74ea26e03fdc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0.03843947217441193\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<AxesSubplot:xlabel='real'>"
|
||
]
|
||
},
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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JqQoAKMWMVquFXm/6rdHmtrflGGv24ehYUdnXj5vZhcz169cRERGBU6dOISoqCo0aNcKmTZuM2gwZMgRHjuT+ToctW7agcePGys+pqamYPXs2du3ahezsbLRv3x7vvvsuatSoYcFQiIiIzKPRaODsXBWVKlUGoIezsyOSk9NNOjNhZ6eBs3N5k9vbcow1+7Czs4dWa70rWcwuZKKiorB//354eXlBr9fne5FUq1atMHnyZKNtbm5uRj+PHz8ely9fxvTp01G2bFl8+umnGDlyJH744QfY2/NkERERFQ2tVgt7e3uUK1cOGRk6kxass7fXmtXelmOKKi9rMLtaCAwMRNeuXQEAU6ZMwZkzZ/Js5+TkBG9v73yf58SJEzh48CAiIiIQEBAAAGjYsCF69eqFHTt2oFevXuamRkRERKWM2ed6HtfpoQMHDsDJyQn+/v7KtkaNGqFZs2Y4cODAY+mDiIiISjarfWh15MgReHt7o2XLlhg8eDCOHj1q9PiVK1fQsGFDaDTG95Q3atQIV65csVZaREREVIJY5UIUX19fBAcHo0GDBoiNjUVERASGDx+Or7/+Gj4+PgCAlJQUVKpUKVess7Nzvh9Xmcre3rg+s7PTGv3fFLYaw7xK71hsNS9LYmw1L0tiTG3/uB7nPmZe1oix1bxMYZVCZuzYsUY/d+rUCX369MGSJUuwYsUKa3Sp0Go1qFy5Qp6POTk5mv18thrDvErvWGw1L0tibDUvS2Is6cOSeO5j6/ZhSYyt5mVJjK3mVZAiuTWofPny6NixI7Zv365sc3Jywu3bt3O1TU5OhrOzs8V96fWClJR0o212dlo4OTkiJSUDOp1pV1bbagzzKr1jsdW8LImx1bwsiTG1vaFdfkyN5z5mXtaIKe68nJwcLT5LU2z3ODdq1AiHDx+GiBhdJ3P16lU0adLkkZ47v9vAdDq92beI2WoM8yq9Y7HVvCyJsdW8LImxpA9L4rmPmZc1Y2w1r4IUyXctpaenY9++fWjZsqWyrUOHDkhOTsbhw4eVbVevXsW5c+fQoUOHokiLiIiIVM7sMzIZGRnYv38/AODmzZtIS0vDtm3bAAB+fn64cuUKVq5ciW7dusHV1RWxsbFYtWoV4uLisHDhQuV5fHx8EBAQgLfffhuTJ09G2bJlsWDBAri7uyMoKOgxDY+IiIhKMrMLmYSEBIwbN85om+Hn1atXo1atWsjOzsaCBQuQlJQER0dH+Pj4YMaMGfD09DSK+/TTTzF79mxMmzYNOTk5CAgIwLvvvstVfYmIiMgkZlcMbm5uuHjxYoFtIiIiTHquSpUqYdasWZg1a5a5aRAREREVzTUyRERERNbAQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKrFQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKrFQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKrFQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKrFQoaIiIhUy764EyCyBq1WA61WAwCws9Ma/V+vF+j1Umy5ERHR48NChkocrVaDKi6O0NjZGW13cnIEAIhOh8SkDBYzREQlAAsZKnG0Ws39IiYiAoiJMX6wdm1oQkOh1WpYyBARlQAsZKjkiokBoqOLOwsiIrIiswuZ69evIyIiAqdOnUJUVBQaNWqETZs2KY+npaVh1apV2L9/P65duwYHBwd4enpiwoQJcHd3V9r9/fff6NKlS67n9/Lywrp16ywcDhEREZUmZhcyUVFR2L9/P7y8vKDX6yFifHr+1q1b+P777/Hss89i/PjxuHfvHr744gsMGjQIP/zwAxo3bmzUfuLEiWjTpo3yc4UKFSwcChEREZU2ZhcygYGB6Nq1KwBgypQpOHPmjNHjbm5u2LlzJxwdHZVtbdu2RWBgIL755htMnTrVqH39+vXh7e1tQepERERU2pldyGi1BS89U758+VzbKlSogHr16iE2Ntbc7oiIiIjyVSQX+6akpCAqKgpPP/10rsemT5+OCRMmwMXFBV26dMGkSZPg4uLySP3Z2xsXWw+vI2IKW41hXoXHmPJ8psTbwljUkJclMbaalyUxprZ/XI9zHzMva8TYal6mKJJC5uOPP4ZGo8Hzzz+vbHNwcMDzzz+PgIAAODk54dSpU1i2bBnOnDmD9evXo0yZMhb1pdVqULly3tfZGNYRMYetxjAv82PMjbfVsdhqXpbE2GpelsQUxTFpST+2ur8siWFepXss+bF6IfPDDz9g3bp1mDNnDmrVqqVsr1GjBqZPn6787OfnhyeffBJhYWHYuXMnevXqZVF/er0gJSXdaJudnRZOTo5IScmATqc36XlsNYZ5FR5jaFMQU+JtYSxqyMuSGFvNy5IYU9sXdlyaGs99zLysEVPceTk5OVp8lsaqhcz+/fsxbdo0jBo1CgMGDCi0fceOHVG+fHmcPXvW4kIGAHJy8t6hOp0+38fyY6sxzMv8GHPjbXUstpqXJTG2mpclMUVxTBZVXrYaw7xK91jyY7UvjTx58iTGjRuH/v37Y9y4cdbqhoiIiEoxqxQyly9fRlhYGNq2bYsZM2aYHLd3716kp6ejZcuW1kiLiIiIShizP1rKyMjA/v37AQA3b95EWloatm3bBuD+dS4igtDQUJQtWxZDhw41WmemYsWKeOKJJwAAc+bMgUajgbe3N5ycnBAZGYnw8HB4eHgo69QQERERFcTsQiYhISHXR0WGn1evXg0AuH37NgBg2LBhRu38/Pzw9ddfAwAaN26Mb7/9FuvWrUNmZiZq1qyJgQMHYuzYsbC351dAERERUeHMrhjc3Nxw8eLFAtsU9jgAhISEICQkxNzuiYiIiBRWu9iXiIiIyNpYyBAREZFqsZAhIiIi1WIhQ0RERKrFQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKrFr5kmVdBqNdBqNbCzu197G/6v1wv0einO1IiIqBixkCGbp9VqUMXFERo7O2Wbk5MjAEB0OiQmZbCYISIqpVjIkM3TajX3i5iICCAm5t8HateGJjQUWq2GhQwRUSnFQobUIyYGiI4u7iyIiMiG8GJfIiIiUi0WMkRERKRaLGSIiIhItVjIEBERkWqxkCEiIiLVYiFDREREqsXbr4mIiGwQVzQ3DQsZIiIiG8MVzU3HQoaIqJTjX/62hyuam46FDBFRKca//G0cVzQvFAsZIqJSjH/5k9qxkCEiIv7lT6rFQoaIiIhMZmvXVLGQISIiIpPY4jVVLGSIiIjIJLZ4TRULGSIiIjKPDV1Txa8oICIiItViIUNERESqxUKGiIiIVIuFDBEREakWCxkiIiJSLRYyREREpFpmFzLXr1/HtGnTEBwcjObNm6NPnz55tlu/fj26d++Oli1bol+/fti7d2+uNqmpqXj77bfh5+cHHx8fjB07FrGxseaPgohUQavVwN5ea7QiqL39/f+0Wk0xZ0dEamR2IRMVFYX9+/ejfv36aNy4cZ5tNm/ejKlTp6Jnz55YsWIFvL298frrr+PkyZNG7caPH49Dhw5h+vTpmDdvHq5evYqRI0ciJyfHosEQke0yrAhauXIFZSVQJ6f7P1euXAFVXBxZzBCR2cxeEC8wMBBdu3YFAEyZMgVnzpzJ1eazzz5D7969MX78eABA27ZtcenSJXz++edYsWIFAODEiRM4ePAgIiIiEBAQAABo2LAhevXqhR07dqBXr16WjomIbFC+K4IC/KZlIrKY2WdktNqCQ6Kjo3Ht2jX07NnTaHuvXr1w+PBhZGVlAQAOHDgAJycn+Pv7K20aNWqEZs2a4cCBA+amRURqYVgR9MH/Hi5siIhM9Ni/ouDKlSsA7p9deVDjxo2RnZ2N6OhoNG7cGFeuXEHDhg2h0RifSm7UqJHyHJaytzcuth7+hk5T2GpMaczL3MdNyaGgNqVxHxdFTGmfl8f1uNrzsiRG7XNvSQyPF9M99kImOTkZAODk5GS03fCz4fGUlBRUqlQpV7yzs3OeH1eZSqvVoHLlCnk+Zvhc3hy2GlPa8yqKPkv7PrbVubTVsRTVcVxS8rIkpiTNva3Oi63mVZAS96WRer0gJSXdaJudnRZOTo5IScmATqc36XlsNaY05mVol5+H4wtrn1eMJXkVdYyt5mVqTGmfF3OP45KalyUxap97S2JK2/Hi5ORo8Vmax17IODs7A7h/a3X16tWV7SkpKUaPOzk54fbt27nik5OTlTaWysnJeyfqdPp8H8uPrcaU9ryKos/Svo9tdS5tdSxFdRyXlLwsiSlJc2+r82KreRXksS+I16hRIwDIdZ3LlStXUKZMGdStW1dpd/XqVYgY36Fw9epV5TmIiIiICvLYC5m6deuiQYMG2LZtm9H2LVu2oF27dnBwcAAAdOjQAcnJyTh8+LDS5urVqzh37hw6dOjwuNMiIiKiEsjsj5YyMjKwf/9+AMDNmzeRlpamFC1+fn6oUqUKxowZg0mTJqFevXpo06YNtmzZgsjISKxZs0Z5Hh8fHwQEBODtt9/G5MmTUbZsWSxYsADu7u4ICgp6TMMjIiqcVqtRFuN7+K4KvV64tg2RDTO7kElISMC4ceOMthl+Xr16Ndq0aYM+ffogIyMDK1aswPLly9GwYUMsXrwYPj4+RnGffvopZs+ejWnTpiEnJwcBAQF49913YW9f4q5BJiIbZVhxWGNnZ7TdcEGj6HRITMpgMUNko8yuGNzc3HDx4sVC24WEhCAkJKTANpUqVcKsWbMwa9Ysc9MgInosuOIwkbrx1AcREfDvisNEpCqP/WJfIiIioqLCQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKrFQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKrFQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKrFQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKrFQoaIiIhUi4UMERERqRYLGSIiIlItFjJERESkWvbWeNIhQ4bgyJEjeT42f/589O7dO982W7ZsQePGja2RFpEqabUaaLUa2Nnd/7vD8H8A0OsFer0UV2pERMXOKoXMe++9h7S0NKNtX331FXbs2IF27dop21q1aoXJkycbtXNzc7NGSkSqpNVqUMXFERo7O2Wbk5Oj8m/R6ZCYlMFihohKLasUMk888USubW+88Qb8/f1RpUoVZZuTkxO8vb2tkQJRiaDVau4XMRERQEyM8YO1a0MTGgqtVsNChohKLasUMg/7888/8ffff2P8+PFF0R1RyRMTA0RHF3cWREQ2p0gKmU2bNqF8+fLo0qWL0fYjR47A29sbOp0OXl5eGDduHHx9fR+5P3t742uY87q2oDC2GlMa8zL3cVNyKKiNLe1jjqXk5PW4Hn/cr8mizsuSGFs6jouqDx4vprN6IZOTk4OtW7ciMDAQ5cuXV7b7+voiODgYDRo0QGxsLCIiIjB8+HB8/fXX8PHxsbg/rVaDypUr5PnYg9cWmMpWY0p7XkXRp63uY0viORbbzMuS+KJ4TVoSX5LeX2w1L0viS9Lxkh+rFzKHDh1CYmIi+vTpY7R97NixRj936tQJffr0wZIlS7BixQqL+9PrBSkp6Ubb7Oy0cHJyREpKBnQ6vUnPY6sxpTEvQ7v8PBxfWPu8YizJqyhiOJaSk5e5x7Gl/dh6XpbE2NJxXFR9lLbjxcnJ0eKzNFYvZDZt2gQXFxcEBAQU2K58+fLo2LEjtm/f/sh95uTkvRN1On2+j+XHVmNKe15F0aet7mNL4jkW28zLkviieE0WRV6WxNjqcWyrx6Ql/dhqXgWx6oJ4mZmZ2LVrF3r06IEyZcpYsysiIiIqhaxayOzZswfp6eno27dvoW3T09Oxb98+tGzZ0popERERUQli1Y+WfvnlF9SpUwdPPfWU0fZjx45h5cqV6NatG1xdXREbG4tVq1YhLi4OCxcufGz9c0VUIiKiks1qhUxycjJ+/fVXDB06FBqNxuix6tWrIzs7GwsWLEBSUhIcHR3h4+ODGTNmwNPT87H0zxVRiYiISj6rFTLOzs44c+ZMno/Vr18fERER1uoaAFdEJSIiKg2KZEG8YsUVUYmIiEosq17sS0RERGRNLGSIiIhItVjIEBERkWqxkCEiIiLVYiFDREREqsVChoiIiFSLhQwRERGpFgsZIiIiUi0WMkRERKRaLGSIiIhItVjIEBERkWqV/O9aql3btG1ERESkOiW6kBG9HprQ0HwfIyIiInUr0YWMRqvFRhxBPFKNtldDJfTX+uUZo9VqoNVqYGd3/1M3w//1eoFeL9ZNmIiIiMxSogsZADiDaEQjwWhbXVRFf+QuZLRaDaq4OEJjZ6dsc3JyBACITofEpAwWM0RERDakxBcy5tBqNfeLmIgIICbm3wdq14YmNBRarYaFDBERkQ1hIZOXmBggOrq4syAiIqJCsJAhohKH17oRlR4sZIioROG1bkSlCwsZIipReK0bUenCQoaISiZe60ZUKvArCoiIiEi1WMgQERGRarGQISIiItViIUNERESqxUKGiIiIVIuFDBEREakWb78mIiKbxBWayRQsZIiIyOZwhWYyFQsZIiKyOVyhmUzFQoaIiGwXV2imQvBiXyIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhQ0RERKpllULmxx9/hLu7e67/5s2bZ9Ru/fr16N69O1q2bIl+/fph79691kiHyCRarQb29lqjVUTt7e//p9Vqijk7IqKCGd7D8nofK8nvYVZdR2blypWoVKmS8nPNmjWVf2/evBlTp07Fq6++irZt22LLli14/fXXsXbtWnh7e1szLaJcClpFFOBKokRk2/J6DwNKx2rIVi1kWrRogSpVquT52GeffYbevXtj/PjxAIC2bdvi0qVL+Pzzz7FixQprpkWUS76riAJcSZSIbF5pfg8rlpV9o6Ojce3aNfz3v/812t6rVy/MnTsXWVlZcHBwKI7UqLTjKqJEpGal8D3MqoVMnz59cPfuXdSpUwf/+c9/MGLECNjZ2eHKlSsAgIYNGxq1b9y4MbKzsxEdHY3GjRtb3O+Dnw8W5OE2hcWY+rgpfVsaUxR92Fpe5j5ujbkvrI21xm+reVkSU1RjKYrjpSjysrQfW8/L1Bhbzauo+uBxbDqrFDLVq1fHmDFj4OXlBY1Ggz179uDTTz/FnTt3MG3aNCQnJwMAnJycjOIMPxset4RWq0HlyhVMavvgNRCPs725z2vNXIo6pqjyKo4+TYmx1fHb0j561HhbPV6KKq+ieE1aEl/Ux4st5WWrrxVLYmz5eMmPVQqZ9u3bo3379srPAQEBKFu2LL766iu8+uqr1uhSodcLUlLSYWenLXRHpaRkQKfTKz8XFvNw+4cZ4gtr9ygxRdGHreVl7rxYY+7ziskrx8c9flvNy5KYohpLURwvRZGXpf3Yel6mxthqXkXVR2k7jp2cHC0+S1Nk18j07NkTX3zxBc6fPw9nZ2cAQGpqKqpXr660SUlJAQDlcUvl5Jh2QOl0epPbmtPe3Oe1Zi4lNa/i6NOUGFsdvy3to0eNt9XjpajyKorXZFHkZWmMLeZlq68VS2Js+XjJT7EsiNeoUSMAUK6VMbhy5QrKlCmDunXrFkdaREREpDJFVshs2bIFdnZ2aN68OerWrYsGDRpg27Ztudq0a9eOdywRERGRSazy0VJoaCjatGkDd3d3AMDu3buxbt06vPTSS8pHSWPGjMGkSZNQr149tGnTBlu2bEFkZCTWrFljjZSIiIioBLJKIdOwYUP88MMPuH37NvR6PRo0aIC3334bQ4YMUdr06dMHGRkZWLFiBZYvX46GDRti8eLF8PHxsUZKREREVAJZpZB59913TWoXEhKCkJAQa6RAREREpQC//ZqIiIhUi4UMERERqRYLGSIiIlKtYvnSSCKyPVqtBlqtJtd3oej1UiK/MZeISgYWMkQErVaDKi6O0NjZKdsMy5CLTofEpAwWM0Rkk1jIEBG0Ws39IiYiAoiJ+feB2rWhCQ2FVqthIUNENomFDBH9KyYGiI4u7iyIiEzGi32JiIhItVjIEBERkWqxkCEiIiLVYiFDREREqsVChoiIiFSLhQwRERGpFgsZIiIiUi0WMkRERKRaLGSIiIhItVjIEBERkWqxkCEiIiLVYiFDREREqsVChoiIiFSLhQwRERGpFgsZIiIiUi374k6AiKggWq0GWq0Gdnb3/+4y/F+vF+j1UpypEZENYCFDRDZLq9WgiosjNHZ2yjYnJ0cAgOh0SEzKYDFDVMqxkCEim6XVau4XMRERQEzMvw/Urg1NaCi0Wg0LGaJSjoUMEdm+mBggOrq4syAiG8SLfYmIiEi1WMgQERGRarGQISIiItViIUNERESqxUKGiIiIVIuFDBEREakWCxkiIiJSLRYyREREpFosZIiIiEi1WMgQERGRarGQISIiItViIUNERESqZZUvjdy6dSt+/vlnnD17FikpKahfvz6GDBmCZ599FhqNBgAwZMgQHDlyJFfsli1b0LhxY2ukRURERCWMVQqZL7/8Eq6urpgyZQoqV66M3377DVOnTsXt27fx+uuvK+1atWqFyZMnG8W6ublZIyUiIiIqgaxSyCxduhRVqlRRfm7Xrh2SkpKwatUqjBo1Clrt/U+0nJyc4O3tbY0UiIiIqBSwyjUyDxYxBs2aNUNaWhrS09Ot0SURERGVQlY5I5OX48ePo2bNmqhYsaKy7ciRI/D29oZOp4OXlxfGjRsHX1/fR+7L3l4LO7vCa7SH2xQWY+rjpvRtaUxR9GFreZn7uDXmvrA21hp/UeVVFMd+Ub0mi+J4yesxW9jHasjL1Bhbzauo+uBxbLoiKWSOHTuGLVu2GF0P4+vri+DgYDRo0ACxsbGIiIjA8OHD8fXXX8PHx8fivrRaDSpXrmBSWycnR7Oe29T25j6vNXMp6piiyqs4+jQlxlbHX1T7uKjnUu1zb0l8UewjS+KL+ti3pbxs9XVvSYwtHy/5sXohc/v2bUyYMAFt2rTBSy+9pGwfO3asUbtOnTqhT58+WLJkCVasWGFxf3q9ICUlHXZ22kJ3VEpKBnQ6vfJzYTEPt3+YIb6wdo8SY+0+NBoNtNr7/1WsWA5paZnQ6wV6vUBEiiUvc+fFGnOfV0xeOT7u8RdVXkVx7BfVa7Iojpe8+rOFfayGvEyNsdW8iqqP0nYcOzk5WnyWxqqFTEpKCkaOHAkXFxcsWrRIucg3L+XLl0fHjh2xffv2R+43J8e0A0qn05vc1pz25j6vNXMxJ0ar1aCKSzlo7OyUbRUrlgMAiE6HxKQM6PX5FzPWyssa8daKsdXxF9U+Luq5VPvcWxJfFPuoKPKyNMYW87LV170lMbZ8vOTHaoVMZmYmwsLCkJqaiu+//x6VKlWyVlf0mGi1mvtFTEQEEBPz7wO1a0MTGgqtVlNoIUNERFSUrFLI5OTkYPz48bhy5QrWrl2LmjVrFhqTnp6Offv2oWXLltZIicwREwNERxd3FkRERIWySiEzY8YM7N27F1OmTEFaWhpOnjypPNa8eXNERkZi5cqV6NatG1xdXREbG4tVq1YhLi4OCxcutEZKNsVwDQqQ+wpuw/UoRI/CcIzx+CKigpSE9wqrFDKHDh0CAMyZMyfXY7t370b16tWRnZ2NBQsWICkpCY6OjvDx8cGMGTPg6elpjZRsxv3rUByNrkMB/r2C29RrUYjyk9cxxuOLiB5WUt4rrFLI7Nmzp9A2ERER1uja5uV7HQrAa1HoseC1TkRkipLyXlFkC+LRQ3gdClkbjzEiMoXK3ytYyJRgJeGzTyIqGXhtIFkLC5kSqqR89klE6sdrA8maWMiUUCXls08iUj9eG0jWxEKmpFP5Z59EVILw/YisgIUM0SPgdUhERMWLhQyRhXgdEhFR8WMh8xjwr/LSidchkbXZ8p0+fN8jW8FC5hHxr3Li5/5kDbZ8pw/f98iWsJB5RPyrnIiswZbv9OH7HtkSFjKPC/8qJyJrsOX3FlvOjUoNbXEnQERERGQpFjJERESkWvxoiYgsYst31BBR6cFChojMZst31BBR6cJChojMZst31BBR6cJChogsx7tWiKiY8WJfIiIiUi0WMkRERKRaLGSIiIhItVjIEBERkWrxYl96JPl9Ay7AtUSISB34Td7qxkKGLFbQN+ACXEuEiGwfv8lb/VjIkMW4lgiR7eFZUvNY8k3e3Me2hYUMPTquJUJkE3iW9BGY+D7GfWx7WMgQEZUQPEtqfdzHtoeFDBFRScOzpNbHfWwzePs1ERERqRbPyBARUYnBW6lLHxYyRERUIvBW6tKJhYxKlKS/MkrSWKj04nFseyy5lZrUj4WMCpSkvzJK0lio9OJxbON4IW6pwkJGBUrSXxklaSyW4l/y6sfjmMh2sJBRk5L0V0ZJGosZ+Jd8CVNKj2MiW8JChqgI8S95otKJZ2Kth4UMUXHgX/JEpQbPxFoXCxkiIiIr4plY62IhQ0REVBR4JtYqivUrCv766y8MHz4c3t7e8Pf3x9y5c5GVlVWcKREREZGKFNsZmeTkZAwdOhQNGjTAokWLcOfOHcyZMweZmZmYNm1acaVFREREKlJshcx3332Hf/75B4sXL4aLiwsAQKfTYcaMGQgLC0PNmjWLKzWgdu2Cf1aTkjQWIiJSJWvetVVshcyBAwfQrl07pYgBgJ49e+K9997DoUOH8MwzzxRLXqLXQxMamud2tSlJYyEiInUy5a6tR6ERkWK5VLpdu3Z49tlnMWnSJKPt7du3R3BwcK7tphK5X91pNIBWq0UKMqCD8S9uO2jhBEfo9Xo8OHpDTAayoMe/D2ihgSMccrV/MAYpKYBO90AndoCTU7595GpvSUw+7UvqWIolr5I0FlvNqySNxVbzKkljeYx5laSx2GpeRjHp6bljypeHXq+HRqOBRqOBJYrtjExKSgqcnJxybXd2dkZycrLFz6vRaGBn9+/OcIJjvm212ryvdXaEg1nt73eUeywFxuTT3pKYgvIqSWMpzrwsibHVsdhqXpbE2OpYbDUvS2JsdSyPNS9LYmx1LLaaFwCUL29+jAmK9a4lIiIiokdRbIWMk5MTUlNTc21PTk6Gs7NzMWREREREalNshUyjRo1w5coVo22pqamIi4tDo0aNiikrIiIiUpNiK2Q6dOiA3377DSkpKcq2bdu2QavVwt/fv7jSIiIiIhUptruWkpOT0bt3bzRs2BBhYWHKgnh9+/blgnhERERkkmIrZID7X1HwwQcf4MSJE6hQoQKCg4MxYcIEODjkfacNERER0YOKtZAhIiIiehS8/ZqIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBAREZFqsZAhIiIi1WIhUwKZuzQQlxIiIiK1YiHzmOj1erPam1I83LlzBxkZGWbnotForNr+zz//RGZmplkxxcUWizQRscm8LGHJOCwdv7kx5r4mi4KI2GRelrBkDvV6PXQ6ndkx5ijKfWzOPhARs8duqyzZv5bMvalKZCFz/PhxHD9+HCdOnMC5c+eQmppaYHtLD7ALFy7g2rVrAACttuBdmZOTg23btuGPP/5Aenq6Ujzk90LIysrCgAED8M4772Djxo24fft2rrbp6em5+li1apXRF3EaYvLqJzs7G5MnT8b69etNPjCzsrLw6quvYsaMGbhz506BY3hQYmIivvzyS/z444+4dOlSrvzyEhkZiZMnT+LUqVO4cOEC0tLSTMrxQeYWaUVBo9GYnZdOp7Pqm3N8fLxZr4EDBw4gKyvL5HE8OM+WjN8QZ47CXpPFQaPRmJ2XNec+v3kv6HX5KHOv1WphZ2dnVo7m7i9L9rGlCnsff/i4N2XsD8aYM/f37t0zqZ3BzZs3kZOTk2/feY1p8+bNSE5ONnn/Pvj7x5K5N5W9VZ61mJw7dw4rV67E4cOHcffuXdjb26Nhw4Zo1KgRfHx88PTTT+OJJ54wmoR79+6hbNmyyg42HDQFTVRMTAzWrFmDjRs3IiEhAa6urhg1ahSCg4Oh1WpzxZ49exZLly7Fvn37kJOTgzp16mDu3Llo3bo1NBoN0tPTjXIA7hdjiYmJ2Lt3L/bu3YsyZcqgU6dO6N27N7y8vODi4oKXX34Z48ePR9u2bQEA33zzDTZs2ICQkBDleTQajTLGrKwso++x2rhxI44ePQp/f38l5+TkZMTFxSEqKgpVq1aFp6cnypUrZxSTkpKCPXv2IDY2FlOmTMGTTz5Z4LwcOXIECxYswOnTp2Fvb4+yZcviww8/RNeuXaHRaHD37l1UqlQJ9vb3D8dLly7hiy++wIEDB5CYmAh7e3vUqlULzZs3x1NPPQV/f3888cQT0Ov1ec5TUlISbty4gRMnTsDNzQ0eHh5wdnZWxiEi+b4JF/TYw+7du4ebN2/iypUrqF+/fqH7ITY2FkePHsXt27fh6+sLT0/PAvv+559/cP78ebRu3dro2NDpdNBqtXnmmZiYiM2bN2PQoEH5fmfZw/vt7t27mDt3Lpo3b46hQ4cWOv7Y2Fi88sorCAoKwoQJE9CwYcMC2xvG9tdff2HTpk1ITk5GUFAQ2rVrV+j+Tk1Nxd9//40TJ07A1dUVTZs2RaVKlVCuXDlotdpc8YXNvV6vz7eQMnXuzZ13oPC5f3hOzJ37xz3v+e2Hopr7/Obd0dERGo0mz3m8c+cOTp06hRMnTqBBgwZ44oknUKNGDVSvXt3oPcyUfVOQtLQ0nD17FlFRUfDw8IC3t3eBx010dDS2bt2K2NhYdOnSpdCxp6Wl4c8//0THjh2N5j4nJyfP3y/A/YL0q6++QlhYGCpWrFjoGBMTEzFt2jR4enpizJgxyvaCjoE7d+7gjTfeQIsWLTBlyhT4+voCKPh1o9PpcPbsWaxZswbp6ekIDg5GUFCQWe+zpihRhcysWbPg4OCAiRMnokOHDrhx4wZ2796NY8eO4cSJE9ixYwcGDx6MXr16KTH//e9/kZOTg379+qFjx45wdHRUHtPpdHlW93PmzMHNmzcRHByMJk2aYOfOnViwYAFq1aoFf3//XC+KBQsWQKfT4YMPPkCzZs3w4YcfYteuXbCzs0N4eDhEBN7e3ggODkadOnUgImjXrh1ef/11nDp1Ci+//DJOnz6Nn3/+Gb/88gvc3Nzg7u6Os2fPGh2069evR/v27ZUxxMXFYdu2bfjpp5/wzz//wNvbG/369YO/vz8A4Ntvv0WPHj3QvXt3AMDBgwexcuVK/P7773B2dkb16tXh5uaGl19+GX5+fgCA1atX48UXX4S7uzs++eQThIWF4Z133kHnzp2h1WrzfENYtGgRqlWrhpUrV8LDwwNTp07Frl274OjoiCVLlkCj0aBly5YYMGAAmjRpgtmzZyMzMxNhYWHw8/PDpUuX8OOPP2LHjh04efIkDh48iLCwMLRu3TrXMXD48GGsWLECv/32G9zc3BAXFwe9Xg8PDw/06dMHPXv2RJUqVYxi7ty5gzJlyqBKlSpGL678Xmx6vR5RUVFYuXIlfvnlF7i4uMDV1RUvvPACnn322bwOTRw6dAhLly7FsWPHUKdOHWzevBmzZs1C06ZNkZ6ejvLlyxv9dafRaBAREYElS5bA29sbbdq0Qc+ePdG0aVPlzS07OxvR0dGoV6+eUgQuWbIEe/fuxcCBA436T05Oxj///IM6derkmp81a9bg4sWLeOGFF6DRaCAiuHv3Lvbu3QtHR0e4urqiTp06qF69OkQE3333HTQaDQ4ePIjY2FhMmzYNzZs3L3Cfbd++HUuWLMHt27dRq1Yt7Nu3D++//z7at2+P1NRUVKpUKdd+P3bsGFasWIH9+/ejWrVqiI+Ph6OjI9q2bYugoCAEBQWhQoUKRnO/cuVKHDp0yGpzf+HCBURERJg87yKCw4cPY8mSJQXOvWFOzJ37+vXrw87OzuJ5v3DhgsnzrtFoHtvcz5gxAx06dMhz7o8fP46VK1di3759Js07AOzZswfh4eG4cOECGjRogB9//BEpKSmoV68eunTpgl69esHDw8Mo5tq1ayhTpgxcXV2N9k1+RY1er8fp06exfPly7N69G9WqVYODgwMGDBiAMWPG5Dn/u3fvxpIlS3D16lW4urpi165dmDlzJgICApCUlKS8f9vb2yv7Lzw8HCtXrkSNGjXw9NNP47nnnoO3t7fyGs/KysLp06fRsmVLpWhdvnw59u3bh9dee81oX964cQM6nQ6NGjUyGtOaNWuQmJiIrl27QqvVQqfT4ebNm9i2bRu0Wi3q1KmDxo0b48knn1QKxu+++w6Ojo5ISEjA22+/jenTp8Pf37/AgmTz5s1YsmQJsrOzUaNGDUyePBnJyckICQlBXFwcHBwcoNVqUalSpUcqbkrMl0bGxcWhW7duWL16dZ5/6e7fvx9fffUVDh8+jGHDhmHChAlITU1FQEAAatasCQCoVKkSfH190aNHD7Rp08boOVJSUnD48GFUrVoVI0aMwKJFi9C+fXsA9yvuadOm4fr169iwYYPRm+WePXswduxYfPHFF0oxsHfvXsydOxcZGRl48skn8c8//+DEiRPw8/PDggULlPibN29i8uTJqFevHmbNmoWMjAycPHkSBw4cwHfffYfMzEy0aNECgYGBCAgIwH/+8x+sW7cOHh4e0Gq1mDhxIn799Vd4enqiVq1aiIyMRFRUFEJDQxEaGorhw4cjLCxMKey6d++OmjVrYvTo0dBoNPj999/x888/w97eHsuWLUO5cuXQqVMnbNy4EU2bNsXFixcxffp0REdHY/z48bneRIH7f8F1