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328 lines
13 KiB
Plaintext
328 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"import pandas as pd\n",
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"focal_countries_list = [\"Peoples R china\", \"Hong Kong\"]"
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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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"outputs": [],
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"source": [
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"country_mode = \"CU\" #CU-country-region AU-address"
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],
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"metadata": {
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"collapsed": false
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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": 3,
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"outputs": [],
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"source": [
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"# (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"computer vision\") OR TS=(\"pattern recognition\")) AND"
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],
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"metadata": {
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"collapsed": false
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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": 4,
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"outputs": [
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{
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"data": {
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"text/plain": "'TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\")'"
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"keywords_source = r'..\\ai_scope_keywords.txt'\n",
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"with open(keywords_source,'r') as f:\n",
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" keywords = f.readlines()\n",
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"\n",
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"keywords = [c.strip() for c in keywords[0].split(\",\")]\n",
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"\n",
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"keywords_str = ' OR '.join('TS=(\\\"'+k+'\\\")' for k in keywords)\n",
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"keywords_str"
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],
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"metadata": {
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"collapsed": false
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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": 5,
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"outputs": [],
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"source": [
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"scope_country_source = r'..\\eu_scope_countries.txt'\n",
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"\n",
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"with open(scope_country_source,'r') as f:\n",
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" coop_countries = f.readlines()\n",
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"coop_countries = [c.strip().upper() for c in coop_countries[0].split(\",\")]\n",
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"focal_countries = [c.strip().upper() for c in focal_countries_list]\n",
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"eu_countries = coop_countries[0:-7]\n",
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"assoc_countries = coop_countries[-7:]\n",
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"\n",
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"nor_c = [coop_countries[-7],]\n",
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"swi_c = [coop_countries[-6],]\n",
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"uk_c = coop_countries[-5:]\n",
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"\n",
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"foc_str = ' OR '.join([country_mode+'='+c for c in focal_countries])\n",
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"coop_str = ' OR '.join([country_mode+'='+c for c in coop_countries])\n",
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"eu_str = ' OR '.join([country_mode+'='+c for c in eu_countries])\n",
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"assoc_str = ' OR '.join([country_mode+'='+c for c in assoc_countries])\n",
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"\n",
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"nor_str =' OR '.join([country_mode+'='+c for c in nor_c])\n",
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"swi_str =' OR '.join([country_mode+'='+c for c in swi_c])\n",
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"uk_str =' OR '.join([country_mode+'='+c for c in uk_c])\n",
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"eu_sub_str = eu_str.split(' OR ')\n",
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"# eu_sub_str"
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],
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"metadata": {
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"collapsed": false
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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": 5,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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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": 6,
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"outputs": [
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{
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"data": {
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"text/plain": "['UNITED KINGDOM', 'ENGLAND', 'WALES', 'SCOTLAND', 'N IRELAND']"
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"coop_countries[-5:]"
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],
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"metadata": {
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"collapsed": false
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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": 7,
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"outputs": [
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{
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"data": {
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"text/plain": "'CU=PEOPLES R CHINA OR CU=HONG KONG'"
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"foc_str"
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],
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"metadata": {
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"collapsed": false
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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": 8,
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"outputs": [
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{
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"data": {
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"text/plain": "'(CU=PEOPLES R CHINA OR CU=HONG KONG) AND (CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN OR CU=NORWAY OR CU=SWITZERLAND OR CU=UNITED KINGDOM OR CU=ENGLAND OR CU=WALES OR CU=SCOTLAND OR CU=N IRELAND) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\")) AND PY=(2011-2022)'"
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"scope_query = f'({foc_str}) AND ({coop_str}) AND ({keywords_str}) AND PY=(2011-2022)'\n",
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"scope_query"
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],
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"metadata": {
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"collapsed": false
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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": 9,
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"outputs": [
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{
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"data": {
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"text/plain": "'(CU=PEOPLES R CHINA OR CU=HONG KONG) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\"))'"
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ch_scope_query = f'({foc_str}) AND ({keywords_str})'\n",
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"ch_scope_query"
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],
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"metadata": {
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"collapsed": false
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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": 10,
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"outputs": [
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{
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"data": {
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"text/plain": "'(CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\"))'"
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"eu_scope_query = f'({eu_str}) AND ({keywords_str})'\n",
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"eu_scope_query"
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],
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"metadata": {
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"collapsed": false
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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": 11,
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"outputs": [],
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"source": [
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"sub_queries = [f'PY=(2011-2022) AND ({i_str}) AND ({keywords_str})' for i_str in [foc_str,eu_str,assoc_str,nor_str,swi_str,uk_str]+eu_sub_str]"
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],
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"metadata": {
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"collapsed": false
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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": 12,
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"outputs": [],
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"source": [
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"from wossel_miners import wos_fetch_entries,wos_fetch_yearly_output"
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],
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"metadata": {
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"collapsed": false
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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": 13,
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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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"100%|██████████| 33/33 [12:49<00:00, 23.31s/it]\n"
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]
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}
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],
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"source": [
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"wos_fetch_yearly_output(query_str_list=sub_queries)"
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],
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"metadata": {
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"collapsed": false
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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": 14,
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"outputs": [
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{
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"data": {
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"text/plain": "'(CU=PEOPLES R CHINA OR CU=HONG KONG) AND (CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN OR CU=NORWAY OR CU=SWITZERLAND OR CU=UNITED KINGDOM OR CU=ENGLAND OR CU=WALES OR CU=SCOTLAND OR CU=N IRELAND) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\")) AND PY=(2011-2022)'"
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"scope_query"
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],
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"metadata": {
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"collapsed": false
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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": 16,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Hoooold...\n",
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"27672 records found! Here we go in 93 steps...\n"
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]
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},
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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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"100%|██████████| 92/92 [09:38<00:00, 6.29s/it]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"final batch of 27601-27672\n"
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]
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}
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],
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"source": [
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"wos_fetch_entries(query_str=scope_query)"
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],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |