From b2d0b03b72d22ebda6cf27f25425a485b2979090 Mon Sep 17 00:00:00 2001 From: Eddy Zhiqiang Ji Date: Mon, 16 Sep 2024 04:44:19 -0400 Subject: [PATCH] 1st repo update; slightly change to plot1 --- .env.sample | 17 + .gitignore | 5 +- docs/plot1/pci_nko_fg1.py | 2 +- environment_cpu.yml | 28 + environment_cuda.yml | 29 + explore_page_matching.ipynb | 392 ---- .../2022-07_FOR_analysis.docx | Bin 0 -> 39173 bytes .../2022-07_FOR_comparison.png | Bin 0 -> 377204 bytes results/figures/f1_diff_over_time.png | Bin 0 -> 363436 bytes results/figures/f1_scores_over_time.png | Bin 0 -> 434035 bytes rs_scraper.ipynb | 128 -- rs_scraper.py | 65 - src/For_Mac_Torch_MPS_support_verify.py | 15 + src/analysis.ipynb | 1661 +++++++++++++++++ src/analysis_util.py | 684 +++++++ src/config.py | 24 + src/corpus_preparation.py | 100 + src/datasets_preparation.py | 124 ++ src/model.py | 247 +++ src/model_config.py | 142 ++ src/test_device.py | 27 + src/train_pipeline.py | 65 + src/util.py | 61 + 23 files changed, 3228 insertions(+), 588 deletions(-) create mode 100644 .env.sample create mode 100644 environment_cpu.yml create mode 100644 environment_cuda.yml delete mode 100644 explore_page_matching.ipynb create mode 100644 results/analysis_results/2022-07_FOR_analysis.docx create mode 100644 results/analysis_results/2022-07_FOR_comparison.png create mode 100644 results/figures/f1_diff_over_time.png create mode 100644 results/figures/f1_scores_over_time.png delete mode 100644 rs_scraper.ipynb delete mode 100644 rs_scraper.py create mode 100644 src/For_Mac_Torch_MPS_support_verify.py create mode 100644 src/analysis.ipynb create mode 100644 src/analysis_util.py create mode 100644 src/config.py create mode 100644 src/corpus_preparation.py create mode 100644 src/datasets_preparation.py create mode 100644 src/model.py create mode 100644 src/model_config.py create mode 100644 src/test_device.py create mode 100644 src/train_pipeline.py create mode 100644 src/util.py diff --git a/.env.sample b/.env.sample new file mode 100644 index 0000000..d961bdd --- /dev/null +++ b/.env.sample @@ -0,0 +1,17 @@ +# Project Configuration +PROJ_FOLDER=/path/to/your/project + +# Logging Configuration (Optional) +LOGS_FOLDER=/path/to/logs +HYPERPARAMETER_SEARCH_LOGS=/path/to/hyperparameter_search_logs +FULL_TRAINING_LOGS=/path/to/full_training_logs + +# Environment Settings +ENVIRONMENT=production + +# OpenAI API Key +OPENROUTER_API_KEY=your_openrouter_api_key + +# CUDA Configuration (Optional) +# If no CUDA devices are available, you can leave it empty or delete this line +CUDA_VISIBLE_DEVICES=0,1 \ No newline at end of file diff --git a/.gitignore b/.gitignore index 5184e7c..fae0415 100644 --- a/.gitignore +++ b/.gitignore @@ -159,6 +159,7 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +.env .DS_Store -2023-*/ -docs/plot1/plot.html + + diff --git a/docs/plot1/pci_nko_fg1.py b/docs/plot1/pci_nko_fg1.py index 0d9c6e4..0ed962b 100644 --- a/docs/plot1/pci_nko_fg1.py +++ b/docs/plot1/pci_nko_fg1.py @@ -101,7 +101,7 @@ def create_nko_plotly_figure(): hoverdistance=16, spikedistance=-1, yaxis=dict( - title="Monthly PCI for North Korea", + title="Monthly PCI for North Korea (PCI-NKO)", linecolor='black', range=[min(df.y.min(), -0.01), max(df.y.max(), 0.16)], zeroline=False diff --git a/environment_cpu.yml b/environment_cpu.yml new file mode 100644 index 0000000..d9729e7 --- /dev/null +++ b/environment_cpu.yml @@ -0,0 +1,28 @@ +name: PCI-NKO +channels: + - defaults + - conda-forge + - pytorch +dependencies: + - python=3.12 + - python-dotenv + - pandas + - plotly + - transformers + - torch + - optuna + - jupyterlab + - scikit-learn + - matplotlib + - seaborn + - nltk + - spacy + - tqdm + - pip: + - datasets + - evaluate + - openai + - langchain + - sentence-transformers + - python-docx + # Add any additional pip packages if necessary \ No newline at end of file diff --git a/environment_cuda.yml b/environment_cuda.yml new file mode 100644 index 0000000..5343ca5 --- /dev/null +++ b/environment_cuda.yml @@ -0,0 +1,29 @@ +name: PCI-NKO-CUDA +channels: + - defaults + - conda-forge + - pytorch +dependencies: + - python=3.12 + - python-dotenv + - pandas + - plotly + - transformers + - torch + - cudatoolkit=12.1 # Please change to your CUDA version + - optuna + - jupyterlab + - scikit-learn + - matplotlib + - seaborn + - nltk + - spacy + - tqdm + - pip: + - datasets + - evaluate + - openai + - langchain + - sentence-transformers + - python-docx + # Add any additional pip packages if necessary \ No newline at end of file diff --git a/explore_page_matching.ipynb b/explore_page_matching.ipynb deleted file mode 100644 index e60651c..0000000 --- a/explore_page_matching.ipynb +++ /dev/null @@ -1,392 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "b7932dc7-fe02-4ea2-af19-d0a3111c18d1", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import datetime, re, os\n", - "from PIL import Image\n", - "from pdf2image import convert_from_path\n", - "import pytesseract\n", - "from collections import Counter" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f06267ab-a423-4b19-953a-77590682fb6e", - "metadata": {}, - "outputs": [], - "source": [ - "file = '../../../../Public/nk-newspapers-shared/factiva-full-data.csv'\n", - "df = pd.read_csv(file)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1530f85d-a072-4440-ab39-18309b760740", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['source_name', 'title', 'body', 'byline', 'publication_datetime',\n", - " 'modification_datetime', 'subject_codes', 'company_codes',\n", - " 'industry_codes', 'region_codes', 'pub_name', 'pub_date', 'part'],\n", - " dtype='object')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5c5c3317-9216-4040-93e3-1b7211dc8102", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['MINCHO', 'RODONG'], dtype=object)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['pub_name'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7c6a3bb3-1706-43f2-95b3-cb9a3190103e", - "metadata": {}, - "outputs": [], - "source": [ - "df['publication_date'] = df['publication_datetime'].apply(lambda x: datetime.datetime.fromtimestamp(int(x)/1000))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "63d0d195-c145-48d5-ad67-e021c95193b3", - "metadata": {}, - "outputs": [], - "source": [ - "df_test = df[(df['publication_date']>='2023-08-01 00:00:00') & (df['pub_name']=='RODONG')]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "81fe99e5-faa5-4680-805c-be019401cb1e", - "metadata": {}, - "outputs": [], - "source": [ - "def find_matches(row):\n", - " try:\n", - " part1 = re.sub('\\s+', '', row['title'])\n", - " part1 = [*part1]\n", - " except:\n", - " part1 = []\n", - " try:\n", - " part2 = re.sub('\\s+', '', row['body'])\n", - " part2 = [*part2]\n", - " except:\n", - " part2 = []\n", - " small_list = part1 + part2\n", - " \n", - " ocr_folder = '../../../../Public/OCR_test_files/rd_' + row['publication_date'].strftime('%Y-%m-%d') + '_ocr/'\n", - " li_ocr_pages = [file for file in sorted(os.listdir(ocr_folder)) if file[-3:] == 'txt']\n", - " \n", - " matching = {}\n", - " for ocr_page in li_ocr_pages:\n", - " with open(ocr_folder+ocr_page, encoding='utf8') as f:\n", - " lines = f.readlines()\n", - " string = re.sub('\\s+', '', ''.join(lines))\n", - " big_list = [*string]\n", - " overlap = list((Counter(big_list) & Counter(small_list)).elements())\n", - " ratio = len(overlap)/len(small_list) \n", - " matching[ocr_page] = ratio\n", - " sorted_ratios = sorted(matching.items(), key=lambda x:x[1], reverse=True)\n", - "\n", - " row['1st_best_page'], row['1st_best_ratio'] = sorted_ratios[0]\n", - " row['2nd_best_page'], row['2nd_best_ratio'] = sorted_ratios[1]\n", - " row['3rd_best_page'], row['3rd_best_ratio'] = sorted_ratios[2]\n", - " \n", - " return row" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "64bca5e8-70fb-4207-8662-130ccab6bf7d", - "metadata": {}, - "outputs": [], - "source": [ - "df_test = df_test.apply(find_matches, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "77cb7ff2-7cf6-4f2c-9e5c-1e6e09c265c1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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publication_datetitlebody1st_best_page1st_best_ratio2nd_best_page2nd_best_ratio3rd_best_page3rd_best_ratio
119942023-08-01<재해성기후에 대처하기 위한 사업을 계속 강하게 내밀자> 농작물보호대책을 철저히 세워물론 농장에서는 큰물피해를 최소화하는데 큰 의의를 부여하고 이미전부터 이와 관련한 ...rd_2023-08-01_url_5_p5.txt0.971084rd_2023-08-01_url_5_p6.txt0.921687rd_2023-08-01_url_5_p3.txt0.918072
119952023-08-01생산실적과 공급실적이 같은가지난 7월에도 북창지구청년탄광련합기업소, 득장지구탄광련합기업소를 비롯한 여러 련합기...rd_2023-08-01_url_5_p5.txt0.973487rd_2023-08-01_url_5_p3.txt0.884726rd_2023-08-01_url_5_p6.txt0.873775
119962023-08-01한가정의 어머니처럼소대원들의 아픔을 자기 아픔으로 여기고 따뜻이 돌봐주고 생활상애로가 제기될 때마다 ...rd_2023-08-01_url_5_p3.txt0.983333rd_2023-08-01_url_5_p6.txt0.963333rd_2023-08-01_url_5_p4.txt0.960000
119972023-08-01<서포지구 새 거리건설을 제기일에 최상의 수준으로 완공할 기세드높다> 뜨거운 애국의...당의 구상과 의도를 높이 받들고 수도 평양에 웅장화려한 새 거리를 일떠세우는 보람찬...rd_2023-08-01_url_5_p1.txt0.971226rd_2023-08-01_url_5_p2.txt0.886676rd_2023-08-01_url_5_p6.txt0.881806
119982023-08-01괴뢰지역에서 악성비루스감염자 급속히 증가지난 19일 하루동안에만도 4만 7 029명이 악성비루스에 감염되였다.\\n\\n괴뢰지...rd_2023-08-01_url_5_p6.txt0.909091rd_2023-08-01_url_5_p4.txt0.818182rd_2023-08-01_url_5_p1.txt0.808081
119992023-08-01이스라엘의 도발적인 행위 비난알 아크사사원은 이슬람교의 성지라고 하면서 그는 꾸드스의 력사적, 합법적지위를 허물...rd_2023-08-01_url_5_p6.txt0.976744rd_2023-08-01_url_5_p2.txt0.930233rd_2023-08-01_url_5_p1.txt0.906977
120002023-08-01재해성기후에 대처하기 위한 사업을 계속 강하게 내밀자NaNrd_2023-08-01_url_5_p1.txt1.000000rd_2023-08-01_url_5_p2.txt1.000000rd_2023-08-01_url_5_p3.txt1.000000
120012023-08-02진심은 사람을 움직이는 가장 큰 힘이다그것은 바로 사람들을 진심으로 뜨겁게 대하는것이다.\\n\\n경애하는 김정은동지께서는 ...rd_2023-08-02_url_5_p3.txt0.964399rd_2023-08-02_url_5_p2.txt0.923077rd_2023-08-02_url_5_p4.txt0.910998
120022023-08-02<당세포들이 틀어쥐고나가야 할 중요과업> 대중을 당의 로선과 정책으로 튼튼히 무장시...경애하는 김정은동지께서는 다음과 같이 말씀하시였다.\\n\\n《기층조직을 강화하여 전당...rd_2023-08-02_url_5_p3.txt0.974576rd_2023-08-02_url_5_p2.txt0.952542rd_2023-08-02_url_5_p5.txt0.951695
120032023-08-01대를 이어 하는것이 참된 애국이다우리 나라에는 아버지가 섰던 초소를 아들이 지키고 어머니가 맡았던 기대를 딸이 돌리...rd_2023-08-01_url_5_p4.txt0.966276rd_2023-08-01_url_5_p2.txt0.928886rd_2023-08-01_url_5_p6.txt0.923021
\n", - "
" - ], - "text/plain": [ - " publication_date title \\\n", - "11994 2023-08-01 <재해성기후에 대처하기 위한 사업을 계속 강하게 내밀자> 농작물보호대책을 철저히 세워 \n", - "11995 2023-08-01 생산실적과 공급실적이 같은가 \n", - "11996 2023-08-01 한가정의 어머니처럼 \n", - "11997 2023-08-01 <서포지구 새 거리건설을 제기일에 최상의 수준으로 완공할 기세드높다> 뜨거운 애국의... \n", - "11998 2023-08-01 괴뢰지역에서 악성비루스감염자 급속히 증가 \n", - "11999 2023-08-01 이스라엘의 도발적인 행위 비난 \n", - "12000 2023-08-01 재해성기후에 대처하기 위한 사업을 계속 강하게 내밀자 \n", - "12001 2023-08-02 진심은 사람을 움직이는 가장 큰 힘이다 \n", - "12002 2023-08-02 <당세포들이 틀어쥐고나가야 할 중요과업> 대중을 당의 로선과 정책으로 튼튼히 무장시... \n", - "12003 2023-08-01 대를 이어 하는것이 참된 애국이다 \n", - "\n", - " body \\\n", - "11994 물론 농장에서는 큰물피해를 최소화하는데 큰 의의를 부여하고 이미전부터 이와 관련한 ... \n", - "11995 지난 7월에도 북창지구청년탄광련합기업소, 득장지구탄광련합기업소를 비롯한 여러 련합기... \n", - "11996 소대원들의 아픔을 자기 아픔으로 여기고 따뜻이 돌봐주고 생활상애로가 제기될 때마다 ... \n", - "11997 당의 구상과 의도를 높이 받들고 수도 평양에 웅장화려한 새 거리를 일떠세우는 보람찬... \n", - "11998 지난 19일 하루동안에만도 4만 7 029명이 악성비루스에 감염되였다.\\n\\n괴뢰지... \n", - "11999 알 아크사사원은 이슬람교의 성지라고 하면서 그는 꾸드스의 력사적, 합법적지위를 허물... \n", - "12000 NaN \n", - "12001 그것은 바로 사람들을 진심으로 뜨겁게 대하는것이다.\\n\\n경애하는 김정은동지께서는 ... \n", - "12002 경애하는 김정은동지께서는 다음과 같이 말씀하시였다.\\n\\n《기층조직을 강화하여 전당... \n", - "12003 우리 나라에는 아버지가 섰던 초소를 아들이 지키고 어머니가 맡았던 기대를 딸이 돌리... \n", - "\n", - " 1st_best_page 1st_best_ratio 2nd_best_page \\\n", - "11994 rd_2023-08-01_url_5_p5.txt 0.971084 rd_2023-08-01_url_5_p6.txt \n", - "11995 rd_2023-08-01_url_5_p5.txt 0.973487 rd_2023-08-01_url_5_p3.txt \n", - "11996 rd_2023-08-01_url_5_p3.txt 0.983333 rd_2023-08-01_url_5_p6.txt \n", - "11997 rd_2023-08-01_url_5_p1.txt 0.971226 rd_2023-08-01_url_5_p2.txt \n", - "11998 rd_2023-08-01_url_5_p6.txt 0.909091 rd_2023-08-01_url_5_p4.txt \n", - "11999 rd_2023-08-01_url_5_p6.txt 0.976744 rd_2023-08-01_url_5_p2.txt \n", - "12000 rd_2023-08-01_url_5_p1.txt 1.000000 rd_2023-08-01_url_5_p2.txt \n", - "12001 rd_2023-08-02_url_5_p3.txt 0.964399 rd_2023-08-02_url_5_p2.txt \n", - "12002 rd_2023-08-02_url_5_p3.txt 0.974576 rd_2023-08-02_url_5_p2.txt \n", - "12003 rd_2023-08-01_url_5_p4.txt 0.966276 rd_2023-08-01_url_5_p2.txt \n", - "\n", - " 2nd_best_ratio 3rd_best_page 3rd_best_ratio \n", - "11994 0.921687 rd_2023-08-01_url_5_p3.txt 0.918072 \n", - "11995 0.884726 rd_2023-08-01_url_5_p6.txt 0.873775 \n", - "11996 0.963333 rd_2023-08-01_url_5_p4.txt 0.960000 \n", - "11997 0.886676 rd_2023-08-01_url_5_p6.txt 0.881806 \n", - "11998 0.818182 rd_2023-08-01_url_5_p1.txt 0.808081 \n", - "11999 0.930233 rd_2023-08-01_url_5_p1.txt 0.906977 \n", - "12000 1.000000 rd_2023-08-01_url_5_p3.txt 1.000000 \n", - "12001 0.923077 rd_2023-08-02_url_5_p4.txt 0.910998 \n", - "12002 0.952542 rd_2023-08-02_url_5_p5.txt 0.951695 \n", - "12003 0.928886 rd_2023-08-01_url_5_p6.txt 0.923021 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_test[[\n", - " 'publication_date', 'title', 'body',\n", - " '1st_best_page', '1st_best_ratio', '2nd_best_page', '2nd_best_ratio', '3rd_best_page', '3rd_best_ratio'\n", - "]].head(10)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/results/analysis_results/2022-07_FOR_analysis.docx b/results/analysis_results/2022-07_FOR_analysis.docx new file mode 100644 index 0000000000000000000000000000000000000000..2d3f958a9325b7a99f50ba6d2021cc058f6c13f9 GIT binary patch literal 39173 zcmagFb9`jmx-J~s?AW$#J007$Z9D1Mwrx8db<{~Zb~?6ktJm7=oW0Mv_xt{t^{X+) z``~?N)j-WslmP`p0|Ekq0_wzO)T~h|`IZ6<1oRyO2nZF>sx4w~=VEH-qOaoVVCt+( z?