794dX331lTIvp0+fxuuvvw6tVotWrVohLS0NR48ehZubG6ZPn46XXnoJX375pVGhEhcXh7CwMOXAP3XqFObNm4eePXsa9Wd4wx8yZAicnJyQlZWFy5cvY+fOnTh48CAcHBwQGhqKF198UXkzHDNmDCpUqAB/f380b94crq6uRn/B5fUCe+mll3Dv3j0MHToUer0eP/zwA37//XfMmzcPvXv3zhXzzDPPwM3NTfnLd9q0aXj66afh7e2N9evXIyoqCj169EBYWBiqV68OAFi4cCGWLVuGjh074tatW0hKSsITTzyBzp07Y+DAgThw4AC2bt2KOXPmKG9ogYGBGDp0KIYNGwaNRoOYmBh89dVXOHjwIJKSklC1alWEhIRgwIAByvi7deuGQYMG4aWXXoKDgwN+/PFHfPHFF7hz5w7S09NhZ2eHVq1a4b///S9atGgBf39/jB8/HvXr18eMGTOQnp6OyZMno0ePHrnm/8F5efrpp/H888/D3t4eH3zwAapWrYpWrVrhhx9+wI0bNxAYGIjXX38drq6uAIC+ffuiZs2aGDFiBBwdHXHt2jX88ssvOHjwIACgWrVqGDduHPr37w97e3v06tXL6nNv7rwX1dwXxbyLCAICAqw+9+bOu0ajQVBQENq2bYsXXngB1apVg1arxZUrV7Bp0yZs3boVWVlZGDhwIMaMGQMnJycAwNChQwEAbdu2hY+PD5o3b648BuRd0AwaNAgVK1bE8OHDYWdnh59//hkbN27E5MmTMWzYMOh0OqOzKH379oWnpycGDx4MR0dHvP/++6hRowY8PT3x7bff4ubNm2jfvj3Gjh2Lxo0bA7j/x+KCBQvQtWtXXL16FefPn4erqyt69uyJl19+Gb///ju+/fZbfP7553B0dISIGM2/VqvF5cuXERERgUOHDiE+Ph5VqlRBSEgIhg8fDicnJ3Tv3h0DBw7E0KFD4eDggLVr12LVqlVITU2FiCA1NRVPPPEEJkyYgMDAQIgI2rdvj9GjR6Nt27aYOnUqzp8/j1dffRXDhg1DmTJl8jz2e/TogS5duuC5556Dk5MT5s2bh4SEBLRs2RLr169HXFwc2rRpg9GjR8PHxyffY6hQUkKcOnVKOnfuLLt27RIREb1eL1lZWZKdnW3U7rvvvpM2bdrI8ePHZcuWLRIQECDh4eGybt06mTBhgvTu3Vvat28vISEhsmjRIrl48aKIiFy7dk3c3d1l2rRp8uyzz0pcXJzR8167dk2eeuop+eyzz5Rtt2/fliZNmkjXrl0lPj5e9Hq9iIicOXNG3N3dZf369Urb48ePS/PmzZX8H3zeHj16yMyZMyUrK0tERBISEqRFixbywQcfyMSJE8Xd3V0mTJggffr0MXq+Fi1ayA8//KBsS05OlpkzZ4qPj49ERkZKWFiYTJo0SURETp48KS1btpQrV64Y9R8XFyd+fn4SEREhc+fOlf79+0taWpqyj8+ePSsvvfSSNG/eXN5//32JjY0VERGdTiciIgcPHpSgoCA5deqU8pyXLl2Spk2byo4dO4zG6e3tLdOmTZPOnTsr7fV6vfJcO3fulJdffllERBYtWiTPPvusJCQkKM9x8eJF8fHxkfPnz8vDdDqdXLhwQWbNmiX+/v6yePFiJRd3d3fx9vYWd3d36d69u8yaNUt2794t0dHRRsdPYmKizJ8/X/73v/+Jn5+fnD592qiPMWPGSJ8+fYyOjZycHPn000/F29tbzp49azQ/gYGB0rlzZ5kwYYK8//770q5dO3n++eeV+OjoaHn22Wdl5MiRsnfvXpk/f74MHjxYOnXqJEFBQdKqVSsJCQmR5ORkEbn/GvD09JTr168r/QwePFj8/Pxk0qRJMnPmTHnllVfEx8dHPv/8cxG5f4z26dNHNm/erMS0bdtWpkyZIkeOHJG4uDjZuXOnDBgwQNq3by+//PKLtGjRQi5fviwiIocPH5Y+ffqIt7e3rFixQu7du6fMm8GFCxfE19fXaF5u3Lgh/v7+EhQUJO+//758+umnyv6IioqS8+fP59pnIiJ3796V0NBQeeutt2TWrFnSs2dPOXToUJHM/dtvvy2tWrUyed7XrVsnW7ZskVatWll17oti3i9evCinTp2y+txv377drHkXEYmKihIfH59c82KQmpoqq1atkvbt28uUKVPk3r17EhUVJe7u7tK5c2dp06aNBAUFybhx4+Srr76SyMhIZSwiIvHx8fLuu+/K+vXrxc/PT86dO2f0/LNnz5aOHTvKjRs3jOb//fffF29vb6OxX758WRn7Rx99JCtXrpTu3btLly5dlPfe1NRUGTFihDzzzDNy6NAhWbdunYwdO1YCAgLEw8NDPD09ZcCAAZKYmCgi91/3Xl5eRv0/++yz0rFjR5kzZ46sWrVK3nnnHfHw8JC3335bbt26JSEhIUa/f3x9feWDDz5Q5vbUqVMycuRI8fLykj/++CPX3F+8eFFGjhwpLVq0kFmzZklqamqu/X7x4kXx9fU12l/x8fHSpk0b6devn6xYsULWrVsn//nPf6RVq1Zy9OjRPOfPFCWmkNHr9RISEiIhISESHR2d6zHDgZmQkCDPPfecfPjhh/LTTz+Jt7e3sqPT0tLk6NGjsnTpUnnllVekW7du0rFjR3n55Zfltddek1atWsnixYtl2LBhcufOHeW5Db9ow8PDpV27dsov859//lmaN28uY8aMMXqTe+utt2TQoEGSmZmpbEtKSpLBgwfL/PnzlW2G5z116pQ888wz8ssvv4jI/WKsbdu28vfff8u9e/fk+vXrMnToUHF3d5fXX39dIiMjZerUqfL6669Lenq6iNx/YYmIXL16VTp06CCrVq2SDRs2SLdu3eTkyZNy8uRJef755+XChQsiIpKenq6MbfLkyTJs2DDp0KGDRERE5Ln/58+fLz4+PvLmm28aFRe3b9+WgIAAGTNmjCQkJMitW7ckLCxMBgwYIP/8849kZ2eLTqeTe/fuydixY2XEiBHi7+8vb7zxRq4Xx9KlS6VTp04icv+XUPv27Y0O/j///FOCgoJkz549eeYocr+YW7p0qbi7u8uRI0dk3bp10qVLF/ntt9/k7NmzMnHiRPH09JRmzZrJoEGDZMmSJfL7779LfHy8/P777+Lu7i5vv/22DB8+XO7evSsiohxb58+fl/bt28uSJUuU/q5evSrNmzeXZ599VhITE5U53bVrl7Ro0UIOHDig7O+dO3eKu7u7sk1E5Pr16/LCCy/Id999pzzfpk2bZOLEidKsWTNp27at9OnTRyZPniyTJ0+W3r17K/t///794uvrK7t371ae7+bNmzJ9+nRxd3dXjvuwsDD58MMPRUTk119/lXbt2klMTIzRfrt8+bK0bdtW+vfvLyNGjFCKasP4p06dKl5eXvLhhx/mmrcdO3ZIz5495cSJE8q2vXv3SosWLZSCNTs7W44fPy5PPfWUfPnll7Jt2zYJDAyUP//8U3nc8Avyhx9+kKCgIElOTpb3339funTpohTM1pz7Jk2ayMCBA02ed29vb3nzzTflpZdesurcBwUFSc+ePa0678uXL5c333zT6nM/ZcoUs+Y9JSVFoqKipEePHvLTTz8Z9f3ge3NWVpb89NNP4u7uLjt27JAff/xROnXqJPv27ZMrV67InDlzpGfPnsov2XfffVd++uknuXr1qhw+fFjc3d3l3XfflWHDhkl8fLzynCL3i86ePXvK1KlTjea/SZMmEhwcLHFxccoY9uzZIx4eHnLkyBGlraEQebCoTElJkVdeeUUpumNjY+Xo0aMyd+5cadasmXh7e0urVq1k8ODBMnnyZAkMDFR+xxw8eFB8fX3lt99+U57v7t27smTJEmnSpIkcPXpUJk6cKG+++aaIiBw9elT8/f3l9u3byn4Tuf966datm8ybN08mT54sI0eONJp7kfvvyV5eXvLaa68pRY7BwYMHpUePHkrBaRi/p6dnrsK2Q4cOsnDhQrGU7V3WbyGNRoPp06cjLS0NkyZNwtq1a/HXX38pV9cbTr1XqlQJFStWRFZWFtq3b4+pU6eiWbNmAIAKFSqgdevWePXVVzF79mxMmTIFffr0QWZmJvbs2YPg4GB4e3sjLS0NLi4uSr+Ga0Oee+452NvbY+3atQCAn376Ce3atUPnzp1RrVo1JdcWLVrglVdeQdmyZZVtWq0WDg4Oua7wFxF4eHigV69emDt3Li5cuICNGzeiU6dOqFWrFhwcHODm5oZx48Zh/PjxuHXrlvIRU5MmTVCmTBnluQDAyckJ1apVQ0pKCnr27AkvLy+MGTMGGzZswJUrV3Ds2DEAUC6qS09PR0JCAqpXr47nnntOuZ7GwHDHw4gRI/Dqq69i69atGDp0KA4dOgSdToeaNWti7NixOHfuHP7zn/+gW7ducHR0hLOzMxwcHGBvbw+tVoucnBzcu3cPdevWxaRJk3D48GG89957+Omnn3Do0CF88803CA8Px/PPPw8AcHZ2Vj4uM2jWrBlq1KiBZcuW4cKFC8p2vV6v7FcnJyeMHDkSvr6+OHjwoPL5cM2aNdG8eXN88sknysdWDg4OWLhwIV577TW88847WLx4MWrVqoXKlSsjKSlJuTDccGw1bdoUAwcOxKpVqxAdHQ0A2LdvH+zs7NCgQQOUKVNGmYcNGzagbdu2ykeYjo6OaNeuHdq2bYvTp08r+7ZevXoYMmQI1q5diyNHjqBBgwbo3bs36tevjxo1auDll19G/fr1kZCQgHPnziE+Ph5vvvkm1q5di8WLF6Nr167o2LGjsh/q1KmDsLAwVKtWTemnXbt2+Pnnn7Fp0yblugLDvBr2W6NGjdC5c2ecO3cO3bt3V46rnJwcODg4YPTo0Rg4cCBWr16NcePG4eLFi8r+9/LyQlZWFr799ltcunQJBw4cwPLly9GmTRvl+gp7e3t4enqiY8eOOHfuHFq3bg2tVouff/4ZWVlZykcIwP2L+itXrgwnJycMGzZMua3T2nNfoUIFo7tICpv3cuXKoUyZMqhevbpV5z4uLg6JiYlWnfcTJ07gf//7n9XnPiMjw6x5P3v2LOrXr4/GjRvjs88+w/79+5UlKx68vrFMmTLo378/unbtisOHD6NChQpwdHSEm5sbGjZsiMmTJ2PLli34+OOP0bRpUxw8eBDz5s3D22+/jdmzZ8PV1RU1a9ZETEyMsm8M+8HwseGPP/6o7Nt9+/bB0dERTZs2hYODgzKGdevWwdfXF97e3so+evLJJ/H000/j7NmzytxXqlQJoaGh2LZtGzZu3Ijq1aujdevWKFu2LGrVqoUZM2YgJCQEzs7OiI+PR1paGoYOHYqPPvoI8+bNQ5cuXZSLcfV6PVxcXPD888+jfv36iIyMROfOnbFjxw4sW7YMsbGxqFGjBpKTk5W51+v1qFSpEtq3b4/Tp09j48aN6Natm9HcA8DgwYMxevRoHD58GJMmTcLx48eVcbVs2RL29vYIDw/H/v37sXHjRoSHh8PT01P5GA0A6tSpg/bt2+Py5cuwmMUlkI3666+/ZMyYMeLl5SX9+vWTjz76SDZu3ChHjhyRhIQE+eSTT6R169ZGH108yFDBP2jz5s3i7u4uZ86cERHJ9XHVg5YvXy5eXl6ye/du8fT0lD///DNXH3k5c+aM+Pj4GH0E87A5c+ZI69atxd3dXfbv35/rcb1eL8nJyXLo0CFZuHChbNq0KVebc+fOiZeXl9JPRkaGLFq0SLp37y7u7u7i7u4uI0eOlB9//FFOnTolY8eOFX9/f2Xshfn111+lb9++EhAQoPzlYji1v27dOtm3b59cv35dWrVqJcuXL5cbN26ITqeTjRs3iq+vr5w6dUp0Op388ccfEhISIl5eXuLv7y/e3t4yceJEZV9eunRJ+YjsQUePHpWAgADp2rWrfP3115KSkpIrx9TUVBkwYIAsXLhQ/v77b/n2229F5P68Pjy3ycnJ8tVXX0mvXr3E3d1dli5dKgcPHpRx48blOf6UlBTp1KmTzJgxQ7KzsyU4OFjefPPNXH8t7tu3Ty5dumS0LS4uTkJCQvI86zV//nzx8/OTgwcPiohIYGCg8hdMZmam3Lp1S44ePSqffvqpvPDCCxIYGChPPfWUfPPNN8o+M/z/9u3b0qtXL/nyyy+V53/vvfekY8eOMmPGDGnXrp0sWLBAOXVtiAkODpYRI0YYnXF72NatW6Vz584yYMAAo9dYeHi4NG/eXPz8/KRDhw7y3//+VzlL9eC+fuaZZ5SPZ1euXCnu7u4yaNAg+f777+XQoUMyc+ZMadGihbI/Y2JipG/fvrJu3To5cuSIBAQESJcuXUya++joaFm7dq2ImDb3U6ZMkTFjxuQ57rzm/aOPPpLz588bfbwrYtrcP/ieUdjcb9u2TebPn2+1eR8wYIDMmTNH1q1bp7ym85Lf3C9dutSsuV++fLlZ8y5y/wxISEiItGnTRubOnSvnz5+XzMxMo/2YmJgozzzzjMyfP19iY2Nl27ZtInJ/7g1nrA1SU1Plf//7nwwfPlzc3d1lyZIlcurUKZkwYUK+4+/Xr5+MHTtWMjIyJDg4WN566y2lD4OoqCi5du2a0baEhAQZOHCghIeHG82XiMj3338vXl5eyjEUGBgoCxYsUPJOTEyUc+fOyTfffCOTJk2SPn36SJs2beSLL75QfpcZni8uLk569+4tK1euFBGRFStWSFBQkIwfP17atm0r06ZNMzojd/36dRkwYIB8/PHHsmfPngJf9ydOnJD+/ftLx44d5ZdfflH2508//SSdO3eW9u3bi7+/v3z88cfSq1cv+fvvv5XYpKQkefbZZ+XTTz/N9/kLU+IKGYO7d+/Kl19+Kf7+/uLv7y+dOnVSPhN98IWcH8Pk37t3Tz744APx9vY2qd/k5GTp06eP+Pn5KR+D5PfchgMtNjZWZs6caXSNS16ysrJk0aJFMn36dOU0YH6ys7OVj5UMb9Dx8fEya9Ys6d69e672qampsmXLFhk0aJC0a9dOvL29pVmzZjJgwADZsmVLwYMW4xff4cOHC3zBi9wv+Hx8fGTQoEHK/po2bVqudoZrAK5duyYZGRkiInLnzh2ZOnWq9OzZM8/nvn37trz11lvi7e0t3t7eMm7cONm0aZMcPXpUdu7cKe+88460bdtWbt68mW9+OTk5Rm9uhtPLhhe64TqhvGzYsEFatWolmzdvlmbNmuX6qDM/R44ckZYtW+ab15w5cyQ0NFS2b98uTZs2zbdITk9Plz179sisWbNk48aNImI8P0ePHpUWLVrIzZs3jY7zJUuWKK8Tw8eUK1askFWrVsnQoUOlc+fO+R53Op1O9Hq9ZGdny86dO6Vz587i4eFhVEwkJCTIhg0b5MSJExITEyNdunSRb775Ru7duydxcXESHh4uTz/9tNy6dUuJ+e2335TrO5o1ayaBgYEye/Zso7F4eHgo++zWrVvy3//+V7y8vMya+wc/wiho7vV6vTLOh/d9YfNe0B9Aec39g89vytynpaXJzp07ZebMmYXOu4HhD5kOHTqYNe8P7h9T5j4+Pl6+//57OX78uElzf+jQIXn55ZeV96HC5l3k/nvYvHnzpF27duLh4SGDBw+W8PBw+d///ifffvutjBo1SgICAoyOL0P+D47