{ "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 14:59:03.349141\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 the data with full news content" ] }, { "cell_type": "code", "execution_count": 58, "id": "49222182-7811-4fa6-8c0a-21d3a546863e", "metadata": {}, "outputs": [], "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": [ "
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idHeadlineDetailsSeverityCategoryRegionDatetimeYearlatlon...if_labeledMonthWeekHeadline_Detailsurltitlecontentcleaned_contentbinary_contentword_count
01.0Grasberg Mine- Grasberg mine workers extend st...Media sources indicate that workers at the Gra...ModerateMine Workers StrikeIndonesia28/5/17 17:082017.0-4.05608137.11302...False5.021.0Grasberg Mine- Grasberg mine workers extend st...https://news.google.com/rss/articles/CBMiZ2h0d...Freeport Indonesia mine workers extend strike ...Trucks are seen on a road in the Grasberg copp...[truck, be, see, on, road, in, grasberg, coppe...[adkerson_jakarta_try, agreement_freeport_indo...53
13.0Shanghai port congestion impacts terminals in ...The persisting port congestion at Shanghai’s Y...MinorPort CongestionChina27/4/17 9:162017.029.52000121.33190...False4.017.0Shanghai port congestion impacts terminals in ...https://news.google.com/rss/articles/CBMiVWh0d...Typhoon Muifa to shut China ports for second t...By Sam Whelan 13/09/2022\\n\\nAnother typhoon ha...[by, sam, whelan, typhoon, have, prompt, port,...[additional_ripple_effect, avoid_path_typhoon,...44
25.0UPDATE - Indonesia: Police confirm two explosi...According to local police in Jakarta, two expl...ExtremeBombing, Police OperationsIndonesia24/5/17 16:202017.0NaNNaN...True5.021.0UPDATE - Indonesia: Police confirm two explosi...https://news.google.com/rss/articles/CBMiZWh0d...Jakarta Police Receive 2 More Reports on Coldp...TEMPO.CO, Jakarta - South Jakarta Metro Police...[jakarta, south, jakarta, metro, police, recei...[actress_accord, available_day_concert, click_...24
36.0UPDATE - Indonesia: Severe winds damage infras...Severe winds have downed billboards and trees ...ModerateRoadway Closure / Disruption, Flooding, Severe...Indonesia19/4/17 9:102017.0-6.91264107.65700...True4.016.0UPDATE - Indonesia: Severe winds damage infras...https://news.google.com/rss/articles/CBMiSWh0d...Indonesia hit by some of strongest winds recordedA man stands near damaged houses following a t...[man, stand, near, damage, house, follow, torn...[bbc_indonesia, climatologist_government_resea...28
414.02 miles E of Chesterfield - A tornado has touc...Government sources are reporting a tornado has...MinorTornadoUnited States17/9/18 19:552018.037.51000-77.61000...True9.038.02 miles E of Chesterfield - A tornado has touc...https://news.google.com/rss/articles/CBMigAFod...UPDATE: Number of homes without power down to ...More than 90,000 homes and businesses across t...[more, than, home, business, across, richmond,...[advise_seek_alternate, affect_richmond, alter...134
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" ], "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([\"Extreme\", \"Severe\"])]\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(['amish_family', 'central_wisconsin', 'clark_county_sheriff',\n", " 'clark_county_wi', 'close_part_state', 'community_crash_fun',\n", " 'county_highway_j', 'crash_semi_van', 'dead_crash_wisconsin',\n", " 'dewhurst_locate_mile', 'driver_van', 'early_information_suggest',\n", " 'east_state_highway', 'eric_chaloux', 'eyewitness_news_none',\n", " 'family_crash_victim', 'first_responder_work',\n", " 'friday_morning_state', 'gross_tell_survivor', 'harm_burn',\n", " 'highway_area_county', 'highway_direction_accord',\n", " 'highway_j_township', 'hospital_marshville',\n", " 'injury_law_enforcement', 'interview_milwaukee_driver',\n", " 'investigation_wisconsin_gov', 'lavada_stout', 'man_life',\n", " 'march_wisconsin_highway', 'miss_mom', 'north_semi',\n", " 'office_crash_happen', 'official_tell', 'open_friday_sheriff',\n", " 'place_stuff', 'remember_victim_authority', 'semi_head',\n", " 'several_hour', 'sheriff_office', 'southeast_eau_claire',\n", " 'stout_state', 'support_scene', 'van_die',\n", " 'van_enter_intersection', 'victim_area', 'victim_area_pass',\n", " 'wisconsin_pick_grandson', 'wisconsin_state_patrol',\n", " 'x_heart_prayer'], 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: 28944\n", "Number of articles: 294\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": "stdout", "output_type": "stream", "text": [ "[('heavy_rain', 14),\n", " ('global_supply_chain', 14),\n", " ('national_hurricane_center', 14),\n", " ('heavy_rainfall', 12),\n", " ('united_state', 12),\n", " ('port_los', 12),\n", " ('hong_kong', 12),\n", " ('critical_destination_port', 11),\n", " ('global_port_tracker', 11),\n", " ('global_shipping_disruption', 11),\n", " ('sign_confidence_consumer', 11),\n", " ('upgrade_import_forecast', 11),\n", " ('national_weather_service', 10),\n", " ('social_medium', 10),\n", " ('moment_exception_request', 10),\n", " ('sorry_site', 10),\n", " ('technical_difficulty_please', 10),\n", " ('trade_statement', 9),\n", " ('tropical_storm', 9),\n", " ('help_business', 9),\n", " ('meet_firm', 9),\n", " ('website_see_service', 9),\n", " ('supply_chain', 8),\n", " ('strong_wind', 8),\n", " ('asian_country', 7),\n", " ('day_trade_asia', 7),\n", " ('global_demand', 7),\n", " ('global_economy', 7),\n", " ('high_yard_density', 7),\n", " ('inch_rain', 7),\n", " ('coast_port', 7),\n", " ('empty_container', 7),\n", " ('union_worker', 7),\n", " ('coastal_area', 6),\n", " ('many_area', 6),\n", " ('customer_demand', 6),\n", " ('economic_growth', 6),\n", " ('free_day', 6),\n", " ('full_network', 6),\n", " ('import_volume', 6),\n", " ('major_economy', 6),\n", " ('negative_impact', 6),\n", " ('vertical_insight', 6),\n", " ('supply_chain_issue', 6),\n", " ('economic_recovery', 6),\n", " ('america_trade_statement', 6),\n", " ('average_day', 6),\n", " ('geological_survey', 6),\n", " ('high_wind', 5),\n", " ('paul_brashier_vice', 5),\n", " ('president_drayage', 5),\n", " ('strike_action', 5),\n", " ('large_number', 5),\n", " ('current_situation', 5),\n", " ('high_inflation', 5),\n", " ('severe_weather_event', 5),\n", " ('accurate_quote_market', 5),\n", " ('adapt_supply_chain', 5),\n", " ('america_space', 5),\n", " ('apapa_tin_tema', 5),\n", " ('asia_day', 5),\n", " ('china_area_chb', 5),\n", " ('china_area_warehouse', 5),\n", " ('critical_resource', 5),\n", " ('date_change', 5),\n", " ('energy_price', 5),\n", " ('energy_price_fall', 5),\n", " ('export_volume', 5),\n", " ('fourth_quarter', 5),\n", " ('full_truck', 5),\n", " ('future_consumer_demand', 5),\n", " ('general_administration_custom', 5),\n", " ('high_flexibility_transit', 5),\n", " ('high_interest_rate', 5),\n", " ('high_inventory', 5),\n", " ('increase_capacity', 5),\n", " ('india_freight_cost', 5),\n", " ('indirect_service', 5),\n", " ('january_trade_maersk', 5),\n", " ('load_truck_box', 5),\n", " ('low_confidence', 5),\n", " ('low_figure', 5),\n", " ('main_product_continue', 5),\n", " ('main_route_area', 5),\n", " ('many_company', 5),\n", " ('market_average', 5),\n", " ('matadi_cape_town', 5),\n", " ('mile_service', 5),\n", " ('monetary_fund', 5),\n", " ('new_air', 5),\n", " ('new_law_limit', 5),\n", " ('new_sea_rail', 5),\n", " ('ocean_market', 5),\n", " ('ocean_network', 5),\n", " ('ok_day', 5),\n", " ('operational_disruption', 5),\n", " ('relevant_rate', 5),\n", " ('retail_sale', 5),\n", " ('rotterdam_felixstowe_valencia', 5),\n", " ('schedule_reliability', 5)]\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": [ "# 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": "aacc2eb2-bce9-462f-b048-cc26baa2383d", "metadata": {}, "source": [ "We initially selected a fixed topic number for model pipelien development and benchmark model setup. Then we used the full dataset for fine-tuning and evaluation." ] }, { "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", " '0.001*\"website_see_service\" + 0.001*\"help_business\" + 0.001*\"meet_firm\" + '\n", " '0.000*\"heavy_rain\" + 0.000*\"port_los\" + 0.000*\"heavy_rainfall\" + '\n", " '0.000*\"big_ship\" + 0.000*\"strong_wind\" + 0.000*\"national_hurricane_center\" '\n", " '+ 0.000*\"national_weather_service\"'),\n", " (1,\n", " '0.001*\"global_shipping_disruption\" + 0.001*\"upgrade_import_forecast\" + '\n", " '0.001*\"sign_confidence_consumer\" + 0.001*\"global_port_tracker\" + '\n", " '0.001*\"global_supply_chain\" + 0.000*\"national_hurricane_center\" + '\n", " '0.000*\"critical_destination_port\" + 0.000*\"trade_statement\" + '\n", " '0.000*\"tropical_storm\" + 0.000*\"economic_growth\"'),\n", " (2,\n", " '0.001*\"moment_exception_request\" + 0.001*\"sorry_site\" + '\n", " '0.001*\"technical_difficulty_please\" + 0.000*\"united_state\" + '\n", " '0.000*\"disruption_air_traffic\" + 0.000*\"major_travel_disruption\" + '\n", " '0.000*\"passenger_service\" + 0.000*\"bus_metro_network\" + '\n", " '0.000*\"likely_disrupt_flight\" + 0.000*\"public_transport\"'),\n", " (3,\n", " '0.000*\"hong_kong\" + 0.000*\"coast_port\" + 0.000*\"social_medium\" + '\n", " '0.000*\"port_los\" + 0.000*\"union_worker\" + 0.000*\"union_pacific_maritime\" + '\n", " '0.000*\"terminal_port\" + 0.000*\"empty_container\" + 0.000*\"coastal_area\" + '\n", " '0.000*\"san_pedro_california\"')]\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: 0.5156580167169962\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": "stdout", "output_type": "stream", "text": [ "\n", "Perplexity for LDAModel: -10.522998996358806\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": [ "# 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_severe.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": [ "
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Topic KeywordsTopic ID
00.001*\"website_see_service\" + 0.001*\"help_business\" + 0.001*\"meet_firm\" + 0.000*\"heavy_rain\" + 0.000*\"port_los\" + 0.000*\"heavy_rainfall\"0
10.001*\"global_shipping_disruption\" + 0.001*\"upgrade_import_forecast\" + 0.001*\"sign_confidence_consumer\" + 0.001*\"global_port_tracker\" + 0.001*\"global_supply_chain\" + 0.000*\"national_hurricane_center\"1
20.001*\"moment_exception_request\" + 0.001*\"sorry_site\" + 0.001*\"technical_difficulty_please\" + 0.000*\"united_state\" + 0.000*\"disruption_air_traffic\" + 0.000*\"major_travel_disruption\"2
30.000*\"hong_kong\" + 0.000*\"coast_port\" + 0.000*\"social_medium\" + 0.000*\"port_los\" + 0.000*\"union_worker\" + 0.000*\"union_pacific_maritime\"3
\n", "
" ], "text/plain": [ " Topic Keywords \\\n", "0 0.001*\"website_see_service\" + 0.001*\"help_business\" + 0.001*\"meet_firm\" + 0.000*\"heavy_rain\" + 0.000*\"port_los\" + 0.000*\"heavy_rainfall\" \n", "1 0.001*\"global_shipping_disruption\" + 0.001*\"upgrade_import_forecast\" + 0.001*\"sign_confidence_consumer\" + 0.001*\"global_port_tracker\" + 0.001*\"global_supply_chain\" + 0.000*\"national_hurricane_center\" \n", "2 0.001*\"moment_exception_request\" + 0.001*\"sorry_site\" + 0.001*\"technical_difficulty_please\" + 0.000*\"united_state\" + 0.000*\"disruption_air_traffic\" + 0.000*\"major_travel_disruption\" \n", "3 0.000*\"hong_kong\" + 0.000*\"coast_port\" + 0.000*\"social_medium\" + 0.000*\"port_los\" + 0.000*\"union_worker\" + 0.000*\"union_pacific_maritime\" \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" ] }, { "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 14:59:24.215253\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: 0.4937577623548033, PV Score: -10.478323004526313, Alpha: 0.31, Beta: 0.31\n", "#Topics: 5, CV Score: 0.5541567363382776, PV Score: -10.43385471040428, Alpha: 0.31, Beta: 0.31\n", "#Topics: 6, CV Score: 0.5583581440340063, PV Score: -10.39832867553369, Alpha: 0.31, Beta: 0.31\n", "#Topics: 7, CV Score: 0.539339746681579, PV Score: -10.391387051762422, Alpha: 0.31, Beta: 0.31\n", "#Topics: 8, CV Score: 0.5802000843907773, PV Score: -10.373703237875723, Alpha: 0.31, Beta: 0.31\n", "#Topics: 9, CV Score: 0.5394146721179509, PV Score: -10.356885263952421, Alpha: 0.31, Beta: 0.31\n", "#Topics: 10, CV Score: 0.5659231559333903, PV Score: -10.36000367507295, Alpha: 0.31, Beta: 0.31\n", "#Topics: 11, CV Score: 0.532285458710766, PV Score: -10.348691633330558, Alpha: 0.31, Beta: 0.31\n", "#Topics: 12, CV Score: 0.5758791159524114, PV Score: -10.34564875995922, Alpha: 0.31, Beta: 0.31\n", "#Topics: 4, CV Score: 0.5056394246858962, PV Score: -10.376932079419479, Alpha: 0.31, Beta: 0.61\n", "#Topics: 5, CV Score: 0.5876914987616891, PV Score: -10.353323740677272, Alpha: 0.31, Beta: 0.61\n", "#Topics: 6, CV Score: 0.5508346273927331, PV Score: -10.336962850546312, Alpha: 0.31, Beta: 0.61\n", "#Topics: 7, CV Score: 0.523092035699816, PV Score: -10.340501003106022, Alpha: 0.31, Beta: 0.61\n", "#Topics: 8, CV Score: 0.5605624564507745, PV Score: -10.3324007391524, Alpha: 0.31, Beta: 0.61\n", "#Topics: 9, CV Score: 0.5299227412122862, PV Score: -10.327807889441948, Alpha: 0.31, Beta: 0.61\n", "#Topics: 10, CV Score: 0.5620617700584865, PV Score: -10.327997994099468, Alpha: 0.31, Beta: 0.61\n", "#Topics: 11, CV Score: 0.5246279758719217, PV Score: -10.329595015802536, Alpha: 0.31, Beta: 0.61\n", "#Topics: 12, CV Score: 0.5913485825867103, PV Score: -10.328887268850167, Alpha: 0.31, Beta: 0.61\n", "#Topics: 4, CV Score: 0.5034743642341697, PV Score: -10.34393767685639, Alpha: 0.31, Beta: 0.9099999999999999\n", "#Topics: 5, CV Score: 0.5731847592093106, PV Score: -10.329603465410381, Alpha: 0.31, Beta: 0.9099999999999999\n", "#Topics: 6, CV Score: 0.5626723514700681, PV Score: -10.322376130193135, Alpha: 0.31, Beta: 0.9099999999999999\n", "#Topics: 7, CV Score: 0.5013628695897919, PV Score: -10.326102353233654, Alpha: 0.31, Beta: 0.9099999999999999\n", "#Topics: 8, CV Score: 0.5827930840908885, PV Score: -10.324082355677463, Alpha: 0.31, Beta: 0.9099999999999999\n", "#Topics: 9, CV Score: 0.529518466567958, PV Score: -10.324051462371564, Alpha: 0.31, Beta: 0.9099999999999999\n", "#Topics: 10, CV Score: 0.5195429723670609, PV Score: -10.325669262179376, Alpha: 0.31, Beta: 0.9099999999999999\n", "#Topics: 11, CV Score: 0.5834576545310179, PV Score: -10.329158548667246, Alpha: 0.31, Beta: 0.9099999999999999\n", "#Topics: 12, CV Score: 0.615909382376136, PV Score: -10.330987397510244, Alpha: 0.31, Beta: 0.9099999999999999\n", "#Topics: 4, CV Score: 0.513652755028171, PV