_q1xk|L+DCWsVv`Hr5-z)$Ecf{0$Z>p##niGee8{DR8nw-p49UIpMN)S9o zVeOIT_ig*>-6z#qP9Pd9V(yf+Hc_lX_s{LEL&)nFjzM$PKkaz(PuJ9FEV{Kc<5YYt 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plot_results(df, event_dates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. LLM analysis (for individual episodes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Load data and best model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# List the start dates of the episodes to be evaluated\n", + "start_date_list =[\n", + " \"2018-01-01\",\n", + " \"2018-07-01\",\n", + " \"2018-12-01\",\n", + "]\n", + "\n", + "# List the paths of the best models for the episodes\n", + "model_path_list = [f'{model_path}/{d}/best_model' for d in start_date_list]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Select a start date" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train_start: 2018-07-01 00:00:00\n", + "train_end: 2022-07-01 00:00:00\n", + "pred_start: 2022-07-01 00:00:00\n", + "pred_end: 2022-10-01 00:00:00\n" + ] + } + ], + "source": [ + "d = start_date_list[1]\n", + "model_path = model_path_list[1]\n", + "train_start, train_end, pred_start, pred_end, train_start_YM, train_end_YM, pred_start_YM, pred_end_YM = generate_dates(d)\n", + "print(f\"train_start: {train_start}\\ntrain_end: {train_end}\\npred_start: {pred_start}\\npred_end: {pred_end}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3220, 3)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Prepare test set from the prediction window\n", + "df_path = \"../data/raw/df_matched_clean_translated.csv\"\n", + "df = pd.read_csv(df_path)\n", + "pred_df = load_and_split_data(df, d)[1][['publication_date', 'text', 'front_page']]\n", + "pred_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    publication_datetextfront_page
    02022-07-01몇해사이에 축산물생산을 획기적으로 늘인 비결 《집짐승을 대대적으로 길러 고기를 비롯...0
    12022-07-01대중적기술혁신운동을 활발히 벌리며 지난 기간 공장에서는 연소모형에 의한 주조공정을 ...0
    22022-07-01<확대되고있는 신형코로나비루스감염증피해> 유럽과 아메리카지역에서 북아메리카에서 1억...0
    32022-07-01<확대되고있는 신형코로나비루스감염증피해> 세계적으로 5억 5 152만 8 600여명...0
    42022-07-01《축하합니다!》 꽃다발을 안겨주는 공장기동예술선동대원들의 얼굴에도, 꽃다발을 받아안...0
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    100202022-06-24조선로동당 중앙군사위원회 제8기 제3차확대회의에 관한 보도 확대회의에는 조선로동당 ...1
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    100222022-06-24세계각국의 보건위기상황에서 찾게 되는 교훈 세계의 많은 나라들에서 방역위기가 완화될...0
    100232022-06-24확대되고있는 신형코로나비루스감염증피해 아시아의 대류행병전파상황\\n\\n아시아에서 22...0
    100242022-06-24<방역대전에서 누구나 알아야 할 상식> 기관지감염증상이 나타날 때 어떻게 할것인가 ...0
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    " + ], + "text/plain": [ + " publication_date text \\\n", + "10020 2022-06-24 조선로동당 중앙군사위원회 제8기 제3차확대회의에 관한 보도 확대회의에는 조선로동당 ... \n", + "10021 2022-06-24 <당정책관철을 위한 도들의 경쟁열의가 더욱 고조된다-육아정책을 높이 받들고> 사덕등... \n", + "10022 2022-06-24 세계각국의 보건위기상황에서 찾게 되는 교훈 세계의 많은 나라들에서 방역위기가 완화될... \n", + "10023 2022-06-24 확대되고있는 신형코로나비루스감염증피해 아시아의 대류행병전파상황\\n\\n아시아에서 22... \n", + "10024 2022-06-24 <방역대전에서 누구나 알아야 할 상식> 기관지감염증상이 나타날 때 어떻게 할것인가 ... \n", + "\n", + " front_page \n", + "10020 1 \n", + "10021 0 \n", + "10022 0 \n", + "10023 0 \n", + "10024 0 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Prepare the test set from the training window\n", + "try:\n", + " dataset_path = \"../data/processed/whole_sets_2018-01_to_2024-01\"\n", + " train_df = test_set_to_dataframe(dataset_path)\n", + " \n", + "except Exception as e:\n", + " print(f\"An error occurred: {str(e)}\")\n", + " import traceback\n", + " print(\"\\nDetailed error traceback:\")\n", + " print(traceback.format_exc())\n", + "\n", + "train_df = extract_data_by_date_range(train_df, train_start, train_end)\n", + "train_df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "System platform: darwin\n", + "Checking for Apple MPS...\n", + "MPS is available.\n", + "System platform: darwin\n", + "Checking for Apple MPS...\n", + "MPS is available.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/py312/lib/python3.12/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "RobertaForSequenceClassification(\n", + " (roberta): RobertaModel(\n", + " (embeddings): RobertaEmbeddings(\n", + " (word_embeddings): Embedding(32000, 768, padding_idx=1)\n", + " (position_embeddings): Embedding(514, 768, padding_idx=1)\n", + " (token_type_embeddings): Embedding(1, 768)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " )\n", + " (encoder): RobertaEncoder(\n", + " (layer): ModuleList(\n", + " (0-11): 12 x RobertaLayer(\n", + " (attention): RobertaAttention(\n", + " (self): RobertaSelfAttention(\n", + " (query): Linear(in_features=768, out_features=768, bias=True)\n", + " (key): Linear(in_features=768, out_features=768, bias=True)\n", + " (value): Linear(in_features=768, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " )\n", + " (output): RobertaSelfOutput(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " )\n", + " )\n", + " (intermediate): RobertaIntermediate(\n", + " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", + " (intermediate_act_fn): GELUActivation()\n", + " )\n", + " (output): RobertaOutput(\n", + " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", + " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (classifier): RobertaClassificationHead(\n", + " (dense): Linear(in_features=768, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " (out_proj): Linear(in_features=768, out_features=2, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Prepare the best model\n", + "model = model_init(model_path)\n", + "tokenizer = get_tokenizer()\n", + "device = get_device()\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Run the model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 278/278 [01:30<00:00, 3.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of 'result' column: (8896,)\n", + "Distribution of 'result' column: result\n", + "0 0.847684\n", + "1 0.152316\n", + "Name: proportion, dtype: float64\n", + "Shape of 'front_page' column: (8896,)\n", + "Distribution of 'front_page' column: front_page\n", + "0 0.895459\n", + "1 0.104541\n", + "Name: proportion, dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 101/101 [00:32<00:00, 3.08it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of 'result' column: (3220,)\n", + "Distribution of 'result' column: result\n", + "0 0.87764\n", + "1 0.12236\n", + "Name: proportion, dtype: float64\n", + "Shape of 'front_page' column: (3220,)\n", + "Distribution of 'front_page' column: front_page\n", + "0 0.906522\n", + "1 0.093478\n", + "Name: proportion, dtype: float64\n", + "\n", + "Training Set Results:\n", + "result\n", + "0 7541\n", + "1 1355\n", + "Name: count, dtype: int64\n", + "prediction_type\n", + "TN 7269\n", + "FP 697\n", + "TP 658\n", + "FN 272\n", + "Name: count, dtype: int64\n", + "Accuracy: 0.8911\n", + "Precision: 0.4856\n", + "Recall: 0.7075\n", + "F1 Score: 0.5759\n", + "\n", + "Prediction Set Results:\n", + "result\n", + "0 2826\n", + "1 394\n", + "Name: count, dtype: int64\n", + "prediction_type\n", + "TN 2696\n", + "FP 223\n", + "TP 171\n", + "FN 130\n", + "Name: count, dtype: int64\n", + "Accuracy: 0.8904\n", + "Precision: 0.4340\n", + "Recall: 0.5681\n", + "F1 Score: 0.4921\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Evaluate the model on both training and prediction sets\n", + "try:\n", + " train_df = classify_texts(train_df, model, tokenizer, device, label_column='front_page', max_length=256, threshold=0.5, invert_predictions=True)\n", + "\n", + " pred_df = classify_texts(pred_df, model, tokenizer, device, label_column='front_page', max_length=256, threshold=0.5, invert_predictions=True)\n", + "\n", + " for df_name, df in [(\"Training\", train_df), (\"Prediction\", pred_df)]:\n", + " print(f\"\\n{df_name} Set Results:\")\n", + " print(df['result'].value_counts())\n", + " print(df['prediction_type'].value_counts())\n", + " \n", + " metrics = calculate_metrics(df, label_column='front_page')\n", + " for metric_name, metric_value in metrics.items():\n", + " print(f\"{metric_name}: {metric_value:.4f}\")\n", + "# Save the results to csv if needed\n", + " # test_df.to_csv('test_results.csv', index=False) \n", + " # pred_df.to_csv('pred_results.csv', index=False)\n", + "\n", + "except Exception as e:\n", + " print(f\"An error occurred: {str(e)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    publication_datetextfront_pageresultprediction_type
    8582022-07-26김덕훈 내각총리 황해남도의 농업부문 사업을 현지료해 당면한 영농공정수행에서 나타나고...01FP
    15632022-08-16열파현상 지속 지중해연안지역에 영향을 미친 열파는 현재 프랑스의 서남부지역으로 번져...00TN
    26872022-09-17재해성기상현상에 계속 철저히 대처하자 특파기자00TN
    26032022-09-15생산에서 제기되는 과학기술적문제들을 적극 풀어나간다. 특파기자00TN
    31752022-09-29이스라엘의 불법무도한 만행 규탄 성명은 이스라엘의 행위는 국제법에 대한 란폭한 위반...00TN
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    " + ], + "text/plain": [ + " publication_date text \\\n", + "858 2022-07-26 김덕훈 내각총리 황해남도의 농업부문 사업을 현지료해 당면한 영농공정수행에서 나타나고... \n", + "1563 2022-08-16 열파현상 지속 지중해연안지역에 영향을 미친 열파는 현재 프랑스의 서남부지역으로 번져... \n", + "2687 2022-09-17 재해성기상현상에 계속 철저히 대처하자 특파기자 \n", + "2603 2022-09-15 생산에서 제기되는 과학기술적문제들을 적극 풀어나간다. 특파기자 \n", + "3175 2022-09-29 이스라엘의 불법무도한 만행 규탄 성명은 이스라엘의 행위는 국제법에 대한 란폭한 위반... \n", + "\n", + " front_page result prediction_type \n", + "858 0 1 FP \n", + "1563 0 0 TN \n", + "2687 0 0 TN \n", + "2603 0 0 TN \n", + "3175 0 0 TN " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_df.sample(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train sample count:\n", + "prediction_type\n", + "FN 130\n", + "TN 130\n", + "Name: count, dtype: int64\n", + "\n", + "Pred sample count:\n", + "prediction_type\n", + "FN 130\n", + "TN 130\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "def sample_for_FOR(train_df, pred_df, tn_fn_ratio=1, random_seed=23):\n", + "\n", + " if random_seed is not None:\n", + " np.random.seed(random_seed)\n", + " \n", + " pred_fn = pred_df[pred_df['prediction_type'] == 'FN']\n", + " pred_tn = pred_df[pred_df['prediction_type'] == 'TN']\n", + " \n", + " pred_tn_sampled = pred_tn.sample(n=min(len(pred_tn), len(pred_fn) * tn_fn_ratio), replace=False, random_state=random_seed)\n", + " pred_sample = pd.concat([pred_fn, pred_tn_sampled])\n", + " \n", + " train_fn = train_df[train_df['prediction_type'] == 'FN']\n", + " train_tn = train_df[train_df['prediction_type'] == 'TN']\n", + " \n", + " train_fn_sampled = train_fn.sample(n=min(len(train_fn), len(pred_fn)), replace=False, random_state=random_seed)\n", + " train_tn_sampled = train_tn.sample(n=min(len(train_tn), len(train_fn_sampled) * tn_fn_ratio), replace=False, random_state=random_seed)\n", + " train_sample = pd.concat([train_fn_sampled, train_tn_sampled])\n", + "\n", + " print(\"Train sample count:\")\n", + " print(train_sample['prediction_type'].value_counts())\n", + " print(\"\\nPred sample count:\")\n", + " print(pred_sample['prediction_type'].value_counts())\n", + " \n", + " return train_sample, pred_sample\n", + "# Sample the data for comparing False Omission Rate (FOR)\n", + "train_sample, pred_sample = sample_for_FOR(train_df, pred_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the sampled data to csv\n", + "train_sample.to_csv('train_sample.csv', index=False)\n", + "pred_sample.to_csv('pred_sample.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** Processing text datasets with LLMs can be time-consuming. To save time during iterative experiments and prevent data loss from unexpected interruptions, it's advisable to save intermediate results to disk. However, feel free to delete these tentatively generated files once you've obtained the final analysis results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Use LLM to process the sampled data" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the OpenRouter API and models\n", + "# Install OpenAI with pip first if not already installed\n", + "\n", + "openrouter_key = os.getenv(\"OPENROUTER_API_KEY\")\n", + "\n", + "# candidate models as of Aug 2024\n", + "or_model1 = \"meta-llama/llama-3.1-8b-instruct\"\n", + "or_model2 = \"meta-llama/llama-3.1-70b-instruct\"\n", + "or_model3 = \"openai/gpt-4o-mini-2024-07-18\"\n", + "or_model4 = \"openai/gpt-4o-2024-08-06\"\n", + "or_model5 = \"anthropic/claude-3.5-sonnet\"\n", + "or_model6 = \"meta-llama/llama-3.1-405b-instruct\"\n", + "or_model7 = \"google/gemini-pro-1.5-exp\"\n", + "or_model8 = \"deepseek/deepseek-chat\"\n", + "\n", + "# Set models for translation and analysis tasks separately\n", + "translation_model = or_model3\n", + "backup_model = or_model4\n", + "analysis_model = or_model5\n", + "\n", + "client = OpenAI(\n", + " base_url=\"https://openrouter.ai/api/v1\",\n", + " api_key=openrouter_key,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** Please find details on the LLM prompts for translation and analysis in the `analysis_util.py` file." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total rows in input file: 260\n", + "Resuming from existing output file: pred_sample_tran.csv\n", + "Translations completed: 260\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Translating: 100%|██████████| 260/260 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    publication_datetextfront_pageresultprediction_typeTranslation_EN-US
    02022-07-01위대한 년대의 불멸할 자욱을 길이 전하는 고귀한 재보 조국해방전쟁승리기념관을 세계적...10FN**Translation:**\\n\\nIn order to splendidly est...
    12022-07-03최악의 역경을 강용히 뚫고나아가는 우리 인민의 억센 기상 《사회주의경제건설은 오늘 ...10FN**Our People’s Indomitable Spirit Overcoming t...
    22022-07-03생산장성의 담보를 마련해가고있다. 리동명10FN\"Securing the guarantee for production growth....
    32022-07-10과학기술보급사업을 활발히 벌려나가고있다. 특파기자10FNThe dissemination of scientific and technologi...
    42022-07-12당정책은 인민의 요구와 리익실현의 절대적인 지침이며 위력한 무기이다 경애하는 김정은...10FNThe Party policy is an absolute guideline for ...
    \n", + "" + ], + "text/plain": [ + " publication_date text \\\n", + "0 2022-07-01 위대한 년대의 불멸할 자욱을 길이 전하는 고귀한 재보 조국해방전쟁승리기념관을 세계적... \n", + "1 2022-07-03 최악의 역경을 강용히 뚫고나아가는 우리 인민의 억센 기상 《사회주의경제건설은 오늘 ... \n", + "2 2022-07-03 생산장성의 담보를 마련해가고있다. 리동명 \n", + "3 2022-07-10 과학기술보급사업을 활발히 벌려나가고있다. 특파기자 \n", + "4 2022-07-12 당정책은 인민의 요구와 리익실현의 절대적인 지침이며 위력한 무기이다 경애하는 김정은... \n", + "\n", + " front_page result prediction_type \\\n", + "0 1 0 FN \n", + "1 1 0 FN \n", + "2 1 0 FN \n", + "3 1 0 FN \n", + "4 1 0 FN \n", + "\n", + " Translation_EN-US \n", + "0 **Translation:**\\n\\nIn order to splendidly est... \n", + "1 **Our People’s Indomitable Spirit Overcoming t... \n", + "2 \"Securing the guarantee for production growth.... \n", + "3 The dissemination of scientific and technologi... \n", + "4 The Party policy is an absolute guideline for ... " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_sample_tran = pd.read_csv('train_sample_tran.csv')\n", + "pred_sample_tran = pd.read_csv('pred_sample_tran.csv')\n", + "train_sample_tran.head()\n", + "pred_sample_tran.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing entries: 100%|██████████| 260/260 [14:21<00:00, 3.31s/it]\n", + "Processing entries: 17%|█▋ | 45/260 [02:25<11:02, 3.08s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Attempt 1: Error occurred: Expecting value: line 5 column 1 (char 22). Retrying in 5 seconds...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing entries: 100%|██████████| 260/260 [14:18<00:00, 3.30s/it]\n" + ] + } + ], + "source": [ + "train_sample_proc = process_df(train_sample_tran)\n", + "pred_sample_proc = process_df(pred_sample_tran)\n", + "train_sample_proc.head()\n", + "pred_sample_proc.head()\n", + "# Save the processed data to csv\n", + "train_sample_proc.to_csv('train_sample_proc.csv', index=False)\n", + "pred_sample_proc.to_csv('pred_sample_proc.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Policy Area\n", + "Social Policy and Ideology 78\n", + "Agriculture and Food Security 63\n", + "Economic and Industrial Policy 40\n", + "Public Health and Safety 38\n", + "Infrastructure and Public Works 20\n", + "Defense and National Security 6\n", + "Education and Human Capital 6\n", + "Foreign Relations and Diplomacy 4\n", + "Science and Technology 3\n", + "Miscellaneous 1\n", + "Labor and Employment 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_sample_proc['Policy Area'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Policy Area\n", + "Social Policy and Ideology 80\n", + "Agriculture and Food Security 52\n", + "Economic and Industrial Policy 34\n", + "Public Health and Safety 34\n", + "Infrastructure and Public Works 18\n", + "Foreign Relations and Diplomacy 15\n", + "Science and Technology 11\n", + "Education and Human Capital 10\n", + "Defense and National Security 4\n", + "Miscellaneous 1\n", + "Labor and Employment 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_sample_proc['Policy Area'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5 Calculate False Omission Rate (FOR) to identify policy areas that need focused analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Policy areas defined in this analysis include:\n", + "ALL_POLICY_AREAS = [\n", + " 'Economic and Industrial Policy', \n", + " 'Agriculture and Food Security',\n", + " 'Infrastructure and Public Works',\n", + " 'Public Health and Safety', \n", + " 'Defense and National Security', \n", + " 'Social Policy and Ideology',\n", + " 'Labor and Employment', \n", + " 'Education and Human Capital', \n", + " 'Foreign Relations and Diplomacy', \n", + " 'Science and Technology', \n", + " 'Miscellaneous'\n", + "]\n", + "Please customize the policy areas in the `analysis_util.py` file as needed.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Sample False Omission Rates:\n", + "Policy Area\n", + "Economic and Industrial Policy 0.735294\n", + "Agriculture and Food Security 0.500000\n", + "Infrastructure and Public Works 0.833333\n", + "Public Health and Safety 0.205882\n", + "Defense and National Security 0.750000\n", + "Social Policy and Ideology 0.525000\n", + "Labor and Employment 0.000000\n", + "Education and Human Capital 0.500000\n", + "Foreign Relations and Diplomacy 0.066667\n", + "Science and Technology 0.454545\n", + "Miscellaneous 1.000000\n", + "Name: prediction_type, dtype: float64\n", + "\n", + "Prediction Sample False Omission Rates:\n", + "Policy Area\n", + "Economic and Industrial Policy 0.775000\n", + "Agriculture and Food Security 0.587302\n", + "Infrastructure and Public Works 0.450000\n", + "Public Health and Safety 0.368421\n", + "Defense and National Security 0.333333\n", + "Social Policy and Ideology 0.435897\n", + "Labor and Employment 0.000000\n", + "Education and Human Capital 0.333333\n", + "Foreign Relations and Diplomacy 0.000000\n", + "Science and Technology 0.333333\n", + "Miscellaneous 0.000000\n", + "Name: prediction_type, dtype: float64\n" + ] + } + ], + "source": [ + "train_for = calculate_false_omission_rate(train_sample_proc)\n", + "pred_for = calculate_false_omission_rate(pred_sample_proc)\n", + "\n", + "print(\"Training Sample False Omission Rates:\")\n", + "print(train_for)\n", + "print(\"\\nPrediction Sample False Omission Rates:\")\n", + "print(pred_for)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Sample Counts:\n", + "Economic and Industrial Policy: FN = 25, Total = 34\n", + "Agriculture and Food Security: FN = 26, Total = 52\n", + "Infrastructure and Public Works: FN = 15, Total = 18\n", + "Public Health and Safety: FN = 7, Total = 34\n", + "Defense and National Security: FN = 3, Total = 4\n", + "Social Policy and Ideology: FN = 42, Total = 80\n", + "Labor and Employment: FN = 0, Total = 1\n", + "Education and Human Capital: FN = 5, Total = 10\n", + "Foreign Relations and Diplomacy: FN = 1, Total = 15\n", + "Science and Technology: FN = 5, Total = 11\n", + "Miscellaneous: FN = 1, Total = 1\n", + "\n", + "\n", + "Economic and Industrial Policy: FN = 31, Total = 40\n", + "Agriculture and Food Security: FN = 37, Total = 63\n", + "Infrastructure and Public Works: FN = 9, Total = 20\n", + "Public Health and Safety: FN = 14, Total = 38\n", + "Defense and National Security: FN = 2, Total = 6\n", + "Social Policy and Ideology: FN = 34, Total = 78\n", + "Labor and Employment: FN = 0, Total = 1\n", + "Education and Human Capital: FN = 2, Total = 6\n", + "Foreign Relations and Diplomacy: FN = 0, Total = 4\n", + "Science and Technology: FN = 1, Total = 3\n", + "Miscellaneous: FN = 0, Total = 1\n" + ] + } + ], + "source": [ + "train_counts = generate_counts(train_sample_proc, ALL_POLICY_AREAS)\n", + "pred_counts = generate_counts(pred_sample_proc, ALL_POLICY_AREAS)\n", + "\n", + "print(\"Training Sample Counts:\")\n", + "\n", + "for policy, (fn, total) in zip(ALL_POLICY_AREAS, train_counts):\n", + " print(f\"{policy}: FN = {fn}, Total = {total}\")\n", + "print(\"\\n\")\n", + "for policy, (fn, total) in zip(ALL_POLICY_AREAS, pred_counts):\n", + " print(f\"{policy}: FN = {fn}, Total = {total}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_end_YM_new = get_previous_month(train_end_YM)\n", + "pred_end_YM_new = get_previous_month(pred_end_YM)\n", + "train_date_range = f\"{train_start_YM} - {train_end_YM_new}\" \n", + "pred_date_range = f\"{pred_start_YM} - {pred_end_YM_new}\" \n", + "pred_start_date = pred_start_YM \n", + "\n", + "plot_false_omission_rates(train_for, pred_for, train_counts, pred_counts,train_date_range, pred_date_range,pred_start_date)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6 Focused analysis on policy areas with significant changes in False Omission Rate (FOR)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Change in False Omission Rate (Prediction - Training):\n", + "Policy Area\n", + "Public Health and Safety 0.162539\n", + "Agriculture and Food Security 0.087302\n", + "Economic and Industrial Policy 0.039706\n", + "Labor and Employment 0.000000\n", + "Foreign Relations and Diplomacy -0.066667\n", + "Social Policy and Ideology -0.089103\n", + "Science and Technology -0.121212\n", + "Education and Human Capital -0.166667\n", + "Infrastructure and Public Works -0.383333\n", + "Defense and National Security -0.416667\n", + "Miscellaneous -1.000000\n", + "Name: prediction_type, dtype: float64\n", + "\n", + "Sample counts for each Policy Area:\n", + " Train Prediction\n", + "Policy Area \n", + "Economic and Industrial Policy 34 40\n", + "Agriculture and Food Security 52 63\n", + "Infrastructure and Public Works 18 20\n", + "Public Health and Safety 34 38\n", + "Defense and National Security 4 6\n", + "Social Policy and Ideology 80 78\n", + "Labor and Employment 1 1\n", + "Education and Human Capital 10 6\n", + "Foreign Relations and Diplomacy 15 4\n", + "Science and Technology 11 3\n", + "Miscellaneous 1 1\n", + "The focused policy areas for analysis include: ['Public Health and Safety']\n" + ] + } + ], + "source": [ + "# Find the policy areas that have significant changes in False Omission Rate\n", + "for_change = pred_for - train_for\n", + "print(\"\\nChange in False Omission Rate (Prediction - Training):\")\n", + "print(for_change.sort_values(ascending=False))\n", + "\n", + "print(\"\\nSample counts for each Policy Area:\")\n", + "train_counts = train_sample_proc['Policy Area'].value_counts().reindex(ALL_POLICY_AREAS, fill_value=0)\n", + "pred_counts = pred_sample_proc['Policy Area'].value_counts().reindex(ALL_POLICY_AREAS, fill_value=0)\n", + "all_counts = pd.DataFrame({'Train': train_counts, 'Prediction': pred_counts}).fillna(0).astype(int)\n", + "print(all_counts)\n", + "\n", + "\n", + "results = filter_policy_areas(for_change, all_counts)\n", + "focused_policies = [area[0] for area in results['significant_change'] + results['potential_change']]\n", + "print(f'The focused policy areas for analysis include: {focused_policies}')" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Based on the analysis of the article summaries and tags from both periods, here's a comprehensive breakdown of the main themes, changes, and potential policy shifts observed:\n", + "\n", + "1. Emerging or increasing themes in Period 2:\n", + "\n", + "a) Natural Disaster Preparedness:\n", + "There's a significant increase in focus on natural disaster preparedness, particularly for typhoons, heavy rains, and flooding. For example:\n", + "- \"North Korean authorities calling for strengthened measures against heavy rain and flooding\"\n", + "- \"Kim Jong Un presided over the National Disaster Prevention Work Review Meeting\"\n", + "- Multiple summaries detailing typhoon preparation efforts and weather forecasts\n", + "\n", + "This suggests a heightened awareness of climate-related risks and a shift towards proactive disaster management.