rwXn67bffjF73ecUY9vHevXvFw8ND1q1bl2v+8/oD2eDw4cPi6emZ7+t+9erVMnjwYPnhhx+kWbNmcuLEiTxf9/fu3ZOjR4/KihUrlOsPH+z3jz/+kBYtWihFxL179+Sbb76RoKAgZe4HDBgg7733nsybN0/69+8v3bt3zzX3Dz6nTqdTjoVjx47JwIEDxd3dXZKSkpQ2KSkpsmvXLvnrr7/k7t270qdPH/n444/l6tWrEhUVJfPmzRN/f/9c82KOEnPXUkHu3LmDS5cuoXz58qhevTrq1atncmxOTg7Wr1+PnJwcDBkypMC28v9Xba9fvx5Tp07FhAkTEBYWVmgfK1aswK5du/Diiy+iX79+hfaRmZlpdJu4qZYsWYKNGzdi5MiRRmvNPOzu3buIi4uDo6MjKlasiMqVK5vdlymio6Px448/4ubNm/D390fnzp2N7hrIz7Vr17Bz5040bNgQXbt2BWC8qKG9vT3u3buHM2fO4NChQ/j1119x8eJFiAicnZ1RpUoVjBo1Ct27d0d2djbKlClT4G1/aWlpmD17No4ePYrt27crizvlJykpCaGhoTh79iw8PT2xZs2aQtf2uHDhAsLDw5GQkICvvvoKer3eaG0jrVaLlJQUvPPOO9i9ezf8/PywcOFCODs7Kyvl5uTkwM7OzuiuCcP4cnJyYG9vj6ioKCxevBjx8fFYs2aNsh6HYTx37tzB0aNHsXfvXhw8eBDZ2dmoWrUq6tWrh9dffx2enp4mLWp148YN/PLLLxg1ahSys7Oh1WqV20cNFi1ahM8//1y5jTUzMxODBg3C2LFjkZ2drXzsePfuXaSnpyM9PR3lypVD3bp1Adw/fpYtW4YbN27gnXfeQZkyZZRT1pGRkdi3b58y94bT64a579GjB86dOwdHR8cC10J5cO4XLVqEcuXKoX79+vnO+4gRI3DmzBl4eXnh+++/x/nz51GuXLk8+8hr7levXo2LFy+ibNmyaNCggdI2OTkZ7777bp5zf/78eZQtW9bodP3D83758mUsWrQI8fHxWLt2LS5cuAAHBwc0atQIwP11RQ4dOoR9+/blmvcxY8bA29sbFy5cQLly5YzyKmjuR48ejfPnz8PBwcEot4LmPigoCGXLllXyunPnDlJSUvKd91WrVmHXrl1wdnaGn58f7OzsEBMTg4MHD2L37t34888/kZ2djcqVK6NOnToYPXo0fH19sWPHDlStWhUtW7ZE+fLlAeS+Qy0tLQ1z587Fb7/9hm3bthUak5mZiVdffRW///47vLy8MHTo0EL7iIyMxPLly5GRkYHw8HDs3r0bLi4uSoxer8e9e/fw/vvv46effkKbNm2wcOFCuLi4KK/5nTt3onLlynjqqaeU9xnDmmGGY+zs2bNYsmQJkpOTMWTIEDg7O+Opp55SPio6duwYduzYgQMHDiAnJweOjo5o0qQJRo8ejbp162LXrl2oUqVKgWMB7i/SunnzZgwYMAA7duxA5cqVlXkx+PbbbzFnzhwAQLly5VC2bFkMHToUoaGhBR5XBSkVhUxRExFcunQJtWvXNukXs16vR2JiIpydnZUDyxr0ej3u3LmDqlWr5vuLtTjk9YIojOGX9oML/hk8eNtkVlYW0tLSkJ2djb///hsigmbNmikL8z0YA+S9EGJmZiY2bNiAWrVqKYVTYTHfffcd3nvvPbz//vsYNGhQoe3Xrl2LLVu2IDQ0FIGBgfn2ceHCBXz77bdo3749AgMDodVqc40/JycHer0+zwJtzZo12LBhA8LCwoxuXc/Jyclz5dHLly+jYsWKcHZ2NiqeCxqLYf8/vEZITk4ORMToGP/tt99w4sQJxMfHo2vXrvDy8sp3Qa+HnTt3Dps2bUJsbCwOHz6MhIQE1KlTB+PGjUNwcDAyMjKQkZFhNPdNmzZFamqqyQtaZmZmIiIiAhcvXsSxY8eQmJgIV1dXjB49Gv369cvV/rvvvsP06dPxxhtvICkpyaQ+1qxZgy1btmDQoEG4dOlSnjH29vY4f/48vvvuO2Xu79y5YzSOOnXq4LXXXkP//v1hZ2dn1MfXX3+NH374AS+++CKuXbtmFDN69Gj0799fmU+NRqPMe/Xq1REbG2vy/jLcfnzjxg18//33uXIbMGCAUtA+OPc+Pj44f/48fv75ZyQkJMDNzQ1jxoxB7969cxXA586dw759+1CrVi3s2bNHWWi0Vq1amDdvnrJsQ3p6OhwdHXH16lVotVq4urri0qVL+S5OaogxLE6alZWFAwcO4O7du9i/f79JMVu3bsX48ePRtGlT/PXXX4W2X7t2LbZu3YrevXsrhWReMdeuXcP27dvx1FNPKWvKPLzQau3atTFnzhzl2qsH+1mzZg3WrFkDFxcXnDlzRtlfc+bMURZUNYiNjUXFihVRvnz5AhdzfbgPQ/F8+vRphIeHG83L3LlzleVHAOCvv/7CxYsX8ddff6FTp07w8PB4pAXyWMiQ6o0dOzbfRQ0ticlvIURzYwYPHoxKlSohODi40PYpKSnIzs7GjBkzCuzDUGg8uEqzOXnFxMQgOzsbH3/8MXQ6XZ4xer0eer3e6BeIOX0YCtOCYgxnCsyZl+zs7Fy/oMeNG4ebN2/Cz88PTZo0wfbt2xEZGYmPPvoIAQEBuZ4/r5gdO3YgMjISc+bMQUBAQK71Q8xtf+TIEaxZswa3bt0yKeaff/5BZmYm3n///QL7MTDMvTl5xcbGIjMzE5988kmumFOnTin7K695MXf8psQ8/MdLYfP48PoswP0bDAzHcLNmzTBz5ky0aNECPXr0wPLly6HT6dCqVSsMGjRIOaucV4yHhwd69OiBZcuWQUTg4+ODfv36oU6dOhbFDBkyBHZ2dggODi6wfXBwMGrXro2srCyMGjWqwD4cHBzQokUL9OzZU/lEwdy8DGsMPdy+e/fuWLZsGXJycuDr64uBAwcqN6c87v21dOlS6PV6PPXUU3juueeUm2YemcUfShHZgLi4OHF3d5cOHTpIhw4dpHfv3jJ9+nT5/fffc7VNTEyUrVu3yrFjx0yOSU5Oli1btsjx48dNjrl7966sW7fO5PYpKSmyfft2s/JKSUmRbdu2mRWTmpoqO3fuNCvG3LEkJyebnVdSUpJs3rzZ7Ji1a9dKy5YtjW5ZvnHjhgwbNkw6d+5sdHFiTk6OnDhxQqKiosTLy8ukGJH7t0p7enqa1F6n01nUx8mTJ+Xy5ctmxRhuJjC1/alTp8zKy9KxmBuj1+tlx44dJo9Fr9fL8ePH5fLly9KiRQv5448/lMf27NkjPXr0kI4dO8qIESPk+eefl6ZNm8rgwYMlISFBYmJiTI556aWXJD4+Xm7fvm1yzJAhQ+TcuXNmtY+LizOpj0GDBkmzZs1k6NChFo3l/PnzZuVlSR/mxgwePNjo1vRHwUKGVM2SRQ3nzZtndszHH39scoxhDQk/Pz+T25vbhyVjsbQfc8Zi6f6yZF6aNGki3bp1M3lxSksXtLRmHzExMeLu7i5Tp061Wl6GPswde1HFWLKP33rrLRkwYIBZC41GRERYPWby5Mk2mdeUKVNsMq+HF4C1VIn6riUqfe7du4e0tDS0b98ezZo1Q69evXD+/Hnl+7V+/vlnbNiwAY0bN0bZsmVRoUIFNG7c2OyYJ554wqwYw5d0WrOP0j4WBwcHuLq6KtfryP9f9Fy/fn288sor+PLLL/Hcc8+hevXqOHLkCBwdHVGjRg3cuHHD5BjDd/FYq4+jR4/C0dERNWvWRHR0tFXyMvRh7tiLKsaSfezs7Aw3Nzejj6jWrl0Lb29v9O3bV3l/aNy4MVq1aoXIyEhkZWVZPebKlSs2mddff/1lk3lFRkaiS5cueFQsZEjV8lvUsHXr1khMTMTJkyfx559/4sSJEzh06BBeeOGFIol55pln0Lp1a5vLqySNpXPnzoiPjzdanNLwzcjPPfcc1qxZg7Vr12L8+PHYuHEjAgMD4e3tjX379pkcY/guMGv2UZLyKoqxdO7cGX379sXFixdzLTTatWvXPBca1ev1Vo8pU6YM6tatC19fX+ZlYow8rkt0H8t5HaJiZMmihkURY6t5laSxmLs4pYj5C1oWRR8lKa+iGoslC40WRQzzejwLwJqjxHxFAZVeD9+29+DdM8D9uzz+/PNPODo6okWLFkUWY6t5laSx5HXnk8GgQYNQt25dvPXWW6hSpYry7brmxhRFHyUpr6Iay8PHiuEYMXxEFRcXh40bN8LV1RWenp5FFsO8zI95VCxkqMQyvKC0Wi0aN26MiRMn2kSMreZlSYyt5iUicHJywksvvYTk5GQ899xzhfZhbkxR9FGS8rL2WB48RgBg48aNiIyMxMiRI4s1hnmZH2MuriNDRCWWmLk4pSUxRdFHScrLkhhL+rBkodGiiGFej38BWBYyREREpFr8aImIiIhUi4UMERERqRYLGSIiIlItFjJERESkWixkiIiISLVYyBBRibVo0SK4u7sXdxpEZEUsZIiIiEi1WMgQERGRarGQISKbkJ6eXtwpEJEKsZAhoiJnuHbl8uXLeOONN+Dr64sXXngBAPC///0PzzzzDDw9PeHn54cJEyYgJibGKP7YsWMYO3YsOnXqBA8PD3Ts2BGzZs1CZmZmcQyHiIpR/l85SkRkZePGjUP9+vUxYcIEiAiWLl2KhQsXomfPnhg4cCASExOxZs0avPjii9i4caPyPTvbtm1DZmYmnn/+ebi4uCAyMhJr1qzB7du38dlnnxXzqIioKLGQIaJi07RpU3zyyScAgJs3b6Jbt24YP348Xn31VaVNUFAQBgwYgG+++UbZPmnSJJQrV05pM2jQINSvXx/z58/HrVu3UKdOnaIdCBEVG360RETF5rnnnlP+vXPnTuj1evTs2ROJiYnKf9WqVUP9+vXxxx9/KG0fLGLS09ORmJgIHx8fiAjOnTtXpGMgouLFMzJEVGzc3NyUf1+7dg0igqCgoDzb2tv/+3Z169YtfPbZZ9izZw+Sk5ON2qWlpVknWSKySSxkiKjYlC1bVvm3Xq+HRqPBihUrYGdnl6tt+fLlAQA6nQ7Dhw9HcnIyRowYgUaNGqF8+fK4c+cOpkyZAr1eX2T5E1HxYyFDRDahXr16EBG4ubmhYcOG+ba7dOkSrl27ho8++gj9+/dXth86dKgIsiQiW8NrZIjIJgQFBcHOzg6LFy+GiBg9JiK4e/cuAECr1SrbHnx89erVRZcsEdkMnpEhIptQr149jB8/Hp988glu3ryJrl27okKFCvj777+xa9cu/Oc//0FoaCgaNWqEevXq4aOPPsKdO3dQsWJFbN++HSkpKcU9BCIqBixkiMhmvPLKK2jQoAG+/PJLfP755wCAWrVqwd/fH4GBgQCAMmXKYNmyZZg5cybCw8NRtmxZdOvWDS+++CKCg4OLM30iKgYaefgcLhEREZFK8BoZIiIiUi0WMkRERKRaLGSIiIhItVjIEBERkWqxkCEiIiLVYiFDREREqsVChoiIiFSLhQwRERGpFgsZIiIiUi0WMkRERKRaLGSIiIhItVjIEBERkWr9HwVgYPGDaABkAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def classes_plot(y_val,y_pred):\n",
|
||
" vd = pd.DataFrame([y_pred,y_val],index=['pred','real']).T\n",
|
||
" vd_pred_true = vd[vd.pred==vd.real].groupby(by='real').count()\n",
|
||
" vd_pred_all = vd.groupby(by='real').count()\n",
|
||
" vd_pred = pd.concat([vd_pred_true,vd_pred_all-vd_pred_true],axis=1)\n",
|
||
" # vd_pred.false[vd_pred.isna().iloc[:,0]]=vd.real[vd_pred.isna().iloc[:,0]].values\n",
|
||
" vd_pred.columns = ['true','false']\n",
|
||
" #print(vd_pred_all.pred[vd_pred.false.isna().values])\n",
|
||
" #print(vd_pred_true.pred[vd_pred_true[vd_pred.false.isna()]])\n",
|
||
" vd_pred.false[vd_pred.false.isna()]=vd_pred_all.pred[vd_pred.false.isna().values]\n",
|
||
" vd_pred = vd_pred.fillna(0)\n",
|
||
" vd_pred_sum = vd_pred.sum()\n",
|
||
" print(vd_pred_sum.true/(vd_pred_sum.true+vd_pred_sum.false))\n",
|
||
" return vd_pred.plot(kind='bar',stacked=True,color=['#66FF99','#ff6666'],rot=70)\n",
|
||
"\n",
|
||
"classes_plot(y_val, y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"id": "78fc9075-6f7f-442a-addc-42eee1a6d83e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0.6523235800344234\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<AxesSubplot:xlabel='real'>"
|
||
]
|
||
},
|
||