Score: -10.530697138966529, Alpha: 0.31, Beta: symmetric\n", "#Topics: 5, CV Score: 0.5609544173986023, PV Score: -10.52498355737737, Alpha: 0.31, Beta: symmetric\n", "#Topics: 6, CV Score: 0.560991877759878, PV Score: -10.511712731054295, Alpha: 0.31, Beta: symmetric\n", "#Topics: 7, CV Score: 0.5452827763370146, PV Score: -10.529071747764636, Alpha: 0.31, Beta: symmetric\n", "#Topics: 8, CV Score: 0.5595075000103662, PV Score: -10.527327877930517, Alpha: 0.31, Beta: symmetric\n", "#Topics: 9, CV Score: 0.5884981194444401, PV Score: -10.516942290461627, Alpha: 0.31, Beta: symmetric\n", "#Topics: 10, CV Score: 0.5653127280555911, PV Score: -10.549998756715572, Alpha: 0.31, Beta: symmetric\n", "#Topics: 11, CV Score: 0.546862127045898, PV Score: -10.525887433659394, Alpha: 0.31, Beta: symmetric\n", "#Topics: 12, CV Score: 0.5631780414776316, PV Score: -10.537973652971452, Alpha: 0.31, Beta: symmetric\n", "#Topics: 4, CV Score: 0.5987359257864653, PV Score: -10.502104647001486, Alpha: 0.61, Beta: 0.31\n", "#Topics: 5, CV Score: 0.5319776401030758, PV Score: -10.460688118045315, Alpha: 0.61, Beta: 0.31\n", "#Topics: 6, CV Score: 0.5721994510586489, PV Score: -10.431896801394789, Alpha: 0.61, Beta: 0.31\n", "#Topics: 7, CV Score: 0.6074211262171534, PV Score: -10.429427017656822, Alpha: 0.61, Beta: 0.31\n", "#Topics: 8, CV Score: 0.5497108392137032, PV Score: -10.414302533270286, Alpha: 0.61, Beta: 0.31\n", "#Topics: 9, CV Score: 0.48025012317775256, PV Score: -10.409777385056554, Alpha: 0.61, Beta: 0.31\n", "#Topics: 10, CV Score: 0.594546729436719, PV Score: -10.419798207082145, Alpha: 0.61, Beta: 0.31\n", "#Topics: 11, CV Score: 0.525350091242622, PV Score: -10.404321642574638, Alpha: 0.61, Beta: 0.31\n", "#Topics: 12, CV Score: 0.5193272627676494, PV Score: -10.410478542336001, Alpha: 0.61, Beta: 0.31\n", "#Topics: 4, CV Score: 0.5771156140206035, PV Score: -10.39861156826831, Alpha: 0.61, Beta: 0.61\n", "#Topics: 5, CV Score: 0.5472851664426466, PV Score: -10.380624704903518, Alpha: 0.61, Beta: 0.61\n", "#Topics: 6, CV Score: 0.5535663656718838, PV Score: -10.369497348780808, Alpha: 0.61, Beta: 0.61\n", "#Topics: 7, CV Score: 0.589806252792333, PV Score: -10.37438342073445, Alpha: 0.61, Beta: 0.61\n", "#Topics: 8, CV Score: 0.4919317455363664, PV Score: -10.372444734111793, Alpha: 0.61, Beta: 0.61\n", "#Topics: 9, CV Score: 0.4623437599219733, PV Score: -10.374125531020487, Alpha: 0.61, Beta: 0.61\n", "#Topics: 10, CV Score: 0.5610807357758321, PV Score: -10.380085587250212, Alpha: 0.61, Beta: 0.61\n", "#Topics: 11, CV Score: 0.518765746961452, PV Score: -10.385561909864869, Alpha: 0.61, Beta: 0.61\n", "#Topics: 12, CV Score: 0.5539371322145971, PV Score: -10.38968949386607, Alpha: 0.61, Beta: 0.61\n", "#Topics: 4, CV Score: 0.5769992210148425, PV Score: -10.363951461348382, Alpha: 0.61, Beta: 0.9099999999999999\n", "#Topics: 5, CV Score: 0.5210611283596341, PV Score: -10.355199552132229, Alpha: 0.61, Beta: 0.9099999999999999\n", "#Topics: 6, CV Score: 0.5356346163762745, PV Score: -10.353779928243933, Alpha: 0.61, Beta: 0.9099999999999999\n", "#Topics: 7, CV Score: 0.6004085675754204, PV Score: -10.361129953396913, Alpha: 0.61, Beta: 0.9099999999999999\n", "#Topics: 8, CV Score: 0.5028586167571227, PV Score: -10.365141017322628, Alpha: 0.61, Beta: 0.9099999999999999\n", "#Topics: 9, CV Score: 0.4242566951022723, PV Score: -10.367868103323051, Alpha: 0.61, Beta: 0.9099999999999999\n", "#Topics: 10, CV Score: 0.5464170398947601, PV Score: -10.377525665209653, Alpha: 0.61, Beta: 0.9099999999999999\n", "#Topics: 11, CV Score: 0.4915117185232479, PV Score: -10.382585965807905, Alpha: 0.61, Beta: 0.9099999999999999\n", "#Topics: 12, CV Score: 0.5272472345077327, PV Score: -10.38904948449824, Alpha: 0.61, Beta: 0.9099999999999999\n", "#Topics: 4, CV Score: 0.5987359257864653, PV Score: -10.551716435375159, Alpha: 0.61, Beta: symmetric\n", "#Topics: 5, CV Score: 0.5506030894292873, PV Score: -10.55231946403784, Alpha: 0.61, Beta: symmetric\n", "#Topics: 6, CV Score: 0.5930043987200657, PV Score: -10.548420561460622, Alpha: 0.61, Beta: symmetric\n", "#Topics: 7, CV Score: 0.5070045369691704, PV Score: -10.571103793182651, Alpha: 0.61, Beta: symmetric\n", "#Topics: 8, CV Score: 0.5071819389599823, PV Score: -10.57314725291321, Alpha: 0.61, Beta: symmetric\n", "#Topics: 9, CV Score: 0.5135165802725025, PV Score: -10.57736916322149, Alpha: 0.61, Beta: symmetric\n", "#Topics: 10, CV Score: 0.6109951273175733, PV Score: -10.600071610330593, Alpha: 0.61, Beta: symmetric\n", "#Topics: 11, CV Score: 0.5336329887668941, PV Score: -10.577532449902474, Alpha: 0.61, Beta: symmetric\n", "#Topics: 12, CV Score: 0.524091927432904, PV Score: -10.62590555179827, Alpha: 0.61, Beta: symmetric\n", "#Topics: 4, CV Score: 0.4692695101969563, PV Score: -10.521682697883469, Alpha: 0.9099999999999999, Beta: 0.31\n", "#Topics: 5, CV Score: 0.49240121475021936, PV Score: -10.486623124181422, Alpha: 0.9099999999999999, Beta: 0.31\n", "#Topics: 6, CV Score: 0.5532189471624976, PV Score: -10.462317970536132, Alpha: 0.9099999999999999, Beta: 0.31\n", "#Topics: 7, CV Score: 0.5275559338750061, PV Score: -10.461789514396694, Alpha: 0.9099999999999999, Beta: 0.31\n", "#Topics: 8, CV Score: 0.49936658225921154, PV Score: -10.452406327236009, Alpha: 0.9099999999999999, Beta: 0.31\n", "#Topics: 9, CV Score: 0.4568419781244587, PV Score: -10.449107139883802, Alpha: 0.9099999999999999, Beta: 0.31\n", "#Topics: 10, CV Score: 0.6070143423417578, PV Score: -10.463497844519804, Alpha: 0.9099999999999999, Beta: 0.31\n", "#Topics: 11, CV Score: 0.5361036932798751, PV Score: -10.451786586872329, Alpha: 0.9099999999999999, Beta: 0.31\n", "#Topics: 12, CV Score: 0.507276488943149, PV Score: -10.462487060733528, Alpha: 0.9099999999999999, Beta: 0.31\n", "#Topics: 4, CV Score: 0.49705751265233244, PV Score: -10.41623205983431, Alpha: 0.9099999999999999, Beta: 0.61\n", "#Topics: 5, CV Score: 0.51953468148397, PV Score: -10.403481500231821, Alpha: 0.9099999999999999, Beta: 0.61\n", "#Topics: 6, CV Score: 0.5405764915187027, PV Score: -10.397574381729704, Alpha: 0.9099999999999999, Beta: 0.61\n", "#Topics: 7, CV Score: 0.5443502362221223, PV Score: -10.405549844997248, Alpha: 0.9099999999999999, Beta: 0.61\n", "#Topics: 8, CV Score: 0.5098407213336632, PV Score: -10.40653620738565, Alpha: 0.9099999999999999, Beta: 0.61\n", "#Topics: 9, CV Score: 0.4359168634991304, PV Score: -10.413591625773561, Alpha: 0.9099999999999999, Beta: 0.61\n", "#Topics: 10, CV Score: 0.6515947831380515, PV Score: -10.424243821667405, Alpha: 0.9099999999999999, Beta: 0.61\n", "#Topics: 11, CV Score: 0.5233542585101998, PV Score: -10.429293410414461, Alpha: 0.9099999999999999, Beta: 0.61\n", "#Topics: 12, CV Score: 0.48122619517681936, PV Score: -10.440076662085376, Alpha: 0.9099999999999999, Beta: 0.61\n", "#Topics: 4, CV Score: 0.5095298236282476, PV Score: -10.381856777972521, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n", "#Topics: 5, CV Score: 0.5180510274909387, PV Score: -10.377321268747115, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n", "#Topics: 6, CV Score: 0.517275578097018, PV Score: -10.380129953932807, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n", "#Topics: 7, CV Score: 0.5677738932026478, PV Score: -10.390100878763198, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n", "#Topics: 8, CV Score: 0.49305895223475493, PV Score: -10.395536833517784, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n", "#Topics: 9, CV Score: 0.43162729671878064, PV Score: -10.403902236035274, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n", "#Topics: 10, CV Score: 0.5859648190470326, PV Score: -10.416300777094142, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n", "#Topics: 11, CV Score: 0.5070549799480709, PV Score: -10.424308453813268, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n", "#Topics: 12, CV Score: 0.5528265906955795, PV Score: -10.435456372974953, Alpha: 0.9099999999999999, Beta: 0.9099999999999999\n", "#Topics: 4, CV Score: 0.47116294495054634, PV Score: -10.570334712998344, Alpha: 0.9099999999999999, Beta: symmetric\n", "#Topics: 5, CV Score: 0.50454790291799, PV Score: -10.574437357876596, Alpha: 0.9099999999999999, Beta: symmetric\n", "#Topics: 6, CV Score: 0.5502111092758252, PV Score: -10.581437378335211, Alpha: 0.9099999999999999, Beta: symmetric\n", "#Topics: 7, CV Score: 0.46715231998005935, PV Score: -10.604227194503197, Alpha: 0.9099999999999999, Beta: symmetric\n", "#Topics: 8, CV Score: 0.48904864041287, PV Score: -10.607711283151284, Alpha: 0.9099999999999999, Beta: symmetric\n", "#Topics: 9, CV Score: 0.5218340028562694, PV Score: -10.609744339496189, Alpha: 0.9099999999999999, Beta: symmetric\n", "#Topics: 10, CV Score: 0.608031433186686, PV Score: -10.637373013372788, Alpha: 0.9099999999999999, Beta: symmetric\n", "#Topics: 11, CV Score: 0.5200109696080161, PV Score: -10.641065826255563, Alpha: 0.9099999999999999, Beta: symmetric\n", "#Topics: 12, CV Score: 0.4893513014273499, PV Score: -10.65542122741711, Alpha: 0.9099999999999999, Beta: symmetric\n", "#Topics: 4, CV Score: 0.4969035332505414, PV Score: -10.473345242584637, Alpha: symmetric, Beta: 0.31\n", "#Topics: 5, CV Score: 0.5576500664971396, PV Score: -10.42187869252464, Alpha: symmetric, Beta: 0.31\n", "#Topics: 6, CV Score: 0.5444759567377432, PV Score: -10.379709362480417, Alpha: symmetric, Beta: 0.31\n", "#Topics: 7, CV Score: 0.552573453447162, PV Score: -10.366131186064113, Alpha: symmetric, Beta: 0.31\n", "#Topics: 8, CV Score: 0.5789142181443101, PV Score: -10.341909638957011, Alpha: symmetric, Beta: 0.31\n", "#Topics: 9, CV Score: 0.5745088211069743, PV Score: -10.316761213358948, Alpha: symmetric, Beta: 0.31\n", "#Topics: 10, CV Score: 0.5760328384249952, PV Score: -10.309220682210613, Alpha: symmetric, Beta: 0.31\n", "#Topics: 11, CV Score: 0.6037836478711097, PV Score: -10.294462946777967, Alpha: symmetric, Beta: 0.31\n", "#Topics: 12, CV Score: 0.5484303260549442, PV Score: -10.282989952516502, Alpha: symmetric, Beta: 0.31\n", "#Topics: 4, CV Score: 0.5075230775704007, PV Score: -10.37182832365566, Alpha: symmetric, Beta: 0.61\n", "#Topics: 5, CV Score: 0.5877306226521412, PV Score: -10.341524533383195, Alpha: symmetric, Beta: 0.61\n", "#Topics: 6, CV Score: 0.5313625834186885, PV Score: -10.318238659735055, Alpha: symmetric, Beta: 0.61\n", "#Topics: 7, CV Score: 0.5158447054678449, PV Score: -10.3138500903392, Alpha: symmetric, Beta: 0.61\n", "#Topics: 8, CV Score: 0.5596782430827049, PV Score: -10.29649515873157, Alpha: symmetric, Beta: 0.61\n", "#Topics: 9, CV Score: 0.564954002157332, PV Score: -10.288190133232916, Alpha: symmetric, Beta: 0.61\n", "#Topics: 10, CV Score: 0.5315048228812047, PV Score: -10.27980111358745, Alpha: symmetric, Beta: 0.61\n", "#Topics: 11, CV Score: 0.5868622902701924, PV Score: -10.273718857935581, Alpha: symmetric, Beta: 0.61\n", "#Topics: 12, CV Score: 0.5934959635252037, PV Score: -10.266428413838572, Alpha: symmetric, Beta: 0.61\n", "#Topics: 4, CV Score: 0.5031550559384641, PV Score: -10.339118975478312, Alpha: symmetric, Beta: 0.9099999999999999\n", "#Topics: 5, CV Score: 0.575531856170343, PV Score: -10.319613327707565, Alpha: symmetric, Beta: 0.9099999999999999\n", "#Topics: 6, CV Score: 0.568072217095823, PV Score: -10.30311643476823, Alpha: symmetric, Beta: 0.9099999999999999\n", "#Topics: 7, CV Score: 0.5131930615421415, PV Score: -10.300102606563774, Alpha: symmetric, Beta: 0.9099999999999999\n", "#Topics: 8, CV Score: 0.5319131739524877, PV Score: -10.291310081568206, Alpha: symmetric, Beta: 0.9099999999999999\n", "#Topics: 9, CV Score: 0.5740497927436715, PV Score: -10.283258852906055, Alpha: symmetric, Beta: 0.9099999999999999\n", "#Topics: 10, CV Score: 0.5353822329603416, PV Score: -10.278880825795703, Alpha: symmetric, Beta: 0.9099999999999999\n", "#Topics: 11, CV Score: 0.5756254349145407, PV Score: -10.275045257250902, Alpha: symmetric, Beta: 0.9099999999999999\n", "#Topics: 12, CV Score: 0.5885140508767818, PV Score: -10.26989478282425, Alpha: symmetric, Beta: 0.9099999999999999\n", "#Topics: 4, CV Score: 0.5156580167169962, PV Score: -10.522998996168337, Alpha: symmetric, Beta: symmetric\n", "#Topics: 5, CV Score: 0.5506868197398487, PV Score: -10.512972754707231, Alpha: symmetric, Beta: symmetric\n", "#Topics: 6, CV Score: 0.554279406777444, PV Score: -10.493652864676617, Alpha: symmetric, Beta: symmetric\n", "#Topics: 7, CV Score: 0.5320818945741242, PV Score: -10.505992273321151, Alpha: symmetric, Beta: symmetric\n", "#Topics: 8, CV Score: 0.6045191930636338, PV Score: -10.492115753417218, Alpha: symmetric, Beta: symmetric\n", "#Topics: 9, CV Score: 0.6149045315197557, PV Score: -10.47519918876149, Alpha: symmetric, Beta: symmetric\n", "#Topics: 10, CV Score: 0.5628353635434522, PV Score: -10.505280064451007, Alpha: symmetric, Beta: symmetric\n", "#Topics: 11, CV Score: 0.5664668023374665, PV Score: -10.463365299486844, Alpha: symmetric, Beta: symmetric\n", "#Topics: 12, CV Score: 0.56897333099906, PV Score: -10.477288042932678, Alpha: symmetric, Beta: symmetric\n", "#Topics: 4, CV Score: 0.6265803518412584, PV Score: -10.471013622650181, Alpha: asymmetric, Beta: 0.31\n", "#Topics: 5, CV Score: 0.5370301270453918, PV Score: -10.424003880489423, Alpha: asymmetric, Beta: 0.31\n", "#Topics: 6, CV Score: 0.6092678747552095, PV Score: -10.386193442661115, Alpha: asymmetric, Beta: 0.31\n", "#Topics: 7, CV Score: 0.5461390597947411, PV Score: -10.358411807676191, Alpha: asymmetric, Beta: 0.31\n", "#Topics: 8, CV Score: 0.548378457816151, PV Score: -10.343645691512856, Alpha: asymmetric, Beta: 0.31\n", "#Topics: 9, CV Score: 0.5299957377409616, PV Score: -10.316787179695952, Alpha: asymmetric, Beta: 0.31\n", "#Topics: 10, CV Score: 0.5793506276784866, PV Score: -10.309249872525184, Alpha: asymmetric, Beta: 0.31\n", "#Topics: 11, CV Score: 0.6051713764150699, PV Score: -10.29619897538942, Alpha: asymmetric, Beta: 0.31\n", "#Topics: 12, CV Score: 0.5383826515403402, PV Score: -10.282753244912747, Alpha: asymmetric, Beta: 0.31\n", "#Topics: 4, CV Score: 0.6025799840553558, PV Score: -10.367545107397191, Alpha: asymmetric, Beta: 0.61\n", "#Topics: 5, CV Score: 0.45513176259769417, PV Score: -10.340142524489346, Alpha: asymmetric, Beta: 0.61\n", "#Topics: 6, CV Score: 0.5843715526202624, PV Score: -10.318892094855599, Alpha: asymmetric, Beta: 0.61\n", "#Topics: 7, CV Score: 0.5554190723347731, PV Score: -10.309853574842794, Alpha: asymmetric, Beta: 0.61\n", "#Topics: 8, CV Score: 0.4917766496095347, PV Score: -10.29716489054611, Alpha: asymmetric, Beta: 0.61\n", "#Topics: 9, CV Score: 0.5294606758566227, PV Score: -10.284945618871486, Alpha: asymmetric, Beta: 0.61\n", "#Topics: 10, CV Score: 0.5417968557253837, PV Score: -10.278257553082913, Alpha: asymmetric, Beta: 0.61\n", "#Topics: 11, CV Score: 0.5741145391999866, PV Score: -10.272950008122585, Alpha: asymmetric, Beta: 0.61\n", "#Topics: 12, CV Score: 0.558032658811334, PV Score: -10.265164329486101, Alpha: asymmetric, Beta: 0.61\n", "#Topics: 4, CV Score: 0.6119812549945736, PV Score: -10.336192865999546, Alpha: asymmetric, Beta: 0.9099999999999999\n", "#Topics: 5, CV Score: 0.4383130803370408, PV Score: -10.31792634671193, Alpha: asymmetric, Beta: 0.9099999999999999\n", "#Topics: 6, CV Score: 0.572308259728283, PV Score: -10.304067084222433, Alpha: asymmetric, Beta: 0.9099999999999999\n", "#Topics: 7, CV Score: 0.550028629796201, PV Score: -10.29658897321457, Alpha: asymmetric, Beta: 0.9099999999999999\n", "#Topics: 8, CV Score: 0.4894734131315978, PV Score: -10.287490041322812, Alpha: asymmetric, Beta: 0.9099999999999999\n", "#Topics: 9, CV Score: 0.48420584454897636, PV Score: -10.281273878219984, Alpha: asymmetric, Beta: 0.9099999999999999\n", "#Topics: 10, CV Score: 0.5429957082089111, PV Score: -10.275424450882161, Alpha: asymmetric, Beta: 0.9099999999999999\n", "#Topics: 11, CV Score: 0.5642947107202422, PV Score: -10.272108941199727, Alpha: asymmetric, Beta: 0.9099999999999999\n", "#Topics: 12, CV Score: 0.5774549906211149, PV Score: -10.268362837057476, Alpha: asymmetric, Beta: 0.9099999999999999\n", "#Topics: 4, CV Score: 0.6266036828634418, PV Score: -10.519981771184684, Alpha: asymmetric, Beta: symmetric\n", "#Topics: 5, CV Score: 0.49093758956116806, PV Score: -10.513923089447863, Alpha: asymmetric, Beta: symmetric\n", "#Topics: 6, CV Score: 0.6059090718840544, PV Score: -10.5023638309932, Alpha: asymmetric, Beta: symmetric\n", "#Topics: 7, CV Score: 0.5753495186879819, PV Score: -10.488029149143129, Alpha: asymmetric, Beta: symmetric\n", "#Topics: 8, CV Score: 0.6309264127222964, PV Score: -10.496005225348071, Alpha: asymmetric, Beta: symmetric\n", "#Topics: 9, CV Score: 0.6358452879415664, PV Score: -10.475434391808438, Alpha: asymmetric, Beta: symmetric\n", "#Topics: 10, CV Score: 0.5896712489200012, PV Score: -10.490075672708167, Alpha: asymmetric, Beta: symmetric\n", "#Topics: 11, CV Score: 0.5505855337667278, PV Score: -10.466304093987537, Alpha: asymmetric, Beta: symmetric\n", "#Topics: 12, CV Score: 0.5750824625138335, PV Score: -10.463431619663394, Alpha: asymmetric, Beta: symmetric\n", "2024-04-14 15:44:43.243190\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": [ "
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TopicsCoherence ScorePerplexity ScoreAlphaBeta
87100.651595-10.4242440.910.61
17690.635845-10.475434asymmetricsymmetric
17580.630926-10.496005asymmetricsymmetric
17140.626604-10.519982asymmetricsymmetric
14440.626580-10.471014asymmetric0.31
26120.615909-10.3309870.310.91
14090.614905-10.475199symmetricsymmetric
16240.611981-10.336193asymmetric0.91
69100.610995-10.6000720.61symmetric
14660.609268-10.386193asymmetric0.31
105100.608031-10.6373730.91symmetric
3970.607421-10.4294270.610.31
78100.607014-10.4634980.910.31
17360.605909-10.502364asymmetricsymmetric
151110.605171-10.296199asymmetric0.31
13980.604519-10.492116symmetricsymmetric
115110.603784-10.294463symmetric0.31
15340.602580-10.367545asymmetric0.61
5770.600409-10.3611300.610.91
6340.598736-10.5517160.61symmetric
\n", "
" ], "text/plain": [ " Topics Coherence Score Perplexity Score Alpha Beta\n", "87 10 0.651595 -10.424244 0.91 0.61\n", "176 9 0.635845 -10.475434 asymmetric symmetric\n", "175 8 0.630926 -10.496005 asymmetric symmetric\n", "171 4 0.626604 -10.519982 asymmetric symmetric\n", "144 4 0.626580 -10.471014 asymmetric 0.31\n", "26 12 0.615909 -10.330987 0.31 0.91\n", "140 9 0.614905 -10.475199 symmetric symmetric\n", "162 4 0.611981 -10.336193 asymmetric 0.91\n", "69 10 0.610995 -10.600072 0.61 symmetric\n", "146 6 0.609268 -10.386193 asymmetric 0.31\n", "105 10 0.608031 -10.637373 0.91 symmetric\n", "39 7 0.607421 -10.429427 0.61 0.31\n", "78 10 0.607014 -10.463498 0.91 0.31\n", "173 6 0.605909 -10.502364 asymmetric symmetric\n", "151 11 0.605171 -10.296199 asymmetric 0.31\n", "139 8 0.604519 -10.492116 symmetric symmetric\n", "115 11 0.603784 -10.294463 symmetric 0.31\n", "153 4 0.602580 -10.367545 asymmetric 0.61\n", "57 7 0.600409 -10.361130 0.61 0.91\n", "63 4 0.598736 -10.551716 0.61 symmetric" ] }, "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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", 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", 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", 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", 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", 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" ] }, "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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", 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" ] }, "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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CnG4f5cgsvsu5YGtFNnvr8nT9sJp8p/49n+sc44lDrZOyvxpLPPze/hoe3lxyU8egYAnJRZKkF4DiNLs+J4T4xVSnpRlL67cTQnwO+JwaY/lD4M/meP5Xga+C4habYj4ZzBMJWdDU7+OVqwN872gHLYMLC26aDBJVeQ6q85xU5DrIspt1YkgmDLvZiM2SfnuxayB84RhN/X6u9vq42ufnQvfYot/AQHG7TQyQJ7uuCt22ZVG5XU2QZUH3mGLFtQ4FaBsKcrHbu6gdGpPxyLYydtXk0lDspr7QldbSi8QT/OxUJ//1WuukGp5Htpfx2J5qtlZkL8n8ViOWjFyEEPfM47ROoCJpuxzonuGc7wJPopDLfM7PYBEw6I/wqzPdfPdIO03909dPbCrL0omiUl2WqbpNyyn6N1sEo3Ga+vwcax3m5SsDi34Dy3aYU9NydfJQrJCcWbrdbjbEEzLdo2GVPAK0DgVp6vdzsGlg1tI1lbkOSrJsXOzxpi1KnYiafCdv21bGjqoc1ha7yZ9Fv5Ou0RDfOdw2qULebTXxsTvqePctS9s3ZbVitbnFfgl8V5Kkf0QJyNcDRyceJElSvRBCkwF9C3B5LudnsDD4wjF+cbqb7xxpnzEV9p07y3lgYzEbSrNWdTplTNVjeuXqAC9d6edg0+IQiCRBkduWtiiwXHW/LYUE+42CWEKmcySkEMigQiAtgwEOXRsimphd467SLBu31uXhsBjxheNc6vHqBaLtw0Hap2jje/e6Qt66rYxtFdmUZdvnVHgohOC15iGeONTKcxf7Uvbtqc3jd/dVc/e6wps+g246rFQq8tuAfwUKgCclSTothLhfCHFBkqQfAheBOPAHWqaXJElfA76iZpb9tSRJa1EyztqAjwFMd34Gc4cQgn5fhJev9POdI+0zynU8sq2MR3dWsLUie9VpOHnDMVoHAxxvHeFY6zDHWkcY9Kdv/TofbK/Mpq7AteIS7KsRkXiCjuGQbn20qYH0k20ji9IzvnsszE9Pdk25P8dh5u3by3nL1lLWFXsW9PfwhmP85EQnX3+1ZVKK+GN7qvjArVXUFy1v35TVihVJRV5tyKQiK82Grg34Od0xyo+Od8yYPfPmLaW8d1clu2tyV1xqIq4WAF7p83GsZVgnj6VAZa6Dt24tVWQ2ch2rQoJ9NSAcS9A+rKTwtg0FaVFdWee7vPMqyJwPCt1WNpZlcXdjIQ9vKiHbYVm0a1/p9fHEoVa+cyS1u2N5jp3fP1DLI9vL3jAZd8lYdanIGawsRgJRLvV4udjj5fmLfTMGoR/YUMx7dldya23uimQd+SNxukZCnO0c5VjrMEdbhmkdSu/qWCyYjRJv2VLG3Y2F7KzKWXbRv9WIYDSupkEHaFEzsVqHAjT1+edVWzNb5LusbCrzsKksi03l2Wwqy6LIs/Qu1lhC6e74xOttkwpjb+TalOVChlxuYiRkQetQgEs9Xi71eDnRNsLh69MTyT2NhTyyvXzZbqiyrLje2oYCHL4+zG8u901q07pUKMu2U+C24raZ8NjNNBa72VGVuyrdessFXziWkoGlWSLXB/2Lniqe67QohFGWxaZyZVmSZVvxm3W/V+nu+B+vXE9x20mSojD8vt2VlOc4VnCGNwYy5HITYSwY43jbMEdU19CpGVxbtzcUcM/6IrZVZLOu2L0kwcdgNE73aIjm/gDffL2VQ9eHFv09klGb72RdiZt1xR7qC11kOxRJeo/NrBdM3iyy7PPFWDBGq2p1TCSSxbBAsh3mcdJQiaMs277ipDEdhBAcbRnmicNtPDlB8HNjmYcP7n1j1KYsJjLkcgNj0B/hWItCJoevD02rc7SrOpdba3PZVpnD1opscpwL90fLsmAwoIgdatIjmsBj92iIC92zF1WcDuuK3UrFc7GbytzxXh43owT7YkAIwYhKIJoab3IwfSS48BjI7x+o4bE91ZTnrG7SmAmBSJyfn+7iG6+1Tkqhf2RbGY/tfWPVpiwmMuRyA6FnLMTRlmEOXx/maMvQlJ368l0W7lhbyC3VOWyrzKGuwDWvIrtwLKEThrJUCWQkRPdYiJ7R8KzTRaeD3WykodjN2iIXDUWKbMbaYveqTl1eaWjNxpJJQ18OBvDOoqZDQ2Wug+p8J7Vq3/ijLSO8cKlv0nGP7ijn/33z+ptCGv/agJ9vHWrjG6+3poxnO8z8/oFa3nVLxaxqXDKYGplsMVZntpgQgvbhIEdahjlyfZijrUNTamh5bCZuayhgb10++9bkUZnrmPGmLITQJeC71e6HmvXRPaYQyFIGaWcDSQKnxYTTasRpNeGymnBYjLisJpxWEw6LCZe6TzlOOdal7xs/12k14TAbF93SiSfGuz5G4zIRtQtkLGksGpeJTNhOPiaSNJZyTlzpGdMyGFj0eIfRIFGT76Qy14EQgpeuTN23aEtFNsWeyTdao0HCbDRgMhiwmCRMBgNmowGzUR1Xl+PbBixG9ThT6rrZIGE2GTAZtHPGz0t7LYM05wePhCx48VIf3zrcNqmOaU9tHo/vreaexjd2bcpckckWuwEghKC538+RFiUb6mjLML3e9Aq5DouRvXV57FHJZG2Re9IPLRJP0JPGVZXsvpqo7ruYeM+uCipyHUqvdLeVAreVQreVHIcFWQgC0QSBSFx5qev+CdvKS90XVbaDEUXAMRAd3zexN8p0cFqMOKzjxKORkMNiRBaCaFyoN/lE2hu+vq0ul6g1/aKiLNuO06rECiRVISmakGnu99M8hZqC0SBRX+gCIBJL0DYhO08ISAhBPCETS2qxHE8o319cFpOapS02zBo5TSAhu1n5Gzstyt83Ek9MWRRb4Lbysdvr2FqRhcNions0jMNqxGkxYTMbMpbzApCxXFgZyyUhCy71eHUiOdo6zPAUloLFZOCW6hz21uWzty6PTWVZ+MLxFNLQXFWa6yqdtPdSYXN5Fn/7js2sK/Ys23smIyELgirZjBPUOPkEVGLyRxIE1W1/JJncFNIyGCQsRgMWk2F8OXE9eXuKY6zqtlkdMxkkBgNRekZDumy/tpyrdWg1GajJd1Kd56SmwEmN6s6qyXeSO4MsfiSe4JWrg3zx1xcmWcGlWTY+/6b1NBS58YVjeMNxZRlS2iJr68n7tJ4wSmsCKWlduaYQEJMVAkqorZrjstLRUlvXCCkhC2KyPGtZl5WC26pkFmY7zGQlLbPsFnVpxmUbJzbtAcZpNWI1GTEbJSwmA2aDYVZWtFA7gSbk8XbSCbW1tCyPdwnV1hP6uvKdRhMy4ZhMOJZQX4pFHInJhOMJhIB376qYd4nBdJZLhlxYHnKJJWTOdY3pZHKsdXharaMCt5XqPAdVeU6KPTaGAhHVdaVYH3N5Wp8NCtxWtlZks60ym60V2eQ6Lbx4qZ+/e/ZK2uM/fkcdf3jnmje0dEky4gmZrtFQSgW6lonVMRxM20NmKhgNEhU5doVEdPJwUVPgpMRjm3RTEkLgj8TxhRUiGCeBGEP+KE+d65m2KNYgMaMFZjRIeGwm3DYzHrsJt9WMyag1N1PngRhfF8r2+Lo22dRxdQjtPiQmjJN2fOL7jH8P4VhiyWugbjZ85LZa/vShxnmdm3GLrQDCsQSnO0Z1MjnRNjInQhjwRRjwRRal0rw6z6FniW2rzKahyD0ppTIhC852jvLvv73O3z4zmVAK3Vb+/Hc2cm9j0Rs2Qysal+kcUQhjYgZW50hozt0diz22CQSiCHnaTEYGAxF6x8L0joUZ8Ee5PtiH91SqFaGsK0QyVw/UzqocirNseOxmPVXbYzfjSUrbVraVdYfFuOwuIiGEbt0kuyI1N1zy2LUBP//1WmtaYjEbJR7dWUGh20osIROJyQSiCUJRxQWrWb3BaJxgNEFQdcsupdt4NSG8yA+qGjLkskgIROKcaBvRyeR0x+iiZFLNFtsrs7lvQzH3NBZSkz+77LABX4SXrvTzD89doc872Y12/4YiPnPf2jeUVlI4lqBzJJhSga5ZIF0joTnfxLPsZmoLnNTkOSnJtuGwmLCbjVjNBryhOH3eMD1jIS52j+mdCtO9h+aO0YigNNvGWptbIQO7GZvZyLnOMZ650Jt2Hp99cB1v215GoXu8MFYkuU5icUEkkSCWEOoNXLlpd4+FiA0lJyBox09IXFCvEU26RvrjRPpzk4gjeWwxHCuxhOC7qmyLUXV9Ku4pIxbNTaW6MfNdJt3FqcVykt2e5uRl0rnjYxOPk9KeazWNJy0kn7uQBzfN9RibSMIJme7REL86083T53r1Ft61BU4e3lTCB/ZULfxLToOMW4z5u8ViCZl/+00zL18d4HzX2JIHMO1mI/dtKGLfmnw2lHqoL3TPSYQvnpA51THK0+d6+c/XWtIe898fWMs7dpSn3IQWC5oQpgQ4lih7azYIRRO0DU+2PtqGgnSPheZ8Q7OZDeS7rNjV5mNafxm72UggGqdDVead7t/DIEGu00qe00Key0Ku00Ke00KO04LDYiQhM37TV28eoWiCl6700z9NfM1iMlCeY1duOknEEFnEm3cylAyy1NiVOenmaTYZsBoNmE3S5LGkm6/ZJKUZU44LRuP8/HQ3pztGJ73/umI3v3eglluqc1LeX4uBvZH63vT7wjx7vpcnz/VwtGUYWUCdSigPbS5Jmwg0V2TcYksEfzjO1w5en5OyqySBQZKmJaJ8l5UNpR71lcX6Ug9VuY553Yh7x8K8cnWAH5/sTNvVsKHIxR/d08DtDQWLHj/xR+Kc7RjlVMcop9pHONU+OimAbTcb9aCnw2JUU4yVbB0ta0dfTtjvtCoZPSnpqgYDshCMBKM09/tp6vfT1Kc085pPN8jZIByTJynkzhWyUIpiB/0RmFxiMicYDRK7a3JTnpYt+o1cSjOWfFzqk7R54lO7UUk7TnlaX6ab9/muMb51qI0fHO9IGc93WfjgvppMbQoKoTxzvpcnz/ZwtHUYIWBNoYs/vKuehzeV0FDkWjb3ZoZcFgDB5DaXZqOkt8gtzbaTkAVjoRijwSijoRjDgSijSRXSVXmOcRIpUQhloZpeF7u9/PJMN//1Wktav/Hbt5fz9h1l3FKdO6MUimZqR+LJ5racNKa4McKxREr2m2Z6T0SW3YwshJ7MEIol1FjUytbULAYcFiOVuQ4qch2U59hxWU3zcqOMu0yUMYMkcbx1mOcu9vHjCW1zQXFfvnNnBbc1FNx00jaReIKnz/XyxKHWSUkJSm1KFfc0Fr2ha1P6vWGeVi2UYyqh1Be6+NRd9Ty8uYSGFXJrZ9xizN8tlpAFvzzThdFgoMhtJRhL0O8Nc6nHx4XuMS71+PCrN1mTQWJNoYsNpVm6VdJY6sFpMaW4OmIpS5HGrz3Zh60Fmn98onPaDLSKXDu1+S7icuq56Uljaeo4JAn9iTnZBaI9+U68sSraZOElszomwmY2UJJlp9hjozhLfanrJep6nsu65O6VaFzm9WuDPHO+l+8f65i0f1d1Lu+6pYL7Nxbjugkz9rpHQ3z3SDvfPNQ66X/6/bdW8tie6hW7aa4GTEUoD20qWVZCybjFlgixhMyZDiW9uKnfN2W6qSQpqb7eUIzfXu3n+Yu9KkEsfaEZKKSiPSWPBKP6jdxuGXdzaGPJbhBtDKB5QOkRf30KyZm1RW521eQqfcaL3ApJTHDHWFS3SbJZLoRgOBBNkTBRlHgDXOwJpvQC0YhJaLn9QqSkqCbDYTFSkz9eB1KtrtfkO/U+HwlZEIoptS/BaIIchwWP3bRihXOhaILfXh3gq69cS5s6XF/o4l23VPCWLaU3ZQsAIQSvX1O6Oz57IdU3WJPv5PE9VTyyo/ymkJ+ZD/q8YZ4+18NT53o51qYQSkORi/92t+LyWm2JNyvVifJR4AtAI7BL7S6p7fss8GEgAXxKCPFsmvP/HHgrSifKfuB3hRDdkiRVA5cALZf2sBDiY0v1OSIxmSMtw/jCMSpyHalP41Nko6RmnkhYjEb9xjvZ1z3Z/20ySBxvHeHJcz2caJucpvy7e6t5z65K1hbP7x9NCEHXaIhT7aOc7hjl9WuDnO/2ElXda0UeK9v1tOYcNpVlzShPL4RgwB/RlXdbk4Pog8EUF5pBUlxnWu2D22pCoJBJXBYpbj6zUaIy10FNvkvJyFKLC2sLnLPSJTMaJFxqtf5KICELznWN8a8vNvHi5f60x9QXurh/QzG/s62UNYWr6+axWPCFY/z0ZBdPHGqdpJd3T2MRj++tYl9d/hsyBb53LMzT53t46lwPx9tGEEJ5kPtvd9dz97oiynLs+gPS4etDDPojehmDvu6PMOiLMuCPpH2Y/c/f3cld64oWfe4r4haTJKkRhRj+HfiMRi6SJK0HvgfsAkqBF4CGia2KJUnyCCG86vqngPVCiI+p5PJrIcTGucxnNWqLTcSVXh8/PdXJL051T5KF2VKRzft2V/LmzaVz7kMSiMQ52znGqY4RTrcrwXetut9qMrC5PCulRqYky572OlpfFk2Jt2VCJlYwKenBaJAoUm/+ugUilKB2XJZTYlKSBKVZdp08kutCyrLtN4yvPZ6QuTYQ4FzXGK82DfDz091pj6vJd/Kh/TVsq8imvsi1Is3ZlgtX+5Tujt8+nNrd0WEx8tieat63u5KK3Bu/b0o0LtM+HGQ0GFVijFElzhiIJBjyKzf/ZDJY7iLQr35gB/dtKJ7XuavOLSaEuASke7J8K/B9IUQEaJEkqRmFaA5NOD9Zy93J5Lj6TYEBX4RfnO7iZ6e6JsnXmwwS77qlgvfurmRDadasrifLguuDfk62j+qWyZVerx5Xqcl3cmBNPlsrs9lWkcO6EndKgFiWFaumbTCgtrEdbybVNhwgHEu1KsqyFXdcSZZNr6QWQrE+er3hlHhOvsuik0dNvktfr8pz3HA9NKJxmat9StztfJeXc11jadNmQVHh/cx9a3l0Z/lNTSQaYgmZ5y/28c3XWyd1QN1Y5uHxPdW8eUvpqv2bxxMygUgCXySmyw35I3H84Tgtg37Odo5xrmuMnrH0uoBLAZfVRIHbSr7LQoHbSoHLqm4rS209x2FZVr201RZzKQMOJ213qmOTIEnSXwKPAWPAnUm7aiRJOgV4gc8LIQ5Ocf5HgI8AVFZWLnzmi4RwLMFzF/v46clODjYNTjJjN5Z5eN/uKt6ypXTG1OGRQJTTWhpwh0ImWnDUbTOxtSKbe++qZ1tFNltUyZd4QqZnLEzLYIDvH21PiYW0Dwd19xgoNRSVuQ6cVhMNRW7dAhECwvEEncOhlEJSl9VETb6TzVospEBxY1XnO8my35h+9HAswZVeH+e7xzjfpZDJlV7ftAW0H729ljdtKmVjmecNI4zY7wvzvSMdfPtI2yTdu7dtK+MDe6rYVpG9JN9HJJ7QdeZ8YU1bTiEETTBV06TzJ+nT6ceo2nS+8MKq9k0GKeWGH0vIHG+drNzx0KZiHt1ZweayLJxWE1bTjSmguWTkIknSC0A6W+tzQohfTHVamrG0VokQ4nPA59QYzR8Cfwb0AJVCiCFJknYAP5ckacMES0c7/6vAV0Fxi834gZYQsiw40jLMT0928vT5Xj3DTIPdbOStW0t57+5KNpdnT3mdhCw42T7Ccxd6efFyvx58N0iwttjDmzaXsrk8i3XFbgySxPVBRRX3+8fa+YsnL07ZH2YqROPylKq6Gjw2k56aqykia90hAYLRBIP+CAZJIZ/V/CMKRRNc7PGqJDLG+W4vTX0+XfYly27GYTFOIpbGEg8PbizmgY3F1BcuX53BSkMIwfG2EZ441MavzqS6AQvdVh7bU8W7bqmkwG2ddF4kLitEMOnmP4EItGOi6ckiEEnMWilDqblSaqdGg7FJv8Op4LKaUlo1by7PoiInfV1a92iIp871pOi9NZZ4eHhTMQ9tKqG2wDWr97wRsGTkIoS4Zx6ndQIVSdvlQHrn9Di+CzwJ/JnqTouo739CkqRrQAOwKgMqzf1+fnaqk5+f6k6baruu2M37dlfy1m1lkzJktH4srYMBfnS8c1JhWTJkAZd6vFzq8fK9o4v+MaaE2SgRSyh9aXrGwpgMEgZJYiQYTfsEaDcbKfQo0vyFHkWqX5PsV8aV9WyHeclv0P5InAsqgVzoUlwd1wb8uisv12lhY1kWd64twGiQ6BwJca5rTCfbrRXZOqFU5TmXdK4rgZh6w9bcpkIIglHlqX/AF+E7R9r53tH2tOcWe2zsqcvjQreX/+eHp1OIwBeOEYgmZpVFmdzvx6W3UTBR6XTo6y6bOm4x4rKZx/v/WE2EYwkGfBH6vGH6vBHahgJcHwzQPhRM0YnLdph18dDaAid1Bcr6bF22XaMhnj7Xw5PnevTW4+tLPPzJ/Wt5cGPxTUUoyVhtbrFfAt+VJOkfUQL69cCk26EkSfVCiCZ18y3AZXW8ABgWQiQkSapVz7++LDOfJYb8EX51ppufneriTOfYpP1Wk4E3bynlkW1lFHps9Iwp/5hdo2F61EZeV3p9i948ajrU5jupV7tE1he5qc134rSaMBkkTEk9NUzG8WZPBiltTA1QbkTeUJx+X5h+X0RZeiPqekSpFer28ltfJO3To8Vo0F0LE4kneX229SiyrDxhn+4Y4VyXQiYtQwE9xbnQbWVTWRYPbiphY6mHDWVZ9HkVaY1fn+2hfTiIQYJdNbm8f/d67t9YPGXiw2pCQhbj7QjC2tN+An8kprck0KyApj4/rzQNpLhF54teb5ifnepKGdPcRXWFLj1mUOC2kuMwpxCHThazlA8KRuO0DAa4PhBQl359OzlL0WI0UJ3voKHQzf0biqlV3ba1+a55tQSfjlAe2lRCTf7N98AxESuVLfY24F+BAmAUOC2EuF/d9zngQ0Ac+CMhxNPq+NeArwghjkuS9BNgLUrGWRvwMSFElyRJbwe+qJ6bQLFmfjXTfJY6WywcS/DipX5+dqqTl68MTKueW5FrJxBJTNnbZTpkO8xU5ylBcIfFiCxr9SAwForSMhigfYL8u9tm0hV5NVn3WjWQ7l4F9QTBaDyJeBQS6vOFGUge80VSMsw0GCTlppVCPm4rBapV5A/HebV5kFebB/U4QFm2nQ2lHjaWKS4OTTEhnpA51jrCM+d7ePZCH73eMGajxN66fB7cWMw964uWRXoklpCTGqtNIILwVO6j5OPHx4NzkC1aKRgNEg6zEZvFiMNi1PXbxtdN2M0GRRDUYsRmMjLgD+sEMjGwXpplo7bApcf8avKd1BW4KM22L7gwtnMkyNPnlMJGLYFjQ6lHKWzcVEL1TUgomX4uM2CxycUXjtE5EuKXZ7r5vy9fW7TrJqM6z8Gd6woBtSugLBgORmkZUOpIkm8cFpOBmjyVQObYYOpGQSSuuDgUyyfCgGYVeSNJFlJk2iZqbpuJylzFpeK2mbCYDJzpGEvrsnxkexlv3VpGaZYtyfViSvskrQWU9Zt/dCYimEAWScfPNqBsNRnGn/aTOm/qriHLuBUQiCS42ufjSp9vUgytwG3l1to8tldms6Mqh8YSD2ajgeFAlB8c6+Dbh9smfT/3NBbx2J4q9q+ZXJuiCW6GYorEvZaaG1THxvcpkvjj6+PjYXU5vh7X12MJoTwwFbio0xNHxrMP55qqPxM6hoM8fb6HJ8/1ckYllI1lCqE8tPHmJJRkZMhlBsyXXOIJme8d6+Byj1fvLni517cEM1Rw17pCsh1mtRAxmGLdGCSoyHWkVKVP12DqjYRYQua3Vwb4yclOnr3Qq8dN9tTmsacuDyFgwB/GG4oz6I/w+rWhFZ1vkUdxCblSiMGUhiw0gjBOGp9KYyyWkLnU4+VE2wgn20c52Taik4PFaGBTeRbbK7PZXpnD9qociiYoAZzuGOWJQ6389GSqW8ttNfHeWyt5/+6qFa1NiSVkTBNUIBYbHcNBPSivubY3lWUphLLp5oyxTYUMucyA+ZLLkD/CHX//8rR6XksBk0FiQ6mH9Uk6ZeuKPYv+VHYjQZYFwVgiJYX0aMsw3zzUOkmxeFd1LhW5DgKROGOhGOe6xmadGbRcMEjgtJpwW03YVHePIudvwGYy6mM2swG7WZH5t5kN6nJ8OxhRMtwudnu52ONN+Zwem4m9dfnsrFaIZEOpJ22tTTiW4Ndne3jiUCtnJ8QJN5Z5eGxPNW9ZxbUpi4HpCOXhTSVU5t34xZ7zQYZcZsB8ySUcS/DpH5zm6fPpGzRNRK7Tgj8SX5SgaDqYjRJ287h8ve6btijBT23Mbk4aTzpO8WObxtct49dailx7LaA8Zb2Bmjk0dU3CeCwhEI0vWm+SbZXZ3L2uMKkfumoV2FItB/s8+tFMlWarub3SbYdiCcLRBOG40gM9pK5H1N7oIbU/+nxl6ixGQwox2VViGvRHpxUMfceOct6ypZSKXAel2babrgi0YzjIkyqhaKS6uTxLd3m9UQklGRlymQHzJRdvOMaHv3EMo0Giqc8/qVfJlvIsSrLstA4FpnWX1Re6eGxvNetLPNTmO/XslHhCJpjkkw5G40nrCUKxuO6P1sdU/3OyL1u5Ruqxc2m5DMqTtN1sxGIykJBFkngkJIRAaOtLIMRpNkrpXULqy2SUOHRtiOuDk+t0NpZ58IbidIwEU8inwG1lXbGbxhIPjSVu1hV7WFvkvqHchyOBKKc6RhQXV9sox9uGU5I1avKdrC/xsL7Uw5pCF0Io8Z9wTCWp2Ph6WF0PxRI8f7FvzsF+SVJSjCtylJqm8lwHFTl2KtQWBMUe2w3RqKt9aJxQznUphLJFI5RNJTeFHM1iIkMuM2C+5CLLgn95sYnvHGmbU2rw/RuKeGxPNbtrcpdcG0t7UtbrCFTZCl84xpBfEbPr94Z1fSMt6L3aM4mcFuOsmrQZJKVIbV3xOImsK3GvuqZS6frmaG0QDAYJoyTROhTgnCovkiwxYjRINJa4lThJZQ47qnIoz7HPydIcC8b40QklQD9R2+rW2lwe31PNPeuL9FhOQhb0ecN0DAfpGAmpyyCdwyE6RoL0esMpZK71OarIcVCRa6c8RyEdjYDyVjCxpG0ooBPK+S6l3npLRTYPbyrmwY0ZQpkOGXKZAQuJudz1D7/FaTFSrQoqxuIyV/v9NPf59JtfQ5GibHvf+uJZSX4IIQjHZJ0IdNkKNXNIX4/E8SUXoEWSxpNkK6ZqBTARWqdHt2oZKMVpSnaR3WJkLBTT04IHfJEprZ8ch5mqPKfeB6XIY9OD1HFZ6NbTaDCq9pAP68vesfC0qdpLgUK3lSKPDbvZiMkoqa16lfYASi2PUr+jtfBVxg1q4Fjp0T6xmZrWfyd5mdzbPBKXicbVfvMJecEWn8NiHFfeVmuOtB7w5qR1i2m8FslsMtDc5+dK39RW9SPbythQlpV0zfG+8G6bmVyn0pI5x2lOcYtF4gl6RsN0jATpUAlHI6LO4WDajqQ66aiEU64SUUWuY9Fl9lsHxwlF0+3LEMrckSGXGbCQVORkyZVnL/TRPqw89dnNRnbV5LJvTR75LutkIpiBLGZzr5EkcFmSiCBNmulUriSn1YjbZtKrlZ0WEwZJEctsGQzor+vqsn0omCKj4baa9LTm8Qw1F9X5C6uPEULQ541wqVdRFLjc4+N46zDdUwgB7q7JpSzbTpbDrAhijo2T1XxqhZYLBgmyHRay7Gay7GbisszVPv+U8ThNoXpTWTal2TbisiCeUBq+xdQmcnFZ6A3lYgmZmCzG17XjEjLBaGJJshpdVhM5TjO5Do1wLMq6S1nmOC3kqeM2sxFvKEbXSEgnoM6RcfKZ2Mk0y25WiCbJ4lFcb4obbjbJBOkIZWtFNg9vKuHBTcWU52QIZa7IkMsMmC+5jAajPPylV+fUJdFokHBajLhtZpxpUkjTppmqZGE2GpBlJbYhhCAmC/zhON5wDF84hjcUV5ZqMNhiNGBNyibSCtCi6k24eyyk34ynypaqynOwtsit1go4dMXifNfC3RjhmFJfcbnHN04mvb60BZEaHtlWxqfvbVjQk6UQSpOwsZDynSnLGJKkyJkkhCCRUPrHxGXFqoirjd20McX6mDreNWlMjX3Nx0LJcZj1IkFHUkJGypjFiMOcJkFDS9pQb+Y/O9XFL053T9LbuqexkMf2VOu1KULtn6MQlyAmy/q61gnVq7btHg5GGQlEGQooy+FgjOFAhJGAsn8qC9cgQY5KOrnOcQLKdZoxShKB6Hj2nyYtM+iP0DkSmkTCBW6rbvEUuKwKaTvMjARivNY8yNHWcQXmzeVZvGVLKQ9uKqEse/UrKaxmZMhlBsyXXCLxBP/yQhPhmDxlvYG27rAYEShFZAoZxPGG1OWEbV8klSg04phJgM8ggdtmxq0SUUuaAPd8IKnBfI2gJhKWlmmkj1mM2EyGlBRau9lILCHzyzPd9HsVWZeesZBuodnNRhqK3ZgNEhe6vfoNKctu5s1bSnj79nK2LpFq7lIjnpC52OPlaMswr18b4mDTQIqr0mMzsaE0i41lHmoLXKrrME2yhprAMT4e10krGE0sKAuxJt+ZlEU4gbwWmF0YiiYmE5D2mjA+ElTGp+Jgm9lAjsNCXBYEI/FZxd3SwWU1kWU347GbyVatR42Qsuypr+ykMbfNfEMkJiwXVl0/l5sFZoOBDaVZdIwE8YZidAxHpySO2dRR2M1GPHYTbpsZj81EtsOi+JvtCmF41PHkbbfNjMduIp4QXOzxcqxlmGOtw5yf0P8l32WlJl9xIxRn2SjJtlPisVHosSIL9PRWLeU1FB1Pc42oy1BSllHy2HAgqp+bfMxsUZXnYDgQ1SucAe5eV8g7dpRzV2PhDZfiGojEOd0xyrFW5W9xqn1UT5CoyLXz5s2l7KzO5ZbqHOoKXIuWoRZPyHqWYP8M4pGVuQ7ubixEqH/75IxCX1iR3AnG4inZhXN5DpUkcJjHCclhUR48Uiwq9UGkJt/JhlKPfqzNbFAsw3RZbaq1OBaKjRNSIDrJjTYTtDTvuXgdtM/ltppSSCjbbsEzBRklk5Z7lat+LzYylgvzt1wGfBH2/81vZiXJ4baaqC10sabARV2hkzUFLkqy7DqZaNbGbNE9GuJY6zBHW5RXkyrbYTEZ2Fqezc7qHNYWuxclDjIfaFlqz17o5fM/P48vHMdtM/HpexrId1u51u/nm4dap3SBWUwGKnLsVOY6qFTTWStzHVTmKQQ5Uy+b5cSAL8KJtmGOtY5wXCX2hCyQJGgs9nBLdY5KJrkUZ9lmvuAC0NTn41uH2/jR8c5JBP/WraU8tqea7ZVztwC1JJPgJNdfEgGp26np8+nT4zX5F+28uSZxWE2GGX93mir3aoHZKJHrtJDntJLnspDvspLntJDnUrYL1GWeOn4jFKVm3GIzYCEB/ZGAUmiWquwbnqBzFUnr0tIk5guSxBU1pd8CTW7eo3SQax0KcLRlmGMtwxxtHdarzl1WEzuqcthVo9y8NpdnrYp/yiu9Pv7yqUu8cnWAqjwHn31wHfdvKOZ8l5cfn+jgl2e6GQnGKHBbedu2Mt68uRS3zUT7sNKUrENdtg8HaR+aHODNd1nGCSeZfHIdFC1hTYUQgrahIEdbhzneOszx1hG9vsZqMrC1IptbqnP1qvfFznJKh7ja3fGJQ20cup4qXVPksfK+3VW8e1cFhe6lJbaFQNMcC06q3Yqn1Gtd6Brjxyc605JRVZ6SVbaY7sKVhMtqUshGJaB81zgx5bms5CcRU47DsiLuugWRiyRJRcD/BkqFEA+qfe73CCG+vvhTXRkstSqyEIKxUGyccJKIaCBJ2XfAG5m1ee+2mXjT5hJuqy+gOMtGocdGgcuKxbSyPeUHfBH+6YWrfP9oOy6riU/dXc8H9lRxvsvLP79wlYNNg1hNBu7bUMwj28s4sCZ/xlof7ftrTyKcZPLpHg2nBMotRgPlanB3IvlU5NrnZMXFEzKXenw6mRxrHWHQr4hfZjvM7KzK1S2TjWXp5VOWCgO+CN8/2s53j7ZPUv/dXZPL43uruTepNuVGRXO/jyfP9vLUuR49bfqW6hwe2lTCgxtLZrQGE7IYF8pMsajGxTMnWlnJSRgTxybGC1cTXFYTVXkOqvIcFLptKZZRMjktVmO+hZLL08B/oXSQ3CJJkgk4JYTYtOCZrRIsNbnMhHAswdnOMY61DvPbKwMpmS3JqMi147SYGPRHGApE0/rAcxzmcavHbaVggkWkNeJyLbJbKRxL8PVXW/i/L18jHEvw/lur+G9319M6FOCfXmjilasD5DktfPT2Wt51S+WitjWOJWR6RsNTks9YKNX1lutMtnoU11tFjoN8txWrSUmEONOh/D1Oto+kxEtuqcpdknjJbCGE4ITa3fGXE7o7WkwG3rGjnMf2VLGu2LOs81psNPX59LThq31+JAl2Vs2eUJYLE92FyYrNExMwfOHkB8zxh8yFtE5eDHz+4UZ+70DtvM5dKLkcE0LcIknSKSHENnXstBBi67xmswqx3OTiC8c40TaiBHxbRjjdOaqb7g1FLm6pztXdXKVTpErGErJSYZ9k+Wjy8smV9lO55BwWYxLhqOSThohyHJZpb6BCCH55ppu/feYKXaMh7l1fxGcfXIc3HOefX7jKy1cGyHVa+OhttXxgTxUOy/LHSsaCMTpGFKJpGQxwqn2Uk+0jc6qD2VGVw7piNzkOC9kOM9kOC9l2MzlOM1l2CzlqEHepFBdC0QS/ON3FE4fauNiTmqxRk+/kA7dW8fYd5YtK2suNdIRyS1UuD20q5sFNJZMUmt8ISOcu1FKy+70RVWEj2RsSZmSaVP502FSWxa8+uX9e81totlhAkqQ81F72kiTdCkxuoZjBlBj0RzjeOswRNZPrYrcXWSg1LxvLsnh8TxW3qAHf2Xa9MxsNFGfZ1Ce4rCmPE0IwGoyl/hP6kv8xw1zq8fLbq+m7PpoMUkrXx4Kkjo/tw0G++sp1hFBI8bu/vxuX1cRfPHmJ31zuJ8dh5n88sI7H9lQtaQBelgUjQUXKZtAXZcCvEOygSr7KurIcDqa3+DRkO8zUFyq1PHFZMBaMMRJUUmSfPt/LaHDqNFlQ3JXZDjM5aoFkOjLKto+P5TjMeGzmKQm8dTCgBug78E5Q3757XSGP7a3mQJq+KTcKrvb5ePKsQihN/SqhVOfyv96ygQc2Fr8hCSUZFpOiqpDF3B4aYgmZU+2jHGwa4GDTIGc7R5GFIpu0py6fA/X57K3LUxIMlkgKaTaWy3aUrpEbgfMo3SPfIYQ4O+83laRHgS8AjcAuIcTxpH2fBT6M0knyU0KIZ6e5zmeAvwMKhBCDcz1fw2JaLkIIOkdCehrqkZZhrg+MB3y3VWazqyaPXdW5bKvMXlVZT8FoPCURQY8FJbUfHvBFJkl3TIXH9lRRnedMakesENNsPrPWClkhiqiufaaRRPL6UCCatjjRahpvh6y10dWWSitdCwUuG/luy6wtKlkW+CJxnXRGQzFGg1FGte2guh2KMRIc3+cNx6YkNUlCTWlVCMdjN3O6fWQSmYDyQPLh/TW8f3fVDavKe7XPx69VQmlWCWVXdS4Pby7hgQ3FFL7BCWU+EEJwbSDAqyqZHL4+RCCawCApKgT76ws4UJ/P1orsRY3BzdstJkmSEfgUCrmsBSTgihBibnbX5Os2orQo/nfgMxq5qMkC3wN2AaXAC0CDEGJS0YQkSRXA14B1wA4hxOBczk/GQshFlgXNA34lk6tVyebSpErcNpNukeyqyWVTWdaKB9wXgrFQjP/zUjP/cVCxVu5eV0h9kZuv/Da12+aaQhfBSHxKyZaFQLOkkkki321RycJGvsuiE8piBS0XAwlZ4A2lI6QYY8EoI8EYrUMBDjYNzngtl9Wk1CrpL7uyzLbrY6uhRbUGIQRX+/y6yytDKIuDIX+E164NcfDqAK82D+pJHVV5Dg7U57N/TQF76vKW1FU6b7eYECIhSdJbhRD/BFxYrAkJIS6pE5u4663A94UQEaBFkqRmFKI4lOYy/wT8d+AX8zx/wZhO/sVuNlKb78QfjnOwaYBD14cwGyRMmgCgYVxc0GQwYFKFBTWhxOT9ZqO6Xz0ueX/yeLpraeeaDAYs2rp6zmxdKbGEzDdfb+UvnrwEKH3m99Tl8eMTnbx4uV8/bmtFNrGErEqALK6uV1Weg42lWdQVuijyjCcsFHoUolntGVFGg0SOqqs1EWc7R3niUBuHr0/ugPnQpmLu31BMabZdFfYM0T0aVmV7Qlzp9THgj0yyilxWEyWq27Q0y64ss20UZ9kpVceXkoCEEFzp8/HU2R6ePNfDtYEABgl21eTy+J4N3L+xeFWnRq9GhGMJTrSNcLBpkINNA7o+msdmYt+afD55l2KdrBbRzdn4Al6TJOnfgB8Aup6IEOLkEsynDDictN2pjqVAkqS3AF1CiDMTCGpW5y8W7BYjj2wvY8AXIZZQNKfiqkCgpsukbQejCUWTStVpiicU4cGYKkAYTySNr8YcxyR0jYb48YnOSeOnkyrsQbmhpnNX5TjMGCSlFa1BUvrCzNSyoG0oSNsEKfiJqC1wUpZt1+uDUuqFVCJaiYSCdAjHEjx5tocnDrelKBPA3GtTonGZPm+YXm+Y7tFxvbiesfHW2wO+yKTz3JoFpKo1lGRPtoTmklUohOByr4+nzimEcl0llN01efzuvhoe2FBMgXt1tTpYzdC+z1ebBjnYPMjRliHCMaWN8/aqHP743gYONBSwqSxrVUrSzOY/Z6+6/GLSmADumu4kSZJeAIrT7PqcEOIXacZBcbtNRMrdSZIkB/A54L75nJ90nY8AHwGorKycYjrTw2oy8sf3rZ3XudNBEw1MIZwkItIUbtPt7xoJcbFnjMs9PnrGwvT7wktWpWyQ0LPZ4olxQcd4QiYQnVqkca7ZLLPF9YGAHt+aD/KcFspy7JRm2clzWXR5+aksxaksUEuStaidr1mRfd4wPzjewZNneya9/46qHD64r5r7NxTPyRKzmAx6U66poBFQMun0jqlk5A1zsdur1+8kw20zpRKOTjw2qvMUMtcI5alzPVwfVAjl1to8PrSvhvszhAIov2lI661JQb83zMGmQV5tHuRg06D+N1lT6OLdt1RyW0M+u2vyVlWsdirMOEMhxJ3zubAQ4p55nNYJVCRtlwPdE46pA2oAzWopB05KkrRrludr8/sq8FVQYi7zmOuSQZIk1R0GdtIX5Q36I1zt83G118fVfr+y7POlBIGNBokch6Iym+Ow6JW8uc6kpa5GaybPacUXjvH3z13hRyc6ybKbeXBjCUP+CM9d7MNlNfGhfdV8eH8tWY7JLhUhBC9d6efLL13jeNsIuU4LH9xbzbt3VeKwGFPIULPuNLKMy+PS8FOR6sTzhwJROkdUqfbhUNqb41wwpAooTuwTv1w40aZ0lTQnEZN5GqJKT3RTuFX188evVZxlozzHrl9LCBgORFOSJQb9EbpGQlzt88/qM+yuyeXT9zawsypnyRvhrVZoKg5nOkc51znG2c4xznePEU8IpSWBU5F3yXVacFiMNPf7udjjTWnQZ5Dg4c2lHKhXMrtKsm489ebZZItlAX8G3KYO/Rb4ohBiwb9ASZJeJjWgvwH4LuMB+ReB+ukC8pIktQI71YD+nM+HlS+inA6jwShX+/xc7fPR1OfjSp9vUkvlLLuZtUVu6otcrC12U1+orM+lu18omuA/Dl7nK7+9Riwhs7cuH1kIXm0exGkx8cF91Xx4fw3Zjskxg3hC5slzPfzfl69xuddHWbad3z9Qw7tuqcRuWVkpmnAsQb83Qq/qNuobU5a9Scu5iheuFkiqZH22Koo4nbWrjcfU1gErhQK3FZvZME6KU5Bm8nh60pwYr5zLtcaJ2Wyafr9yvjTl70gIQa83zJmOMc52jnKuSyETrXDXajKwodTD5vJsbGYjg/4Ir1wdoD+Nm3IilIfD8YZseU7r+LrLMmk8x7F0dVZTYaF1Lv+JkoL8TnX7AygV+48sYEJvQ8lAKwCeVIsy7xdCXJAk6YfARSAO/IFGDJIkfQ34SnLa8kRMd/5qhy8co6nfrxBIr5+mfh9Xen0p/4Quq4n6Ihf3ri+ivsjN2iI3DUUuCtzWeWdFybLgZ6e6+Ltnr9DrDVNf6MJhMfJK0wAOs5FP3FHH7+2vTRuIDscS/ORkJ//+2+u0DwepK3Dy949u4a1bS1dNgN1mNlKZ55g2bVerk4nEld4tsuqWlGVBQig3bFmtxFZcS+NuJW3Z651fZpzTYiRXk+VQbxouq1mdg0IKWp2SVjAbjilFsZqlMRyIYjJIegZdmduuZs9NeKljDosxyVpUyScpRjg+LjjTOcovT3dPUo2wGA28bVsZG8o8mAwG4rLMcCDKpR4vl3p8etO8iUgX+6nIVVyRAiUdXpnLdPMaJ83l4kmTSnDadz8b5LusrC/1MByI8I3XW6c8bm2Rm7XF7tR2HOGYml0Ym3ULdS2lPVdtyqaQz7iVlI6QljJ7dTaWy6Rq/EyF/vwRiiZo7verFsi4JZL89GwzG2goUiyQhiIXDcVuGorclGbZFjW19vD1If7iyYuc7/Lisprw2Ez0eMPYzUYe31vN7x+oJTcNqfgjcb57pI2vHWyh3xdhS3kWH79jDfetL7phi/kWimhc5pkLvXzrUCvHWkfSHnP3ukJsZmNKIWu61gQWoyGlcHWicoLDYkJSEyE0N5b+mlAHNJebr8VowGoyzFm+vsBtpTLXQdUE9epKtXFXIBrn2kCAq30+mtUHqKZ+vy6+qr13Tb6T+iKXbnnXF7qozndO+6CSkNO7WNMl1iST5kTrTunkmUq2I8Eop9pHOdE2MqNES5HHSn2hG0mC0WCMc10zO3bcNlPKvFYCbquJzz3cyLt3zS/uvFDLJSRJ0n4hxKvqxfYBN6YfYRkRiSe41h+gqV+JhWjWSPtwUE8btZgM1BW42Fmdw3uLKmlQrZHyHPuS3qRbBgP81VOXeO5inz7mj8RJyIKP3FbLRw7Upq3aHQ5E+cZrLXzzUBtjoRj71uTxT+/ayt66vFVTT7Lc6B0L892jSt+UiU/l60s8PL63irdsKZvSPeiPxOn3hnW9Kc06GVCFTVuHAhy6NjTnG/58EE3IMzaky3GY1a6kTpwWI5IkEYjE6RgJcqRlmJ+d7kpJi7aaDJPEQ3dV51KZ5yDPaaFrNERTn1+32s92jvHkuR79GiaDpJPOGvVhq77QTXW+A6vJiNEgYTQs3PUajiW40D3GmY4xznWNcaZzNCU5pDLXwabyLLaUZ7G5PJuNZVl6Jl08IXOmc4yDTQO82jSoy/PYzUZurc3VCxjrC13Tute0TqfjscfUJJ7ZkOZE6y6VNCdbq3FZZm2xe8HfXzrMxnLZCnyTcY2REeB3hRBnlmRGK4CFWC6xhEzrYICrfanWSNtQUPdtaz+QhmI3DYVu1ha7qC9yU5XrWFYf6WgwypdebOaJQ60p6c52s5HH9lTxkdvSk0r3aIj/OHid7x/tIBRLcN/6Ij5x5xq2VmQv29xXE4QQHL4+zLcOt/L0+d6Um6nRIPHwphIe21PFjqqcOZGuEIKO4ZDit+8a5XzXGBe6vdO2fZ4PsuxmaguchKJKm+lk68ZtM+nqEXFZTLKI+n2RSRL2mhJvdZ6TkiwbZpMBgwQGSSIUTai6biHahwKTOkcWqlZPMvkUuK3EZZnRYEyxdPr9NPf7aRsK6HM1GiSq8hw0qFbOmkKFdGoLnDO2nIglZK70+jjTOcrZjjHOdo1xtc+n/14L3VY2l2crRFKRzeayrBS3sBawP9g8yMGrSg2bLxxHkhSdLq2AcXtV9g3X6G6uWJR+LpIkeQCEEN6Zjr3RMF9yGQ5EuesfXk774zdIsKE0i901uWyvyqHIYyXboWRmZdmn1pJaCkTjMt863MaXXmxKUQi2mQ184NYqPnp7HflpSOXagJ+vvHyNn6tPo2/ZWsrHb6+jvmhpnnRWO/yROD871cW3DrVOyp4qdCu1Ke/ZVTGranNNJuh8l3JzOz8hEGw2Sqwr9rCxLEutuNc6j5r07qNKZ1IzLptJr3OQZcFwMJpWxLTfF+bFS/3TungsJkNSf6EJQqaqe85mNpKQBd1jIVoHA7QNBWkdUpYdw8GUBxeHxUhVnpPqPAdVeU48dhMGtb4pEpN1QdH2oSA93nAKUdvMBipyxomn0GMlkRBEE4qYY+dIiKv9qQ9yBgmKPDaKPErNTqHbij+SmFLYMdthZlNZFlvKs9lcnsWWiuy0emajwajaolopYNRcemXZdjWjq4C9dXmz1ga8WbBQVeT/DfytEGJU3c4B/lgI8fnFnuhKYb7kEkvIfOO1VlqHAowGYwyrPcBHglFGArEpXQwGCUXI0GFW04CVTI/xtGBLSgpxzjwJSQjBcxf7+KunLtGaVIBoNRl4/61VfPT22rRFeuc6x/jyy808c6EXi9HAu2+p4Pdvq6U8Z3VU/i43mvv9fOtQKz852TVJ3HNXdS6P7a2atjZFCEH3WJhzSdlE57vG9JucySCxrsTNprIsNpVls6ksi4Zi16I89QohONs5ptShnO+hYziEySCxb00+tzcUsKUim2hc1kVMk3sOaetTPTzlOpNJRyGeHKdFLxoOReP4Iwn6vGFaBwN0jARTYgs2s4HqPKdu9ZRm2zEZJQySRDQu06kSj0ZaU1k9RVk2ZFnpfBqOJTjZPjKrwLvTYmRHdS6lWQoZFWfZKFaXeU4L1wcDSgFj0wBnu8YQQolR3FqXpxNKdZ7jDesShoWTiy61nzR2UgixfRHnuKJYioC+EIJANKH0+A5Gx4knEJt2e6rAXjpCynVYyHaa0xJS92iYL73YlJLlYzUZeN/uKj52x2RS0Vw9X365mYNNg7htJh7bU8UH99WktWpudsQTMi9c6udbh1t5rTlVlsVmNvC2bUrflMaS1L4pWmqqRiBnOxUfvibvbzJINBSpRFKexebyLNYWuxfVfaIRiqbl1TmiEMr++nwe2lTCfeuL0qaUT4VIPJHaWdUfYSApTqQR0aA/vYCo22ai0G0lz2UlrhKP1mVSW58Iq8mgNr1yUpPvpDLXgcduxihJxBIK8bQNjffumWj1WEwG1pd42FKeRW2BiyKPDafVOJ6WPiElfTCNhM5E7KjK4f4NRZTnOHTLqMC9+qWHlhILJZezwC2qXheSJNmB40KIDYs+0xXCaqlzmYqQhgOK0OFcCWkitlRks67InWIpZdvNnOoY5XtH2xkNxshzWvi9A7W879bKZWnRu9ow6Fe7Ox5pnyS8WZXn4AO3VvHojgqyHGaEEPR5I5zrGtOtknNdY3rqqNEgUV/oYnN5lkom2awrdi9JG2ohBGc0C2URCGU+SMhK9lqymvaAqqY9MWFhLim9E2EySFTnK6626jwnVflOSrNsmI0GErKgwG2locg9Y5rtoD/Ca2ol/MtX+iel/Nao7xGIJnQSmhhvkiQl5bh4guWjLTWLaLGb860WLJRc/jvwFpTaFgF8CPilEOJvF3uiK4XVQi7zQTIhHWwa5E9/dm7SMdsrs7GYDCmuu+kISSvIynVa9DhRetedcsx0/UhuBAghONk+yrcOtfLkuZ5J381d6wp1KyXZGjnXNaZniBkkaChys7FMsUY2lmWxvsSzJESSPO/THaMqofTSNRrCbJTYv0YjlOK0SgorDSGUtgXJcaHktg7JbR4mdhGdLdxWE3WFSg2Y1s7AZjZytc/Hua6xSW2h799QxB1rC9m/ZmrhR603Us9YWNdy6xmbXJibbs5uq4nyXAe1+YolVpPvpKbASW2+c8lJfymx4IC+JEkPAPegaHc9N5seKTcSbmRyAcWF8+WXr/GPz1/Vx962rYz//sDaSbIR4ViCHx3v4B+fv6r7/N+2rYy9dXl4w3HdatIso9kQkuay08hGixPlOBU33USCWi2EFIom+OUZpbujpjCbjLoCJ1sqsvGGlLqFPu84kawpdClEorq31pdkLYsagRCCUx2jPHW2h6fPjxPKgfoCHtpUwr2NRauSUOaLcCyRkpQwMKHrap83Mqkz53zhsprIspvx2M1k2U0qKVnIUruMKuMKUWUlvTx2c4pwZCia0HXcNBLqHQvrnVDbh4Mp7sMch1klHBe1BePkU53nXHGFi5mwUMvFCYSEELIkSWtR+ro8vdCeLqsJNzK5NPf7+eA3jtIxrGSvZDvM/Nt7trO/Pj/lOG84xrcPt/Gfr7Yy6I+wrTKbT9yxhrvXFc7qJi+EwB+J62QzHIyqrrrYJELS3HYzEZImXZKOkFK3Z+7YOB20YG8kniASl7na5+NrB1v47dWBGc+VJKgrcLG5LEu3StaXepZVXVkjlCfP9vD0uR66x8KYjRK3qYRyz/qiG7q98WIgIQuGAhHOd43xk5NdaYVBlxJumymFcLInEZKFkiwbVXkOSrLs9IyFaBkM0DIY4PpggJYBZX2i0kNplo0anXBcuuVTnmNfFdptCyWXE8ABIAdFzv44EBRCvG+xJ7pSuBHJRZYF//V6K3/+64v62CfvWsMf3LkmxRUz6I/wX6+18MShNnzhOAfq8/nEHWu4tTZ3ybNc0hFS31iYjpEgPaPjT3bdY6EF+eCng9kozan6ubbAqVojStbW+lLPivjLZVm1UM6NE4rFaOCAGkPJEIqCYDTOkevDvKIWMDb1Kyni+S4L+9YoGV371+Sr7cDVDqfhuG4BDUzTdTVdJ9CpYDJIWE0GrGYjFqPSmjgUSzAWnJw1ajRIlOfYqVZTtKvznVTnO6nJc5LjtNA5olg4GuFcHwxwfcCfMh+TQaIyL9nNphS21hY4KVyAHNRcsVByOSmE2C5J0icBuxDib9NlkN3IuNHIpXMkyNv/7+u6m6Y6z8F/PLYzpf6kcyTIf7xyne8f6yCakHlwYzEfv30Nm8qzprrskiGekHmlaYAfHuvkxct9KyZ1MRUe2V7G1opstlXkUJnrwG0zrYjLTiGUEZ4828vT53voUQnltoZxQnkjJlkkIyELzneN8WrzIK9cHeBk+wixhMBqMrCrJlcvYFxX7F7w3zDVJZfqjhtP2Y4wNIXMTpbdTKHbisdu1h9ytCy5QGQ8Wy4ZJo14VLeYRj41+YqLrGM4yHWVdJJfybVLDotRd63VqrEdjXwW+4FkwanIwCdQOj9+WBWHPCeE2LSos1xB3CjkIoTgO0fa+fzPz+tjf/XIJt61s0L/Ifkjcb70YhP/+WoLkqTEUz56ex11Ba5ln2/LYIAfHe/gJyc76fNGyHNaeOvWMqrzHdhMRqxmA1Z9acBmNqYsrSYjNvUYs3F6ZVp/JD6eRReMcvDqIP/5Wsu85q257PR0b811pyU1JLUryFHjSfMlJFkWnGwf4clzPTxzvjeJUAp4eHMxdzdmCKVjOKj2Nxng9WtDet3N+hKPXm+yszpnSZMnpkNCFgz5U7Ph+r2T64XSqRvMBSaDREWuI4VwqvKcVOU6MBok2oaCtAz6FTeb+uoYDqYQX57TMimh4EB9wbz7wyyUXG4DPgO8JoT4G0mSaoE/EkJ8al6zWYW4EchlwBfhrr9/WdeY2lGVw1fev0NvxCSE4Oenu/irpy7T74vwzp3l/NE9DXozr+VCIBLnqXM9/Oh4J0dbhzFIcOfaQh7dWcFd6wqXVIU1Ek/w1LkenjjUxqn20ZR9Sm1KGR+4tZr1pZ60hDQSSEpiULe11G8txjSV1WU0SGTbzWkJaeJ2tt1My2CAg02DPHO+l15vGIvJwO0NBTy8qYS7GwuXtAXxaoc3HOPQtSG9gFErAC722Niv9jfZtyb/hqu/EkLgDcUnEM44EenE5Ivgm4NLDhT3r0I8isVTk68QUGm2nYSsyNWc7hjh56e6J7WY+MhttfzpQ43z+kyLIv9yM2O1k8t3j7SnpBh/80O7uL2hQN++0D3GF355gWOtI2wpz+J/vXXjsup+Kam8I/zwWCe/PttNIJqgNt/JozsreGR7WVo5jcVE12iI7xxu4wfHOlL63MDk2pSFIJmQhnUlhsmENHF7Nm2rG0s8VObaUyymSenfC7CQVjNiCZkzHaN6B8bTHaMkZIHDYuTW2jz2r1EIZc00wo83G7Q+RAP+VCtIK2LVxoYCMxd/ToW71hXy8KYS7t9YPO+44kJVkTNYIYwFY2z54nP69n3ri/jSe7bp5v9oMMo/PHeV7xxpI9th4W/evolHd1Qs282n3xfmZye7+OHxDq4NBHBYjLxpcwnv3FkxZ9HGuUIIwWvNQ3zzUCsvXuqb5PO+c20Bj+2t5vb6gkX7PiRJwm0z47aZp+0Po0GWBcfbRvjBMcU1mIzN5Vlsr8whmpB1QmodDHIyODotIWkNpMbTu2eoR1qFhCSEoGUwoLfyPayqPhsk2FSezcdvr2N/fT7bK3OW1NJdzZhNHyJQ4plDgSgtgwGev9jHM2p6+mzwm8v9vNo8SDQh8555Su5Phwy5rFJ88/VW/uyXF/TtJz+1nw2lSjA+IQt+cKyDv3v2MmOhGI/tqebT9zQsS31DLCHz0uV+fni8k5eu9JOQBTurcvjbt9fx0OaSJc+s8oZj/OREJ9863JYiiQ7gsZl4584K3n9rFdX5ziWdx1RIyILjrcNKltf5Xvp9EawmA/dvKOKhTSXc3Vg043ekFRmOBlLdc+PadTGdkFoGA5xsnx0hjad3TyakifVIHptpUR8ORgJRXrs2qLq6BvUbYHmOnTdtUdr57q3Lu6ELCpcLmirzibYRTrYrrbE1dWtJgoYiFzuqcthWmcOOqhxq1d/CWCg2yR036I9MkjBaLMwm5pIrhBie9qAbHKvJLTYajLL1i8/r2+/ZVcFf/s4m/cnzZPsIf/aLC5zrGmNXTS7/6y0bluyfIxnN/T5+eLyTn57sZNAfpcBt5e3by3l0Z/myJAtc7vXyxKE2fn6qa1KGTWOJh8f3VPHWrVP3TVlKaITypEooAyqh3Lm2kIc2l3DXusIlJ92ZCGmShJBKULMiJOfUBbLJ28mEFIknONk2qvQ4aR7kXJLw4941eUqPkzX5VL3BhR9ng1A0wZnOUU62j3CyTVlqWnVuq4mtldlsV4lka2X2siaALNQtdkSSpNMo8i9Pi0UI0kiS9CjwBaAR2JXculiSpM8CHwYSwKemUwOQJOkzwN8BBUKIQUmSqoFLwBX1kMNCiI8tdL7LhS+/3MzfPnNF3z743+/UpSgGfBH+5pnL/PhEJ0UeK//y7q28ZUvpkv4wfeEYvz7bww+Pd3CqfRSTQeLuxkLeubOC2xsKlryIK5aQefZCL08cauNoS+rzjckg8cDGYh7fW83OJXbBpUNCFhxLslAmEsrd6wrnnYEzH0iSpEvwz8ZlB+OEpBDRuDU0XhA7TkgtgwFOtI0yGpxdDGki3DYTb9lSyu0NBRS4rTohZZAKIQRdoyFOtI3oXTAv9Xj177w238ld6wp1MllT6EpRB1hNmI3lIqFIv3wI2AX8APiGEOLqtCdOf81GQAb+HfiMRi6SJK0Hvqe+TynwAtAghJgkmypJUgXwNWAdsCOJXH4thNg4l/mstOXS7w2z63+/qG9//I46/scD6wDlBvvEoTb++fmrhOMJPry/lk/etWbJblxCCI62DPPD4508da6HUCxBfaGLd+6s4G3by5YlQ6fPG+a7R5Tujv0TujsWuK28d1cl791dueSJAhORkJXv5qlzPTxzQSEUm1kllE2KhbKchLLUiMaVvinBWFyV0E8onTN9EXpGQ/jCca72+Xj+Ut+UQWWb2aB2QEx/gMkg6dJB44kMSeneE7azHYvvsltJROIJznd5OZnk4tL+5+1mI1srstlepVgm2ypz0rYdX0ksyHJRLZXngeclSboT+DbwCUmSzgD/UwhxaK4TEkJcUic2cddbge+rCswtkiQ1oxBNuvf4J+C/A7+Y6/uvFggh+KunL/PVV67rY8c+d4+eXvx68yBf+NUFrvb5ub2hgD9783pql8gF1TsW5icnO/nR8Q5ah4K4rCZ+Z1sZ79xZztaK7GWp5j/aMswTh9t49nzvpJvRLdU5PLanmvs3FC9rkDchC460DCmEcr6PQb9CKHetUwjlzrUrSygJWaRI1wejcYUQJkjah6IJAin7xvfrBBJJukYsMedi1zWFLj56W+0kwcxkC2miXp1mIWluvOuDfobbYtNaSOkJaYLrbpUSUp83rMRKVDI53+XVK/grcu3srctje1UO2ytzWFfsXhUSL/PFjL8KSZLygPcDHwD6gE8CvwS2Aj8CahZxPmUoEjMaOtWxiXN6C9AlhDiT5h+mRi389AKfF0IcTPdGkiR9BPgIQGXl4mdKzITmfj/3/ONv9e0/uX8tf3DnGkBpK/yXT17iyXM9VOTa+Y/HdnJPY+Gi/zgi8QQvXurnh8c7eOXqALKA3TW5fOrueh7YWLws+lkBtbvjtw+3cbnXl7LPZjbwO1vL+MCeKj2ZYTmgEcqTZ3t49kIvg/6oTigPbyrlznUFS/LdROIJukfDdI4E6RwJ0TkSpGskhDccJxBRbvjJRBGMJuZclGc1GXBYjDgsJuwWo7pupNBtw55nxGE24rQq++xmI61DAU62jaQ0mwOl+rw6z6G7ti71eGnu9/MnPz7Ln3CWsmw7G0o9bCjNYmOZsqzMVfqzzAazJaThJEIaCabvJwPjhDSVkOo4EY3XI7mtcyekWEKmfTjItX4/V3p9/PpsD1f6fFMef6A+n0e2l7FvTX7axn03MmbzCzkEfAv4HSFEcj7lcUmSvjLVSZIkvQAUp9n1OSHEVNZGur9kyn+LJEkO4HPAfWmO7QEqhRBDkiTtAH4uSdKGdK2ZhRBfBb4Kiltsqs+x2IjEE3z2p+f46ckufez45+8h32UlEk/wtYMt/NtvmpGF4NP3NPDR22sXvfL4cq+XHx7r5GenOhkJxij22PjEHWt4x47yZcuyujbg51uH2vjJiU69MFRDZa6Dx/YsTm3KbBFPyBxtUYLyGqHYzcZxC2URCCUdeShLZV2T89FgMkgUZ9nIdphxmE3kOi2U5xixm00KKViNONT1caIw6YRhtxhxWpL3m2b0z/eMhZR6k6ZBXmse1OuG1ha52V+fz/76fHbX5Kb9Lgb9ES50e7nQPcaFbi8Xu708d7FP35/ntLBeJRyFeDxU5znTpkknx5AWg5CGk+NK8yCkiYkMRklSEydiDAUijARiNA/4p7zWVDioZs8VuK3UaF05NemXfIWMb9ReMLOZ9eeFED9MHpAk6VEhxI+EEH8z1UlCiHvmMZ9OoCJpuxzonnBMHYq1pFkt5cBJSZJ2CSF6gYj6/ickSboGNKCIba44jlwf4l1fHTfMPv9wIx/eX4MkSfzmch//61cXaRsK8sCGYj73cOOUfSXmg7FQjF+e6eZHxzs42zmG2Shx3/piHt1ZzoH6gmUJCsYTMi9e7udbh9p4tXlw0v471hbw+J5qbm9YvNqUmeZzRCOU870MBVRCaVSKy+5YOzdCmQ95lGTbKM92cFt9AeU5Dspz7Mor10GR27rkbpFAJM7h60N6AWOzLvxo5bYGRfRxf33+rOJb+S4rtzcUpBT4+iNxLvV4udA1phKPl6+/el13uTktRhpLPLqVs77UM6tGX+kwX0LyhuNpm/EN+qNc6B7jeOsIV/v8c56PhjvWFrCh1KMTVK7TgsVkwBuKMRaKMRSI0joYoG0oyMtXBxg4kVoTle+yUpM/3pVTawtdnb+6iWfWwpUzjc3rzSXpZVID+huA7zIe0H8RqE8X0E+6RiuwUw3oFwDDQoiEKlNzENg0Uyr1Ugf0R4NRPvX907ySJPF++LN3U5xlo3UwwBd/fZHfXO6nrsDJF96ygQP1BdNcbfaQZcHh60P84HgHz5zvJRKXWVfs5l23VPDWrWXLFhwc8kf4/rEOvnukfVKBl8dm4tGdFXxgmWpT4gmZw9cVQnnugkIoDotRr1a+Y23hlOnMCyEPhTSWnzwmIiELznaOKvUmzYOcbBshLivCj7tr8zigksm6YveSxSgi8QRNfX4udns5r1o5l3q8eoq52ShRX+jWrZsNZVk0liydOnUwGuf6QIBrA36uact+/yRByJmgWR/lOXaC0URSSvjMFlKybp3VbMQbiimvsEJAU8W/8l3WcWXlJKunKs+xLBJC85J/kSTpQeAh4J0oGWIaPMB6IcSuBUzobcC/AgXAKHBaCHG/uu9zKJlpcRQNs6fV8a8BX0lOW1bHWxknl7cDX1TPTQB/JoT41UzzWSpy0fS+Pv2DM/qYZq2EYgn+z0vN/McrLZiNEn90TwOP761elGC1Nxzjm6+18oPjHXSOhPDYTLx1axnv3FnBxjLPsgQ2tR4k3zrUxpNneybJjq8rdvP43mreurV0yWM744TSzbMX+hhOIpQ3bS7h9gaFUOZDHqXZ9nHCyEklkSKPbVWkiXYMB3VJ+tevDemdEjeUejhQX8CB+nx2VK2c8CMopNc6FNDdahdVK0er55AkqMlzTnKr5c0ye1EIQb8vwrV+fwqJXB8IpDzwGCQoy1H0+MaCsUmy+0aDxLpit54KvL0yh4pc+4y/Kc1CGpki1TtZw24kqRZprm62ZOS7LCrROCdZPotFPPMlly0oQfsvAv9f0i4f8JIQYmRRZrcKsBTk0joY4BPfOal3yXNajDz5qQNU5Tl46lwvf/HkRXrGwjyyrYz/+eA6ChchrTYal/nOkTa+9GITI8EY+9fk8+jOcu7fULxsN45wLMEvz3TzrUNtnOsaS3tMscfGjqocBAJZRlkK5QeYvJSFQEyxlIVAQNLx2rWU7WhCnlTBn4xcpwWL0aBeS7mmdiPTcKOQx0SMhWIcujaou7ra1GB8SZZNkaSvL2BfXd6sb8wrBSEEvd4wF7q8KbGcZDIo9th0olmvkk44lhgnkCQy8SfF9hwWI3UFLuoKnBR5bPgiccbUGM2F7jGdVLIdZp1ItlVms6U8e17ZgeGYYpXN5Xc4G0JKjiXNhZDynBZKsm0Ue2x8cF8N+9bkz3hOOixUFdkkhJibROcNhsUkl2hc5quvXOPvnxsvA/qT+9fysdvruDbg589+cYFD14dYX+Lhi2/dwM7q3AW/pxCCZ8738jfPXKZ1KMi+NXl89sFGNpYtX4ZV+1CQbx9p44fHO3RJ9HTw2Ezku6xIkuIjN0hgkBQ5fQkwGMa3DZKS4WGQJHWMtEuDei0tyytd4zGtHsVuNk54X+VcowEK3bYbgjwmIpaQOd0xysGrAxxsHuRMxyiyUB5obq3N0wmlrsC5KtJxF4rRYFS3bDTCuTbgT9tTpSTLppNIXaGL2nyl6LBrNKRWvI9wpc+H0KRTCt16Xcl2VTplId9ZOJbgP19r4csvXSMUS1BX4GR9iYfGEg/rS5Vlcu2YEErX1FA0QSiWIBxLWkblNGPaukw4liAQiTPgj9A9GqJnNDwpWWYqtP71w/P6fPO1XH4ohHinJEnnmJCxBSCE2Dyv2axCLBa5HGsd5lPfO0XPmNKqNN9l4du/t5vSbDv//HwT3zzUistq4jP3r+W9uyoX5cZ1om2Y//3UZU60jdBQ5OKzDzVyR0PBstxEZFnw26YBvnWojZeu9E9ZSJfjMPOmzaU8sLGY3TW5ixpniCVkXr82xFNne3j2Yi+jwRhOi5F71itaXrc3FKyou2cpIITg+mBAl6Q/fH0Yvyr8uLk8W+9xsrUi+w0j/BiKJrjc6+VSj0+3SmoKnBglKUk6ZYST7aPLIp0iy4Jfne3mr5++rN8PlhIWkwGbyaCnkNvMSoagzWScMGbQx2xmZXxNoYvbGuYX551vEeV/U5dvmte7voEwGozy109f5vvHOvSxP7xzDZ+8ew2/OtPD+792hKFAlPfsquQz961dlEB662CAv3nmMk+f76XQbeWvH9nEO3aUL0uAeDQY5UfHO/n2kTbd5ZIOH95fw4Mbi9lembOo2V8aoTx5tpvnLvYxGozhspq4W83yuu0mJJThQJTXmgd1QulWb1iVuQ7esrWU2+rz2VObv2yp26sNdotSzZ7vsnKyfYSfnOycLJ1SoEinaLGSpZJOea15kPd97UjKmMVooCzHrt7QDYRjMq1DgUk6eRPhsBgVK6oym22VOeS7rNgtSgO9ZNJYjRb2lOQihOhRV51CiIvJ+yRJugNoW7pp3Tj45Zlu/vSn51L8uX/+OxuxGg3s+PMX8EfiZDvM/Ot7trGlPJtgNE4knsCoun6MBklZN4BR3Zak5PXUf5rhQJQvvdjEtw+3YTEZ+PQ9Dfz+bTXLUvB4vmuMbx1q4xdnuqbsef/e3ZW8+5YKNpVlLar1FEvIvNY8yFPnelII5Z5GpQ7lZiOUSDzBidYRDqodGC90exFCcSvurcvnE3cqPU5mm3J7MyIcS3Che4yTbaO6QvBE6ZSP3l6rxEsqcpZUy8wfifPN11v5u2evpIw/tKmYBzaWcOfaghmD6NG4THO/n4s9SvbcxW4vl3q9vNo8qKfuV+U5aCxOdqu5KVvmhoCzxWxiLudRiij/FrCpy51CiD1LP73lwXzdYn3eMLuTNMGWCkaDNGWQLt9lUUgqmZg0wlLXDeo+gyRhMEgYtViFepwW59CPUWMQBjWOkVwINxX2rcljfYln/JrJ15dQ1g3jsY7U+SQdk/Q5hIBD14Z45kKvnuGkfGYrb9qs1KHY1ae2FKLWvoNZvp9B/Q5SP/94LGc5IITgSp9Pl6TXYkcmg8T2yhy9gHFzWdYNLQmyECRLp5xoH+FCknRKZa6D7ZXZyyqdMuSP8MKlPn5wrIOTEzqf/p/3bufuxsIFP/AIIegZC3NJI5wexfXXOhTQ3dAemyklhrO+xEN9kQuraekfthYa0HcCfwPsANzAd4C/EULMvxn0KsNikUuB28qA+uRUlm3nk3etwWE1IctKNlNCXcqC8XVZkBCMH6ONyUoGU1yW+dnJLt0NAopkRG2+UzlWPVe5HhPeJ2lcFmmOn3BM0nrXSHBSGmY6ZNnNSZ8j9b1uhianmhWpE1AycepklERM2jEaqevrTDp+yB/l+uDUGW1Wk4G9dXm4bebJ76VdM/m90s1He1CYRPpMOG8WJJz0EBKIxPHYzJiMhlkTder1032X48fIQnC5x6fEStpHOdk2omeJWUwGtqjN1rarWVzLJZ3SORLk2Qt9PHuhd5JSd12Bk+/83q0UZy39XAKROJd7fSlWzpVeHyE1K81kkFhT6KKxRLFu1pdk0VjiXvQMwYWSiwX4S+BewIVSsf/9RZ3hCmO+5BJPyHzzUBt//usUryG3VOfwiTvWsKcub0FPLq81D/K/n7rEhW4vm8qy+NOHGtlTlzfv680Erbvj//jJ2Sm72f3hnWv4xJ11s3LDiYlEmpbsBJGYzG+vDvCrM90cSfrBbirL4oGNxeyty8NkMOjHa+SoEdj4ukLKCVldV98v7TH6etIx+vFpjkl+r5Trk/6YlOuPt0g+0jJ9a6Rcp4VCt1WfT+pDx8SHB/W7TXfMTULu6WAzG5Q4g+5aJtXNnMa1POmYCRauYv2TtD5+nWsDAS71TFKQSsGB+nzdcp8d6Sc/JEz1EDLuQdCvk/b6yjEAHSMhrvQqRHOlz0fHcOrvuMhj1a2bRvVVm59egmc2WGg/l2MoysO3AHnAv0uS9A4hxDvmNZubCMOBKOfVWg6rycD+NfmYjQZeaRrgg984ht1s5EB9Pvc0FnHnukJd7XgmXOn18VdPX+LlKwOUZdv5l3dv5c2bS5dMEmUsFOPzPz/Pr85MVNpRsKs6lz++r4FdNblzchMl/2AnIhqXebV5gCfP9vL8xV684Thum4lHtpfx8KYS9tfnL4tZv5SQZcHFHq9ewHhKdZ1YjAZuqclh/5qClJvSYmMiuY9by6QS9QSLdjQU40TbCC+pbXDng7oCJ+tLlaflkizb+HvKgpgsuNTj5Xjr8LSyKmsKXWwpz6bIY00i2okPK5OtfnkiMctMeLgZ/w6icVk9d/zzJ2QxrdjkRLitJk60jXC0ZTjlIWU1os8boc87wMtXxtVC/vjeBj55d/2iv9dsyOXDSVXxvcBbJUn6wKLP5AbEUCDKM+d7Jz3Nh2MJDl8f4sVL/bxwqY/nLvYhSbC1Ipt7Gou4d30R9YWuSTfqfm+Yf3z+Kj883oHTauJPH1rHY3uqlyRQHU/IfPtwG1/41cW0+81GiY/fsYb37qpcNDM/Ek/watMgT57r4fmLffhUQrlvfTEPby5m35obn1C6RkO82jTAQbUaXkt7VRQJqthfX8Cu6txl6Zg5Hblr0NKaT7SNcLxV6eMzFT68v4b37q5Mqf1oHQzwtVev8+3D7SnHKhXwAX51Bj1+lg75Lis7qsbTgTeWZa1IYkYsIXP4+hDPXujluQtKjNFkkNhTl8f9G4ppLPHwX6+18OuzPRS4rfzxvQ08urNixu82rUUrT7CSp7Owp3Vfp3lQmNI9TtqHCVkI7l5XtCTf6Wybhb0PqBVCfFGSpEqgWAhxdElmtAJYSJ3LWDA2bfqnEIIL3V6daLSq9YpcO/c0FnFPYxEbS7P4+mst/Mcr14nLMh+4tZpP3rVm0bNbIvEEr1wd5P/5wekpi6t2VuXw2N5qHlikvimReIKDV5Usr+cvKYTisZm4b0MxD28qYd+a/Bu6FsMfiXP42hAHm5QCRk0VoMBt5cCafA405K8qOfVgNM6ZjjG9MdVvLvdPeezv7q3mwY3FbKvMmfFvFI3LPH+xj+8caeP1a0Ozmktplo37NxazsTSLjWVZ1BU4lzVZIRiN88rVAZ690MeLl/rwhuPYzUbuWFvA/RuKuXNdIQBffqmZ/3qtFYMBPnKglo/eXndTNYVbCBYac/m/KF0j7xJCNEqSlAM8J4S4ZfGnujJYzk6UvWNhXrzcxwsX+zjYNDipIdKv/nA/m8oXr7I+FE3w26sDfOtwK681p//RW03jfVMWo6pfI5Qnz/XwwsU+fJEkQtlcwr66G5dQ4gmZs11jer3JqfZR4rLAZjawuyZPL2BsKJpsmS43hBB0joxXop9Qm1NNhUd3lHP72gL21eXP+sHGF45xpmNMTwU+1T6SNhFk35o8vvDmDUQTMhe6FMHK811jXOzx6mntVpOBxhIPG8s8OuEsdtbTaDDKC5f6efZCL69cHSASl8l2mLmnsYj7NxRzoD4fm9lILCHz3SPt/PMLVxkNxXhkWzmfub+BkqzVmfa7UlgouZwUQmyXJOmUEGKbOnZGCLFlCea6IljuNsdCCH5zuZ//7xcXJgXOjQaJW6pzdKtmPkrB0bjSe17r7z4VKnLtfODWKt65s4Jsx8KspHAswcEmxULRCCXLbua+9UU8dIMTSttQQO27McDr14bwheNIEmwszWJ/fb4u/LjSLr3klrknVDIZmNAmOhl3ri3gQH0BtzXkU1cwMxkKIWgdCupElV46RSn421GVQ1mOnecv9vG9o+281jyE0SBxT2Mh79lVyW31SluFhCy4PuBXycarEE63V7eszUaJhiK3SjaKftj6Es+c3Io9YyGeUzO8jrQMk5AFpVk27ttQzH0bithVPa4aIYTghUv9/NXTl7g+EGBPbR6fe3h5pZRuJCyUXI4Ae4FjKskUoFgu2xZ/qiuD5SSXc51j/OVTFzl8fZiafCf/88F13Le+CCHgdOcoL1zs48VL/XpAcU2hi7sbC7m3sYhtlTnT+ngDkTjfP9bBv/6maVp9r9sbCnh8bxW3NxQuqLI3HEvwytUBhVAu9eNXCeX+DYr0yj41wWG1IhiNc6nHx8XuMa72+bFbjBR5bDgsRloGA1zt83Gl16fLd5Rl2/X+JvvW5K94P/N+te7jRJqWuemwsSxVBXkmMgxFE5zpVAoUT7Wnl07Rqt1nkk5pGQzw/aPt/OhEJ8OBKGXZdt6zq4J37qyYJNoqy4KOkaBCNqqFc75rjBH1f9ogQV2Bi41lilDlxjKlD0zy+zf3+9X4SS9nOhVX9JpCF/dvUCyUdEW+yb/NugInf/pQI3etW/wOsDcTFkou7wPeBWwHvgm8AyUd+UeLPdGVwnKQS8dwkL9/7gq/ON1NrtPCH91Tz3t2VU55820fCvLCpT5evNzHkevDxGVBrtPCnWsLuXd9IQfqC3S/75A/wjdfb+VLv2me8v3dNhOP7qjgA3uqqFlA35RwTHGzPXWuhxdVQsl2mLl/fTEPbS5hb13eqiQUTe32QpLgYctgYNbpui6riVtr8yjOslLssVGcZVeXVoo8tiXvnRFPyFzu9elkciKp7mMqFHmsOpnsW5OfIpA4ERNdaCfbR7nY49WznmoLnCky8/WFrnlluEXiCZ67oFgzr18bt2beu7uKA2vyp7ymVkx4vmuM893jzcd6veP1X9V5DtYVe2jq93FNjX1tqcjWCaWuwJX22t2jIf7+2Sv89FQXuU4Ln76nnndP89vMYBwLIhf1AuuAu1FEal8UQlxa3CmuLJaSXMaCMf7Py81847VWJAl+70ANH7u9bk43o7FQjFeuDvDCpT5eutyPNxzHYjRQmefQOwdOhbVFSt+U39k2/74pGqE8ebaHFy/1EYgmdEJ5eHMJe1YRoWg3yQvdSjXzRZVQksUDS7Ns2MxGhoPRFAvPIEFVnpP6Qhf1RS6KPTYG/VH6vGF6xsL0ecP0esNprUKnxUhxlo3iLBtFHptKPMp6SZayneeyzspSTMiClsGAXpF9qn2EMx1jeoFcoduKw2LEH0kQjMZ1fSqt4ddts4j7TCed4rAY2VKuWiVV2UsmnTLRminPsfOeXZU8uqN81i0oBnwR/aHhfNcYl3q8lOXYuX9DMfeuL5o2RuKPxPnKy9f4j4PXEcCH9tXwiTvrFk288o2A+aoiT6sFP1N3xxsJS0Eu0bjMtw638a+/aWJMDQj+8X0NlC5QByiWkPnONCnEoNwkH9xYwmN7quZcm6IhHEvw8hXNQlEIJcdh5v4NxTy0aW6EEo4lONk+wuFrQ5zqGCXPaaG+yM2aQhf1hS4qcx3zyhKKJ2SuDwaUm4va8+Nij1eXijFIUFvgYkOph+IsG2NBpaXs+a4xnWyq8hyKJP2aAvbU5ZFln/nGEo4lUglnTCEdfX0sTL8vMilZw2iQKHRbdcJRLB5TSmvd4UCU1qGAHuQ2GSTWl3qoyHEQjMYJRJReJVpv+8YSj04mO6unbvjVOxbWM8ROTiGdovQsWR7plGRo1sx3j7Rz6PoQJoPEPY1FvHd3JfunsWbmi3hC5ofHO/nH568y6I/wli2l/Mn9axe1rfgbBfMllxYUqf10f1khhKhdwIQeBb4ANAK7krtLSpL0WeDDKJ0kPyWEeDbN+V8Afh/QKoH+VAjx1GzPn4jFJBchBE+e6+Fvn7lC+3CQA/X5/M8H17GhdGEBQSEEx1pH+Pi3T+g3lunw3t2V3NNYyN66/FnXDSiE0s+T53r5TRKhPLBRIZRba2dHKLGEzNnOMQ5dU2o9jreNEI3LGCRYV+xhLBRLcelYjAZqC5wq2bipL1JIpyrPqScBaJLqmlvrYvcYl3t9ehtaq8nAumK33jBqTaELfzjOsbZhXm0a5EK3kiXlsZkUnS61gHGpbiiyLBgMROgbi9DrDdMzFuJk2wgvXRlI0UmbDgVuK7trcmnu93O5V4nB5busCplMk+IcS8hc7PbqZHKqfVT/vq0mA5uTpFO2V+bMurh3OXB9wM/3j3XwY9Waqci18+5bKnl0Z/mC07mFELx8dYC/euoSV/v83FKdw+ceXs/WiuzFmfwbEAt2iy02JElqRElv/nfgMxq5SJK0HvgesAsoBV4AGoQQiQnnfwHwCyH+fsL4rM6fiMXs5/KXT17idMco64rdfPahRm6fZ58EDbKskNUnv3dqymN2VOXw2J4qdtfk8WrzIC9c7OOVpgGC0QR2s5H99fncO4VKQCiqEUoPv7ncTzCaINdp4X61DuXW2pn7ryTUiuvXrw1y6NoQR1uGCaiumsYSD3vr8thbl8ctNbm6y8EfiXOt309Tv5+mfh/Nfcp6x0hwxjiIzWxgW0WO2n1QaXtbW+Ckud+v1Js0DXK0ZZhIXBV+rMrhNrVh1qayrCWXJw/H1B7xPWNKwoCq/+RTU3QltWVvY4mHilwHeU4LOU4LEugWkGYVDfgi1BW49BTndcXuSU/yg/6IHic52TbC2a5R3fIpzbKxrSqHHSqZrC/x3BBZe5F4gmcv9PG9JGvm3vVFvGfX/KyZSz1e/vdTlzjYNEhVnoPPPriO+zcUZ4L1C8RC5V+QJOkRYD+KJXNQCPHzhUxIi9mk+cO+Ffi+ECICtEiS1IxCFIdmeemFnj8vXB/w89dPX+a5i30Ueaz87ds38/Yd5Qu6iUXjMv/npWb+5cWmtPutJgNv3VrKY3uqU9Ik37GjnHfsKJ+kEvB8kkrA/jX5GA0STf1+XlIJJc9p4Xe2KdIrMzX0EkLQ3O/n9WtDvH5tkMPXh/Wn8boCJ49sL2dPXR631uZNmVHlsprYXJ5FgdtKlt2M3WzEYJDwR+KT2g2n+276vGGCsQRPnuuZ1IxpTaFLSXdtyGd3Td6SFrz1+8Jc6vGNS6T3eLk+GNAD4Q6LkXXFbt6ypVRXrV1X7J53/CuekLnU7dWJ5GT7iN5Tx2yU2FCaxXt3Venxkhu1LsNqMvKWLaW8ZUtpijXz9PneOVkz/d4w//DcVX50ogO3zcz/+6b1fODWqhuCYG90zCZb7MvAGhSLAJTMsWtCiD9Y8JtL0sukWi7/BhwWQnxb3f468LQQ4scTzvsC8LuAFzgO/LEQYmS256v7PgJ8BKCysnJHW9vc29MkZMGf//oi3z7cRlwWbC7P4lN31eu+dOVlntM/sj8S5xPfOckrVwfS7i/PsfPYnioe3VEx6yCrEIKT7SN87mfndfdKMv7gzjo+eVf9lO4zIQTtw0GVTIY4dG2IQX9En49imeSzpy6PoikCsYFInJbBAM39fi71jGdsaemlkgQ1+U42qG6tDaWKuJ6m4hqOJbjY4+Xbh9v46cmuKT+rxWjgQH0+a4pUF1uhizWFrgUTjBbf0QhEkz7XvgdQrASNQDRxwMpcx4JiBqPBKKfaR3UX15mOUd0qXC3SKcsFzZr57pE2Dl8fxmSQuG+DYs3sq0u1ZoLROP/xSgv//so1YgmZx/YoqhcLrefKIBULTUW+AGwU6oGSJBmAc0KIDTOc9wJQnGbX54QQv1CPeZlUcvk/wKEJ5PCUEOInE65dBAyiWFJ/DpQIIT402/MnYr5usX5fmLv//rcz9qm2mQ24bWbcNhOepKXHrpCP22piKBDlG6+3TnmN2xoKeHxPFXesnX1tSjAa56XLSlD+N5f7CcUS5LssbK3IwWY24A3HOXx9iGhcxm0zcXtDAfeuL+KOhkKCsTiHkshE89kXuq0pZJIcs5BlQY83zLV+P9cH/FwbCHB90M/1gUCKdWExGlhb7B4nkdIs1hW7JxFAQhZc6B7TCxhPto0STchYTAZ2Veeyvz6fW2vzsJuNXBvw09Snutj6lfdMrvkoy7brCQT1RS7WFCoJBekC+GOhmN4/QyOSq31+omp8x2I0UF/kSiGRxhL3gm9csixoHvDrFsmJthE9pdZokGgscaekA5fn2N+wbp1rA36+f7SdH5/oZCQYozLXwbt3VfCO7eW8fHWAf3juCn3eCA9tKuZ/PLDuDd1UbSmxUHL5KfBpIUSbul0F/LUQ4j2LMLGXSSWXzwIIIf5K3X4W+IIQYkq3liRJ1cCvhRAb53M+LCzmkpAF/nAcbziGNxzDF47jC8fxhmL4tO2Itq0dF9f3TVdBnQyX1ZRiDXnUpdtmwmM36+Mmg8Tx1hFebR6gzzvele+R7WU8vLmE3TV5KeQUjMZ5tWmQHx7v4IVL6XWmHlRl7/fU5VNX4CQYTdAyGOCaRiDqsmXQn9Kh0m0zUVvgoq7ASZ26rC1wUZPvnDIxoHMkqEirNA/yWvOgnvbbWOJRs7ry2VWTO+MTejwh0z4c5Gqfn+Z+nxLb6fNzbcCvJwGAUgtSX+imOt9B71iESz3