\n", + "\n", + "b) Monkeypox Concerns:\n", + "Period 2 introduces mentions of monkeypox, indicating North Korea's awareness of emerging global health threats:\n", + "- \"North Korea emphasizes the importance of rapid testing for suspected monkeypox cases\"\n", + "\n", + "c) Dengue Fever Outbreak:\n", + "Multiple summaries in Period 2 mention dengue fever outbreaks, both in North Korea and neighboring countries:\n", + "- \"Dengue fever cases in North Korea have increased significantly\"\n", + "- \"Vietnam reports 211,388 cases of dengue fever nationwide, with 87 fatalities\"\n", + "\n", + "2. Decreasing or disappearing themes:\n", + "\n", + "a) COVID-19 Specific Measures:\n", + "While COVID-19 is still mentioned in global context, there's less emphasis on specific internal measures compared to Period 1. The focus seems to have shifted to broader epidemic prevention and disaster response.\n", + "\n", + "b) Disinfection and Quarantine Details:\n", + "Period 2 has fewer detailed descriptions of disinfection procedures and quarantine measures, suggesting a possible normalization of these practices or a shift in priorities.\n", + "\n", + "3. Surprising elements or unexpected developments:\n", + "\n", + "a) Declaration of Victory:\n", + "- \"Kim Jong Un met with military personnel who participated in the emergency epidemic prevention campaign\" and \"declared victory over the epidemic\"\n", + "This declaration of victory is surprising given the ongoing global nature of the pandemic.\n", + "\n", + "b) Bomb Explosion:\n", + "- \"Bomb explosion reported in North Korea\"\n", + "This isolated mention of a security incident stands out and could indicate internal security challenges.\n", + "\n", + "4. Implications for North Korea's priorities or challenges:\n", + "\n", + "a) Climate Resilience:\n", + "The increased focus on natural disaster preparedness suggests North Korea is prioritizing climate resilience and infrastructure protection.\n", + "\n", + "b) Public Health System Strengthening:\n", + "Continued emphasis on epidemic prevention and pharmaceutical production indicates ongoing efforts to bolster the healthcare system:\n", + "- \"North Korea emphasizes strengthening the healthcare sector's material and technological foundation\"\n", + "\n", + "c) Food Security:\n", + "Multiple mentions of protecting crops from natural disasters imply concerns about food security:\n", + "- \"Emphasis on minimizing crop damage to ensure successful farming year\"\n", + "\n", + "5. Consistent themes across both periods and their implications:\n", + "\n", + "a) Ideological Framing of Health Measures:\n", + "Both periods consistently frame public health and safety measures in ideological terms:\n", + "- \"North Korea frames epidemic control as a patriotic duty\"\n", + "This suggests a continued use of health crises to reinforce political loyalty and national unity.\n", + "\n", + "b) Global Health Awareness:\n", + "Both periods show North Korea's awareness of global health situations, implying ongoing engagement with international health information despite isolation.\n", + "\n", + "c) Emphasis on Leadership:\n", + "Kim Jong Un's role in guiding responses to health crises and natural disasters is consistently highlighted, reinforcing the personality cult.\n", + "\n", + "6. Potential future policy directions based on these trends:\n", + "\n", + "a) Enhanced Disaster Response Infrastructure:\n", + "Given the increased focus on natural disasters, North Korea may invest more in early warning systems, evacuation procedures, and infrastructure resilience.\n", + "\n", + "b) Diversified Health Threats Management:\n", + "The shift from COVID-19 specific measures to broader epidemic prevention and emerging threats like monkeypox suggests a more diversified approach to health security.\n", + "\n", + "c) Strengthened Domestic Pharmaceutical Industry:\n", + "Emphasis on pharmaceutical production and Korean traditional medicine may indicate future policies to boost domestic medical capabilities and reduce reliance on imports.\n", + "\n", + "d) Climate Change Adaptation:\n", + "The focus on weather-related disasters might lead to more policies addressing climate change impacts, potentially including agricultural adaptations and water management strategies.\n", + "\n", + "e) Continued Ideological Integration:\n", + "Health and safety measures will likely remain closely tied to ideological goals, with potential increases in \"patriotic health campaigns\" or similar initiatives.\n", + "\n", + "In conclusion, the analysis suggests a shift in North Korea's focus from immediate COVID-19 response to broader disaster preparedness and health system strengthening, while maintaining its characteristic ideological framing of these efforts. The country appears to be grappling with multiple health and environmental challenges, which may shape its domestic and international policies in the coming years.\n" + ] + } + ], + "source": [ + "analysis_result = analyze_policy_changes(train_sample_proc, pred_sample_proc, focused_policies)\n", + "print(analysis_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Word document to save the analysis results\n", + "from docx import Document\n", + "\n", + "doc = Document()\n", + "doc.add_heading('Policy Analysis Report', 0)\n", + "\n", + "doc.add_heading('Policy areas requiring focused analysis:', level=1)\n", + "for category, areas in results.items():\n", + " doc.add_heading(f\"{category.capitalize()}:\", level=2)\n", + " for area in areas:\n", + " doc.add_paragraph(f\"{area[0]}: FOR change = {area[1]:.4f}, Total sample = {area[2]}\")\n", + "\n", + "doc.add_heading('Detailed Analysis', level=1)\n", + "doc.add_paragraph(analysis_result)\n", + "\n", + "doc.save(f'../results/analysis_results/{pred_start_date}_FOR_analysis.docx')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/analysis_util.py b/src/analysis_util.py new file mode 100644 index 0000000..a46b99f --- /dev/null +++ b/src/analysis_util.py @@ -0,0 +1,684 @@ +import os +import sys +from pathlib import Path +import pandas as pd +import numpy as np +from datasets import load_from_disk +import pyarrow as pa +from model_config import * +import matplotlib.pyplot as plt +import matplotlib.dates as mdates +from datetime import datetime, timedelta +from dateutil.relativedelta import relativedelta +import seaborn as sns +from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score +import torch +from tqdm import tqdm +import time +from docx import Document +from dotenv import load_dotenv +from openai import OpenAI + +load_dotenv() + +# Define the policy areas +ALL_POLICY_AREAS = [ + 'Economic and Industrial Policy', + 'Agriculture and Food Security', + 'Infrastructure and Public Works', + 'Public Health and Safety', + 'Defense and National Security', + #'Nuclear Power', + 'Social Policy and Ideology', + 'Labor and Employment', + 'Education and Human Capital', + 'Foreign Relations and Diplomacy', + 'Science and Technology', + 'Miscellaneous' +] + +# Define LLM models +# candidate models as of Aug 2024 +or_model1 = "meta-llama/llama-3.1-8b-instruct" +or_model2 = "meta-llama/llama-3.1-70b-instruct" +or_model3 = "openai/gpt-4o-mini-2024-07-18" +or_model4 = "openai/gpt-4o-2024-08-06" +or_model5 = "anthropic/claude-3.5-sonnet" +or_model6 = "meta-llama/llama-3.1-405b-instruct" +or_model7 = "google/gemini-pro-1.5-exp" +or_model8 = "deepseek/deepseek-chat" + +# Set models for translation and analysis tasks separately +translation_model = or_model3 +backup_model = or_model4 +analysis_model = or_model5 + +##################################################################### + +## 1. For plotting the results +def plot_results(df, event_dates, output_dir='../results/figures'): + # Create the output directory if it doesn't exist + os.makedirs(output_dir, exist_ok=True) + + output_file_1 = os.path.join(output_dir, 'f1_scores_over_time.png') + output_file_2 = os.path.join(output_dir, 'f1_diff_over_time.png') + + # Read in the results + results = df + + # Get the columns for plotting + expected_columns = ['Pred End', 'Test F1', 'Pred F1'] + plot_df = results[expected_columns].copy() + + # Convert the date to datetime + plot_df['Pred End'] = pd.to_datetime(plot_df['Pred End']) + + # Extract the year and month and store in new columns in %Y-%m format + plot_df['year_month'] = plot_df['Pred End'].dt.strftime('%Y-%m') + + # Plot the results + plt.figure(figsize=(12, 6)) + sns.set_style("whitegrid") # Use white background with grid + + ax = sns.lineplot(data=plot_df, x='Pred End', y='Test F1', label='Test F1', color='navy') + sns.lineplot(data=plot_df, x='Pred End', y='Pred F1', label='Pred F1', color='crimson') + + # Add vertical lines for important events and event descriptions + y_min, y_max = ax.get_ylim() + y_range = y_max - y_min + num_events = len(event_dates) + event_positions = [y_min + i * y_range / (num_events + 1) for i in range(1, num_events + 1)] + + for (event_date, event_desc), event_position in zip(event_dates, event_positions): + ax.axvline(event_date, color='gray', linestyle='--', alpha=0.5) + ax.text(event_date, event_position, event_desc, rotation=0, ha='left', va='center', fontsize=8) + + plt.title('Test and Prediction F1 Over Time', fontsize=16) + plt.xlabel('Prediction End Date', fontsize=14) + plt.ylabel('F1 Score', fontsize=14) + plt.xticks(rotation=45) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) + + # Set x-axis ticks to every two months + ax.xaxis.set_major_locator(mdates.MonthLocator(interval=2)) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) + + # Adjust the x-axis limits to show the desired time range + ax.set_xlim([datetime(2022, 2, 1), datetime(2024, 4, 30)]) + + plt.tight_layout() # Adjust the plot to ensure everything fits without overlapping + plt.savefig(output_file_1, dpi=300, bbox_inches='tight', facecolor='white') # Set facecolor to 'white' + plt.show() + + # Second plot to show the difference between the test and prediction F1 + plt.figure(figsize=(12, 6)) + sns.set_style("whitegrid") # Use white background with grid + + plot_df['F1 Diff'] = plot_df['Test F1'] - plot_df['Pred F1'] + ax = sns.lineplot(data=plot_df, x='Pred End', y='F1 Diff') + + # Add vertical lines for important events and event descriptions + y_min, y_max = ax.get_ylim() + y_range = y_max - y_min + num_events = len(event_dates) + event_positions = [y_min + i * y_range / (num_events + 1) for i in range(1, num_events + 1)] + + for (event_date, event_desc), event_position in zip(event_dates, event_positions): + ax.axvline(event_date, color='gray', linestyle='--', alpha=0.5) + ax.text(event_date, event_position, event_desc, rotation=0, ha='left', va='center', fontsize=8) + + plt.ylabel('Policy Change Index for North Korea', fontsize=14) + plt.axhline(0, color='red', linestyle='--') # Add a horizontal line at 0 + plt.xticks(rotation=45) + + # Set x-axis ticks to every two months + ax.xaxis.set_major_locator(mdates.MonthLocator(interval=2)) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) + + # Adjust the x-axis limits to show the desired time range + ax.set_xlim([datetime(2022, 2, 1), datetime(2024, 4, 30)]) + + plt.tight_layout() + plt.savefig(output_file_2, dpi=300, bbox_inches='tight', facecolor='white') # Set facecolor to 'white' + plt.show() + + +########################################################### +## 2. LLM analysis + +# Generate dates +def generate_dates(date1, train_years=INITIAL_TRAIN_YEARS, pred_months=PREDICTION_PERIOD): + train_start = pd.Timestamp(date1).normalize() + train_end = (train_start + pd.DateOffset(years=train_years)).normalize() + pred_start = train_end + pred_end = (pred_start + pd.DateOffset(months=pred_months)).normalize() + + # Generate date strings + train_start_YM = train_start.strftime('%Y-%m') + train_end_YM = train_end.strftime('%Y-%m') + pred_start_YM = pred_start.strftime('%Y-%m') + pred_end_YM = pred_end.strftime('%Y-%m') + return train_start, train_end, pred_start, pred_end, train_start_YM, train_end_YM, pred_start_YM, pred_end_YM + +def get_previous_month(date_str): + """ + Takes a date string in 'YYYY-MM' format and returns the previous month in the same format. + """ + # Parse the input string to a datetime object + current_date = datetime.strptime(date_str, '%Y-%m') + + # Subtract one month + previous_month = current_date - relativedelta(months=1) + + # Format the result back to 'YYYY-MM' string + return previous_month.strftime('%Y-%m') + +# Extract data by date range +def extract_data_by_date_range(df, start_date, end_date, random_seed=None): + df['publication_date'] = pd.to_datetime(df['publication_date']) + + start_date = pd.to_datetime(start_date) + end_date = pd.to_datetime(end_date) + + mask = (df['publication_date'] >= start_date) & (df['publication_date'] < end_date) + filtered_df = df.loc[mask] + + if random_seed is not None: + np.random.seed(random_seed) + filtered_df = filtered_df.sample(frac=1, random_state=random_seed) + + return filtered_df + +# Load data by date range +def load_and_split_data(df, train_start, train_years=INITIAL_TRAIN_YEARS, pred_months=PREDICTION_PERIOD): + df['publication_date'] = pd.to_datetime(df['publication_date']).dt.normalize() + train_start, train_end, pred_start, pred_end = generate_dates(train_start, train_years, pred_months)[:4] + train_df = df[(df['publication_date'] >= train_start) & (df['publication_date'] < train_end)].copy().reset_index(drop=True) + pred_df = df[(df['publication_date'] >= pred_start) & (df['publication_date'] < pred_end)].copy().reset_index(drop=True) + return train_df, pred_df + +# Convert test set to pandas DataFrame +def test_set_to_dataframe(dataset_path): + print("Loading dataset...") + dataset = load_from_disk(dataset_path) + print("Dataset loaded successfully.") + + print("Converting test set to pandas DataFrame...") + test_df = dataset['test'].to_pandas() + + # Convert publication_date to datetime + test_df['publication_date'] = pd.to_datetime(test_df['publication_date'], errors='coerce') + + print("Conversion completed.") + return test_df + +# The three functions below are for evaluating the model +def preprocess_batch(texts, tokenizer, max_length=256): + return tokenizer( + texts, + truncation=True, + padding=True, + max_length=max_length, + return_tensors="pt" + ) + +def classify_texts(df, model, tokenizer, device, label_column='front_page', batch_size=32, max_length=256, threshold=0.5, invert_predictions=False): + model.eval() + all_predictions = [] + + for i in tqdm(range(0, len(df), batch_size)): + batch_texts = df['text'][i:i+batch_size].tolist() + inputs = preprocess_batch(batch_texts, tokenizer, max_length) + inputs = {k: v.to(device) for k, v in inputs.items()} + + with torch.no_grad(): + outputs = model(**inputs) + predictions = torch.sigmoid(outputs.logits).squeeze().cpu().numpy() + + # Ensure predictions is two-dimensional + if predictions.ndim == 1: + predictions = predictions.reshape(-1, 1) + + all_predictions.append(predictions) + + # Concatenate all batches of predictions + all_predictions = np.vstack(all_predictions) + + # Apply threshold and possibly invert predictions + if invert_predictions: + df['result'] = (all_predictions[:, 0] < threshold).astype(int) + else: + df['result'] = (all_predictions[:, 0] >= threshold).astype(int) + + # Print some debugging information + print(f"Shape of 'result' column: {df['result'].shape}") + # print(f"First few values of 'result' column: {df['result'].head()}") + print(f"Distribution of 'result' column: {df['result'].value_counts(normalize=True)}") + print(f"Shape of '{label_column}' column: {df[label_column].shape}") + # print(f"First few values of '{label_column}' column: {df[label_column].head()}") + print(f"Distribution of '{label_column}' column: {df[label_column].value_counts(normalize=True)}") + + # Use numpy for element-wise operations + df['prediction_type'] = np.select([ + (df[label_column] == 1) & (df['result'] == 1), + (df[label_column] == 0) & (df['result'] == 0), + (df[label_column] == 0) & (df['result'] == 1), + (df[label_column] == 1) & (df['result'] == 0) + ], ['TP', 'TN', 'FP', 'FN'], default='Unknown') + + return df + +def calculate_metrics(df, label_column='front_page'): + y_true = df[label_column] + y_pred = df['result'] + + # Ensure y_true and y_pred are one-dimensional arrays + y_true = np.ravel(y_true) + y_pred = np.ravel(y_pred) + + accuracy = (y_true == y_pred).mean() + precision = precision_score(y_true, y_pred, average='binary') + recall = recall_score(y_true, y_pred, average='binary') + f1 = f1_score(y_true, y_pred, average='binary') + + return { + 'Accuracy': accuracy, + 'Precision': precision, + 'Recall': recall, + 'F1 Score': f1 + } + +# Sample for FOR +def sample_for_FOR(train_df, pred_df, tn_fn_ratio=1, random_seed=23): + + if random_seed is not None: + np.random.seed(random_seed) + + pred_fn = pred_df[pred_df['prediction_type'] == 'FN'] + pred_tn = pred_df[pred_df['prediction_type'] == 'TN'] + + pred_tn_sampled = pred_tn.sample(n=min(len(pred_tn), len(pred_fn) * tn_fn_ratio), replace=False, random_state=random_seed) + pred_sample = pd.concat([pred_fn, pred_tn_sampled]) + + train_fn = train_df[train_df['prediction_type'] == 'FN'] + train_tn = train_df[train_df['prediction_type'] == 'TN'] + + train_fn_sampled = train_fn.sample(n=min(len(train_fn), len(pred_fn)), replace=False, random_state=random_seed) + train_tn_sampled = train_tn.sample(n=min(len(train_tn), len(train_fn_sampled) * tn_fn_ratio), replace=False, random_state=random_seed) + train_sample = pd.concat([train_fn_sampled, train_tn_sampled]) + + print("Train sample count:") + print(train_sample['prediction_type'].value_counts()) + print("\nPred sample count:") + print(pred_sample['prediction_type'].value_counts()) + + return train_sample, pred_sample + +# Translate text using LLM with interruption-proof +def RD_translate(text, primary_model=translation_model, backup_model=backup_model): + max_retries = 3 + base_delay = 2 + + def try_translation(model): + for attempt in range(max_retries): + try: + completion = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": "You are a professional translator specializing in Korean to English translation, with expertise in North Korean official documents and news articles."}, + {"role": "user", "content": f"Translate the following North Korean text from Korean to English. Maintain the original tone and style while ensuring clarity in English. Pay attention to specific political terms and expressions common in North Korean media: {text}"} + ], + max_tokens=1000, + temperature=0.5 + ) + return completion.choices[0].message.content + except Exception as e: + if attempt < max_retries - 1: + delay = base_delay * (2 ** attempt) + random.uniform(0, 1) + print(f"Error occurred with model {model}: {e}. Retrying in {delay:.2f} seconds...") + time.sleep(delay) + else: + print(f"Failed after {max_retries} attempts with model {model}. Error: {e}") + return None + + # Try with primary model + result = try_translation(primary_model) + if result: + return result + + print(f"Switching to backup model {backup_model}") + + # Try with backup model + result = try_translation(backup_model) + if result: + return result + + return f"Error in translation: Failed with both models" + +def calculate_false_omission_rate(df): + grouped = df.groupby('Policy Area') + false_omission_rates = grouped['prediction_type'].apply(lambda x: + sum(x == 'FN') / (sum(x == 'FN') + sum(x == 'TN')) + if len(x) > 0 else 0 + ) + false_omission_rates = false_omission_rates.reindex(ALL_POLICY_AREAS, fill_value=0) + # Convert series to dataframe + return false_omission_rates.reset_index(name='False Omission Rate') + +# Note: Translation can take a long time (30 min for approx 500 rows). +# This function is to resume the translation process from interrupted point. +def translate_csv(input_file, output_file, batch_size=20): + if not os.path.exists(input_file): + raise FileNotFoundError(f"Input file '{input_file}' does not exist.") + + df_input = pd.read_csv(input_file) + total_rows = len(df_input) + print(f"Total rows in input file: {total_rows}") + + if os.path.exists(output_file): + df = pd.read_csv(output_file) + print(f"Resuming from existing output file: {output_file}") + completed_translations = df['Translation_EN-US'].notna().sum() + else: + df = df_input.copy() + df['Translation_EN-US'] = "" + df['Translation_EN-US'] = df['Translation_EN-US'].astype(str) # Ensure string type + df.to_csv(output_file, index=False) + print(f"Created new output file: {output_file}") + completed_translations = 0 + + print(f"Translations completed: {completed_translations}") + + with tqdm(total=total_rows, desc="Translating", unit="row", initial=completed_translations) as pbar: + for index, row in df.iterrows(): + if pd.isna(row['Translation_EN-US']) or row['Translation_EN-US'] == "": + korean_text = row['text'] + english_translation = RD_translate(korean_text) + df.at[index, 'Translation_EN-US'] = english_translation + pbar.update(1) + if (pbar.n % batch_size == 0) or (pbar.n == total_rows): + df.to_csv(output_file, index=False) + print(f"Translated and saved up to row {pbar.n}") + + print("Translation process completed.") + df.to_csv(output_file, index=False) + print("Final save completed.") + +# Summarizing and tagging texts +def clean_text(text): + return re.sub(r'^\s*\n+', '', text.strip()) + +def process_entry(entry, analysis_model=analysis_model, max_retries=3): + prompt = f"""Analyze the following news article from North Korea and provide: +News text: '{entry}' + +1. A concise summary in 3-5 bullet points. +2. Tags in two categories: + a) 1 relevant policy area selected from the following list: + ['Economic and Industrial Policy', 'Agriculture and Food Security', 'Infrastructure and Public Works', + 'Public Health and Safety', 'Defense and National Security', 'Nuclear Power', 'Social Policy and Ideology', + 'Labor and Employment', 'Education and Human Capital', 'Foreign Relations and Diplomacy', 'Science and Technology', 'Miscellaneous'] + b) 3-5 specific keywords or concepts mentioned in the article +Use the following format: +Summary: +- +- +- +Tags: +[Policy Area 1], [Keyword 1], [Keyword 2], [Keyword 3], [Keyword 4], [Keyword 5] +""" + for attempt in range(max_retries): + try: + response = client.chat.completions.create( + model=analysis_model, + messages=[ + {"role": "system", "content": "You are an expert analyst on North Korean politics and policies. Your task is to summarize news articles and identify key policy areas. Provide the summary and tags directly without any introductory text."}, + {"role": "user", "content": prompt} + ], + max_tokens=350, + n=1, + temperature=0.2, + ) + + result = response.choices[0].message.content.strip() + + # Check if "Tags:" exists in the result + if "Tags:" in result: + summary, tags = result.split("Tags:") + summary = clean_text(summary.replace("Summary:", "")) + tags = clean_text(tags) + tags = re.sub(r'\[|\]', '', tags) + policy_area = tags.split(",")[0].strip() + + # If tags or policy area is "N/A", retry + if tags == "N/A" or policy_area == "N/A": + print(f"Attempt {attempt + 1}: Tags or Policy Area is N/A. Retrying...") + continue + + return summary, tags, policy_area + else: + print(f"Attempt {attempt + 1}: No Tags found in the response. Retrying...") + continue + + except Exception as e: + print(f"Attempt {attempt + 1}: Error occurred: {e}. Retrying in 5 seconds...") + time.sleep(5) + + print(f"Failed after {max_retries} attempts. Returning error message.") + return "Error in processing", "Error in processing", "Error in processing" + +# Process the entire dataframe +def process_df(df): + tqdm.pandas(desc="Processing entries") + df['Summary'], df['Tags'], df['Policy Area'] = zip(*df['Translation_EN-US'].progress_apply(process_entry)) + return df + + +# Plotting FOR +def ensure_dir(file_path): + directory = os.path.dirname(file_path) + if not os.path.exists(directory): + os.makedirs(directory) + +def plot_false_omission_rates(train_for, pred_for, train_counts, pred_counts, train_date_range, pred_date_range, pred_start_date): + plt.rcdefaults() + sns.set_style("whitegrid", {'axes.facecolor': 'white'}) + + fig, ax = plt.subplots(figsize=(16, 10), facecolor='white') + + x = np.arange(len(ALL_POLICY_AREAS)) + width = 0.35 + + train_bars = ax.barh(x - width/2, train_for, width, label=train_date_range, color='#1f77b4', alpha=0.8) + pred_bars = ax.barh(x + width/2, pred_for, width, label=pred_date_range, color='#ff7f0e', alpha=0.8) + + ax.set_xlabel('False Omission Rate', color='black') + ax.set_title('False Omission Rate by Policy Area', fontsize=16, color='black') + ax.set_yticks(x) + ax.set_yticklabels(ALL_POLICY_AREAS, color='black') + ax.legend(fontsize=12) + + ax.tick_params(axis='x', colors='black') + ax.tick_params(axis='y', colors='black') + + for text in ax.get_legend().get_texts(): + text.set_color('black') + + def autolabel(rects, counts): + for rect, (fn, total) in zip(rects, counts): + width = rect.get_width() + # Add percentage label + ax.annotate(f'{width:.0%}', + xy=(width, rect.get_y() + rect.get_height()*0.25), + xytext=(5, 0), # 5 points horizontal offset + textcoords="offset points", + ha='left', va='bottom', + color='black') + # Add fn / total label + ax.text(rect.get_x() + rect.get_width() / 2, + rect.get_y() + rect.get_height() / 2, + f'{fn} / {total}', + ha='center', va='center', + fontweight='bold', color='black', + fontsize=8) + + autolabel(train_bars, train_counts) + autolabel(pred_bars, pred_counts) + + ax.grid(color='lightgray', linestyle='-', linewidth=0.5) + + fig.patch.set_facecolor('white') + + plt.tight_layout() + file_path = f'../results/analysis_results/{pred_start_date}_FOR_comparison.png' + ensure_dir(file_path) + plt.savefig(file_path, dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') + plt.show() + plt.close() + +# Locate the policy areas that have significant changes in False Omission Rate for further