{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46322fb5-5918-4b70-9689-9e0781439ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "daf1e3d1-75ac-4299-8bed-2f413a49f9a6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "import gensim\n",
    "from gensim import corpora\n",
    "from gensim import similarities\n",
    "from gensim import models\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "# from wordcloud import WordCloud, ImageColorGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import re\n",
    "import os\n",
    "import datetime\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from pprint import pprint\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models as gensimvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c673c907-e1d8-4d64-9a73-c15c15b78e7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-04-14 14:58:01.527521\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "print(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e6de6b-71bd-4948-8827-52601406058f",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49222182-7811-4fa6-8c0a-21d3a546863e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_parquet(\"processed_data1.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fb59a30",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Headline</th>\n",
       "      <th>Details</th>\n",
       "      <th>Severity</th>\n",
       "      <th>Category</th>\n",
       "      <th>Region</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Year</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>...</th>\n",
       "      <th>if_labeled</th>\n",
       "      <th>Month</th>\n",
       "      <th>Week</th>\n",
       "      <th>Headline_Details</th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "      <th>cleaned_content</th>\n",
       "      <th>binary_content</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Grasberg Mine- Grasberg mine workers extend st...</td>\n",
       "      <td>Media sources indicate that workers at the Gra...</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Mine Workers Strike</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>28/5/17 17:08</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>-4.05608</td>\n",
       "      <td>137.11302</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Grasberg Mine- Grasberg mine workers extend st...</td>\n",
       "      <td>https://news.google.com/rss/articles/CBMiZ2h0d...</td>\n",
       "      <td>Freeport Indonesia mine workers extend strike ...</td>\n",
       "      <td>Trucks are seen on a road in the Grasberg copp...</td>\n",
       "      <td>[truck, be, see, on, road, in, grasberg, coppe...</td>\n",
       "      <td>[adkerson_jakarta_try, agreement_freeport_indo...</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Shanghai port congestion impacts terminals in ...</td>\n",
       "      <td>The persisting port congestion at Shanghai’s Y...</td>\n",
       "      <td>Minor</td>\n",
       "      <td>Port Congestion</td>\n",
       "      <td>China</td>\n",
       "      <td>27/4/17 9:16</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>29.52000</td>\n",
       "      <td>121.33190</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Shanghai port congestion impacts terminals in ...</td>\n",
       "      <td>https://news.google.com/rss/articles/CBMiVWh0d...</td>\n",
       "      <td>Typhoon Muifa to shut China ports for second t...</td>\n",
       "      <td>By Sam Whelan 13/09/2022\\n\\nAnother typhoon ha...</td>\n",
       "      <td>[by, sam, whelan, typhoon, have, prompt, port,...</td>\n",
       "      <td>[additional_ripple_effect, avoid_path_typhoon,...</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>UPDATE - Indonesia: Police confirm two explosi...</td>\n",
       "      <td>According to local police in Jakarta, two expl...</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>Bombing, Police Operations</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>24/5/17 16:20</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>UPDATE - Indonesia: Police confirm two explosi...</td>\n",
       "      <td>https://news.google.com/rss/articles/CBMiZWh0d...</td>\n",
       "      <td>Jakarta Police Receive 2 More Reports on Coldp...</td>\n",
       "      <td>TEMPO.CO, Jakarta - South Jakarta Metro Police...</td>\n",
       "      <td>[jakarta, south, jakarta, metro, police, recei...</td>\n",
       "      <td>[actress_accord, available_day_concert, click_...</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>UPDATE - Indonesia: Severe winds damage infras...</td>\n",
       "      <td>Severe winds have downed billboards and trees ...</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Roadway Closure / Disruption, Flooding, Severe...</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>19/4/17 9:10</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>-6.91264</td>\n",
       "      <td>107.65700</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>UPDATE - Indonesia: Severe winds damage infras...</td>\n",
       "      <td>https://news.google.com/rss/articles/CBMiSWh0d...</td>\n",
       "      <td>Indonesia hit by some of strongest winds recorded</td>\n",
       "      <td>A man stands near damaged houses following a t...</td>\n",
       "      <td>[man, stand, near, damage, house, follow, torn...</td>\n",
       "      <td>[bbc_indonesia, climatologist_government_resea...</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.0</td>\n",
       "      <td>2 miles E of Chesterfield - A tornado has touc...</td>\n",
       "      <td>Government sources are reporting a tornado has...</td>\n",
       "      <td>Minor</td>\n",
       "      <td>Tornado</td>\n",
       "      <td>United States</td>\n",
       "      <td>17/9/18 19:55</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>37.51000</td>\n",
       "      <td>-77.61000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>9.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2 miles E of Chesterfield - A tornado has touc...</td>\n",
       "      <td>https://news.google.com/rss/articles/CBMigAFod...</td>\n",
       "      <td>UPDATE: Number of homes without power down to ...</td>\n",
       "      <td>More than 90,000 homes and businesses across t...</td>\n",
       "      <td>[more, than, home, business, across, richmond,...</td>\n",
       "      <td>[advise_seek_alternate, affect_richmond, alter...</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                           Headline  \\\n",
       "0   1.0  Grasberg Mine- Grasberg mine workers extend st...   \n",
       "1   3.0  Shanghai port congestion impacts terminals in ...   \n",
       "2   5.0  UPDATE - Indonesia: Police confirm two explosi...   \n",
       "3   6.0  UPDATE - Indonesia: Severe winds damage infras...   \n",
       "4  14.0  2 miles E of Chesterfield - A tornado has touc...   \n",
       "\n",
       "                                             Details  Severity  \\\n",
       "0  Media sources indicate that workers at the Gra...  Moderate   \n",
       "1  The persisting port congestion at Shanghai’s Y...     Minor   \n",
       "2  According to local police in Jakarta, two expl...   Extreme   \n",
       "3  Severe winds have downed billboards and trees ...  Moderate   \n",
       "4  Government sources are reporting a tornado has...     Minor   \n",
       "\n",
       "                                            Category         Region  \\\n",
       "0                                Mine Workers Strike      Indonesia   \n",
       "1                                    Port Congestion          China   \n",
       "2                         Bombing, Police Operations      Indonesia   \n",
       "3  Roadway Closure / Disruption, Flooding, Severe...      Indonesia   \n",
       "4                                            Tornado  United States   \n",
       "\n",
       "        Datetime    Year       lat        lon  ...  if_labeled Month  Week  \\\n",
       "0  28/5/17 17:08  2017.0  -4.05608  137.11302  ...       False   5.0  21.0   \n",
       "1   27/4/17 9:16  2017.0  29.52000  121.33190  ...       False   4.0  17.0   \n",
       "2  24/5/17 16:20  2017.0       NaN        NaN  ...        True   5.0  21.0   \n",
       "3   19/4/17 9:10  2017.0  -6.91264  107.65700  ...        True   4.0  16.0   \n",
       "4  17/9/18 19:55  2018.0  37.51000  -77.61000  ...        True   9.0  38.0   \n",
       "\n",
       "                                    Headline_Details  \\\n",
       "0  Grasberg Mine- Grasberg mine workers extend st...   \n",
       "1  Shanghai port congestion impacts terminals in ...   \n",
       "2  UPDATE - Indonesia: Police confirm two explosi...   \n",
       "3  UPDATE - Indonesia: Severe winds damage infras...   \n",
       "4  2 miles E of Chesterfield - A tornado has touc...   \n",
       "\n",
       "                                                 url  \\\n",
       "0  https://news.google.com/rss/articles/CBMiZ2h0d...   \n",
       "1  https://news.google.com/rss/articles/CBMiVWh0d...   \n",
       "2  https://news.google.com/rss/articles/CBMiZWh0d...   \n",
       "3  https://news.google.com/rss/articles/CBMiSWh0d...   \n",
       "4  https://news.google.com/rss/articles/CBMigAFod...   \n",
       "\n",
       "                                               title  \\\n",
       "0  Freeport Indonesia mine workers extend strike ...   \n",
       "1  Typhoon Muifa to shut China ports for second t...   \n",
       "2  Jakarta Police Receive 2 More Reports on Coldp...   \n",
       "3  Indonesia hit by some of strongest winds recorded   \n",
       "4  UPDATE: Number of homes without power down to ...   \n",
       "\n",
       "                                             content  \\\n",
       "0  Trucks are seen on a road in the Grasberg copp...   \n",
       "1  By Sam Whelan 13/09/2022\\n\\nAnother typhoon ha...   \n",
       "2  TEMPO.CO, Jakarta - South Jakarta Metro Police...   \n",
       "3  A man stands near damaged houses following a t...   \n",
       "4  More than 90,000 homes and businesses across t...   \n",
       "\n",
       "                                     cleaned_content  \\\n",
