{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Classifying Information about Kaggle Competitions - Zero-Shot Classification of Text Data - 2023 Kaggle AI Report  \n*Anthony Wynne | June 2023*\n\n## Key points  \n\n* This noteebook is about preparing the data to analyse for the 2023 Kaggle AI Report. Rather than the report itself!  \n* Of the ~5500 competitions described in Meta Kaggle, only a few hundred have data type tags that relate them to the report topics.\n* The main goal of this notebook is to classify the Kaggle competition data into each report topic area using Zero-Shot classification.  \n* The main findings:\n  * Zero-Shot Classification saves time using transfer learning but requires optimisation to get the best results.\n  * The length of text data used for Zero-Shot classification is important.  \n  * In large datasets, the Zero-Shot Classification can be slow, there is a linear increase in time per input word.  \n  * Minimal preprocessing of the text data is required for Zero-Shot Classification. But combining steps, such as language detection, can be important.  \n  * Once classified, the large dataset of Kaggle competition data has some interesting characteristics to investigate further for each report topic.     \n* Zero-Shot classification is statistical in nature each time this notebook is run the results may vary slightly from the last run - but the conclusions should remain the same even if the details are slightly different!\n* The datasets used are 'live' and growing, so some of the numbers quoted will change over time.\n\n## Introduction  \nThe data available for the 2023 AI report Kaggle competition describes the previous Kaggle competitions. The competition write-ups and the Meta Kaggle dataset they were extracted from have little specific detail of how each past competition relates to this competition's report topics. There are only a few hundred of the over 5500 competitions described with data type tags that can connect them to the report topics.\n  \n\"....describe what the community has learned over the past two years of working and experimenting with:\n1) Text data  \n2) Image and/or video data  \n3) Tabular and/or time series data  \n4) Kaggle Competitions  \n5) Generative AI  \n6) AI ethics  \n7) Other (anything that does not fall into any other category).\"  \n   \nIn addition to the write-up data provided, I have created a [dataset](https://www.kaggle.com/datasets/anthony35813/webpage-data-for-kaggle-competitions) by [scraping](https://www.kaggle.com/code/anthony35813/automate-updating-dataset) the competition web pages on Kaggle to collect text data about the original competition description, evaluation and dataset. For full details of the data and the scraping methodology see the details on the dataset page. For the write-ups, I will use the edited code from the [original notebook](https://www.kaggle.com/code/paultimothymooney/every-kaggle-competition-solution-writeup-to-csv/notebook) that produced them so I can generate the IDs that join to the other Meta Kaggle data that I am using.\n\nThe main problem with classifying the dataset is the limited labels to train a classifier. This notebook will explore the use of the Zero-Shot Classification model<sup>1,3</sup> (inspired by Leonid Kulyk's [notebook](https://www.kaggle.com/code/leonidkulyk/kaggle-ai-report-topic-selection)) on the data to classify the competition text data into the report topics above. Then see how the results compare to the descriptive tags available in the Meta Kaggle dataset. I have previously tried and failed to achieve a satisfactory classification with the BERTtopic model<sup>7</sup>, which led me to generate the dataset of scraped competition web pages. BERTtopic tends to focus on the competition's theme, and it is hard to direct it to focus on the information relating to the data source. \n\nZero-shot classification takes a set of class labels. Then it uses transfer learning from a pre-trained model to provide a probability that the text submitted for classification belongs to each class. Yin *et al*<sup>2</sup> descibed it as \"Zero-Shot aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e.g., topic, emotion, event, etc.) described by the label.,\n\nThe theory being tested is that Zero-shot classification will be good enough to discover information about each report topic in the larger dataset of untagged competitions (approx.5000). The Meta Kaggle tags will be used to score the classification; they are only available for a few competitions(approx.300). However, it will be enough to indicate how well the Zero-Shot classification has worked and if it can be successfully applied to the rest of the competition data. After the initial results, optimisations will be explored to improve the classification results and understand the method's limitations.  \n\n![image.png](attachment:4d6491c4-6b8c-4d70-afca-0eb681995ee4.png)  \nFigure 1: The Experimental Flow Diagram   \n\n*n.b. Many of the cells below are hidden these contain the technical details of the data joins etc. are included for reproducability and hidden to make the notebook easier to read.* 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"}}},{"cell_type":"markdown","source":"## Import and install packages  \nCells hidden, unhide for details.","metadata":{}},{"cell_type":"code","source":"# install packages not in kaggle container\n!pip install -U accelerate\n!pip install -U language_detector\n!pip install langdetect -U","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-06-14T20:52:38.345878Z","iopub.execute_input":"2023-06-14T20:52:38.346335Z","iopub.status.idle":"2023-06-14T20:53:14.655503Z","shell.execute_reply.started":"2023-06-14T20:52:38.346303Z","shell.execute_reply":"2023-06-14T20:53:14.654206Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport re\nimport torch\nimport os\nimport time\nimport matplotlib.pyplot as plt\nfrom scipy import stats\nimport seaborn as sns\nfrom tqdm import tqdm\nfrom bs4 import BeautifulSoup\nfrom language_detector import detect_language\nfrom langdetect import detect\nfrom transformers import pipeline\nprint(\"torch version: \", torch.__version__)\ntry:\n    assert torch.cuda.is_available(), \"No GPU/CUDA available the notebook will take hours to run\"\nexcept AssertionError as e:\n    print(e)\n\n# ignore warnings - remove for debugging\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":0.017791,"end_time":"2023-06-06T22:10:07.427718","exception":false,"start_time":"2023-06-06T22:10:07.409927","status":"completed"},"tags":[],"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-06-14T20:53:14.658749Z","iopub.execute_input":"2023-06-14T20:53:14.659176Z","iopub.status.idle":"2023-06-14T20:53:14.670019Z","shell.execute_reply.started":"2023-06-14T20:53:14.659138Z","shell.execute_reply":"2023-06-14T20:53:14.669036Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data preparation  \nThe data loaded below was processed and joined together to create the dataset for the Zero-Shot Classification.  \n* competition_descriptions.csv from [Kaggle Competition Descriptions](https://www.kaggle.com/datasets/anthony35813/webpage-data-for-kaggle-competitions) dataset.\n* Teams.csv, ForumTopics.csv, ForumMessages.csv and Competitions.csv from the [Meta Kaggle](https://www.kaggle.com/datasets/kaggle/meta-kaggle) dataset.\n  \n  \n![image.png](attachment:d712f71c-10bf-4ade-a440-1eacc5f8b9cf.png)\nFigure 2: The Data preparation and joins process","metadata":{},"attachments":{"d712f71c-10bf-4ade-a440-1eacc5f8b9cf.png":{"image/png":"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"}}},{"cell_type":"code","source":"# TODO - add your own path to the data\ndata_dir = '/kaggle/input/'\n# unquote for debugging\nfor dirname, _, filenames in os.walk(data_dir):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\nmeta_dir = 'meta-kaggle/'\nweb_dir = 'webpage-data-for-kaggle-competitions/'\nai_report_dir = '2023-kaggle-ai-report/'\ndata_joins = 'ai-report-meta-kaggle-data-joins/'","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":0.036569,"end_time":"2023-06-06T22:10:07.477575","exception":false,"start_time":"2023-06-06T22:10:07.441006","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-06-14T20:53:14.671148Z","iopub.execute_input":"2023-06-14T20:53:14.671743Z","iopub.status.idle":"2023-06-14T20:53:14.700908Z","shell.execute_reply.started":"2023-06-14T20:53:14.671707Z","shell.execute_reply":"2023-06-14T20:53:14.699843Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def date_cols(filename):\n    \"\"\"Find the date columns in the\n     Meta Kaggle csv files\n    \"\"\"\n    return list(pd.read_csv(filename, nrows=1).filter(regex='\\w*Date').columns)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:53:14.702269Z","iopub.execute_input":"2023-06-14T20:53:14.702589Z","iopub.status.idle":"2023-06-14T20:53:14.711202Z","shell.execute_reply.started":"2023-06-14T20:53:14.702563Z","shell.execute_reply":"2023-06-14T20:53:14.710141Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load the data\nCompetition_Description = pd.read_csv(\n    f'{data_dir}{web_dir}competition_descriptions.csv',\n    parse_dates=date_cols(f'{data_dir}{web_dir}competition_descriptions.csv'))\nassert len(Competition_Description\n          ) > 5000, \"Competition_Description has not loaded correctly\"\n\nTeams = pd.read_csv(\n    f'{data_dir}{meta_dir}Teams.csv',\n    parse_dates=date_cols(f'{data_dir}{meta_dir}Teams.csv'))\nassert len(Teams) > 6320000, \"Teams has not loaded correctly\"\n\nCompetitions = pd.read_csv(\n    f'{data_dir}{meta_dir}Competitions.csv',\n    parse_dates=date_cols(f'{data_dir}{meta_dir}Competitions.csv'))\nassert len(Competitions) > 5000, \"Competitions has not loaded correctly\"\n\nForumTopics = pd.read_csv(\n    f'{data_dir}{meta_dir}ForumTopics.csv',\n    parse_dates=date_cols(f'{data_dir}{meta_dir}ForumTopics.csv'))\nassert len(ForumTopics) > 328000, \"ForumTopics has not loaded correctly\"\n\nForumMessages = pd.read_csv(\n    f'{data_dir}{meta_dir}ForumMessages.csv',\n    parse_dates=date_cols(f'{data_dir}{meta_dir}ForumMessages.csv'))\nassert len(ForumMessages) > 1926000, \"ForumMessages has not loaded correctly\"","metadata":{"papermill":{"duration":0.467857,"end_time":"2023-06-06T22:10:07.949890","exception":false,"start_time":"2023-06-06T22:10:07.482033","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-06-14T20:53:14.715510Z","iopub.execute_input":"2023-06-14T20:53:14.715793Z","iopub.status.idle":"2023-06-14T20:58:57.218167Z","shell.execute_reply.started":"2023-06-14T20:53:14.715768Z","shell.execute_reply":"2023-06-14T20:58:57.217058Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Inspect the data for debugging\n# Competition_Description.info(show_counts=True)\n# Teams.info(show_counts=True)\n# Competitions.info(show_counts=True)\n# ForumTopics.info(show_counts=True)\n# ForumMessages.info(show_counts=True)","metadata":{"papermill":{"duration":0.03521,"end_time":"2023-06-06T22:10:08.056650","exception":false,"start_time":"2023-06-06T22:10:08.021440","status":"completed"},"tags":[],"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-06-14T20:58:57.220090Z","iopub.execute_input":"2023-06-14T20:58:57.220515Z","iopub.status.idle":"2023-06-14T20:58:57.226022Z","shell.execute_reply.started":"2023-06-14T20:58:57.220479Z","shell.execute_reply":"2023-06-14T20:58:57.225085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# rename the Id columns to match the other dataframes\nTeams = Teams.rename(columns={\n    'Id': 'TeamsId',\n    'Medal': 'TeamMedal',\n    'MedalAwardDate': 'TeamMedalAwardDate'\n})\n# rename the writeup columns for clarity\nForumTopics = ForumTopics.rename(columns={\n    'Id': 'ForumTopicId',\n    'Title': 'WriteupTitle',\n    'CreationDate': 'WriteupDate'\n})\nForumMessages = ForumMessages.rename(\n    columns={\n        'Id': 'ForumMessageId',\n        'Message': 'WriteupBody',\n        'Medal': 'WriteupMedal',\n        'MedalAwardDate': 'WriteupMedalAwardDate'\n    })\nCompetitions = Competitions.rename(columns={'Id': 'CompetitionId'})","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:58:57.227456Z","iopub.execute_input":"2023-06-14T20:58:57.228455Z","iopub.status.idle":"2023-06-14T20:58:57.631936Z","shell.execute_reply.started":"2023-06-14T20:58:57.228422Z","shell.execute_reply":"2023-06-14T20:58:57.630971Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# join the data\nprint(\"Start of merge df len: \", len(Teams))\n# get the teams with a writeup\ndf = Teams.drop(\n    'CompetitionId', axis=1).merge(\n        right=ForumTopics,\n        how='inner',\n        left_on='WriteUpForumTopicId',\n        right_on='ForumTopicId')\nprint(\"After Teams and Forum Topics merge: \", len(df))\n# get the messages for the writeups\ndf = df.merge(\n    right=ForumMessages,\n    how='inner',\n    left_on='ForumTopicId',\n    right_on='ForumTopicId')\nprint(\"After Teams, Forum Topics and messages merge: \", len(df))\n# Get the competitions with a writeup\ndf = df.merge(\n    right=Competitions, how='inner', left_on='ForumId', right_on='ForumId')\nprint(\"After Teams, Forum Topics, messages and competitions merge: \", len(df))\n# inspect the data\n# df.info(show_counts=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:58:57.633532Z","iopub.execute_input":"2023-06-14T20:58:57.637120Z","iopub.status.idle":"2023-06-14T20:58:59.712616Z","shell.execute_reply.started":"2023-06-14T20:58:57.637080Z","shell.execute_reply":"2023-06-14T20:58:59.711609Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# restrict the data to the featured and research competitions\ndf_seg = df[df['HostSegmentTitle'].isin(['Featured', 'Research'])]\ndf['HostSegmentTitle'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:58:59.714063Z","iopub.execute_input":"2023-06-14T20:58:59.715270Z","iopub.status.idle":"2023-06-14T20:58:59.769776Z","shell.execute_reply.started":"2023-06-14T20:58:59.715234Z","shell.execute_reply":"2023-06-14T20:58:59.768664Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# select the columns we want\ndf_text = df_seg[[ \n       'CompetitionId',\n       'EnabledDate',\n       'Title',\n       'Subtitle',\n       'WriteupTitle',  \n       'WriteupBody', \n       'EvaluationAlgorithmName', \n       'EvaluationAlgorithmDescription',\n       ]].copy().fillna('None')","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:58:59.771436Z","iopub.execute_input":"2023-06-14T20:58:59.771825Z","iopub.status.idle":"2023-06-14T20:58:59.868387Z","shell.execute_reply.started":"2023-06-14T20:58:59.771792Z","shell.execute_reply":"2023-06-14T20:58:59.867376Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# groupby the competition id and join the text for all the writeups\ndf_text = df_text.groupby('CompetitionId').agg({\n    'EnabledDate': 'first',\n    'Title': 'first',\n    'Subtitle': 'first',\n    'WriteupTitle': 'first',\n    # There are multiple forum write ups and replies for each competition,\n    #  these are grouped together and the text is combined into a single string.\n    'WriteupBody': ' '.join,\n    'EvaluationAlgorithmName': 'first',\n    'EvaluationAlgorithmDescription': 'first',\n}).reset_index()\n","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:58:59.870048Z","iopub.execute_input":"2023-06-14T20:58:59.870610Z","iopub.status.idle":"2023-06-14T20:58:59.976749Z","shell.execute_reply.started":"2023-06-14T20:58:59.870576Z","shell.execute_reply":"2023-06-14T20:58:59.973029Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### The text clean up  \nA series of functions are required to clean up the text data. For example, the write-ups from the forums still have their HTML tags. There are tables of numbers that can be stripped out, website addresses and line returns and tabs. ","metadata":{}},{"cell_type":"code","source":"def clean_html(html):\n    \"\"\"Clean the html text\n    \"\"\"\n    soup = BeautifulSoup(html, \"html.parser\")\n    return soup.get_text(\" \", strip=True)\n\n\n# tests\nprint(clean_html(\"<div><br>A </div><div>test</div>\"))\n\n\ndef split_joined_words(w):\n    \"\"\"Split joined words, sometimes the \n    parsed text has joined words, they can \n    be split by capital letters in the middle,\n    but watch out for acronyms!\n    \"\"\"\n    return re.sub(r'(?<=[a-z])(?