analysis +def filter_policy_areas(for_changes, sample_counts, for_threshold=0.15, sample_threshold=10, additional_threshold=0.10): + results = { + 'significant_change': [], + 'potential_change': [], + 'stable': [], + 'insufficient_data': [] + } + + for policy in for_changes.index: + change = for_changes[policy] + train_sample = sample_counts.loc[policy, 'Train'] + pred_sample = sample_counts.loc[policy, 'Prediction'] + total_sample = train_sample + pred_sample + + if total_sample >= sample_threshold: + if change >= for_threshold: + results['significant_change'].append((policy, change, total_sample)) + elif change >= additional_threshold: + results['potential_change'].append((policy, change, total_sample)) + else: + results['stable'].append((policy, change, total_sample)) + elif total_sample >= sample_threshold // 2: + if change >= for_threshold: + results['potential_change'].append((policy, change, total_sample)) + else: + results['stable'].append((policy, change, total_sample)) + else: + results['insufficient_data'].append((policy, change, total_sample)) + + for key in results: + results[key] = sorted(results[key], key=lambda x: abs(x[1]), reverse=True) + + return results + +def analyze_policy_changes(train_sample, pred_sample, focused_policies, model=analysis_model, batch_size=None): + def prepare_data(sample, policies): + filtered_data = sample[sample['Policy Area'].isin(policies)] + articles = [] + for _, row in filtered_data.iterrows(): + article = f"Summary: {row['Summary']}\nTags: {row['Tags']}\n" + articles.append(article) + return articles + + train_articles = prepare_data(train_sample, focused_policies) + pred_articles = prepare_data(pred_sample, focused_policies) + + def analyze_full_batch(train_batch, pred_batch): + prompt = f"""As an expert analyst on North Korean politics and policies, analyze these sets of article summaries and tags from two periods related to {', '.join(focused_policies)}: + + Period 1 (Training Sample): + {'-' * 40} + {"".join(train_batch)} + {'-' * 40} + + Period 2 (Prediction Sample): + {'-' * 40} + {"".join(pred_batch)} + {'-' * 40} + + Please provide a comprehensive analysis of the main themes, changes, and potential policy shifts observed. Focus on: + 1. Emerging or increasing themes in Period 2 + 2. Decreasing or disappearing themes + 3. Any surprising elements or unexpected developments + 4. Implications for North Korea's priorities or challenges + 5. Consistent themes across both periods and their implications + 6. Potential future policy directions based on these trends + + Provide a structured analysis, using specific examples from the summaries and tags to support your observations and conclusions. + """ + + max_retries = 3 + for attempt in range(max_retries): + try: + response = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": "You are an expert analyst on North Korean politics and policies. Your task is to analyze sets of article summaries and tags from different periods and provide insights on potential policy changes and focus areas."}, + {"role": "user", "content": prompt} + ], + max_tokens=2000, + n=1, + temperature=0.3 + ) + return response.choices[0].message.content + + except Exception as e: + if attempt < max_retries - 1: + print(f"Error occurred: {e}. Retrying in 5 seconds...") + sleep(5) + else: + print(f"Failed after {max_retries} attempts. Error: {e}") + return f"An error occurred while analyzing the data: {str(e)}" + + if batch_size is None or (len(train_articles) <= batch_size and len(pred_articles) <= batch_size): + + return analyze_full_batch(train_articles, pred_articles) + else: + + batch_analyses = [] + for i in range(0, max(len(train_articles), len(pred_articles)), batch_size): + train_batch = train_articles[i:i+batch_size] + pred_batch = pred_articles[i:i+batch_size] + batch_analysis = analyze_full_batch(train_batch, pred_batch) + batch_analyses.append(batch_analysis) + + final_prompt = f"""You have analyzed multiple batches of article summaries and tags from two periods related to North Korean policies in {', '.join(focused_policies)}. Based on these analyses, provide a comprehensive overview of the changes and trends observed. Please address: + + 1. What are the most significant themes or topics that have become more prevalent in Period 2? What new themes have emerged, and which ones have decreased in importance? + 2. What might these changes suggest about potential policy shifts or new focus areas in North Korea? How do these align with known domestic and international developments? + 3. Are there any surprising elements in the Period 2 data that might indicate unexpected developments or policy changes? How do these differ from North Korea's traditional policy positions? + 4. How do these differences reflect changes in North Korea's priorities or challenges? What implications might these have for North Korea's domestic and foreign policy? + 5. What themes remain consistent across both periods? What might this suggest about North Korea's long-term strategies or core interests? + 6. Based on these trends, what predictions can you make about future policy directions in North Korea? How might these changes affect international engagement with North Korea? + + Please provide a structured analysis, synthesizing the information from all batches and using specific examples to support your observations and conclusions. + + Here are the summaries of each batch analysis: + + {'-' * 40} + {"".join(batch_analyses)} + {'-' * 40} + """ + + max_retries = 3 + for attempt in range(max_retries): + try: + response = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": "You are an expert analyst on North Korean politics and policies. Your task is to synthesize analyses of multiple batches of article summaries and tags to provide a comprehensive overview of policy changes and trends."}, + {"role": "user", "content": final_prompt} + ], + max_tokens=2000, + n=1, + temperature=0.3 + ) + return response.choices[0].message.content + + except Exception as e: + if attempt < max_retries - 1: + print(f"Error occurred: {e}. Retrying in 5 seconds...") + sleep(5) + else: + print(f"Failed after {max_retries} attempts. Error: {e}") + return f"An error occurred while analyzing the data: {str(e)}" \ No newline at end of file diff --git a/src/config.py b/src/config.py new file mode 100644 index 0000000..5990d25 --- /dev/null +++ b/src/config.py @@ -0,0 +1,24 @@ +import os +from dotenv import load_dotenv + +# Load environment variables from .env +load_dotenv() + +# Project Folder +PROJ_FOLDER = os.getenv("PROJ_FOLDER") +LOGS_FOLDER = os.getenv("LOGS_FOLDER") +HYPERPARAMETER_SEARCH_LOGS = os.getenv("HYPERPARAMETER_SEARCH_LOGS") +FULL_TRAINING_LOGS = os.getenv("FULL_TRAINING_LOGS") +ENVIRONMENT = os.getenv("ENVIRONMENT", "development") +CUDA_VISIBLE_DEVICES = os.getenv("CUDA_VISIBLE_DEVICES") + +# Check if all required environment variables are set +required_vars = ["PROJ_FOLDER", "LOGS_FOLDER", "HYPERPARAMETER_SEARCH_LOGS", "FULL_TRAINING_LOGS"] +missing_vars = [var for var in required_vars if not os.getenv(var)] + +if missing_vars: + raise EnvironmentError(f"Missing required environment variables: {', '.join(missing_vars)}") + +# Set CUDA_VISIBLE_DEVICES if available +if CUDA_VISIBLE_DEVICES: + os.environ["CUDA_VISIBLE_DEVICES"] = CUDA_VISIBLE_DEVICES \ No newline at end of file diff --git a/src/corpus_preparation.py b/src/corpus_preparation.py new file mode 100644 index 0000000..419836f --- /dev/null +++ b/src/corpus_preparation.py @@ -0,0 +1,100 @@ +import argparse +import pandas as pd +from datetime import datetime, timedelta +import numpy as np +import os +from datasets import Dataset, DatasetDict +from config import PROJ_FOLDER + +def read_data(file_path): + df = pd.read_csv(file_path) + df['publication_date'] = pd.to_datetime(df['publication_date'], errors='coerce') + df.dropna(subset=['publication_date', 'text', 'front_page'], inplace=True) + return df + +def get_date_range(df): + min_date = df['publication_date'].min().strftime('%Y-%m-%d') + max_date = df['publication_date'].max().strftime('%Y-%m-%d') + return min_date, max_date + +def select_columns(df, columns_to_keep): + return df[columns_to_keep] + +def stratified_date_split(df, start_date, end_date, block_size=20, train_ratio=14, validate_ratio=3, test_ratio=3): + # Ensure the ratios add up to the block size + assert train_ratio + validate_ratio + test_ratio == block_size + + # Initialize lists to store the dataframes + train_frames = [] + validate_frames = [] + test_frames = [] + + # Get the minimum and maximum dates + min_date = max(pd.to_datetime(start_date), df['publication_date'].min()) + max_date = min(pd.to_datetime(end_date), df['publication_date'].max()) + + current_date = min_date + while current_date <= max_date: + end_date = current_date + pd.Timedelta(days=block_size) + + # Select the block of data + block = df[(df['publication_date'] >= current_date) & (df['publication_date'] < end_date)] + + # Get the unique dates in the block + days = block['publication_date'].dt.date.unique() + + # Shuffle the dates + np.random.shuffle(days) + + # Split the dates into train, validate, and test sets + train_days = days[:train_ratio] if len(days) > train_ratio else days + valid_days = days[train_ratio:train_ratio + validate_ratio] if len(days) > train_ratio + validate_ratio else days[len(train_days):] + test_days = days[train_ratio + validate_ratio:train_ratio + validate_ratio + test_ratio] if len(days) > train_ratio + validate_ratio + test_ratio else days[len(train_days) + len(valid_days):] + + # Append the dataframes to the respective lists + if len(train_days) > 0: + train_frames.append(block[block['publication_date'].dt.date.isin(train_days)]) + if len(valid_days) > 0: + validate_frames.append(block[block['publication_date'].dt.date.isin(valid_days)]) + if len(test_days) > 0: + test_frames.append(block[block['publication_date'].dt.date.isin(test_days)]) + + # Update the current date + current_date = end_date + + # Concatenate the dataframes + train = pd.concat(train_frames) if train_frames else pd.DataFrame() + validate = pd.concat(validate_frames) if validate_frames else pd.DataFrame() + test = pd.concat(test_frames) if test_frames else pd.DataFrame() + + # Reset the index + train.reset_index(drop=True, inplace=True) + validate.reset_index(drop=True, inplace=True) + test.reset_index(drop=True, inplace=True) + + return train, validate, test + + +def save_to_datasets(train, validate, test, save_path, dataset_name): + dataset_dict = DatasetDict({ + 'train': Dataset.from_pandas(train), + 'validate': Dataset.from_pandas(validate), + 'test': Dataset.from_pandas(test) + }) + dataset_dict.save_to_disk(os.path.join(save_path, dataset_name)) # Save the whole DatasetDict + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Prepare datasets for analysis based on specified date range") + parser.add_argument("file_path", type=str, help="File path for the input CSV data file") + parser.add_argument("--start_date", type=str, help="Start date for the dataset processing in YYYY-MM-DD format") + parser.add_argument("--end_date", type=str, help="End date for the dataset processing in YYYY-MM-DD format") + parser.add_argument("--save_path", type=str, default=f"{PROJ_FOLDER}/data/processed/", help="Directory to save the processed datasets") + args = parser.parse_known_args()[0] + df = read_data(args.file_path) + df = select_columns(df, ['publication_date', 'text', 'front_page']) + if not args.start_date or not args.end_date: + args.start_date, args.end_date = get_date_range(df) + train, validate, test = stratified_date_split(df, args.start_date, args.end_date) + dataset_name = f"whole_sets_{pd.to_datetime(args.start_date).strftime('%Y-%m')}_to_{pd.to_datetime(args.end_date).strftime('%Y-%m')}" + save_to_datasets(train, validate, test, args.save_path, dataset_name) + print("Datasets successfully saved.") diff --git a/src/datasets_preparation.py b/src/datasets_preparation.py new file mode 100644 index 0000000..125ba51 --- /dev/null +++ b/src/datasets_preparation.py @@ -0,0 +1,124 @@ +import os +import argparse +from datetime import datetime +from datasets import Dataset, DatasetDict, load_from_disk +import logging +import random +from config import PROJ_FOLDER +from model_config import INITIAL_TRAIN_YEARS, PREDICTION_PERIOD, STEP_MONTHS + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s: %(message)s') + +def parse_date(date_str): + try: + return datetime.strptime(date_str, '%Y-%m-%d') + except ValueError: + logging.error(f"Date format error with '{date_str}'. Expected format: YYYY-MM-DD.") + raise + +def safe_date_filter(example, start_date, end_date): + """Safely filter examples based on publication date.""" + date = example['publication_date'] + return