"execution_count": 49,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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j4lIFAJRiRqvVQq83/dZoc9dXc4w1t+HsXFHZ10XN7ELm2rVriIqKwokTJxAdHY1GjRph48aNRusMGjQIhw/nfqbD5s2b0bhxY+Xn1NRUzJw5Ezt37kR2djbatWuHd999FzVq1LCgKURERObRaDRwda2KSpUqA9DD1dUZycnpJp2ZcHDQwNW1vMnrqznGmttwcHCEVmu9kSxmFzLR0dHYt28ffHx8oNfr8x0k1apVK0ycONFomYeHh9HPY8eOxaVLlzB16lSULVsW8+bNw7Bhw7B+/Xo4OvJkERER2YZWq4WjoyPKlSuHjAydSRPWOTpqzVpfzTG2yssazK4WQkJC0KVLFwDApEmTcPr06TzXc3Fxga+vb77vc/z4cRw4cABRUVEIDg4GADRs2BA9e/bE9u3b0bNnT3NTIyIiolLG7HM9RXV6aP/+/XBxcUFQUJCyrFGjRmjWrBn2799fJNsgIiKiks1qF60OHz4MX19ftGzZEgMHDsSRI0eMXr98+TIaNmwIjcb4nvJGjRrh8uXL1kqLiIiIShCrDETx9/dHWFgYGjRogLi4OERFReGll17C119/DT8/PwBASkoKKlWqlCvW1dU138tVpnJ0NK7PHBy0Rv83hVpjmFfpbYta87IkRq15WRJj6vpF9Tr3MfOyRoxa8zKFVQqZ0aNHG/3csWNH9O7dG4sWLcKyZcussUmFVqtB5coV8nzNxcXZ7PdTawzzKr1tUWtelsSoNS9LYizZhiXx3MfW3YYlMWrNy5IYteZVEJvcGlS+fHl06NAB27ZtU5a5uLjg1q1budZNTk6Gq6urxdvS6wUpKelGyxwctHBxcUZKSgZ0OtNGVqs1hnmV3raoNS9LYtSalyUxpq5vWC8/psZzHzMva8QUd14uLs4Wn6UptnucGzVqhEOHDkFEjMbJXLlyBU2aNHmk987vNjCdTm/2LWJqjWFepbctas3Lkhi15mVJjCXbsCSe+5h5WTNGrXkVxCbPWkpPT8fevXvRsmVLZVn79u2RnJyMQ4cOKcuuXLmCs2fPon379rZIi4iIiOyc2WdkMjIysG/fPgDAjRs3kJaWhq1btwIAAgICcPnyZSxfvhxdu3aFu7s74uLisGLFCsTHx2P+/PnK+/j5+SE4OBhvv/02Jk6ciLJly2Lu3Lnw9PREaGhoETWPiIiISjKzC5nExESMGTPGaJnh55UrV6JWrVrIzs7G3LlzcffuXTg7O8PPzw/Tpk2Dt7e3Udy8efMwc+ZMTJkyBTk5OQgODsa7777LWX2JiIjIJGZXDB4eHrhw4UKB60RFRZn0XpUqVcKMGTMwY8YMc9MgIiIiss0YGSIiIiJrYCFDREREdouFDBEREdktFjJERERkt1jIEBERkd1iIUNERER2i4UMERER2S0WMkRERGS3WMgQERGR3WIhQ0RERHaLhQwRERHZLRYyREREZLdYyBAREZHdYiFDREREdouFDBEREdktFjJERERkt1jIEBERkd1iIUNERER2i4UMERER2S0WMkRERGS3WMgQERGR3WIhQ0RERHaLhQwRERHZLRYyREREZLdYyBAREZHdYiFDREREdouFDBEREdktFjJERERkt1jIEBERkd1iIUNERER2i4UMERER2S0WMkRERGS3WMgQERGR3WIhQ0RERHbLsbgTILIGrVYDrVYDAHBw0Br9X68X6PVSbLkREVHRYSFDJY5Wq0EVN2doHByMlru4OAMARKdD0t0MFjNERCUACxkqcbRazf0iJioKiI01frF2bWgiIqDValjIEBGVACxkqOSKjQViYoo7CyIisiKzC5lr164hKioKJ06cQHR0NBo1aoSNGzcqr6elpWHFihXYt28frl69CicnJ3h7e2PcuHHw9PRU1vv777/RuXPnXO/v4+ODNWvWWNgcIiIiKk3MLmSio6Oxb98++Pj4QK/XQ8T49PzNmzfx/fff45lnnsHYsWNx7949fPHFFxgwYADWr1+Pxo0bG60/fvx4tGnTRvm5QoUKFjaFiIiIShuzC5mQkBB06dIFADBp0iScPn3a6HUPDw/s2LEDzs7OyrLAwECEhITgm2++weTJk43Wr1+/Pnx9fS1InYiIiEo7swsZrbbgqWfKly+fa1mFChVQr149xMXFmbs5IiIionzZZLBvSkoKoqOj8eSTT+Z6berUqRg3bhzc3NzQuXNnTJgwAW5ubo+0PUdH42Lr4XlETKHWGOZVeIwp72dKvBraYg95WRKj1rwsiTF1/aJ6nfuYeVkjRq15mcImhcwnn3wCjUaD5557Tlnm5OSE5557DsHBwXBxccGJEyewZMkSnD59GmvXrkWZMmUs2pZWq0HlynmPszHMI2IOtcYwL/NjzI1Xa1vUmpclMWrNy5IYWxyTlmxHrfvLkhjmVbrbkh+rFzLr16/HmjVrMGvWLNSqVUtZXqNGDUydOlX5OSAgAI8//jiGDx+OHTt2oGfPnhZtT68XpKSkGy1zcNDCxcUZKSkZ0On0Jr2PWmOYV+ExhnUKYkq8GtpiD3lZEqPWvCyJMXX9wo5LU+O5j5mXNWKKOy8XF2eLz9JYtZDZt28fpkyZghEjRqBfv36Frt+hQweUL18eZ86csbiQAYCcnLx3qE6nz/e1/Kg1hnmZH2NuvFrbota8LIlRa16WxNjimLRVXmqNYV6luy35sdpDI//880+MGTMGffv2xZgxY6y1GSIiIirFrFLIXLp0CcOHD0dgYCCmTZtmctyePXuQnp6Oli1bWiMtIiIiKmHMvrSUkZGBffv2AQBu3LiBtLQ0bN26FcD9cS4igoiICJQtWxaDBw82mmemYsWKeOyxxwAAs2bNgkajga+vL1xcXHDy5ElERkbCy8tLmaeGiIiIqCBmFzKJiYm5LhUZfl65ciUA4NatWwCAIUOGGK0XEBCAr7/+GgDQuHFjfPvtt1izZg0yMzNRs2ZN9O/fH6NHj4ajIx8BRURERIUzu2Lw8PDAhQsXClynsNcBIDw8HOHh4eZunoiIiEhhtcG+RERERNbGQoaIiIjsFgsZIiIislssZIiIiMhusZAhIiIiu8VChoiIiOwWCxkiIiKyWyxkiIiIyG6xkCEiIiK7xUKGiIiI7BYLGSIiIrJbLGSIiIjIbvEx02QXtFoNtFoNHBzu196G/+v1Ar1eijM1IiIqRixkSPW0Wg2quDlD4+CgLHNxcQYAiE6HpLsZLGaIiEopFjKkelqt5n4RExUFxMb++0Lt2tBERECr1bCQISIqpVjIkP2IjQViYoo7CyIiUhEO9iUiIiK7xUKGiIiI7BYLGSIiIrJbLGSIiIjIbrGQISIiIrvFQoaIiIjsFm+/JiIiUiHOaG4aFjJEREQqwxnNTcdChoiolONf/urDGc1Nx0KGiKgU41/+KscZzQvFQoaIqBTjX/5k71jIEBER//Inu8VChoiIiEymtjFVLGSIiIjIJGocU8VChoiIiEyixjFVLGSIiIjIPCoaU8VHFBAREZHdYiFDREREdouFDBEREdktFjJERERkt1jIEBERkd1iIUNERER2y+xC5tq1a5gyZQrCwsLQvHlz9O7dO8/11q5di27duqFly5Z46qmnsGfPnlzrpKam4u2330ZAQAD8/PwwevRoxMXFmd8KorzUrg3UrWv8X+3axZ1VqabVauDoqDWaEdTR8f5/Wq2mmLMjIntk9jwy0dHR2LdvH3x8fKDX6yGSe+KbTZs2YfLkyXj11VcRGBiIzZs34/XXX8fq1avh6+urrDd27FhcunQJU6dORdmyZTFv3jwMGzYM69evh6Mjp7ghy4leD01ERL6vke0VNCMowCctE5FlzK4WQkJC0KVLFwDApEmTcPr06VzrfPbZZ+jVqxfGjh0LAAgMDMTFixfx+eefY9myZQCA48eP48CBA4iKikJwcDAAoGHDhujZsye2b9+Onj17WtomImi0WmzAYSQg1Wh5NVRCX21AMWVVuuU7IyjAJy0TkcXMvrSk1RYcEhMTg6tXr6JHjx5Gy3v27IlDhw4hKysLALB//364uLggKChIWadRo0Zo1qwZ9u/fb25aRLmcRgyO4C+j/05DHTNRlmqGGUEf/O/hwoaIyERFfv3m8uXLAO6fXXlQ48aNkZ2djZiYGDRu3BiXL19Gw4YNodEYXxdv1KiR8h6WcnQ0LrYefkKnKdQaUxrzMvd1U3IoaJ3SuI9tEVPa+6WoXrf3vCyJsfe+tySGx4vpiryQSU5OBgC4uLgYLTf8bHg9JSUFlSpVyhXv6uqa5+UqU2m1GlSuXCHP1x68Hm8qtcaU9rxssc3Svo/V2pdqbYutjuOSkpclMSWp79XaL2rNqyAlbkStXi9ISUk3WubgoIWLizNSUjKg05k20FOtMaUxL8N6+Xk4vrD184qxJC9bx6g1L1NjSnu/mHscl9S8LImx9763JKa0HS8uLs4Wn6Up8kLG1dUVwP1bq6tXr64sT0lJMXrdxcUFt27dyhWfnJysrGOpnJy8d6JOp8/3tfyoNaa052WLbZb2fazWvlRrW2x1HJeUvCyJKUl9r9Z+UWteBSnyCfEaNWoEALnGuVy+fBllypRB3bp1lfWuXLmS6/btK1euKO9BREREVJAiL2Tq1q2LBg0aYOvWrUbLN2/ejLZt28LJyQkA0L59eyQnJ+PQoUPKOleuXMHZs2fRvn37ok6LiIiISiCzLy1lZGRg3759AIAbN24gLS1NKVoCAgJQpUoVjBo1ChMmTEC9evXQpk0bbN68GSdPnsSqVauU9/Hz80NwcDDefvttTJw4EWXLlsXcuXPh6emJ0NDQImoeEVHhtFqNMrPww3dV6PXCuW2IVMzsQiYxMRFjxowxWmb4eeXKlWjTpg169+6NjIwMLFu2DEuXLkXDhg2xcOFC+Pn5GcXNmzcPM2fOxJQpU5CTk4Pg4GC8++67nNWXiGwmrxmHgX/vquCMw0TqZnbF4OHhgQsXLhS6Xnh4OMLDwwtcp1KlSpgxYwZmzJhhbhpEREWCMw4T2Tee+iAiAv6dcZiI7EqRD/YlIiIishUWMkRERGS3WMgQERGR3WIhQ0RERHaLhQwRERHZLRYyREREZLdYyBAREZHdYiFDREREdouFDBEREdktFjJERERkt1jIEBERkd1iIUNERER2i4UMERER2S0WMkRERGS3WMgQERGR3WIhQ0RERHaLhQwRERHZLcfiToCIiFSgdu2CfyZ6kIqOFxYyRESlnOj10ERE5Lmc6GFqO15YyBARlXIarRYbcBgJSFWWVUMl9NUGFGNWpFZqO15YyBAREU4jBjFIVH6ui6roCxYylDc1HS8c7EtERER2i4UMERER2S0WMkRERGS3WMgQERGR3WIhQ0RERHaLhQwRERHZLRYyREREZLdYyBAREZHdYiFDREREdouFDBEREdktFjJERERkt1jIEBERkd1iIUNERER2i4UMERER2S0WMkRERGS3WMgQERGR3XK0xpsOGjQIhw8fzvO1OXPmoFevXvmus3nzZjRu3NgaaRHZJa1WA61WAweH+393GP4PAHq9QK+X4krNMrVrm7aMiMgEVilk3nvvPaSlpRkt++qrr7B9+3a0bdtWWdaqVStMnDjRaD0PDw9rpERkl7RaDaq4OUPj4KAsc3FxVv4tOh2S7mbYTTEjej00ERH5vkZEZC6rFDKPPfZYrmVvvPEGgoKCUKVKFWWZi4sLfH19rZECUYmg1WruFzFRUUBsrPGLtWtDExEBrVZjN4WMRqvFBhxGAlKNlldDJfTVBhRTVjb28Nknno0ieiRWKWQe9scff+Dvv//G2LFjbbE5opInNhaIiSnuLIrEacQgBolGy+qiKvqi5Bcy+Z2R4tkoIsvZpJDZuHEjypcvj86dOxstP3z4MHx9faHT6eDj44MxY8bA39//kbfn6Gg8hjmvsQWFUWtMacxLeT2fv2Qfjjclh4LWUdM+ZltKTl4ODto8z0gZzkaZ+jko6s9kUb1ur98vas3L1v2i1rxMYfVCJicnB1u2bEFISAjKly+vLPf390dYWBgaNGiAuLg4REVF4aWXXsLXX38NPz8/i7en1WpQuXKFPF97cGyBqdQaU9ryKugvWWttU6372JJ4tkU9eT18RspwNsrUeFt8Ji2Jt+fvl0eNUesxacl21JpXQaxeyBw8eBBJSUno3bu30fLRo0cb/dyxY0f07t0bixYtwrJlyyzenl4vSElJN1rm4KCFi4szUlIyoNOZdgpXrTGlMS/Devn9JftwvGH9ghS0TTXtY7al5ORV2HZMjS/qz6St87IkRk3Hsa22UdqOFxcXZ4vP0li9kNm4cSPc3NwQHBxc4Hrly5dHhw4dsG3btkfeZk5O3jtRp9Pn+1p+1BpTGvPK7y9Za21TrfvYkni2RZ15WRJvi8+kLfKyJEatx7Faj0lLtqPWvApi1QnxMjMzsXPnTnTv3h1lypSx5qaIiIioFLJqIbN7926kp6ejT58+ha6bnp6OvXv3omXLltZMiYiIiEoQq15a+vnnn1GnTh088cQTRsuPHj2K5cuXo2vXrnB3d0dcXBxWrFiB+Ph4zJ8/v8i2X+JmRCUiIiIjVitkkpOT8csvv2Dw4MHQaDRGr1WvXh3Z2dmYO3cu7t69C2dnZ/j5+WHatGnw9vYuku2XtBlRiYiIKDerFTKurq44ffp0nq/Vr18fUVFR1to0gJI3IyoRERHlZpMJ8YpVCZoRlYiIiIxZdbAvERERkTWxkCEiIiK7VfIvLRERmSKvp1DzydREqsdChohKvfye5WV4jYjUi4UMEZV6eT2VGvj3eV5EpF4sZIiIkPtZXsC/z/MiIvXiYF8iIiKyWyxkiIiIyG6xkCEiIiK7xUKGiIiI7BYLGSIiIrJbLGSIiIjIbrGQISIiIrvFeWQeotVqoNVq4OBwv8Yz/F+vF+j1UpypERER0UNYyDxAq9WgipszNA4OyjIXF2cAgOh0SLqbwWKGiIhIRVjIPECr1dwvYqKigNjYf1+oXRuaiAhotRoWMkRERCrCQiYvsbFATExxZ0FERESFKPmFTO3api0johKDY92ISo8SXciIXg9NRES+rxFRycOxbkSlS4kuZDRaLTbgMBKQarS8Giqhr5ZPtCUqiTjWjah0KdGFDACcRgxikGi0rC6qoi9YyBCVaBzrRlQqcEI8IiIislssZIiIiMhusZAhIiIiu8VChoiIiOwWCxkiIiKyWyxkiIiIyG6V+NuviYjIPnGGZjIFCxkiIlIdztBMpmIhQ0REqsMZmslULGSIiEi9OEMzFYKDfYmIiMhusZAhIiIiu8VChoiIiOwWCxkiIiKyWyxkiIiIyG6xkCEiIiK7ZZVC5ocffoCnp2eu/2bPnm203tq1a9GtWze0bNkSTz31FPbs2WONdIhMotVq4OioNZpF1NHx/n9araaYsyMiKpjhOyyv77GS/B1m1Xlkli9fjkqVKik/16xZU/n3pk2bMHnyZLz66qsIDAzE5s2b8frrr2P16tXw9fW1ZlpEuRQ0iyjAmUSJSN3y+g4DSsdsyFYtZFq0aIEqVark+dpnn32GXr16YezYsQCAwMBAXLx4EZ9//jmWLVtmzbSIcsl3FlGAM4kSkeqV5u+wYpnZNyYmBlevXsV///tfo+U9e/bExx9/jKysLDg5ORVHalTacRZRIrJnpfA7zKqFTO/evXHnzh3UqVMH//nPfzB06FA4ODjg8uXLAICGDRsard+4cWNkZ2cjJiYGjRs3tni7D14fLMjD6yg/165tvOL//1zYez78hFZTmBtji22oLS9zX3+kvrcwD2u1X615WRJjq7bY4nixRV6WbkfteZkao9a8bLUNHsems0ohU716dYwaNQo+Pj7QaDTYvXs35s2bh9u3b2PKlClITk4GALi4uBjFGX42vG4JrVaDypUrmLTug2MgDESvhyYiIs/lea1v6vsWdYwttmFJjK3yKo5tmhKj1varaR89arxajxdb5WWLz6Ql8bY+XtSUl1o/K5bEqPl4yY9VCpl27dqhXbt2ys/BwcEoW7YsvvrqK7z66qvW2KRCrxekpKTDwUFb6I5KScmATqdXfjbEbMBhJCBVWV4NldBXG5Br/YcZ4gtb71FibLENteVVWF/m148FKYqYvHIs6varNS9LYmzVFmU7+ZxZLa6+N/c4tnQ7as/L1Bi15mWrbdjie88WeZm6HRcXZ4vP0thsjEyPHj3wxRdf4Ny5c3B1dQUApKamonr16so6KSkpAKC8bqmcHNMOKJ1On+e6pxGDGCQqP9dFVfRFQL7rm/q+RRlji22oOa/i2KYpMWptv5r20aPGm7JOQWdW1dT3lsTb4jNpi7wsjVFjXmr9rFgSo+bjJT/FMti3UaNGAIDLly8r/zb8XKZMGdStW7c40iKiEkKj1eZ7ZpWIShabzey7efNmODg4oHnz5qhbty4aNGiArVu35lqnbdu2vGOJiB7ZacTgCP5S/juN0nUnB1FpYZUzMhEREWjTpg08PT0BALt27cKaNWvw4osvKpeSRo0ahQkTJqBevXpo06YNNm/ejJMnT2LVqlXWSImIiMj+5DPWi/5llUKmYcOGWL9+PW7dugW9Xo8GDRrg7bffxqBBg5R1evfujYyMDCxbtgxLly5Fw4YNsXDhQvj5+VkjJSIiIrtS0Fgv+pdVCpl3333XpPXCw8MRHh5ujRSIiIjsGsd6maZYBvsSERFR4fK7i5b+ZbPBvkRERERFjYUMERER2S1eWiIiAPcf76HVanI9C0WvlxL5xFwiKhlYyBARtFoNqrg5Q+PgoCwzTEMuOh2S7mawmCEiVWIhQ0TQajX3i5ioKCA29t8XateGJiICWq2GhQwRqRILGSL6V2wsEMMZcInIfnCwLxEREdktFjJERERkt3hpiYiI1IvPGqJCsJAhIiJV4rOGyBQsZIiISJX4rCEyBQsZIiJSLT5riArDwb5ERERkt1jIEBERkd3ipSUiIqKSIq+7ukr4nV4sZIiIiEqA/O7yMrxWUrGQISIiKgHyussLKPl3erGQISIiKiEevssLKPl3enGwLxEREdktnpEhsgelcAAfEZEpWMgQqVxpHcBHRGQKFjJEKldaB/AZaLUaaLUaODjcvxJu+L9eL9DrpThTIyIVYCFDZAdK4wA+4H4RU8XNGRoHB2WZi4szAEB0OiTdzWAxQ1TKsZAhon89PO6mmMfhaLWa+0VMVBQQG/vvC7VrQxMRAa1Ww0KGqJRjIUNEAPIfi6OKcTixsUBMTHFnQUQqxEKGiADkPRantIzDISL7xUKGiBQPj8UpDeNwiMi+cUI8IiIislssZIiIiMhusZAhIiIiu8VChoiIiOwWCxkiIiKyWyxkiIiIyG6xkCEiIiK7xUKGiIiI7BYLGSIiIrJbLGSIiIjIblnlEQVbtmzBTz/9hDNnziAlJQX169fHoEGD8Mwzz0Cj0QAABg0ahMOHD+eK3bx5Mxo3bmyNtIiIiKiEsUoh8+WXX8Ld3R2TJk1C5cqV8euvv2Ly5Mm4desWXn/9dWW9Vq1aYeLEiUaxHh4e1kiJiIiISiCrFDKLFy9GlSpVlJ/btm2Lu3fvYsWKFRgxYgS02vtXtFxcXODr62uNFIiIiKgUsMoYmQeLGINmzZohLS0N6enp1tgkERERlUJWOSOTl2PHjqFmzZqoWLGisuzw4cPw9fWFTqeDj48PxowZA39//0felqOjFg4OhddoD69TWIypr5uybUtjbLENteVl7uvW6PvC1rFW+22Vly2OfVt9Jm1xvOT1mhr2sT3kZWqMWvOy1TZ4HJvOJoXM0aNHsXnzZqPxMP7+/ggLC0ODBg0QFxeHqKgovPTSS/j666/h5+dn8ba0Wg0qV65g0rouLs5mvbep65v7vtbMxdYxtsqrOLZpSoxa22+rfWzrvrT3vrck3hb7yJJ4Wx/7aspLrZ97S2LUfLzkx+qFzK1btzBu3Di0adMGL774orJ89OjRRut17NgRvXv3xqJFi7Bs2TKLt6fXC1JS0uHgoC10R6WkZECn0ys/Fxbz8PoPM8QXtt6jxFh7GxqNBlrt/f8qViyHtLRM6PUCvV4gIsWSl7n9Yo2+zysmrxyLuv22yssWx76tPpO2OF7y2p4a9rE95GVqjFrzstU2Sttx7OLibPFZGqsWMikpKRg2bBjc3NywYMECZZBvXsqXL48OHTpg27Ztj7zdnBzTDiidTm/yuuasb+77WjMXc2K0Wg2quJWDxsFBWVaxYjkAgOh0SLqbAb0+/2LGWnlZI95aMWptv632sa370t773pJ4W+wjW+RlaYwa81Lr596SGDUfL/mxWiGTmZmJ4cOHIzU1Fd9//z0qVapkrU1REdFqNfeLmKgoIDb23xdq14YmIgJarabQQoaIiMiWrFLI5OTkYOzYsbh8+TJWr16NmjVrFhqTnp6OvXv3omXLltZIicwRGwvExBR3FkRERIWySiEzbdo07NmzB5MmTUJaWhr+/PNP5bXmzZvj5MmTWL58Obp27Qp3d3fExcVhxYoViI+Px/z5862RkqoYxqAAuUdwG8ajED0KwzHG44uIClISviusUsgcPHgQADBr1qxcr+3atQvVq1dHdnY25s6di7t378LZ2Rl+fn6YNm0avL29rZGSatwfh+JsNA4F+HcEt6ljUYjyk9cxxuOLiB5WUr4rrFLI7N69u9B1oqKirLFp1ct3HArAsShUJDjWiYhMUVK+K2w2IR49hONQyNp4jBGRKez8u4KFTAlWEq59EgEAatcu+GdSPY4NJGthIVNClZRrn0Si10MTEZHncrIPHBtI1sRCpoQqKdc+bS6vv/T513+x0mi12IDDSECqsqwaKqGvNqAYsyJzcGwgWRMLmZLOzq992lJ+f/kbXqPicxoxiEGi8nNdVEVfsJCxO/w+IitgIUP0//L6yx8o+K9/jkMiIipeLGSIHvDwX/5A/n/9cxwSEVHxYyFTBErcX+W8Q8Qkqh+HxH60e2q+08ei7z2OQSMrYCHziEraX+W8Q8QCKrzuz360f2q+08eS7z2OQSNrYSHziFT/V7mZeIdIycB+tH9qvtPHku89S8agEZmChUxRUeFf5ZbiHSIlA/uxhFDzd4uZuZkzBo3IVCxkiIjUjONKyNrsfDwdCxkiIpXiuBKytpIwno6FDBFZRM131JQUHFdC1lYSxtOxkCEis6n5jpqShuNKyNrsfTwdCxkiMpua76ghotKFhQwRWU7Nd9QQUamgLe4EiIiIiCzFQoaIiIjsFgsZIiIislssZIiIiMhucbAvPZL8noALcC4RIrIPFj3Jm1SDhQxZrKAn4AKcS4SI1M+SJ3mTurCQKSpqfVaFFfNS5hLZtAlINJ6wC1WrQtOrF+cSIbIxniU1T77fYwV8h3EfqwsLmSKg1mdV2CIv0euh6dUr39eIyHZ4ltQy+X2P5fUdxn2sPixkioBan1Vhi7z4LBgi9eCMy5Yx57uS+1h9WMgUEbOfVZHXJR4rXI6yxTM0Sv2zYNR6WZFKL864bDazvyu5j1WDhUwxyO+Sj+E1sh9qvaxIRFRasJApBrwcU3Ko9bIikU2o8Gwkb6UufVjIFJNSfzmmBLHF5TsitVHj2UjeSl06sZCxEyXpr4yS1JZSz0ZjvdSotB/Hajwbacmt1GT/WMjYgZL0V0ZJaktpV5rHevE4vk+NZyPNuZWaSgYWMnYg39v97PBWv5LUFkuVlL/kS/NYLx7H6qXGM0VkXSxk7ElJut2vJLXFDCXtL/lSP9arlB7HaqfGM0VkPSxkiGyI1/CJSqeSciZWjVjIENkYr+ETlS4l7Uys2rCQIbIxXsMnKl04psq6WMgQFQNewycqhTimyiqKtZD566+/MH36dBw/fhwVKlRAWFgYxo4dCycnp+JMS71UOIumxUpSW6j0UutxXIrn97EZS/axWo8XO1dshUxycjIGDx6MBg0aYMGCBbh9+zZmzZqFzMxMTJkypbjSUi01zqJpqZLUFiq91Hocl+b5fWzFkn2s1uOlJCi2Qua7777DP//8g4ULF8LNzQ0AoNPpMG3aNAwfPhw1a9YsrtRUqSSNqyhJbaHSS63HcWme38dWLNnHaj1ebMWad20VWyGzf/9+tG3bViliAKBHjx547733cPDgQTz99NPFlZpqlaRxFSWpLVR6qfU4LvXz+9iAJftYrceLtZly19aj0IhIsQyVbtu2LZ555hlMmDDBaHm7du0QFhaWa7mpRO5XdxoNoNVqkYIM6GB86s4BWrjAGXq9Hg+2Pr+Y/Na3JEateZWkthRlXiWpLWrNqyS1Ra15laS28HvPvvJ6MAbp6YBO90BiDkD58tDr9dBoNNBoNLBEsZ2RSUlJgYuLS67lrq6uSE5Otvh9NRoNHBz+3RkucM53Xa1Wm+fy/GLyW9+SGLXmZUmMWttSlHlZEqPWtqg1L0ti1NoWteZlSYxa28LvPfvKCwBQvrz5MSZ4tGgiIiKiYlRshYyLiwtSU1NzLU9OToarq2sxZERERET2ptgKmUaNGuHy5ctGy1JTUxEfH49GjRoVU1ZERERkT4qtkGnfvj1+/fVXpKSkKMu2bt0KrVaLoKCg4kqLiIiI7Eix3bWUnJyMXr16oWHDhhg+fLgyIV6fPn04IR4RERGZpNgKGeD+Iwo++OADo0cUjBs3jo8oICIiIpMUayFDRERE9Ch4+zURERHZLRYyREREZLdYyBAREZHdYiFDREREdouFDBEREdktFjJERERkt1jIEBERkd1iIVMCmTs1EKcSIiIie8VCpojo9Xqz1jeleLh9+zYyMjLMzkWj0Vh1/T/++AOZmZlmxRQXNRZpIqLKvCxhSTssbb+5MeZ+Jm1BRFSZlyUs6UO9Xg+dTmd2jDlsuY/N2QciYnbb1cqS/WtJ35uqRBYyx44dw7Fjx3D8+HGcPXsWqampBa5v6QF2/vx5XL16FQCg1Ra8K3NycrB161b8/vvvSE9PV4qH/D4IWVlZ6NevH9555x1s2LABt27dyrVuenp6rm2sWLHC6EGchpi8tpOdnY2JEydi7dq1Jh+YWVlZePXVVzFt2jTcvn27wDY8KCkpCV9++SV++OEHXLx4MVd+eTl58iT+/PNPnDhxAufPn0daWppJOT7I3CLNFjQajdl56XQ6q345JyQkmPUZ2L9/P7Kyskxux4P9bEn7DXHmKOwzWRw0Go3ZeVmz7/Pr94I+l4/S91qtFg4ODmblaO7+smQfW6qw7/GHj3tT2v5gjDl9f+/ePZPWM7hx4wZycnLy3XZebdq0aROSk5NN3r8P/v6xpO9N5WiVdy0mZ8+exfLly3Ho0CHcuXMHjo6OaNiwIRo1agQ/Pz88+eSTeOyxx4w64d69eyhbtqyygw0HTUEdFRsbi1WrVmHDhg1ITEyEu7s7RowYgbCwMGi12lyxZ86cweLFi7F3717k5OSgTp06+Pjjj9G6dWtoNBqkp6cb5QDcL8aSkpKwZ88e7NmzB2XKlEHHjh3Rq1cv+Pj4wM3NDS+//DLGjh2LwMBAAMA333yDdevWITw8XHkfjUajtDErK8voOVYbNmzAkSNHEBQUpOScnJyM+Ph4REdHo2rVqvD29ka5cuWMYlJSUrB7927ExcVh0qRJePzxxwvsl8OHD2Pu3Lk4deoUHB0dUbZsWXz44Yfo0qULNBoN7ty5g0qVKsHR8f7hePHiRXzxxRfYv38/kpKS4OjoiFq1aqF58+Z44oknEBQUhMceewx6vT7Pfrp79y6uX7+O48ePw8PDA15eXnB1dVXaISL5fgkX9NrD7t27hxs3buDy5cuoX79+ofshLi4OR44cwa1bt+Dv7w9vb+8Ct/3PP//g3LlzaN26tdGxodPpoNVq88wzKSkJmzZtwoABA/J9ZtnD++3OnTv4+OOP0bx5cwwePLjQ9sfFxeGVV15BaGgoxo0bh4YNGxa4vqFtf/31FzZu3Ijk5GSEhoaibdu2he7v1NRU/P333zh+/Djc3d3RtGlTVKpUCeXKlYNWq80VX1jf6/X6fAspU/ve3H4HCu/7h/vE3L4v6n7Pbz/Yqu/z63dnZ2doNJo8+/H27ds4ceIEjh8/jgYNGuCxxx5DjRo1UL16daPvMFP2TUHS0tJw5swZREdHw8vLC76+vgUeNzExMdiyZQvi4uLQuXPnQtuelpaGP/74Ax06dDDq+5ycnDx/vwD3C9KvvvoKw4cPR8WKFQttY1JSEqZMmQJvb2+MGjVKWV7QMXD79m288cYbaNGiBSZNmgR/f38ABX9udDodzpw5g1WrViE9PR1hYWEIDQ0163vWFCWqkJkxYwacnJwwfvx4tG/fHtevX8euXbtw9OhRHD9+HNu3b8fAgQPRs2dPJea///0vcnJy8NRTT6FDhw5wdnZWXtPpdHlW97NmzcKNGzcQFhaGJk2aYMeOHZg7dy5q1aqFoKCgXB+KuXPnQqfT4YMPPkCzZs3w4YcfYufOnXBwcEBkZCREBL6+vggLC0OdOnUgImjbti1ef/11nDhxAi+//DJOnTqFn376CT///DM8PDzg6emJM2fOGB20a9euRbt27ZQ2xMfHY+vWrfjxxx/xzz//wNfXF0899RSCgoIAAN9++y26d++Obt26AQAOHDiA5cuX47fffoOrqyuqV68ODw8PvPzyywgICAAArFy5Ei+88AI8PT3x6aefYvjw4XjnnXfQqVMnaLXaPL8QFixYgGrVqmH58uXw8vLC5MmTsXPnTjg7O2PRokXQaDRo2bIl+vXrhyZNmmDmzJnIzMzE8OHDERAQgIsXL+KHH37A9u3b8eeff+LAgQMYPnw4WrdunesYOHToEJYtW4Zff/0VHh4eiI+Ph16vh5eXF3r37o0ePXqgSpUqRjG3b99GmTJlUKVKFaMPV34fNr1ej+joaCxfvhw///wz3Nzc4O7ujueffx7PPPNMXocmDh48iMWLF+Po0aOoU6cONm3ahBkzZqBp06ZIT09H+fLljf6602g0iIqKwqJFi+Dr64s2bdqgR48eaNq0qfLllp2djZiYGNSrV08pAhctWoQ9e/agf//+RttPTk7GP//8gzp16uTqn1WrVuHChQt4/vnnodFoICK4c+cO9uzZA2dnZ7i7u6NOnTqoXr06RATfffcdNBoNDhw4gLi4OEyZMgXNmzcvcJ9t27YNixYtwq1bt1CrVi3s3bsX77//Ptq1a4fU1FRUqlQp134/evQoli1bhn379qFatWpISEiAs7MzAgMDERoaitDQUFSoUMGo75cvX46DBw9are/Pnz+PqKgok/tdRHDo0CEsWrSowL439Im5fV+/fn04ODhY3O/nz583ud81Gk2R9f20adPQvn37PPv+2LFjWL58Ofbu3WtSvwPA7t27ERkZifPnz6NBgwb44YcfkJKSgnr16qFz587o2bMnvLy8jGKuXr2KMmXKwN3d3Wjf5FfU6PV6nDp1CkuXLsWuXbtQrVo1ODk5oV+/fhg1alSe/b9r1y4sWrQIV65cgbu7O3bu3Inp06cjODgYd+/eVb6/HR0dlf0XGRmJ5cuXo0aNGnjyySfx7LPPwtfXV/mMZ2Vl4dSpU2jZsqVStC5duhR79+7Fa6+9ZrQvr1+/Dp1Oh0aNGhm1adWqVUhKSkKXLl2g1Wqh0+lw48YNbN26FVqtFnXq1EHjxo3x+OOPKwXjd999B2dnZyQmJuLtt9/G1KlTERQUVGBBsmnTJixatAjZ2dmoUaMGJk6ciOTkZISHhyM+Ph5OTk7QarWoVKnSIxU3JeahkfHx8ejatStWrlyZ51+6+/btw1dffYVDhw5hyJAhGDduHFJTUxEcHIyaNWsCACpVqgR/f390794dbdq0MXqPlJQUHDp0CFWrVsXQoUOxYMECtGvXDsD9invKlCm4du0a1q1bZ/RluXv3bowePRpffPGFUgzs2bMHH3/8MTIyMvD444/jn3/+wfHjxxEQEIC5c+cq8Tdu3