=[A-Z0-9])', ' ', w)\n\n\n# tests\nw = 'test word split delightSince'\nprint(split_joined_words(w))\nx = 'test keep acronyms ABC'\nprint(split_joined_words(x))\n\n\ndef remove_numbers(s):\n    \"\"\"remove numbers and numerical data\n    from the text\"\"\"\n    return ''.join([char for char in s if not char.isdigit()])\n\n\n# tests\nprint(remove_numbers('this is a number test 123'))\n\n\ndef remove_website_addresses(s):\n    \"\"\"remove website addresses from the text\"\"\"\n    return re.sub(r'http\\S+', '', s)\n\n\n# tests\nprint(\n    remove_website_addresses(\n        'this is a website test http://www.test.com/test/this/website'))\n\n\ndef remove_repeated_word(s):\n    \"\"\"These often occur with formulas\"\"\"\n    return re.sub(r'\\b(\\w+)( \\1\\b)+', r'\\1', s)\n\n\n#tests\nprint(remove_repeated_word('this is is a repeat word test test test'))\n\n\ndef remove_non_printing_strings(s):\n    \"\"\"remove non printing characters\"\"\"\n    return re.sub('\\s+', ' ', s)\n\n\n# tests\nprint(remove_non_printing_strings('this \\n is a non \\tprinting test\\r!!!'))","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:58:59.978469Z","iopub.execute_input":"2023-06-14T20:58:59.979623Z","iopub.status.idle":"2023-06-14T20:58:59.992739Z","shell.execute_reply.started":"2023-06-14T20:58:59.979586Z","shell.execute_reply":"2023-06-14T20:58:59.991666Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def clean_up(text):\n    \"\"\"Clean up the text\"\"\"\n    text = clean_html(text)\n    text = split_joined_words(text)\n    text = remove_website_addresses(text)\n    text = remove_numbers(text)\n    text = remove_repeated_word(text)\n    text = remove_non_printing_strings(text)\n    return text\n\ndf_text['WriteupBody'] = df_text['WriteupBody'].apply(clean_up)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:58:59.994649Z","iopub.execute_input":"2023-06-14T20:58:59.994988Z","iopub.status.idle":"2023-06-14T20:59:26.485748Z","shell.execute_reply.started":"2023-06-14T20:58:59.994959Z","shell.execute_reply":"2023-06-14T20:59:26.484671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# combine the web description, evaluation, dataset description in to a single string\nCompetition_Description['comp_info'] = Competition_Description.apply(\n    lambda x: ' \\n'.join([\n        str(x[col])\n        for col in Competition_Description.columns\n        if col not in ['CompetitionId', 'Slug']\n    ]),\n    axis=1)\n# clean the text\nCompetition_Description['comp_info'] = Competition_Description[\n    'comp_info'].apply(clean_up)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:59:26.492229Z","iopub.execute_input":"2023-06-14T20:59:26.492562Z","iopub.status.idle":"2023-06-14T20:59:33.132177Z","shell.execute_reply.started":"2023-06-14T20:59:26.492535Z","shell.execute_reply":"2023-06-14T20:59:33.131264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# add the competition description to the df_text\ncompetition_info = Competition_Description[['CompetitionId',\n                                            'comp_info']].copy()\n\ndf_text = df_text.merge(\n    right=competition_info,\n    how='left',\n    left_on='CompetitionId',\n    right_on='CompetitionId').fillna('None')\ndf_text.info(show_counts=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:59:33.133513Z","iopub.execute_input":"2023-06-14T20:59:33.133876Z","iopub.status.idle":"2023-06-14T20:59:33.161730Z","shell.execute_reply.started":"2023-06-14T20:59:33.133846Z","shell.execute_reply":"2023-06-14T20:59:33.160721Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# show the data\ndf_text.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:59:33.163155Z","iopub.execute_input":"2023-06-14T20:59:33.163463Z","iopub.status.idle":"2023-06-14T20:59:33.184911Z","shell.execute_reply.started":"2023-06-14T20:59:33.163434Z","shell.execute_reply":"2023-06-14T20:59:33.184041Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Zero-Shot classification  \n* There are 315 unique competitions with a write-up (at the time of writing).  \n* The two different sources of text, competition write-ups and website descriptions will be classified separately, and the results will be compared.  \n* The results will be scored against the tags available.   \n* Zero-Shot classification requires the labels to classify the documents against; I will split the labels derived from the competition report topics into separate categories to be closer to the tags available. I.e., Image and video data will be split into Image data and Video data.  \n* I'm leaving out the Kaggle competitions topic as they are all Kaggle competitions. ","metadata":{}},{"cell_type":"code","source":"# set up gpu use for the pipeline, device=0 is the first gpu\npipe = pipeline(model=\"facebook/bart-large-mnli\", device=0)\n\n# the labels for the model\ncandidate_labels = [\n    \"Text data\", \n    \"Image data\",\n    \"Video data\", \n    \"Tabular data\",\n    \"Time series data\", \n    \"Generative AI\", \n    \"AI ethics\",\n    \"Other\"\n]","metadata":{"papermill":{"duration":45.866121,"end_time":"2023-06-06T22:10:54.301701","exception":false,"start_time":"2023-06-06T22:10:08.435580","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-06-14T20:59:33.186894Z","iopub.execute_input":"2023-06-14T20:59:33.188096Z","iopub.status.idle":"2023-06-14T20:59:56.501828Z","shell.execute_reply.started":"2023-06-14T20:59:33.188062Z","shell.execute_reply":"2023-06-14T20:59:56.500795Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Run the classification and generate the results  ","metadata":{"execution":{"iopub.execute_input":"2023-06-06T21:01:45.817012Z","iopub.status.busy":"2023-06-06T21:01:45.815920Z","iopub.status.idle":"2023-06-06T21:01:45.823596Z","shell.execute_reply":"2023-06-06T21:01:45.822274Z","shell.execute_reply.started":"2023-06-06T21:01:45.816971Z"},"papermill":{"duration":0.006131,"end_time":"2023-06-06T22:10:56.030136","exception":false,"start_time":"2023-06-06T22:10:56.024005","status":"completed"},"tags":[]}},{"cell_type":"code","source":"results = pd.DataFrame(columns=['CompetitionId'])\n\n\ndef get_scores(Id, text_data, text_data_label):\n    result = pipe(text_data, candidate_labels=candidate_labels)\n    result_df = pd.DataFrame(columns=result[\"labels\"], data=[result[\"scores\"]])\n    result_df['CompetitionId'] = Id\n    result_df['Data Source'] = text_data_label\n    return result_df\n\n\n# loop through the data and get the predictions\nfor row in tqdm(\n        df_text.itertuples(),\n        total=len(df_text),\n        desc='Processing',\n        unit='rows',\n        ncols=100,\n        position=0,\n        leave=True):\n    Id = row.CompetitionId\n    doc_writeup = row.WriteupBody\n    doc_comp = row.comp_info\n    _df = get_scores(Id, doc_writeup, 'Writeup')\n    results = pd.concat([results, _df], ignore_index=True)\n    _df = get_scores(Id, doc_comp, 'Competition')\n    results = pd.concat([results, _df], ignore_index=True)\n\n# save the results\nresults.to_csv('ZeroShot_text_data_source_results.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T20:59:56.503576Z","iopub.execute_input":"2023-06-14T20:59:56.503982Z","iopub.status.idle":"2023-06-14T21:20:52.156378Z","shell.execute_reply.started":"2023-06-14T20:59:56.503944Z","shell.execute_reply":"2023-06-14T21:20:52.155208Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### An example result for one competition  \n* The table below shows the result of the Zero Shot classification for a single competition.   \n* The model produces a probability for each of the candidate topics.   \n* You can see that the two different text data sources have been recorded for the same competition and they can lead to different results.  \n* Note that each loop takes ~8sec per loop on Telsa T4 GPU and 80-160secs per loop on a CPU  \n    \nWhich data source has produced the most accurate classification? ","metadata":{}},{"cell_type":"code","source":"results.head(2)","metadata":{"papermill":{"duration":0.006104,"end_time":"2023-06-06T22:10:56.042673","exception":false,"start_time":"2023-06-06T22:10:56.036569","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-06-14T21:20:52.158502Z","iopub.execute_input":"2023-06-14T21:20:52.159116Z","iopub.status.idle":"2023-06-14T21:20:52.181981Z","shell.execute_reply.started":"2023-06-14T21:20:52.159082Z","shell.execute_reply":"2023-06-14T21:20:52.180545Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Tags - Bring in the Meta Kaggle tags to score the classification results  \nThe Meta Kaggle data set contains many tags that can be used to group and categorise the data—a selection of these tags are similar to the report topics. The tags, therefore, can be used to score the Zero Shot classification results. The tags are unavailable for all competitions, so the results are only scored for those with tags. The loading of the tag data and the selection of the appropriate tags are described below. ","metadata":{}},{"cell_type":"code","source":"# load the data\ntags = pd.read_csv(f'{data_dir}{meta_dir}Tags.csv')\n# rename the Id column\ntags.rename(columns={'Id' : 'TagId'}, inplace=True)\ntags.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:52.183579Z","iopub.execute_input":"2023-06-14T21:20:52.184141Z","iopub.status.idle":"2023-06-14T21:20:52.228289Z","shell.execute_reply.started":"2023-06-14T21:20:52.184108Z","shell.execute_reply":"2023-06-14T21:20:52.227239Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tags.info(show_counts=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:52.229799Z","iopub.execute_input":"2023-06-14T21:20:52.230427Z","iopub.status.idle":"2023-06-14T21:20:52.249925Z","shell.execute_reply.started":"2023-06-14T21:20:52.230392Z","shell.execute_reply":"2023-06-14T21:20:52.248963Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are 788 unique tags (at the time of writing). They have a tag name and a Full path which is more descriptive as to their categorisation, i.e., name=text, FullPath= data type > text. For some of the tags, there is a full description. Below, a graph shows all the available tags, their full path, and their occurrence in the data. \n\n* The tags are joined to the competitions via a linking table called CompetitionTags.  \n* When the tags are joined to the linking table, the count of rows increases, indicating that there is more than one tag per competition.  ","metadata":{}},{"cell_type":"code","source":"competitionTags = pd.read_csv(f'{data_dir}{meta_dir}CompetitionTags.csv')\n# rename the Id column\ncompetitionTags.rename(columns={'Id': 'CompetitionTagId'}, inplace=True)\n# un comment for debugging\n# competitionTags.head()\n# join the tags to the competition tags\ncompetitionWithTags = competitionTags.merge(\n    tags, on='TagId').drop([\n        'CompetitionTagId', 'DatasetCount', 'KernelCount', 'CompetitionCount'\n    ],\n                           axis=1)\n# un comment for debugging\n# competitionWithTags.head()\ncompetitionWithTags.info(show_counts=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:52.253380Z","iopub.execute_input":"2023-06-14T21:20:52.256974Z","iopub.status.idle":"2023-06-14T21:20:52.292736Z","shell.execute_reply.started":"2023-06-14T21:20:52.256929Z","shell.execute_reply":"2023-06-14T21:20:52.291356Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"competitionWithTags['FullPath'].value_counts(sort='descending').plot(\n    kind='barh', figsize=(5, 20))\nplt.title('The Full path name of the available Competition Tags\\n')\nplt.xlabel('Count')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:52.294804Z","iopub.execute_input":"2023-06-14T21:20:52.295238Z","iopub.status.idle":"2023-06-14T21:20:54.379937Z","shell.execute_reply.started":"2023-06-14T21:20:52.295202Z","shell.execute_reply":"2023-06-14T21:20:54.379084Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Tags that are similar to the report categories  \nThe code cell below shows the tags selected to that most closely represent the report categories. image-to-text was the only available tag that was clearly aligned to Generative AI; no tags represent AI ethics or the Other category. The results will be filtered to only the competitions with these tags for evaluation. Then we will check how many competitions have multiple tags, as mentioned above.","metadata":{}},{"cell_type":"code","source":"# selected tags\nreport_cat_tags = [\n    'data type > text', \n    'data type > image', \n    'data type > tabular',\n    'data type > video data', \n    'technique > time series analysis',\n    'task > image-to-text'\n]\n# filter the competition with the selected tags\ncompetitionWithSelectedTags = competitionWithTags[\n    competitionWithTags['FullPath'].isin(report_cat_tags)]\ncompetitionWithSelectedTags.info(show_counts=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.380964Z","iopub.execute_input":"2023-06-14T21:20:54.381425Z","iopub.status.idle":"2023-06-14T21:20:54.402108Z","shell.execute_reply.started":"2023-06-14T21:20:54.381389Z","shell.execute_reply":"2023-06-14T21:20:54.400576Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# how many competitions have more than one tag\nprint(\n    f'n.b. The number of unique competitionIds: {competitionWithSelectedTags[\"CompetitionId\"].nunique()}'\n)\ncompetitionWithSelectedTags['CompetitionId'].value_counts().value_counts(\n).sort_index().plot(\n    kind='barh', figsize=(6, 4))\nplt.xlabel('Number of tags')\nplt.ylabel('Number of competitions')\nplt.title('Number of competitions with multiple tags\\n')\n# add number to the bars\nfor i, v in enumerate(competitionWithSelectedTags['CompetitionId']\n                      .value_counts().value_counts().sort_index()):\n    plt.text(v + 3, i + .25, str(v), color='black', fontweight='bold')\nplt.title('The Number of Tags per Competition in the test set\\n ')\nplt.xlabel('Number of Competitions')\nplt.ylabel('Number of Tags')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.404152Z","iopub.execute_input":"2023-06-14T21:20:54.404951Z","iopub.status.idle":"2023-06-14T21:20:54.672995Z","shell.execute_reply.started":"2023-06-14T21:20:54.404911Z","shell.execute_reply":"2023-06-14T21:20:54.671971Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# show the tags grouped by competition\ntag_analysis = competitionWithSelectedTags.groupby(\n    'CompetitionId', as_index=False)['Name'].agg({\n        'number of tags': 'count',\n        'tags': lambda x: ', '.join(x)\n    }).sort_values(\n        'number of tags', ascending=False)\ntag_analysis.head(20)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.674412Z","iopub.execute_input":"2023-06-14T21:20:54.675661Z","iopub.status.idle":"2023-06-14T21:20:54.708152Z","shell.execute_reply.started":"2023-06-14T21:20:54.675618Z","shell.execute_reply":"2023-06-14T21:20:54.706903Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\n    \"The table below shows the number of competitions with the same tag combinations\"\n)\ntag_analysis['tags'].value_counts().rename_axis(\n    'tags combinations').reset_index(name='counts').head(20)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.709916Z","iopub.execute_input":"2023-06-14T21:20:54.710344Z","iopub.status.idle":"2023-06-14T21:20:54.725553Z","shell.execute_reply.started":"2023-06-14T21:20:54.710306Z","shell.execute_reply":"2023-06-14T21:20:54.724276Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Multiple tags per competition  \nThe top competition with multiple tags 8586 has three tags (image, tabular, text), and from its [webpage](https://www.kaggle.com/c/8586), it looks like the three tags are appropriate. This competition could be viewed as belonging in any of the three corresponding classifications.  \n\n![image.png](attachment:image.png)  \n\"Avito is challenging you to predict demand for an online advertisement based on its full description (title, description, images, etc.), its context (geographically where it was posted, similar ads already posted) and historical demand for similar ads in similar contexts. With this information, Avito can inform sellers on how to best optimize their listing and provide some indication of how much interest they should realistically expect to receive.\"  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n17wBT2FXA2NLdbc0uHpxOgjFdDlRCf4PmbqPeLjwekBEh6hwTl7eBAvIMTDd/gIOcDxANsQ7Dzo9g7E29cwoCHJW4kXveQF2xcH9S9A7FJUgkCPR6paDyOPpXNIHuBInti5b44pbW/X3A6qPh1fkD1xuJURrrPZyvGkJj0DKrh5UZvCHQ4oLQMxPXa6lXrVKeKbEtVhVVWVtjGrZU2bfpMvXeCNTXXWlFhkRq9VCFrv40vyLJDpk/0OHcl+wo4H3jgAbv44oudtESyczn66KPtL3/5i5WUlMTO7Fz2FDhjbfyrF6aJtbQCVgMOmn1iVK0CiIKifBs3dqzlFxZaZ0+X1QmE01QRO8W6urTB8Opr6xycK6qrfI4uitSvyoOKdEjxaVHjpaSwx14pSqKUrx+F1R/h+gRYKDlsUGpvnQKy7LR0AWiCgC3RAc7VTQqDyqfod7KAIUHKxsZ5FBHWAUPsG+z39+GuBCk4TAdglNo6OEo1ucEreIIUMlegmp2SZjnaO0hK2jvbbWt1pVXVVutY4CUwcM0dlSKgUroBlcCkUS6RN4CnrsaTV+SlA2XAPAEtB039cS5kq4QjvLLUEhRXggAoTuXFdb+muImXTCcegJSy078YmHo0Hm/AevUvlpYgHXoOidJGuVM+fp/KAch2AFW6gnQEjQDp83skHFOOhOEZMM5UNaiALve6haCGlDzo7SZEiqntVL3sflWNWCSvLRkx4ERgiN0ydaiMVNpkmdbdAr2aqmpVvh6vqKvXrbXnliy2VrWSnR2dYjrNVlhUaIkK6yClCgwbwxR2hdQ9mNSDAjISi9lcnFtkhdl5/gzMc5QmVczItVUK0h27H1aZl55pmTKT02Uup0jx0nWe5xBnoitiwKxcAFDt+vVfm0C9T3Z3nMKgaF39vUTt19u6AjcA4N3Z3yNAF5QqHp6Zo+elac89WWKthQVFlpGaQewxiT1rFAmYB0gBniQvcFVIADYu6qRKQdaqjsU2kQCMBD7xAi+xSQCNvEQ4psESdbT+RDG7RBwkyhHukQC4wVGQGw56algU1JJjbNPBGSBTGVAOPFoFp/uCMgG4ud7v6VCa9cz4eLFjAZ1qnofrl81PGcVQ2duuOKURME8Uc9b/OhFvlTVV1t7SZBPHjbHJkyfpFAHFHMUaccHg/mnXhk0TSSRIoAUjIFQpGJ/qW9BqA4SqpPiICvILrFlstLquJjDDVenTxTS7ursFtr1WU1froAtgJuGLFLOB4eEjxEyiwqewV9wwwdCXCUPsELvsFhvo1TGVnPt4PtdRplSxB1hlu8Kh/rxwcnxiALhSusDXBQtNcvMMUMXcr2trsYa2Vj0zUMwepY3wxI90KH6YLL95J8xIlBPAgKGioKkCa9wGpJ+n8Ch/3GgTf0VKLtg7A1T6A9DUOW/QMKs9QAzUgnL2Uwrv5/jzPVFiHisnBF78cT5gs1xV/IqT+Ik3iJObgDuOA+shBFrCkYcEDM9xg9cTB0xcCIG5jsBMSfM21qnN0+XvE9xPmmGxWEZZWdnW2tLiPs7GhkY/bm9rlwnc6mYwW4vOUV8jiQQZMeCk0ofLYCULrMomlTlo1tXVCXS6HVBQFsAEUwgz3RVL97TFKiVMp1tmbX9vn6VIKVAefHT9PX3WI+ZK5S/OzfcOGe9ckdlOxe8W6AKEdBalJQkopTCAKGDqy+EpbXQCoTSAnPvaYvrn4KnzsBO/D9hDqfUsfx9db8TXKVbZJZsNWNBVxRewVXyfKCrpcZeB4gSwk1NSrVOAXtfcYLVNddbU2hRT2dEnoSmOsGcDgwKsCsxX8om9gxDn9TIOiAIgGgzyiveDAfYDhojuxSTmBvaUCXv+OAcb3AbQPFB7oJO73V/qz1J+ax88N2i4nLEqLX0qX2/49M+fr2NPt9+rA5IQu5cLnMF9wMOpS172QuQk1UncCbiFuJ/n9+Lz1jlPL+lWXQtiiCQS1c/Y/lULFRSHeNjqb9q0yVtpKmBNTa3V1tRYuwCotr7el6nrE9PsErC4X1H3hornvdYCSXrBEwS86WlpYp0pbn4TACBqbG12JYRNekWXAgDMriz6l5mWrhfDPxkwF9KUqGfQueOpQ9kUWaIAkmuExWwPFEOb7kHoMOpWugDl5o4OZ5Id3ikU+E65t1OAz11Bx4eYrOLOSEm39JQ0GxCY9vR0BcDjcQI1o1ECVhgyQvLM84Erep8AkAAj5RQYJNPbZKIPJoltC297ZLKz71VrhEHrezVE4ugKrPgUD6Z2f6LKOVGAlaSGBrNbe+7r07Vehe/DtNZvvAGE5ZzXCsWhDPR0kFYHQqWPhtjTy2WKjTgUDh+1vwPp5iUkNISwUOIIfKeDbjXEJaYrfSpPlVlre5eAVSCqP+pgsuogxwP9g5Yan2rxPCiSSCTUwJERFEyVNWCWYo5dAkWBXV8vrbVMaJ0HqLhGpaQnerAvVrmlDwApPk8qNoyyrrFBoNrjrJEwAF+y4oBhUH1RIczvTvyqAjiGMnGvM08xiECxYJ5KkzOoOI/L/Wk6RhzG/J6gYwBW6gxJ4j47gWmP7ucMTJVhR94rr9sB+lax5l5nUaTGvMOLhgC3Q4LO9fX3Cjz7pJZBDzTst02gP9rG3oWszPNV6XbGGTt2CX5oF8s35SGsWkcOXsGdgRAPeQNwkb+Bie85rTwOwnB/KKHrw4GZAOxjadn2XI8riNs7rvw4YKYIsQeNpkAuls4wvoCJEiZgvZ4W/SAcJnlmRoZ+B5YL1g094kEYNcZJNCQ8IGhUtuVHJK97GTHgpEp1ChAZa5mZlenKhw8qIwMWKmUSMAKsKAoggrhvysEwUAyc9gj+zAxV6FABMKVQF+JPFiOAXWAWA8Lc6z3dAijCus9MQi9okKpg56AO4/BzgckXKCKaoWeKLeJa4DmcIR430xQWEMCs7BLg4VPtkQJlyBTPULrcZ6s78JuyB4QdZPX8BIBbcaWlZ7kLoqu709IzMre9/2gRzFAAMAA78ip4d47JYy8jsUjyAAboHt3BXjUAA1bf0GqNbR0qA/1WQ9fXrwaMO+NVLn3dfg9DxdyVoTLiGfwmdgDLM9vLMQArZ4bBSf+jrP3YATMoqyAst+m3/iWKnQZgF5QFQoMZNIFBGD8ibn8WLocEL+PE5ERZQI0OjASkTuDrTlT9xd9NE+JAr/NBWg5QiXu3/rtU7/Ez7U/XRodlkKNJ8TqOT7O0uESbrnxJ1bk58RPsjUkp9pXCZDv30BIrTY63Q7Mz7L1H5NqPv/keu/TSS4dsP7OvX3yCvevQbCtKi7NsWSKZshKzZV1kq76jWzwnT3GkydJAxuXH2dcvMvvZp80+UmL2mRyzD+eavT/L7LwUs4+PMXtTtlmuEAqQCjfBi8pHB/u5LBIu+fznv5O3m4GhezIWsFOm7BNPPGFtne1uIufl5Uo1Biw7J1tsUkxPGUaHiVdoVVyKDAVLFkjiX0LXABsUi0qLn4xB8t0xh7wrnDb8pwx4xyQmHIpG7zm97VR+wpGrsN1AGZTRmNA8QAJgsrkfTH8wJx+SpD3DnPBd0uMO24Rhxuk5KBDKxLXwmTBSOod4Fte991bReBhPR+AfhaF2KE8AClwP/IaFBqb7roWOMRT5lQrMFrDanfTIKuhSGXm+cELvF/wFx4MCzYEE3i9JZTBo7bIMsCzwRT+7YImtWLfOigvznFFTvnHke5zeUSY85eSAyHsoLh93ScQ8w9/NnxL81hYcB+AYO/SSCzcHdB0QJ+EB334BJk5r8hSfpYMoYfU79Ld6fCqn4LnBc5pb2qxLZnhdXa21t7f5gPeU9AwHdqyTdMx23dan91RhW1Fulo0vyvN7dyUQBrZXKqQ7rPe7kg0bNtitt94a+PF3I4nT/scG0z6h9znO4vqP0RkmmjT5Pn5QZTXY441Ju3Kcupyi8tuoBnKr8mdaRq6dmJJsEw+eY+84ca6947PfthPOeIsde+yx27ZDEjda2cZl9nh5l83Oz7Rp8461IxI7bPLRF1lD5WZr626zCdKhNsWdruwfL2D83iVm55wRbzmPDtoRKprjlLXHCUDn6fpZR5mdJkB9oc5snbKCUqThmjkn38aWTbC6mmZv8PdExo8fb+edd55lZmbGzuxcdjf+tqmp2cDLV66VOxCUA99kS0urD77OUCWcVDbJJkwuc99TAB5ilarEwfCRBO9JJwNQRB9oLHEFhEVqjwnVJbPfWYvACHOYcx6HChkQQ1CWQFEwE/u9cymYFaRQCoObALCDPThocb/OO6sQoAEwii0AbIFbVkqaFWdmWwYMV2kno/CRwjoB1B6lp1N7gMJBU7KN7eg3espZxp4W5BdZQV6RWHSmd4b4xVEkQa807x74g8mXELD9jZReN5/1Iy8z3eorq625qd2Wv7jILv/Fpfan3/7BvvT5r9gnPvpJ+/AHPmKXXXa5rd2wVe8OWyMvcFuoXLSRl5STg5vymvLyY8+zIB/5zXMBO8Ky6aSDJsdcIy3hb13yPXWsRyDarT1WSHBvMCbXGzLVv+A57IkvAF/qKuOB3crgHp2nPiKE474DXU58hwDpq2YH/9gs++zJ+k8nbLq2FMGj8kNHfYMp1jUgfdRx7UCntSgP1/b028Mbqy29o8sqyjfarY8vtfaGrQoxVOIsK6fQkmtarahvwCqb+mz12s22uKbPFm6utOrudtWpLNval2DCaOvVw0pz4i2rV3UsT7onDK/VYX6ZtnlmE6cqjMCyTQmpDaqEpYvJnn9mpv3zHz+1X/zwE/aOs9KsTOC7v2REgRPf5eaN5e6rbGxUa6ZKt2LFcmd0hx98qM2eMdOHGAFUrqBqgYIqGfgDYYZhp4vXVwl+Jq/sAldYIHv8owBdEuFU2TsEpvSIBkoltpuRLQXPtoKsXB0HrQx+ThSIZ8GCADnd7WZ1L+MwdZymtKXoeYAi4OFMGKYscCU93tmkA0AA8HYw0O8APPsdWB18FBsNCDOI0mXSM6qAc6Sfp8bgaNSI57U2GDdCY6OXdvDkEr+TdJAuLOmREvzrxlvtwx/9oj32+FNWkJNq65e9aI/fe7etX7rIajYst2v/eqV97uMftyefeFoWSJd1Ka9Qll7FwxafkKz4xcrJDVE65aiXMWzU81JX+J9xougNlgbp4L+g1DDFPUd1HCd21mUNNfW2pXyLrVm1ytauWWmby8tt66ZNVrF5k5WvXWWLnnnCbrvxP3b3HXf6uaQ4ADtOZa/609lhwMVArHHo6+3Su3uq/HyC6oSsTNUZT8UBKe8TRv7g/Wa//KTZP/6eZB/59ql6r4/qikA0Jv2DbeS63lsWpN4VHouFUd7dY9+tFyhOKLSxA922ZeFSgr9MEkqnWe6b3monf/Riq0gvtpa+XotP6bT1L9wjC6vLCsbOsvb+bsXLcyhalWmqrMulvXbCGwTsSkq5OMWfnzZbIVxetdlMRWrtse6ASZNz7ZMXFooJ32qrFv3GxvW32gkxy2F/yIgCJ4DHlEjYC6ZtbW2dlDHJJk+c5E73jNR0H9NJ5XSaDegIbNxkUyXGxE0VVYZSO0BJ4TDLfTC74gVUOc9geDqW6PEOhxwxZc9ZoyKnpxvQYiwnZjzZy56ec0xtzG9O0lOPGSbjW6Z+ijV2tFm7CpzeVqZqorCkKTs1zbIFhJnacgSEMFneISg2wBeFJ854qRnA2qd0MXc+YFn4N+saZQ4yLlQA4QgwioR3QQAtZ2JD0kd+Uk5AfrkYx5IlSwRCj1t9zVa75qa77IUNVZaaO8aKJ860k8842/73/75mX//K5+xtb3+H7kg2NYWWGp9iaaoHna3tAl5melHuPAOA5iE6wnUCUFM2ngaYHykJrBPSxm+EOkBec//GjZvs17/6jX30I5+yT3zyf+zLX/9/9rFPftXe+b5P20Xv/ZS9/R0fs7dd9AG7+COftW/834/s29+51D7yoU9abU2T1y+mlLbKQlL1sySVLc+jTlJO9NL7ExPjVU+wF4PnH4iCa3GKzOATtT9bJvGn35dgqUXnKgs/oqsTtEn//P9gCyV8Y0rriodfsEXr66y7uTo4OUQGMzJszHGH26GTM21qQrVlNG5UfMmWINDMKxlvY1OyLVsP8O+2KrJ+vDndIiJNg5Yy1uyZRWb/c7vOF5jNVnKy9eB6IXhqVsD8S4pzLXncmVa7ocBKi+fbxvi59ljf/usrGDHgJGMxd3p6giaiu6vXOsU2ampr7bnFC30wcWFRsY0dO9bZGAoJmLnPT0oB++R+QMb9OyirzhEuNzvHSvILLT8n13JY4EMACHtkOBPjP2GdGTKt8WsCVq2dbVbX0miN7S3OAhnfyfjRLAFgr4DSzW2BYyiw0TSFwT+JwEjbFaZNDLpXz8H0ZqgRlQhfaaKUHAV3dqRnc92VWSH4H4HJIpiI4WIRAEAAnKNLQhOVfGQfMPMAoFAiB1O9R2FRkU2fPsPmHzzHWlorrLlqg+WliUH2tVlDbbk9cN+D9tOf/cH+9Z8bZKqtsX4xwXUrV9mKpUttzbIVVltVbR3NLV7OviiLnkO8/lyeFUPw4LkwyyA3uU4atm2AqBpeGsrKiiq74+Y7bPniF6yjsc7SrMPS4nqsp7XGejvrLVOKl1c6yTIKp1hGwWSxyjhLUiOcmKKGtUt1R3+MgiBNPJ40kY5w4xnUbth3mL4DUcjJUm2N2oCiWQKoKe8WGZj1FsvIOFPvlmrYZnhwuT5Br8r3HoW1xgxv9hOSZJIXFlp/huCv/+ULiyRktVvW3Kl28oklNiu+z374zc/YVTf9P5sxJssyVDdSuh+x9x5udmhWEH+uHpYxPt5a15k9fIvZY/eZyk16U2mmIrVHdP6ZjWZNHYG+rFiz1b5x6a32rR8+aH+8coFYbIVNSqBs9o+MGHCGVcp9WqrouPLS0lPFNMXSBHwZmRk+HhOzzwdZCxTx9+UJDGWRuQnvHTaqoCgV7AIQhMEydZMVkXAFdHR1uBntKw9JeWCQJXmF1iMlAPjc56laguIx/o758SgHCgE7TdZzmX6ZJFORueUIQ4iYYslqR9nJwfRM3gdF4T58mo0y57phkvoNWZL+umnLECnMfsCH4U90LPF8N/VRNm3h8J6gg4QsD3NrdEiIB4HfOeYL1t9AksApSe+sCjqofEtIlzUhwDnu+OMtPSXF3v62i+yK3//B8vOy7aC5c+yt77xIjWKcu2sGejotNSXOcvIzraAwV2Zbgk8CiBPQMtwMgAaUemU5MCoNk9DHX3rx8YcEDRTZ5QxQG24VwnX3dqjhbLd5hxxkV11zlV17+3V21fV/te//7If2xjPfYG87/yw74pCZAvdN1tWw1XKSe+youZPsB9/+kv3t71d6ze9W3ejtcoPU0uLp+aVepejZKi+mb2Kh4DpS4MF+sd6+IFUHqgCEdAkhKiY7/myB6ZHj7JTj36R6P1NWQYJNzkq2TF3LUf5M1papynFmfqp965Rp9pbJeXbSsUU29SQRkuZHFEtIArosbultNnjP7bZldYUt6Y63/NnzrWRqqeUWF1htYqYtq+u33z6XYc90Z1qn4tc/sdEBa5U+1IltJsssf8dUMc0GszsFnhRLVZvM9fbgGS11Pbbk8Sp7bEG53fdkhV1/d4M9WRk+f98LWjyigkJgOmMGud9KylFTW2PrNqy3au0nlpXZNLGW7Kxgea6UNKZUDsqE6/bee1p/VxbMObHD/t5+a29vt4raKmeaAJGzOykaJjfKVF5dYZVNddbS1e4ABhJwnkrPkmkwSqo8QA0QJgig0wXIHWKszioUFmUMWIbC6T98nW4+ct1Bl04s3ilQHuJAeF/ChJ0pbp7rac44FXbo6khJzGpy8PSgo0Zir6SyChqrgHnCrQOw4l194RPeSWcbGxutWdv8eQdZRnqCHXfMYVZbW2/TZ8+2X/35z/bzKy63c95ypt43zt01lF9xXp7Cpvq7k2chs+1V+Xrvf8y9Qb65D7mfY0BS9UnXGBxPo4XFgEUC+MLkGYmRlp4mcM6zlLh+NYbxdtqZp9qUaTPstDPOsh/+9Mf2699eZn+56gr7/g+/bceecLzXIeLgeS3NzaonQScl5cQiMiQSsCSNlLcXl2x5r1sHqLTH9hjlCLX1uLlmhYem2Mfff7JddOzZdvb0SXbJqXPto0eW2aTURDuiIM1OK0q3t1x8qL3729+wiy79i134pWIryPqXNdf/SzHAOun9T7T+1LOtKzvVmja3Wd4YNWzJl9vapV+0+e2NNks6/v3zEuyCI+fbWy+cZ3l6eHaHLL32JKtal2B1ymB8n6u36ryOE3UNYG/Whv0qVbTjDjErE+3NSIy3kydkuuuB8/tLAm0fAaFKYbo6Y8HE5pwqYWdHlw80BiABmK0VW6yuvtZ/UzHramp8wDpgyHhBj4eOF0zpWKUtKSz2ZeG6dI5lxXztTpmBgFBygkx9KRdjPGEwKCOADXAzX76rNxiIT6WX7rkvlFlAjW2tHj8px6fFQPZ2KaH0w5WbtBI37gR66CkobwgUjueRXkx6WDLAzGwiVyv9550rEhgu6crPybf87FwPhwHKI0ajBDwvxpR1RANCYwGQ+Xm9l06pgVPNloydUGopYpXvfPeFlpmdabfefLM99ujjtmLlBntxyUrLyy2yMSUlXs61AlrKzvNPZeGgqXwnL3kqHUMsQcj0WcAREGfyRK+uMzIhnMEFoHM/4I5biIkTzNAKwF6aNCALJCPBxpUW2OGHH2HzD59rEyePsyQx3r6+YLhbKJj7DKNj3DCL01AfaOh5b0CUZwW5oj9dC34fmLI5tncfY0xOKBJITdFBYoYdNXa8ffK4Y+wYMfg3H15kxWWDdqLy/ief77I3DFZbRnaTHXvuUbLGkszqGmQ5bLWuDuB4jTbx2HEdln5Yv1VV1tjRYwvtsCnNNm9KtV08rdlOSa22N5/QZ986e6F99/Ql9qDM9dNylZbKXute3GvNQscUVYKpKoLx4hhdqmPUMDoUqRuoaYXM9k1ipfGqF6VqiFP1IkeKoe4vGTHgDBUNgekxyNvNHAELxwN64U1bNllTY5O1qpVvbGhwlskiH84CFRYl5R5ANVFgmpYSdCix2hKLCAOU3cq00vwiy8vKsazUDJ8PDmD6gsI6BiTxm1LZOc9AdSRki4kypxHuAyzjAFQPELBE2A2934wLTfUmTQqjgsNHabqX0PzmXX2IE5veEaAFGDFznY0qDCCBK6FZJirsqDfWuz76JACHBLUOsGzSCpDF9Q5Y0kC8JfbrvI7VCilPVcuTlQ/KHzpYWJ8zNTXJ3vyW023T+nV2350P2qyZM2zuwXOlX1XKswF7vqPZbtmwyupo3AbVSEktBgc7rSuBXtZ+KYpM8ESxWtVGth4VUYe2bs5hvisMvdz9CQLTJO1xsaWoyFLECgXcgyoatvjkeGsbaFa5JViS2E9WDmXPO+m36pWqjxpPVrli7VXMQdWbzlaBv+oYdUHPZMRvXK8a5X7KioZYjanu6R8Uu8KndIDKWtVHUQWX0DM4Ce1XFj27ctBWVYyx6kYY96AVZxTYrI50+05dt33pP/12z/It9tjHLrXqa75orU/db7ak3/LbKi1+5d+sfvWj9tCX32BrfvNba76x3gZT0+xDJ0+w0rLLLWXcHdZw4hhLKxCTHJto4yd1W0nyoE3LNDtjmvJ/IM5aqoP05CstBdqek3m+VVVM1r7qhydTZaBzCtejd5im8705IkUq//N3P6R2rwlZNyKid5LEWmoxAICqVxUTwAE8xpWWWldnl9XW1TkjYzFiwCYxJcmZRMBWE4KpmgJUB1EBV3tXh2/OGqXMrN/JNQAVtgJYIZh7hPH579oDUAAlrATmB7uhpx3whUG1d3Vam9gGQMl10uimoI4BEd4FekVr5wwWxqE9mAhowqydXbOXxvEuoRnPKAAWWqbH1lmSzvNMBswHXG50CWkn7/V6QUFq492C14mxLb8Y5Om8eQdbdk6uPfzQY7a5vNKamzvt+KOOtXe87S2W0NcsTehUgxVvBfm5tnZLlT3YWGu3dnfYLartf2zrtdu7euxO5fN1agT/KPZ6hfa/b2+1P3S22J+62+0v2q7ubLN/dnfav3u67HqB9XVqdG7VPXdKse+WWXCv8vzBxBR7JCnDHk3JtMfSMu2BpBR7NCnH/rhskU2bOt0bYQaih+/HO3HMX0dHp4/YYAynu1J4WV2DzfpvhfFOqiHlyvGBKmvrzDaI0VGKcMTAhjDLElm88al4KysptQE1jNXLVttTSzZYVv5UO1LXk543u/PhfnumvMM+8dMe++3XREruVA5WVVjylkvVqC63479Qay8OdNgTVXXW0VxhaRnrbM2Tz1n36hXWmVtqt9Zn2P/8bsA+9Zs4+9qV/fbdnnirFHgObsHi1F4JWa7tKqWP/nphp21QAutjBJ8B87rk6T2u2KxKxZGuxrMoBqz7Q0Zs5hDjKB988EGrkemNzwmTiC0EosbmJpszc7Yv9EEnz9gxY1UxGRdIlmgvsMvKyvKKHIDLoJtQBbn5dvic+VZVV205WdkC4LFin6liPj1u/tJp1K57YIssrAFjArjwmfF8pkiiCJif1H/cAqQJQMtKSzMWBEEtSAvpdJeB6AhDjELARV/cfNUG+GG6w1wBW9hloJT+GpKgQwnlhLkShrSwvBznASDef0+UcF/NHOqWSdynRsmz3YfgBJMDYFiAxrY0OIAMWG5enrWpPO+5+34rKhljx594nFVt2WpHH324HX3ckVZYXKBGstPyinOsNj3R7qirsCO6+q1IYNi+caM1r9toLVurrGLZcqtftcrqly+36qXLbeuylbblhWW2Sdv6pUutfPVae+GZZ2zRwoX2wqJFvn9uySJbsHihPfvCElskxVy4aoU9vmyZrW2otQV1lbZInHH58mV2Qnah5SudpD0+To03jFNlR7mwr6qutUGVyeatWwSgLKzd40PmaJTJiASsJK8Z+qW8oAEeV1Jo4/Vuu5PROHOowS6wu5Pn2zViajeLnd8gW/g/68wW3iHL+4kBm5hZrvjutGfXLbXfL6+yca0l9kJPrZUpD6ZLOU6f2mlZL66zp0RRzz2+1xKrxMhzOqw7V43VpVvt4QUplr9ijXWUTbbNjzZZZvY4Kzt1htV359jjdyyx7vVqTGsG7c4NQrs5JXbCm1stW5b+0gfNmpTNtUqjrHH3xZLrz2njuxOUxgk5MuPFQtNivyuUNVTJmQLf+yp0YjeyN2YOjRxwCrzuf+ABq66u9oJHB6m0mERMtcRcTxK7HKOWjQHrzKJhZXjMazYU1D8BgHLGQCVHrAbQYSV14i/OK7CC7DwpereNKS51UK2orHAQJLfd5ylgZNA5LgL3pQmoesRYYKYcE7Mg3YEiLzPLw8IMcWIGc5MDs5zwmbFMxDzHVeCLlCheFhzB9HPTXOAYAGtwP34wfKk8iHdCYX2qpd6/U6yL9/BOrlEEnEy5pHfbmVWc0q8XYN43bx4AZ8DSfCIAZat3POLww23xkiX29DML3Y/Y2zNg4yeW+FjI+JRENaQ9lp6bLRMx0Rb2dtj/zj/cjigqsmMnTrBjJo21o7WdOH2ynTp9mp0xe4adNWeWnXPQHDtn9iw7V8fnzz3YzpXJf/7cg+zt8w6xCw8+2N528CF27rSZ9rY58+yCGXPs/Ckz7MKyqfbGcePtiEE1dmKvy55bYmcdPM/OmDxVTIbyYgSH0IKecjV2DL6nPDcJ6AdVJtV1AlCdZ959ohgrAJrIcDmVW78aZ89/FRWN6NiiAptQTN/0rmU0Amdiy3nW3jzfNi6Ns+Zys1VPmq27T7r9oKyy9TU22HSXPVV1rz3RXG29KWNtQ1yyDaROsca+bpua3GEHn6QcXNFvf5cVUPHCgP362QE78f0F9vjDZfbXmyrtu3/6ov3zlvstvaHTHliyyeadcp5NPfEC27Cm3J669n57x+FmDzcO2gRZoceqPpx2tMqrLt6efVx6r/TRzQRoUluZCMoQqC3a0JILi8wydWG5wLNfIPqiELVRwF+UqmPGV+1GRv2US1c89gCWCp8KAGAydGjK5MmWIqXCZGVQ/IaNG8ROq31MJ77MkoJChcOnqMwSqLA1tTRbQ1OT2GqjM8U2sdWttdWuxM2tLbZUjMPNYLHWxvoGW7d2na1eudramlo8DSh+ip4FkwTgANY2KVdzW7vSFzPX9butq8M7spgZhKLRs9op0AEgGfuJvzMJYNTvoIOE9HlSnZn5fXocvlWuAcR0KiGklffvYbZTb8CCw3waLRLMjApYJnsY90vATodOkGbSjpB6GooL33ae5eZm2Xe//WN3z8DsabgC3za+0VSr0n56RrplOHMP8i0AI+UZW0K/Nhq0Xkvo77Ek7ZOkPon9XZY02K1N5wfwOXZZfH+npSl8cly3Jcd3Woq2pMQey0nrs0llRdY7vcyOP/RgW7B8oT1VvlkMP2g0huY5bheEBgVCQJpZ/pA9jQL38P7hOFHqjr87jW6YJQeg9DUus76H11rcdcrHf/db0vV9lvZojxU2Vlji4H22uvY+q+6p19smWtpgrc1MX2P5GetF8xLtRQHU00KxZ0X50iYL1D4bZ+/5UZx1PVVrd133qH3uk7MsJ/U2m5qYYCVN7XZq3qBNyldjvPVZ62pqNNl/1rSi11bUDtg0paVYvDFf+ZkkK6QevdE5Zk8yg/4QbXO00TwBYVPEZJ7YanavUHVA5vmmZvRM4bPMKvcANPeWjChwIqxUQyX0WT6qiPSQMgWzUi381i1bbJFYyuYtm91cLd+82VqUsbOmTXdlorKGSopp1C4GypAkGGCjwlXX11pVTZVt3Kr7Wlud2W3evNUefOAhtXyP2bNPPWOPP/a43X73PVaztTJQFlV2wAvWCWtIS0mxbJlkgCDjM9sEaO0Cz2AojJQDpVGh8s0ietVhmnzMDfALFgHh/XAmvKRFAAL3oHigCiyZBoIGAQDhXektxq/rJvAokwGxy0EW5aBjROljRALlEfaik48s9Nwn013Q72Mt6bKZJXb40Y99yObNmSE6Ioaqe+JV/kmDrBoVZ60C13Vd7TYngc9Z8AzFpQj90xf4hb0x4zh4VtA5pThUf/jGlOxlHTPzi3Av7RPipUH8hj0qngSZ4i3xKdahenJmWamdNHe+PdbR4EOkeonf09+nFPCOvXouVlSXtbaKtoipYukkiSXHJYiLsliI8kNJ8LKjnGmE8csH0HtgykDfLdbb+j0bbPiDtS66xXqWPWC96+6yttrfS0//Zi2DCxWqwDvHBrPm2DH5fTaloMVmlFVbglDsEbHUJSqSiVPMPnnJoL3jXWa5A+n27iPG2pErVlvW5Y/bu9Lbber4QZuvsKkDWywxpda6OzZYXl6/Pds9YHOEhAtUTej8GWwYtKpFAdMEIAFPOn6wfeHPy7QJGy1L5YAa1+n4DaUC7gyzUt2ADfEk1HQ/yYgCJ0rnq9+oijk7QQnF3FCEJgFdfUODX8NUBViCTheWJqv3lZAwb/hwFqahO+ldqai6MJMEN0mYFQTQrdu80Woa6uy+e++xCoFyu+LvFkDjM23m+zGTynxpOu9NVRzMFKLi9zJ0RfFxHkbJholNmhgAjmTx0TUUGMXRvTBOvogJgHt6Ajz2LWRpnKdn38Fa4ue0B6gzRP9h1pjODOCGxYwuoaxgVZjn4B/5D/uijJRW/SbXAEw2wLNf+6TUVBs/abx9+ZtfscJxY6y2sdW6+gatS+EBvcd7W2xze5NNj0u1XsZz6R9vzmLB7m9UnuuXgAmWnizzGDAMysHXb6WBUpEk0IuvfZxAOS6JE7hLFI4IKQxxmgWDPVL+TpugsnqTzPnm5AHb3NBkre3tUkQ1ajxbW6/eS1VAdaTdPybIsCdfgpCGoV8arRSq2PXuwTKI7pNW40r9G3XFNgwZtLUqy2uV29+zuIFvycL6pojD5dbed7vKdq1yiHWRNql8VZ/b66wgPc6e3dpl/1oxYOviMmzGlFQ7+cR4++RByhv8ismyGht7bELXBkv6S43F/7nbNjzZazXVPXZDTpKtqlphDTXLLbVpg33q7F77y7Vx9s9Llc0yu69S/svit/LKwGeJnUnWtirfX9SeoUjh2k0lBWbnHWb2pmLdq2rarPuyhbJn8Xs/loeSOjLigCKFCXsvXaR0KF6vwKyrvcMd77AZAITP8AKUMLFNFVusfNMm7xEnPKBJThLehyyJDQKIDIXB3A3NcD4zjLmF2e1sVUoAQ6WjatzkKTZ91kHe+xv0vEtrJDBPzHs6jZraW52F8jE3qZbCJVpOevBddaZwsidu0sTmIKvtpSmWgYTgThjeHcYNQ4GxYO4RL3G5uUf4WFpGi/AmpCkETN4D0PRrXg40hto7kAR5wZaUlmqdApiKulrLzCuy+uYONYLt1tneKaaX6A7+/sQ+K0zPFAvFjRH4iINnBHHC3jlHKsK880VgdCaoUwJNPSsIEzBShGucR3kaxDrvj+u0SZ2DVpiUZqVxyTY9Md9Sysps7dLlKmMBprCxp2fQOvvEhLv7rbGty+sOvkRAMRijizkepI9y41k8g9908o2+Bm84QkPea2nWYDm2XIpP6TxkBZlilHFAVL9lSwuy1chlJHTaslqzw2fG2eECyv/33j47flKXTT6k0GYdpTLqSrTVd5fZxqJ8e0Jl1ahsgSGWig0+2NFvTycP2vXXLrYvf+wf9qdrFlpikXQnXXnfJsBUMY4R6KXkSrdUlLBLfJz0nHeqnOh6o4RlUzirXCeQ/I+qymoB7lOip60CzkqZ7eUy00cMvF6BjNizvYLFWGToW+K74P6pVR27yS2gZIAxvd7MEOoSQ2xTq98qtkgvbG+3gLFH92IaSaFYFBlBcdMFbgwngh109XSJ3fW50gQgGgoKHcxcuuuRh62+rdUSGBMaM7O9o0bKgLmdn5njTJLfnQCz0uU+T216E48NMCV2V1wpPQAYMNWEgPFIyRD2IUNVYG8YAGgG7afIPAcw6fnnPZJlZqKco0qUPgdF5WUAHMoBnesWt+weZNYOTE3Wg2hmr/BUOW+9om8DMskLC8ZbR6vY3abNPpe9s6vbFi1ba+t7OmxVXI815JTa0sQ03cdcfzVG/ficMXvVmPHRPB3HiZ1yPChayZcqdSLIe6XFwTOWX4BsCJ4hgDKq4dm4Lqtqbbfj07MtweNIsKlZGba6o90mTyuzTZsrrU/hAz4pJtPS4oDc1TNgnd16G5VJv14SwpmQkKKGkVEVADMsU3xI+4EBOsb8kQesUJvpgOmIV71MGWsHzzrRMnO61GDA8fqc5bUOdFp+T72Vt2bY1I7pdpzyuzCh3/ozEi13Uqt1JA5aW8KAbakst5zBRrv5wQFbqkblMVkBf+9JtHuqEm1lfZ/1L+uwtc/U2Z1LOuzNYprTzzc7+0dmawTI06crLU0CUuUn4++LVbwZ2pzIasMsJ6uztK1npaSVZkvXma2oD87XqxG8X8BJqveXjChoAzCBfyzoRc7JyQ7mp6uSYnoBdLCwnNxcSwRQUYDEcLk3UXb9hrUyBIgWXlf9Wr+ACGYACPHJ3QCcemz58hX+TLQhZAOAU29nty1duMBefH6RNTU3O4OhFz1RCgj7xE3AizMUiTTQu16SW+D+zE58q10CZgGf97Jrg2WxAZAMQ3JwQYv0SM7xG2AFPDkmTag6H6SDJTdLUQnvrglMddI8ioTUvJQm/+XvyykADCYWrODOsCX8z4PW3dWnMmC0RY+Nm1BkxcVFtnHjBhXwoI0pKbQNVVusf91qG1j8vN3W12orW/n+VKcUj5WOMIsDoHYhD/mL5aX+cfJl+cQScKHAPgnDXka0raQTqb3R5qbneENK+Y5LSbcnGtdbnfZ4N71+6KY+WRp1VXQwDlhrV6s1a8Nfi4uJ2UO+mAtx6F2pG4G7IsifoVbGgSqTx82WOZ4kc7fPjj64y7JSIQoB0UELyYvNfYP2ovTmHy+usr+9OGj/fnKsbegrsZY2ha1S+Qthi8ccYRvXp1p56gS7Ytpcuz0xw57tEWftTbd0KdeDikvGnoNjjQit2i4rFyJ2qXGaNl4BOuOsTefxabJkYb3CCRutXBsD9dlw7MUpUVJH31RELuAIg+H3Z2mM8HCk+92PydxfGIHgRC1bmgMHs0t85SRVXuLD5Gbspn+TJ2aaoRteUVVxAV7v3VRlpZfbw4tp8umMoKMlWRV8wGoqgyWuQqXD7CoYW2pTZs+yDatWW3Ndg2VnZbmSBX5MsQi0zu8RMCh+Fu3A/GYAfOgTZcYRPlZmrqCIUmO/n3PcQwyAPHHof3/fkIEGKyMFflTWemT4FWH4Q4F9TKHC70721XCkLgEGQ5JCXCCNQb7ohF4J0Ny0eYs988yz3vn26COPaXvcnnriKVu06HlbvmylNdTX+md2UzMzbfXWOtuiRql/ZbmdpnKsb2qwR5sbrUTmfIneh7UtyQsyz7PM97H8iT1T7RAXgt/ezMGG2Qd5TPqoJ+u1v7Wj1t6VXGQTdQ0fNsDHalcbGmpsMW6cxUtt7Zo19tDd99h9d9xljz34iC159jlbvfxF2yw6U1dR5TPaqE98uYD7yTvqjE+mUNqoo6V52VZWemCO4wylraPVV/Tne1mLlm+SVZdore0BcQkFnyPL3PQn51tbf5elVnXYg8822eMPCADF9PKy46xxabxd/3ijFVW02i/qK+zcgR6bMdhp/8oosnFpjdauagdI1gnsYnjnQuldfFaiFa7qtzvWi4EKEHlTTH3GcTJgCI2mlx12vCPkIY5YVd0jGfXjOO+7/36rrKr0ikd99zGNqnRcA3wSZHrj/M/OzhJAyjRub3Ng8EyIKQ8tvyuu9lQ+4oLNuRtAwajAmPuALOZ81ZZKv8+BSxs+srTMDDvosEOsetMWW/Picps8qczyCgpUgIM+XAahl5zl5ShUV1o9w0FKyhLTTWe+IRjSMeSAyXMJJ+Ea7+nP1jHsFAGE6YxiI90+tjQ5TSxWjEZhGLYUxrEr2WfA2dMvwNGLOKsLmDFNQ7cy58UXltnlv/qt/fPqf9lD9z9oi599yla9uMS2lK+38vXrbfWLywRAy+zpBYvskQcftvbmVpswb769OHuqFdQ32dmTZlhxe7etSWi350uKLFfvP1bmf4LKtY+GUXtQ0q0PXlVJoJx5Ppe8Hqllw2rBhKc3njfqHhSwiXY8ppCb6hvsnXljfbUdZ4aqd8z0ql+2zm6uWm9xaytty+r1du/NN1m9wqaqHvIZjAyVSZsUoa5yq5WvWWnLFi+yzWvXWklJiWVkZwpg6EjqsYQkeubjbGxRnk2kt2I3MpqBkw7AAV+4OwDLAYHmmDSzliHYOVNl0Kx9l949R3lwnPI5WXXhS7pt/WazJ9ekW/PWRtuQWGSpm1vsTbo3S9e3Ko/+NJhhTV0t1qpzDFhncDvlOVQ+eIbZC9eLyYq5Pqv7AMxV2oq0MdUSn+dYbYw2Ajy3l+3j250cAMB5nw+A3wYs2uHn40UxYwEQWJo75VXYaalpPlvImaUqO2HcRJeSsJwcIEXHjffAA1g6IQPPMtLSnAVVVVRbc0OTwqHwegZMF0ZJ5sNylQlN1bVWVFrk40h9qJP+SB9x+lcMdcxvZ7CkVAqHqwHwg3GEPfukjXsdKFH02DNDYPXf+kel9/GA2mCorI6Um5VjjS1N/hzCjT7gZExjMI9eKfdzq1atst//5vf2h9/9Tqy9zrIFNEccdbidfOLxdtQRh9lBs2fbUUcebocfcYjNnDnNSksLbdbUyXbmKSfbn55+whr0e+biF2zahEKZZmPsqOxiW99cZ9UCq7KEVEuhjBPUYKm8vAyUL/BxxPPY2bxSFJxyATQRRmr0Sqk74pPtP23Vdmhyth02kCizEGtF9aKqyq69+hq75oo/mFU1W8nJh9hhmXmWmpZvb3rzmdbV2SoFSXN/bFFxkc2cM9smTCxTPYyzlroaW/TMUyq/XsvPL/KyVqYojWKc+TDOQk/DrmQ0A+d/ibK4tz/eP0AYCgAGaGXKJn6f9um6tFj72dpKFX6pGGtyW7+94YR+y1mtshCz7NH5P8Xn2tq4GhuXrsZF7HOasqpe0IFFl5MUJx2L91EXXzgp0da+MGAPCjh5Dm9KrQMwWewD3yWrDDCbiOFKr1ZG/8yh+x/wJeRQdkCJhOI3ojOIsYyYfvgxveNH52AH/t305FSZs8HqRoHScD3JwYsxkYBpmkCE8CEYwEi2lm/xXvcQkLx3VDQFfaNGFJSWWmtDoxWIbZZOGOfz0t0UVzgGqEsfHEBD09kHssfikTpTpySD7gsNASwESDchSat++7Nje4RLbkoq/SSGEQHu3439jTZTvbO7Vw1ZMDifz1Dcdtst9qtfXmYVFZV2zDFH26GHHWqnnn6yzZ8/12ZMn2rzDp7rywPOnjPdpkwts3HjxtqE8UU2adoUW7z4Ods8b5Y1rFtvCy/9la0uL7fCvGKbMq7EZqZlWFmP8r6wUCZgt38QjFWlcOoMknV6ftgoYQEEeUx5BnkbMNCgDmB/bOgZsEd7GuzsQYZ7xVlfe489/vCj9offXmHLFz9v8w+db+OnTLbytBQ7tDDPjjzqUJstkKf2jJ802eJTMywjN98KS0rFuHstOUUNu9hwTk6W2PRq27yu3MaMGat6nOFjfEvzc2zy2OIg03YhBxRwShLjkiwvOd3a1RghPJk1M+OVp23K8ye1tek337xfJ/bdVVJsE8el2vjCDusRNFyVNGjLhHYD8X22RdT0pBlxVqK6n1mSaw0NXVaYnGKHZ5jlFw/YRiHl1w8fsMoVZpUCTkzyKm3UUprFLG0Mfqefv0YbOvpqZdTPHAorOOYyH/NnppADp9gcYFGYm+cAyBx0FicGEJubm32ZOGYGMZyIAfN0pKDwmOQMF2KVdxQaFgKowVCY897Z3u5ASVgqPsOe8IV2tXdaY3WNtdRU61qvdXUItGX68wnbcPql6oKzSRQTvyqVlT2dP3BL74XXc7wXnXOxd/MteF1PJz8454KiS/gZjg9NElPhYQCDr8akvQPCKBLvUCP/lI83XH+9XS7QZCTC8accZzPnzrIL3n6BHTF/vs0Uay/Kz6MDXO8DdAWmcbbM2rKpE2xM6Tg788LzbfJhh9nEhno76+xzZAJvsEt/epk9/vQzbqZPVIO6adU6645Ltoa+eOtWealQbKBHnMN7r8kglYB+9vUSf7AhZBvPo7ytN9Fu7Wu0XKWdVXX6Vcb33nGX/f5Xf7Cu5g674MK32smnnmLve8dbLbc4zwpmzbbSMfleRzIyc6xToMuUSj7hrMpp+ePH2USx6CNPO80SU5Nt+rSZ1qt6+di9d1lHe6vSxTC23TdCB5qQs+0DyofsNCsuCtwQmNjU2RaZ6KtVDixJBzO8T4GfkOne31BrVV3Ntmpprz2fPmAniIr2TxMTVbnkCEjz4zssU+Fq6/tti35v7eq1h5r7LTOjUA2lGjiBJgMW5quomSn0Tm2wWcxzgHO9Nsz30ZzbIwqcgAgtLSDUIWCrktkO+PHZDCo7YMf6iXGq7OEycTAq2CjrdiLc2yOzkdkcPWJCMBAYCeBLz7yzKCkbAMw0P3ylIRAN3WPK11bV+HCoDsVFCw7rQxgvymrx9HrjhwSY6QQASGHKDFPC91YgBcvPzA5YqtIAQDoj5VjxkH5eOjhPr3rAngKXQ8AqfTaRaqErv9IFQI02IW24Rx568AG7+q9X2bix4+ycN77JzjjrLDvmuGO97HhHsjcYqxu4VPA3864wtUEZV6kCvLUCHYZ3feC0k+3wE4+0k0461tLSk+zH3/ux3XP3vSqvdjtcTKSvvt5adc+WvjjrVK7RWPkICpUnZccxyot/MwRLrA9nV/pd0dttjyR32MyOPkvo7LFH7r3frrn6rzZpUqmd99bzbNrcqTb38DmWn8LQpEx7qrdZwJ3uLiK+088QON6DcszMDL4uQKOclJFjh5xwmnWo7uWJWbUztffZZ7fVuwNVMlRVp6rBo+NnexFHsEpZiu1tvF/CNqYHw2eIEkwQ1elRuG6Z2gua+2xMYY9duyrJCoqT7N4NydYmdjp3ptnRZQKVmjhb1ZpgPSIwIqb+1dFW3bd8ea29Q0q4YpmYmx7A08oU+Qwl6hQd881NnsMzXzlf3zcyosDpgBVjb2xUNExhZmgARLBGGBdsbGrZZDvk4Hleiam+vgakFIaKzEwbr9L6559ZULxdAlPW7sRvSBwwTZgSc8T942vaAuAMTDukqZ52MjDHQ0VEAQFJ4mWQe1aKqDn3kWbt+T6Rn8c8Bxz0PIQ4w+FJIVCSRp401ExHGcPxnow7VaqCTib9hb3y7jcbRULZbFi/zn73299YVk6elU2bYYfLRC8pLfWe8nSZqvEy0ZjlBWtmfGWirAlvEvS+g3oncXTrS0u0p1P6raZ2ixWlJsq0P9jOPe8cO/bYI2zc+DF29ZV/tcULFltcT5eNTVAD2lgt9ppqlYqzS6zHGxkpVsj+g73MVjFSyowPpnWKJXbp3GMDnZZQ3mSHp+TYkzLP//6nv9phh8xzoJ97yFw76KCDgnJR8R2WV2SLWxutSe+AiyceN43iZIQG7hC3XPS8QdU/6myKyv7wE09xoM7Lz7cXFy606s2bvN4dqHKS8uEiVbsCNXzbg6ey06W9k0FA1FiJ3lXkMKjrKmMHUqkQhgHXHlxtVt7Za1ff32uTx/WLwQ9ao2z8U0Qd79o6aNXtfbaqRdfS4yxNz0aL6Lznu0I5ssMT28w/yCYj05boXIU2qBNsMysjzvIKnPeOWhlZ4JQCBuxEhaHSoEeZYxQgMLcw48XoBEzrpajrN24M2KYANSgtCiqYmeMf1BJw4VnOzghm8zBgHsE/GXCFQGCSgwpHJxMME8FdkCGmkZub451GDJQGsMFIKkOHWCamNt8MIqYAFHVeSg1wFsA0eX5MqDywK58VpD3pCY/9un4DiDDNxAQpZCKMOrge9MYHQ2t8PGf4sqNESNOlP/upGEeHFRcW2UXvfLdlMghPOeOddnrPwUS9M2un6j0YAM+ndLsFNLLpLWjuBH7StMV9nTZDZZERnyQFjbMJZePtwnecZxMnjPGG5MorrrI+5T2LRJcKaOvXbbC2eDHPgRSlg/G2fENfrDMGnLCeYGX4PusSk+1XA9rQ3W8L9ey5NICby+2m6260g+fNt5kzZ9hRxx5rY8aNUw7HyXpIldXSbzNyCiylTQy1q8WE115X2tu7rLWpxRrra235iy9YzZYKX9+gurxc+63W2FBvc+Yd6l8mLR0z1p64/27rV2N7oMrzeu9/Kvl1ytBxqrLMXFVRvkySVd3LSlItSefRE2WT90vQ0dmtYzpvmqXGaEXVVt2vOHoV9sYn+i0v2axB6vl8pVlrSrxNFhMVrtoqnQOYJ4ufQHJyZItvyhE4amP1I9itsNeu17X7dMzg9+YOAW/jrruFSDodSPtLRhY4+VNGA47O/lQy+Cz9GqChQsNkxpxnbnqdKmc70/MUBkYRBAzYqrTHgQxggzEG885Z4CH4mBubl25MyEiAe8zYMTZ+wngBaLL1S9mK84ucRXrTpuv0utLxg3LyGWE20gno5Yp9FqRm2ECvoEBRA85MuUScYXq4l0xzhGOeDdhzTErIB8Z/uugc89z9HqU9WEFpRLP9VcvypUtt5YqlVizT9LAjjrCCwkIHSwcuyoJ31OYuCIASdkbW6z0472Wun6vi+6y9rs3OzMS9r/xKpMGIt3yxtg98+IM2ZdpU/+rpL39+mZ+PV55NL86ynoY6WynT+Skxng6VO/PE8bdSxs40dYw10i66E9+bZGsGe62iqcYuLC6wB25/yOKSsiy3MNvGTpzoaQ/qWjAqgjxPGYyzeWrMbmuosXqlkZWw6CCik7KlWWCq1PaqUe7v6racjEzr0zFbbWODZciMzxbrbm9sscpN4QcoDjyhs4WJeKcKsOZli+2p/DK3Q54e5UtTh0oO5JQo+7btQ03j1MQ8/ZZKoRpqjxwYH15ndvwk1YFqs2lFcZaROGD5dAhJkYSh1q548WFOLTabO1ngK3JLN1uzIuY7SMXiO1naoD0JOuef296JoD2w5pHocX+lMuIaTIWlwodKRyWm8sIeUTL2PsdcFTM3J8c7icKxm5jGZBcfaON7M26eK64OmVJt7W3un2JKJiY78YZKi3BMpxHfw8kS0/QeND0/OztbZkSXLyTCtE/MelwGpI11QB2cBfSY080d7Vbf1mI1LQ3W2sk6VrwQ+MyzAhYbvkswVAkwVRZyUULLzDg5FovwIUkCBgfeWMXzsYiSIPTokYULn3NmV1JaYjPmzPGFPMhWhuAMxuFXhv0pKzgn25dFOnyxFPJGVWiA/JOyLRnotWP7421CaroliF16L5LyhzATJ02wE0483hu2e+9/yFYsW+H5EBc3YOOTZFXIlN6gvHqgttHnv/cLHPmqaFc3i1HLFNTGx/Xa+7rsmb5mG6t9enWjPfHci95QFufn2vS5B/nkAwdbLwvKJsFauzvs+OJx1lJZYU92t6j8+ywhQ4w4Pcsy8gssLSffCsZOtDFlU6xrMMESM3IEDOmWXzTOCsdNsKbWZpswaYq1NDK68cCUg6UOPyozu2mq2ZVTzN6s3+IsLqrO26S5tUN6oRJT4STFzjPENxTKbGWDWaXYK+AVXgIYb18Wb5XiM62iqitbmc5qtlrnq4RwNKV8nrhYoJ2uZzPsCGOcr1/M0HZGSXAOQNrek8y5oUDFM2M0a7/J0PS8KuFlYHywEcAuNLW8U4ceapUCHTqwUbSSqZA9dAiJCQ5IQRiy0yngcpYhgARwYKfMSe9QCXeoZHr1my8mup8S0JMJDmAidFA4sGljaFQ7flWYkf7mzD/Iw+JH5RtDzjopJB3DbDDhSS8KxgIhHb3dAs8mq29p9HvCdwFA2fBV4i4IlpoLwJQt8BnENlUxemwBc9gt7BV3AEDL1dEkdN6Nn1Dm+V06fpw3KJQD0yuDzpk4XaNcYH8w6WBGGJ+fABgx3ys6+6QsjXbBmIm6TH5I6/T+PlWSMtI2/5CD7dBD51tCcorddvNt3ogpayxdYQ8rzLMusd5fd9XZgyrvPgEjaxd0COQ6u3qlhH020NZvW4XWL7Q32ZyEdLv5X7dZZn62DfZ22qlnnL3NjUAvOb64tg69h/a9AoLc+GSb0TNgy5sabHXlJpun9z3t9FPs3De/0d79nnfaG899k38B880XvNnOf/sF9pa3vkXXzrZz3/ImWUeJlqkGmK8OHKhysdjlW1UkmeIDRbK5fyHTeb4DY4J07b+7YjDTleUvk0DTzKdRUtX5EmVYl2WBW3PbgK2RrV1RP2B1jYPWLDuesbrUGKZPApKpQssUPXeLVJ+FPWDAs/R4gPUooeFbtH+vtpO1HauNfn5GzgK6EGSmr4Tp2J8yYsDpLxN7I0AmFI4x0RlqxBcfmYLIECGCsnBHfl6+Ay4fcyMcxxQG4zMBLBz3MAhW3MFkx7xiQZDOtg7vZQ+ZJ/f6eDsBLR0WALGDmQoOHyMACa8FlH1cpcKjuHGkT+c86Xowz0iWIvP8xjaBu67hh3UwVxyAImHxX/JHR1Aya0QKQADKEERpLABy1vTEvAVAB8WunBnzrFEkmM100o0dW+qLrzCEjM8qBytSBR185HMwVAwzmHLQW/i5HvcXX9/VbLMHMyxbzJB8DBsZNioG5VZYXGwlJcU2oWySrVm5ymqqazwvYOq9Cnchq793x9vf6yvsetmAHcxo6glWMKLe1NXX2NNbVllhW5flVTXa/Xc8aDMnT7GDxDS74lLEjATwYpmdYr29LNShcmlVHJ0dfbalfLPN6Ry0+rqt1isbcv3aVbZuxTJ7fsGz9uLiRbZy6RJbtfQFW6rjxQuesZUvvmhLVyy3LTWVMuebxJIyrUtWz4EqCZjUNdKrcpWNwC1XxXSHQGwesCZ2v70kSnXwgyIOWCoozlGabDMEvDkpqc46+dA3CxTnKwxm97iuOCtW4NR4mekCyYNEXzHLl2srV9szoHsL9FjAd3mLWKnSsVDI2sJvhalUPMVpcTYmLdlydYyJP0nbZB2/WXtANEPb/ux5HzHgHCovKUxwDHj60m/aAENYJ2yF76xXV1UKfuhoYfZQYOb3ivEErDL4VCwtPXDK8KT2VjFOxYEy+TjKmKCYDHXqFDPNKQymV2bkZIvdJLpi8hE4WCsgiBJ2dum4W0rJkCSdGwRw8X0BFHqurwiv7GHIE2P/BnsZJaD0SxHpXGAKHn8OliFo8qc9K9yzB1xhpPTeYto76CjNQc6MHiE9TMMrLMpXI9LlK+r3iG7Q2JDXAKiKTo1a0CAxDZHwygLbsHmTXbt5g1Urt05OyrBERQbTHyA/le+UP4ST1YVYQGPi5Ik2fuw4a2tps7rqWuWgGpNB1QnlN+bh+QUFdozu/WdLvd3R2WZb6qtVRyps4/pVtmDhc/ZcaoKlPL3E1j/ypLXKhG6tr7X0jFRrami0rRu22PoVK221AI+56auXL7c1zy+1J556wu555BFrXrfYijessI3NldbTVWGbt5Rbi+rhhq3ltrWqyqpqq62qukrgWm9VqpeVem59XZOzbVhy0KQfmKJitT7hfgKkWYbCoEzoIuHlt5TnfKZie8FvyYYAmAwr4jeAoVusVRyivrvLzW38klsFkOnjFFbUs0q6OJiTZseo7h8uO/8wkZT/U5jTtN25Ns4++5TZLbqfhe3uV5auUt3YFCPz5PAiPWB1foJVji+yBlmqSzmnbZM2VUNfUUmPcga7v2TEgDNQvkBRhjJOxM1pgQsVsKtLSimAA0ABLKbPoWi6yRldwFAFoArnCiiNBaB8aJOYKuE72gJzPShCLLNEKU/w1UviLcwW/xfDKygudNOb3mL24XhNALnDv5wpYBYoOxDr9wCLNkqR8aUynpAFSPC3+jAVHbOQqg8WV1p4DufYszlTprEgI7R5rzvMcxuAipkKQH26JfkxioTyAeQaG+tt8+b1VldbZY1N9conNWA0ZHrPnp6gg4YsD/Ksz2674Ua76j+32ZL0TLtQZvTE/jhra5eJTcOjvCBPvEeebNFG+UydMc19zW0qk6aGJs9nRlywT1T4HOXTO/MK7S1NnXZld53dWttq99x2l/31r1dbbWObnTNlrlVXbLIHbrnNGTyD2FcufNYqyze62ycnM82KivIsKzfP4seNtwXJ3bY8vteS81Otp3mz1T1Xa2vV+CaovsydM9PXMZg1Vax1cplNGTvGJo8fY6VFuVY2YYyNH1NkY0sKZF6met1Mxs48QKVFIMkwTVRzQMDZJ+DsUHVnbngwDuXlghUPm0RYs4COmLdNNJupwDBMfJz4PplLjk/83Qp8fq3Z0Yo7L77fqhniJd0p1b3rdJ0FihnsfrrCdSrcbUrLjfr9sLZVekCF4oNFztWWo3teqOizZ9dWWJOICudhnRl6HguArNTGsKX96XEeQcYZmLAOHrG9+/O0B2AAPkxYGFhnh0pN4QFQB1spDAAlbbR+MRoUK+igYNEPho4ErNBBVmEDcNVe4VB6PiOM7409g5m3Vmx1ME1JT3XApMPGgRrg1DNJIp9OwHfahs9USsv19o4269H5LoFoa2uLNbY22ea6CqtqqrNabfXNjdbS1mItusaKT7gefDiVngEY854BS471BCvOWGY4+PMX9saPLlGGyG66/bbb7Gc//bHdfPN1Ans1EAJOb1xiDUm4wj1sepWY3fNr11vTtBLruvofNp4GJK7XMlN7fAyr+3NV3gAO+RDmR2ZWpsqzxZLE4NoEYCFo46rp65T2qHFlXc6Tk5LsqOXr7Ynkdqs+dKrNO/kM6zztBFvwxFOW0yrFFojlip3WNjXY5u4WGxiTZ81ZaVYnQFwjy2JNF6xxvZ2WO8Y+OO8IO2POXMvIHOf3DXT02oqUTMvJSrfivCwbM6bYSseX2OSpE23qlDI7ePYsn3c/VyA/Jj/fctQQ93Z02YSJGKIHpqCVAJ0wyadUrk+Js0tmjbVLdQIGh9k7tJOd8ITF9zhL2wQB4lOifMo6B1rAk/v4Nvu/xTI/LfP7ZIHhB1Xl8VMWqiif0Z5xCFghjyvcQ9LpG6oG7TTZ2rBcngnRZCUlVnWHCt2jDWbJUnOJqmsAOA4SzjEwHgBGAHXeY3/JXtViGIYDozIA9ujgJSUMWF8ALgBeALba2AkovfdbwOgKJYbnfS66l3A+IB7WJwXzThcpZ3KqzChdp0MCPycgy9J2+BV5Hh1K3IeSAmxutstEw5fn5n97qzNQAL1N4MlH4viWux+3NVt9a4NVtdTZ1vpKq6ir9m1LzVbbVLnZNlduter6GqsQS2Orbqh1YAVQw04yhLTz/NEofGOHvGS909zsAutuEztXw9DT3WF9PfiWu2Wm4S5hRAP5P2gLnlpgScW51l9fZfOTsyw7g+84iVn3xlucQDBeZYf/WJXA4tUAUriAb7wKk0Wj6TSk8wk3CswkHh+36kuvnhOn39V1VTZ5Y4Xlv7DMlk8psYUTx9iTUrNFzz1nydl51pKUbNnpudaosM1jJ9tKMd1mmf8NjQ02mCbgLiqywalTbXVJtj2b2GtP5aXbegFh+smz3Uc2LWuMNQgMu/iwH/VJacLXiwsGlwp1EdfBlo2bLDE5w+vIxEmiXAeoAIKoF+AECP2vUHTTygr7qk4yX5xrdMSEfkPhoI3Xhu9ysjbO42ccp4gO056ZPpTq8brxGF3IFRLOEJpOUiSpeshZqvafELoAdNy7SeEWC+1qk8VAhYRjdQ0mSRyqIt7h9KiOSR+LjADMzCwXJntaSB+sF28sgAnIh2ndHzJywKk3C/HPuZVAAtAEOJwh6mLIOhh+5IviYvoRVgAXmnW+wpHOsuwYC0Ckpcq8TRCrFIuBrXovtjbvPNL93MO1cLxlU3OTD1/iUx10CDQ3sc4ivkWUNMY6AW6Bc2CK9jmAArZc6+rED8q3i2CnTMeUcvmHvVrFOBusrqXeapprrKqh2ioFGhtqttgWKXkFYwSltLDUapm5MFP3wwIgyouh22gTGihf+k/l09zcYhs2bAxGMcTYpjNP34IRBt1dPXbMscdaclWdZa/caKccLVVSmVL2WAg0ZpQn2sr7BiyVMZXBd5nwaTNOLyExmJ5LjsTHyWIY7LWW+iZForJoUxm2tNrHiifZwVtrbfPkHOtOj7dG2Yaph5xgaedcZM1f/YIlfusblviRD9tTR82y6w4ab3fL9H4st9RWdslSqa6zIm2zVA4n9SbYOcl5dpFAfkp9nW1Z+JSlquwLcrMsLYGpowne6ciXVMkHqjL1prx8ozPbro4WGz/hwAZOgIe8vl7bKqnLp7Vn1fVHtAFYrHA0IVY9YXk+GF0b91VqW6uNaREALWDaKyA8UwgmQ8P6ZUSmlemkgHONdvNFSyfqeqbiW6Lf7crQlXpmt6zAfyni1UoQZj757KKGND0tzqYJDQFrgJFtvjbAEyCFfQKc7GGqpHl/yYiujnTPPXf7kl4OEDoXsixnlNrQJegjSsQpBP+WfrpSSdt0Ta2+FI+T/WKl3vutcFzmGr2/qtG+cAeKjHkO42R8J/fROZSZnWkdrW3OZFiBPicvz4cOwQADJQ6GR/mDtaHozoxVvbgOMyKBpB2mjA90oK/Xx3q6v0+bAvg/gBxFA4y5l9eCOTEHngD+7fXYBuA7E/Y32r3sq9WRnnr6GXvkscf90xcM+M7Jy7Wi4kL/9AmLnYTi5QpjFIhmZGb6LJ1jjjjaSsZPsBS9F40TjRiFRVl4OepYp73cyZ+KLZW2aNFSa21utlNPO8VKxxV5fvDxt8XPr7Srfn2ZTRxfalNnH2HFYyfauLIyOzQz3wo3Ndvc2h5rKsqw3nEZltDbaBWP3m+5G9bbjI1r7ezWTjulsd6Ok2VweG2lTW6qtSKVQUrvgCXJvuysqLL2DlkSfXFWkF9q8w8/UvmbYL1xfGpF6hirB/ilKX9863UC2EcfeFQNR6+dec7ZNmvmNMvLggftWkbj6kgsogHYock/0TYv9vsybSyogXANcxhTPFbDnd0xeB5agnlcq/+qFGCNAjRLhSbOSrNVjf22QteurDH7jbbnFbhFRb88JUFgOeizjYiXoUStndIh7YmPmoUmAOpxIkEdOkn1ydIJ3uhgbYA1x5minbmKoFE3Ex62isNvT2TUr44UwxIpSMxEd6Tkt/aqmJzn2K+5NnFNmYh/SxlHC+8ghemkyrqNreo85xzEVEkwy73TKFbZMeWJiN++uEZcgo1VZuUW5Fk2K3orPCDiiUPYK/e9s0P3AbpB54dAU2khPgdDGKn2ARuViSogppc9UcDLnnuJG8amBHl4KjybfwxOpn51S6PVaqtrxeRv1nGzr9A0mmT27Nk+BZJ56PmF+XbiqSdYSnqCDXaLE4h9D3aLgffCODvViMLIYJ69liLgSUpn8kAXCxrpPExem4CH6ZGsqB+UX9D5o9KyZS+sVCOW5l//zC9QBVRmq1Rt/fq19qff/dG2VDVbTV2TyrjbUhJ6bcPaVVa+foPNSkuyhJqtVnDfg3ZOVZu9sS/Zkm9/1JIWid+UV1vjhk22ae0Wq67ttqq2QWvsSbGeAYanJVj3YKJlTRhrk+dMs6mzZtjBh8+3Ep4dn2TdnXQQMpKjyzq72vV+rQJPVseKs1uvu0ENdaZlS+HmzjsoKOcDVPgkBf7G+7UBRJjDv9IGMA1VCwAtNzHOslPjHOh4Y/ZAzvkyLM7WjZOEdrDOBF2M6xqwFkX4z3o9IzHexs/OtMlZcZaeE2e5+ck2XjhEHMTNeM0jcxlHEfxOV3jiBTxhkZysFlIzvTNHPwFywrGv039bVB0JS/r1uP0qIwuciHIlAMyAaaI0iJs/+g1Y+Tx2VybM9eC8/ulc0BMLaAKU3jmk3/ieArNecROea4TVTUytdHMbv6WvqMQ86CSfPsgq3gC2l5TuwXcqZPaB7yHY8hxPg1pGOih6u6X0MkXbxGDo+WWGE2wa1g0w0NPuKzwJKfAOALY+m0aAHedsi4+CKS6xS7yCrVJIwHNrfbVtlkm/qbrCP6UxmiQnK9kOm3+QdTQL6FlRv6ffspJlKCnfaKDYmJFFPoSztoLywzcYNHCB2yTRfbv+JVFdx29K3gKaNCrNajQ2bdpktcqH/PwsKyjO84aHBm/1irV6do1l5hTYlFlzLSmer4/itum25Uufs1uu/bttXLbI5paVWHJfu1r9LPu/n/7c3nHxxXbYcafb5NlH2ow5R1hWboniKLTi4nwrKU6ysROSZGLnWRpMYiApWMsg1oDjE3c3j9LgyxaqqvCbhvKh+++36ooGXzT7lNNP9GshETgQ5QVtfJ6CXmyabVZcZ4jPjprwnoRBK0oa9DGZDPk5TttZ2tp1Q7dsdnrk3exX/Whd222bhSInzIiz04tVWivb7adquD5XO2jF5Z02RlQTcKSDCYb4kUPgrQFAN0jfWIYHkMSfit9zUl6irdSenvOnlOfhB9ykng5WAOdoENIyYuLA6GYOFRO0CoDJmaP2KEkIluyHMlBXPt84B94FwEsrT+cF4zwRN6fwg6omYw6lqWJnZGWIGcg84muJEvxzdXW1zjLp8OkQS4IJtbO8nBird1AJYBny1MfwKIEuvb+ExxRnqifpALQxy5l6mJicYgMCyEGxlH7t+/R8//QDPfpKTx97KaKfU9qZA9+i57E8Xqu2RpmmdFzUNzYqLbs3n/elkIennnqSjZF53qP8eOqJp5WXfAmRxaYDJu2MQLWdzjTKEndH2MveqYZApelMk3GzgKc3Mio7WGcX5a28XL1ipW3ZvNW2lq+z095wmqUprE98EOiWjBtnJWXj7AMfvsjGlOa7fztLYHfwjKl20fnn2mc+9kH78HsvsnPOOs3mTh5vM0ukbs1V9pef/9Cuv/qPtvr5Jyw1sc3ys3osN0MmeoIAuS/F4nvzVJbM3gL0GIuZYF0+LI6yRn0DNw2tRL9YdZwaiXVr1tnSJStt8uTpNmFiqRUrPdTB8LvzB6IAQDdp68TclZo8reMQNJnLwAD3UDp64myMqCI+RjqBAFAuA2AAJsYsfkhWg8f3eVP1oGU9L7BdO2hn9w7aQmX1X3Q+dAAyiIuOJVUJK9/631DNOFFcNXNVDAXNzLIzS9XDsoSxDIAv0nZefpydI6pbpPOkhW1/yogCJxICYcAYA+ALgTRglLHzvsFogt8+JAgzWTmCH41zrI3J0Ba/rkpOnOEzuBf2EPhGMa0SxCrSfMwdIMtYS84xLIZnOihLKWBG+OLws9Kjy8D59DRVBf32TirtBwBpgSEzkOKYc52cbD0wSW2dUjAZptahWsDcaT561U6nQnubD6xnmFKjtua2VoFlk9U01FutNkCzQaBJOPehjiJhxk3PQJ+dePKxlpAUb9U1TXb77ffJZG4I1kZRucHmu9q7xcb69Z4AT58ahm6rb2q3qlq9J2y1qc3qmsXOe/qsSWy9VQ1QbX2zVdc3WkVVnd1z5/1WUVltkyaMt0NlLre3NVlbY711Kp+mjS+xL3/hIzZ71nRrbWlT/inemlrbtKXCmppbraq61vp7lPvKu76udilOr02fNcXmzp5pfILl6ScXuBuAL3C2dzCbqd9qm1usjuXkZD00NrdbXSONV5s1NKmc2rqssYkyareW9k6VW6t16dyzzyywxx572nJzx6iceuyg+XNVvgrf2qx3Hl2WwnCEGsesHIb+1Gjb3tRVtm0TaZfVV3e6Oc+wIQabY5ozNIl55XSRAYb4SatLUnzsJT3tc2SpTFU8oe8RoEU9s7RnYzX5pKYAeJOTUgJ/uKRddYxvszPaAeZZoBvTdUmk1z/bwTCqjTLTl28etAbZ6dwV3Ln/JG79ho2DDALeldTX796jUFtba1/44hftuQULnC2yBYOfQ5AMfJAAlU9B5Big0l/ILslI7uHLe3yLpltmMmFgloAN99G5AjNkjc+s3BxnjfT+4uAvKi6wRAEdjDYtM90ys7KcifLMzIwsB1rmtCdr799o71fr5+nQ80kLLFL3MpWTFex5Lh06vrSWgN87ecQqmXPPfcxxh3n5/SpcOoX0On6NxUMAbRddDDrEuGh22tHH2/gSquKuxTtnYpXrlUjoYtidXH3NNXbfgw9Yamq6bd642Za+uNSy8/Jl8qbZ4Ucf7XnGFEwbFKtPSbV4gSufQ0lIUiNFHundgrwNvhMVFxdYHdyH4vSrjJ585GFfQKJ89Xr7yGc+ZgUF2coqmf2Y+JS78hJLAPOZPOdG0h4/GCxDyPoCfZ0takjVgKkuwG5bWjqsvrbFHrzvQZs5Z67V1tXYvPkHWTbOe93TrYaWBjQuLtHaZYW4PzpWBuQrdY9zSUo3/uuNYsRpmVmWrvKtqa62o088wRtbb8D1zkcefJCdfuxRfv+uBAa/u06GXQl1kC8j7E4eeOABu/jii92q2VMBDOktD3JhxwLThOWdokBv1DE92tQigBNQrDhEgLlU5wXAN43NtotyBu0Nq1utXIScVMMe8afOVhn+SHS2uweyYd5Tf5JU4napSXcKI2EGrVv57irDpv8OVXWHxTILtEvomSTUHBQgQ5nohScdADKCr3NP5GjV4b/85S/+Eb7dCZ/Z2ZVs2FjukyZGlHG689w1B6zAbA9Mm3DVcO9ZlmLgM+O672GhAIuuwxZROEzlNrEMzGUAEYWivpPBdB55RguUYJTp6ak+a4j7OYd5SUdOEguKsI4WjDIj07/HzmeCM9IzLC9LgCRlgH3iH2XuYKpAOS89K1jYWOc7YIxqMBrq6qylodE6lJ6mOrHHqmrbXL7J6qprrE6NBQ1GtZiRT9VraPCl8up1T63CMjSqUefq6/ld5z4+wIx3HE0yGK88k7q098ZZycQpNueQQ93PV1/XYk8/sVAg0mTxyWmWrLyLF1AmJqQI7PjwHgY8ZjDrEPRYi/KoWSyurbVbbLJX792ircnWbygXiMVZbUWFnX722ZaSkWvtPQLKgTTrS8qxtr5ka+jkG+lp1jmYIjafaCJ/aqxSrVPF09Dabk0d3dbcn2A1nYNW2dprVS29vnBuQmaOHXr0MbZu3ToBbpItfHaRvfD8cquoqNrWaMTpfLrKPCMn37Ky88QmCwTqGZaSlmsJqdmqIqnW3NBuY8dP8bJhWNnsQw+xfr0bjUOc3pdvkQfDsQ9sCcdA7kwABGW5FUvJYH8szsE91Fh61zdoW7XELE+B6H2fXdFit8b12tbpYpVC2xTp/vSkODuxJM+eGFdqVYIAznEfvfh8P91zkSm9MdDEB1osCnvhrFQ7VKh4kH4frnsKOtQACzShDiAJadBp71Hnvv0pIwucQjcHRCkSFfClLTgf+jG5Hh6HvwnH/GU/h18xFiciSBToBqwEip8Qz1JnYp0yxVJlpuXl5jl4Aq58K8dXZBKIdjMLyLegVxhTmkCwwTwp3JjCYpsg5jd/9lzfivIKLEPx0dEEeDNMhs9oZAqE8bkB8rBLGIkvchx7V/ylPMcZnlgyPj46G9r1m6EiMAj2sJ42ncOPOpoEds/C0QkwROXflJmz7JAjj/Keb8Z1rlm11pYtWWrVlZU+b598oRb3qwzYWCwF2wEzmjyBhTP6gVk3nbIMVorJrXx+iR134sk2Taa1StsrPnehCkkC4eRkAZRUikWgk8UW09SIJaVmWLJYMKvNx1OmAjEl0BIF4ikCvgxZEWkZGTZp+gw74bTT3J0DGA6IGS97fpktfHqBbVy7wRcspg6xtivpr1dDV6dGcewkPjY31xLjVJ5STaZy5hXkW/H4cd6Iw0ppjKlXAwJ+9ge6wBj/28sYCDoHm2OPeU5YgBMWydrw9MBz70xt8DKA8Hhts5Z32dcU8LZ5Bdbz4TPs2XPL7KfZCfaryjpLkV4DlF/Xhr+UlY7ocMLUD5/FfqI4TlpFj6n9svV6yNPK66k6T8cRz0aoLYSFaaJBIwpew5QRHcd51113qaWvUGULYC80wdm2F86hYH6sDbD0mqmNzh8qbTgezns/pdyYybBQ0gM4Ef/EsglSGCmwlIZ48vPzgg+/CYBZi7NFLG/61Ok2d/YcmydzbuK4CZadmW2lxSWWkZZuWVK+7KxMMcUGn8vO0BOWVWNIEe4BOnn4/nqbnumArjTTKQIoAiB85oMVkgBjngtAsvF+pBczNviUBu+S6NNEWdGnQKbw7mRfjeNctmatbdy81Z/HO1Alc5W+MqWzrSlg2eQV01E3btgg86rXXRnKDJVT4EbxfJF10CB23SBQ4qNuK5evsCcefsQ6mhrtre98j42dOMHvi6dzj4ZH91NT3OrQnsaTcbrkHdaIryIyKMgDLMX64sWM9UQfzpSg30mJatDUkGK+8wFAvlbJgiAMYZowcZJuT7Itm/V7zTpbA3i/uNzWrFsjS6LBiguV/0rz1hXLbMkTD0oR+2z2/HmWX1Ts+UZacnJyg0aYuqdtQmmxTRaL2p2E9faVCs8e6XGcocDa/lsbA6EscK2UaTtUx4y95BwmMhtPYc44TggANF2BsxRZlo6fU4C7N3TY3xevtztXNNma+g4r03t8UtXpK6KM6SpOmCVgS3h6zYmDjRxl2PN7ZPIfLqiBYWLqs7jHoUpAntrLHGXnZlVl0oPZj1YAyLzP7mRvjOMcUR/nJZdcYosWLfKKHwrghqlOaW0PAu5nAmBUKYPWPZghNPQ3ysV9KCedOYiz05hfB3CEBfpzdF9pSYn7I9vE7rJzsp090uuOvzBTJnujlLi5tcUmTShT2GLrFEjSuQMQ02nUQueN4nfllmIxgJ7XoXcYNwEvgmIBGFmZwar0uABgmPTSci+Fyz3pYi25Sh+9xnwgrqmpyRnyey98u82ehpt917KvfJz/ufUuu/9x+llVRnpeisAtJZUV9Fnwud9qxDQ3rl1rLa2tMnWz/Js8lDBuT2b/wPLJF9haqu5trK3w+iCaZuMnjLNDjjjCsgTEfBLaASUp3csQwKRuBOWd5PmPVUFHIkAcrJjlyZIAsglBJ6IaT6oGloyvnDVIx2K/2KTKsbPNasUo169a6bPHMrNUB3q4N6hH+F/xedPpSKOaJfN90vSZMuNzXGloPOgkXL9uvZWOGeO/SR/QftjMSXbyIQwl37WMZh/nroR6m5cYZ0f3DTrjpFRpRvFx8jb4RwkDgG6cnW3tKq/ZK1udkQKshOVTGP2oucrtWO1na49JT1ztQjqmXqbq3I2K6AYdA84MVQIE40Qv3ypkhpU+02S2RpQ3R2DZqIsLFJA6B1NFIwBPKMG26rEL2Rs+zhEFzs9+7nO2aOFC/+2gJ3HFj1Xy4EwAoGy05mxIWDk9dwgP8kicmboyBZ1IhGOP4rERlopOeAc8HXMOQIO5YuLTeSSN1GmZ3lIEFprgWpZaIDomYJqAIcNqSGVGphRI5iEmtccLYPJshXUmKtaYGuvBp6MI1kmrzzsyQwmlxM9KOriHDhAGxHMvPfgfe8/77eBZc/z9diX7CjhvuvsBu++xpzw/E1QeKL6PWFAe4YqgY4b1UGurq5xR1qus21qb9V69avmxHBjUTAeaylv3FY8psTHjxtq4CRPFAgU+Osf3f1gdyuuFwBXw43n8ZtICnXi8a1DuqgmwUI4VnD2/US86FHheWAcYkBsfHxv76yw1CE95untEoNLe1uUuBfzZ4rLewcWnW0JQpIxY8s4BPImZXcGXBNatX2+Tysq8DtE4HzajzE4+9LUNnDDCM7Vhg8LPqH28CSyR7syt2jAEHnxLqU1aUmcF5X12tIAtRVkPrUAA0cXa1mk7QxvsFZO/QPcxLIoe/Ft1fKX2vCU+VxYGAWVeEMLmypbn21Ctqrr1It5dCgewYuJjpwGYpGP3nDyQUQ+cnwM4xTiRoHUPgI4KDvigGA5sEgAT9hGwN7EcKRfhqfRUHPd9innAZriHjfCE4/tDxMliGh0yH31Ikc6lpvBJBz5D3OEMhDCudNpcQWUqA3goF9dgVyibM1mFUSAP52n39KPjUjL8f4RVmhgPSscJcfJ5iMRk5tBrU9p8LrxYDumJj5MSCgzw+7EILorEoHv8bJ/84EfsoJm0s7uWfQWcN9/7kN0v4AQ8MKFT9L6MTqBnmnxCofhuEoAVsj3cEHzVK2jwlH0CFho+WHqGTGhGPySqPJJSAEQd649PMsM61cQYC68QT5L3vOvAsz/Ie/I2bHhw0fhlHqI4qC+EAWC5j99xfONdvwFVlWoszwBUGGrgR6eMiG9AWkc6aeR4NzYVlzcAIUBS/3geHXukMy9PKqt7DxVwnvIaBk4AjLnhgB0dMDBFwBJWp9ffNpOnqTDR7r1+lr3lspWWf0ufHQpX0YWN2uixZ4wnWo45zoyl4/UjVXQ1Xa/UKqpIJ9NGRfgjhQdIEZ7L3HmQhkHxD2ljjjv+VHycnAeYWZEJ8x5mSw6Rnt3JqO9VDyv+UHG2JqEyU7mpFFRW2BcFzp7K2djY6BtgyPfX8TNS6UMQZeMYIKipqbGtfImwscl6MLHFJACs6qpqq66otNbmVje9HXz1rKD3vNc7cPhuTEtTs7W1tHpveauOO1ranFF1CWi6tfE1Tea6M5Ae5SHeyi1braqyyhfMddapOPvFuFj0l06odsWHD9DHgkopGTTOKAPAiPPNug/fH0vR8R6jSWBg6Vm5lpmdqwYoS4qfJTAR0IjFpahMU/UOyQK/NAFqNm6P7AzLzk63DDUMuZkZlqEGKS81w/LTMi0nJd3iWTxaisHEgb5egZtM5f445YOqGx9jY3gK3zJSQJWPwK1P57voQGMtVHrogyFEdMgkCITj4lFhxQlYKi3sQ5aYIpBKSErTOXysfSrfOtWJdvcr03j5FFkde0PHBAa+B6VyY+1VlivISFWDp980tiwlSAcenUy4VQTp1lRTb22qM4zsiKH3a1LQWsCSjTUvASYADMACuGB3mNtIfl2fve/cpdb1qCwv/a4VstYra8Led+5jgyGOUcSV7zEr/WucrwcKGwVcj1b4i7VncBfslpxlz0D5k7Sx0jvuAsCX5wOWAHc4dRQTf3+WxogCJyAVAmMIdhyHW/gbgTnirGUfspbwHio6g5oZQgTgAqIAYcig2ANe9GaHAExFZ1UkzDFfgFhKj3mG4uAfRXEw1d0005adnWVjx46x4uJi/7gbTBBjHl+bf7+I37BOJczjI22KG5MQoPSedCkZ3zZqEzvoVBr4zAcAymwl3pMe9k5W+YmlGTZM+n0I1CgSZ4kZmW66wuAAkAY1GOHkBapoWlrg/2P4F3mXkZHu+d6m929hCToBC3mDbxLG7Q2f3pfz/m0i5yzUkUFntGAQ/ucmNX4M5cIvWVdXb3W1dX6O8vLGUelgZSoAza0N1Rk6oShLyok0YYnQ+89U2ZQUsfu0HNULpoL2+fAzXAVYCdwPoyQp6bI8OAcrxU/OlyyxWigf/KzEyTTN0tISHzNMfaROvFaFUqYHG/8loImZHQIgQMe+WWiBbxEqNCCSK57jfk2ylPsx5wt1DP/FNKfE8VeOF6K++MigPa1ADMJ/VhvTKY/W/kPaPq7trdqIm+cDrlXaAF5ynLhCs5y4qU0Biuw/GVHgpLLvaAuvURmhwjNnzrSJEyduU1Q2zGBXUgXnvAMmQ3pgpVKwEDBRVo79nphiA2ooKUzPzxGXNoae8IE3QFS2na8P6ecVH4yyXkpaLRaJYqAUKAfPbxdj7RQAtjdJGQV83WKjvZ0CAvyYsBWBMc+DxWL+06OL2Ygvk2P8ZwhMhufBYHkfzE7uiWHIqBFMYdwcsGTyfsyYMTZr1iwbO36cjRs3zsZqYzHfbG05YqX4hplAQNHicuHDbawrkJub70PD8PnSCAFOmOY0buQPPeqUDwweX3DYy0m+A17M/CI+gJe4sWBg6AxGr62rdUvjxRdeUJ1odbdICMxeNwSsNHqkA3BuV7lhydTVCYjVqAK+jPHFjAZIMaUBzfBe6sGW8k22Yf16a1FDyHA2GuJJZZMcgFkEhGFXr1WBOVItMaNrtfGmDEeC7QFmjMMsl3oCooSB+cFGAbjVCgy4MXCd7xQBjqu1AXZ1uqf6WrObf2z2uI5ZCxTa0KB7MNMBS0CQeO7WxrMBJWwM/JpBD8jok5GdOfSFL9hzzz3nIOkgGJPQhA/9V9tfCzc387WPaY37OEPgRcL7h54L7x0q4bkwXHg9jN9/cV07xcYvryluZuueoJc+YE6BuZ3g1wBBfJkJAkvGjYa+rLS0VFdUpE17AICFSAqLC72Tio4TWBSr0QM4X/3Cl+yIQ5mktmvZVz7OG+9+wJau3uDTRRmOw6IlNHIAKcDHqvrdMp+JKyszRVuaf5MnSWa5MEphmN2T7YBJQzIw2KP6UGPjBTqVtfVe3jQYlEOWmB3gTI95fX2Dg1LoWwxAWOxQDR7uGhrCgoJ8W7N6jTdGvqCLwJL0lJSWejxMg6Q8sVK6BKL4BsNGl7LjmdRfQBLfOH6uwPLp9wYT/2um2HSbGkka6IaGeissLPI4AGXSxDNWrVlm77nwzXbCobvv1DsQfZyAFODJHgmcI0HHENoK2PEbkxlaAOABajR9XOc+mCisFVOfcHygDe3ieshCeQb3E3+IAtTQkEXyDMqT+IeefzWyN3ycIzqO884773Tzantw4xjlCSrsSx02oYS/PYwqjTNIbb4KEuCJdmqjQyC8b2gc4X5o3OFxKOHvbXs2PQtTHBNcRCV4Dv/0TEqP3zAbovG59qRJ97MCOqyT5e0Ij2mO75NPHvM9bxiMM0uBgftvdS1FQHTskUfZycefYAfzGQcp8e4EYHCwf4UCK0cJdycvLF9lTzyzwEEEExdTmraGz4swIgFA6esN8g0WnZWV4f7o4pJSB9igQcPNQY+08nMgYIGAJFM1ARFcATBMQA/QAiw9vB4EOFPmubBVhSNT/b2Vt3x3P1f1kxEM3FtQWODslRWuKD8WaGH6pY+A0DnMd9LGlFAaLd6DRpBnA0YAIUL8NAw0+DnMJFMYN88FoqQF8KMRoF5nMOwsOcFSE+JszjT6nXctxMP2SoU6CkvfnbyScZw7E2W1gx1PZYNtAnaY6DBMfIrhHgBkz7XQHwpjZA+j5Dph6fmGVcJEoV7EyX0cExZ2yUY8PAvqAcMlDGFJD+l6tTKq1+McCkpseyLhPWxU1hBY2XQxiEvK4GBFWIaw4LMEtDjv5166J7w/jJc95wCPoefY68CVlt8okZthbLGiAgCcbXJNP3hOeJ6eYthwszKRKZbVMiWZ9YR55zOKdB+zmliBvqGu0TuluHPd+nX2yKOP+gK5o0kACzqDenpYXq3HfZaA/wCuCMxZgcfWis0CmWrbvGWzyUqxVKZf6p7+/jgxNxqKoCeaFfMBRVjQho0bFVerTOxa5VGVd+ax0DVm88aNG1zhATsADOG55eXlvvQcvk6Av729zSqrtioMPsokH2PKJIdWmeuE3aB4qiorg0YGQNVWVlYmdlEs5ligfal+TxR7z7acXPiQlNTjrBQIF9rY0jHeucgQNWeKsiTCupSaquelMs+91ZlvXt6uCUYkrx8ZMeB8pQJ4hdtQAdDCLXYiBmwBeDnASemGSgBywdVte7E+Zy8c61wYH8oRhvFz/NM5Z5UCaO9kQoG0p9MDBumdRDoHy4GFudJ3BJ09fWJR+Maa6ekXS/MFLxQ3X+SEdW5Yv8EWL15iy1avcpN4NAmdN7k5+WJjKZYnUGL4Df5Jb6j0bspCscsCmzJ1kh00d66lCDRTUtMFhABspypRovsRATtcFRzDqPGV5gtwAB3MX1wPhQIrWCVlQkmwhwXCBmCV3AegERZzngWTE8T06JRqVLzr16+3LVsrrFtlUTy21ErGjlE5xltlRaU3kHR04VOmLBmfC0gDwPg/majA+MxSgSl+W93orpOtm7d4Q4G7wBdx0bszpre6plJAygf/2lTWwdcEIokE2avAuQ30thMAC4UJe7iHAlsoQ4+RMC72vsUYp5/mONx0Ipz77hvHgGHsvlBCECXM0Guhn5O4QjbrH4rzuAPmSSeRuxSkTG4Oa+M3LNQ7KwQmDHdCGTvEbtrEPjukwIAtw51C9jqaJEugAvPkfWBbKSmp3gmUX1Dgps64ceOdceGDZIA/wAaQ0KmD+cw1zHNMe3yCxAcLBMDIaxoTWhL8ofg1MWVh7DBKnutAqgyGGebn51uRwHPChAk2adKkbX5J/Jg+AkJAC8AC9EVFRb5oNbOAiJPefMqgS3lNx6J/kVT5zzk6AAFX/J95Obn+Hg0NjTqf4AuyUAO8LuqADivCtuheQB2rgroaSSTIXgXO7cEvlBCwQjOa/VDw2l7C8zu8HrsPBeXYWaj/ju19CxgmgMWzwi0ECbYwPSGouW/V4wn2vIkPlhcD81kv2hjuRE8693i4foUnPr2TM1SeqQ1xV4OOYaue1lEkCYkpvi5ld3e/QL7Dmutbrb9HabcEMa5smbF5ApIssbnkoMEQ8FVV1ggY092PCMtjiBDTISnztBSBW1K63pNFp+n4SbAcASxABXrC1AsKCm3atGk2cWJZwEgFgCXFxcHQoPRMKxCwZSSzmEqK5eYWytxvUD4zhEl52xcsWUcHDqAHiLMg9WB3r1WVb/EPvbU1NFlrfZN1t7RbY3Wt9bSqERNDTuiTldAmRqr0psSpAcjMUprETrvaxE5ZZLrTunraraGJKaN9Vl9XrcauzXq7xKQb8d5FEsleBs5XIzsEyZgEYPjfWwhUHOu/ADxhipyLXeP80HDhfYAmxyGI+kaHUOyab/hXff/SGFE6sXwvRXQgVRyxVLpp7z5ZgS3P7e8OfHo7f7P9I3SCMIsqWQyRTwTTMbFV5rDaBp9GqSxwQJSd4HkCs8NsZlhRRWWFbdmyxTtk8D8CknwHnw40wDQ9Lc0bKHq2a2XKs8c/idBoskBLdXWNbSrf5HuW5ivfuNHWr1kXLN9XV+cdNMSNSQ4T5hkM7XLgFAulcwn/JONrGQbFBId2xn62CgxbWnxYGuWPDxrWSvxrVq2Wib7ZGwJ60klTS2uztbUrvNhxYiJh49VgMIQKBp7s7DiSSJB9CpwvgcrelRAQw+PgINjc5N4GppwQm4ydD8EURskeVhneH4Ipv/1+7nMwIYqXOpoAym0byKMA/iziIqwU1VnxKBJhoYMh78XqSDBIwAnAYyC8rwbF4P4OZng1ySznez7FzjDx79KY4J8kDo4ZxoVZm5Od5aCG2Y5ZjZkNWAK09JYDRj5+U/fSw+6f29D9LEDNAi1MIGAWjw9TEoBhsuMjHVNa6vFRHqGMHTPG04OZP2H8BO+Bh4kCqKSL59Ipx9KClBPPBZQ3bd7kaSUuGgxd0jv0+VAl/KPBxAsxWJn9oUspkkhGbBxnJCMvKP6+GMfJM17Nc14vEjDk3Q/v2lfjOCMZednni3xEMvKyr4AzkpGVCDgPXNkvi3xEEkkkkbweJALOSCKJJJJhSgSckUQSSSTDlAg4I4kkkkiGKRFwRhJJJJEMU/YbcPbW1lt3ZfV/bX2NrKvyKmRw0Lorqqxj9Xrra375nPD2Zausp5J1WSIZaenobrHOnmBg+3BlU+1K3RsNLt8bgg6gVz1VNdbfuvfyeLC317rKt1in9K6/45WP5ED/O1azjPLolv02HGnlBy+xro1bHOj6WWAhI81nhuQcf4RN/iFfYR6+9DW12ObL/mBti5daUkGe9VTX2piPvseKLnijX1/96a9b3inHWdHbWZh/9MuBNBzp17d/1rLS8+y9J3/LEhN2v6Qa8/z9u0WSP9z9ZTvrsA/apOKD/PcrFWY1sToSsw6WrH/Iqps32VmHfiC4uA9ltAxHYuJF+fcvs5annrO4lGTrb2mzvNNOsIlf/5zFJY7cvPu+llZb96XvKv5WS8hIt96mZiv7389Z1hGHxELsuTQ//qw13P2gTf7B17R/yHW49AMX+bv4BO69PAlh1A9HmvXXX9khD99gB918lSVkZdhB1/3Jf28DTaZw7Eh2Meum8s//9Bk8cxXnrKt+aVN/8i3b8M2fONNE+tvabaBnyNqFPGNH0e3o2TtLTyTW3F5nayoW2aaaFdbVGyzovCvp7G6z/3fdexSWVRfN54YzrXFwh4VB1u/+fF9/r111/zetXOwVGZM/xWaMefli0TuL5zUret++hiYbd8lH7OBb/mYHXftHa7jvYau55oZYAMnO8mQP8wp2ufTCiy1z3myb/fff2My/XGbjPv0h2/jtnzuR2SMZ8iyYK0QKSZ8xxbIOm+fXK6642upuvdfPjwYZsYWMX6kMdHVb7X9ucRZIazXQ3WNNDz5utTfeYe0vrrTkkiJLzMmSmdFu9bfeYw133m9d6zdZ6oSxFp/GetKBkNmbL73CJnzp45ZcFLQaySWFljn/IEsuLbbE7Cyru/kuS5s6SYU8x9qeX2Y1/7rJWp5eqOdm+HNokWuuudEaH3jMTfqM2dPdvKm7+W5vBbu3VFrqxHHW8swiN4G4h4JuvP8xSy4utHi16iMp+2oh41crty24wkrzJllacpb19vdYWVHwJcgFa+4Ws+yznIxCAVuPPbnyVrcqbl/wB1tbuURtYL/NGHeEPbP6Dp1PsOfW3GNb6lZbcc4ES01Ot4a2Knv4xf/YonX3W09flxXljLeWzgZ7bu19trFmmT2x4mad71T4iXbnwj/bQp3HXZCTHpQ/gFyYPc5aOxvt8eU36r57PU7iT0rYex9lYIon2yuVcPrnqxYBTuM9D1vatEmWPn2K61f3pkrr2VppOScdKyC6x+pvu9c6Vq21dOlFfGqKTOVm6cXNAthH3eWVPnOaTxNuuOMBS8hUmdz1oNd74kKaVPfrpZPTf/3//H4E3Wxd8LxM982WechB/hyez8pj7ctXW9tzz1va9MnWtmSZVUsHW6VPcQkJljKuVFboZqVnneWffap1SwcHhD3dmyus+m/XOvskfYP9fdYv/UsqLAj085a7LX3WdF9C8tXKPl/IeKQEUALgis4/W4WTZlt+faWzxNobBKRLV1nR295s3Sr4qn9c7/Q9lI6Va9z8SBnLB01fkpwTjrKU8S8/17l2o638wCWWdfh8yxCIrnjfp91tUPWP69y/UnDumaoMD1irCrjxoSesc816K3rrudb+wgo/NyATmMrF87u3VlndTXfKhHj1hXYgSndvpz34wjV20kFvs4MnnWh3LbwydsXssWU32Pqq5/24V4zwviV/t+TEVDuo7HjLTMu1WeOPlvkVZ01tNVbfstVOmHOBAHGpLVjL12cEyM9e4Wb/SXPfZk+uuMXBtqGt0q574lLFk2JHTDvD7l50lVU2rrfZE44RSI61qWMOsfyssQ6sz298xJnsfUuutqb2WjtxzoW2autztnDdfR7/6036BULNjz/j5AGCABEpvOCN3t+w7ivfFwnoc1cXAFV43lketvKqf9ugyEz1v2+yNZ/7phOduKSX3EcAYfbRh1lCevCdLSROjUb6wbOt6bFnxEi7HPR8jQZJ25KlVnPdbe5z3fzz31nuycda+uwZtunHv/HrQ6Vd5IY4UkRWSDPgmHXEfCcwtdffEYSRNQnRGQnQHI6MOuBsvO8Ryzn1eEubMdVyTzvRWxsKrunhJ9QKnWKpUyZawZveIBBdua0wkMGuHm/RLCF4pa0C3BfOebctPvF8W/vF7/i5UGghc08+Rq3uMZZ3+olqFeda44OPqTVrcUaETPnBV72QMO996TilYdynPmg5Jx5tubqvt67BC7/m2tssQ6w2IXP3n8J4LcryzU+KHWY6gyvIHGObxRjXxcASn+NL5vGgM8z0lCxnpLDTsuI57lrJSsuzI6afZRMKZ9r8yacIRCsc8NZULLSS3EluhhfnThAIv+Dxjc2fbIdMPtWmyxQvEqPs6G61ycUHKZ4CjwPGGSwLOOAMuLxmuR09441uvr/12EtsWumhsTS9PqTqqv/Y2i9821a+77NikFOt4IJzVN+fsIlf12+ZwyXve7vV33GftS560Tt3xn/hYwKpad43UPG7vzpztf5+G/+ZD7u/MTG2kj6CDsbHPkcyVFhtCyDmXha42Sa42vQ7qSDfJn//a7Imsy0hK9N6aurELvlSwktCWUNOsBiTx5RYatl4gex0yxQoty56wcO3iczkHHek0Pp1DpwAVWgGxCcn+fqWrNrjHUixVi1OBRUWSihp0ya7vyXslS9539ts5p8vtcILz/E4h0qf6DYFEUqSzGzMdO5JzMtRRfu3bfjmj2XGL3JnOoVcpVZz4w8uUyu8wJ8Pi+1YuU7s+E7vcHo9Cmb48xsetSklc+3RZdc7k5taOt9N6O39lf0DfEFmx5Io0A1NZ1Zm4s5umeCNYqLLNj3pJjwdPuMKpnuYxISUbR1QQUP38mcNFcCzT89OEkNF8jJL3K3wepJcEZExH36XTb30Ozb98h84wRjo6nKTG0nMznTg6a2tU91OCgiIJCE7y3rrX1qDNH3OjNjRS5I6aYK1LX7x5eAovezcUO5m+vYSkp1ukY7KK6+RmX2PtTz5rEzuVl8Za08EBppcWiSwx21XLstxXuzKvpNRB5wAGkyOzKe3LiE91U3wpMJ8NykQ/IswvLCAEcAvZWyptTy1yPWIVjEpL9fqbwnMvqGSWjbBWp5dHPzQc1oXPh/4Lp96zkre/3Yr+/aXLGPuLPfNYIrkn3OaTfrWF9zsb12wWOlJtAy1epV/+of33qepFX89SmNbtdW3Vth7T/mWnX/MZ337+Nk/tVVbFrj5jVkedhatrVjiexcpqQPrkIZve0lNynCAO372efaWoz/pjLEoe3zs6o6FOAHKocLyeCyGzHAphN52XAivJ0mdNF71dZYzNnql0Z1E6QYME+nasNn7CzIEjABqX2zYEqCENbZNhuhbKHlvOMl9oQ33PBQ7Y9a5bqO1LXzBxn78/a67cbIC+5qDoWqda/jQsFmHTGz8l1hxeWec4ub9LgVGGQNW/KFF54sN//5vTqjwje5r2e+dQ4M9Pd65UvjmM91PArvDfwhINt77iGUePt+HNcBCOd/X0Gj1t9/vPhgcztsounapkyda5R//7s5l/JHVV1+nQjnZ/TL5Z5xkzY8+ZelTJzsQ4jPtWrtRJsvjXiATvvQJa1NFqvzLv6xfhQyIjvnQO71nsPofN3jB48TOlWmfpudgYsJCx3/6w17h9oaM9s6hiob11txeY4dNfYODJCwQs3vV1gXeAVMooHtUIFXXstU2Vi91UDty+tmWnZZvjy69zjbVrbQ5E46zlVuesWljD7XcjCKrad5snTK9Z40/ytq7mr1Dp7mjzp5aeZtNG3OIAyHjPnkmfi18lhNl+udnltqaykW2tPxJTwsgCmudPf4YmetdAssbraG10pZseMgOn3aGM8+9JaOnc8i8zuIfpM6GQsNPZ+mWS6/wTlf6FMZ+/H1u8gKcFb+5yvWv6YHHbNL/fcmtMIYJ5b3hREtIe8mXiaCzOScc7a4xgBa9q/jd3/yZ2UfME6Ep8s7c2mtvdb3s3lLhnb2wYDqa6KhqWbDE+w1oSJNLiq2notJyZcV1CYBZqT/7yEOcDdded5v3KdA3kaJGYMsvrrDc0463PMW1DQdepexp59B+X1YO6k6G4mvxVkeZ17Vpq7dMsLn0OdMtPiXF/Yydazd4xqeMG2NphFfLs730VNVax4rVHm+qCi+lVAUhBps6pcw7hRJzs7w3jla1fclSlXyCZR12sMXzfRw9o/3FFW7Kp4wf6z1/9Jp3rFrvcdCycY6KRycS5vysqy73Tqy9IaN9HGdrZ4N3DhVkj/VOnlCqm8rdhwmIrq1c7D3iAGNN0yYbmz/V+KJmVeNGB9TZE44WWG5yIINltnU2bouT+zbVrvBzYwumiXFOcDAEAMfk0Wiax5mdXqDnZVpLR71tqV9tBVkqO/3u7e/2Y3ykW+pWWZMAGBZbkkPDt/eMrVGzrBy6VL7F/YiA38tEoNq5boPrWrLALeMgNf5YAiIR9B/01jc62EJGONe5TmU6Rfm2k/pIh1LHyrVOUnCxtYlRQkRmXPETBz/YbYJYbfLYEjHNbgGr9FFMt2drhRix4k1OFjlpdn8mQ6hwAdCPQMcwliT+zI4Va/x31lGH+nNefON73P0wkqZ6tB7nXpTqf98sM/2fVvyO82zsx94bOzvyEq3HeWBKtB6ncFlgS79BYm72iLHBUBruedg2/fjX7kaY+vP/2yGBeqUSAedeFFrX/rYO9xmN5AyM7SUCzgNTIuDcuwIThREzQmCkJVrIeC8KvZGYGnsTNCOJJJIdCx3FewM0hyMRcEYSSSSRDFMi4IwkkkgiGaZEwBlJJJFEMkyJgDOSSCKJZJiyR73qfJi/PzZqP5J9Iwywz8zMtIRXMdSip6fHe9Yj2bdCj3ryDuZv76mga21tbfa6WwZvPwu6lpWVFfu1YxnWcCQKMALOfSeAJturmTWEUG7MQokUcN9JWG7sX42E5RaV3b4TgHN35TYs4IwkkkgiiSQaxxlJJJFE8oolAs5IIokkkmFKBJyRRBJJJMOUCDgjiSSSSIYpEXBGEkkkkQxTIuCMJJJIIhmmRMAZSSSRRDJMiYAzkkgiiWSYEgFnJJFEEskwJQLOSCKJJJJhSgSckUQSSSTDlAg4I4kkkkiGKRFwRhJJJJEMUyLgjCSSSCIZpkTAGUkkkUQyTImAM5JIIolkmBIBZySRRBLJMCUCzkgiiSSSYUoEnJFEEkkkw5QIOCOJJJJIhikRcEYSSSSRDFMi4IwkkkgiGaZEwBlJJJFEMkx5TQMnH/VvbW192dbZ2enndyUtLS1WXV3tx+3t7X7fSElHR4dVVVVZb29v7Ewg/f39/kyevbeF929oaNj2jqF0dXX5uw4ODsbO7Fi6u7v9HcrLy//rPfaVkF9NTU2en69USPvQukFcu6sbr1bCOtnW1hY7s2cS1kPe+9UKcfB88o/61tPTE7sSyZ7Kaxo4AYf3v//99oEPfMA++tGP2sUXX2xf/OIX7d57742F+G+hYv/xj3+0t7zlLV7Bvve979nXvva12NVXLw888IC96U1vspUrV8bOBNLY2Gjvfve77Q9/+EPszN4TFPD//u//7F3velfsTCD/+te/7JOf/KQ1NzfHzvy3oGRXXnmlvfGNb7Rf/vKXVltbG7uyd4VyIc9efPFF/7169Wq78MIL7YorrvDfr0QeffRR++AHP2jvfe97vY587nOfsz/96U9WUVERCzHyAlhRn77zne/EzuxYAPGFCxdaZWWl//7+979vH/vYx2z9+vX++5UKdfrf//63lzP17X3ve5/9/Oc/32fl+FqR1zRwwijWrVtnaWlp9j//8z/29a9/3QHqsssui4XYtcTFxdl5551nF110UezMa1vq6+ttw4YNu2Q1gO6iRYts3LhxDgDFxcWxK3tXQsC+/PLL/Xdpaal99rOftdNOO81/vxKBbVE/zjrrLPvyl79sU6dOtd/+9rf2+9//3t9zb0hGRobXp/PPPz92ZscCeNNoA+4IDTkgX1RU5L9fqdx44432q1/9yiZMmGA//vGPPR233Xab/eIXv4iFiGRPJOGSz3/+O3m5ubGfry3BHPnHP/5hkydPdnaFssFaNm3a5EwU0xQGCMt76qmnLFf5UFJS4sfLly+3j3zkI/bss886c503b56bp1dffbUzM+KYOHGiLV261G6++WaviCjFY4895r8POeQQu+++++yqq66yBx980NLT023MmDG2du1ae/jhh+3QQw/1lv/xxx/3eJKTk+3666+3adOm2XHHHWcrVqxw9nPrrbe6mTZp0iRLSkqKvZk5EyEt//nPf5x9lZWVWUpKil166aXOGJ944gmPHxDk3r6+Pk8PcW7evNm2bNnijQhMK5Qnn3zS84e8Il94d+LiHgBm5syZ/i4wdsz1rKwsf+4jjzziYVBy8hDlJk1/+9vf/PkPPfSQh/3zn//sjdmdd95pt9xyi+cJv3/3u9/Zc889Z3PmzPF3JO8pkzvuuMPBfPr06Z6nbPxGsrOz7emnn/bwMDPSe8QRR/g1wG/r1q0O7nfffbeXAWHHjh1reXl53iAivCtpf9vb3mannnqqnXjiibZ48WJPyymnnOLPIF7ejXA5OTleP3D3UFbkPwyYsidsXV2d1zfynfcfP378tjLhniVLltiyZcu2uRjIu2uvvdbzlu0vf/mLg3lhYaEzadIBgFOvcKtQ5uQFdeX+++/3dFGXyFvSxTuSr9T7v//97y97Z4S8/vznP2+ZmZneUMydO9frHnGvWrXK3vrWt1pNTY3nF+8Au0V3cN3ccMMNHh9pp2yoP8R90003ub4cdthhrie/+c1vvL7SCL0Wpamp2cDL10XnEBUAcwTzCOV+z3ve4+epZD/96U8dEADRL33pS864QqHCUEFRPhgP91N5qCSco8UuKChwBaKSUzGvueYa9/3BygAKQITKj6kF2CIheKBsAO1Xv/rVl7E8wnGO+wBSKiMAOVRQRhSRtKN8PAtlJC0oHUoGePBcjlEwGEx8fLxXcBRlV0K6UJB77rnH3x0WRn6RJ+EGeP7zn//0tKamprrS4xIBWDdu3OjKC7snD8lf0kY8KBYA8q1vfcvTyu/rrrvOfvKTn3haeV8AkryFYaK0PA/Aw2Qnr7hO/OQB+QVIA2KAIa4WGgf2P/jBDxwc+E1DyPVdybHHHusNCgABKPzsZz9zAObZWCwAIgAOuAAOvCe/yVPcQNSLKVOm+J685zzvTf2D7QGud911lzdigCn1gPd94YUXttUT3jcUGjzelwaL+4mPsqYuAsqUOfWWek1ekC5AHXAl/bD00IdJw88zANLQUiCPySOeSZ5SJpQzoM47UJ7kHfWAuGiQqb/UP8CVBp4ypc5DAmg4qBevdXldACct8kEHHWTz58/3VvL22293JUehqXwwifz8fK9UVOodCSBEy8r93EOFozKzJ25YFXGiAGeffbbNmjXLPv3pTzsboaKGlRYBvPCrfeYzn7EzzjjDGRbxh0LlBEBgqLAyGAdpHir4a/GVknZcETAAwAkhPTAL/FcAAApBxecdL7nkEvvEJz7hzGV3Qr4RD7412DpKcsIJJ/g7ASYXXHCBvy/hABX8ZgAMTBGFRwAXQAPwQo466ihXdMxj3vFTn/qUb1wnLtgWynr88cf7ewMACxYssJNOOsnTQH7z7rwLwvPIQ8CFRo0GAhZKg0J5kTbi5F7y4plnnvH7dibEx5aQkOBgTINAA8f7wsxgpKQbVkeZkdewU8qQ9J955pmev//v//0/e/vb3+7pR3i/7373u25yby+AGAzw4x//uLNBGl3i5N6TTz7Z8yoxMdHDUocAROoGrgoAFKuAdwfQeHfM76985SteB6kXYaNMmYQNEHVwe6EBwoKCgePvJV+xkMh/hHhweRE3cVAfjj76aG/MAE0sFHSDOvJal9cFcGLG4KvEBAVMYA2YXlR+WmwUDJ/SMccc44q5I6HCUnFoWQkPoGHaISg552ihYZgzZsxwoMMHCDijsNxLpUVQShSEysue86QjFMJTGanEKCrmEmZsKKQbloeJSxjiJnwYP2DBMwAehGsoF89CGVGu8NquhHcEOFBaNhQvBBaE5wLWxIXChO8Ek+IaQl4AWqQHAei5n3gRyoa4SRP3oKj/+7//62wSNsc5GEz43FDxhwoWAGCOggOcMD4AGyDhXsCS8gOMduWThZkRljLE5UA5kNdYEzB0QAzAph7RCFPmmNewauoF6SIvyAOej7tmKHCSRvJ0eyFPeH+ukU/k39B3HHrM+5DOMHxYptxD3oTnSQNlEtYJhPfiHHUB5ovwjnTywZaJg/gJx/28C88OG3zKjEaahgShPqAz/IZwoFdveMMbtrkGXsvyugBOzBtYHAwRIKNyUYlnz57tFelDH/qQsyfMExRiR8I1KlTIJDHpOKaS4P+kUsFQADjAgIqEjwsfImBMBQ5ZJRUfUxizEfOKOEhPKKSBNNLxAfsiTsAhFFwPgAqKTIMAQPCOIePckcAweT5AjEk71CXxSgWA5h3xi5G/mIukhfSGgLE9yO1OMAthdCHr5H6UHIUGVFB0gGzouxKGfKYRwVcYdqIAktwDu4cJksc0QtvL888/7w0dHSSA4emnn+6WBaDAOwKUdOhwP4AIs4JpYa7SeMLoAWvqEuVJ/uLGCTsjkV3lA/UAPy3PpgGlPhEX98DqKG8aPwRAJw08E5aJqc81XDohK92ZALK8B9YPrh/eA/Mf0576Rr2lkUNHyEvyhfP4QhHynjLGzYDAwqnb9BcAmtRJ8vr1IK8L4FyzZo19+9vfdrMGxcLcoHK+4x3v8ILHHOUawAIQ7EhQIExRKg7AiZ8pZKcAKJWIyoxC0SoDxigNfkUqLApL5wtCS07FxGwO90MZICAJIOI74hpMdiiwYobCnlCaX//6127+YZLx/J0Jjn+ABHOReFHMVyswG94TVglIEC+dLJwbLmCGQl5SNvgWsQr4TfnhBwVIyVN6hbcfg4o5yzvB3gA+WBAuAPIV1wCmMwCPibu9ABw//OEPHfxxn1DO3EfeEx9+P1gwgM5vQBnggfUDeAzNoswwYTHXqU80jJi6e8K+AENAm/yjMQf4cQHw7riA8D+Grg+AjPRQltStH/3oR/6cc845x1ni7gQ/L1YXfmjcA3Qw0UCQdsCfBojGlXcP/dCkAyFP6BilX4AORwATsH7nO9/pDRTkgvS/HiRu/YaNg5MnlcV+vv6E1hqFAASowLtTeBgBDIjWeXfgg5kJ0wyZwPZmJkCHIoSmz/YSskiUZEdsAnZHhUVhto97RwJrY7weaee5IymAEmkEKF4paA4V8pkGiLgoI/ZssHVAJLy2OyFfAFmUnsZvuEIZcj/PGwqCmLWcp/EcGi/lBbgDrrtjgJQFAEXDSgcRJjSuAMo0FPKB39vXNfKE+zlPvR2u4IKAeXIv7zA0L8ljypPzvDf1HbKBGwWrDeYZujx4V/IhBGT8tK9l2bCx3MDL1z1wRhLJ/hIaRkYS0GhjEY1WARxxS+BGwR01VPCP0vGIe4YOJVjna1ki4Iwkkv0ssFnYG2wvHCUwGoV0woaxcLBWhgrgjxsFq4ltT6yAA1ki4IwkkkgiGaaEwPm66ByKJJJIIhlJiYAzkkgiiWSYEgFnJJFEEskwJQLOSCKJJJJhSgSckUQSSSTDlAg4I4kkkkiGKRFwRhJJJJEMUyLgjCSSSCIZpkTAGUkkkUQyTImAM5JIIolkmBIBZySRRBLJMCUCzkgiiSSSYcoeLfLB+nysjPJaX/lktAhrSLLGJqvKv5o8Dz87Ecm+FdbIHLqm5nCF8mddz3Ad0kj2vpDnrFEbfrlgZzKs1ZFYOoqII9m3wmrju1sMd1fCIrgoYFR2+04AOho81th8pcJCzSzjFsm+Fcpud8v7DWt1pEjxDlyJym7fSpTfB64Mp+wiH2ckkUQSyTAlAs5IIokkkmFKBJyRRBJJJMOUCDgjiSSSSIYpEXBGEkkkkQxTIuCMJJJIIhmmRMAZSSSRRDJM2aMB8PX19bGjnQszXTIzM1/VgO3XgzBWrK2tzWdi7U5GYgB8Z2dn7NfOhcHaDNqOZNfCZALydHeSlpa2TwbAM9MFnYtmF+1ayE90jplYu5OCgoLY0Y5lr30emEKMtl1vo1F2lM5oe/k2GmVH6Yy2l297QyJTPZJIIolkmBIBZySRRBLJMCUCzkgiiSSSYUoEnJFEEkkkw5QIOCOJJJJIhikJl3z+89/Jy82N/dyx7MmQFnqvWAR0Zwu4XnrppfbEE0/Y0qVLfWHkCRMmxK7smTzzzDO+MG/ukLTW1dVZTU2NrVq1ytrb262wsDB2ZXQL778nQyMYIsQwr1cqDHliKMbuhGEtbDuSF1980ctu/fr1tnr1aps8efJuF3sdKps3b/b7xo4d+7IezhUrVnge3HTTTTZ//vzY2dEt5OWeDCPbVX7uiZAve7IANbrGosk76zm+44477Prrr/e8Zk1ddG44Cyw/9NBDXgezsrJiZ4K1eRsbG33//PPP26RJk2JXRq+Qn+jcniwbt7thZE1NzQZe7jPg/Nvf/mZnnXWWzZw50773ve/ZiSeeaPfee68D4rhx4+yee+6xhx9+2CZOnOh7rlFgy5Yt8wIkfsbQLV682B5//HEfU/enP/3JnnrqKS9cxl+Vl5fbrbfe6mPbtm7davfff79vACoAMFoK+UACzjVr1jjwffCDH7QbbrjBFZoG6+677/b0cf2WW27x+ysqKuzGG2+01tZW27Jli19bu3at14tHH33U40GJq6ur7cc//rEVFRV5OBT6uuuu8/CU+WOPPWa33367lznhWVyWsZH7Ww404Hz66addt04++WT717/+5fmNLkFe0JfbbrvNFixYYMXFxX4evYOYkPeUMeUxZcoUu/POO+3ZZ591sCQMjd2MGTN8fDcgym/yhrJEzx544AErKSmxa6+91mbNmjWshnZvyN4Azn1qqqMUBx98sFe+u+66ywGZF/r1r39t69ats0MOOcQWLlxomzZtslNOOcUefPBBbzGnTp26rVCuueYaO/XUU+2RRx6xI4880ubOneuAWlVV5dcA5E996lP23HPP+QBy4kcRGUgeLTL7ygTlLC0ttcMOO8wVhzI699xz7Qc/+IGzmjPOOMOZJccf/vCH/ZiGEGWjzGGr//73v12JuRelo4EEMLFCUFTK8cknn3RwRrEvvvhiV3ZkZ8AQya6FfMvIyPB8HjNmjJdBaJn9+c9/9nw/6qijXFfCckQP0SMaNxpCBuJT5ieddJJ94xvf8HI74YQT3PpDP4nj05/+tBOUm2++2e8BlCE4AOZrtez2KXDW1ta6otCqAWooEa3YQQcd5IziN7/5jSsNAAdr5DzKBkCGTDY7O9vBkAIBDMNZOLQq7GnpOYap8RzYCuHGjx8fKeArFJQN5oh1AMABhLDMo48+2mbPnu0KCesgf1taWrxxpDzf+MY3vuxTBCETg01RVuEsHJSQMqXM2cOAKDvqAYoKs41k+IIeoF+wdohFXl6em9fLly/3MmK78sorXQ9p4CgbSArlCAEJWT66F+okzJIypszYAEfAlXv5jSsHsObZ8+bN8/OvRdlnpjotGPQfE/ud73ynm+zEi1LQ6mHeUbCwSRQKAMW0oEBhKFyn8DD3eA6FjhJjTnAfBY55ACs9//zzXfm4H9Ck8Hj2tGnTRgV4Ag5UrN3JaDDVKaMNGza4csA4cbcQlncANFEuyhxrAVfIokWL/LmwDsoEtkoZ0GACorAdGAv1gWvcT5lT3jSKmJWcQwEJ09TU5I3nq5nCOFJyoJnqgCaNHOY0jBEwRI/IZ0gJZYrOcA3QIyzlxnsee+yxroeUKW4z0oSeQmLQubKyMtc7GjbKnLIOrQiAkzLEKsR/TRr3p5B26uueWJx7aqqP+Fz1V1NhdidLliyxf/zjH/bzn/88dubAEwqPyrsnCrgv56qH7GJvyQUXXGCXX365K9aBKuRlyJJ3JeTlqwF6gGtP56pDLPY2GfjKV75i3/zmNx1wD0RB10Z6rvoBBZwIwDMaWOMrldcrcB7o5Ya8XoHzQJe9AZz71Mc5EhJVkgNTonKL5LUk+ww4aa3pdcPH+c9//tP3jL984YUXvIXFB8bwCYTeWMIi+F5+9KMfeW8dcvXVV7ufE2FIC0ObvvWtb9nKlSt9GAT+FnpmYTg4wnGKM9bzj3/8o/3kJz/Zdi/CEAxaIVwADJdB6OjAH4JfB6c5vYy4BjBVeBZpxMdDmv7617+6L4peSVo03oPhHXvCSg4UCTv0EMqLEQr7Quh8ovw2btwYO7NjIc+HCiMyGGYTCuX+WiqP4Qg+Szp/qM97Q7a3mhhNEeptKIyG+cUvfuF6PBwhzejzaC27fQKcVG7GcQJuDF8AeBi3Ry8tIEfmMu6SYzKYys9YTUxagJUeXUyXEARDwPvSl75kH/vYx9yBjWOaeCk84kXpiJM4KIAzzzzTvvjFL3rcYYHT8UB89AiTNsICooA6QM04NJzgn//85z38Zz7zGf8NeH/uc5/zDipAnmdS0GHa98QMP1CEBgEFRGj8KAvyiHekUaF8yC/KkN53OnQIz/jbUFk4R6cdeUO+0njRGRR2gOAKIk7KgnKm7HgO12kAKVfiIp/Zh4BKI8lYXjo/uJ9wpJfOLMJQvtQZ0g3o88ztgfa1LJQBpifjm3l38om85zx5hA5xTN6gK5Qf5cN5dIH8ozwoW8qI3nTKm3In/GWXXeZhKFt67jmmXCgLjhGuoTPoB2VCXJQH8RA35Uu5UpbUI9LFUCbCc51nch91Z09M7X0l+6RXHf8nvdz0vE6fPt172tiz0WPOPWQavcj49CgkQIkC4pjz9NpRCKeffrorGL2BOKsZYxaOC4T54fOh1xa2SEYzY4WCeMMb3uBgTfyco6cvrBg8h15+KhgDflFO/IvseQa98zyHeBnKQW8iQy0Y20gvMWyVykJloPee3uddDaGhUuxJJRgNverkEZWWMgOEUAyUCoVEeVAE8pTGjDLkmZQDz+U9KXeAFP83vazkNee4jzpD/nFM+P/85z9eF3hnyoz8hXEwxpAeXZgk5YsiER/1jTQwRhGQpfyoq4ADykpaUUru4TygyvsQ/pUIadyTRnFX+bknQt0YiV51yo28DAEK64E8ZjQKQEX5kD+UG3EBoqEuVFZWOohBDGiQaPwoK3rqOUcclBG+XMqAPSAHAUFfyGtGQ1Am1A/0kTqDxUL+M6j+8MMP97Tfd9993hPPu6DbpIn72XgGOEGZMkSNcMMV8pN3JQ92J7vzTe/zAfAAGxlJ5rEH+FAgQI+MYiYKBQA7RAAmFBBHOwBC4aKA9KrTulHAtJTci1IPzRQyl+EWABlCJaYivulNb/LCpfARlA92CqiGsypOO+20bTOQqCzMTBoqACqVisKgQgKkjBF9//vf74O2D4QpaK9GKEfKkEqNVcCgdRo3ztF4oAicQwmprDQ+VHpAj/KD1TCMCSUMzTDKkXLnOmVDIxk2YMQB6HFMePIaVsQAexpAGjbuwbUCGBCWNHI/DAsB8JlwgRK+ms62A02o0+gC7w04UW/Jwzlz5vh1iAj5BCFAX9AF8hOw5V7qfwiE6Cb3Uk6AIEPLADLihOSgg+g0oyZo8Cg3hEYLoOQedBo9Rld5LtcoD4gIVh9AT91B53g2QhqpOzSur4ZEjLTss3GcuxLMiPe85z3bZqAQD+P4KCgKjQyjNUJpGAPKeEyUBQYZMiHAFQWmQLmHAkR5uU4rQqsJaNKiAdYUGoCKsl144YVe0DCpI444wtPw7ne/28cXUmGIi9aXuFDWUEEBZpQUJkJjQHy06oTnHXYmPOtAYZy8J/lGq88eACM/adCo3JQT95IPlNdxxx3ne1gLG4oBIHLMe3MfCkRclCkASkNI/tEQwWQARsqA8kbJUCyYPNc5Rx0jfwlLGfA8pvZRBwFwyor7KCuAmnA00txLw0aD+UrkQGOc5DHlRwNCHhCeMqCMADwsQHSP8gHIIAyAHGydeAFNzpN31EPeP2T65Dc6hS6iZ5QPeUO+U5bsAVDKjLg4plzIF+KkXHgO9Qkd45h7eG/qEnlAWZIensWe8nsl+bo3GOeoGI5E5sLkEDI0LCBelkrBb+JnA0xC9gHQhZmBcnGeyhFWXMyykNpTsFwnHAWJQnMvz6blpNBpDXkubApgRUgPz+RZYWtMugBgQACgpnIShrSSLs4TbkfCM4lrTxRwNAxHIu/DfOG9+c17ki4UMixv8pl3Yk++cw8MhLDkMflI/hGG/A7LFiUkPwhPnpEO7idNuECID0YUumwoP5Se6+QP5cp5nkFY0hOWJYpHHpB2GloUKEzTKxHykvh2J7zD7hRwV0Ie8z67E96Verqz9wnBAgBlT7o4F1oM5BcNPTpDWcImmTxCHiGECcsX3aGsCI/+cC/XiZM9QnkQf6ij7AHCsL5AcKgDnCdcmEe8K3EArpQTzyEPiJv70DXKkPhfSdlRH4h/T8gKebMr2efjOOn4oQWjkJjbiokLLac1g/HhN2GjQJidgrnGC+NbhFVgDuDfJEMx12GoFAAm2DnnnOPPoCcWak9ajj/++G0VJ8xsCuCYY45x0xFlpMBgN5gudEZQqLSGe1MONOBEMJ3wTcEEmH2C75B8pCyHCh1/WA0o1u4EBWEdAsoANgTTx1WC24R5z29/+9s9j3j/sBHC5Dz00EP9eFeCJUI6UcSRlAMNOIcj6Arx7Ex/D2TZG8C5z0x1KjOAyeIdtDwA4ic+8Qm/D+c/DOG9732v03x8m5jTMAvA9kMf+pC3dLANnMoADxUG0OVelBjB7GB+ND3ogCo+EyoErR+VEeBEqfHFAdyco5XFDAB0ATX8b3tbSNOeFCLvsDPmuidCheEddycoy84UBvMN5vfJT37S850ywUTnmLKDzdDo8BzKivwDWPFJ4TKhDGhQMesIwz5kFlRmGkBMNZ5DGigDfKeULcyUuPBncy/PxPymIeU8eUgjCEgRH8eUL/40zDrqWZiOV5OPoZCGPWnwdpWfeyK810iY6sMR4tqZ7h7oQn6GJGp3srsGb7+sjvT3v//dARRmCYOk0PGzoIhUSpgkzBTFohCp8LBAhj2EPYSwDtgEILh9RmC2MQ6TsWsodFiBAfRwDzDTkcMUQFgpoExaOAejjeTlQn7TcUBZkV80aIAHwEZjg1mNuwLmj5DvWBKAFr4v2CONIuP7aBBDpz8SMhx8WIQ5++yz/fp5553ndQXzkQ4LnhVaHigBzBRFuPbaax18AUzAlWukFwmHxwC24blIIhkp2afASa8znS6YfCgIFZ2KDUhiksE46WwIGQJMkUUFmCsLgAKq3INJCKsAbAFP9oSlo4H4P/rRjzozoSOADccze0xywuDD4TqsleehwFznGZG8XAAtyor8ZQ9AUT7kF6AFc4f14/hHYKRMEgA0uQdAhD1htWDC4oIJGVJYdgAk1yhzABKwhYHyPFhn2IkQWj6UG+dgB5QlIAo4ch8NLIKFwkgNztNgRhLJSMo+M9XpuYPZwTAwjcMByrAQeq9hMYAYfj0Uhp5p2Av+NcwwOhdQno985CM+ZAhlQ3m4H8XkHvYoLhvpCc10wJY911FETMf9KQeSqQ77x7WBz409gEkjB3OH6VFelAPvwzFj88hnABImiRlOOZL/lC/uFhoownA//nP8zPS0U8+4TuMHy8UFgM+JukAY8oPGj2MaX+oA1xBcAIx64Dkh2FLPaCDxW+M7f7VCXr5WTfXXspCfI22q77POIZRnqCMbJkLFRxlQFpzuHPNyVJqwswJGysa9VBRejDgIT6UhbdzDNYA7BHkUZfsKhVIR76up1K9WSCvvsycKOFo6h4iDRo48ZSPtlBXvgVCWxEH+Uk6cp6KGvbeE5z0or9C6ICxgzDXqDfESD/WIcgzziWOeRcPJOcKGIx0Iz/UQaMLnsHGe+3kOVsZIgAv58FrtHHotC/VipDuH9pmpDssYWsBUfBgBoIlQ0VCaoeDIxn2wG/ZDwZAwhMWUxFxDOQjDMVuojEM3ru9P0DwQBd8lQAUYMTefMoIp0umC0rKR94BaWMbssSgwl7mOD5RzsE+m3lKBKTvOU1aUDUKdAPCQMB7OETdmd2gpEJ772VOeADJhqfQ0NuH5cExvWGcieblgQdDA7U