elGSDPKclpetfY4mHugLXoqRe+8IxTneMKunA7SOcTpJOyXaY9TjJ9soctlRkLUu30RsNkXiCZ8738r2j7Ry+Pp7AurUim88/3MjO6mmTXjNYIBZKLr9FkdvXhCpvQYlhBAGEEG9ZwMReJpVcNgDfZTwg/yJQnyagX6K1YZYk6dPAbiHEu2d7/kQst/xLNC7zzy9c5csvX5vymAP1+dy9rpC4LHTC8k0kMHV9phgFKGm5LqtGRMrT+kw9KibCajKQZTfr9RDadStyHdTmO1UicVGrkonWJXM6+MIxDl0b4tVmpQNji9o8q8hj1TO69q3JX7SMpoQs6BwJqlbOuKXTMhCgKMuW1FxJsUgK3NZFsQ406RQtFXiidMraIjfbKselU2qSlIczmB2uDfj55elu6otcPLypJPP9LQMWSi63T7dfCPHbeUzobcC/AgXAKHBaCHG/uu9zwIeAOPBHQoin1fGvAV8RQhyXJOlbwFYUt1gr8NEkskl7/nRYLnLpGQvx6R+cTnnC0mA0SNy/oYjH9lSzexa1KYFInN9c7uepcz28dKVftxj2rcnjQH0Ba4vdBCLjpNQ7FuHlK/3Tdj6cKxwWI7trcsl3WXUrSrOkPLqFZU6xuAwSnOkc5WDToNLjpGOUhCywm43cWpvLfrWiPF1LghsJwWics51jU0qnbEvS4NpSMb10SgYZrFbMm1wkSTICzwoh7lmqya0GLCW5CCE4dG2Ix//rKLHE5O8632Xhvbsqec/uyhkzewKROC9e7uepswqhROIyBW4rD6p1KLdU5+our0g8wan2UV6/NsRrzYOcaBuZ1XwtRgMFbmvqy2UlmpD59dnuSZ3tFgP5Lgu3NRQoJGU1Jbn6kghLJSqXzbTkqcRzRTr14Us9vhTpFM3FtaMqhzUF85NOySCD1YaFWi6/BD4ghBhbismtBiwFufgjcX5wrGNSC2QN2yuzeXxvNQ9sLJ5WQNAfifPipT6eOtfDy1cUifBClVDuWFtIjtPC2c5RTncoL62fyHSYKhZSleeYlbJvLCFzvHWEFy718cKlPj0VtjrPQZ7Lqmanjc54nflitvEnT1IChTspgcJpMS7IKtKkU060jejxkoFllk7JIIPVgIWSyw+BW4HnAf3OJYT41GJOciWxmOTS1OfjK7+9zk9OTu7oZzJIvG1bGY/tqU7bs0UIQSCaoH0oyI9OdPDEobYFt0vdqbpd5hoLmQ4JWUlNvtLr5XKvj1+c7tZjJelQkmXjD+5cwwMbi8l3Wen3hjneNsLx1hFOtA1zodurS6VU5zlYW+xmbbGHhiIXOQ6LGmtKTpSYHH/yJSVKpLMQkzEx/qRYRumJyG0zEYwmaO7309Tn42qfPyXgX5nrULO3stlelcPaouWVTskgg5XEQsnl8XTjQohvLsLcVgUWSi7xhNKF719ebEpbRwJw17pC7mksQhaC4UBUf3WMBDnTMcp8OWRdsZutFdlsrcimJt9JrtNCrtNCtsOyKO6jIX+EK70+LvX6uNLr5UqvcoPVRBSnQr7LgjccJxqXZ1QJ0LojHm8d5rga8Naq2fNdVnZW5bCzWnEpbSjNmrZuSAhBJC7jDaVm5Y0TUQxvKHk8dd9IIKrXkcwGVXmOlLiSsj67+NNqL+RLyIKesRDtQ0HahoO0DgWU9aEgnSNBtlRk86F9NdzeUJBx871BsRiqyHagUghxZbEntxowX3IRQvDvr1znr5++vASzgt01uWyrzGFrRTaNJW5Ks+1Lpj6sSZZcVq2RK70+LvemFgmmQ5HHyn3rlQ5+t9blpQSmJ6oE9IyFdZUArRlaOuVeWRZc7fepls0Ix9uG9ViPzWxgS3k2O6tz2FmVy/bKnGnbTM8ETTrlRPsIp9pGOdM5qqcqSxJKXYyqd1ae4yAUS4wTU5IVlUpgcfwz1D5pn8VtS+/G89jNevxJsaSWJv4UiSfoHFEIpHUoQNtQkHaVSDqHQym1QmajREWug6pcB8VZNn5zuZ8+b4TaAicf3FfD27eXZdKl32BYqOXyZuDvAYsQokaSpK3AFxeSgrzaMF9y6RoNse+vf7Moc7ilOoeP3V7HnWsLl/QpUFZdWhqBXOnzcrnHR+tQQLeerCYDVXkOvQ4nWd0322FmX12+Kv44e+FHIQQXe7y8cFEhmnNdSgivItfO3euKuHd9Ebtqcqckzz5vmOOtCtGcaBvhQvd4r5GGIhc7q3MVC6cql4rc9MWF8YTMlT7ftNIpWoHiQqVTkuufJltO4/VPmiWV7rjkmqGp4LQYJ8WZ3BPce0ZJYiSoWGXDqvKytj4WiqVouTktRirznFTlOqjKd1CV66Q6z0FlnoOSLHsKmUXjMk+d6+Hrr7ZwrmuMLLuZ9+6u5LE9VTes7EwGc8NCyeUEcBfwclKb43NCiE2LPtMVwnzJ5fqAn7v+YfpM7D21imBj50iQ811jNPf7kYUSh3hwYwkPby5mW0XOkhDKcCDK5Z4kS6TPR1OfT+//IUlQlavGOIrcCJT+GG1DQU62jxCJy5iNEjuqcjhQX8D+NflsXCThx96xMC9eVrpuvto8qKgEWE3cvnZcJWA6iyQYjXO6Y5QTrSMcaxvhVNuIrpRQ4FZcaVpDq3BM5mznaIp0SoHbqqcCb69cndIp0bg8XoibFGdKF39qG1b+v4JzcOlNRKHbSnW+c9r4UzKBaUub2cDxthG+frCF5y72YpAkHtpUwof317Alozh8U2PBbY6FELslSTqVRC5nhRCbl2CuK4L5kks0LvNvLzXjDcUIRRMEYwlCUaXXh/Zkng4f3FfNu26pmFOlt6wWU44Eo2qTq/EeIEofkBij6lPpaDDGoD+SIs2f67SwrtjN2mK3uvTgsZk40TbCq81KzYl2fEORSy9g3FWTu6TCjzCuEvDCpT5+c7mfQX8Uo0Hiluoc3X1WPUP3zHhC5qnzvXz1lWuc75q6OPRt28r4vQM1rC/x3FB1NLIs6POFaR0M0j6suK/ahoK0DQdoGwxOkiAqybJRqbqvchwWPRaX47AgEKnuvNB4/Ck5MUI7ZqZ4oNko6eQzFopNaqa2tsjN23eUke2wpGb2JRHWao8/LQSjwSgvXxlAUtsz1xW4sFtW14PMfLFQcvk6SqX7/wTeDnwKMAshPrbYE10pLFa22FgoxgsX1bThqwMpmV6lWTZsFiNtQ8FJGWBOi5FCj40Cl5UCjxWnxchoUCMOxZ0xGopNmTlmNEjkOCzkOMzK0mkm12mhrsClZl65KXBZCUYTHGkZ0gsYm9Qulvkui9obXrFOirMW1jdjIZBlwenOUV64qFg1V/qUBIm6Aif3rC/i3sYitlXm6JaLlgp8KikJIMdhZntlDmU5doRQHgIuqhphCVkgSdBQ6GZHdQ63qLGb6XS6hBAkZEEsIYgmZGLaKz5hOyETjYvU7YQgFpeJy+PrKfsSsj6mbyddKxJP0DsWpn04mCJbYzJIlOfYqcpzUpXnUJa5DqryHFTkOhbNCtMyGCcT0WQSSk6U6B4N0T1BrXo6rIb402Ki3xfmuQt9PHO+l0PXh1J+u5KUqnW3RnsVuBcUP1wJLJRcHMDngPvUoWeBvxBCzP4/Z5VjIeQyForxvEooB5sG9DRYs1Fia0U2lblOxkLjJNHnDc8pGwkUteBba3OpK3DpT585TpVMnBbcVlPaG2NCFpzrGuNVtcfJyfYRYgmB1WRgV02u3oExXY+Q1YKO4SDPX+zj66+2TNkzfl2xIp2ipQRPJZ0SiMQ50zHKMTV2c6p9VA+857usuKzGVAKJy/r2UsFiNGAySpiNBsxGAxajhNlkSNku8tjGCSTPQXWek5Is2w2R8pyQBc9e6OVLSZmUjSUe3r69jFynZZywIuNWU2q6+fzjTxOtI82950mT2eexL7z+qWM4yLMXennmfC8n2kcQQvntPrCxmPs3FOOwGGnu9ytp7ery+gStu3yXlTWFTupVYVXtVbhIMkSLjfl2orQBH0OR2z8HfF0IMXMKzA2I+ZLLcCDK7X/70pSqyFaTIYkMVKsimRiS1s1GAwO+CJ0jIa72acF2X4puWJ7TwtpiNw1Fbt0iaShy40pyW3UMBxXLpHmA15qH9D4r60s8HGjI58Ca6dvhrgZoqclaxfupjtFp9dNubyjgnsZC7m4smlMb6YQsuNLr40TbMKc7xoglZOWGbhq/2es3fKNBv+lbksjAZJSwaMeaDJiTt9Ney4BZHTMZpFV5w1gqnOsc4z9fa+HXZ7uJy4J7Gov48P6aWckdReMy/sjkOidvcjwqlD71XNuebf1TsnU0Mf40sVC33xvWVTA0zb3GEg8PbCjmwU3FM8oYJWRB10iI5gEfTX0K4TQPKEvNEgel8FmxblwppFOe41hRi22+5PIDIAYcBB4EWoUQf7RUk1xJzJdc4gmZ7x/rQBaCbIeFXIeFbIdZJ5SF+lWFEAz6ozrRXE0TlJ8KeU4Ld64r1IUf812rp5VtMmaSTqkrcI7LzCdJp2gqAS+qKgGtatbX+hIP96wv4p7GQjaWZq1ai+yNjD5vmG8dauM7R9oYCcbYUOrhQ/tqeNOWklkpRMwHev3TpDRyjYhSFc3nG38CJb6ZajVNrnPyTEiUmBh/EkIw4IvoFo7+GvCnKKlbTQZqNcJJIp7q/NmpbSwU8yUXPSNMkiQTcFQIsX3pprlyWG5V5IUglpA51T7K94+289NTUzfNMhokavOdNBS7WVfkVpbFbipyFta8aqGYSTpla8V486ttldmz6pEihODaQECRo7nYx8n2EWSh1ODcta6Ie9cXsrcuf1Vba29EhKIJfn66i/98tYWmfj8FbisfuLWK9+2u1JvErQYkZKXZ3tPnevnZqU69wZ3FZGD/mnz21OZhtxinLNRNtqRmU/9kNRmmkTEyIQT0+yL0ecP6cuLDptEgUZXroK4wlXjqCl0pno6FYr7kcjKZTCZu30xYzeQihKBlMMCrzYO8cnWQw9eH8EfiGCTYVJ7NgTX5HKjPZ1tlDgAtgwEu93pV15qfK33eFLFJu9lIfZGLtUmutbVF7kWTlp+I3rGwLjOv1KeM6e6JpZJOGfJHeOnKAC9e6uO3VwcIRhMzqgRksHIQQvBK0yD/+WoLv706gNVk4G3byvjgvhrWFrtXZE6xhMyha0M8c6GX5y70MeiP6F1O799YzD2NRVO28J4OE+uf0llHyVZTukLd2cSfpkNplm2cdAqV9gTzbXQ3X3JJMK4lJgF2lB4uEiCEEJ55zWYVYrWRy0ggymvXlIyug02DeiC7PMfOAVWSfm9d3qz/IQKROFf7fCmEc6XXn1J9n+Mwp8Ry1ha5qS9yp+3UOBViCZmL3d6UniVaxpDVpFTWb6vKZkdlDtsqc5blBj9flYAMVgZNfT7+6/VWfnqyk3BM5kB9Ph/aX8Pt9UsvMROOJXjl6gDPXOjlhYt9eMNxHBYjd64t5IGNxdyxtkDvhbSSSB9/mlriyBeZHI9Kjj99/I46/scD6+Y1lwXLv9zsWGlyicQTSr2JSibnu8cQQun7sXdNntLjZE0+VXmORb0JDvkjeiznippEcLXPn2K6l2bZaEginIYiJYvFZjYy4IsoJJJGOqU0y6Z3UdxRlUNjiWfFaxmSVQJevNzH2c65qQRksHwYCUT57tF2vvl6K/2+CHWqxMwjiywx4wvHeOnKAM+cV1THg9EEWXYz9zQW8cBGRdboZnOnJsef/OE4lbmOeXsMMuQyA5abXIQQXO3zc7BpgFebBzlyfZhQLIHRILGtIluphq/PZ0t51rKnmwoh6BpVMtYu96pJBL2K1TNdMDPbYeZ3tpZxS3XugqVTlgt93rBu0cxHJSCDpcdUEjOP76medz3WcCDKCxf7eOZCL682DRJNyOS7rNy/QSGUW2vzMg8Ys8SqIxdJkh4FvgA0Aru0Nsfqvs8CHwYSwKeEEM+mOf8LwO8DA+rQnwohnpIkqRq4BGgCm4dnU+y5HOTS7wvzmtrK99Wm8bTF2nynUm9SX8CttbmrwuwGpar4VPuo7uI63jZCND69r9dqMlBf5FLca0kutmKPbdW7nRZDJSCDpYMQgmOtI/znq+MSMw9vLuFD+2YnMdM7Fua5i708fa6XIy1DyEIpZHxwYzEPbCxmW2XOqirCvFGwGsmlEZCBfwc+o5GLJEnrge8Bu4BS4AWgQQiRmHD+FwC/EOLvJ4xXA78WQmycy3yWglxC0QRHW4f1AkatgCzHYWbvmnxuUwmlbA51GUsFWRY0D/iVVGCVTK6pTceMBonGErfeSXF7ZU5KNXsomqCpX63L0VKm+3z0ecfjOR6bSa/JWVPoYn2Jh7XF7nkHEZcamkrAi5f6eOHi1CoBmZvRyqBjOMg3Xm/lB8c68Efi7KzK4cP7a7h3fVGKpd82FOCZ8708c6GXU2rzujWFLh7YoBDKhtIbSwJoNWLVkYv+5pL0Mqnk8lkAIcRfqdvPAl8QQhyacN4XWGXkIsuKP18rYDzWqjzpW4wGdlTlsL8+n9vqC9hQ6lnx2gtfOKaIPrYpvd3TSadoRLK5PAtJAr+aRhmIJPBHtPX4pHVt2TUS4kznzM1LD9Tns04lnnXFHtYUrj7dpY7hoN5188j1YeKyINdp4c61hdzTWMiBhoJFTe/MYHbwhWP88Hgn33i9hY7hEGXZdh7fW0UoKvPMhV4u9SgacxvLPDy4sYT7NxSxpnBlss9uVtxI5PJvKK6sb6vbXweeFkL8eMJ5XwB+F/ACx4E/FkKMqORyAbiq7vu8EOLgTPNYSD+XX5/t4bmLff9/e2ceXndd5/vXJznZl6ZZm6aE0j0pa4AKilBoEVRGpOhVH+9cr3pl9Lo/lwfl4jiOoyOoM8/MODqOF5UZR5nLhQIiytJAqSClQEtL2yy0pVv2rUlOlrN+7h+/3zk5SZNmO8k5aT+v58nz23/nk5z2vM/vu7zfvHSoc9Qs8mWLs7huTQlXrSgiN8ODCKSIuD8g7jIlJXbb3Scy8fnuPom5dtzzEQYDQbzDQfY397KjsZMdjR2jzCzHUlGQRXFuOgP+kCMSw0EG/MEpTRwTgdx0DzkZHnIyUsnNTCM3I5WcdA+5mR5yM5xjHf0+Hn799JTO2PvEOjWvXZLP2iW5LC/KSQq7k77hAC80dLCtro3n69vpGw6SnprCVSuLuHEGLgHG7AmFlWcPtvGLF99m19FuRJwE1pvWO7YrU42FMKZPQsRFRLYBS8Y5dI+qPu6es53R4vJj4OUx4vJ7VX1kzL3LgE5Agb8BylX1UyKSAeSqapeIXA48BqxX1dNsckXkDuAOgMrKysuPHTs27d+xvX+Yd37vuVF5JwuNRVlp5GZEPvxTyXHXI2KQm+GIg7OeSm5GmiMeY87LnqYvU/eAn+frnVFbz9d3nJZsmZfpYcA3ImzpqSmsLM1lbVluVHDWlOVRUTCx4eRcYy4BycfhDi95mR5K8xJnvnousZCeXKbULDbmHsuZoCls7P0nYjbNYl1eH33DQcKqqCphhbAq4TDuPnfbPRY5JxQec/6oczn9fjrizjvyGk7wl5NH3z3uE0ZmWgrXri7h2jUlXLKsgPysEdHI8KQkRZuzLxjilSPd7od0e3Rez7oleSwvyuG8wixSRKLDpVti3HZzMzysiQhOWa7rRJA/owlusyHWJaC2ro3Xj5lLgHH2s5DEZT3wG0Y69GuB1eN06Jeraou7/lXgHar6UREpAbpVNSQiK3B80S5S1e4z1ZHoeS5TZTgQYn9Tb3S2++7jp06zTomEX03VOiXZUFUa2vrZdtARmjdOnAKcJrtNbrNTdXk+x7oGokOkI2FoEZNOcNxl10VNPh3xWV2aO+fZNBFiXQJ2NHYwYC4BxllI0omLiNwG/AgoAU4Bb6jqTe6xe4BPAUHgK6r6B3f//cBPVfU1EfkVcClOs9hR4C9UtUVEbge+7V4bAv5KVZ+YrJ5kFZeW3iF2Hzs1rnXK+UXZMR3vBXGzTkk22vuHeb6+nW117bz4VidDgRA56alcu6aETVVlXL+2hKLcjKjRX6zgRFwJYu0yKguzRwnO2rI8VpTkzOm8Bl8wxM4j3W5GTRvN5hJgnCUknbgkG8kgLpFAq4gz8J4zWKfUnL84aV2O55LhQIg/He5kW107tXVttPX5SBGoqVzMpiqnj2PVGIvzUFg50T0YbVKLOBIc6RyIOi+npQorinOj5p7OyDWnPyfe/SURl4DI5M3xXAKuXF6YcDcDw5gKJi6TkAhxibVO2X2sh30ne6PWKRUFWVwWk++eDNYpyYaqsr+pz+njqG+LRhufX5TNpnWO0Fx5BisXXzDEkY6B0XEGrf2jAsmy01NZXTbaVXqNa/IZL2JdAl461InPXAKMBYSJyyTMtbgEQ2HqW/vZc9zpJ3n9WA/Hu52RRWmpwoUVi0YySyoXJzRmeKHS0jtErftE89LhLsfKJdPDRncuylQ/pPuHAzS2eUcC2yYIbRsb2LamLHfW7grmEmAsNExcJiHe4tIz4GfPCTev5FgPe0+eiuYtlORluE1bzpPJ+qWLbARRnBnwBXnxUCe1E3xIb6oq44Jpfkh39PtOE5zGMaFtFQVZo1yl1y5x+nNmEtpkLgHGQsDEZRJmIy4R6xQn/MrpLzkSY51SXZ4fzSsZa51izD1n/JB2haamsmBGgyHCYcfksyHGVbqhtZ/DHd7o3KfUFOGC4pxRgrO2LI/zCqcXT2suAUYyYuIyCTMVl54BPzf/445RPloRlhdlc/XKYkrzMsjLHJmM6GR0e8jNSItu52Z47BvoPHGiezA6n+aVt7sIhJTF2WlsXFtK+aJM0j0pzk9qChmRdU8K6amppHtSSEsV0j3uMXdf7DXpnhRSBNdZ2jsqziDSFArO/KM1ZSODByLNbKVTCG2LuATU1rXxfEMHvUMBcwkwEoKJyyTMVFx8wRA/ef4wzaeGoh5bkSjTiBfXVGJNwek8jghQXowQ5WakTSBOnpjz09xZ9Knzkpt9ttA/HGBHo9PH8ce3Ojg1GIir24InRUYJTyisZ7TfGUt2eioXVSzissrFVBRkkpeZNvL+u+97VnoqDa39bG9oN5cAY94xcZmEuezQD4eVAf+I4PTHCs+o7cC44tQfI1KhKXzwpXtSoh9ARTnplORlUJKXQXGusyyJLN191t8zmnBY8YfC+IJh/MEw/pC7jG6H8AXDBEI6al9k3TfuNSPrvnGOtfYOjxqlNhMy05z3vdM7sXi944JCblq/hOK8DPJiRSozzZ6gjRlh4jIJyTDPZTJUleFAmH5fYAJxihUjJ9a0a8BHR7+PTq9/1GinWPIzPRSPEZ1YESrOzaA0L4PCnPSzcpJmsqKqNPcO09Dax94Tvbx+rIfXjnWfMT99cXYaSwuyKMxJZ8gf4lj3YNTBYarkpKeOEpy8zJEn5bH7LqoooKo8z/oQJyEcVupa+1i6KIvF82xLNNeYuEzCQhCX2RIIheny+uno99HhHXaWrvBE1ju8znK8pjwRZwhuce7pAjR2fVFWmn3gzBHBUJijXYMxgwj6aGzzcrRrgMh/5XRPCqtLc6MDCNYsyeOCohwOtvTx2J4mnjnYNuqe5xdl844LCilflBXzpSUQfWruH3b3DQcY8I82GI215blqRaE1y7qMHrHYQafXx6KsNL7x/io+dPmys+b/h4nLJJwL4jIdBv1BOvv9rgj5o6ITK0Kd7ro/dPo36bRUGbcZriTPeQq6/PxC89WKM0P+EIfavVHBaXAHE7T2jZh85mV6WOtOCFVVTnQPcbx7MDrQYCouASG3mffUQICdR7p4tq7tjLY85xJNp4Z4zh0s8vKR0XOtrllVxMOvn+TVoz28e3Uxf3vbRWdFFICJyySYuMwMVaVvOHjak89pIuT10eX1RV2bzVdr/jg16KexzesKTj+NrV7qW/voGz7zQBNPinDzhUvYXFXGxrUlZzRBHQ6EePmwIzTj2fLcWF3KypKz7z0Oh5W9J0/xnOt9FwknW16UzaaqMjZVlXLl8hGXiHBY+fWu49z3h3pCYeV/vWcNn3zXBQu6n8vEZRJMXOaeUFjpHvBzsmeQP7qz0Mfz1dpwBssWIz6oKm19Pupb+9yJoV4a2vp4q80btSAaj49ccR6fufaCM6Y5TmrLUz36A3ehMV5zV4rAFcsL2VxVyg3rylhZknNGIW0+NcRfPraf2vp2Llm2iPs+dDHrluTP428RP0xcJsHEJTG09g5TW99GbV07Lx7qnLFlixEfQmHlWNdAVHDqWvp46kDrhOffsK6Uj155HtVL8ycMbRvPlifffY83LZD3eKLmruvWlEzpyW48VJUn9rXw1789QO9QgM9tXMkXbli14PqsTFwmwcQl8Uzmq3VjdRnnF5mvViIYDoQ43OGltq6dn2w/NOGItbVledScXzDiu1aWN6rfJfKtf9tB5z3uGnDe4w3LC9lUVZo03mnTbe6aDT0Dfv7mdwfZuqeJlSU53Hf7xVyxvHDW950vTFwmwcQluYhYtjiBYW00tnkBWFWa6wpNKZeeZ75aiaRvOMCT+1r4yfZDnOieeI5OcW5GNJJ6XdTkM4/MtFTeOOHY8tTWjdjyrCrNZVNV6bx7pw36g/zxrdk1d82G7Q3t3PPofpp7h/jzq87nrpvXLQg7HxOXSTBxSW6Od434au162/HVKspJ5/p1zrfdd68unreESeN0gqEwrx3riX4ZiLgERBBxMolin3jOK8waGSpdlkdOuocjnV5eaOwY5Z22cW0JN1aVzYl3WvOpIUfc6tv50+H4NHfNhgFfkB8+08ADfzrKkvxMvnvbhdywrmzeXn8mJJ24iMiHgW8BVcCG2Ix7Ebkb+DROkuSXVPXpCe7xReALOKmTT6rqXdO5PhYTl4VD71CAFxo72Hawje0N7fQNB0n3pPDOlUXR0WcWWZA4VJXDHQNOh35dG68f6yGsjhv4mrJcKguzKcrJ4GiXk6UTG9rmSRFWlOSwtCCL1t5hTvYMRedcpaem8I4VhdxY7ZiNVszAOy0cVvY19Ua95eayuWs27D7ew9cf2Udjm5dbL13KN2+pTtph3ckoLlVAGPhX4M6IuIhINfAgsAFYCmwD1qhqaMz11wP3AO9XVZ+IlKpq+1SvH4uJy8IkEArz6tFuth10fLUi8zUurMiPCs36pfln3RDYhUT3gN+NqW5jR2MHA/4QWWmpXLO62HFzXl1C33BgJMrAnRx6sufMdjjFuRl8bMN5bKoq4+KKib3TztTctWmdM/lzLpu7Zoo/GOYn2w/x4+cPkZvh4Zt/Vs0HL61IujqTTlyiLy6yndHicjeAqn7P3X4a+JaqvjzmuoeAn6nqtjH7p3T9WExcFj6qyqF2L9vcVMfdx3tQhfJFmdHO4qtXFi240ThnE75giJ1Hut34gzaae4cnnPPk9QVpjEkIbXTzc87knfa5jSv55LuWEwwptfXOCLVkaO6aDY1t/XztkX3sOX6KjWtL+M4HL2TZ4uSZfLmQxOWfgZ2q+h/u9s+BP6jqw2OuewN4HLgZGHbv8epUrx+LicvZR6fXx3PuB8yOxpEZ5O9eXcLm6nNzBnkyoaocbOmLRjxH5jwtW5wVHR04nktAp9cXFZxX3u7i6QNt490+SlFOOp/buJKPbahcsP1yobDyq5eP8v2nGwC466a1/PnVy5NiQEtCxEVEtgFLxjl0j6o+7p6zndHi8mPg5THi8HtVfWTMvfcDzwFfBq4E/i+wAvjnqVzvHrsDuAOgsrLy8mPHjs36dzaSk8gM8siggNgZ5I4tfXI2jZxLtPUNR+fDvHioE18wTF6Gh+vWTv60EQ4rR7sG+NXOY/zypaOA00ejKIHQmNC2srxREdWV0wxtSyQnewa559H9vNDYQU1lAffdfjGryyae0DofLKQnl6k2iz0F3Kuq293tw8BVwP+YyvVjsSeXc4fYGeTb6to40Dwyg7yyMJustFRyMjxkp8cs0z1kZzjLrDHbsedleFJMoOJAZM5TbV07tfVt0TlPV5y/ONqhP5WY6kAozNudzsCBxraR5rXj3YNRk8/MtBRWl8aEti1xllMJbUsEqspjbzTx7ScO4vUF+fz1q/ifG1eN6wM3HywkcVkP/IaRDvlaYPU4HfqfBZaq6jdFZI17XiVQPZXrx2Licu7SfGqI2vp2djR20OX1MegPuT9BBnwhhgJn/KczihRhtPBkpJKd7iEn3VmOEqwY4cpKTyUneu6Y69NSz+mog8iExm3jxVS7T50105wPM+gP8labN5oQGhGe2HiCRVlpo1yl1y3JY01pXtK4CXR5ffz1Ewf57d5m1pTlcu/tF1NTuXje60g6cRGR24AfASXAKeANVb3JPXYP8CmcIcZfUdU/uPvvB36qqq+JSDrwC+BSwI8jUM+d6fozYeJiTEQorAwFQgz6ggz4Qwz4glHxGYzZHvAHGfSNLAej14w5z11OJ/EyNUWiEcrjRzCPsz7evkkinGPPzfCkkDbOPTLc9UQlW57oHnSHObez80hXXOfDdA/4XeubkWjqxtZ++mMiKJbkZ0ab1CLis6o0N2Ghe8/Vt/GNR/fT0jfMf3/ncu58z9p57VtKOnFJNkxcjPnGHww7T0f+EeGKLmMEacAXwhcMEQiNJFf6Jki5HJV4GbMduTaeEc5pqUJN5WJur1nGey9aQl7m/H+j7xsOsMOd8/R8Qwe9QwHSU1O4amURN7oZM0tnMB8mFlWlpXd4lOA0tPZzqMOL3zX5TBFYXpQzqi9nTVkey4uy5+Wp0+sL8v2n6vn3l49RUZDF3265iOvWlMz564KJy6SYuBjnAqGwEphChHOsWAUmEKwBX5DaunaOdA6Q4UnhpvVL2FJTwTWrihPSjDeRS0B1eT6bq0rZXF3GhUsnng8zk9c72jU40pfjNq8d7RqIRkuke1JYVZIb7cuJPOmUL8qck/6c145287VH9nG4Y4Atl1Xwl7dUz3nypYnLJJi4GMb0UVXeOHGKrbub+O3eZnqHApTmZfDByyrYUlORMBv5iEtArTtoI+ISUJqXEc2XeefK4jlpyhoOOKFtkcEDkSediULb1saYfMZDCIYDIX78/CH+ZfthFmWl8VcfWM+fXVw+Z4MTTFwmwcTFMGaHLxji+foOtu4+yXP17QTDSnV5PltqKrj10oqEJo9GXAJq69t4oeF0l4Ab1pXNeX29gwGnWc2dGNrQ2n9aaFtpXka0SS0iOKvLcslOn34fSl1LH19/ZB97T/ayaV0p37ntQsoXza6JcDxMXCbBxMUw4kf3gJ8n9jazdfdJ9p7sJTVFuG5NCVtqKthcVZawzm+YnkvAXBMJbWsYx4kgEtomApWF2aNcpdctyWN5cc6kHmihsPLLl97mh8804ElJ4WvvXcfHN1TGdTCGicskmLgYxtxwqL2frbubeHRPEy29w+Rlerjl4nK21CzjivMXJ3QuyWQuAZurnGTU+Z5DEgorx7sHo01qTr9OH0e7BqMmn2mpwsqS3OiTTkR4KgqyThOP412D/O9H3+TFQ51sWF7I926/iJUluXGp1cRlEkxcDGNuCYWVnUe6eGT3SZ7a38qgP0RlYTZbairYctkyKosS75c1kUvAte4w50R7kg0HQhzpGKChrc+Jpm7to7HNS9OpEZPPnPTU0/py1izJoygnnYdfP8l3nqxjKBDiy5tWc8e1K2btAG3iMgkmLoYxfwz4gjx9oJWtu5t46XAnqnDl8sVsqVnG+y4qZ1FW4icqDvqDvHSoi20H22blEjAf9A0HeMuNpm5o7YsOme4ZDETPKc5NZ01ZHoU56Tx7sA1fMExVeT733X4RFy8rmPFrm7hMgomLYSSGlt4hHtvTzCO7T3Ko3Uu6J4Ubq8u4vaaCd68uSXi2Cox2Caita6e+dfYuAXONqtLh9dHY6qW+tc8ZudbmpbG1f5TrRIrADz50CbdfvmxGr2PiMgkmLoaRWFSVN5t62bq7icffaKJnMEBxbjofuKSC2y+voLo8eXJ5TnQPRgPH4u0SMNeEw8rJnqGo4LzV7gSSzTTx0sRlEkxcDCN58AfDvNDoDGuurWvHHwqzbkledFhzWX7yJI3Oh0tAMmPiMgkmLoaRnJwa9PPEvha27j7JnuOnSBG4ZnUJt9dU8J7qJWSlJ0/423y7BCQDJi6TYOJiGMnPkQ4vj+5pYuvuJppODZGb4eF9Fy1hS80yNiwvTKoP7clcAjZXlfKuVXPjEjCfmLhMgomLYSwcwmFl19Futu4+ye/fbMXrC1JRkMWWmgpuu6yCFXGawxFPxnMJyExLcZJR58klYC4wcZkEExfDWJgM+UM8c7CVR3Y38eJbHYQVct2snNwMTzQ3JzfDQ3aGh9xoVo6znp3uOe38nIxI3o6TxRNvI86JXAIuWVbAjdXz6xIwW0xcJsHExTAWPm19w/xuXwtNPUMM+oN4fUEntiAmRyeyb9A/9RC4DE/KhEIVEaeccYQqKmYZTghcWX7mabP9VZW6ln53mHMbe5PEJWCqmLhMgomLYZxbhMMaDXTzjhGegWiWjpOnM+APjmxHjo06x9kOTZKXk5YqrC7NY/3SfOenYhFV5fmjhi0nu0vAWExcJsHExTCM2aCq+Nycm/GEyjsc5EjnAAdb+jjY3Eun1w84xpTLi3KojgjO0kWsX5pPcW4GQ/4QLx7qTGqXgKQTFxH5MPAtoArYoKqvxRy7G/g0EAK+pKpPT3CPLwJfwIkzflJV7xKR5UAd0OCetlNVPztZPSYuhmHMF6pKe7+PA829HGjq40BzHwdaejnRPeIRVpafERWa6vJ8qsrz6R70UzueS0BVGZurE+MSkIziUgWEgX8F7oyIi4hUAw8CG4ClwDZgjaqGxlx/PXAP8H5V9YlIqaq2u+LyO1W9cDr1mLgYhpFoeocCHGzu40Bzr7vs41CHN9rclpfpobrcebpZlJVGa98Qb3cO8NrRHoJhZXF2GtevK51Xl4AziUtCPApUtQ4YbzTErcB/qqoPeFtEDuEIzctjzvsccK97HqraPrcVG4ZhzC2LstK4emURV68siu4bDoRoaO3nQHMfB1t6OdDcx292HWM44OS9pHtSqCzMptPro2cwwNbdzjygiEvAZtcloCIBLgHJZoBTAeyM2T7p7hvLGuDdIvJdYBjn6edV99gFIrIH6AO+oap/HO+FROQO4A6AysrKOJVvGIYRPzLTUrnkvAIuOa8gui8UVt7u9DrNae6TTvegf9R1/lCYHY0d7Gjs4JuPH6CqPJ8b59klYM7ERUS2AUvGOXSPqj4+0WXj7Buv3c4DLAauAq4EHhKRFUALUKmqXSJyOfCYiKxX1b7Tbqr6M+Bn4DSLTfoLGYZhJAGpKcKq0jxWleZx66XOd29Vpbl3mANNvVHROdjcS3PvMODEHte19PFPzx2aN5eAORMXVd08g8tOAufFbC8Dmic4b6s6HUa7RCQMFKtqBxBpKntdRA7jPOVYh4phGGctIkJFQRYVBVm8Z/3Id/qeAf+oJrUDzX0c6fDy4K7jPLjrOJlpKXz3gxfN2HL/TCRbs9hvgd+IyN/jdOivBnaNc95jwA3AdhFZA6QDnSJSAnSrash9klkNHJmXyg3DMJKMxTnpXLO6mGtWF0f3DfqD1Lv9OPUtfZQXzI3LdELERURuA34ElABPisgbqnqTqh4QkYeAgzhDjD8fGSkmIvcDP3VHlv0C+IWI7Af8wCdUVUXkWuDbIhLEGcr8WVXtnv/f0DAMIznJTvdQU7mYmsrFc/o6NokSG4psGIYxE840FDk5DWsMwzCMBY2Ji2EYhhF3TFwMwzCMuGPiYhiGYcQdExfDMAwj7pi4GIZhGHHHxMUwDMOIOzbPBRCRDuDYLG5RDHTGqZx4YnVND6trelhd0+NsrOt8VS0Z74CJSxwQkdcmmkiUSKyu6WF1TQ+ra3qca3VZs5hhGIYRd0xcDMMwjLhj4hIffpboAibA6poeVtf0sLqmxzlVl/W5GIZhGHHHnlwMwzCMuGPiMktEJFVE9ojI7xJdSwQROSoib4rIGyKSNFkCIlIgIg+LSL2I1InI1UlQ01r37xT56RORryS6LgAR+aqIHBCR/SLyoIjMTarTNBGRL7s1HUj030pEfiEi7W62U2RfoYg8KyJvucu5DS6Zel0fdv9mYRFJyKixCer6gft/cp+IPCoiBfF4LROX2fNloC7RRYzD9ap6aZINffxH4ClVXQdcQhL83VS1wf07XQpcDgwCjya2KhCRCuBLwBWqeiGQCnw0sVWBiFwIfAbYgPMe3iIiqxNY0gPAzWP2fR2oVdXVQK27Pd88wOl17Qe2ADvmvZoRHuD0up4FLlTVi4FG4O54vJCJyywQkWXA+4H7E11LsiMi+cC1wM8BVNWvqqcSWtTpbAIOq+psJtTGEw+QJSIeIBtoTnA9AFXATlUdVNUg8AJwW6KKUdUdwNi02VuBf3PX/w344HzWBOPXpap1qtow37WMqWG8up5x30uAncCyeLyWicvs+AfgLiCc4DrGosAzIvK6iNyR6GJcVgAdwC/dZsT7RSQn0UWN4aPAg4kuAkBVm4AfAseBFqBXVZ9JbFWA8+37WhEpEpFs4H3AeQmuaSxlqtoC4C5LE1zPQuJTwB/icSMTlxkiIrcA7ar6eqJrGYd3qWoN8F7g8yJybaILwvkWXgP8i6peBgyQmOaKcRGRdOADwP9LdC0Abj/BrcAFwFIgR0T+a2Krcr59A/fhNKU8BewFgme8yFgQiMg9OO/lr+NxPxOXmfMu4AMichT4T+AGEfmPxJbkoKrN7rIdp/9gQ2IrAuAkcFJVX3G3H8YRm2ThvcBuVW1LdCEum4G3VbVDVQPAVuCdCa4JAFX9uarWqOq1OE0sbyW6pjG0iUg5gLtsT3A9SY+IfAK4Bfi4xml+ionLDFHVu1V1maoux2lOeU5VE/7NUkRyRCQvsg68B6cpI6GoaitwQkTWurs2AQcTWNJYPkaSNIm5HAeuEpFsERGcv1fCB0AAiEipu6zE6aBOpr8bwG+BT7jrnwAeT2AtSY+I3Ax8DfiAqg7G676eeN3ISBrKgEedzyM8wG9U9anElhTli8Cv3SaoI8AnE1wPAG7fwY3AXyS6lgiq+oqIPAzsxmmq2EPyzPB+RESKgADweVXtSVQhIvIgsBEoFpGTwF8B9wIPicincUT6w0lSVzfwI6AEeFJE3lDVm5KgrruBDOBZ93Njp6p+dtavZTP0DcMwjHhjzWKGYRhG3DFxMQzDMOKOiYthGIYRd0xcDMMwjLhj4mIYhmHEHRMXw5gmIqIi8ncx23eKyLfi/BqfjHFq9se4XN87zfv8Pl4ut4YxHWwosmFMExEZxvH7ulJVO0XkTiBXVb81R693FMcduXMu7m8Yc4E9uRjG9AniTGj86tgDIvKAiHwoZtvrLjeKyAsi8pCINIrIvSLycRHZ5T6VrJzsRcXhB26Wypsi8pGYe+9wszgOishPRSTFPXZURIrd9f/mZnbsFZFfufs+7N5vr4gk0greOMuwGfqGMTN+DOwTke9P45pLcCzru3EcCu5X1Q0i8mUc94KvTHL9FuBS9z7FwKsxgrABqAaO4RhKbsHxbwNARNYD9+CYmnaKSKF76JvATaraZM1nRjyxJxfDmAGq2gf8O06g11R5VVVbVNUHHAYiFvpvAsuncP01wIOqGnINNl8ArnSP7VLVI6oawvH6umbMtTcAD0ea1lQ1kunxEvCAiHwGJ5DMMOKCiYthzJx/AD4NxObSBHH/X7mGk+kxx3wx6+GY7TBTa0WQMxwb23k6dlvG2YfrIfUNnEyWN1zPMMOYNSYuhjFD3G//D+EITISjOHHJ4OSxpMXxJXcAHxGRVBEpwUn23OUe2yAiF7h9LR8BXhxzbS3wXyLiEWkWE5GVqvqKqn4T6CT5gr+MBYqJi2HMjr/D6f+I8H+A60RkF/AOnFC0ePEosA8noOs54C43ygDgZRw34P3A2+65UVT1APBd4AUR2Qv8vXvoB+7ggP044rU3jvUa5zA2FNkwFjgishG4U1VvSXAphhHFnlwMwzCMuGNPLoZhGEbcsScXwzAMI+6YuBiGYRhxx8TFMAzDiDsmLoZhGEbcMXExDMMw4o6Ji2EYhhF3/j9LK4I1Yld4MQAAAABJRU5ErkJggg==", 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" ] }, "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": "markdown", "id": "8a4196d2-0f8a-4b0b-a6dd-ead9441af44e", "metadata": {}, "source": [ "Topic 4, 8, 9 and 10 were selected for further evaluation using the visual graphs, considering that the best combination does not always yield the best result since a model with higher number of topics tends to have a better measurable result but may not fit the data the most. \n", "\n", "However, take note that even the random_state was preset and all other parameters were fixed, there are still randomness found that the model may produce inconsistant output each time. " ] }, { "cell_type": "markdown", "id": "df1c00ad-ba54-4686-ac75-ef1033066dce", "metadata": {}, "source": [ "unfortunately, the alter of the number of topics has no much effect on the results, and the news are not clustered into relevant topics properly. also, most topics are stacked together, indicating high similarity and ambiguity among them due to the multi-aspect nature of the news contents. As a result, LDA may not be a suitable solution for this kind of news content. same result goes for moderate and minor." ] }, { "cell_type": "code", "execution_count": 31, "id": "490734ed-077c-4fb0-930c-0b42f4f63c94", "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": [ "# realised that there may be some overlaps for 8 topics, thus 4-6 topics are optimal\n", "k = 8\n", "a = \"asymmetric\"\n", "# a = 0.91\n", "# b = 0.61\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": 32, "id": "afe8abf0-2d12-414e-92be-a655865addb1", "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" ] }, { "data": { "text/plain": [ "(0.6309264127222964, -10.496005226704286)" ] }, "execution_count": 32, "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": 33, "id": "8430a827-6dbb-4737-8ccc-78ed17a01234", "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": [ "# 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_viz8_severe_training.html\")" ] }, { "cell_type": "code", "execution_count": 34, "id": "5e30d71a-a3c7-40c7-94c0-7eea1bedc887", "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" ] }, { "data": { "text/plain": [ "[(0,\n", " '0.001*\"upgrade_import_forecast\" + 0.001*\"sign_confidence_consumer\" + 0.001*\"global_shipping_disruption\" + 0.001*\"global_port_tracker\" + 0.001*\"website_see_service\" + 0.001*\"help_business\" + 0.001*\"meet_firm\" + 0.000*\"heavy_rain\" + 0.000*\"united_state\" + 0.000*\"new_york\" + 0.000*\"new_york_city\" + 0.000*\"large_number\" + 0.000*\"national_hurricane_center\" + 0.000*\"big_ship\" + 0.000*\"empty_container\" + 0.000*\"social_medium\" + 0.000*\"several_hour\" + 0.000*\"houston_ship_channel\" + 0.000*\"nautical_mile\" + 0.000*\"second_half\" + 0.000*\"second_quarter\" + 0.000*\"makilala_town\" + 0.000*\"new_oil_gas\" + 0.000*\"country_tremor\" + 0.000*\"official_tell\" + 0.000*\"executive_director\" + 0.000*\"major_quake\" + 0.000*\"cent_world_earthquake\" + 0.000*\"strong_hurricane\" + 0.000*\"interview_milwaukee_driver\"'),\n", " (1,\n", " '0.001*\"sorry_site\" + 0.001*\"technical_difficulty_please\" + 0.001*\"moment_exception_request\" + 0.001*\"global_supply_chain\" + 0.001*\"economic_growth\" + 0.001*\"critical_destination_port\" + 0.000*\"asian_country\" + 0.000*\"vertical_insight\" + 0.000*\"trade_statement\" + 0.000*\"high_flexibility_transit\" + 0.000*\"situation_schedule_port\" + 0.000*\"major_economy\" + 0.000*\"rotterdam_felixstowe_valencia\" + 0.000*\"day_trade_asia\" + 0.000*\"high_yard_density\" + 0.000*\"negative_impact\" + 0.000*\"operational_disruption\" + 0.000*\"global_economy\" + 0.000*\"taiwan_site_operation\" + 0.000*\"monetary_fund\" + 0.000*\"strong_recovery\" + 0.000*\"ocean_network\" + 0.000*\"low_figure\" + 0.000*\"site_operation\" + 0.000*\"severe_weather_event\" + 0.000*\"china_area_warehouse\" + 0.000*\"national_hurricane_center\" + 0.000*\"heavy_rainfall\" + 0.000*\"increase_capacity\" + 0.000*\"retail_sale\"'),\n", " (2,\n", " '0.000*\"heavy_rainfall\" + 0.000*\"united_state\" + 0.000*\"global_supply_chain\" + 0.000*\"asian_country\" + 0.000*\"significant_supply_chain\" + 0.000*\"use_term\" + 0.000*\"crucial_business\" + 0.000*\"south_korea_date\" + 0.000*\"lunar_calendar\" + 0.000*\"advance_factory\" + 0.000*\"saturday_festivity_conclude\" + 0.000*\"new_fall\" + 0.000*\"new_spring_festival\" + 0.000*\"new_commence\" + 0.000*\"new_supply_chain\" + 0.000*\"cny_impact\" + 0.000*\"new_family\" + 0.000*\"february_celebration_day\" + 0.000*\"february_friday\" + 0.000*\"business_present\" + 0.000*\"new_celebrate\" + 0.000*\"slow_operation_limit\" + 0.000*\"disruption_ship\" + 0.000*\"festival_note_preparation\" + 0.000*\"slow_shut_operation\" + 0.000*\"logistical_challenge_production\" + 0.000*\"shipment_cny_note\" + 0.000*\"hometown_celebrate\" + 0.000*\"main_festival_fall\" + 0.000*\"important_holiday_period\"'),\n", " (3,\n", " '0.001*\"coast_port\" + 0.001*\"terminal_port\" + 0.001*\"president_drayage\" + 0.001*\"paul_brashier_vice\" + 0.001*\"social_medium\" + 0.001*\"port_los\" + 0.001*\"san_pedro_california\" + 0.001*\"seattle_labor_slowdown\" + 0.001*\"union_worker\" + 0.001*\"proactive_move\" + 0.001*\"empty_container\" + 0.001*\"union_pacific_maritime\" + 0.001*\"port_oakland_friday\" + 0.001*\"agriculture_industry_use\" + 0.001*\"congestion_issue_plague\" + 0.001*\"pma_comment_cite\" + 0.001*\"select_terminal\" + 0.001*\"intend_destination\" + 0.001*\"good_railroad\" + 0.001*\"pick_container_variety\" + 0.001*\"union_pacific\" + 0.001*\"sealand_maersk_company\" + 0.001*\"coast_supply_chain\" + 0.001*\"cnbc_supply_chain\" + 0.001*\"negotiation_ilwu_longshoreman\" + 0.001*\"rail_shipment_shipper\" + 0.001*\"carrier_port_los\" + 0.001*\"busy_port_process\" + 0.001*\"specialized_worker_work\" + 0.001*\"coast_port_bnsf\"'),\n", " (4,\n", " '0.001*\"notice_become\" + 0.001*\"seek_pay_rise\" + 0.001*\"announce_strike_notice\" + 0.001*\"strike_march_lufthansa\" + 0.001*\"public_transport_company\" + 0.001*\"march_walkout_stag\" + 0.001*\"strike_notice\" + 0.001*\"march_passenger\" + 0.001*\"public_transport_worker\" + 0.001*\"verona_airport_passenger\" + 0.001*\"work_hour\" + 0.001*\"strike_information\" + 0.001*\"cancel_flight_lufthansa\" + 0.001*\"villafranca_airport_march\" + 0.001*\"private_transport_operator\" + 0.001*\"walk_pay_strike\" + 0.001*\"saturday_flight_delay\" + 0.001*\"spanish_union_railway\" + 0.001*\"stage_strike\" + 0.001*\"nationwide_railway_strike\" + 0.001*\"ballot_strike_action\" + 0.001*\"ground_staff\" + 0.001*\"uk_border_force\" + 0.001*\"european_strike_announce\" + 0.001*\"major_travel_disruption\" + 0.001*\"driver_employment_plan\" + 0.001*\"queue_delay_london\" + 0.001*\"cancel_departure\" + 0.001*\"big_strike\" + 0.001*\"italy_railway_company\"'),\n", " (5,\n", " '0.001*\"coast_container\" + 0.000*\"global_supply_chain\" + 0.000*\"strong_wind\" + 0.000*\"average_day\" + 0.000*\"critical_destination_port\" + 0.000*\"manageable_level\" + 0.000*\"european_hub\" + 0.000*\"europe_hub_port\" + 0.000*\"dwell_day\" + 0.000*\"affect_truck_disruption\" + 0.000*\"arrival_cluster_ship\" + 0.000*\"lead_delay_ship\" + 0.000*\"trade_statement\" + 0.000*\"north_sea\" + 0.000*\"temporary_closure\" + 0.000*\"vicinity_benelux_port\" + 0.000*\"savannah_vessel_berth\" + 0.000*\"wait_port\" + 0.000*\"arrival_departure\" + 0.000*\"new_export_surge\" + 0.000*\"berth_port\" + 0.000*\"hamburg_consequence\" + 0.000*\"huge_increase_vessel\" + 0.000*\"couple_day_berth\" + 0.000*\"single_transaction\" + 0.000*\"diversion_panama\" + 0.000*\"port_los\" + 0.000*\"terminal_operation\" + 0.000*\"rotterdam_hamburg_southampton\" + 0.000*\"outlook_hub\"'),\n", " (6,\n", " '0.000*\"national_hurricane_center\" + 0.000*\"port_los\" + 0.000*\"tropical_storm\" + 0.000*\"new_holiday\" + 0.000*\"director_gene\" + 0.000*\"marine_exchange\" + 0.000*\"next_day\" + 0.000*\"high_degree\" + 0.000*\"familiar_matter\" + 0.000*\"high_fuel_cost\" + 0.000*\"employee_work\" + 0.000*\"city_limit\" + 0.000*\"postal_service\" + 0.000*\"heavy_rain\" + 0.000*\"critical_destination_port\" + 0.000*\"global_supply_chain\" + 0.000*\"trade_statement\" + 0.000*\"social_medium\" + 0.000*\"severe_weather\" + 0.000*\"declare_state_emergency\" + 0.000*\"supply_chain\" + 0.000*\"export_volume\" + 0.000*\"high_yard_density\" + 0.000*\"travel_restriction\" + 0.000*\"day_trade_asia\" + 0.000*\"loss_damage\" + 0.000*\"america_trade_statement\" + 0.000*\"relevant_rate\" + 0.000*\"accurate_quote_market\" + 0.000*\"free_day\"'),\n", " (7,\n", " '0.000*\"national_hurricane_center\" + 0.000*\"heavy_rain\" + 0.000*\"hong_kong\" + 0.000*\"united_state\" + 0.000*\"next_hour\" + 0.000*\"tropical_storm\" + 0.000*\"global_economy\" + 0.000*\"social_medium\" + 0.000*\"high_wind\" + 0.000*\"fire_arc\" + 0.000*\"coastal_area\" + 0.000*\"national_weather_service\" + 0.000*\"tropical_storm_force\" + 0.000*\"storm_surge\" + 0.000*\"several_airport\" + 0.000*\"storm_surge_foot\" + 0.000*\"tropical_storm_strength\" + 0.000*\"public_health_emergency\" + 0.000*\"carolina_emergency_management\" + 0.000*\"wind_storm_surge\" + 0.000*\"president_joe\" + 0.000*\"port_los\" + 0.000*\"global_trade\" + 0.000*\"container_volume\" + 0.000*\"southern_california_port\" + 0.000*\"arrest_warrant\" + 0.000*\"vulnerable_community\" + 0.000*\"high_volume\" + 0.000*\"volunteer_organization\" + 0.000*\"seroka_executive_director\"')]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_model.print_topics(num_words=30)" ] }, { "cell_type": "markdown", "id": "4fc5e753-e0e7-4520-9e7d-10e26e4d580d", "metadata": {}, "source": [ "This allows ease access to the trained model for future prediction work." ] }, { "cell_type": "code", "execution_count": null, "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_8_asym_sym\")" ] }, { "cell_type": "markdown", "id": "466c3952-69d1-4d19-b49c-d4b1e1844572", "metadata": {}, "source": [ "get dominant topics and topic percentage contribution.\n", "Made use of gensim lda's own function: https://radimrehurek.com/gensim/models/ldamodel.html" ] }, { "cell_type": "code", "execution_count": 35, "id": "cd88034c-2fb8-4f1f-a4e8-85d09b4fc1dc", "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": [ "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": 48, "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": 49, "id": "c88b088b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Document_NoDominant_TopicTopic_Perc_ContribTopic_DistributionText
0070.9627[(7, 0.96268755)]UPDATE - Indonesia: Police confirm two explosions in East Jakarta, further casualties reported According to local police in Jakarta, two explosions have now been confirmed close to the Terminal Ka...