       "0  [truck, be, see, on, road, in, grasberg, coppe...   \n",
       "1  [by, sam, whelan, typhoon, have, prompt, port,...   \n",
       "2  [jakarta, south, jakarta, metro, police, recei...   \n",
       "3  [man, stand, near, damage, house, follow, torn...   \n",
       "4  [more, than, home, business, across, richmond,...   \n",
       "\n",
       "                                      binary_content word_count  \n",
       "0  [adkerson_jakarta_try, agreement_freeport_indo...         53  \n",
       "1  [additional_ripple_effect, avoid_path_typhoon,...         44  \n",
       "2  [actress_accord, available_day_concert, click_...         24  \n",
       "3  [bbc_indonesia, climatologist_government_resea...         28  \n",
       "4  [advise_seek_alternate, affect_richmond, alter...        134  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09113e88-66cc-414c-a953-da04db83c4ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3555, 23)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "037e74fc-bbcd-43e3-8346-799920cca8d8",
   "metadata": {},
   "source": [
    "# Vectorisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67cef3a-59fb-4dd8-adc8-2cf288b90728",
   "metadata": {},
   "source": [
    "NLP vectorization refers to the process of converting text data into numerical vectors that machine learning algorithms can understand and process. \n",
    "\n",
    "Bag-of-Words (BoW) is used here that represents text as a collection of unique words along with their frequencies. Each word is assigned an index, and the vector contains the count of each word present in the document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c95b7b8a-9767-469d-812d-c9a9d9fee0e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfb2001e-04c1-49dc-b423-a64ea47af5a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# choose only the extreme and severe cases for modelling\n",
    "cleaned = df_copy[df_copy[\"Severity\"].isin([\"Moderate\"])]\n",
    "cleaned.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de71c523-a59e-44b2-aa96-5f17d872c9c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "headline = cleaned.binary_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b1e34e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['heavy_rainfall', 'accord_state_medium', 'administrative_unit',\n",
       "       'admiral_armand_balilo', 'alert_china_brace', 'babuyan_island',\n",
       "       'banca_cause_boat', 'beijing_launch_emergency',\n",
       "       'binangonan_town_rizal', 'boat_capsize_mbca', 'china_affect',\n",
       "       'coastal_city', 'coastal_community', 'coastguard_spokesman',\n",
       "       'control_operation_country', 'dead_country_passenger',\n",
       "       'early_hour_friday', 'east_manila_pummel', 'eastern_part_island',\n",
       "       'eastern_taiwan_rainfall', 'eastern_taiwan_shut',\n",
       "       'fifteen_province', 'flight_cancel', 'flooding_city_luzhou',\n",
       "       'forecast_storm_cut', 'fujian_china', 'fujian_province',\n",
       "       'fujian_province_gmt', 'gale_hail', 'heavy_rain',\n",
       "       'heavy_rainfall_country', 'imminent_landfall_typhoon',\n",
       "       'issue_railway_service', 'landfall_china', 'landfall_state_news',\n",
       "       'landslide_typhoon', 'lose_strength_lash', 'main_island_luzon',\n",
       "       'major_port_city', 'manila_radio_station',\n",
       "       'national_observatory_classify', 'national_observatory_renew',\n",
       "       'northern_province_trigger', 'northwest_taiwan_strait',\n",
       "       'part_coastal_zhejiang', 'philippine_taiwan', 'point_doksuri',\n",
       "       'port_side_motor', 'power_force_evacuation', 'power_household',\n",
       "       'princess_aya_metre', 'record_area_rain',\n",
       "       'serious_impact_philippine', 'severe_weather',\n",
       "       'severe_weather_alert', 'ship_fishing_boat',\n",
       "       'shut_business_school', 'shut_school_business',\n",
       "       'sichuan_guizhou_yunnan', 'sichuan_province_sweep',\n",
       "       'social_medium_passenger', 'strong_typhoon_maximum', 'strong_wind',\n",
       "       'super_typhoon', 'taiwan_issue_weather', 'taiwan_majority',\n",
       "       'talim_island_gust', 'thursday_flood_control', 'topple_tree_knock',\n",
       "       'torrential_rain', 'tree_trunk_accord', 'typhoon_doksuri_batter',\n",
       "       'typhoon_savage_region', 'weather_bureau_issue',\n",
       "       'wind_cause_passenger'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headline[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "677055b4-978e-4253-90f4-3f903662e225",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# vectorise the words\n",
    "doc_dict = gensim.corpora.Dictionary(headline)\n",
    "docs_vecs = [doc_dict.doc2bow(doc) for doc in headline]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a54d1768-b069-4936-a156-deaf0b506d93",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 117199\n",
      "Number of articles: 1696\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of unique tokens: %d\" % len(doc_dict))\n",
    "print(\"Number of articles: %d\" % len(docs_vecs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9147fa86-1503-4252-bd9b-92fea1e6a926",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('heavy_rain', 125),\n",
      " ('supply_chain', 90),\n",
      " ('national_weather_service', 69),\n",
      " ('strong_wind', 68),\n",
      " ('critical_destination_port', 58),\n",
      " ('tropical_storm', 53),\n",
      " ('industrial_action', 52),\n",
      " ('global_supply_chain', 48),\n",
      " ('heavy_rainfall', 46),\n",
      " ('international_airport', 43),\n",
      " ('united_state', 43),\n",
      " ('high_yard_density', 42),\n",
      " ('social_medium', 40),\n",
      " ('global_port_tracker', 39),\n",
      " ('global_shipping_disruption', 38),\n",
      " ('sign_confidence_consumer', 38),\n",
      " ('upgrade_import_forecast', 38),\n",
      " ('hong_kong', 37),\n",
      " ('new_york_city', 36),\n",
      " ('schedule_reliability', 36),\n",
      " ('national_hurricane_center', 35),\n",
      " ('port_authority', 33),\n",
      " ('economic_growth', 33),\n",
      " ('mediterranean_demand', 33),\n",
      " ('trade_trade_statement', 33),\n",
      " ('global_economy', 33),\n",
      " ('east_coast', 32),\n",
      " ('customer_demand', 32),\n",
      " ('trade_statement', 32),\n",
      " ('new_york', 31),\n",
      " ('severe_weather', 31),\n",
      " ('american_market', 31),\n",
      " ('relevant_information', 31),\n",
      " ('matadi_cape_town', 31),\n",
      " ('pacific_trade_statement', 31),\n",
      " ('situation_port_face', 31),\n",
      " ('boost_business', 30),\n",
      " ('important_transport_route', 30),\n",
      " ('industry_share_market', 30),\n",
      " ('part_commitment_provide', 30),\n",
      " ('relevant_news_hope', 30),\n",
      " ('state_port', 30),\n",
      " ('severe_thunderstorm', 30),\n",
      " ('high_wind', 30),\n",
      " ('fourth_quarter', 29),\n",
      " ('shanghai_ningbo_shekou', 29),\n",
      " ('additional_capacity_cater', 29),\n",
      " ('full_network', 29),\n",
      " ('cargo_move_supply', 28),\n",
      " ('tropical_cyclone', 28),\n",
      " ('several_day', 28),\n",
      " ('coastal_area', 28),\n",
      " ('great_china', 28),\n",
      " ('current_situation', 28),\n",
      " ('many_company', 28),\n",
      " ('day_trade_asia', 28),\n",
      " ('coast_port', 27),\n",
      " ('international_container', 27),\n",
      " ('abijian_conakry_maputo', 27),\n",
      " ('america_latin', 27),\n",
      " ('america_trade_maersk', 27),\n",
      " ('arrival_trade_asia', 27),\n",
      " ('click_link_stay', 27),\n",
      " ('connect_customer_supply', 27),\n",
      " ('currency_depreciation_demand', 27),\n",
      " ('day_coast_day', 27),\n",
      " ('fiscal_support_consumer', 27),\n",
      " ('lift_outlook', 27),\n",
      " ('lome_onne', 27),\n",
      " ('monthly_please', 27),\n",
      " ('pacific_america_trade', 27),\n",
      " ('pointe_noire_balboa', 27),\n",
      " ('post_subscribe_asia', 27),\n",
      " ('professional_find_market', 27),\n",
      " ('question_supply_chain', 27),\n",
      " ('service_china', 27),\n",
      " ('useful_subscribe_maersk', 27),\n",
      " ('help_business', 26),\n",
      " ('power_outage', 26),\n",
      " ('asia_trade_statement', 26),\n",
      " ('new_zealand', 26),\n",
      " ('america_trade_statement', 26),\n",
      " ('many_country', 25),\n",
      " ('crew_member', 25),\n",
      " ('meet_firm', 25),\n",
      " ('website_see_service', 25),\n",
      " ('port_congestion', 25),\n",
      " ('situation_trade', 25),\n",
      " ('vietnam_cambodia_myanmar', 25),\n",
      " ('high_inflation', 24),\n",
      " ('international_longshore_warehouse', 24),\n",
      " ('tuesday_morning', 24),\n",
      " ('main_port', 24),\n",
      " ('high_level', 24),\n",
      " ('china_area', 24),\n",
      " ('next_day', 23),\n",
      " ('passenger_service', 23),\n",
      " ('storm_surge', 23),\n",
      " ('new_jersey', 23),\n",
      " ('stable_service', 23)]\n"
     ]
    }
   ],
   "source": [
    "# Calculate word frequencies\n",
    "word_frequencies = {doc_dict[word_id]: freq for word_id, freq in doc_dict.cfs.items()}\n",
    "sorted_words = sorted(word_frequencies.items(), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "pprint(sorted_words[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed78239-2ce1-4784-a8f4-4c7438c8627b",
   "metadata": {},
   "source": [
    "# LDA Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db83273-461d-4f70-b23f-ec967579d94f",
   "metadata": {},
   "source": [
    "## Benchmark Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6d577bd-9936-4d45-be90-345af2eb4827",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# Build LDA benchmark model\n",
    "lda_model = gensim.models.LdaMulticore(\n",
    "    corpus=docs_vecs,\n",
    "    id2word=doc_dict,\n",
    "    num_topics=4,\n",
    "    random_state=42,\n",
    "    chunksize=100,\n",
    "    passes=10,\n",
    "    per_word_topics=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4f1521f-5f43-40d2-a3a3-a8ac2ca6fec2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.000*\"supply_chain\" + 0.000*\"industrial_action\" + 0.000*\"heavy_rain\" + '\n",