4IQ2fcjoQyxSLEVbOjuLAEcbvg86bzdk/Aa08FtwwN+v6QferjjOTAExo3FvlgmBcVlYHp9J4DoHQC4YLB38zq/SgJnTkoJKAJwPKb1h5BcQA1FA0XDEqH2U2nD+cQfKZ0HiKAL3EjWCcoH9d2psSRvFxg4eRryF4BmaFMlhk5sGisQcLBzAA/Nu5lg7HTcOISwwUG4wb8KDfKlTKlXrC4TWjdEIb4YM7M6AJUf/e733l8hKEcuRZancSzvVVLY0kcxMUx9YnwPJtj4sJ/TR3bE3Y+0jIqZg693oSKuSctL2wLgHqlgiK8Wh8nbBLWiflJrzqAB6sLTWgAlaE/KA8Khk+UusA0SqwBno9i4mukwsNc+aAbK4zTiYRyAMb4OLlGByDxwR5htXQy4aMERAFmwjFzi/hgmvtSeBfydHeyq/zcE6FuAAy7E3RtVz5O1gYgrxj9gM+XsiPPYeH4gRlmRl7TGAGAABxASYPIMQ0U9/HedNpRPjSENHi4QYib8DB7gI9ZRNQLRrZQ/lyjfpBGGlOuA3bgCeVJZx7lT1iej/88dBkxKoKxwzyDOHkuYRCGq4VAShpJGyNtdibkJzoHAO9Odudi2S/DkSI58ARlwUSm5adzD0XALAYomf8MsKE4VHgAks4ahiwBfHQQcY1KiwC43AvjoHMn9H8yHRMQRriPgfEwGhQCpUZJUTbYBXOaAeg9acxf7wKg0lkHUDIagsaGj+yFywQiMD7cJe9617ucVQJIlCnslC1sACgvOnFpGClDgJI6QNkMFeoAz2U8Ne4VOvlocHk2oATo0lDSmUej/OY3v3nbzKDQTYOwFgHjsekoJP1cA3ypL4QnzaSN6aPUnT1p0EZSIuCMZLcCy6OSU1EZHoZfGvBixg/+Z0ATZYG9wAxQzLCTB2XBz4lwjNIxKgK2A5MEKEPfJYIpz+pSKCTKxqpIIZvh43CM+wWQYSKR7Fook5CNUkaAFoyTvAwF0MEE54uVzKYD0Mh/yolGM7R4GCONOU5ZwEgpJ8pt+waM51A3eM72s7yoNwAezJJypX4QP6MvaChhlDBRhLURqEvUEyweGDjpgb2SfhpVJHy/fS0H3KczDnTBXAAU9qSFHC296qQVswslwzzCROQ9YIykD2CEaVCpeWZoQqIYmEmEpV5g8nFMZccvFQImdSesN+QN4VAy2BDnuc49xMt9KC3pCQF5Xwl5yfvtTsjL3Zl8uxLef0/8duQNebAz8KB8CEPZkGcwSPIf8KLccH1gspPPPBPXCmEATcqcsqMOktdgAOBFHaA8yAfipkwoR66RFn6HPkzio76QH9xP48uzyMcw3cRPeJ5HGgFL2CrxhTP9eA5gTNpJG3lDnGEdo84Q987ygbjJiz1xj9GQ70r2+Vz1sNeNzMF8owDIRAqPzICOU8AUCH4VWjgyio3C5OU5pkLCVLiPAqD1IkNoqThPhhMX/jEym0pCuigc4ofFoJRkPq0c8VIAxBuakqG5wXOJi4LheaSb+Kgcr1R4hwMNOCMJ5EADzj0RXC+4YF7LsjeAc5+Z6ldffbWbCvSg8jF8gIx557RImAD4sqDlfOoAkMIxzDkAFwDjPpzbYYVi2t2vfvUrzxTC8FkLqD5xAHz4yQBmpvrhG+NeQBXHMi0ox/hamPVCOPw6OKQR9pgsxMEe5zr34NgmLZFEciAIuoEZDhBDWiBAEBKEOo8uhSYvQt0O9Yuw6BHCeUgIjQadOWHn5p40Iq9V2We96vTwseINMz+YMomJANOjN4xjwIoCBvEpPAqdjgZ8WjimiR/miJ8LASRxVMPgGB4DO8PHRsvCMwBEmCXfjKaQ8bvBBHCWwyqJKzRTmCMNC2ToC6BNKwyzhLXif8EpTSUhXnx6sOtXI2HF252EZtErFfKQPN2dwFx2ZilE8pKQl+Tp7uTV5id1g4Z9d4KuoUM7Y5yYvQzvgjQQJzrI8CAsJ64xM4t6zyI5dAqxjgR1nyFo6A86iX/7G9/4hls+kBp0k2MAGfKDPox24d3RObBid7I7S2G/9aqTeAqaihW+SLjnPE5hCpEWj4qxqwrI8nMwT8IMNTXJKOa3Y5Z/9atf9f3HP/5x97lcc801Xll2JJjlTAsMFxlBaAxY1YlOEZ5FZYskkgNBqO8IlhINMJ01NP5YUZAMiEworBJG59AHPvABJxUQB4AT3cQdxkgJOmXolac3nS8kENfrVfYpcNJzR4bTK8oGQGI6swFutGSwLMb4cW53QniALXRKh4I5wXNgygAhpjzPxtymQuyshab1pDLgYw19i7SwtL7ECRumhY8kkgNBYJiQCDaYcljvqcNYUzDR0KJhSBiWFQDKeWb7hB1K6BgCOQktQiw8APb1Kvusc4g4KEAEU5nCoADwowCWmNWYJ9zPNUyBoUAF1Qb4QmYJa4RWY0IDclznPsJQ4JjcYXiAFeAj/rADiErEde7nOtdoXUkLflcE4CUuzH+eF5r3O3NH7InwDOLaE5Mv6hwaXXKgdQ4BeqFbiDpPveU3ukqdpm5xjjpNGIhMqMfcG17jndGDUFdY/pElGA8EMx1B10a6c2if+TjD2QYAB2BI5aBV46U4h9+F59AShmMGATsKCiHeEJSZ5ge4ESdDFogj9GFQ+IAmGYDpTYYxu4CKAFCGlQyADJ3fzFzgOv5OAJz04b/BkU6aUATMHgYRkyYaAdKLwxwfEqY9z6ezift3J2Fl3p2MBh8n7x8OC6EMyNuwrEdK6LxDWV9NI7Ev5EDzcVJ/ALyQ0JC/hKfsOAe4EwYhDs4RnrDoHcc8gzDhHkKDnjGG90AR8jPEh93JqPNx4lsEiPAR0qsOQOKgBnwAQV6KXmzMYkAM4Bra4zdU6FwCEAEqnN0oN2Hpead3HEAM5zhT0QFYeulpMcMecwSFJT2//OUvPR1sYeYycwEQBtxJOxnPKICh4TD/caiTVsB0+4/xvxaEdyOPmR4XjooIG6dQYEc0OkN7YTnHnkob5hf3UQbkJWFDcKCsaNwoR8ITLryHOkNclCNlTnnQACKcJ3z4XOJjo15Qp4iDfZgW0h+WbySvTHCp7Wp64+tF9mmv+lvf+lbvpWYmADQfBsc9oDwMDgVlWhcgRGsHuO5ojBlTw5iOFZoUfKcG5zXpxDcKY+UbNqGZTvxcI24YI8OfaDnpmaciMOyJnnsYBfEgONSZ4RL2quMbJRwmz/9v70zAq6zOPP6Sm52QEED2yCabUKxWnLFaoVNstTMdeJQRHTcqLkNtsRbGdkYcGXGjaBVtRyuKiNYFxAVEQFARURCKIPsiEAIhCwnZ9+Rmzu/kHrzkCbn3Qrgk8P6e5z73W8/33bP8z/u+53zfxQokuM5I/ejRo+1TLPweRJv4bCBaksVJ/mKRMyBAfpI/5B+/2T1+N23aNCuc5CdWPh0gMx0QMMqXp0gQQ8qNzgrx4i39pIUryKAbgooLSJrkN1PAKF+Emg6K30LniNdCvSEmRwdKvXv++eft0y6UD50bv4VrcT+kzbWZ58s6dcENmoRKS7M4lTrIzxZrcTr4EfwAGhRiROOhsdAQEUsaCo3UWRXBwg9m1O/OO++0lYmG2ZA4UckQS66L2CFMVEDWmUBfH+6VdEiTRs5xDB4x4ERj576xgoLpXFoqlAuDbHRG5DMWHJ2Tq2SIJh0IZer+KoPOjxcLI27kHdY4riJliygwBYZ8BISMmBnTz+iMsAw5B8Elb+lA8BLoRN2zzcAoMB4K27kOIRyEnClolBXCzTPYlDcdGsf7W8qKcqKEVTixKHlJA+JDBacS0yCwaBCi6667zk53QIRoXMwp49VXWH88H+sm7x4P0sHixDJAjI8H1iKWJA0V4USkuQ/ceNxBByKA5YNwYBFjfXEcliUNGpcFKxYBwILF7ed+CUGcadCBYGkifuQfAuimZtEZOauHWQnkF3/aRkdEWSBiCCXnMo+WjgqRdZCviCQCzEshsFrxALAi6VxZp87Ut77ZTj1x/w+OQHIM4oubTh1zFpn/PSrKyRLWN8A7C5CGBYgUldlZfVgwrFPpaVic4xoZ52J50Pg4jzTYhnXj0sMcpxECLrobKUYY2cc630Cabhn3j+uAc8W5B+eWcV0aIA3bNV7W+a3cD/cA3BfrbHf3VB8EqCWNqiN8zm0kLfe4KnnKMvdHnrvRV/LFlQGdDds4hnzjPPKGa7nyBPKDPKRMnEXr6hP7gPxkG+eTpstf9pMeec+55Ctl5/azj22u3Jz3cCLw+7l2ILims8ZPBPKcPAgEeUE91w6hcSh/6ofTn8YIdlQ9bMKJSOFKUSkQBNwzLBbWqWi4gsSi3Cg5lRuLgvRwCxFR0sDScWJCnIvj2I7FSoMhXVxw4l2kS6XCQiJtrBYaNZWSj2usWCqcyz0gbAxU0EBYJn0aAfeJZcn1EVlipdwnVlEotDThDAZikMSD/aGScm3yCpFlbiCuO7+b/GYfeVFfYDiWvCbfiUtyTrBQDyhTOj8H16DMjzfbgfvAs+FtPYFQ4WyZnArhDJurjstLUJ9KMXfuXDt4gLhR0WkswA9DOAns82OJUeEWA8tM/Zk5c6ZNgw+NAng+nUEHGimuOukQ++IY0kGweVyMDgCB5T64Ju4h7j/LhBHALTOARfoMVHAPc+bMse65exaeRs39nw0wso74kDd0GKw7YaS8eNjAQX4SgqGD4YXFHIulTvmzzHYgD0kLgSDM4ax2ypmBRNbJa/YBVi8vZXFlDlyLMuW+SId74cMxXMuVLTFTOis3lQw4jv10mFxHUUIhbMLJ6ObVV19t41EMMiCOzk2n5+SDZUiMC8sOS4Oem3ghDRBBpEHQYBEwGifnsE7ckVF2zuH/0hFQGoobtGGdeOT06dPtveBW8vgZcTSuRxytPtwb1h4WBi7n9ddfb/8egmtgWWOpnqjL15Ig7svAC8KHqDGCzvQs8pz/D+JJE/IYmAXBPuLRWJh0OhyH4NUH4aKzmzhxon0hMtPF3HYGCCk/PBI6WWKrdGaky6AfENN866237HZi4BMmTLB1jE6RF+jymTp1qu046XCZAodlzO8gPaaREcPmnhUlVMImnIgQrhTQyzsXG0sGM7ohEFUGcJyYOqFlvf5gAdYlkB4Ci9i58wD3nYGJ41kXzpIhHdKlwdGosFIY6OB5XUST+8ENIyTAyxDOdHB7GWijEyJPGFTjrdt0XHQqY8aMOdqBuI6G2CUTpHmslnNd2fiDJUj5k5/Dhw+3HSDQiZHPhGkYaGLwjdAJ4kgZsQx4C2PHjrXhFUSW8hk2bJi9RwSSN4ezDVyMlk6Vzpl6xMAVlmgwrrei1CdswnnNNdfY/yLBhcZNJ6ZEo6KyY4U2BAJGPIxYJg0MaGBYG7jKuGccQ0N74403rJXBxHWOwSXjONxp1yhpOMQ6nYXkcNYl7jnp0OgRDOaH8tIR3HtAILgewg+cd6aDlUcHwm8l3/j97ndjyWNxuvx1goR7jXuO2DrX/HiQnj9Y8pyHleqfv5QDZUR9ATozQgQciwfgb/0z9Yj5nP7WpH9a1B0sYe7fhQgUJRTCNjhE48KyxCLAYqOiY4lwvH+l5ji204CwSljnGLeM9eCOx5XnumzHcqARcA9YPFghrlH6W0Q0fr5JE4uHZY5jO3E30iQN9pEO98v1OI5ru/viHrGAQh1U4RzujXQD0RwGh8gH0uE+yEfyGsHhd7CPfGCdPGMfeQisU97uPI71LwfO43i+yWdX5mzjPLaxTDmxD7gP7pN9lAEdGGXDNvLT1SnCBIRhmIg/adIke23ScsexTFqkzcftCwR5yXmBIC0dHGo+UOZNPTgUNuFU6mhpwolLjhhi8QUDgoZwEUdGUPEWXAdGxXUdEqJ3vClb9cGlxjshvfqQj8Qw8TochGuIdxJWIC5L/Ls+DB7REbpQTjCocLZMToVwhs1VV1omuN24tm4kHaseN5pl3HFXKR0II4MuiKV7PwHiy4cwCYM5nI81yHl8uAZigeAxOORiknQwQFwToUVMmCHh7oOBIMSMp7c4h22kRwdOmIVpbLjs3CNuO/eGsLNM+MWFYBQlVPR/1U8DznUMBJaZs9ZOBATDubmN4VzWhkAwETvEBkuKEXasOGKXbkoW8WesY+B6PHOOgFFviEUyik38GFeaQR6sMWZJMHjDNDHyA4GdMmXK0bg1ljnnMQjI6D11i9F3RBbh5XzumalL1DkEkX28MARrl5kY3COii2XLN2KNBc/0Mu6b67sBpGDgt5GngWgsP4OBuuE6j8bgd5OnanE2DvlJHaNOBSKQp6D/q64EDYMt/PMgE8Vx2xmxZvoQo95YkvVfmsFg37hx4+zMBgSRgTaEDTFg4I1nyXH9sTCpqIgsLjADS5yHMM+fP9++dR+o9IgWz5qPGjXKTlcihslA0ciRI+0xNA53Db7pdLhPlpnW5Fxswk7MCuDvUhTlRFHhVAKChcY0LqxHrDde2IGoMTuC6T3M9WROJSIFuMC8yci53+znyR2WsWCZv4mQuePZxzQkxI70sQpxzesLsrOsOB63n5AAFizglru3L3Ec6WMpY6liEWMBYr1ijTKViRkXuPru9YOKEgo6OBRmWtrgkHMb+eY45/7jIrLNhRIQQUQKON6JHPdP7NH9DncO3xzDh/0IJts4Djcby5SBJcDixC11x3Mt8pHf5waY2I/lyvkIMGlyDOtuH/fvlt3vJT13b4HQwaGWCXWWMqd+BUIHh5QmAZFDCBAoGiji6ASIhss3HyeagKiyzofzOd6tI2ru2x2HK+/S44EDHlZwogkcw37SckLHsa5j4cN2RIR7ZdmJlzuXYzmHNLgey3yCFU1F8UeFU2lW0OPzCjpFac6ocCqKooSICqeiKEqIqHAqiqKEiAqnoihKiKhwKoqihIgKp6IoSoiocCqKooRIkz05xKRjJkkz2VhpHB4n5OmQQITrySE3GV1pHJ6I4omkQITrySHqhntySjk+PGlGmwvmJR9hfx8nuCc7lMbh0a9gCjFcwqnlFhyUWTCP7YVLOCkzyk5pnGDLDU6LcCpNS7iEU2lawiWcStOjz6oriqKcIlQ4FUVRQkSFU1EUJURUOBVFUUJEhVNRFCVEmqVw1tZ4xVtWb76c12yrqPStNExtVbVUF9b942Itbwn3/TVDTXGJeCsDv3EdqgsKxVse+I+yzlaqa6qkxtt4XtbWeqWkolAKSg77toSX6prg/pjrbIA2U1NSKjWlZbZN0bZOBbVVpl5wHdoa7fQE8592WnO0DXuPtuHmRrMUzqL1m2TXr/7gW6ujLPWApD3+bKPiWbJlh+x74HGpra6R/E+/kILPv7LbOa/g8zV2ORC7J0yWnPeX+NaU+ny2dZ6s3rHIt9YwuzM2yIwF42Xhur/6tpxaKqrKJO3wdt+ayMsf/49k5qf61s5eao2xcfCpF2TPxCmy/5GnZd+U6XLwmZnGuKj72+WmomzPfkl79BlJ/d8nJPWhJ833k5Lva3uhUvjFWtn34B/rlteslyMffmyXq7JzpDqv+UzRapbCSc9Vvi/Nt1ZHrRHMikOZ1vIMhviB/SS+/3m+NaWpKCzNleLyPN9aw2Qe2Sv9uw2Vfx/2374tp5aMvL3yyscP+tZErhh0rSTFd/CtncUYq68yI0uShv9Qut9zh3S96xbJ/3iV5MxvvOMLBSzMnbf/TqJTusq59/1aeky+V5J/fJkV7KrDJzf/O+68XtL6gkH2dxxZukKK1m307Tn9tMgYZ0V6phR8uc5k5qeSNu3PUrh2g81cf6qP5ElV/R7KHFO45msp2brTLmPZpk3/P8mc/ZZUF9W5Bw6s1dKd39pleu6sOfOaVY/XHNi4b4XsSl8v81fPkKVfzzaCmi/7s7fL5v2rJDV7q3yx7T3xGrc9LXubzPviSfnw7zMlpzDdnsv+fVlb5PNt78iezG9k3e4lVpQ/WPeCLF4/y1iRpbI5daXMXfWE7Dy4zhRXrfl4Zb+xLN8x11tk0iopL5C84ixZtmGOHMjZKau2vSuFZUfkYO5uKa8qlrUmzYKSHHs9b22NLN/0NymtKJLMvFR5d/Uz5lp/ldyiQ3b/mUxkUhuJ7thB4nr3kKTLLrGeGWGtglVrrTd2eN5C68qDt7xcDr/zoaQ99qzkLV9pvLdqqTXuc+7iT6S6oEhyF350jCDmGSGuzi+QzmPHSFSHdhKZ2EaSR/xIYnumSMZLr9uwV86Cj4663LSpvGWf2fZXcSBd0p+dJQdnzJTSXXvsfn+q8/OlKueIFH2zVbLNPeaa9p7z7mIp+nqzsXLrPAqMLO7Xa35POGmRwlluMi3t0RlSeShLos5pL/sfflq8lce68AVfrJPCr/z++tUUVO4HyyXrtbclsm2iFK5eL+nPvCQJgwdY12X/1KfE6/eUTWVWtnE7nrLnFX+9SdKfe0Ui4mJ9exVY/PcXjfDNl5T2fa34rdg8VzyeSImOipUoT4zERbeWVCOOb69+Wnp3GiIREZHy2oqpUlVTIVvTvpTZnzxo93u91fLRxjmyYO1zkpzQSbYfXGNc/bsl/cgeaZ/YVR6dd6NkGdc7p/CQzFp2v5zbYaARwEJ57O2b7WOH7dp0Nt8eiTXXi2gVIW+ufFxyCzNlT8ZGI8Iv2nvddmCNfLrpTckuSJNXVzwknZN7SXRkrLy8/AEbtz2T8ZaWGxEqNKK1R/I/Wy2th5wvWW+8K4dmviZtLv6+FBth2vv7h81xZcYQ+YuUbNombYZeINlzF0rmK/OsmGbNflP2PTBNKg5mGNX4TjbKdu+VxEsvllb+j37y3orz+0rhug1W2DJeeNXGQKFo3TemDb5jRRjBjDm3mxVcrk9b8wfDJu+TVRIRFSXRpp17YmPEk9Da3MMhyXr1bXsM935kyafmmPD+6V7zFc56mWjx24QZ3+nm0dJh5FUSER1le8bGyFu2Ur6dNEW6/eY2ienWRdL/8rJEm+8IUxCtBw+U8tQ0qcz+ridNHjFMSjZsthXlyOJPpdNNoyXCFJzyHVFGeIb2vUou6fdzuXTAv8iRogzpYgTpvC4XSs+O58sPzvupFcF+XS+Wi84bIcMG/ZuxJMvlUG6dddGv6w/kxuGTpZsRXhg++Dq5bOBIubjPlZJTlC4/vfBW+achN0h8bKIVzcT4dnLXVU9IUutzJDGuvezN2iStY9tKX5N+fEwbudhcL8GsA4/e/3DAv8rKrfOt9bp6+0L52YW3yN7MTRIbFS8JccnSIbG7HDLifKbHQ9P/PEt23jFR0h55Wtr/809M3b7CWn097v+ttQ67jh9rLcmijVukbNdeSZk03h7T8fpRxiJ8yZeKSIdRV0nX/7hFoton+7aYJmm8sYgGHgtuxbZGBqI88XHS7Z47TDvuKRExMVJpvEgEuiHiB/a1nzZDL5TkK6+QBOO+Y3Uy2FW8cau0HXap78jw0SyFs1Wkx8ZO/MXTmvqs+15GQc/DcqsIs05v14DOOkiLwaLYHt2NC77WplORnmHcg28l9/2lNgBNofgLI1Zpu19cKYffWyLFpgfufPO1vj2KIzLCWAJGPMFjlo0zbZf9KS7LN+KWKKakjNDGmE+0FPlipB2TulsL0YEwQoQnypyTZPfZ8yKiTcpeOZizSx6eO0aWbnjZxjWd+348ehjxbp/YTb7csVAy8vcaIR1pXfWs/P2yZucHsn7PMhmY8o/H3MOZSMp9v5JB82bKgDnPGsNhnBGqKCM6FRLdqS4OTF2nfeEWi2l7EdF1f/UcmZxotn1nTLT+3sCj7c8R1yPFCu4xo9+mXMr2plmBM8V3DO64ysM5smfSQ3Lgj8bC3bzdDvp6gxzxpx3HdO8iOe8tlorUA9Lmou/59oSPZlljYo35Tm+Cu22nT5herdgUTlS7ttLKE/ottzI9Wsp9d0u/56ZJ3kcrTKHut7GexIuGSO/H/kt6TplkM58K5E/nW6+Tw3PftwXlSWzj26qEQtd2fWz8kRhjfkm2VFSWSnefhRkqX+1aLBf1HiHjr/6TsWgH2201NdVGtD1SWV1mpyHV52cXjpUlX8+SPp0vMJoQbazMrub6/eSXI6bKTcbaHdLzCunUtvGX3JxpeBISJLpLRxufRORwzQl5EbYyGWrbB5ZkyabtknT5P/jOapik4Zda0csxBoab8VJkPLWy3Xts3BNrFEOo4kCGjXeWbttljyndukui2ibJgNkzpN3Pf2KPYQD4uJh2X+M3DtFx9C/k0Iuvi7eqyv6WcNMshTMmpav0fOQPkvrQn2TbDeNly7XjJHfRx8Z1GGkyOMp3VPAQ/4hMTpK4Pj0l8fJL5PC8D6T7vXfYQPWOcb+T7TfdbV2FVp5jXY74vr2tmLYZ+n3fFiVUBvf4kbU6n3z3dnlh6e9lSK8rTnjEm3M3pa60Mc/DhQeke4d+RkwXSUqH/tbiffztW2R/9jbf0XVc0GuYlFUWy8DudQIw6NzLxGMs2kfn3ijPLPy17MvafMZbnPXBs+py2w2S8eLfZPutEyTtieelz/QHJaZninQcM0p2T7hfdt52rx3J7mXaYWNEdzpHBhrxy120XHbc8hvZbj677vxPO/iEqBEKa/vjy2XHL++RXXfdJ1X5dQOsscZFr87Lly2jb5fsuQskrn8fG/NsCOKn8QP6Ssas12X7jXfbuZ6Jlw0Vb0mpxPY8VyKTjjV4wkGzfq0cAz7EGG1w2JjmTf3OSOKijBB6TM/nqTfwwz4C2/smT7NxndaD+vv2hI8z5bVyuNSMXrvY4snAKH15ZYnERSfY+uD11kiEsThx5UvLiyTWbMcCDURBaa6xGlpJQnyyDQc0JS3mtXKErA5lGk8u+ZiBTwSvKitHojp2sOMHQWEsVMYICIt5EuIlZ8FSyXzpTbngk3niaR0vVbl5NqwWmVwXg4Zab60VT4waxJGR8cYGeWqYwG+sUsJ0DORuuWac9J42uUlddX0f50nCdCR63Mh2baXb+FtPy4i6vo+zZaLv4zQYUa7MzDZudCffhqaD0fbst963A0w9Jv+2biCqiVDhPEnoIZmfFt25o+0xTwcqnC0TFc5TC49F0z5jjCg3tUGjLzI+SZhyQUz0dImmoigNQ0yTyfync161CqeiKEqIqHAqiqKEiAqnoihKiKhwNmNOdvqVvpPy9HCy+d7U0+6Upico4Yz2PYKlhA+PJ/BcxEBQbvq/2+GF/G6K9tIU5a+ERijlFtR0JEVRFEWnIymKopwwKpyKoighosKpKIoSIiqciqIoIaLCqSiKEiIqnIqiKCGiwqkoihIiKpyKoighIfL/JVDnqihOv64AAAAASUVORK5CYII="}}},{"cell_type":"markdown","source":"## Examine and score the first set of results    \nThe first table below shows the distribution of the model's probability outputs for both text data sources (315 competitions with tags x 2). The table shows that the classifier can be up to approx. 70% confident in its predictions. It is the least confident in the Generative AI and AI ethics classes. The maximum of the choices is selected as the classification result for each competition.\n\nThe second table below shows the total counts of each predicted classification. The 'Other' class has been predicted more than anything else. ","metadata":{}},{"cell_type":"code","source":"results = pd.read_csv('ZeroShot_text_data_source_results.csv')\n# results.info(show_counts=True)\nresults.drop('CompetitionId', axis=1).describe().round(2)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.727708Z","iopub.execute_input":"2023-06-14T21:20:54.728549Z","iopub.status.idle":"2023-06-14T21:20:54.789319Z","shell.execute_reply.started":"2023-06-14T21:20:54.728511Z","shell.execute_reply":"2023-06-14T21:20:54.788057Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# for each row return the col name with the highest value\nresults['top_result'] = results.drop(['CompetitionId', 'Data Source'],\n                                     axis=1).idxmax(axis=1)\nresults['top_result'].value_counts().rename_axis('Report Topic').reset_index(\n    name='counts').head(20)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.791861Z","iopub.execute_input":"2023-06-14T21:20:54.793212Z","iopub.status.idle":"2023-06-14T21:20:54.811027Z","shell.execute_reply.started":"2023-06-14T21:20:54.793158Z","shell.execute_reply":"2023-06-14T21:20:54.809713Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Checking the accuracy of the classification against a single tag   \n* The tags selected above were merged into the results table, and the tag names were mapped to the predicted report topics.   \n* Then, the results were scored and separated for the data source, either the write-up text or the website description text. \n* Initially, to simplify things, only the competitions with a single tag were selected for the analysis.   \n* The first graph with the green bars scores the classification accuracy against the tags, excluding the 'Other'and 'AI ethics' report topics that did not occur in the tags.   \n* The second graph with the red bars then scores the accuracy of the miss classification where the model has predicted 'Other' or 'AI ethics', but it should have predicted one of the available tags that align with the report topics. ","metadata":{}},{"cell_type":"code","source":"tag_map = {\n    'Text data': 'text',\n    'Tabular data': 'tabular',\n    'Generative AI': 'image-to-text',\n    'Time series data': 'time series analysis',\n    'Image data': 'image',\n    'Video data': 'video',\n    'Other': 'other',\n    'AI ethics': 'ethics'\n}\n\nresults['mapped_result'] = results['top_result'].map(tag_map)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.812647Z","iopub.execute_input":"2023-06-14T21:20:54.813789Z","iopub.status.idle":"2023-06-14T21:20:54.828302Z","shell.execute_reply.started":"2023-06-14T21:20:54.813759Z","shell.execute_reply":"2023-06-14T21:20:54.827103Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# merge the tag analysis with the results\nresults = results.merge(tag_analysis, on='CompetitionId', how='left')\n# select just the competitions with a single tag\nresults_single_tag = results[results['number of tags'] == 1].copy()\n#  generate the score\nresults_single_tag['score'] = (results_single_tag['mapped_result'] ==\n                               results_single_tag['tags']).astype(int)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.830166Z","iopub.execute_input":"2023-06-14T21:20:54.830589Z","iopub.status.idle":"2023-06-14T21:20:54.846126Z","shell.execute_reply.started":"2023-06-14T21:20:54.830550Z","shell.execute_reply":"2023-06-14T21:20:54.844799Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results_single_tag.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.847933Z","iopub.execute_input":"2023-06-14T21:20:54.848635Z","iopub.status.idle":"2023-06-14T21:20:54.870640Z","shell.execute_reply.started":"2023-06-14T21:20:54.848601Z","shell.execute_reply":"2023-06-14T21:20:54.869609Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results_single_tag[~results_single_tag['mapped_result'].isin(\n    ['other', 'ethics']\n)].groupby('Data Source')['score'].mean().sort_values().plot(\n    kind='barh',\n    title='Percentage of competitions correctly classified against the Kaggle tag\\n excluding Other and AI ethics\\n',\n    color='green',\n    figsize=(6, 4))\nplt.xlabel('Correctly classified')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:54.872438Z","iopub.execute_input":"2023-06-14T21:20:54.873149Z","iopub.status.idle":"2023-06-14T21:20:55.138803Z","shell.execute_reply.started":"2023-06-14T21:20:54.873114Z","shell.execute_reply":"2023-06-14T21:20:55.137882Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# how many of the competitions classified as other or ethics should have been classified as a different category\n\nmiss_tagged = results_single_tag[results_single_tag['mapped_result'].isin(\n    ['other', 'ethics'])].groupby(['Data Source'])['tags'].count().mul(-1)\n# percentage\nmiss_tagged.div(results_single_tag.groupby([\n    'Data Source'\n])['tags'].count()).sort_values().plot(\n    kind='barh',\n    color='red',\n    title='Percentage of competitions miss-classified against the Kaggle tag as Other and AI ethics\\n',\n    figsize=(6, 4))\nplt.xlabel('Miss-classified')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:55.140449Z","iopub.execute_input":"2023-06-14T21:20:55.141092Z","iopub.status.idle":"2023-06-14T21:20:55.391780Z","shell.execute_reply.started":"2023-06-14T21:20:55.141058Z","shell.execute_reply":"2023-06-14T21:20:55.390734Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot the overall accuracy\n# percentage\nresults_single_tag.groupby(['Data Source'])['score'].mean().sort_values().plot(\n    kind='barh',\n    color='blue',\n    title='Overall classification accuracy against a single Kaggle tag\\n',\n    figsize=(6, 4))\nplt.xlabel('Single tag accuracy')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:55.395313Z","iopub.execute_input":"2023-06-14T21:20:55.395632Z","iopub.status.idle":"2023-06-14T21:20:55.652331Z","shell.execute_reply.started":"2023-06-14T21:20:55.395605Z","shell.execute_reply":"2023-06-14T21:20:55.651296Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Single-tag conclusions  \nThe text data from the Competition webpage description data source has produced the most accurate results in all of the graphs above. However, a result of only 25-35% (varies with each run) accuracy could be better! Can that result be improved? The model may be predicting the 'Other' category too often.  \n   \nFor further analysis, I will drop the poorly performing write-up data and examine the competition data source more deeply. The next step is to explore the distributions of the predictions for that data source more closely. ","metadata":{}},{"cell_type":"code","source":"# keep only the competition results\ncomp_results = results[results['Data Source'] == 'Competition'].copy()\n# plot a histogram of the scores for each topic\nsns.set_style('whitegrid')\nsns.set_context('notebook')\nsns.set_palette('colorblind')\nplt.figure(figsize=(10, 6))\nsns.kdeplot(\n    comp_results.drop([\n        'CompetitionId', 'Data Source', 'number of tags', 'tags', 'top_result',\n        'mapped_result'\n    ],\n                      axis=1))\nplt.title(\n    'Is there a threshold where a tag should be considered?\\n The Distribution of Classification Scores\\n'\n)\nplt.xticks(np.arange(0, 1, 0.05))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:55.655766Z","iopub.execute_input":"2023-06-14T21:20:55.656575Z","iopub.status.idle":"2023-06-14T21:20:56.432925Z","shell.execute_reply.started":"2023-06-14T21:20:55.656535Z","shell.execute_reply":"2023-06-14T21:20:56.431949Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The figure above shows that the 'Other' classification is predicted too often. The tags show that tabular data is the most frequent data type. Video and AI ethics are predicted less often, which aligns with expectations. What happens if we increase the amount of data by including the competitions with multiple tags?\n\n### Include the competitions with multiple tags    \nFor the previous analysis, only the highest-scoring topic was used. The multiple tags appear valid from checking the websites above, so what happens if we score the prediction as correct if the predicted tag is included in the multiple tags for each competition?   \n  \nThe table below shows the results sorted to display the multiple tag competitions first for illustration. ","metadata":{}},{"cell_type":"code","source":"comp_results.sort_values('number of tags', ascending=False).head(10)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:56.434546Z","iopub.execute_input":"2023-06-14T21:20:56.435622Z","iopub.status.idle":"2023-06-14T21:20:56.462924Z","shell.execute_reply.started":"2023-06-14T21:20:56.435585Z","shell.execute_reply":"2023-06-14T21:20:56.461646Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"comp_results['score'] = (comp_results['mapped_result'].isin(\n    comp_results['tags'])).astype(int)\nprint(\n    \"The accuracy of only the competition data source including multiple tags: \",\n    comp_results[~comp_results['tags'].isnull()]['score'].mean().round(2))","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:56.464799Z","iopub.execute_input":"2023-06-14T21:20:56.465691Z","iopub.status.idle":"2023-06-14T21:20:56.477526Z","shell.execute_reply.started":"2023-06-14T21:20:56.465664Z","shell.execute_reply":"2023-06-14T21:20:56.476477Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Penalise 'Other' classification    \nThe result above 0.4 to 0.5 depending on the notebook run is still not satisfactory. Can the classification be improved by reducing the predicted probability of the 'Other' class?   \n  \nIn the distribution graph above it looked like the 'Other' category score could be reduced by approximately 1/2 from to make it closer to the other distributions. Let's test that theory by applying a range of penalties and plotting the change in accuracy.","metadata":{}},{"cell_type":"code","source":"opt_df = pd.DataFrame(columns=['penalty', 'accuracy'])\n# experimental_results = comp_results[~comp_results['tags'].isnull()].copy()\n\nfor i, penalty in enumerate(np.arange(0.1, 1.1, 0.05)):\n    experimental_results = comp_results[~comp_results['tags'].isnull()].copy()\n    # reduce the score of other\n    experimental_results['Other'] = experimental_results['Other'].mul(penalty)\n    # for each row return the col name with the highest value\n    experimental_results['top_result'] = experimental_results.drop(\n        [\n            'CompetitionId', 'Data Source', 'top_result', 'mapped_result',\n            'number of tags', 'tags', 'score'\n        ],\n        axis=1).idxmax(axis=1)\n    experimental_results['mapped_result'] = experimental_results[\n        'top_result'].map(tag_map)\n    opt_df.loc[i] = [\n        penalty, experimental_results['mapped_result'].isin(\n            experimental_results['tags']).astype(int).mean().round(2)\n    ]\n\nopt_df.plot(x='penalty', y='accuracy', kind='line', figsize=(10, 6))\nplt.title('Accuracy vs Penalty applied to category Other\\n')\nplt.xlabel('Penalty')\nplt.ylabel('Accuracy')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:20:56.478818Z","iopub.execute_input":"2023-06-14T21:20:56.479196Z","iopub.status.idle":"2023-06-14T21:20:56.999059Z","shell.execute_reply.started":"2023-06-14T21:20:56.479148Z","shell.execute_reply":"2023-06-14T21:20:56.998014Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Conclusions so far   \nFrom the optimisation graph above, penalising the other category dramatically improves classification accuracy, which rises to 0.85 to 0.95 depending on the notebook run, with a 0.35 to 0.45 penalty.   \n  \nI am happy with that level of accuracy acheived for the Zero Shot classification in this notebook. Now it's time to try and classify all the previous competitions (~5000) that did not have any tags.  ","metadata":{}},{"cell_type":"markdown","source":"## Classify the competitions with no tags   \nOnly the competition webpage descriptions will be used, and a 0.35 penalty will be applied to the 'Other' predicted probability before the top result is selected. However, before that classification can be carried out, another problem exists. So far, all the testing and optimisation has been done on English text. I noticed that some of the competitions are in other languages. So, these languages need to be identified and excluded.  \n### Identify the languages  - keep only English text\nLanguage detection is problematic and can easily take up a whole experimental notebook to itself; in summary, it often takes more than one language detection model to get a good result. The aim here is not to accurately describe the languages but to remove the non-English text. To that end, I will use three techniques to identify the languages. First, the langdect model<sup>4</sup>, which is a good starting point but misses some Asian languages, esp. Mandarin. Then the language_detect model<sup>8</sup>, which is not very good with European languages and gets English, French and German confused, it is good at detecting Mandarin, so it compliments langdetect. Finally, I will use a simple character detection to limit the threshold of non-ASCII characters.   \n#### langdetect","metadata":{}},{"cell_type":"code","source":"# add a column for the language\ncompetition_info['Language'] = competition_info['comp_info'].apply(\n    lambda x: detect(x))\n# convert the iso code to the language name\nlanguage_codes = pd.read_csv(\n    f'{data_dir}language-iso-codes/language_codes.csv', index_col='639-1_code').to_dict()\ncompetition_info['Language'] = competition_info['Language'].map(\n    language_codes['language_name'])\n# plot the languages\ncompetition_info['Language'].value_counts().plot(kind='barh', figsize=(6, 6))\n# add numbers to the plot\nfor i, v in enumerate(competition_info['Language'].value_counts()):\n    plt.text(v + 3, i + .25, str(v), color='black', fontweight='normal')\nplt.xlabel('Number of competitions')\nplt.ylabel('Language')\nplt.title('Languages of the competition descriptions using langdetect\\n')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:27:28.190089Z","iopub.execute_input":"2023-06-14T21:27:28.190503Z","iopub.status.idle":"2023-06-14T21:29:10.451855Z","shell.execute_reply.started":"2023-06-14T21:27:28.190472Z","shell.execute_reply":"2023-06-14T21:29:10.450972Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Check some of the true positives for langdetect","metadata":{}},{"cell_type":"code","source":"# check the result\nfor i, row in enumerate(competition_info[competition_info['Language'] !=\n                                         'English'].itertuples()):\n    print(row[3], ':\\n', row[2][:200].strip())\n    print('\\n')\n    if i == 10:\n        break","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:29:10.454090Z","iopub.execute_input":"2023-06-14T21:29:10.455147Z","iopub.status.idle":"2023-06-14T21:29:10.466166Z","shell.execute_reply.started":"2023-06-14T21:29:10.455111Z","shell.execute_reply":"2023-06-14T21:29:10.464978Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### language_detector  \nAfter removing the non english languages detected by langdetect, apply language_detector to the remaining text.","metadata":{}},{"cell_type":"code","source":"# keep just the English competitions\nenglish_competition_info = competition_info[competition_info['Language'] ==\n                                            'English'].copy()\n# use language_detector to check the language\nenglish_competition_info['Language'] = english_competition_info[\n    'comp_info'].apply(lambda x: detect_language(x))\n# plot the languages\nenglish_competition_info['Language'].value_counts().plot(\n    kind='barh', figsize=(6, 6))\n# add numbers to the plot\nfor i, v in enumerate(english_competition_info['Language'].value_counts()):\n    plt.text(v + 3, i + .25, str(v), color='black', fontweight='normal')\nplt.xlabel('Number of competitions')\nplt.ylabel('Language')\nplt.title(\n    'Languages of the competition descriptions using language_detector \\nafter removing langdect recognised non English text\\n'\n)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:29:10.468106Z","iopub.execute_input":"2023-06-14T21:29:10.468687Z","iopub.status.idle":"2023-06-14T21:29:11.017331Z","shell.execute_reply.started":"2023-06-14T21:29:10.468639Z","shell.execute_reply":"2023-06-14T21:29:11.015774Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# keep just the English (French and German) competitions\nenglish_competition_info = english_competition_info[\n    english_competition_info['Language'].isin(['English', 'French',\n                                               'German'])].copy()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:29:11.020609Z","iopub.execute_input":"2023-06-14T21:29:11.020972Z","iopub.status.idle":"2023-06-14T21:29:11.028251Z","shell.execute_reply.started":"2023-06-14T21:29:11.020944Z","shell.execute_reply":"2023-06-14T21:29:11.027214Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Check the progress  of the language cleanup  \nThe table below shows that there are still some texts in different languages. It was made by sorting the rows by text length(in words); smaller word counts are a good way to find non-English texts, as other languages often tend not to split into words in the same way as English. The table shows that there are still some non-English languages in the data. The next step is to try and remove them with non ASCII charcter limits.","metadata":{}},{"cell_type":"code","source":"english_competition_info['comp_info_len'] = english_competition_info[\n    'comp_info'].apply(lambda x: len(x.split(' ')))\nenglish_competition_info[english_competition_info['comp_info_len'] <\n                         100].sort_values('comp_info_len').head(20)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:29:11.030216Z","iopub.execute_input":"2023-06-14T21:29:11.030973Z","iopub.status.idle":"2023-06-14T21:29:11.151659Z","shell.execute_reply.started":"2023-06-14T21:29:11.030939Z","shell.execute_reply":"2023-06-14T21:29:11.150670Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Use the percentage of ASCII to remove the final non-English text","metadata":{}},{"cell_type":"code","source":"# get the percentage ascii characters in the string\ndef get_ascii_percentage(string):\n    return len([c for c in string if ord(c) < 128]) / len(string)\n\n\n# add a column for the percentage of ascii characters\nenglish_competition_info['ascii_percentage'] = english_competition_info[\n    'comp_info'].apply(get_ascii_percentage)\n# distribution of ascii percentage\nsns.kdeplot(data=english_competition_info['ascii_percentage'], shade=True)\nplt.title('KDE Distribution of the ASCII character percentage\\n')\nplt.xlabel('ASCII character percentage')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:29:11.153238Z","iopub.execute_input":"2023-06-14T21:29:11.153819Z","iopub.status.idle":"2023-06-14T21:29:12.716895Z","shell.execute_reply.started":"2023-06-14T21:29:11.153785Z","shell.execute_reply":"2023-06-14T21:29:12.715909Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Is greater than 0.90 a good threshold to remove non english text?  \nBelow are 5 text samples with between 0.90 and 0.95 percent ASCII","metadata":{}},{"cell_type":"code","source":"# show the comp_info for 0.90 < ascii_percentage < 0.95\nfor i, row in enumerate(english_competition_info[\n    (english_competition_info['ascii_percentage'] > 0.90)\n        & (english_competition_info['ascii_percentage'] < 0.95)].itertuples()):\n    print(i, row[2][:200].strip())\n    print('\\n')\n    if i == 5:\n        break","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:29:12.718716Z","iopub.execute_input":"2023-06-14T21:29:12.719404Z","iopub.status.idle":"2023-06-14T21:29:12.734350Z","shell.execute_reply.started":"2023-06-14T21:29:12.719371Z","shell.execute_reply":"2023-06-14T21:29:12.733172Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Is greater than 0.95 high enough to exclude the non enlish text?  \nAbove 0.95 was not quite high enough what about above that. Below are 5 text samples with between 0.9 and 0.95 percent ASCII","metadata":{}},{"cell_type":"code","source":"# show the comp_info for > 0.95 < ascii_percentage < 1\nfor i, row in enumerate(english_competition_info[\n    (english_competition_info['ascii_percentage'] > 0.95)\n        & (english_competition_info['ascii_percentage'] < 1)].itertuples()):\n    print(i, row[2][:200].strip())\n    print('\\n')\n    if i == 5:\n        break","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:29:12.736294Z","iopub.execute_input":"2023-06-14T21:29:12.736586Z","iopub.status.idle":"2023-06-14T21:29:12.748988Z","shell.execute_reply.started":"2023-06-14T21:29:12.736560Z","shell.execute_reply":"2023-06-14T21:29:12.748088Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Looks good; keep only the competitions with a percentage of ASCII characters greater than 0.95  \nThe table below shows that there are still over 4000 competitions to classify, which should be a good test of the model. ","metadata":{}},{"cell_type":"code","source":"english_competition_info = english_competition_info[\n    english_competition_info['ascii_percentage'] > 0.95].copy()\nenglish_competition_info.info(show_counts=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:29:12.750624Z","iopub.execute_input":"2023-06-14T21:29:12.751400Z","iopub.status.idle":"2023-06-14T21:29:12.768204Z","shell.execute_reply.started":"2023-06-14T21:29:12.751370Z","shell.execute_reply":"2023-06-14T21:29:12.767017Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"##  Use the optimised Zero-Shot approach to classify the larger dataset of competitions with no tags  \nNote that it will take a couple of seconds for a loop with a T4 GPU to classify each competition and around a minute with a CPU for each loop. This processing time can be a significant limitation to using transformer-based transfer learning with large datasets.","metadata":{}},{"cell_type":"code","source":"results_2 = pd.DataFrame(columns=['CompetitionId'])\n\n\ndef get_scores(Id, text_data, text_data_label):\n    result = pipe(text_data, candidate_labels=candidate_labels)\n    result_df = pd.DataFrame(columns=result[\"labels\"], data=[result[\"scores\"]])\n    result_df['CompetitionId'] = Id\n    result_df['Data Source'] = text_data_label\n    return result_df\n\n\n# Note ~1-2sec per loop on T4 ~20secs per loop on CPU\nfor row in tqdm(\n        english_competition_info.itertuples(),\n        total=len(english_competition_info),\n        desc='Processing',\n        unit='row',\n        ncols=100,\n        position=0,\n        leave=True):\n    Id = row.CompetitionId\n    doc_comp = row.comp_info\n    df = get_scores(Id, doc_comp, 'Competition')\n    results_2 = pd.concat([results_2, df], ignore_index=True)\n\n# save the results\nresults_2.to_csv('ZeroShot_text_final_results.