isinstance(date, datetime) and start_date <= date <= end_date + +def slice_dataset(dataset, start_date, end_date): + if not (isinstance(start_date, datetime) and isinstance(end_date, datetime)): + raise ValueError("Start date and end date must be datetime objects") + + return { + key: dataset[key].filter(lambda example: safe_date_filter(example, start_date, end_date)) + for key in ['train', 'validate', 'test'] + } + +def oversample(dataset): + """Oversample 'True' instances in the training dataset based on the 'front_page' column.""" + true_docs = dataset.filter(lambda x: x['front_page'] == 1) + false_docs = dataset.filter(lambda x: x['front_page'] == 0) + + count_true = len(true_docs) + count_false = len(false_docs) + oversample_factor = count_false // count_true + oversampled_true_docs = Dataset.concatenate([true_docs] * oversample_factor) + difference = count_false - len(oversampled_true_docs) + + if difference > 0: + oversampled_true_docs = Dataset.concatenate([ + oversampled_true_docs, + true_docs.shuffle(seed=42).select(range(difference)) + ]) + + balanced_train_set = Dataset.concatenate([oversampled_true_docs, false_docs]) + balanced_train_set = balanced_train_set.shuffle(seed=42) + return balanced_train_set + +def prepare_datasets(start_date_train, end_date_train, start_date_pred, end_date_pred, dataset): + train_data = slice_dataset(dataset, parse_date(start_date_train), parse_date(end_date_train)) + oversampled_train_data = oversample(train_data['train']) + + train_dataset = DatasetDict({ + 'train': oversampled_train_data, + 'validate': train_data['validate'], + 'test': train_data['test'] + }) + + # For prediction data, convert to Dataset + pred_dataset = Dataset.concatenate([ + slice_dataset(dataset, parse_date(start_date_pred), parse_date(end_date_pred))[key] + for key in ['train', 'validate', 'test'] + ]) + + # Print label distribution + unique_front_pages = set(pred_dataset['front_page']) + print("Prediction Dataset Label Distribution:") + print(unique_front_pages) + + return train_dataset, pred_dataset + +## Save to DatasetDict +def save_datasets(train_dataset, pred_dataset, start_date_train, end_date_train, start_date_pred, end_date_pred): + save_dir = os.path.join(PROJ_FOLDER, "data", "temp", f'{INITIAL_TRAIN_YEARS}Y{PREDICTION_PERIOD}M{STEP_MONTHS}S') + os.makedirs(save_dir, exist_ok=True) + + train_filename = f"train_{start_date_train.strftime('%Y-%m')}_{end_date_train.strftime('%Y-%m')}" + pred_filename = f"pred_{start_date_pred.strftime('%Y-%m')}_{end_date_pred.strftime('%Y-%m')}" + + train_dataset.save_to_disk(os.path.join(save_dir, train_filename)) + pred_dataset.save_to_disk(os.path.join(save_dir, pred_filename)) + + # Print unique front_page values + unique_front_pages = set(pred_dataset['front_page']) + print("Saving Prediction Dataset Label Distribution:") + print(unique_front_pages) + + logging.info(f"Datasets saved as {train_filename} and {pred_filename} in {save_dir}.") + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Prepare and save datasets for training and prediction using specified date ranges.") + parser.add_argument("start_date_train", type=str, help="Start date for the training dataset in YYYY-MM-DD format.") + parser.add_argument("end_date_train", type=str, help="End date for the training dataset in YYYY-MM-DD format.") + parser.add_argument("start_date_pred", type=str, help="Start date for the prediction dataset in YYYY-MM-DD format.") + parser.add_argument("end_date_pred", type=str, help="End date for the prediction dataset in YYYY-MM-DD format.") + + args = parser.parse_args() + + # Assuming the training dataset directory is set in an environment variable + whole_datasets = load_from_disk(os.path.join(PROJ_FOLDER, "data/processed/whole_sets_2018-01_to_2024-01")) + + train_dataset, pred_dataset = prepare_datasets( + args.start_date_train, + args.end_date_train, + args.start_date_pred, + args.end_date_pred, + whole_datasets + ) + + save_datasets( + train_dataset, + pred_dataset, + parse_date(args.start_date_train), + parse_date(args.end_date_train), + parse_date(args.start_date_pred), + parse_date(args.end_date_pred) + ) + + logging.info("Datasets have been prepared and are ready for training and prediction.") diff --git a/src/model.py b/src/model.py new file mode 100644 index 0000000..9dd7706 --- /dev/null +++ b/src/model.py @@ -0,0 +1,247 @@ +import os +import json +import optuna +import argparse +import evaluate +from transformers import Trainer, TrainingArguments, EarlyStoppingCallback +from datasets import load_from_disk +from datetime import datetime +from dateutil.relativedelta import relativedelta +import pandas as pd +import torch +from util import create_logs_folders, compute_metrics, save_best_model, load_best_hyperparams, preprocess_function +from config import PROJ_FOLDER, HYPERPARAMETER_SEARCH_LOGS, FULL_TRAINING_LOGS +from model_config import MODEL_CHECKPOINT, NUM_LABELS, MAX_LENGTH, MAX_ATTEMPTS, N_TRIALS, INITIAL_TRAIN_START, model_name, scheme_name, model_path, get_tokenizer, get_device, model_init, get_optuna_settings +from datasets_preparation import prepare_datasets, save_datasets + +device = get_device() +tokenizer = get_tokenizer() +task_evaluator = evaluate.evaluator("text-classification") + +def get_previous_date_str(start_date_str, step_months=1, initial_train_start=INITIAL_TRAIN_START): + current_start_date = datetime.strptime(start_date_str, '%Y-%m-%d') + initial_train_start_date = datetime.strptime(initial_train_start, '%Y-%m-%d') + + previous_start_date = current_start_date - relativedelta(months=step_months) + + if previous_start_date < initial_train_start_date: + return None + + previous_start_date_str = previous_start_date.strftime('%Y-%m-%d') + return previous_start_date_str + + +def run_hyperparameter_search(start_date_str, + encoded_dataset, + n_trials=N_TRIALS): + create_logs_folders() + # Calculate previous hyperparameters path + previous_date_str= get_previous_date_str(start_date_str) + initial_hyperparams = None # Alternative: load_best_hyperparams(previous_date_str) + + if not initial_hyperparams: + print("Warning: No initial hyperparameters found, starting from default settings.") + + def objective(trial): + hyperparams = get_optuna_settings(trial, initial_hyperparams=initial_hyperparams) + output_dir = os.path.join(HYPERPARAMETER_SEARCH_LOGS, model_name, scheme_name, start_date_str) + os.makedirs(output_dir, exist_ok=True) + + search_args = TrainingArguments( + output_dir=output_dir, + evaluation_strategy="epoch", + save_strategy="epoch", + load_best_model_at_end=True, + metric_for_best_model="f1", + logging_dir=os.path.join(output_dir, 'logs'), + logging_steps=10, + logging_strategy="epoch", + save_total_limit=2, + report_to=None, + seed=42, + **hyperparams + ) + + model = model_init().to(device) + search_trainer = Trainer( + model=model, + args=search_args, + train_dataset=encoded_dataset["train"].shard(index=1, num_shards=10), # Use 1/10 of the training data for faster training + eval_dataset=encoded_dataset["validate"], + tokenizer=tokenizer, + compute_metrics=compute_metrics, + callbacks=[EarlyStoppingCallback(early_stopping_patience=3)] + ) + + search_trainer.train() + eval_results = search_trainer.evaluate() + + return eval_results["eval_f1"] + + study = optuna.create_study(direction="maximize", study_name="Model Hyperparameter Optimization") + study.optimize(objective, n_trials=n_trials, gc_after_trial=True) + + + print("Number of finished trials: ", len(study.trials)) + print("Best trial:") + trial = study.best_trial + print(" Value: ", trial.value) + print(" Params: ") + for key, value in trial.params.items(): + print(f" {key}: {value}") + + + return study.best_trial + +def run_full_training(start_date_str, encoded_dataset): + best_f1 = 0 + best_model_dir = None + best_hyperparams = None + max_attempts = MAX_ATTEMPTS # Number of attempts to try before stopping + + for attempt in range(max_attempts): + print(f"\n\033[91mAttempt {attempt + 1}...\033[0m") + if best_hyperparams is None: + print("\n\033[91mRunning hyperparameter search...\033[0m") + best_trial = run_hyperparameter_search(start_date_str, encoded_dataset) + attempt_hyperparams = best_trial.params + print(f"Using hyperparameters: {attempt_hyperparams}") + + output_dir = os.path.join(HYPERPARAMETER_SEARCH_LOGS, start_date_str) + os.makedirs(output_dir, exist_ok=True) + + + training_args = TrainingArguments( + output_dir=output_dir, + evaluation_strategy="epoch", + save_strategy="epoch", + per_device_eval_batch_size=128, + load_best_model_at_end=True, + metric_for_best_model="eval_f1", + learning_rate=attempt_hyperparams['learning_rate'], + per_device_train_batch_size=attempt_hyperparams['per_device_train_batch_size'], + num_train_epochs=30, + weight_decay=attempt_hyperparams['weight_decay'], + warmup_steps=attempt_hyperparams['warmup_steps'], + lr_scheduler_type="cosine_with_restarts", + gradient_accumulation_steps=attempt_hyperparams['gradient_accumulation_steps'], + max_grad_norm=attempt_hyperparams['max_grad_norm'], + adam_epsilon=attempt_hyperparams['adam_epsilon'], + push_to_hub=False, + report_to=None, + logging_dir=output_dir, + save_total_limit=2 + ) + + model = model_init().to(device) + trainer = Trainer( + model=model, + args=training_args, + train_dataset=encoded_dataset["train"], + eval_dataset=encoded_dataset["validate"], + compute_metrics=compute_metrics, + callbacks=[EarlyStoppingCallback(early_stopping_patience=3)] + ) + trainer.train() + test_result = trainer.evaluate(encoded_dataset["test"]) + eval_f1 = test_result['eval_f1'] + + if eval_f1 > best_f1: + best_f1 = eval_f1 + best_model_dir = output_dir + best_hyperparams = attempt_hyperparams + print(f"New best F1 score: {best_f1} achieved on attempt {attempt + 1}") + + if best_model_dir: + save_best_model(model, start_date_str, best_f1, best_hyperparams) + print(f"\033[94mBest model and hyperparameters saved to disk with F1 score: {best_f1}.\033[0m") + + return best_hyperparams, best_f1 + +def run_prediction_evaluation(start_date_train, encoded_pred_dataset): + best_model_path = os.path.join(model_path, start_date_train, "best_model") + + model = model_init(best_model_path).to(device) + + task_evaluator = evaluate.evaluator("text-classification") + eval_results = task_evaluator.compute( + model_or_pipeline=model, + tokenizer=get_tokenizer(), + data=encoded_pred_dataset, + input_column="text", + label_column="front_page", + label_mapping={'LABEL_0': 0, 'LABEL_1': 1}, + metric='f1' + ) + pred_f1_score = eval_results['f1'] + + print(f"\n\033[91mEval results on prediction dataset: {pred_f1_score}\033[0m") + + result = {'f1': pred_f1_score} + print(json.dumps(result)) + return pred_f1_score + + + + +def parse_args(): + parser = argparse.ArgumentParser(description="Run model training and evaluation.") + parser.add_argument("start_date_train", type=str, help="Start date for the training dataset in YYYY-MM-DD format.") + parser.add_argument("end_date_train", type=str, help="End date for the training dataset in YYYY-MM-DD format.") + parser.add_argument("start_date_pred", type=str, help="Start date for the prediction dataset in YYYY-MM-DD format.") + parser.add_argument("end_date_pred", type=str, help="End date for the prediction dataset in YYYY-MM-DD format.") + parser.add_argument("--n_trials", type=int, default=10, help="Number of trials for hyperparameter optimization.") + parser.add_argument("--previous_hyperparams_path", type=str, default=None, help="Path to the JSON file containing the best hyperparameters from the last period.") + return parser.parse_args() + +def main(args): + start_date_train = datetime.strptime(args.start_date_train, '%Y-%m-%d') + end_date_train = datetime.strptime(args.end_date_train, '%Y-%m-%d') + start_date_pred = datetime.strptime(args.start_date_pred, '%Y-%m-%d') + end_date_pred = datetime.strptime(args.end_date_pred, '%Y-%m-%d') + + # Load the file to store results + # current_date = datetime.now().strftime("%Y-%m-%d") + all_res_file = os.path.join(PROJ_FOLDER, "results", f"{scheme_name}_all_results.csv") + all_res = pd.read_csv(all_res_file) + + data_dir = os.path.join(PROJ_FOLDER, "data", "temp", scheme_name) + + train_filename = f"train_{start_date_train.strftime('%Y-%m')}_{end_date_train.strftime('%Y-%m')}" + pred_filename = f"pred_{start_date_pred.strftime('%Y-%m')}_{end_date_pred.strftime('%Y-%m')}" + + train_dataset_path = os.path.join(data_dir, train_filename) + pred_dataset_path = os.path.join(data_dir, pred_filename) + + # Load encoded datasets + encoded_train_dataset = load_from_disk(train_dataset_path).map(preprocess_function, batched=True) + encoded_pred_dataset = load_from_disk(pred_dataset_path).map(preprocess_function, batched=True, remove_columns=["publication_date"]) + + # Hyperparameter search