      "  '0.000*\"national_weather_service\" + 0.000*\"tropical_storm\" + '\n",
      "  '0.000*\"power_outage\" + 0.000*\"customer_demand\" + '\n",
      "  '0.000*\"global_supply_chain\" + 0.000*\"many_country\" + '\n",
      "  '0.000*\"critical_destination_port\"'),\n",
      " (1,\n",
      "  '0.001*\"heavy_rain\" + 0.000*\"supply_chain\" + 0.000*\"main_port\" + '\n",
      "  '0.000*\"shanghai_port\" + 0.000*\"port_congestion\" + 0.000*\"united_state\" + '\n",
      "  '0.000*\"landside_operation\" + 0.000*\"relevant_information\" + '\n",
      "  '0.000*\"american_market\" + 0.000*\"port_authority\"'),\n",
      " (2,\n",
      "  '0.000*\"global_port_tracker\" + 0.000*\"global_shipping_disruption\" + '\n",
      "  '0.000*\"upgrade_import_forecast\" + 0.000*\"sign_confidence_consumer\" + '\n",
      "  '0.000*\"asian_country\" + 0.000*\"heavy_rain\" + 0.000*\"strong_wind\" + '\n",
      "  '0.000*\"global_supply_chain\" + 0.000*\"high_level\" + 0.000*\"global_economy\"'),\n",
      " (3,\n",
      "  '0.001*\"heavy_rain\" + 0.000*\"national_weather_service\" + '\n",
      "  '0.000*\"critical_destination_port\" + 0.000*\"help_business\" + '\n",
      "  '0.000*\"website_see_service\" + 0.000*\"meet_firm\" + 0.000*\"supply_chain\" + '\n",
      "  '0.000*\"heavy_rainfall\" + 0.000*\"economic_damage\" + '\n",
      "  '0.000*\"high_yard_density\"')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "# Print the Keyword in the 10 topics\n",
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[docs_vecs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd57b1f4-a6cd-41e8-964f-d8a1d30aa3c9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coherence Score LDAModel:  0.37313480890940176\n"
     ]
    }
   ],
   "source": [
    "# Compute Benchmark Coherence Score\n",
    "coherence_model_lda = CoherenceModel(\n",
    "    model=lda_model, texts=headline, dictionary=doc_dict, coherence=\"c_v\"\n",
    ")\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print(\"\\nCoherence Score LDAModel: \", coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "152e5a3a-7afe-4fb8-a02f-d7492ad80936",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity for LDAModel:  -11.567552981485052\n"
     ]
    }
   ],
   "source": [
    "# Compute Benchmark Perplexity\n",
    "perplex = lda_model.log_perplexity(docs_vecs, total_docs=None)  # For LDAModel\n",
    "# a measure of how good the model is. lower the better.\n",
    "\n",
    "print(\"\\nPerplexity for LDAModel: \", perplex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7dd3a60a-5c6f-4249-9868-30528a5b0ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models as gensimvis\n",
    "\n",
    "# feed the LDA model into the pyLDAvis instance\n",
    "pyLDAvis.enable_notebook()\n",
    "visual = gensimvis.prepare(lda_model, docs_vecs, doc_dict)\n",
    "\n",
    "# Save the output to the html file\n",
    "pyLDAvis.save_html(visual, \"topic_viz_benchmark_moderate.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3a5612f7-6358-49c8-aba9-8aa54e275c6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic Keywords</th>\n",
       "      <th>Topic ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000*\"supply_chain\" + 0.000*\"industrial_action\" + 0.000*\"heavy_rain\" + 0.000*\"national_weather_service\" + 0.000*\"tropical_storm\" + 0.000*\"power_outage\"</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001*\"heavy_rain\" + 0.000*\"supply_chain\" + 0.000*\"main_port\" + 0.000*\"shanghai_port\" + 0.000*\"port_congestion\" + 0.000*\"united_state\"</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000*\"global_port_tracker\" + 0.000*\"global_shipping_disruption\" + 0.000*\"upgrade_import_forecast\" + 0.000*\"sign_confidence_consumer\" + 0.000*\"asian_country\" + 0.000*\"heavy_rain\"</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001*\"heavy_rain\" + 0.000*\"national_weather_service\" + 0.000*\"critical_destination_port\" + 0.000*\"help_business\" + 0.000*\"website_see_service\" + 0.000*\"meet_firm\"</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                       Topic Keywords  \\\n",
       "0                            0.000*\"supply_chain\" + 0.000*\"industrial_action\" + 0.000*\"heavy_rain\" + 0.000*\"national_weather_service\" + 0.000*\"tropical_storm\" + 0.000*\"power_outage\"   \n",
       "1                                              0.001*\"heavy_rain\" + 0.000*\"supply_chain\" + 0.000*\"main_port\" + 0.000*\"shanghai_port\" + 0.000*\"port_congestion\" + 0.000*\"united_state\"   \n",
       "2  0.000*\"global_port_tracker\" + 0.000*\"global_shipping_disruption\" + 0.000*\"upgrade_import_forecast\" + 0.000*\"sign_confidence_consumer\" + 0.000*\"asian_country\" + 0.000*\"heavy_rain\"   \n",
       "3                 0.001*\"heavy_rain\" + 0.000*\"national_weather_service\" + 0.000*\"critical_destination_port\" + 0.000*\"help_business\" + 0.000*\"website_see_service\" + 0.000*\"meet_firm\"   \n",
       "\n",
       "   Topic ID  \n",
       "0         0  \n",
       "1         1  \n",
       "2         2  \n",
       "3         3  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"max_colwidth\", 200)\n",
    "# Get the topics and their top keywords into a dataframe\n",
    "topics = lda_model.show_topics(num_words=6)\n",
    "\n",
    "topic_keywords = pd.DataFrame()\n",
    "for topic_id, topic in topics:\n",
    "    topic_keywords.at[topic_id, \"Topic Keywords\"] = topic\n",
    "\n",
    "topic_keywords[\"Topic ID\"] = topic_keywords.index\n",
    "# topic_keywords['Topic Name'] = topic_mapping\n",
    "topic_keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "26da4eea-06a0-4ff7-ae14-2f40fa0db04b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1895598f-3e5f-4acd-83a6-4491cc90f695",
   "metadata": {},
   "source": [
    "# Hyper-Perameter Tuning and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47136c89-ff7b-4ac9-840f-04122fe62160",
   "metadata": {},
   "source": [
    "Run the cells below only for re-modelling with new datasets, the whole tuning and evaluation process may take hours to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c79ca5c4-e078-43ce-a430-8c1ed93dcd64",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# hyper-perameter tuning (alpha and beta)\n",
    "def compute_coherence_values(corpus, dictionary, k, a, b):\n",
    "\n",
    "    lda_model = gensim.models.LdaMulticore(\n",
    "        corpus=corpus,\n",
    "        id2word=dictionary,\n",
    "        num_topics=k,\n",
    "        random_state=42,\n",
    "        chunksize=100,\n",
    "        passes=10,\n",
    "        alpha=a,\n",
    "        eta=b,\n",
    "    )\n",
    "\n",
    "    coherence_model_lda = CoherenceModel(\n",
    "        model=lda_model, texts=headline, dictionary=doc_dict, coherence=\"c_v\"\n",
    "    )\n",
    "    coherence = coherence_model_lda.get_coherence()\n",
    "    perplex = lda_model.log_perplexity(docs_vecs, total_docs=None)\n",
    "\n",
    "    return coherence, perplex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c3c8478-9336-40f2-bb30-a37db4243b67",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# setup\n",
    "import numpy as np\n",
    "\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "model_list = []\n",
    "coherence_values = []\n",
    "perplexity_values = []\n",
    "model_topics = []\n",
    "alpha_result = []\n",
    "beta_result = []\n",
    "\n",
    "# topic ranges\n",
    "num_topics = range(4, 13)\n",
    "\n",
    "# Alpha parameter\n",
    "alpha = list(np.arange(0.31, 1, 0.3))\n",
    "alpha.append(\"symmetric\")\n",
    "alpha.append(\"asymmetric\")\n",
    "\n",
    "# Beta parameter\n",
    "beta = list(np.arange(0.31, 1, 0.3))\n",
    "beta.append(\"symmetric\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e6bc53-0b57-4858-879a-644eca54ddbc",
   "metadata": {},
   "source": [
    "Rational behind the alpha and eta: https://stats.stackexchange.com/questions/37405/natural-interpretation-for-lda-hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02877b81-32df-4168-8e62-4cbca2be100b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic range:  range(4, 13)\n",
      "Alpha:  [0.31, 0.61, 0.9099999999999999, 'symmetric', 'asymmetric']\n",
      "Beta:  [0.31, 0.61, 0.9099999999999999, 'symmetric']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "print(\"Topic range: \", num_topics)\n",
    "print(\"Alpha: \", alpha)\n",
    "print(\"Beta: \", beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3c1f703c-4778-467f-a12e-0c18eeb274c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-04-14 14:58:21.806761\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Topics: 4, CV Score: 0.4639120100070641, PV Score: -11.464789668175275, Alpha: 0.31, Beta: 0.31\n",
      "#Topics: 5, CV Score: 0.5201240130087459, PV Score: -11.419304959935175, Alpha: 0.31, Beta: 0.31\n",
      "#Topics: 6, CV Score: 0.5306883696213397, PV Score: -11.389754078309831, Alpha: 0.31, Beta: 0.31\n",
      "#Topics: 7, CV Score: 0.4501096856569587, PV Score: -11.359337921238922, Alpha: 0.31, Beta: 0.31\n",
      "#Topics: 8, CV Score: 0.5544991941875896, PV Score: -11.348405818868896, Alpha: 0.31, Beta: 0.31\n",
      "#Topics: 9, CV Score: 0.4574277500533525, PV Score: -11.32308976427175, Alpha: 0.31, Beta: 0.31\n",
      "#Topics: 10, CV Score: 0.5857931657753643, PV Score: -11.324607585088907, Alpha: 0.31, Beta: 0.31\n",
      "#Topics: 11, CV Score: 0.556279187731952, PV Score: -11.329564884447464, Alpha: 0.31, Beta: 0.31\n",
      "#Topics: 12, CV Score: 0.5255844718897261, PV Score: -11.322565125444651, Alpha: 0.31, Beta: 0.31\n",
      "#Topics: 4, CV Score: 0.5198268311028859, PV Score: -11.444283658927644, Alpha: 0.31, Beta: 0.61\n",
      "#Topics: 5, CV Score: 0.521955032774801, PV Score: -11.426177735995873, Alpha: 0.31, Beta: 0.61\n",
      "#Topics: 6, CV Score: 0.5172539440373629, PV Score: -11.419638945858873, Alpha: 0.31, Beta: 0.61\n",
      "#Topics: 7, CV Score: 0.4135467326193986, PV Score: -11.397548635422106, Alpha: 0.31, Beta: 0.61\n",