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T21:29:12.772405Z","iopub.execute_input":"2023-06-14T21:29:12.772682Z","iopub.status.idle":"2023-06-14T22:31:23.206674Z","shell.execute_reply.started":"2023-06-14T21:29:12.772657Z","shell.execute_reply":"2023-06-14T22:31:23.205515Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Apply the penalty to the 'Other' class  \nThen plot the results.","metadata":{}},{"cell_type":"code","source":"# penalise other\npenalty = 0.35\nresults_2['Other'] = results_2['Other'].mul(penalty)\n\n# for each row return the col name with the highest value\nresults_2['top_result'] = results_2.drop([\n    'CompetitionId',\n    'Data Source',\n],\n                                         axis=1).idxmax(axis=1)\n\nresults_2['top_result'].value_counts().plot(kind='barh', figsize=(6, 4))\nplt.title('Number of competitions classified as each report topic\\n')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T22:31:23.208435Z","iopub.execute_input":"2023-06-14T22:31:23.210288Z","iopub.status.idle":"2023-06-14T22:31:23.639131Z","shell.execute_reply.started":"2023-06-14T22:31:23.210249Z","shell.execute_reply":"2023-06-14T22:31:23.638071Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Check the Zero Shot classification results  \nThe competition title and subtitle were added, and then a random sample of the results is shown in the table below to eyeball them. This table will change every time the cell is run. A quick survey shows that the classification has gone well; I see some issues with Generative AI and AI ethics. Potentially these classes need to be optimised further in the same way as the 'Other' class was. To check the website for the competition, use the competitionId to look up the competition on the Kaggle website, i.e., https://www.kaggle.com/c/{competitionId}  \n\n### The large dataset of competitions with no tags can now be used to analyse the trends in Kaggle competitions.    \nWith the proviso that more optimisation and cross-checking of the Zero-Shot classification results are advised before any firm conclusions are drawn. For example, the following types of analysis are now possible on the Kaggle competitions in relation to the report topics.   \n   \nThe two charts below show two types of analysis enabled by the Zero-Shot classification. \n* The first chart shows the volume of Kaggle competition topics; there is a drop in all competitions in 2022!\n* The second chart shows that Tabular data has always been the highest proportion data type in Kaggle competitions. Time series data has been decreasing, and Generative AI has been increasing. \n\nA potential next step would be to take the top two predicted classes, as tabular data is often combined with the other types and see how the classification pairs have trended over the last few years.   ","metadata":{}},{"cell_type":"code","source":"# bring in the competition dates, titles and subtitle\nresults_2 = results_2.merge(\n    Competitions[['CompetitionId', 'EnabledDate', 'Title', 'Subtitle']],\n    on='CompetitionId')\n# results_2.info(show_counts=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T22:31:23.640842Z","iopub.execute_input":"2023-06-14T22:31:23.641249Z","iopub.status.idle":"2023-06-14T22:31:23.660763Z","shell.execute_reply.started":"2023-06-14T22:31:23.641203Z","shell.execute_reply":"2023-06-14T22:31:23.659632Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# sample the results how do they look - rerun cell to continue sampling\nresults_2[['CompetitionId', 'EnabledDate', 'Title', 'Subtitle',\n           'top_result']].sample(20)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T22:31:23.662479Z","iopub.execute_input":"2023-06-14T22:31:23.662936Z","iopub.status.idle":"2023-06-14T22:31:23.687253Z","shell.execute_reply.started":"2023-06-14T22:31:23.662895Z","shell.execute_reply":"2023-06-14T22:31:23.686140Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# get the proportion of competitions classified as each topic each year\nresults_2['year'] = results_2['EnabledDate'].dt.year\n# count the number of competitions classified as each topic each year\ncounts_per_year = results_2.groupby(\n    ['year', 'top_result'])['CompetitionId'].count().unstack().fillna(0)\n# drop 2000 and 2001 as there are only a few competitions\ncounts_per_year = counts_per_year.drop([2000, 2001])\n# plot the results\ncounts_per_year.plot(kind='bar', stacked=True, figsize=(10, 6))\nplt.title('Number of competitions classified as each report topic each year\\n')\nplt.xlabel('Year')\nplt.ylabel('Number of competitions')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T22:31:23.688893Z","iopub.execute_input":"2023-06-14T22:31:23.689320Z","iopub.status.idle":"2023-06-14T22:31:24.497844Z","shell.execute_reply.started":"2023-06-14T22:31:23.689284Z","shell.execute_reply":"2023-06-14T22:31:24.496921Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# get the proportion of competitions classified as each topic each year\ncounts_per_proportions = counts_per_year.div(\n    counts_per_year.sum(axis=1), axis=0)\n# create a rolling average of the proportions\nproportions_rolling = counts_per_proportions.drop([2023]).rolling(\n    3, min_periods=1).mean()\n# set the figure size\nplt.figure(figsize=(10, 6))\n# plot the proportions\nsns.lineplot(data=proportions_rolling, dashes=True, linewidth=3.5)\n# legend top left\nplt.legend(bbox_to_anchor=(1.01, 1), loc='upper left')\nplt.title(\n    'The Trend in the Proportion of Competition Topics each Year\\n (3 year rolling average)\\n'\n)\nplt.xlabel('Year')\nplt.ylabel('Proportion of competitions')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T22:31:24.499536Z","iopub.execute_input":"2023-06-14T22:31:24.500223Z","iopub.status.idle":"2023-06-14T22:31:25.130797Z","shell.execute_reply.started":"2023-06-14T22:31:24.500188Z","shell.execute_reply":"2023-06-14T22:31:25.129918Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Further Discussion  on Zero-Shot Classification\nThe exploration of Zero-Shot classification on the Kaggle competitions above has shown it is a valuable tool for classifying large datasets with limited class labels. However, it could be better, and some issues need to be addressed. The one that has been bugging me is uncovering why the write-up classification performed so poorly compared to the competition descriptions.  \n### How do the two text sources compare? \nGerber *et al*<sup>6</sup> suggested that the text input length significantly impacts the Zero-Shot classification accuracy. Is the write-up text length substantially different from the competition description text?","metadata":{}},{"cell_type":"code","source":"# compare the word length of the text sections\ndf_text['WriteupBody_len'] = df_text['WriteupBody'].apply(\n    lambda x: len(x.split(' ')))\ndf_text['comp_info_len'] = df_text['comp_info'].apply(\n    lambda x: len(x.split(' ')))\n# plot the word length\nplt.figure(figsize=(10, 6))\nsns.boxplot(data=df_text[['WriteupBody_len', 'comp_info_len']], orient='h')\nplt.xlabel('Words')\nplt.ylabel('Section')\nplt.yticks([0, 1], ['Write up Body', 'Competition Description'])\nplt.title(\n    'Text length of the different text sections is dramatically different!\\n')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T22:31:25.132549Z","iopub.execute_input":"2023-06-14T22:31:25.133324Z","iopub.status.idle":"2023-06-14T22:31:25.699989Z","shell.execute_reply.started":"2023-06-14T22:31:25.133284Z","shell.execute_reply":"2023-06-14T22:31:25.698969Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Test the length of the text hypothesis\nDoes the write-up accuracy decrease with the length of the text? What happens if the write-ups are shortened to the same length as the competition descriptions?  \nThe table below describes the competition description text in terms of words.  \n  \nThen the write-up text is processed by the Zero-Shot classifier with 8 different text lengths. From 100 words doubling each time to 12,800 words. The time for each loop will also be recorded to see if there is a significant increase in processing time with longer texts. Once the classification is complete, the 'Other' penalty will be applied and the accuracy against the tags scored. The results are shown in the optimisation chart below.","metadata":{}},{"cell_type":"code","source":"# median word length of the competition description\ndf_text[['comp_info_len']].describe().round(2)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T22:31:25.701750Z","iopub.execute_input":"2023-06-14T22:31:25.702434Z","iopub.status.idle":"2023-06-14T22:31:25.719362Z","shell.execute_reply.started":"2023-06-14T22:31:25.702398Z","shell.execute_reply":"2023-06-14T22:31:25.718185Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_text['WriteupBody_wordlist'] = df_text['WriteupBody'].apply(\n    lambda x: x.split(' '))\n\n# retest the write ups with shortened word length\nresults_3 = pd.DataFrame(columns=['CompetitionId'])\n\n# processing time per word\ntime_dict = {}\n\nwords = [100, 200, 400, 800, 1600, 3200, 6400, 12800]\n# loop through the data and get the predictions\nfor i in tqdm(\n        words,\n        total=len(words),\n        desc='Processing',\n        unit='row',\n        ncols=100,\n        position=0,\n        leave=True):\n    time_dict[f'start_{i}'] = time.time()\n    for row in df_text.itertuples():\n        Id = row.CompetitionId\n        if len(row.WriteupBody_wordlist) > i:\n            doc_writeup = ' '.join(row.WriteupBody_wordlist[:i])\n        else:\n            doc_writeup = row.WriteupBody\n        _df = get_scores(Id, doc_writeup, f'Writeup length {i}')\n        results_3 = pd.concat([results_3, _df], ignore_index=True)\n    time_dict[f'end_{i}'] = time.time()\n\n# save the results\nresults_3.to_csv('ZeroShot_text_final_results_3.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T22:31:25.721437Z","iopub.execute_input":"2023-06-14T22:31:25.722057Z","iopub.status.idle":"2023-06-14T23:31:09.129124Z","shell.execute_reply.started":"2023-06-14T22:31:25.721996Z","shell.execute_reply":"2023-06-14T23:31:09.128069Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load the results\nresults_3 = pd.read_csv('ZeroShot_text_final_results_3.csv')\n# penalise other\n# penalty = 0.35\nresults_3['Other'] = results_3['Other'].mul(penalty)\n\n# for each row return the col name with the highest value\nresults_3['top_result'] = results_3.drop([\n    'CompetitionId',\n    'Data Source',\n],\n                                         axis=1).idxmax(axis=1)\n\nresults_3['mapped_result'] = results_3['top_result'].map(tag_map)\n# merge tags\nresults_3 = results_3.merge(tag_analysis, on='CompetitionId', how='left')\nresults_3WithTag = results_3[results_3['tags'].notnull()]\n# score\nresults_3WithTag['score'] = results_3WithTag['mapped_result'].isin(\n    results_3WithTag['tags']).astype(int)\nresults_3WithTag.head()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-06-14T23:31:09.133798Z","iopub.execute_input":"2023-06-14T23:31:09.136481Z","iopub.status.idle":"2023-06-14T23:31:09.202656Z","shell.execute_reply.started":"2023-06-14T23:31:09.136444Z","shell.execute_reply":"2023-06-14T23:31:09.201750Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# group by write up length\ntext_length_score = results_3WithTag.groupby(\n    'Data Source')['score'].mean().reset_index()\ntext_length_score['Words'] = text_length_score['Data Source'].str.split(\n    ' ').str[-1].astype(int)\ntext_length_score.sort_values('Words', inplace=True)\ntext_length_score.reset_index(drop=True, inplace=True)\ntext_length_score.plot(\n    x='Words',\n    y='score',\n    kind='line',\n    figsize=(10, 6),\n    linewidth=3.5,\n    marker='o',\n    legend=False)\nplt.title('Accuracy vs Text length of the write-ups\\n')\nplt.xlabel('Text length (words)')\nplt.ticklabel_format(style='plain', axis='x')\n# log xaixs\nplt.xscale('log')\n# add a label to the points\nfor i, txt in enumerate(text_length_score['Words']):\n    plt.annotate(\n        str(txt),\n        (text_length_score['Words'][i], text_length_score['score'][i]),\n    )\nplt.ylabel('Accuracy')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-06-14T23:31:09.207242Z","iopub.execute_input":"2023-06-14T23:31:09.209548Z","iopub.status.idle":"2023-06-14T23:31:10.298075Z","shell.execute_reply.started":"2023-06-14T23:31:09.209513Z","shell.execute_reply":"2023-06-14T23:31:10.296990Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot the time taken for each word length\ntime_taken = pd.DataFrame.from_dict(time_dict, orient='index').T\ntime_taken['100'] = time_taken['end_100'] - time_taken['start_100']\ntime_taken['200'] = time_taken['end_200'] - time_taken['start_200']\ntime_taken['400'] = time_taken['end_400'] - time_taken['start_400']\ntime_taken['800'] = time_taken['end_800'] - time_taken['start_800']\ntime_taken['1600'] = time_taken['end_1600'] - time_taken['start_1600']\ntime_taken['3200'] = time_taken['end_3200'] - time_taken['start_3200']\ntime_taken['6400'] = time_taken['end_6400'] - time_taken['start_6400']\ntime_taken['12800'] = time_taken['end_12800'] - time_taken['start_12800']\ntime_taken = time_taken[[\n    '100', '200', '400', '800', '1600', '3200', '6400', '12800'\n]]\ntime_taken = time_taken.T\ntime_taken.reset_index(inplace=True)\ntime_taken.rename(columns={'index': 'Words', 0: 'Time'}, inplace=True)\ntime_taken['Words'] = time_taken['Words'].str.split('_').str[-1].astype(int)\ntime_taken.sort_values('Words', inplace=True)\ntime_taken.reset_index(drop=True, inplace=True)\ntime_taken.plot(\n    x='Words',\n    y='Time',\n    kind='line',\n    figsize=(10, 6),\n    linewidth=3.5,\n    marker='o',\n    legend=False)\nplt.title('Time taken vs Text length of the write-ups\\n')\nplt.xlabel('Text length (words)')\nplt.ticklabel_format(style='plain', axis='x')\n# log xaixs\nplt.xscale('log')\n# add a label to the points\nfor i, txt in enumerate(time_taken['Words']):\n    plt.annotate(\n        str(txt),\n        (time_taken['Words'][i], time_taken['Time'][i]),\n    )\nplt.ylabel('Time taken (seconds)')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-06-14T23:31:10.299630Z","iopub.execute_input":"2023-06-14T23:31:10.300411Z","iopub.status.idle":"2023-06-14T23:31:11.146206Z","shell.execute_reply.started":"2023-06-14T23:31:10.300376Z","shell.execute_reply":"2023-06-14T23:31:11.145166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Is that a good linear fit up to 800 words?","metadata":{}},{"cell_type":"code","source":"# generate the regression stats\nslope, intercept, r_value, p_value, std_err = stats.linregress(\n    time_taken[:4]['Words'], time_taken[:4]['Time'])\nprint(\n    f'Linear fit up to 800 words: \\nSlope: {slope.round(2)}\\nIntercept: {intercept.round(2)}\\nR-squared: {(r_value**2).round(2)}\\nP-value: {p_value.round(5)}\\nStandard error: {std_err.round(2)}'\n)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-06-14T23:31:11.147811Z","iopub.execute_input":"2023-06-14T23:31:11.148452Z","iopub.status.idle":"2023-06-14T23:31:11.158373Z","shell.execute_reply.started":"2023-06-14T23:31:11.148414Z","shell.execute_reply":"2023-06-14T23:31:11.157153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Overall conclusions for the experimentation with Zero-Shot classification on Kaggle competitions data   \n* Human sampling and evaluation of Zero-Shot classification results are essential to ensure the results are accurate for all the classes.  \n\n* Although time is saved upfront, not labelling the data and training a classifier, there is a need for some post-processing work to optimise and adjust the probability outputs for each classification class.  \n\n* Class labels are required for a proportion of the data to be classified for reassurance that the classification is accurate. And the class labels are necessary to carry out any optimisation work. The class labels were available for around 8% of this experiment's data.  \n\n* Although the Zero-Shot classification methodology saved time by not having to do more than a quick text cleanup, beware of multiple languages in large datasets that may need to be removed or translated in the preprocessing - another problem 😃\n\n* The length of the input text is important to both the accuracy of the Zero-Shot classification and the processing time to get the class prediction. The text length optimisation shown in the chart above suggests that the text needs to be at least 400 words and less than 800. The highest accuracy was 400 words at 0.84. The competition descriptions data above achieved a similar accuracy at between 0.85 and 0.95 (depending on the notebook run) for a median word length of 572 words. \n\n* However, the competition description text was written to be that length, whereas the write-ups were chopped down to the number of words. It may be that a transformer summariser step in preprocessing, as suggested by Gerber *et al*<sup>6</sup> and set to ~500 words as suggested here, would be the way to deal with the Zero-Shot classification of long texts. But the summariser would add additional processing time. \n\n* The processing time here increased linearly per word between 100 and 800 words, then levelled off and slowly increased to the maximum tested length of 12,800 words. This time scaling is probably related to the 512 token input limit of the transformers, but that has not been explored experimentally here. The processing rate will change if this notebook is rerun on a different system. But at the time of writing, the final cell shows an increase of 0.62 seconds processing time per word between 100 and 800 words, and after that, an increase in the number of words is less of a time problem. \n\nThe use case for Zero-Shot classification is to classify large datasets with sparse class labels. This notebook would suggest that an optimum processing time and accuracy can be acheived using approximately 500 words of text.\n\nI hope you found this notebook useful and I would welcome and comments you have. Thanks for reading this far 😃","metadata":{}},{"cell_type":"markdown","source":"## References  \n1) [Bart Large mnli](https://metatext.io/models/facebook-bart-large-mnli)   \n2) [Yin et al](http://arxiv.org/abs/1909.00161)  \n3) [Hugginface Bart Large mnli](https://huggingface.co/facebook/bart-large-mnli)  \n4) [langdetect](https://github.com/Mimino666/langdetect)  \n5) [ISO 639-1 language codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes)    \n6) [Gerber et al](http://arxiv.org/abs/2201.01337)   \n7) [BERTopic](https://maartengr.github.io/BERTopic/index.html)   \n8) [language-detector](https://github.com/DanielJDufour/language-detector)   \n  ","metadata":{}}]}