1100.9799[(0, 0.9799124)]40 miles W of Columbia- Florence continues to weaken; additional flooding expected. Incident closed. Tropical Depression Florence continues to weaken and is moving north-northwest at ten miles per...
2200.9551[(0, 0.95514065), (1, 0.010377824)]California Wildfires Cause Numerous Fatalities and Extensive Damage; Assessments are Ongoing On Tuesday, damage assessments were ongoing in areas affected by wildfires in California. The Camp Fire...
3330.9740[(3, 0.9739527)]Canada Post backlogs reported in Toronto, Montreal, and Vancouver Local media sources indicated on November 1 that over 150 Canada Post trailers in Toronto, Vancover, and Montreal comprise a deliv...
4410.9705[(1, 0.9704964)]Cargo theft network arrested for stealing USD 4 million worth of goods from truck yards On December 15, it was reported that 8 individuals from New Jersey have been arrested and a multi-million ca...
5500.9851[(0, 0.9850588)]Central & Southern Taiwan Rains and floods subside as relief operations continue. Incident closed. Media sources report that heavy rainfall and flooding have subsided across central and southern...
6610.9975[(1, 0.9974598)]Central Tennessee - Tornadoes touched down in multiple locations across the region. Incident closed. Tornadoes were reported at multiple locations in central Tennessee, including near Hwy 11 in Pu...
7700.9653[(0, 0.9653319)]Coastal Virginia - The mandatory evacuations for Zone A residents have been lifted. Incident closed. The mandatory evacuations for residents in Zone A, the lowest-lying areas of Coastal Virginia a...
8870.9893[(7, 0.98926944)]Considerable congestion continues to be reported at Port of Long Beach Shipping sources indicate on December 5 that significant congestion is still affecting container terminals in the Port of Lon...
9900.9893[(0, 0.9892587)]Criminal Investigation Launched Following Genoa Bridge Collapse On Wednesday, Italian Prime Minister Giuseppe Conte declared a one-year state of emergency in the region of Liguria following the de...
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" ], "text/plain": [ " Document_No Dominant_Topic Topic_Perc_Contrib \\\n", "0 0 7 0.9627 \n", "1 1 0 0.9799 \n", "2 2 0 0.9551 \n", "3 3 3 0.9740 \n", "4 4 1 0.9705 \n", "5 5 0 0.9851 \n", "6 6 1 0.9975 \n", "7 7 0 0.9653 \n", "8 8 7 0.9893 \n", "9 9 0 0.9893 \n", "\n", " Topic_Distribution \\\n", "0 [(7, 0.96268755)] \n", "1 [(0, 0.9799124)] \n", "2 [(0, 0.95514065), (1, 0.010377824)] \n", "3 [(3, 0.9739527)] \n", "4 [(1, 0.9704964)] \n", "5 [(0, 0.9850588)] \n", "6 [(1, 0.9974598)] \n", "7 [(0, 0.9653319)] \n", "8 [(7, 0.98926944)] \n", "9 [(0, 0.9892587)] \n", "\n", " Text \n", "0 UPDATE - Indonesia: Police confirm two explosions in East Jakarta, further casualties reported According to local police in Jakarta, two explosions have now been confirmed close to the Terminal Ka... \n", "1 40 miles W of Columbia- Florence continues to weaken; additional flooding expected. Incident closed. Tropical Depression Florence continues to weaken and is moving north-northwest at ten miles per... \n", "2 California Wildfires Cause Numerous Fatalities and Extensive Damage; Assessments are Ongoing On Tuesday, damage assessments were ongoing in areas affected by wildfires in California. The Camp Fire... \n", "3 Canada Post backlogs reported in Toronto, Montreal, and Vancouver Local media sources indicated on November 1 that over 150 Canada Post trailers in Toronto, Vancover, and Montreal comprise a deliv... \n", "4 Cargo theft network arrested for stealing USD 4 million worth of goods from truck yards On December 15, it was reported that 8 individuals from New Jersey have been arrested and a multi-million ca... \n", "5 Central & Southern Taiwan Rains and floods subside as relief operations continue. Incident closed. Media sources report that heavy rainfall and flooding have subsided across central and southern... \n", "6 Central Tennessee - Tornadoes touched down in multiple locations across the region. Incident closed. Tornadoes were reported at multiple locations in central Tennessee, including near Hwy 11 in Pu... \n", "7 Coastal Virginia - The mandatory evacuations for Zone A residents have been lifted. Incident closed. The mandatory evacuations for residents in Zone A, the lowest-lying areas of Coastal Virginia a... \n", "8 Considerable congestion continues to be reported at Port of Long Beach Shipping sources indicate on December 5 that significant congestion is still affecting container terminals in the Port of Lon... \n", "9 Criminal Investigation Launched Following Genoa Bridge Collapse On Wednesday, Italian Prime Minister Giuseppe Conte declared a one-year state of emergency in the region of Liguria following the de... " ] }, "execution_count": 49, "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": 44, "id": "4fe6b40b-6922-4de3-8d9e-dac7474b6303", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 85\n", "1 60\n", "3 32\n", "7 27\n", "2 27\n", "5 26\n", "4 19\n", "6 18\n", "Name: Dominant_Topic, dtype: int64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_dominant_topic[\"Dominant_Topic\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 45, "id": "b9917340-31cf-48af-871f-b481128fdf22", "metadata": {}, "outputs": [ { "data": { "image/png": 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RJEmSNk8zBWcq8GHg2sz8RUS8AvjvcmNJkiRtumb2xDySmSetmcjMB4C/LS+SJEnS5mmm4FxWDDC+ncaA41sy865yY0mSJG26DRaczDwqIrYB/hI4BvhRROyUmXuUHU6SJGlTbLDgRMTrgTcUX7sBPwRuKTeWJEnSpmvmENXNwFzgAuD6zHy+3EiSJEmbp5mC8xfA64CjgL+NiNXAzzLzY6UmkyRJ2kTNjMF5PCIeAPYBBgOvBbxVgyRJ6rOaGYNzP3Av8D/A14BJHqaSJEl9WTOHqIZm5urSk0iSJPWSZq5kvH9EzImIuwEi4pCI+GgzK4+IByPirohYEBFzi3l7RMQNEXFf8bj7ZuSXJEl6iWYKziU0btWwEiAz7wTevhGfcWxmjszMUcX0NGBOZg6lcXfyaRuxLkmSpA1qpuDskJm3rTNv1WZ85slAZ/G8EzhlM9YlSZL0Es0UnGUR8UogASLiNGBRk+tP4McRMS8iphTzBmXmIoDiceBGZpYkSVqvZgYZvx+YAQyLiEeB3wLvaHL9r8vMxyJiIHBDRNzTbLCiEE0BGDJkSLNvkyRJauo6OA8Ab4yIHYF+mflksyvPzMeKxyURcS1wBLA4Ijoyc1FEdABLenjvDBrFilGjRmWznylJktRjwYmId/UwH4DM/Nb6Vty1EBXPjwM+CVwHTASmF48zNym5JElSD9a3B+cvu5kXwInA3sB6Cw4wCLi2KET9gX/PzFkRcTtwdURMBh4CTt/o1JIkSevRY8HJzL9Z8zwaLWUC8PfArcBnNrTi4tDWod3M/wMwZlPCSpIkNWO9Y3Aioj9wJnAO8HPgtMy8twW5JEmSNtn6xuC8H5hK42J8x2fm71qWSpIkaTOsbw/Ol2mc4fR64AdrBhfTGIeTmXlIydkkSZI2yfoKzn4tSyFJktSL1jfI2ENSkiRpi9TMrRokSZK2KBYcSZJUOz0WnIiYUzx+tnVxJEmSNt/6Bhl3RMTRwEkRcRWNs6fWysw7Sk0mSZK0idZXcP4RmAYMBr6wzrIE/qqsUJIkSZtjfWdRfRf4bkR8LDM/1cJMkiRJm2W9t2oAyMxPRcRJwFHFrJsy84flxpIkSdp0GzyLKiIuoHHLhl8WX1OLeZIkSX3SBvfgAG8BRmbmaoCI6ATmAx8uM5gkSdKmavY6OLt1eb5rCTkkSZJ6TTN7cC4A5kfEf9M4Vfwo3HsjSZL6sGYGGV8ZETcBf0mj4Px9Zv6+7GCSJEmbqpk9OGTmIuC6krNIkiT1Cu9FJUmSaseCI0mSame9BSci+kXE3a0KI0mS1BvWW3CKa98sjIghLcojSZK02ZoZZNwB/CIibgOeWjMzM08qLZUkSdJmaKbgfKL0FJIkSb2omevg3BwRLweGZubsiNgB2Kr8aJIkSZummZttvhv4LvD1YtbewPdLzCRJkrRZmjlN/P3A64AnADLzPmBgmaEkSZI2RzMF57nMfH7NRET0B7K8SJIkSZunmYJzc0R8BNg+IsYC1wA/KDeWJEnSpmum4EwDlgJ3Ae8Brgc+WmYoSZKkzdHMWVSrI6IT+DmNQ1P3ZqaHqCRJUp+1wYITEW8BvgbcDwSwX0S8JzP/s+xwkiRJm6KZC/19Hjg2M38DEBGvBH4ENFVwImIrYC7waGaeEBF7AN8B9gUeBM7IzOUbH12SJKl7zYzBWbKm3BQeAJZsxGdMBX7VZXoaMCczhwJzimlJkqRe02PBiYhxETGOxn2oro+IMyNiIo0zqG5vZuURMRh4C/BvXWafDHQWzzuBUzYluCRJUk/Wd4jqxC7PFwNHF8+XArs3uf4LgfOAnbvMG5SZiwAyc1FEdHvRwIiYAkwBGDLEm5lLkqTm9VhwMnPS5qw4Ik6gcXhrXkQcs7Hvz8wZwAyAUaNGedaWJElqWjNnUe0H/A2NQcFrX5+ZJ23gra8DToqI/wNsB+wSEZcDiyOio9h708HGjeeRJEnaoGbOovo+8A0aY29WN7vizPww8GGAYg/OuZn5joj4HDARmF48ztyoxJIkSRvQTMF5NjO/1IufOR24OiImAw8Bp/fiuiVJkpoqOBdFxPnAj4Hn1szMzDua/ZDMvAm4qXj+B2DMRqWUJEnaCM0UnIOBdwJ/xZ8PUWUxLUmS1Oc0U3DeCrwiM58vO4wkSVJvaOZKxguB3UrOIUmS1Gua2YMzCLgnIm7nxWNwNnSauCRJUiWaKTjnl55CkiSpF22w4GTmza0IIkmS1FuauZLxkzTOmgLYBtgaeCozdykzmCRJ0qZqZg9O1xtlEhGnAEeUFUiSJGlzNXMW1Ytk5vfxGjiSJKkPa+YQ1bguk/2AUfz5kJUkSVKf08xZVCd2eb4KeBA4uZQ0kiRJvaCZMTiTWhFEkiSpt/RYcCLiH9fzvszMT5WQR5IkabOtbw/OU93M2xGYDPwFYMGRJEl9Uo8FJzM/v+Z5ROwMTAUmAVcBn+/pfZIkSVVb7xiciNgD+CAwAegEDs/M5a0IJkmStKnWNwbnc8A4YAZwcGauaFkqSZKkzbC+C/2dA+wFfBR4LCKeKL6ejIgnWhNPqsazzz7LEUccwaGHHspBBx3E+ec37jn7oQ99iGHDhnHIIYfw1re+lccff7zaoJKkbvVYcDKzX2Zun5k7Z+YuXb529j5Uqrttt92WG2+8kYULF7JgwQJmzZrFrbfeytixY7n77ru58847edWrXsUFF1xQdVRJUjc2+lYNUjuICHbaaScAVq5cycqVK4kIjjvuOPr3bxzZPfLII3nkkUeqjClJ6oEFR+rBCy+8wMiRIxk4cCBjx45l9OjRL1p+6aWX8uY3v7midJKk9bHgSD3YaqutWLBgAY888gi33XYbd99999pln/nMZ+jfvz8TJkyoMKEkqScWHGkDdtttN4455hhmzZoFQGdnJz/84Q+54ooriIiK00mSumPBkbqxdOnStWdIPfPMM8yePZthw4Yxa9YsPvvZz3Ldddexww47VBtSktSjZu4mLrWdRYsWMXHiRF544QVWr17NGWecwQknnMD+++/Pc889x9ixY4HGQOOvfe1rFaeVJK3LgiN145BDDmH+/Pkvmf+b3/ymgjSSpI1lwVHbmD5/WdURmHbYnlVHkKS24BgcSZJUOxYcSZJUOxYcSZJUOxYcSZJUO6UVnIjYLiJui4iFEfGLiPhEMX+PiLghIu4rHncvK4MkSWpPZe7BeQ74q8w8FBgJHB8RRwLTgDmZORSYU0xLkiT1mtIKTjasKCa3Lr4SOBnoLOZ3AqeUlUGSJLWnUsfgRMRWEbEAWALckJk/BwZl5iKA4nFgmRkkSVL7KbXgZOYLmTkSGAwcEREjmn1vREyJiLkRMXfp0qWlZZQkSfXTkrOoMvNx4CbgeGBxRHQAFI9LenjPjMwclZmjBgwY0IqYkiSpJso8i2pAROxWPN8eeCNwD3AdMLF42URgZlkZJElSeyrzXlQdQGdEbEWjSF2dmT+MiJ8BV0fEZOAh4PQSM0jaRA8//DDvete7+P3vf0+/fv2YMmUKU6dO5W1vexv33nsvAI8//ji77bYbCxYsqDZsSdwG0partIKTmXcCh3Uz/w/AmLI+V1Lv6N+/P5///Oc5/PDDefLJJ3n1q1/N2LFj+c53vrP2Neeccw677rprhSnL5TaQtlzeTVxStzo6Oujo6ABg5513Zvjw4Tz66KMceOCBAGQmV199NTfeeGOVMUvlNpC2XN6qQdIGPfjgg8yfP5/Ro0evnXfLLbcwaNAghg4dWmGy1nEbSFsWC46k9VqxYgWnnnoqF154Ibvsssva+VdeeSXjx4+vMFnrtPM2ePjhhzn22GMZPnw4Bx10EBdddNHaZV/+8pc54IADOOiggzjvvPMqTCm9lIeoJPVo5cqVnHrqqUyYMIFx48atnb9q1Sq+973vMW/evArTtUa7b4OexiEtXryYmTNncuedd7LtttuyZEm3V/yQKmPBkdStzGTy5MkMHz6cD37wgy9aNnv2bIYNG8bgwYMrStcaboOexyFdcsklTJs2jW233RaAgQO9KL36Fg9RSerW//7v//Ltb3+bG2+8kZEjRzJy5Eiuv/56AK666qraH5oBt8G6uo5D+vWvf80tt9zC6NGjOfroo7n99turjie9iHtwJHXr9a9/PZnZ7bLLLrustWEq4jb4s3XHIa1atYrly5dz6623cvvtt3PGGWfwwAMPEBFVR5UAC47UVqbPX1Z1BKYdtmfVESrfDn1hG2yM7sYhDR48mHHjxhERHHHEEfTr149ly5bhrXXUV3iISpLUo57GIZ1yyilrr//z61//mueff54999yyipvqzT04kqQerRmHdPDBBzNy5EgA/umf/omzzjqLs846ixEjRrDNNtvQ2dnp4Sn1KRYcSVKP1jcO6fLLL29xGql5FhxJakOOQ1LdOQZHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkiTVjgVHkqQNOOussxg4cCAjRoxYO2/hwoW85jWv4eCDD+bEE0/kiSeeqDCh1mXBkSRpA84880xmzZr1onlnn30206dP56677uKtb30rn/vc5ypKp+6UVnAiYp+I+O+I+FVE/CIiphbz94iIGyLivuJx97IySJLUG4466ij22GOPF8279957OeqoowAYO3Ys//Ef/1FFNPWgzD04q4BzMnM4cCTw/og4EJgGzMnMocCcYlqSpC3KiBEjuO666wC45pprePjhhytOpK5KKziZuSgz7yiePwn8CtgbOBnoLF7WCZxSVgZJkspy6aWXcvHFF/PqV7+aJ598km222abqSKXqbhzSggULOPLIIxk5ciSjRo3itttuqzDhi7VkDE5E7AscBvwcGJSZi6BRgoCBPbxnSkTMjYi5S5cubUVMSZKaNmzYMH784x8zb948xo8fzyt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"text/plain": [ "
" ] }, "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": 47, "id": "fffa1e57-f975-4469-a42b-19d76c60fb66", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Document_NoDominant_TopicTopic_Perc_ContribTopic_DistributionText
363600.9990[(0, 0.9989845)]UPDATE - USA, North Carolina: More than a million people face mandatory evacuations along southern East Coast as Florence nears Category 5 status
22722760.9987[(6, 0.9986539)]UPDATE 1 - Vietnam monitoring more than 5,000 Chinese workers for potential signs of coronavirus
898930.9986[(3, 0.99856216)]Strong winds cause closure at Dammam Port
333340.9985[(4, 0.9985456)]UPDATE - USA, North Carolina: Hurricane Florence downgraded to Category Three as projected path shifts southward
434340.9985[(4, 0.9985456)]UPDATE: Hurricane Florence downgraded with states of emergency still in place
15015050.9985[(5, 0.998542)]UPDATE: Waiting times still 5-6 days at Port of Manila as decongestion efforts continue
959570.9985[(7, 0.99846315)]UPDATE - Australia: Fatalities reported due to ongoing bushfires in New South Wales and Queensland; over 125 fires burning
22122110.9984[(1, 0.9983966)]UPDATE 1 - Port closure due to low visibility at Tianjin Port further impacts operations
23823810.9984[(1, 0.9983619)]UPDATE 2 - Waiting times increase to 5 days at the Port of Felixstowe
23623610.9984[(1, 0.9983966)]UPDATE 2 - Longer berthing times and high yard congestion reported at Port of Ningbo
616110.9984[(1, 0.9983966)]Congestion issues continue at the Port of Manila
171710.9984[(1, 0.9983966)]Port of Manila faces congestion due to bad weather and out-of-window arrivals
18618610.9983[(1, 0.99827653)]Port of Busan to temporarily close for 24-hours on Oct 1 due to public holiday
28228220.9982[(2, 0.9982014)]UPDATE 14 - Central North Carolina - Flooding mostly dissipated after rainfall from Hurricane Eta. Event closed.
323210.9981[(1, 0.9980616)]UPDATE - USA, District of Columbia: State of Emergency declared for Virginia in anticipation of Hurricane Florence
17817810.9981[(1, 0.9980577)]Novel Coronavirus Outbreak Provides a New Justification for Unrest for Protesters in Hong Kong
232370.9979[(7, 0.99785054)]South-Central Virginia Power has been restored to most customers in the region. Incident closed.
12412430.9979[(3, 0.99785566)]UPDATE 1: Port of Dalian closed due to strong winds
666670.9979[(7, 0.99787486)]Ecuador No major protest activity reported after an agreement was reached. Incident closed.
27727750.9978[(5, 0.99779654)]UPDATE 1 - Congestion continues at London Gateway, causing low productivity
\n", "
" ], "text/plain": [ " Document_No Dominant_Topic Topic_Perc_Contrib Topic_Distribution \\\n", "36 36 0 0.9990 [(0, 0.9989845)] \n", "227 227 6 0.9987 [(6, 0.9986539)] \n", "89 89 3 0.9986 [(3, 0.99856216)] \n", "33 33 4 0.9985 [(4, 0.9985456)] \n", "43 43 4 0.9985 [(4, 0.9985456)] \n", "150 150 5 0.9985 [(5, 0.998542)] \n", "95 95 7 0.9985 [(7, 0.99846315)] \n", "221 221 1 0.9984 [(1, 0.9983966)] \n", "238 238 1 0.9984 [(1, 0.9983619)] \n", "236 236 1 0.9984 [(1, 0.9983966)] \n", "61 61 1 0.9984 [(1, 0.9983966)] \n", "17 17 1 0.9984 [(1, 0.9983966)] \n", "186 186 1 0.9983 [(1, 0.99827653)] \n", "282 282 2 0.9982 [(2, 0.9982014)] \n", "32 32 1 0.9981 [(1, 0.9980616)] \n", "178 178 1 0.9981 [(1, 0.9980577)] \n", "23 23 7 0.9979 [(7, 0.99785054)] \n", "124 124 3 0.9979 [(3, 0.99785566)] \n", "66 66 7 0.9979 [(7, 0.99787486)] \n", "277 277 5 0.9978 [(5, 0.99779654)] \n", "\n", " Text \n", "36 UPDATE - USA, North Carolina: More than a million people face mandatory evacuations along southern East Coast as Florence nears Category 5 status \n", "227 UPDATE 1 - Vietnam monitoring more than 5,000 Chinese workers for potential signs of coronavirus \n", "89 Strong winds cause closure at Dammam Port \n", "33 UPDATE - USA, North Carolina: Hurricane Florence downgraded to Category Three as projected path shifts southward \n", "43 UPDATE: Hurricane Florence downgraded with states of emergency still in place \n", "150 UPDATE: Waiting times still 5-6 days at Port of Manila as decongestion efforts continue \n", "95 UPDATE - Australia: Fatalities reported due to ongoing bushfires in New South Wales and Queensland; over 125 fires burning \n", "221 UPDATE 1 - Port closure due to low visibility at Tianjin Port further impacts operations \n", "238 UPDATE 2 - Waiting times increase to 5 days at the Port of Felixstowe \n", "236 UPDATE 2 - Longer berthing times and high yard congestion reported at Port of Ningbo \n", "61 Congestion issues continue at the Port of Manila \n", "17 Port of Manila faces congestion due to bad weather and out-of-window arrivals \n", "186 Port of Busan to temporarily close for 24-hours on Oct 1 due to public holiday \n", "282 UPDATE 14 - Central North Carolina - Flooding mostly dissipated after rainfall from Hurricane Eta. Event closed. \n", "32 UPDATE - USA, District of Columbia: State of Emergency declared for Virginia in anticipation of Hurricane Florence \n", "178 Novel Coronavirus Outbreak Provides a New Justification for Unrest for Protesters in Hong Kong \n", "23 South-Central Virginia Power has been restored to most customers in the region. Incident closed. \n", "124 UPDATE 1: Port of Dalian closed due to strong winds \n", "66 Ecuador No major protest activity reported after an agreement was reached. Incident closed. \n", "277 UPDATE 1 - Congestion continues at London Gateway, causing low productivity " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_dominant_topic.sort_values(by=\"Topic_Perc_Contrib\", ascending=False).head(20)" ] }, { "cell_type": "code", "execution_count": 56, "id": "8510f506-141f-4382-b668-251df1afc95f", "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_severe.csv\")" ] }, { "cell_type": "code", "execution_count": null, "id": "16388596-a1d6-4509-acac-6dd57220554a", "metadata": {}, "outputs": [], "source": [] } ], "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 }