      "#Topics: 8, CV Score: 0.4303958375840883, PV Score: -11.390482728554709, Alpha: 0.31, Beta: 0.61\n",
      "#Topics: 9, CV Score: 0.5662576167041697, PV Score: -11.39201745796882, Alpha: 0.31, Beta: 0.61\n",
      "#Topics: 10, CV Score: 0.5566622063665962, PV Score: -11.41095608061675, Alpha: 0.31, Beta: 0.61\n",
      "#Topics: 11, CV Score: 0.547476047658658, PV Score: -11.394978781288104, Alpha: 0.31, Beta: 0.61\n",
      "#Topics: 12, CV Score: 0.5116492407478347, PV Score: -11.393122787570931, Alpha: 0.31, Beta: 0.61\n",
      "#Topics: 4, CV Score: 0.4352207111264508, PV Score: -11.49145948378943, Alpha: 0.31, Beta: 0.9099999999999999\n",
      "#Topics: 5, CV Score: 0.5140847992863881, PV Score: -11.449665615656524, Alpha: 0.31, Beta: 0.9099999999999999\n",
      "#Topics: 6, CV Score: 0.5151141592516707, PV Score: -11.440423990622008, Alpha: 0.31, Beta: 0.9099999999999999\n",
      "#Topics: 7, CV Score: 0.46966749676646674, PV Score: -11.459223690441473, Alpha: 0.31, Beta: 0.9099999999999999\n",
      "#Topics: 8, CV Score: 0.44562849863044174, PV Score: -11.435881399807032, Alpha: 0.31, Beta: 0.9099999999999999\n",
      "#Topics: 9, CV Score: 0.5625414884734794, PV Score: -11.43862533771016, Alpha: 0.31, Beta: 0.9099999999999999\n",
      "#Topics: 10, CV Score: 0.5768093350982684, PV Score: -11.443981200189665, Alpha: 0.31, Beta: 0.9099999999999999\n",
      "#Topics: 11, CV Score: 0.5479399278558044, PV Score: -11.447314042575124, Alpha: 0.31, Beta: 0.9099999999999999\n",
      "#Topics: 12, CV Score: 0.4803052005975812, PV Score: -11.463318960603067, Alpha: 0.31, Beta: 0.9099999999999999\n",
      "#Topics: 4, CV Score: 0.47663663050093946, PV Score: -11.496835683262523, Alpha: 0.31, Beta: symmetric\n",
      "#Topics: 5, CV Score: 0.5111135847021848, PV Score: -11.478000133670788, Alpha: 0.31, Beta: symmetric\n",
      "#Topics: 6, CV Score: 0.4901940315789181, PV Score: -11.45477152709802, Alpha: 0.31, Beta: symmetric\n",
      "#Topics: 7, CV Score: 0.440850217246956, PV Score: -11.432282919782061, Alpha: 0.31, Beta: symmetric\n",
      "#Topics: 8, CV Score: 0.485989937107258, PV Score: -11.400685604043499, Alpha: 0.31, Beta: symmetric\n",
      "#Topics: 9, CV Score: 0.463919720659755, PV Score: -11.386759584899005, Alpha: 0.31, Beta: symmetric\n",
      "#Topics: 10, CV Score: 0.515587801920651, PV Score: -11.407975496203113, Alpha: 0.31, Beta: symmetric\n",
      "#Topics: 11, CV Score: 0.5228863816000231, PV Score: -11.403337181939323, Alpha: 0.31, Beta: symmetric\n",
      "#Topics: 12, CV Score: 0.5382804884501613, PV Score: -11.394899952229625, Alpha: 0.31, Beta: symmetric\n",
      "#Topics: 4, CV Score: 0.5102957552685613, PV Score: -11.490557392163034, Alpha: 0.61, Beta: 0.31\n",
      "#Topics: 5, CV Score: 0.4948538948443534, PV Score: -11.44885551940658, Alpha: 0.61, Beta: 0.31\n",
      "#Topics: 6, CV Score: 0.5258465934181115, PV Score: -11.433492330763162, Alpha: 0.61, Beta: 0.31\n",
      "#Topics: 7, CV Score: 0.41673870819204467, PV Score: -11.402046516678466, Alpha: 0.61, Beta: 0.31\n",
      "#Topics: 8, CV Score: 0.5137798570765933, PV Score: -11.397560155141448, Alpha: 0.61, Beta: 0.31\n",
      "#Topics: 9, CV Score: 0.5084645633311302, PV Score: -11.386805565749032, Alpha: 0.61, Beta: 0.31\n",
      "#Topics: 10, CV Score: 0.5410038157417831, PV Score: -11.388941387492848, Alpha: 0.61, Beta: 0.31\n",
      "#Topics: 11, CV Score: 0.5892196988195931, PV Score: -11.39212828604555, Alpha: 0.61, Beta: 0.31\n",
      "#Topics: 12, CV Score: 0.5542547123471065, PV Score: -11.384326585401412, Alpha: 0.61, Beta: 0.31\n",
      "#Topics: 4, CV Score: 0.501525657655967, PV Score: -11.468571965996519, Alpha: 0.61, Beta: 0.61\n",
      "#Topics: 5, CV Score: 0.5221536335298973, PV Score: -11.448821725997714, Alpha: 0.61, Beta: 0.61\n",
      "#Topics: 6, CV Score: 0.5281358484600871, PV Score: -11.44950215345329, Alpha: 0.61, Beta: 0.61\n",
      "#Topics: 7, CV Score: 0.42394918763038264, PV Score: -11.43923424661328, Alpha: 0.61, Beta: 0.61\n",
      "#Topics: 8, CV Score: 0.5568892365525734, PV Score: -11.43633611670671, Alpha: 0.61, Beta: 0.61\n",
      "#Topics: 9, CV Score: 0.5149348397633609, PV Score: -11.4472120390379, Alpha: 0.61, Beta: 0.61\n",
      "#Topics: 10, CV Score: 0.5630729774225, PV Score: -11.450019709798864, Alpha: 0.61, Beta: 0.61\n",
      "#Topics: 11, CV Score: 0.5660107401990132, PV Score: -11.448195037813493, Alpha: 0.61, Beta: 0.61\n",
      "#Topics: 12, CV Score: 0.6067048487832326, PV Score: -11.517394735026985, Alpha: 0.61, Beta: 0.61\n",
      "#Topics: 4, CV Score: 0.43774939207495733, PV Score: -11.48177003428995, Alpha: 0.61, Beta: 0.9099999999999999\n",
      "#Topics: 5, CV Score: 0.5029369415019748, PV Score: -11.494537749126692, Alpha: 0.61, Beta: 0.9099999999999999\n",
      "#Topics: 6, CV Score: 0.5291135020347877, PV Score: -11.470562843074505, Alpha: 0.61, Beta: 0.9099999999999999\n",
      "#Topics: 7, CV Score: 0.46801939216428334, PV Score: -11.477993250814961, Alpha: 0.61, Beta: 0.9099999999999999\n",
      "#Topics: 8, CV Score: 0.4561116075614552, PV Score: -11.477908631558154, Alpha: 0.61, Beta: 0.9099999999999999\n",
      "#Topics: 9, CV Score: 0.5338538763594571, PV Score: -11.483869637063064, Alpha: 0.61, Beta: 0.9099999999999999\n",
      "#Topics: 10, CV Score: 0.5318066925133764, PV Score: -11.495437235794398, Alpha: 0.61, Beta: 0.9099999999999999\n",
      "#Topics: 11, CV Score: 0.5691724725526975, PV Score: -11.504310405974408, Alpha: 0.61, Beta: 0.9099999999999999\n",
      "#Topics: 12, CV Score: 0.49991529594127626, PV Score: -11.505533982013462, Alpha: 0.61, Beta: 0.9099999999999999\n",
      "#Topics: 4, CV Score: 0.4552237010043355, PV Score: -11.519488759303073, Alpha: 0.61, Beta: symmetric\n",
      "#Topics: 5, CV Score: 0.545320332273874, PV Score: -11.51464607273628, Alpha: 0.61, Beta: symmetric\n",
      "#Topics: 6, CV Score: 0.5037142962003162, PV Score: -11.485375286810592, Alpha: 0.61, Beta: symmetric\n",
      "#Topics: 7, CV Score: 0.5197257954918989, PV Score: -11.46790641794297, Alpha: 0.61, Beta: symmetric\n",
      "#Topics: 8, CV Score: 0.528850760593386, PV Score: -11.473541888744615, Alpha: 0.61, Beta: symmetric\n",
      "#Topics: 9, CV Score: 0.4754354820188108, PV Score: -11.447800585922861, Alpha: 0.61, Beta: symmetric\n",
      "#Topics: 10, CV Score: 0.5545087006492542, PV Score: -11.448617532526365, Alpha: 0.61, Beta: symmetric\n",
      "#Topics: 11, CV Score: 0.5803849173749287, PV Score: -11.484144383689875, Alpha: 0.61, Beta: symmetric\n",
      "#Topics: 12, CV Score: 0.5516526443744681, PV Score: -11.49757894104075, Alpha: 0.61, Beta: symmetric\n",
      "#Topics: 4, CV Score: 0.5027917339086706, PV Score: -11.508321341491556, Alpha: 0.9099999999999999, Beta: 0.31\n",
      "#Topics: 5, CV Score: 0.5473451238570242, PV Score: -11.476199027141465, Alpha: 0.9099999999999999, Beta: 0.31\n",
      "#Topics: 6, CV Score: 0.5263033045341249, PV Score: -11.455818922432687, Alpha: 0.9099999999999999, Beta: 0.31\n",
      "#Topics: 7, CV Score: 0.4249033039770485, PV Score: -11.439072328454124, Alpha: 0.9099999999999999, Beta: 0.31\n",
      "#Topics: 8, CV Score: 0.5257502288456282, PV Score: -11.440670614849461, Alpha: 0.9099999999999999, Beta: 0.31\n",
      "#Topics: 9, CV Score: 0.4937110429111786, PV Score: -11.538683096475324, Alpha: 0.9099999999999999, Beta: 0.31\n",
      "#Topics: 10, CV Score: 0.5171285747211095, PV Score: -11.443715372746375, Alpha: 0.9099999999999999, Beta: 0.31\n",
      "#Topics: 11, CV Score: 0.5637323361926754, PV Score: -11.434902180459567, Alpha: 0.9099999999999999, Beta: 0.31\n",
      "#Topics: 12, CV Score: 0.5489653662753551, PV Score: -11.435895185836689, Alpha: 0.9099999999999999, Beta: 0.31\n",
      "#Topics: 4, CV Score: 0.40738966504264507, PV Score: -11.575187678752403, Alpha: 0.9099999999999999, Beta: 0.61\n",
      "#Topics: 5, CV Score: 0.4480568157698127, PV Score: -11.477576503727905, Alpha: 0.9099999999999999, Beta: 0.61\n",
      "#Topics: 6, CV Score: 0.5330102708251107, PV Score: -11.467808194068539, Alpha: 0.9099999999999999, Beta: 0.61\n",
      "#Topics: 7, CV Score: 0.45779839617515544, PV Score: -11.471211938193846, Alpha: 0.9099999999999999, Beta: 0.61\n",
      "#Topics: 8, CV Score: 0.4870454496099792, PV Score: -11.482466513820956, Alpha: 0.9099999999999999, Beta: 0.61\n",
      "#Topics: 9, CV Score: 0.5303788121961808, PV Score: -11.491231277215055, Alpha: 0.9099999999999999, Beta: 0.61\n",
      "#Topics: 10, CV Score: 0.5112464093427893, PV Score: -11.492803197757006, Alpha: 0.9099999999999999, Beta: 0.61\n",
      "#Topics: 11, CV Score: 0.5537187976761082, PV Score: -11.512950310954386, Alpha: 0.9099999999999999, Beta: 0.61\n",
      "#Topics: 12, CV Score: 0.5679691741128957, PV Score: -11.508771684398054, Alpha: 0.9099999999999999, Beta: 0.61\n",
      "#Topics: 4, CV Score: 0.3933054454053455, PV Score: -11.497518639226415, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n",
      "#Topics: 5, CV Score: 0.589325955132936, PV Score: -11.492435837264503, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n",
      "#Topics: 6, CV Score: 0.5891778217025485, PV Score: -11.498234293625453, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n",
      "#Topics: 7, CV Score: 0.4113895439915415, PV Score: -11.502480619894754, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n",
      "#Topics: 8, CV Score: 0.5013789759224359, PV Score: -11.524500558665608, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n",
      "#Topics: 9, CV Score: 0.5411114714801344, PV Score: -11.52354896574579, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n",
      "#Topics: 10, CV Score: 0.5489605509764599, PV Score: -11.541670601920169, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n",
      "#Topics: 11, CV Score: 0.5521831391698555, PV Score: -11.56505640967607, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n",
      "#Topics: 12, CV Score: 0.5272780937846272, PV Score: -11.549856064732888, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n",
      "#Topics: 4, CV Score: 0.5176701047250883, PV Score: -11.528281447687528, Alpha: 0.9099999999999999, Beta: symmetric\n",
      "#Topics: 5, CV Score: 0.49746849624786044, PV Score: -11.508013562904555, Alpha: 0.9099999999999999, Beta: symmetric\n",
      "#Topics: 6, CV Score: 0.5419489918599717, PV Score: -11.5075154726013, Alpha: 0.9099999999999999, Beta: symmetric\n",
      "#Topics: 7, CV Score: 0.40708441209228247, PV Score: -11.502271043127188, Alpha: 0.9099999999999999, Beta: symmetric\n",
      "#Topics: 8, CV Score: 0.4537366453852746, PV Score: -11.522092334770058, Alpha: 0.9099999999999999, Beta: symmetric\n",
      "#Topics: 9, CV Score: 0.5237313210491747, PV Score: -11.481904335663883, Alpha: 0.9099999999999999, Beta: symmetric\n",
      "#Topics: 10, CV Score: 0.45204516028312547, PV Score: -11.556813995104399, Alpha: 0.9099999999999999, Beta: symmetric\n",
      "#Topics: 11, CV Score: 0.5677359830782438, PV Score: -11.576884230111963, Alpha: 0.9099999999999999, Beta: symmetric\n",
      "#Topics: 12, CV Score: 0.5645545814779469, PV Score: -11.722289564831504, Alpha: 0.9099999999999999, Beta: symmetric\n",
      "#Topics: 4, CV Score: 0.5096801318813404, PV Score: -11.484445801610724, Alpha: symmetric, Beta: 0.31\n",
      "#Topics: 5, CV Score: 0.5407084529182314, PV Score: -11.405665664628687, Alpha: symmetric, Beta: 0.31\n",
      "#Topics: 6, CV Score: 0.5256985307101506, PV Score: -11.384260677896423, Alpha: symmetric, Beta: 0.31\n",
      "#Topics: 7, CV Score: 0.3938497094433955, PV Score: -11.347979193432307, Alpha: symmetric, Beta: 0.31\n",
      "#Topics: 8, CV Score: 0.40870219174466205, PV Score: -11.329357861567477, Alpha: symmetric, Beta: 0.31\n",
      "#Topics: 9, CV Score: 0.4551607767067736, PV Score: -11.305475362227975, Alpha: symmetric, Beta: 0.31\n",
      "#Topics: 10, CV Score: 0.49708257412468687, PV Score: -11.29174902126549, Alpha: symmetric, Beta: 0.31\n",
      "#Topics: 11, CV Score: 0.5632602441940544, PV Score: -11.277388606705703, Alpha: symmetric, Beta: 0.31\n",
      "#Topics: 12, CV Score: 0.5004361850501182, PV Score: -11.252120159656126, Alpha: symmetric, Beta: 0.31\n",
      "#Topics: 4, CV Score: 0.45026631409973844, PV Score: -11.446101604202207, Alpha: symmetric, Beta: 0.61\n",
      "#Topics: 5, CV Score: 0.5345320770421942, PV Score: -11.41745273804171, Alpha: symmetric, Beta: 0.61\n",
      "#Topics: 6, CV Score: 0.47818132958598536, PV Score: -11.396271817339604, Alpha: symmetric, Beta: 0.61\n",
      "#Topics: 7, CV Score: 0.4054910520894842, PV Score: -11.379087105085048, Alpha: symmetric, Beta: 0.61\n",
      "#Topics: 8, CV Score: 0.44436557185657705, PV Score: -11.365027523441828, Alpha: symmetric, Beta: 0.61\n",
      "#Topics: 9, CV Score: 0.5854859503620514, PV Score: -11.352325085632192, Alpha: symmetric, Beta: 0.61\n",
      "#Topics: 10, CV Score: 0.5121731087883664, PV Score: -11.34217716233342, Alpha: symmetric, Beta: 0.61\n",
      "#Topics: 11, CV Score: 0.5797394131562412, PV Score: -11.338405166744872, Alpha: symmetric, Beta: 0.61\n",
      "#Topics: 12, CV Score: 0.4665974675497469, PV Score: -11.325085048036788, Alpha: symmetric, Beta: 0.61\n",
      "#Topics: 4, CV Score: 0.5070656740531808, PV Score: -11.455940443373116, Alpha: symmetric, Beta: 0.9099999999999999\n",
      "#Topics: 5, CV Score: 0.49214748127339714, PV Score: -11.440476724027855, Alpha: symmetric, Beta: 0.9099999999999999\n",
      "#Topics: 6, CV Score: 0.4457998789798427, PV Score: -11.425751243524687, Alpha: symmetric, Beta: 0.9099999999999999\n",
      "#Topics: 7, CV Score: 0.40531947491965725, PV Score: -11.415481333351392, Alpha: symmetric, Beta: 0.9099999999999999\n",
      "#Topics: 8, CV Score: 0.42357436101999135, PV Score: -11.406588641228334, Alpha: symmetric, Beta: 0.9099999999999999\n",
      "#Topics: 9, CV Score: 0.5596409044631243, PV Score: -11.39456815007088, Alpha: symmetric, Beta: 0.9099999999999999\n",
      "#Topics: 10, CV Score: 0.5100688836211684, PV Score: -11.390616508606803, Alpha: symmetric, Beta: 0.9099999999999999\n",
      "#Topics: 11, CV Score: 0.573105206639525, PV Score: -11.388776935130071, Alpha: symmetric, Beta: 0.9099999999999999\n",
      "#Topics: 12, CV Score: 0.5289534597632722, PV Score: -11.376517011780951, Alpha: symmetric, Beta: 0.9099999999999999\n",
      "#Topics: 4, CV Score: 0.44674624460024426, PV Score: -11.500540667422738, Alpha: symmetric, Beta: symmetric\n",
      "#Topics: 5, CV Score: 0.5172711027079705, PV Score: -11.479899607208795, Alpha: symmetric, Beta: symmetric\n",
      "#Topics: 6, CV Score: 0.4845042894779277, PV Score: -11.429329937627926, Alpha: symmetric, Beta: symmetric\n",
      "#Topics: 7, CV Score: 0.41613732469216824, PV Score: -11.428561792106187, Alpha: symmetric, Beta: symmetric\n",
      "#Topics: 8, CV Score: 0.5437367952908457, PV Score: -11.409068228518308, Alpha: symmetric, Beta: symmetric\n",
      "#Topics: 9, CV Score: 0.5969151993457286, PV Score: -11.387578913849133, Alpha: symmetric, Beta: symmetric\n",
      "#Topics: 10, CV Score: 0.45998978533990975, PV Score: -11.353834283194002, Alpha: symmetric, Beta: symmetric\n",
      "#Topics: 11, CV Score: 0.591755664544989, PV Score: -11.364023975222716, Alpha: symmetric, Beta: symmetric\n",
      "#Topics: 12, CV Score: 0.5593625395740002, PV Score: -11.311757568334627, Alpha: symmetric, Beta: symmetric\n",
      "#Topics: 4, CV Score: 0.384400689854963, PV Score: -11.470076592839252, Alpha: asymmetric, Beta: 0.31\n",
      "#Topics: 5, CV Score: 0.4571007296487773, PV Score: -11.4315756013948, Alpha: asymmetric, Beta: 0.31\n",
      "#Topics: 6, CV Score: 0.4948333992004407, PV Score: -11.388666761150796, Alpha: asymmetric, Beta: 0.31\n",
      "#Topics: 7, CV Score: 0.5185428353294149, PV Score: -11.352602918503012, Alpha: asymmetric, Beta: 0.31\n",
      "#Topics: 8, CV Score: 0.3887562449341237, PV Score: -11.3264773180022, Alpha: asymmetric, Beta: 0.31\n",
      "#Topics: 9, CV Score: 0.47176950275011087, PV Score: -11.308984303791025, Alpha: asymmetric, Beta: 0.31\n",
      "#Topics: 10, CV Score: 0.4601005168113895, PV Score: -11.294799693909502, Alpha: asymmetric, Beta: 0.31\n",
      "#Topics: 11, CV Score: 0.5272897293922618, PV Score: -11.277015178297905, Alpha: asymmetric, Beta: 0.31\n",
      "#Topics: 12, CV Score: 0.47818798082805164, PV Score: -11.253522545084454, Alpha: asymmetric, Beta: 0.31\n",
      "#Topics: 4, CV Score: 0.3980028769391756, PV Score: -11.440641713999707, Alpha: asymmetric, Beta: 0.61\n",
      "#Topics: 5, CV Score: 0.4169945716982104, PV Score: -11.4182454018062, Alpha: asymmetric, Beta: 0.61\n",
      "#Topics: 6, CV Score: 0.47158461834366944, PV Score: -11.39427374667421, Alpha: asymmetric, Beta: 0.61\n",
      "#Topics: 7, CV Score: 0.46671070213273264, PV Score: -11.377124266599212, Alpha: asymmetric, Beta: 0.61\n",
      "#Topics: 8, CV Score: 0.43018581749893614, PV Score: -11.361993134233312, Alpha: asymmetric, Beta: 0.61\n",
      "#Topics: 9, CV Score: 0.5339433640015115, PV Score: -11.351370590879824, Alpha: asymmetric, Beta: 0.61\n",
      "#Topics: 10, CV Score: 0.4829161970637215, PV Score: -11.346269615666197, Alpha: asymmetric, Beta: 0.61\n",
      "#Topics: 11, CV Score: 0.4244920010893091, PV Score: -11.340799860916507, Alpha: asymmetric, Beta: 0.61\n",
      "#Topics: 12, CV Score: 0.4326440062751971, PV Score: -11.33263557805337, Alpha: asymmetric, Beta: 0.61\n",
      "#Topics: 4, CV Score: 0.3622240135539393, PV Score: -11.458605289076653, Alpha: asymmetric, Beta: 0.9099999999999999\n",
      "#Topics: 5, CV Score: 0.36017289812747716, PV Score: -11.438062685268541, Alpha: asymmetric, Beta: 0.9099999999999999\n",
      "#Topics: 6, CV Score: 0.4953056861030138, PV Score: -11.423627936511448, Alpha: asymmetric, Beta: 0.9099999999999999\n",
      "#Topics: 7, CV Score: 0.4797225368563036, PV Score: -11.408030205517965, Alpha: asymmetric, Beta: 0.9099999999999999\n",
      "#Topics: 8, CV Score: 0.5612869521290274, PV Score: -11.396620242445366, Alpha: asymmetric, Beta: 0.9099999999999999\n",
      "#Topics: 9, CV Score: 0.4927841695347509, PV Score: -11.395780295178001, Alpha: asymmetric, Beta: 0.9099999999999999\n",
      "#Topics: 10, CV Score: 0.49410434886369325, PV Score: -11.389087688182043, Alpha: asymmetric, Beta: 0.9099999999999999\n",
      "#Topics: 11, CV Score: 0.4905259494290944, PV Score: -11.390083825054806, Alpha: asymmetric, Beta: 0.9099999999999999\n",
      "#Topics: 12, CV Score: 0.4847363490761827, PV Score: -11.376416142414838, Alpha: asymmetric, Beta: 0.9099999999999999\n",
      "#Topics: 4, CV Score: 0.35682127038278, PV Score: -11.509843900194742, Alpha: asymmetric, Beta: symmetric\n",
      "#Topics: 5, CV Score: 0.38952629675726813, PV Score: -11.481359514598278, Alpha: asymmetric, Beta: symmetric\n",
      "#Topics: 6, CV Score: 0.4793190490064719, PV Score: -11.45396832965634, Alpha: asymmetric, Beta: symmetric\n",
      "#Topics: 7, CV Score: 0.5113252809445713, PV Score: -11.422523004615886, Alpha: asymmetric, Beta: symmetric\n",
      "#Topics: 8, CV Score: 0.3869498415939613, PV Score: -11.409792366676964, Alpha: asymmetric, Beta: symmetric\n",
      "#Topics: 9, CV Score: 0.48398529423927517, PV Score: -11.39461496619998, Alpha: asymmetric, Beta: symmetric\n",
      "#Topics: 10, CV Score: 0.45097429380817944, PV Score: -11.394057134079636, Alpha: asymmetric, Beta: symmetric\n",
      "#Topics: 11, CV Score: 0.5223219207403089, PV Score: -11.38585115586793, Alpha: asymmetric, Beta: symmetric\n",
      "#Topics: 12, CV Score: 0.5486117543266827, PV Score: -11.281217472971717, Alpha: asymmetric, Beta: symmetric\n",
      "2024-04-14 16:05:28.210872\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "print(datetime.datetime.now())\n",
    "\n",
    "for a in alpha:\n",
    "    for b in beta:\n",
    "        for num in num_topics:\n",
    "            cv, pv = compute_coherence_values(\n",
    "                corpus=docs_vecs, dictionary=doc_dict, k=num, a=a, b=b\n",
    "            )\n",
    "\n",
    "            model_topics.append(num)\n",
    "            coherence_values.append(cv)\n",
    "            perplexity_values.append(pv)\n",
    "            alpha_result.append(a)\n",
    "            beta_result.append(b)\n",
    "            print(\n",
    "                \"#Topics: \"\n",
    "                + str(num)\n",