and model training + best_hyperparams, best_f1 = run_full_training(args.start_date_train, encoded_train_dataset) + + + # Print the training results + print(f"Training completed for the period starting {start_date_train.strftime('%Y-%m-%d')}.") + print(f"Best F1: {best_f1}, Best Hyperparameters: {best_hyperparams}") + + + # Evaluate the model on prediction dataset + print("\nEvaluating prediction dataset...") + prediction_f1 = run_prediction_evaluation(start_date_train.strftime('%Y-%m-%d'), encoded_pred_dataset) + + # Announce the evaluation results + print(f"\nPrediction F1 score: {prediction_f1}") + + # Concatenate the results to the dataframe + res = pd.DataFrame([[start_date_train.strftime('%Y-%m-%d'), end_date_train.strftime('%Y-%m-%d'), + start_date_pred.strftime('%Y-%m-%d'), end_date_pred.strftime('%Y-%m-%d'), + best_f1, prediction_f1]], + columns=['Train Start', 'Train End', 'Pred Start', 'Pred End', 'Test F1', 'Pred F1']) + + all_res = pd.concat([all_res, res], ignore_index=True) + all_res.to_csv(all_res_file, index=False) + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/src/model_config.py b/src/model_config.py new file mode 100644 index 0000000..5470b17 --- /dev/null +++ b/src/model_config.py @@ -0,0 +1,142 @@ +from transformers import AlbertConfig, AlbertForSequenceClassification, AutoModelForSequenceClassification, RobertaConfig, TrainingArguments, BertTokenizerFast +from transformers import TFAutoModelForSequenceClassification +from config import PROJ_FOLDER +from optuna.trial import Trial +import torch +import sys +import os + +# Model Checkpoint +MODEL_CHECKPOINT = "klue/roberta-base" # Alternative: "kykim/albert-kor-base" +INITIAL_TRAIN_START = "2018-01-01" +INITIAL_TRAIN_YEARS = 4 #year(s) +FINAL_TRAIN_START = "2019-11-01" +PREDICTION_PERIOD = 3 # months +STEP_MONTHS = 1 # month(s) +MAX_PRED_END = "2024-02-01" + +model_name = MODEL_CHECKPOINT.split("/")[-1] +scheme_name = f"{INITIAL_TRAIN_YEARS}Y{PREDICTION_PERIOD}M{STEP_MONTHS}S" +model_path = os.path.join(PROJ_FOLDER, "models", model_name, scheme_name) + + +NUM_LABELS = 2 +MAX_LENGTH = 256 + +# Setup hyperparameter search +OPTUNA_HYPERPARAMETERS = { + "batch_size": [8, 16], + "num_train_epochs": [6, 10], + "learning_rate": (1e-5, 1e-4), + "weight_decay": (0, 0.3), + "warmup_steps": (500, 2000), + "gradient_accumulation_steps": (1, 4), + "max_grad_norm": (0.5, 2.0), + "adam_epsilon": (1e-8, 1e-6) +} + +MAX_ATTEMPTS = 3 +N_TRIALS = 10 + + +def get_tokenizer(): + return BertTokenizerFast.from_pretrained(MODEL_CHECKPOINT) + +def get_device(): + """Determine which device to use (MPS, CUDA, or CPU).""" + print(f"System platform: {sys.platform}") + + if sys.platform == 'darwin': # Check if the operating system is macOS + print("Checking for Apple MPS...") + if torch.backends.mps.is_available(): + print("MPS is available.") + return torch.device("mps") + else: + print("MPS is NOT available.") + + print("Checking for CUDA...") + if torch.cuda.is_available(): + cuda_devices = os.getenv("CUDA_VISIBLE_DEVICES") + print(f"CUDA is available. Using GPU: {cuda_devices}") + return torch.device("cuda") + + print("Neither MPS nor CUDA is available. Using CPU.") + return torch.device("cpu") + + +def model_init(best_model_path=None): + device = get_device() # Get the appropriate device + config = RobertaConfig.from_pretrained( # Or 'AlbertConfig.from_pretrained(' for Albert model + MODEL_CHECKPOINT, + num_labels=NUM_LABELS, + hidden_dropout_prob = 0.2, # Dropout for hidden layers + attention_probs_dropout_prob = 0.2 # Dropout for attention probabilities + ) + if best_model_path: + model = AutoModelForSequenceClassification.from_pretrained( + best_model_path, + config=config, + local_files_only=True, + ignore_mismatched_sizes=True + ).to(device) + else: + # Load model from the default MODEL_CHECKPOINT + model = AutoModelForSequenceClassification.from_pretrained(MODEL_CHECKPOINT, config=config).to(device) + return model + + +def get_optuna_settings(trial, initial_hyperparams=None): + hyperparams = { + "per_device_train_batch_size": trial.suggest_categorical( + name="per_device_train_batch_size", + choices=OPTUNA_HYPERPARAMETERS["batch_size"], + ), + "num_train_epochs": trial.suggest_int( + name="num_train_epochs", + low=OPTUNA_HYPERPARAMETERS["num_train_epochs"][0], + high=OPTUNA_HYPERPARAMETERS["num_train_epochs"][1], + ), + "learning_rate": trial.suggest_float( + name="learning_rate", + low=OPTUNA_HYPERPARAMETERS["learning_rate"][0], + high=OPTUNA_HYPERPARAMETERS["learning_rate"][1], + log=True, + ), + "weight_decay": trial.suggest_float( + name="weight_decay", + low=OPTUNA_HYPERPARAMETERS["weight_decay"][0], + high=OPTUNA_HYPERPARAMETERS["weight_decay"][1], + ), + "warmup_steps": trial.suggest_int( + name="warmup_steps", + low=OPTUNA_HYPERPARAMETERS["warmup_steps"][0], + high=OPTUNA_HYPERPARAMETERS["warmup_steps"][1], + ), + "gradient_accumulation_steps": trial.suggest_int( + name="gradient_accumulation_steps", + low=OPTUNA_HYPERPARAMETERS["gradient_accumulation_steps"][0], + high=OPTUNA_HYPERPARAMETERS["gradient_accumulation_steps"][1], + ), + "max_grad_norm": trial.suggest_float( + name="max_grad_norm", + low=OPTUNA_HYPERPARAMETERS["max_grad_norm"][0], + high=OPTUNA_HYPERPARAMETERS["max_grad_norm"][1], + ), + "adam_epsilon": trial.suggest_float( + name="adam_epsilon", + low=OPTUNA_HYPERPARAMETERS["adam_epsilon"][0], + high=OPTUNA_HYPERPARAMETERS["adam_epsilon"][1], + log=True, + ) + } + # Option to inject initial_hyperparams if they are provided + if initial_hyperparams: + for key, value in initial_hyperparams.items(): + if key in hyperparams and isinstance(hyperparams[key], (int, float)): + hyperparams[key] = value # Update with initial hyperparam value if needed + + return hyperparams + +device = get_device() +print(f"Selected device: {device}") + diff --git a/src/test_device.py b/src/test_device.py new file mode 100644 index 0000000..51022df --- /dev/null +++ b/src/test_device.py @@ -0,0 +1,27 @@ +import sys +import torch + +def get_device(): + """Determine which device to use (MPS, CUDA, or CPU).""" + print(f"System platform: {sys.platform}") + + if sys.platform == 'darwin': # Check if the operating system is macOS + print("Checking for Apple MPS...") + if torch.backends.mps.is_available(): + print("MPS is available.") + return torch.device("mps") + else: + print("MPS is NOT available.") + + print("Checking for CUDA...") + if torch.cuda.is_available(): + cuda_devices = os.getenv("CUDA_VISIBLE_DEVICES", "0") + print(f"CUDA is available. Using GPU: {cuda_devices}") + return torch.device("cuda") + + print("Neither MPS nor CUDA is available. Using CPU.") + return torch.device("cpu") + +if __name__ == "__main__": + device = get_device() + print(f"Selected device: {device}") \ No newline at end of file diff --git a/src/train_pipeline.py b/src/train_pipeline.py new file mode 100644 index 0000000..c2a8104 --- /dev/null +++ b/src/train_pipeline.py @@ -0,0 +1,65 @@ +import subprocess +import os +import csv +from datetime import datetime +from dateutil.relativedelta import relativedelta +from config import PROJ_FOLDER +from model_config import INITIAL_TRAIN_START, INITIAL_TRAIN_YEARS, FINAL_TRAIN_START, STEP_MONTHS, MAX_PRED_END, scheme_name + +initial_train_start, initial_train_years, step_months, final_train_start = INITIAL_TRAIN_START, INITIAL_TRAIN_YEARS, STEP_MONTHS, FINAL_TRAIN_START + +def generate_date_pairs(start_str, initial_train_years, step_months, final_train_start, max_pred_end=MAX_PRED_END): + # Convert string dates to datetime objects + current_train_start = datetime.strptime(start_str, "%Y-%m-%d") + final_train_start = datetime.strptime(final_train_start, "%Y-%m-%d") + max_pred_end = datetime.strptime(max_pred_end, "%Y-%m-%d") + + # Loop through dates, generating pairs until the final train start date is reached + while current_train_start <= final_train_start: + train_start = current_train_start.strftime('%Y-%m-%d') + train_end = (current_train_start + relativedelta(years=initial_train_years)).strftime('%Y-%m-%d') + pred_start = (current_train_start + relativedelta(years=initial_train_years, days=1)).strftime('%Y-%m-%d') + pred_end_date = (current_train_start + relativedelta(years=initial_train_years, months=3)) + # Ensure pred_end does not exceed the maximum allowed date + if pred_end_date > max_pred_end: + pred_end_date = max_pred_end + + pred_end = pred_end_date.strftime('%Y-%m-%d') + yield train_start, train_end, pred_start, pred_end + current_train_start += relativedelta(months=step_months) + +def main(): + # current_date = datetime.now().strftime("%Y-%m-%d") + results_file = os.path.join(PROJ_FOLDER, "results", f"{scheme_name}_all_results.csv") + + if not os.path.exists(results_file): + with open(results_file, 'w', newline='') as file: + writer = csv.writer(file) + writer.writerow(['Train Start', 'Train End', 'Pred Start', 'Pred End', 'Test F1', 'Pred F1']) + + for train_start, train_end, pred_start, pred_end in generate_date_pairs(INITIAL_TRAIN_START, INITIAL_TRAIN_YEARS, STEP_MONTHS, FINAL_TRAIN_START): + print(f"Training dataset: {train_start} to {train_end}, Prediction dataset: {pred_start} to {pred_end}") + + # subprocess.run(['python', 'datasets_preparation.py', train_start, train_end, pred_start, pred_end], check=True) + + # # Print message to indicate that the datasets have been prepared in green + # print("\033[92m" + f"Datasets have been prepared for training and prediction." + "\033[0m") + + # subprocess.run(['python', 'model.py', train_start, train_end, pred_start, pred_end], check=True) + try: + + subprocess.run(['python', 'datasets_preparation.py', train_start, train_end, pred_start, pred_end], check=True) + print("\033[92m" + "Datasets have been prepared for training and prediction." + "\033[0m") + + subprocess.run(['python', 'model.py', train_start, train_end, pred_start, pred_end], check=True) + + except subprocess.CalledProcessError as e: + print("\033[91m" + f"Error occurred while running subprocess: {e}" + "\033[0m") + + except Exception as e: + print("\033[91m" + f"An unexpected error occurred: {str(e)}" + "\033[0m") + + print("All results have been saved to DataFrame.") + +if __name__ == "__main__": + main() diff --git a/src/util.py b/src/util.py new file mode 100644 index 0000000..55673be --- /dev/null +++ b/src/util.py @@ -0,0 +1,61 @@ +import os +import json +import numpy as np +from datasets import load_from_disk +import evaluate as ev +from config import PROJ_FOLDER, LOGS_FOLDER, HYPERPARAMETER_SEARCH_LOGS, FULL_TRAINING_LOGS +from model_config import model_name, scheme_name, model_init, get_tokenizer + + +def create_logs_folders(): + os.makedirs(LOGS_FOLDER, exist_ok=True) + os.makedirs(HYPERPARAMETER_SEARCH_LOGS, exist_ok=True) + os.makedirs(FULL_TRAINING_LOGS, exist_ok=True) + +def compute_metrics(eval_pred): + logits, labels = eval_pred + predictions = np.argmax(logits, axis=-1) + return ev.load('f1').compute(predictions=predictions, references=labels) + +# Load the data +def load_datasets(train_path, pred_path): + return load_from_disk(train_path), load_from_disk(pred_path) + +# Tokenize the data +def preprocess_function(examples): + tokenizer = get_tokenizer() + tokenized_examples = tokenizer(examples["text"], truncation=True, padding=True, max_length=256) + tokenized_examples["labels"] = examples["front_page"] + return tokenized_examples + +# Save and load hyperparameters +def save_hp(hyperparams, file_path): + with open(file_path, 'w') as f: + json.dump(hyperparams, f) + +def load_hp(file_path): + try: + with open(file_path, 'r') as f: + return json.load(f) + except FileNotFoundError: + return None + + +# Save and load the best model and hyperparameters +def save_best_model(model, start_date_str, best_f1, best_hyperparams): + best_model_path = os.path.join(PROJ_FOLDER, "models", model_name, scheme_name, start_date_str, "best_model") + os.makedirs(best_model_path, exist_ok=True) + model.save_pretrained(best_model_path) + print(f"\033[94mBest model saved to {best_model_path} with F1 score: {best_f1}.\033[0m") + + # Save the best hyperparameters for the time period + hyperparams_folder = os.path.join(PROJ_FOLDER, 'models', model_name, scheme_name, start_date_str,'hyperparameters') + os.makedirs(hyperparams_folder, exist_ok=True) + hyperparams_path = os.path.join(hyperparams_folder, f"{start_date_str}_best_hyperparams.json") + save_hp(best_hyperparams, hyperparams_path) + + +def load_best_hyperparams(start_date_str): + hyperparams_folder = os.path.join(PROJ_FOLDER, "models", model_name, scheme_name, start_date_str, "hyperparameters") + hyperparams_path = os.path.join(hyperparams_folder, f"{start_date_str}_best_hyperparams.json") + return load_hp(hyperparams_path) \ No newline at end of file

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