MDEiRNRr149zJgxAxkZGfjzzz+xf/9+fPfdd8jMzESLFi0QEhKC4OBg/Oc//8GaNWvg5eUFrVaL8ePH45dffoG3tzdq1aqFkydPIjo6GhEREYiIiMBLL72E4cOHK4Vdt27dULNmTYwcORIajQa//fYbfvrpJzg6OmLJkiUoV64cOnbsiA0bNqBp06a4cOECpk6dipiYGIwdOzbXlyhw/y+4bt264auvvlL65dSpU3j99deh1WrRqlUrpKWl4ciRI/Dw8MDUqVPx4osv4ssvvzQqVOLj4zF8+HDlwD9x4gRmz56NHj16GG3P8IU/aNAguLi4ICsrC5cuXcKOHTtw4MABODk5ISIiAi+88ILyZThq1ChUqFABQUFBaN68Odzd3Y3+gsvrA/biiy/i3r17GDx4MPR6PdavX4/ffvsNs2fPRq9evXLFPP300/Dw8FD+8p0yZQqefPJJ+Pr6Yu3atYiOjkb37t0xfPhwVK9eHQAwf/58LFmyBB06dMDNmzdx9+5dPPbYY+jUqRP69++P/fv3Y8uWLZg1a5byhRYSEoLBgwdjyJAh0Gg0iI2NxVdffYUDBw7g7t27qFq1KsLDw9GvXz+l/V27dsWAAQPw4osvwsnJCT/88AO++OIL3L59G+np6XBwcECrVq3w3//+Fy1atEBQUBDGjh2L+vXrY9q0aUhPT8fEiRPRvXv3XP3/YL88+eSTeO655+Do6IgPPvgAVatWRatWrbB+/Xpcv34dISEheP311+Hu7g4A6NOnD2rWrImhQ4fC2dkZV69exc8//4wDBw4AAKpVq4YxY8agb9++cHR0RM+ePa3e9+b2u6363hb9LiIIDg62et+b2+8ajQahoaEIDAzE888/j2rVqkGr1eLy5cvYuHEjtmzZgqysLPTv3x+jRo2Ci4sLAGDw4MEAgMDAQPj5+aF58+bKa0DeBc2AAQNQsWJFvPTSS3BwcMBPP/2EDRs2YOLEiRgyZAh0Op3RWZQ+ffrA29sbAwcOhLOzM95//33UqFED3t7e+Pbbb3Hjxg20a9cOo0ePRuPGjQHc/2Nx7ty56NKlC65cuYJz587B3d0dPXr0wMsvv4zffvsN3377LT7//HM4OztDRIz6X6vV4tKlS4iKisLBgweRkJCAKlWqIDw8HC+99BJcXFzQrVs39O/fH4MHD4aTkxNWr16NFStWIDU1FSKC1NRUPPbYYxg3bhxCQkIgImjXrh1GjhyJwMBATJ48GefOncOrr76KIUOGoEyZMnke+927d0fnzp3x7LPPwsXFBbNnz0ZiYiJatmyJtWvXIj4+Hm3atMHIkSPh5+eX7zFUKCkhTpw4IZ06dZKdO3eKiIher5esrCzJzs42Wu+7776TNm3ayLFjx2Tz5s0SHBwskZGRsmbNGhk3bpz06tVL2rVrJ+Hh4bJgwQK5cOGCiIhcvXpVPD09ZcqUKfLMM89IfHy80ftevXpVnnjiCfnss8+UZbdu3ZImTZpIly5dJCEhQfR6vYiInD59Wjw9PWXt2rXKuseOHZPmzZsr+T/4vt27d5fp06dLVlaWiIgkJiZKixYt5IMPPpDx48eLp6enjBs3Tnr37m30fi1atJD169cry5KTk2X69Oni5+cnJ0+elOHDh8uECRNEROTPP/+Uli1byuXLl422Hx8fLwEBARIVFSUff/yx9O3bV9LS0pR9fObMGXnxxRelefPm8v7770tcXJyIiOh0OhEROXDggISGhsqJEyeU97x48aI0bdpUtm/fbtROX19fmTJlinTq1ElZX6/XK++1Y8cOefnll0VEZMGCBfLMM89IYmKi8h4XLlwQPz8/OXfunDxMp9PJ+fPnZcaMGRIUFCQLFy5UcvH09BRfX1/x9PSUbt26yYwZM2TXrl0SExNjdPwkJSXJnDlz5H//+58EBATIqVOnjLYxatQo6d27t9GxkZOTI/PmzRNfX185c+aMUf+EhIRIp06dZNy4cfL+++9L27Zt5bnnnlPiY2Ji5JlnnpFhw4bJnj17ZM6cOTJw4EDp2LGjhIaGSqtWrSQ8PFySk5NF5P5nwNvbW65du6ZsZ+DAgRIQECATJkyQ6dOnyyuvvCJ+fn7y+eefi8j9Y7R3796yadMmJSYwMFAmTZokhw8flvj4eNmxY4f069dP2rVrJz///LO0aNFCLl26JCIihw4dkt69e4uvr68sW7ZM7t27p/Sbwfnz58Xf39+oX65fvy5BQUESGhoq77//vsybN0/ZH9HR0XLu3Llc+0xE5M6dOxIRESFvvfWWzJgxQ3r06CEHDx60Sd+//fbb0qpVK5P7fc2aNbJ582Zp1aqVVfveFv1+4cIFOXHihNX7ftu2bWb1u4hIdHS0+Pn55eoXg9TUVFmxYoW0a9dOJk2aJPfu3ZPo6Gjx9PSUTp06SZs2bSQ0NFTGjBkjX331lZw8eVJpi4hIQkKCvPvuu7J27VoJCAiQs2fPGr3/zJkzpUOHDnL9+nWj/n///ffF19fXqO2XLl1S2v7RRx/J8uXLpVu3btK5c2fluzc1NVWGDh0qTz/9tBw8eFDWrFkjo0ePluDgYPHy8hJvb2/p16+fJCUlicj9z72Pj4/R9p955hnp0KGDzJo1S1asWCHvvPOOeHl5ydtvvy03b96U8PBwo98//v7+8sEHHyh9e+LECRk2bJj4+PjI77//nqvvL1y4IMOGDZMWLVrIjBkzJDU1Ndd+v3Dhgvj7+xvtr4SEBGnTpo089dRTsmzZMlmzZo385z//kVatWsmRI0fy7D9TlJhCRq/XS3h4uISHh0tMTEyu1wwHZmJiojz77LPy4Ycfyo8//ii+vr7Kjk5LS5MjR47I4sWL5ZVXXpGuXbtKhw4d5OWXX5bXXntNWrVqJQsXLpQhQ4bI7du3lfc2/KKNjIyUtm3bKr/Mf/rpJ2nevLmMGjXK6EvurbfekgEDBkhmZqay7O7duzJw4ECZM2eOsszwvidOnJCnn35afv75ZxG5X4wFBgbK33//Lffu3ZNr167J4MGDxdPTU15//XU5efKkTJ48WV5//XVJT08XkfsfLBGRK1euSPv27WXFihWybt066dq1q/z555/y559/ynPPPSfnz58XEZH09HSlbRMnTpQhQ4ZI+/btJSoqKs/9P2fOHPHz85M333zTqLi4deuWBAcHy6hRoyQxMVFu3rwpw4cPl379+sk///wj2dnZotPp5N69ezJ69GgZOnSoBAUFyRtvvJHrw7F48WLp2LGjiNz/JdSuXTujg/+PP/6Q0NBQ2b17d545itwv5hYvXiyenp5y+PBhWbNmjXTu3Fl+/fVXOXPmjIwfP168vb2lWbNmMmDAAFm0aJH89ttvkpCQIL/99pt4enrK22+/LS+99JLcuXNHREQ5ts6dOyft2rWTRYsWKdu7cuWKNG/eXJ555hlJSkpS+nTnzp3SokUL2b9/v7K/d+zYIZ6ensoyEZFr167J888/L999953yfhs3bpTx48dLs2bNJDAwUHr37i0TJ06UiRMnSq9evZT9v2/fPvH395ddu3Yp73fjxg2ZOnWqeHp6Ksf98OHD5cMPPxQRkV9++UXatm0rsbGxRvvt0qVLEhgYKH379pWhQ4cqRbWh/ZMnTxYfHx/58MMPc/Xb9u3bpUePHnL8+HFl2Z49e6RFixZKwZqdnS3Hjh2TJ554Qr788kvZunWrhISEyB9//KG8bvgFuX79egkNDZXk5GR5//33pXPnzkrBbM2+b9KkifTv39/kfvf19ZU333xTXnzxRav2fWhoqPTo0cOq/b506VJ58803rd73kyZNMqvfU1JSJDo6Wrp37y4//vij0bYf/G7OysqSH3/8UTw9PWX79u3yww8/SMeOHWXv3r1y+fJlmTVrlvTo0UP5Jfvuu+/Kjz/+KFeuXJFDhw6Jp6envPvuuzJkyBBJSEhQ3lPkftHZo0cPmTx5slH/N2nSRMLCwiQ+Pl5pw+7du8XLy0sOHz6srGsoRB4sKlNSUuSVV15Riu64uDg5cuSIfPzxx9KsWTPx9fWVVq1aycCBA2XixIkSEhKi/I45cOCA+Pv7y6+//qq83507d2TRokXSpEkTOXLkiIwfP17efPNNERE5cuSIBAUFya1bt5T9JnL/89K1a1eZPXu2TJw4UYYNG2bU9yL3v5N9fHzktddeU4ocgwMHDkj37t2VgtPQfm9v71yFbfv27WX+/PliKfUN67eQRqPB1KlTkZaWhgkTJmD16tX466+/lNH1hlPvlSpVQsWKFZGVlYV27dph8uTJaNasGQCgQoUKaN26NV599VXMnDkTkyZNQu/evZGZmYndu3cjLCwMvr6+SEtLg5ubm7Jdw9iQZ599Fo6Ojli9ejUA4Mcff0Tbtm3RqVMnVKtWTcm1RYsWeOWVV1C2bFllmVarhZOTU64R/iICLy8v9OzZEx9//DHOnz+PDRs2oGPHjqhVqxacnJzg4eGBMWPGYOzYsbh586ZyialJkyYoU6aM8l4A4OLigmrVqiElJQU9evSAj48PRo0ahXXr1uHy5cs4evQoACiD6tLT05GYmIjq1avj2WefVcbTGBjueBg6dCheffVVbNmyBYMHD8bBgweh0+lQs2ZNjB49GmfPnsV//vMfdO3aFc7OznB1dYWTkxMcHR2h1WqRk5ODe/fuoW7dupgwYQIOHTqE9957Dz/++CMOHjyIb775BpGRkXjuuecAAK6ursrlMoNmzZqhRo0aWLJkCc6fP68s1+v1yn51cXHBsGHD4O/vjwMHDijXh2vWrInmzZvj008/VS5bOTk5Yf78+XjttdfwzjvvYOHChahVqxYqV66Mu3fvKgPDDcdW06ZN0b9/f6xYsQIxMTEAgL1798LBwQENGjRAmTJllH5Yt24dAgMDlUuYzs7OaNu2LQIDA3Hq1Cll39arVw+DBg3C6tWrcfjwYTRo0AC9evVC/fr1UaNGDbz88suoX78+EhMTcfbsWSQkJODNN9/E6tWrsXDhQnTp0gUdOnRQ9kOdOnUwfPhwVKtWTdlO27Zt8dNPP2Hjxo3KuAJDvxr2W6NGjdCpUyecPXsW3bp1U46rnJwcODk5YeTIkejfvz9WrlyJMWPG4MKFC8r+9/HxQVZWFr799ltcvHgR+/fvx9KlS9GmTRtlfIWjoyO8vb3RoUMHnD17Fq1bt4ZWq8VPP/2ErKws5RICcH9Qf+XKleHi4oIhQ4Yot3Vau+8rVKhgdBdJYf1erlw5lClTBtWrV7dq38fHxyMpKcmq/X78+HH873//s3rfZ2RkmNXvZ86cQf369dG4cWN89tln2LdvnzJlxYPjG8uUKYO+ffuiS5cuOHToECpUqABnZ2d4eHigYcOGmDhxIjZv3oxPPvkETZs2xYEDBzB79my8/fbbmDlzJtzd3VGzZk3ExsYq+8awHwyXDX/44Qdl3+7duxfOzs5o2rQpnJyclDasWbMG/v7+8PX1VfbR448/jieffBJnzpxR+r5SpUqIiIjA1q1bsWHDBlSvXh2tW7dG2bJlUatWLUybNg3h4eFwdXVFQkIC0tLSMHjwYHz00UeYPXs2OnfurAzG1ev1cHNzw3PPPYf69evj5MmT6NSpE7Zv344lS5YgLi4ONWrUQHJystL3er0elSpVQrt27XDq1Cls2LABXbt2Nep7ABg4cCBGjhyJQ4cOYcKECTh27JjSrpYtW8LR0RGRkZHYt28fNmzYgMjISHh7eyuX0QCgTp06aNeuHS5dugSLWVwCqdRff/0lo0aNEh8fH3nqqafko48+kg0bNsjhw4clMTFRPv30U2ndurXRpYsHGSr4B23atEk8PT3l9OnTIiK5Llc9aOnSpeLj4yO7du0Sb29v+eOPP3JtIy+nT58WPz8/o0swD5s1a5a0bt1aPD09Zd++fble1+v1kpycLAcPHpT58+fLxo0bc61z9uxZ8fHxUbaTkZEhCxYskG7duomnp6d4enrKsGHD5IcffpATJ07I6NGjJSgoSGl7YX755Rfp06ePBAcHK3+5GE7tr1mzRvbu3SvXrl2TVq1aydKlS+X69eui0+lkw4YN4u/vLydOnBCdTie///67hIeHi4+PjwQFBYmvr6+MHz9e2ZcXL15ULpE96MiRIxIcHCxdunSRr7/+WlJSUnLlmJqaKv369ZP58+fL33//Ld9++62I3O/Xh/s2OTlZvvrqK+nZs6d4enrK4sWL5cCBAzJmzJg825+SkiIdO3aUadOmSXZ2toSFhcmbb76Z66/FvXv3ysWLF42WxcfHS3h4eJ5nvebMmSMBAQFy4MABEREJCQlR/oLJzMyUmzdvypEjR2TevHny/PPPS0hIiDzxxBPyzTffKPvM8P9bt25Jz5495csvv1Te/7333pMOHTrItGnTpG3btjJ37lzl1LUhJiwsTIYOHWp0xu1hW7ZskU6dOkm/fv2MPmORkZHSvHlzCQgIkPbt28t///tf5SzVg/v66aefVi7PLl++XDw9PWXAgAHy/fffy8GDB2X69OnSokULZX/GxsZKnz59ZM2aNXL48GEJDg6Wzp07m9T3MTExsnr1ahExre8nTZoko0aNyrPdefX7Rx99JOfOnTO6vCtiWt8/+J1RWN9v3bpV5syZY7V+79evn8yaNUvWrFmjfKbzkl/fL1682Ky+X7p0qVn9LnL/DEh4eLi0adNGPv74Yzl37pxkZmYa7cekpCR5+umnZc6cORIXFydbt24Vkft9bzhjbZCamir/+9//5KWXXhJPT09ZtGiRnDhxQsaNG5dv+5966ikZPXq0ZGRkSFhYmLz11lvKNgyio6Pl6tWrRssSExOlf//+EhkZadRfIiLff/+9+Pj4KMdQSEiIzJ07V8k7KSlJzp49K998841MmDBBevfuLW3atJEvvvhC+V1meL/4+Hjp1auXLF++XEREli1bJqGhoTJ27FgJDAyUKVOmGJ2Ru3btmvTr108++eQT2b17d4Gf++PHj0vfvn2lQ4cO8vPPPyv788cff5ROnTpJu3btJCgoSD755BPp2bOn/P3330rs3bt35ZlnnpF58+bl+/6FKXGFjMGdO3fkyy+/lKCgIAkKCpKOHTsq10Qf/CDnx9D59+7dkw8++EB8fX1N2m5ycrL07t1bAgIClMsg+b234UCLi4uT6dOnG41xyUtWVpYsWLBApk6dqpwGzE92drZyWcnwBZ2QkCAzZsyQbt265Vo/NTVVNm/eLAMGDJC2bduKr6+vNGvWTPr16yebN28uuNFi/OE7dOhQgR94kfsFn5+fnwwYMEDZX1OmTMm1nmEMwNWrVyUjI0NERG7fvi2TJ0+WHj165Pnet27dkrfeekt8fX3F19dXxowZIxs3bpQjR47Ijh075J133pHAwEC5ceNGvvnl5OQYfbkZTi8bPuiGcUJ5WbdunbRq1Uo2bdokzZo1y3WpMz+HDx+Wli1b5pvXrFmzJCIiQrZt2yZNmzbNt0hOT0+X3bt3y4wZM2TDhg0iYtw/R44ckRYtWsiNGzeMjvNFixYpnxPDZcply5bJihUrZPDgwdKpU6d8jzudTid6vV6ys7Nlx44d0qlTJ/Hy8jIqJhITE2XdunVy/PhxiY2Nlc6dO8s333wj9+7dk/j4eImMjJQnn3xSbt68qcT8+uuvyviOZs2aSUhIiMycOdOoLV5eXso+u3nzpvz3v/8VHx8fs/r+wUsYBfW9Xq9X2vnwvi+s3wv6Ayivvn/w/U3p+7S0NNmxY4dMnz690H43MPwh0759e7P6/cH9Y0rfJyQkyPfffy/Hjh0zqe8PHjwoL7/8svI9VFi/i9z/Dps9e7a0bdtWvLy8ZODAgRIZGSn/+9//5Ntvv5URI0ZIcHCw0fFlyP/Bdj3YT7/++qvR5z6vGMM+3rNnj3h5ecmaNWty9X9efyAbHDp0SLy9vfP93K9cuVIGDhwo69evl2bNmsnx48fz/Nzfu3dPjhw5IsuWLVPGHz643d9//11atGihFBH37t2Tb775RkJDQ5W+79evn7z33nsye/Zs6du3r3Tr1i1X3z/4njqdTjkWjh49Kv379xdPT0+5e/eusk5KSors3LlT/vrrL7lz54707t1bPvnkE7ly5YpER0fL7NmzJSgoKFe/mKPE3LVUkNu3b+PixYsoX748qlevjnr16pkcm5OTg7Vr1yInJweDBg0qcF35/1Hba9euxeTJkzFu3DgMHz680G0sW7YMO3fuxAsvvICnnnqq0G1kZmYa3SZuqkWLFmHDhg0YNmyY0VwzD7tz5w7i4+Ph7OyMihUronLlymZvyxQxMTH44YcfcOPGDQQFBaFTp05Gdw3k5+rVq9ixYwcaNmyILl26ADCe1NDR0RH37t3D6dOncfDgQfzyyy+4cOECRASurq6oUqUKRowYgW7duiE7OxtlypQp8La/tLQ0zJw5E0eOHMG2bduUyZ3yc/fuXURERODMmTPw9vbGqlWrCp3b4/z584iMjERiYiK++uor6PV6o7mNtFotUlJS8M4772DXrl0ICAjA/Pnz4erqqsyUm5OTAwcHB6O7Jgzty8nJgaOjI6Kjo7Fw4UIkJCRg1apVynwchvbcvn0bR44cwZ49e3DgwAFkZ2ejatWqqFevHl5//XV4e3ubNKnV9evX8fPPP2PEiBHIzs6GVqtVbh81WLBgAT7//HPlNtbMzEwMGDAAo0ePRnZ2tnLZ8c6dO0hPT0d6ejrKlSuHunXrArh//CxZsgTXr1/HO++8gzJlyiinrE+ePIm9e/cqfW84vW7o++7du+Ps2bNwdnYucC6UB/t+wYIFKFeuHOrXr59vvw8dOhSnT5+Gj48Pvv/+e5w7dw7lypXLcxt59f3KlStx4cIFlC1bFg0aNFDWTU5Oxrvvvptn3587dw5ly5Y1Ol3/cL9funQJCxYsQEJCAlavXo3z58/DyckJjRo1AnB/XpGDBw9i7969ufp91KhR8PX1xfnz51GuXDmjvArq+5EjR+LcuXNwcnIyyq2gvg8NDUXZsmWVvG7fvo2UlJR8+33FihXYuXMnXF1dERAQAAcHB8TGxuLAgQPYtWsX/vjjD2RnZ6Ny5cqoU6cORo4cCX9/f2zfvh1Vq1ZFy5YtUb58eQC571BLS0vDxx9/jF9//RVbt24tNCYzMxOvvvoqfvvtN/j4+GDw4MGFbuPkyZNYunQpMjIyEBkZiV27dsHNzU2J0ev1uHfvHt5//338+OOPaNOmDebPnw83NzflM79jxw5UrlwZTzzxhPI9Y5gzzHCMnTlzBosWLUJycjIGDRoEV1dXPPHEE8qloqNHj2L79u3Yv38/cnJy4OzsjCZNmmDkyJGoW7cudu7ciSpVqhTYFuD+JK2bNm1Cv379sH37dlSuXFnpF4Nvv/0Ws2bNAgCUK1cOZcuWxeDBgxEREVHgcVWQUlHI2JqI4OLFi6hdu7ZJv5j1ej2SkpLg6uqqHFjWoNfrcfv2bVStWjXfX6zFIa8PRGEMv7QfnPDP4MHbJrOyspCWlobs7Gz8/fffEBE0a9ZMmZjvwRgg74kQMzMzsW7dOtSqVUspnAqL+e677/Dee+/h/fffx4ABAwpdf/Xq1di8eTMiIiIQEhKS7zbOnz+Pb7/9Fu3atUNISAi0Wm2u9ufk5ECv1+dZoK1atQrr1q3D8OHDjW5dz8nJyXPm0UuXLqFixYpwdXU1Kp4Laoth/z88R0hOTg5ExOgY//XXX3H8+HEkJCSgS5cu8PHxyXdCr4edPXsWGzduRFxcHA4dOoTExETUqVMHY8aMQVhYGDIyMpCRkWHU902bNkVqaqrJE1pmZmYiKioKFy5cwNGjR5GUlAR3d3eMHDkSTz31VK71v/vuO0ydOhVvvPEG7t69a9I2Vq1ahc2bN2PAgAG4ePFinjGOjo44d+4cvvvuO6Xvb9++bdSOOnXq4LXXXkPfvn3h4OBgtI2vv/4a69evxwsvvICrV68axYwcORJ9+/ZV+lOj0Sj9Xr16dcTFxZm8vwy3H1+/fh3ff/99rtz69eunFLQP9r2fnx/OnTuHn376CYmJifDw8MCoUaPQq1evXAXw2bNnsXfvXtSqVQu7d+9WJhqtVasWZs+erUzbkJ6eDmdnZ1y5cgVarRbu7u64ePFivpOTGmIMk5NmZWVh//79uHPnDvbt22dSzJYtWzB27Fg0bdoUf/31V6Hrr169Glu2bEGvXr2UQjKvmKtXr2Lbtm144oknlDllHp5otXbt2pg1a5Yy9urB7axatQqrVq2Cm5sbTp8+reyvWbNmKROqGsTFxaFixYooX758gZO5PrwNQ/F86tQpREZGGvXLxx9/rEw/AgB//fUXLly4gL/++gsdO3aEl5fXI02Qx0KG7N7o0aPzndTQkpj8JkI0N2bgwIGoVKkSwsLCCl0/JSUF2dnZmDZtWoHbMBQaD87SbE5esbGxyM7OxieffAKdTpdnjF6vh16vN/oFYs42DIVpQTGGMwXm9Et2dnauX9BjxozBjRs3EBAQgCZNmmDbtm04efIkPvroIwQHB+d6/7xitm/fjpMnT2LWrFkIDg7ONX+IuesfPnwYq1atws2bN02K+eeff5CZmYn333+/wO0YGPrenLzi4uKQmZmJTz/9NFfMiRMnlP2VV7+Y235TYh7+46Wwfnx4fhbg/g0GhmO4WbNmmD59Olq0aIHu3btj6dKl0Ol0aNWqFQYMGKCcVc4rxsvLC927d8eSJUsgIvDz88NTTz2FOnXqWBQzaNAgODg4ICwsrMD1w8LCULt2bWRlZWHEiBEFbsPJyQktWrRAjx49lCsK5uZlmGPo4fW7deuGJUuWICcnB/7+/ujfv79yc0pR76/FixdDr9fjiSeewLPPPqvcNPPILL4oRaQC8fHx4unpKe3bt5f27dtLr169ZOrUqfLbb7/lWjcpKUm2bNkiR48eNTkmOTlZNm/eLMeOHTM55s6dO7JmzRqT109JSZFt27aZlVdKSops3brVrJjU1FTZsWOHWTHmtiU5OdnsvO7evSubNm0yO2b16tXSsmVLo1uWr1+/LkOGDJFOnToZDU7MycmR48ePS3R0tPj4+JgUI3L/Vmlvb2+T1tfpdBZt488//5RLly6ZFWO4mcDU9U+cOGFWXpa2xdwYvV4v27dvN7kter1ejh07JpcuXZIWLVrI77//rry2e/du6d69u3To0EGGDh0qzz33nDRt2lQGDhwoiYmJEhsba3LMiy++KAkJCXLr1i2TYwYNGiRnz541a/34+HiTtjFgwABp1qyZDB482KK2nDt3zqy8LNmGuTEDBw40ujX9UbCQIbtmyaSGs2fPNjvmk08+MTnGMIdEQECAyeubuw1L2mLpdsxpi6X7y5J+adKkiXTt2tXkySktndDSmtuIjY0VT09PmTx5stXyMmzD3LbbKsaSffzWW29Jv379zJpoNCoqyuoxEydOVGVekyZNUmVeD08Aa6kS9awlKn3u3buHtLQ0tGvXDs2aNUPPnj1x7tw55flaP/30E9atW4fGjRujbNmyqFChAho3bmx2zGOPPWZWjOEhndbcRmlvi5OTE9zd3ZXxOvL/g57r16+PV155BV9++SWeffZZVK9eHYcPH4azszNq1KiB69evmxxjeBaPtbZx5MgRODs7o2bNmoiJibFKXoZtmNt2W8VYso9dXV3h4eFhdIlq9erV8PX1RZ8+fZTvh8aNG6NVq1Y4efIksrKyrB5z+fJlVeb1119/qTKvkydPonPnznhULGTIruU3qWHr1q2RlJSEP//8E3/88QeOHz+OgwcP4vnnn7dJzNNPP43WrVurLq+S1JZOnTohISHBaHJKw5ORn332WaxatQqrV6/G2LFjsWHDBoSEhMDX1xd79+41OcbwLDBrbqMk5WWLtnTq1Al9+vTBhQsXck002qVLlzwnGtXr9VaPKVOmDOrWrQt/f3/mZWKMFNUQ3SI5r0NUjCyZ1NAWMWrNqyS1xdzJKUXMn9DSFtsoSXnZqi2WTDRqixjmVTQTwJqjxDyigEqvh2/be/DuGeD+XR5//PEHnJ2d0aJFC5vFqDWvktSWvO58MhgwYADq1q2Lt956C1WqVFGermtujC22UZLyslVbHj5WDMeI4RJVfHw8NmzYAHd3d3h7e9sshnmZH/OoWMhQiWX4QGm1WjRu3Bjjx49XRYxa87IkRq15iQhcXFzw4osvIjk5Gc8++2yh2zA3xhbbKEl5WbstDx4jALBhwwacPHkSw4YNK9YY5mV+jLk4jwwRlVhi5uSUlsTYYhslKS9LYizZhiUTjdoihnkV/QSwLGSIiIjIbvHSEhEREdktFjJERERkt1jIEBERkd1iIUNERER2i4UMERER2S0WMkRUYi1YsACenp7FnQYRWRELGSIiIrJbLGSIiIjIbrGQISJVSE9PL+4UiMgOsZAhIpszjF25dOkS3njjDfj7++P5558HAPzvf//D008/DW9vbwQEBGDcuHGIjY01ij969ChGjx6Njh07wsvLCx06dMCMGTOQmZlZHM0homKU/yNHiYisbMyYMahfvz7GjRsHEcHixYsxf/589OjRA/3790dSUhJWrVqFF154ARs2bFCes7N161ZkZmbiueeeg5ubG06ePIlVq1bh1q1b+Oyzz4q5VURkSyxkiKjYNG3aFJ9++ikA4MaNG+jatSvGjh2LV199VVknNDQU/fr1wzfffKMsnzBhAsqVK6esM2DAANSvXx9z5szBzZs3UadOHds2hIiKDS8tEVGxefbZZ5V/79ixA3q9Hj169EBSUpLyX7Vq1VC/fn38/vvvyroPFjHp6elISkqCn58fRARnz561aRuIqHjxjAwRFRsPDw/l31evXoWIIDQ0NM91HR3//bq6efMmPvvsM+zevRvJyclG66WlpVknWSJSJRYyRFRsypYtq/xbr9dDo9Fg2bJlcHBwyLVu+fLlAQA6nQ4vvfQSkpOTMXToUDRq1Ajly5fH7du3MWnSJOj1epvlT0TFj4UMEalCvXr1ICLw8PBAw4YN813v4sWLuHr1Kj766CP07dtXWX7w4EEbZElEasMxMkSkCqGhoXBwcMDChQshIkaviQju3LkDANBqtcqyB19fuXKl7ZIlItXgGRkiUoV69eph7Nix+PTTT3Hjxg106dIFFSpUwN9//42dO3fiP//5DyIiItCoUSPUq1cPH330EW7fvo2KFSti27ZtSElJKe4mEFExYCFDRKrxyiuvoEGDBvjyyy/x+eefAwBq1aqFoKAghISEAADKlCmDJUuWYPr06YiMjETZsmXRtWtXvPDCCwgLCyvO9ImoGGjk4XO4RERERHaCY2SIiIjIbrGQISIiIrvFQoaIiIjsFgsZIiIislssZIiIiMhusZAhIiIiu8VChoiIiOwWCxkiIiKyWyxkiIiIyG6xkCEiIiK7xUKGiIiI7BYLGSIiIrJb/wfe3pF/XifUSQAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#fit svc\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.pipeline import make_pipeline\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"from sklearn.metrics import f1_score\n",
|
||
"\n",
|
||
"from sklearn.svm import SVC\n",
|
||
"clf = make_pipeline(StandardScaler(),\n",
|
||
" SVC(gamma='auto'))\n",
|
||
"clf.fit(X_data, y_data)\n",
|
||
"y_pred = clf.predict(X_val)\n",
|
||
"classes_plot(y_val, y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "2e8eeba1-44d6-4036-8413-785cc99bc9c9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#fit random_forest\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.pipeline import make_pipeline\n",
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"from sklearn.metrics import f1_score\n",
|
||
"\n",
|
||
"from sklearn.svm import SVC\n",
|
||
"clf = make_pipeline(RandomForestClassifier(n_jobs=-1))\n",
|
||
"clf.fit(X_data, y_data)\n",
|
||
"y_pred = clf.predict(X_val)\n",
|
||
"classes_plot(y_val, y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "2cc2f449-c297-4294-a03e-235d76a5ad7f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#fit Linear SVC\n",
|
||
"\n",
|
||
"\n",
|
||
"from sklearn.pipeline import make_pipeline\n",
|
||
"from sklearn.naive_bayes import MultinomialNB, ComplementNB\n",
|
||
"from sklearn.metrics import f1_score\n",
|
||
"\n",
|
||
"from sklearn.svm import LinearSVC\n",
|
||
"clf = make_pipeline(LinearSVC(C=1000, penalty='l1', max_iter=500, dual=False))\n",
|
||
"clf.fit(X_data, y_data)\n",
|
||
"y_pred = clf.predict(X_val)\n",
|
||
"classes_plot(y_val, y_pred)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "hack",
|
||
"language": "python",
|
||
"name": "hack"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.8.10"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|