    "                + \", CV Score: \"\n",
    "                + str(coherence_values[-1])\n",
    "                + \", PV Score: \"\n",
    "                + str(perplexity_values[-1])\n",
    "                + \", Alpha: \"\n",
    "                + str(alpha_result[-1])\n",
    "                + \", Beta: \"\n",
    "                + str(beta_result[-1])\n",
    "            )\n",
    "\n",
    "print(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364ff6d5-e3da-4dde-a2c8-5375fc5d711f",
   "metadata": {},
   "source": [
    "The table below reveals the top 20 fine tuned models with best combinations of coherence score and perplexity score. It was sorted by the coherence score in descending order as a higher coherence score indicates a better model, and sorted the perplexity score in ascending order as a lower perplexity score indicates a better model. While coherence score evaluates the quality of the topics, the perplexity score evaluates the overall performance of the model in predicting new documents. Usually, the coherence score is a better metric to use if the goal is to obtain topics that are semantically coherent and interpretable. Perplexity score, on the other hand, is a better metric to use if the goal is to build a model that generalises well to new data, in other words, how confident the model is in predicting the new data (Sánchez-Aguayo, et al., 2022). Ultimately, we aim to get a balance between the perplexity value and coherence score when determining our final model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "78a60032-a4d7-44d4-841c-a1bd3740d5dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topics</th>\n",
       "      <th>Coherence Score</th>\n",
       "      <th>Perplexity Score</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>Beta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>12</td>\n",
       "      <td>0.606705</td>\n",
       "      <td>-11.517395</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>9</td>\n",
       "      <td>0.596915</td>\n",
       "      <td>-11.387579</td>\n",
       "      <td>symmetric</td>\n",
       "      <td>symmetric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>11</td>\n",
       "      <td>0.591756</td>\n",
       "      <td>-11.364024</td>\n",
       "      <td>symmetric</td>\n",
       "      <td>symmetric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>5</td>\n",
       "      <td>0.589326</td>\n",
       "      <td>-11.492436</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>11</td>\n",
       "      <td>0.589220</td>\n",
       "      <td>-11.392128</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>6</td>\n",
       "      <td>0.589178</td>\n",
       "      <td>-11.498234</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>0.585793</td>\n",
       "      <td>-11.324608</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>9</td>\n",
       "      <td>0.585486</td>\n",
       "      <td>-11.352325</td>\n",
       "      <td>symmetric</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>11</td>\n",
       "      <td>0.580385</td>\n",
       "      <td>-11.484144</td>\n",
       "      <td>0.61</td>\n",
       "      <td>symmetric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>11</td>\n",
       "      <td>0.579739</td>\n",
       "      <td>-11.338405</td>\n",
       "      <td>symmetric</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10</td>\n",
       "      <td>0.576809</td>\n",
       "      <td>-11.443981</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>11</td>\n",
       "      <td>0.573105</td>\n",
       "      <td>-11.388777</td>\n",
       "      <td>symmetric</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>11</td>\n",
       "      <td>0.569172</td>\n",
       "      <td>-11.504310</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>12</td>\n",
       "      <td>0.567969</td>\n",
       "      <td>-11.508772</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>11</td>\n",
       "      <td>0.567736</td>\n",
       "      <td>-11.576884</td>\n",
       "      <td>0.91</td>\n",
       "      <td>symmetric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "      <td>0.566258</td>\n",
       "      <td>-11.392017</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>11</td>\n",
       "      <td>0.566011</td>\n",
       "      <td>-11.448195</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>12</td>\n",
       "      <td>0.564555</td>\n",
       "      <td>-11.722290</td>\n",
       "      <td>0.91</td>\n",
       "      <td>symmetric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>11</td>\n",
       "      <td>0.563732</td>\n",
       "      <td>-11.434902</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>11</td>\n",
       "      <td>0.563260</td>\n",
       "      <td>-11.277389</td>\n",
       "      <td>symmetric</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Topics  Coherence Score  Perplexity Score      Alpha       Beta\n",
       "53       12         0.606705        -11.517395       0.61       0.61\n",
       "140       9         0.596915        -11.387579  symmetric  symmetric\n",
       "142      11         0.591756        -11.364024  symmetric  symmetric\n",
       "91        5         0.589326        -11.492436       0.91       0.91\n",
       "43       11         0.589220        -11.392128       0.61       0.31\n",
       "92        6         0.589178        -11.498234       0.91       0.91\n",
       "6        10         0.585793        -11.324608       0.31       0.31\n",
       "122       9         0.585486        -11.352325  symmetric       0.61\n",
       "70       11         0.580385        -11.484144       0.61  symmetric\n",
       "124      11         0.579739        -11.338405  symmetric       0.61\n",
       "24       10         0.576809        -11.443981       0.31       0.91\n",
       "133      11         0.573105        -11.388777  symmetric       0.91\n",
       "61       11         0.569172        -11.504310       0.61       0.91\n",
       "89       12         0.567969        -11.508772       0.91       0.61\n",
       "106      11         0.567736        -11.576884       0.91  symmetric\n",
       "14        9         0.566258        -11.392017       0.31       0.61\n",
       "52       11         0.566011        -11.448195       0.61       0.61\n",
       "107      12         0.564555        -11.722290       0.91  symmetric\n",
       "79       11         0.563732        -11.434902       0.91       0.31\n",
       "115      11         0.563260        -11.277389  symmetric       0.31"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the top 20 combinations based on Coherence Score and Perplexity Score\n",
    "result = pd.DataFrame(\n",
    "    {\n",
    "        \"Topics\": model_topics,\n",
    "        \"Coherence Score\": coherence_values,\n",
    "        \"Perplexity Score\": perplexity_values,\n",
    "        \"Alpha\": alpha_result,\n",
    "        \"Beta\": beta_result,\n",
    "    }\n",
    ")\n",
    "result.sort_values(\n",
    "    by=[\"Coherence Score\", \"Perplexity Score\"], ascending=[False, True]\n",
    ").head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3461df57-c069-4ad2-80d7-8890dec9438e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "result.to_csv(\"lda_fine_tuning_result.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "800e5a4b-7302-42e8-97b0-5b598c1c80ae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Alpha\n",
       "0.31                  Axes(0.125,0.125;0.775x0.755)\n",
       "0.61                  Axes(0.125,0.125;0.775x0.755)\n",
       "0.9099999999999999    Axes(0.125,0.125;0.775x0.755)\n",
       "asymmetric            Axes(0.125,0.125;0.775x0.755)\n",
       "symmetric             Axes(0.125,0.125;0.775x0.755)\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show graph Topics vs Coherence Score\n",
    "result.groupby(\"Alpha\").plot(x=\"Topics\", y=\"Coherence Score\", legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "26996b89-0e7a-4f2d-8cf7-c4a716569bc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show graph Topics vs Perplexity Score\n",
    "\n",
    "plt.plot(model_topics, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence Score\")\n",
    "plt.legend((\"Coherence Score\"), loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "91d2f4c1-de77-44b6-b41b-fcc9a07233e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wyf/miniconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show graph Topics vs Perplexity Score\n",
    "\n",
    "plt.plot(model_topics, perplexity_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Perplexity score\")\n",
    "plt.legend((\"perplexity_values\"), loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc3ddd2-f743-4e5b-b6c6-2656e0b77aec",
   "metadata": {},
   "source": [
    "## Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "490734ed-077c-4fb0-930c-0b42f4f63c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# realised that there may be some overlaps for more than 5 topics, but below 5 topics results in low differentiation and high ambiguity among the topics.\n",
    "# LDA is not suitable for this dataset\n",
    "k = 9\n",
    "a = \"symmetric\"\n",
    "# a = 0.31\n",
    "# b = 0.31\n",
    "b = \"symmetric\"\n",
    "\n",
    "\n",
    "final_model = gensim.models.LdaMulticore(\n",
    "    corpus=docs_vecs,\n",
    "    id2word=doc_dict,\n",
    "    num_topics=k,\n",
    "    random_state=42,\n",
    "    chunksize=100,\n",
    "    passes=10,\n",
    "    alpha=a,\n",
    "    eta=b,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "afe8abf0-2d12-414e-92be-a655865addb1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5969151993457286, -11.387578913849133)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_coherence_values(corpus=docs_vecs, dictionary=doc_dict, k=k, a=a, b=b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8430a827-6dbb-4737-8ccc-78ed17a01234",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set up the environment to display the graphical outputs\n",
    "# feed the LDA model into the pyLDAvis instance\n",
    "pyLDAvis.enable_notebook()\n",
    "visual = gensimvis.prepare(final_model, docs_vecs, doc_dict)\n",
    "\n",
    "# Save the output to the html file\n",
    "pyLDAvis.save_html(visual, \"topic_viz12_mod_training.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5e30d71a-a3c7-40c7-94c0-7eea1bedc887",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.001*\"supply_chain\" + 0.000*\"critical_destination_port\" + 0.000*\"pacific_trade_statement\" + 0.000*\"additional_capacity_cater\" + 0.000*\"peak_season\" + 0.000*\"terminal_productivity_situation\" + 0.000*\"full_network\" + 0.000*\"major_disruption_trade\" + 0.000*\"international_airport_company\" + 0.000*\"shanghai_shekou\" + 0.000*\"new_cargo_area\" + 0.000*\"narita_airport_concept\" + 0.000*\"composite_pmi\" + 0.000*\"boa_day\" + 0.000*\"container_load_lcl\" + 0.000*\"inflation_rate_peak\" + 0.000*\"squeeze_consumer_purchase\" + 0.000*\"consumer_confidence\" + 0.000*\"heavy_rain\" + 0.000*\"second_quarter\" + 0.000*\"national_weather_service\" + 0.000*\"global_supply_chain\" + 0.000*\"global_economy\" + 0.000*\"many_country\" + 0.000*\"good_trade\" + 0.000*\"geopolitical_risk\" + 0.000*\"express_concern\" + 0.000*\"freight_service\" + 0.000*\"public_holiday\" + 0.000*\"small_medium\"'),\n",
       " (1,\n",
       "  '0.000*\"day_day_day\" + 0.000*\"heavy_rain\" + 0.000*\"american_port\" + 0.000*\"supply_chain\" + 0.000*\"american_market\" + 0.000*\"state_port\" + 0.000*\"customer_demand\" + 0.000*\"boost_business\" + 0.000*\"relevant_information\" + 0.000*\"industry_share_market\" + 0.000*\"part_commitment_provide\" + 0.000*\"relevant_news_hope\" + 0.000*\"cargo_move_supply\" + 0.000*\"important_transport_route\" + 0.000*\"port_tanjung_pelepas\" + 0.000*\"port_change_base\" + 0.000*\"central_america\" + 0.000*\"flow_information_resource\" + 0.000*\"connect_synchronize_operation\" + 0.000*\"lyttelton_port\" + 0.000*\"warehousing_operation\" + 0.000*\"adaptable_process_visualization\" + 0.000*\"santa_marta_turbo\" + 0.000*\"valuable_information_point\" + 0.000*\"fruit_slack_season\" + 0.000*\"critical_topic\" + 0.000*\"good_service_increase\" + 0.000*\"new_strategy\" + 0.000*\"day_operation\" + 0.000*\"main_port_status\"'),\n",
       " (2,\n",
       "  '0.000*\"industrial_action\" + 0.000*\"global_supply_chain\" + 0.000*\"high_level\" + 0.000*\"public_transport\" + 0.000*\"trade_statement\" + 0.000*\"major_travel_disruption\" + 0.000*\"critical_destination_port\" + 0.000*\"economic_growth\" + 0.000*\"work_hour\" + 0.000*\"global_scale\" + 0.000*\"high_interest_rate\" + 0.000*\"general_administration_custom\" + 0.000*\"severe_weather_event\" + 0.000*\"negative_impact\" + 0.000*\"energy_price\" + 0.000*\"economist_intelligence_unit\" + 0.000*\"strong_wind\" + 0.000*\"place_flight_train\" + 0.000*\"cancel_flight_lufthansa\" + 0.000*\"spanish_union_railway\" + 0.000*\"public_transport_worker\" + 0.000*\"strike_march\" + 0.000*\"seek_pay_rise\" + 0.000*\"bus_metro_network\" + 0.000*\"passenger_service\" + 0.000*\"possible_frankfurt_airport\" + 0.000*\"hour_break_disruption\" + 0.000*\"pc_union_member\" + 0.000*\"high_yard_density\" + 0.000*\"strike_march_lufthansa\"'),\n",
       " (3,\n",
       "  '0.001*\"help_business\" + 0.001*\"website_see_service\" + 0.001*\"meet_firm\" + 0.001*\"national_weather_service\" + 0.000*\"tropical_storm\" + 0.000*\"heavy_rain\" + 0.000*\"international_airport\" + 0.000*\"crew_member\" + 0.000*\"average_day\" + 0.000*\"heavy_rainfall\" + 0.000*\"new_york_city\" + 0.000*\"storm_surge\" + 0.000*\"second_half\" + 0.000*\"several_day\" + 0.000*\"durban_container\" + 0.000*\"new_york\" + 0.000*\"terminal_durban\" + 0.000*\"port_congestion\" + 0.000*\"import_export\" + 0.000*\"first_quarter\" + 0.000*\"strong_wind\" + 0.000*\"transnet_operator\" + 0.000*\"fall_ship\" + 0.000*\"merchandise_christmas_problem\" + 0.000*\"mining_industry_transnet\" + 0.000*\"bad_weather_addition\" + 0.000*\"august_buy_gantry\" + 0.000*\"teu_bound_port\" + 0.000*\"urgent_intervention_address\" + 0.000*\"large_container\"'),\n",
       " (4,\n",
       "  '0.000*\"port_authority\" + 0.000*\"hong_kong\" + 0.000*\"chinese_port\" + 0.000*\"international_container\" + 0.000*\"eastern_china\" + 0.000*\"occasional_deal_communication\" + 0.000*\"busy_port\" + 0.000*\"port_group\" + 0.000*\"agree_privacy_policy\" + 0.000*\"read_disclaimer_author\" + 0.000*\"insight_info_share\" + 0.000*\"terminal_operate\" + 0.000*\"available_information_authenticate\" + 0.000*\"statutory_authority_author\" + 0.000*\"chart_use_article\" + 0.000*\"dalian_port\" + 0.000*\"global_shipping_line\" + 0.000*\"view_marine_insight\" + 0.000*\"constitute_opinion_constitute\" + 0.000*\"permission_author_marine\" + 0.000*\"terminal_dct\" + 0.000*\"maritime_gateway\" + 0.000*\"share_use_form\" + 0.000*\"deep_water\" + 0.000*\"email_subscribe\" + 0.000*\"follow_reader_article\" + 0.000*\"share_facebook_twitter\" + 0.000*\"image_reproduce\" + 0.000*\"east_china_sea\" + 0.000*\"linkedin_whatsapp\"'),\n",
       " (5,\n",
       "  '0.001*\"supply_chain\" + 0.000*\"relevant_information\" + 0.000*\"critical_destination_port\" + 0.000*\"important_transport_route\" + 0.000*\"boost_business\" + 0.000*\"american_market\" + 0.000*\"state_port\" + 0.000*\"relevant_news_hope\" + 0.000*\"industry_share_market\" + 0.000*\"part_commitment_provide\" + 0.000*\"daily_life\" + 0.000*\"raw_material\" + 0.000*\"cargo_move_supply\" + 0.000*\"important_aspect\" + 0.000*\"dead_end\" + 0.000*\"collaboration_visibility\" + 0.000*\"food_clothing\" + 0.000*\"finish_product_storage\" + 0.000*\"much_topic\" + 0.000*\"chain_point\" + 0.000*\"efficient_fluent\" + 0.000*\"retail_store_store\" + 0.000*\"agile_supply_chain\" + 0.000*\"single_thing\" + 0.000*\"process_optimization_partner\" + 0.000*\"situation_trade\" + 0.000*\"east_coast\" + 0.000*\"rupert_vancouver\" + 0.000*\"additional_capacity_cater\" + 0.000*\"pacific_trade_statement\"'),\n",
       " (6,\n",
       "  '0.001*\"heavy_rain\" + 0.000*\"strong_wind\" + 0.000*\"hong_kong\" + 0.000*\"durban_container_terminal\" + 0.000*\"durban_container\" + 0.000*\"wait_berth\" + 0.000*\"terminal_service\" + 0.000*\"handle_equipment\" + 0.000*\"terminal_manage_executive\" + 0.000*\"due_combination_inclement\" + 0.000*\"terminal_pier_crane\" + 0.000*\"enough_equipment\" + 0.000*\"ship_sit\" + 0.000*\"reach_stacker_service\" + 0.000*\"pier_result_vessel\" + 0.000*\"inclement_condition\" + 0.000*\"major_ship_line\" + 0.000*\"machine_result_truck\" + 0.000*\"terminal_hour_book\" + 0.000*\"due_transnet_service\" + 0.000*\"congestion_delay\" + 0.000*\"lose_hour_september\" + 0.000*\"truck_congestion_mean\" + 0.000*\"terminal_container_dct\" + 0.000*\"collect_container\" + 0.000*\"road_truck\" + 0.000*\"bad_equipment_break\" + 0.000*\"dozen_vessel_queue\" + 0.000*\"earle_peter_assure\" + 0.000*\"truck_struggle\"'),\n",
       " (7,\n",
       "  '0.001*\"heavy_rain\" + 0.000*\"strong_wind\" + 0.000*\"tropical_storm\" + 0.000*\"coastal_area\" + 0.000*\"national_hurricane_center\" + 0.000*\"heavy_rainfall\" + 0.000*\"international_airport\" + 0.000*\"severe_weather\" + 0.000*\"severe_thunderstorm\" + 0.000*\"next_hour\" + 0.000*\"tropical_depression\" + 0.000*\"tropical_cyclone\" + 0.000*\"united_state\" + 0.000*\"news_agency\" + 0.000*\"coastal_region\" + 0.000*\"storm_move\" + 0.000*\"tropical_storm_force\" + 0.000*\"national_weather_service\" + 0.000*\"power_line\" + 0.000*\"emergency_service\" + 0.000*\"southern_port_city\" + 0.000*\"saturday_morning\" + 0.000*\"tuesday_morning\" + 0.000*\"strong_wind_rain\" + 0.000*\"high_water\" + 0.000*\"china_sea\" + 0.000*\"social_medium\" + 0.000*\"meteorological_agency\" + 0.000*\"monday_morning\" + 0.000*\"urban_area\"'),\n",
       " (8,\n",
       "  '0.001*\"global_port_tracker\" + 0.001*\"global_shipping_disruption\" + 0.001*\"upgrade_import_forecast\" + 0.001*\"sign_confidence_consumer\" + 0.000*\"heavy_rain\" + 0.000*\"moment_exception_request\" + 0.000*\"sorry_site\" + 0.000*\"technical_difficulty_please\" + 0.000*\"severe_thunderstorm\" + 0.000*\"dp_world\" + 0.000*\"southeastern_part\" + 0.000*\"national_weather_service\" + 0.000*\"east_coast\" + 0.000*\"heavy_rainfall\" + 0.000*\"social_medium\" + 0.000*\"united_state\" + 0.000*\"news_agency\" + 0.000*\"significant_damage\" + 0.000*\"supply_chain\" + 0.000*\"heavy_rain_cause\" + 0.000*\"ship_speed\" + 0.000*\"tuesday_afternoon\" + 0.000*\"ship_wait\" + 0.000*\"wind_rain\" + 0.000*\"port_area\" + 0.000*\"strong_wind\" + 0.000*\"port_authority\" + 0.000*\"international_airport\" + 0.000*\"economic_damage\" + 0.000*\"national_hurricane_center\"')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.print_topics(num_words=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607d2cfd-b3ca-4f99-9e01-d320ca98a2a0",
   "metadata": {},
   "source": [
    "# Save the final model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "84eb2746-173a-4283-bca5-681f77548698",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save a model to disk, or reload a pre-trained model\n",
    "# naming convention: final_model_topic_alpha_eta\n",
    "final_model.save(\"final_model_9_sym_sym\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7b6e4d9-a577-4dfb-ba6e-fc74365880f4",
   "metadata": {},
   "source": [
    "# Find dominant topic(s) for each news article"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eeecbcb-358c-44f9-8463-75cdfac0ba90",
   "metadata": {},
   "source": [
    "Attach the dominant topics back to the news dataset for classifying purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bebb269-dbb0-4c46-925c-38de0f2bcfd7",
   "metadata": {},
   "source": [
    "Made use of gensim lda's own function: https://radimrehurek.com/gensim/models/ldamodel.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f585ff52-b60d-4d70-ae64-a7c23d2cc6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "def format_topics_sentences(ldamodel, corpus, data):\n",
    "    # Preallocate memory for the DataFrame\n",
    "    num_docs = len(corpus)\n",
    "    sent_topics = {\n",
    "        \"Dominant_Topic\": [0] * num_docs,\n",
    "        \"Perc_Contribution\": [0.0] * num_docs,\n",
    "        \"Topic_Distribution\": [()] * num_docs,\n",
    "    }\n",
    "\n",
    "    # Get main topic in each document\n",
    "    for i, row in enumerate(ldamodel[corpus]):\n",
    "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
    "        if row:\n",
    "            # Get the Dominant topic, Perc Contribution and Keywords for each document\n",
    "            dominant_topic, perc_contribution = row[0]\n",
    "            topic_distribution = row\n",
    "            sent_topics[\"Dominant_Topic\"][i] = int(dominant_topic)\n",
    "            sent_topics[\"Perc_Contribution\"][i] = round(perc_contribution, 4)\n",
    "            sent_topics[\"Topic_Distribution\"][i] = topic_distribution\n",
    "\n",
    "    # Create the DataFrame\n",
    "    sent_topics_df = pd.DataFrame(sent_topics)\n",
    "    sent_topics_df[\"Text\"] = data\n",
    "\n",
    "    return sent_topics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "24d3ff60-035e-4133-9ffd-88cce5cdccb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topic_sents_keywords = format_topics_sentences(\n",
    "    ldamodel=final_model, corpus=docs_vecs, data=cleaned.Headline_Details\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c88b088b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Document_No</th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Topic_Distribution</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9834</td>\n",
       "      <td>[(6, 0.9834445)]</td>\n",
       "      <td>Grasberg Mine- Grasberg mine workers extend strike for a fourth month. Media sources indicate that workers at the Grasberg mine will extend their strike for a fourth month as disputes regarding la...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9692</td>\n",
       "      <td>[(6, 0.9691763)]</td>\n",
       "      <td>UPDATE - Indonesia: Severe winds damage infrastructure in Bandung, West Java Severe winds have downed billboards and trees in Bandung on Wednesday afternoon. According to local media sources, the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9765</td>\n",
       "      <td>[(7, 0.9764757)]</td>\n",
       "      <td>270 kilograms of heroin discovered in container at Port of Genoa Local media sources indicated on November 8 that 270 kilograms of heroin arrived from Iran were discovered in containers at the Por...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9982</td>\n",
       "      <td>[(1, 0.99823475)]</td>\n",
       "      <td>6 miles S of San Antonio - A magnitude 5.2 earthquake was detected in the region. Incident closed. The European-Mediterranean Seismological Centre (EMSC) reported a magnitude 5.2 earthquake that s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9884</td>\n",
       "      <td>[(0, 0.98838603)]</td>\n",
       "      <td>6 suspects steal truck and take driver hostage in Santos Local media sources indicate on October 18 that 6 suspects hijacked a truck in Santos on the evening of October 15, releasing the driver in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.9882</td>\n",
       "      <td>[(4, 0.98823905)]</td>\n",
       "      <td>7 ships grounded in Taiwan due to Tropical Depression Media sources reported that 6 container ships and a tanker were grounded due to the passing of a Tropical Depression near Taiwan on August 24....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9944</td>\n",
       "      <td>[(6, 0.99439585)]</td>\n",
       "      <td>Adelaide   Utility workers have restored power to all customers. Incident closed. SA Power Networks has restored power to all customers after a wind storm caused thousands to be without power acro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9787</td>\n",
       "      <td>[(0, 0.97869843)]</td>\n",
       "      <td>Airframe Dr &amp; Boeing Blvd - Boeing operations will resume beginning on Sunday night. The Boeing Company operations will resume in North Charleston beginning Sunday night after shutting down due to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0.9867</td>\n",
       "      <td>[(3, 0.98665065)]</td>\n",
       "      <td>All terminals closed at Port of Le Havre Industry sources indicate on December 28 that all terminals are closed at the Port of Le Havre. The closures are believed to be linked to Act VI of the Yel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0.9897</td>\n",
       "      <td>[(5, 0.9897034)]</td>\n",
       "      <td>American Airlines Flight Attendants to Picket at 15 Airports Across the US on Sunday, November 18 On Sunday, November 18, American Airlines flight attendants represented by the Association of Prof...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Document_No  Dominant_Topic  Topic_Perc_Contrib Topic_Distribution  \\\n",
       "0            0               6              0.9834   [(6, 0.9834445)]   \n",
       "1            1               6              0.9692   [(6, 0.9691763)]   \n",
       "2            2               7              0.9765   [(7, 0.9764757)]   \n",
       "3            3               1              0.9982  [(1, 0.99823475)]   \n",
       "4            4               0              0.9884  [(0, 0.98838603)]   \n",
       "5            5               4              0.9882  [(4, 0.98823905)]   \n",
       "6            6               6              0.9944  [(6, 0.99439585)]   \n",
       "7            7               0              0.9787  [(0, 0.97869843)]   \n",
       "8            8               3              0.9867  [(3, 0.98665065)]   \n",
       "9            9               5              0.9897   [(5, 0.9897034)]   \n",
       "\n",
       "                                                                                                                                                                                                      Text  \n",
       "0  Grasberg Mine- Grasberg mine workers extend strike for a fourth month. Media sources indicate that workers at the Grasberg mine will extend their strike for a fourth month as disputes regarding la...  \n",
       "1  UPDATE - Indonesia: Severe winds damage infrastructure in Bandung, West Java Severe winds have downed billboards and trees in Bandung on Wednesday afternoon. According to local media sources, the ...  \n",
       "2  270 kilograms of heroin discovered in container at Port of Genoa Local media sources indicated on November 8 that 270 kilograms of heroin arrived from Iran were discovered in containers at the Por...  \n",
       "3  6 miles S of San Antonio - A magnitude 5.2 earthquake was detected in the region. Incident closed. The European-Mediterranean Seismological Centre (EMSC) reported a magnitude 5.2 earthquake that s...  \n",
       "4  6 suspects steal truck and take driver hostage in Santos Local media sources indicate on October 18 that 6 suspects hijacked a truck in Santos on the evening of October 15, releasing the driver in...  \n",
       "5  7 ships grounded in Taiwan due to Tropical Depression Media sources reported that 6 container ships and a tanker were grounded due to the passing of a Tropical Depression near Taiwan on August 24....  \n",
       "6  Adelaide   Utility workers have restored power to all customers. Incident closed. SA Power Networks has restored power to all customers after a wind storm caused thousands to be without power acro...  \n",
       "7  Airframe Dr & Boeing Blvd - Boeing operations will resume beginning on Sunday night. The Boeing Company operations will resume in North Charleston beginning Sunday night after shutting down due to...  \n",
       "8  All terminals closed at Port of Le Havre Industry sources indicate on December 28 that all terminals are closed at the Port of Le Havre. The closures are believed to be linked to Act VI of the Yel...  \n",
       "9  American Airlines Flight Attendants to Picket at 15 Airports Across the US on Sunday, November 18 On Sunday, November 18, American Airlines flight attendants represented by the Association of Prof...  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format\n",
    "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
    "df_dominant_topic.columns = [\n",
    "    \"Document_No\",\n",
    "    \"Dominant_Topic\",\n",
    "    \"Topic_Perc_Contrib\",\n",
    "    \"Topic_Distribution\",\n",
    "    \"Text\",\n",
    "]\n",
    "\n",
    "# Show\n",
    "df_dominant_topic.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560da382-aa86-4df1-8b85-56b057a27cd4",
   "metadata": {},
   "source": [
    "# Result Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4fe6b40b-6922-4de3-8d9e-dac7474b6303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    251\n",
       "3    217\n",
       "8    207\n",
       "5    206\n",
       "2    173\n",
       "6    164\n",
       "4    163\n",
       "7    159\n",
       "1    156\n",
       "Name: Dominant_Topic, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dominant_topic[\"Dominant_Topic\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b9917340-31cf-48af-871f-b481128fdf22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get value counts of each topic\n",
    "topic_counts = df_dominant_topic[\"Dominant_Topic\"].value_counts()\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "topic_counts.plot(kind=\"bar\", color=\"skyblue\")\n",
    "\n",
    "# Add labels to the bars\n",
    "for i, count in enumerate(topic_counts):\n",
    "    plt.text(i, count, str(count), ha=\"center\", va=\"bottom\")\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Topics\")\n",
    "plt.ylabel(\"Number of News\")\n",
    "plt.title(\"Topic Distribution\")\n",
    "\n",
    "# Show the plot\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "70e7d652-4421-45e0-93f8-aaa51c186422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1696, 5)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dominant_topic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69932a6e-7159-46b1-98f8-827d99b95c54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample 100 rows, can change the random_state for different samples\n",
    "sampled_data = df_dominant_topic.sample(n=100, random_state=42)\n",
    "sampled_df = pd.DataFrame(sampled_data).reset_index()\n",
    "sampled_df.to_csv(\"sample_moderate.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}