main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\gedas\\anaconda3\\envs\\cybercare\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim import AdamW\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import f1_score, accuracy_score, multilabel_confusion_matrix, classification_report\n",
    "from transformers import get_linear_schedule_with_warmup, AutoTokenizer, AutoModel # , BertTokenizer, BertModel\n",
    "from tqdm import tqdm\n",
    "from collections import defaultdict\n",
    "import yaml\n",
    "\n",
    "# from nltk.stem import WordNetLemmatizer, PorterStemmer\n",
    "# from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import model configuration\n",
    "with open('config.yaml', 'r') as file:\n",
    "    config = yaml.safe_load(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 6753 entries, 9062 to 4728\n",
      "Data columns (total 8 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   TITLE                 6753 non-null   object\n",
      " 1   ABSTRACT              6753 non-null   object\n",
      " 2   Computer Science      6753 non-null   int64 \n",
      " 3   Physics               6753 non-null   int64 \n",
      " 4   Mathematics           6753 non-null   int64 \n",
      " 5   Statistics            6753 non-null   int64 \n",
      " 6   Quantitative Biology  6753 non-null   int64 \n",
      " 7   Quantitative Finance  6753 non-null   int64 \n",
      "dtypes: int64(6), object(2)\n",
      "memory usage: 474.8+ KB\n",
      "HEAD:\n",
      "                                                    TITLE  \\\n",
      "9062   Connectivity Properties of Factorization Poset...   \n",
      "11887  InfoVAE: Information Maximizing Variational Au...   \n",
      "6931   The origin and early evolution of life in chem...   \n",
      "8690   Monadic Second Order Logic with Measure and Ca...   \n",
      "13788  Optical reconfiguration and polarization contr...   \n",
      "\n",
      "                                                ABSTRACT  Computer Science  \\\n",
      "9062     We consider three notions of connectivity an...                 0   \n",
      "11887    A key advance in learning generative models ...                 1   \n",
      "6931     Life can be viewed as a localized chemical s...                 0   \n",
      "8690     We investigate the extension of Monadic Seco...                 1   \n",
      "13788    Controlling and confining light by exciting ...                 0   \n",
      "\n",
      "       Physics  Mathematics  Statistics  Quantitative Biology  \\\n",
      "9062         0            1           0                     0   \n",
      "11887        0            0           1                     0   \n",
      "6931         0            0           0                     1   \n",
      "8690         0            1           0                     0   \n",
      "13788        1            0           0                     0   \n",
      "\n",
      "       Quantitative Finance  \n",
      "9062                      0  \n",
      "11887                     0  \n",
      "6931                      0  \n",
      "8690                      0  \n",
      "13788                     0  \n",
      "DESCRIBE:\n",
      "        Computer Science      Physics  Mathematics   Statistics  \\\n",
      "count       6753.000000  6753.000000  6753.000000  6753.000000   \n",
      "mean           0.417888     0.279283     0.271879     0.247446   \n",
      "std            0.493248     0.448680     0.444961     0.431560   \n",
      "min            0.000000     0.000000     0.000000     0.000000   \n",
      "25%            0.000000     0.000000     0.000000     0.000000   \n",
      "50%            0.000000     0.000000     0.000000     0.000000   \n",
      "75%            1.000000     1.000000     1.000000     0.000000   \n",
      "max            1.000000     1.000000     1.000000     1.000000   \n",
      "\n",
      "       Quantitative Biology  Quantitative Finance  \n",
      "count           6753.000000           6753.000000  \n",
      "mean               0.027543              0.010070  \n",
      "std                0.163672              0.099848  \n",
      "min                0.000000              0.000000  \n",
      "25%                0.000000              0.000000  \n",
      "50%                0.000000              0.000000  \n",
      "75%                0.000000              0.000000  \n",
      "max                1.000000              1.000000  \n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "data = pd.read_parquet('cc_data.parquet').sample(frac=0.4, random_state=12345)  # debug\n",
    "# data = pd.read_parquet('cc_data.parquet')\n",
    "\n",
    "# Display basic info\n",
    "print(\"INFO:\")\n",
    "data.info()\n",
    "print(\"HEAD:\\n\", data.head())\n",
    "print(\"DESCRIBE:\\n\", data.describe())\n",
    "\n",
    "# This is for initial data exploration\n",
    "# We are interested to see if there are null values in the data\n",
    "# We also want to see how big is the file\n",
    "# Check for any anomalies in the data, and we notice very low mean values for QB and QF\n",
    "# This means that the data is imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of papers per subject:\n",
      "Computer Science        2822\n",
      "Physics                 1886\n",
      "Mathematics             1836\n",
      "Statistics              1671\n",
      "Quantitative Biology     186\n",
      "Quantitative Finance      68\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. title length: 73.27572930549385\n",
      "Avg. abstract length: 1006.9142603287428\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's visualize numbers for a better picture \n",
    "subject_names = ['Computer Science', 'Physics', 'Mathematics', 'Statistics', 'Quantitative Biology', 'Quantitative Finance']\n",
    "\n",
    "# How many papers for each subject\n",
    "subject_counts = data[subject_names].sum()\n",
    "print(\"Number of papers per subject:\")\n",
    "print(subject_counts)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "subject_counts.plot(kind='bar')\n",
    "plt.title('Distribution of Subjects')\n",
    "plt.xlabel('Subject')\n",
    "plt.ylabel('Number of Papers')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# In fact, these graphs show that the data is imbalanced, especially QB and QF\n",
    "\n",
    "# Now let's see the distribution of lenghts of titles and abstracts\n",
    "data['title_length'] = data['TITLE'].str.len()\n",
    "data['abstract_length'] = data['ABSTRACT'].str.len()\n",
    "\n",
    "print(\"Avg. title length:\", data['title_length'].mean())\n",
    "print(\"Avg. abstract length:\", data['abstract_length'].mean())\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "sns.histplot(data['title_length'], kde=True, ax=ax1)\n",
    "ax1.set_title('Title Lengths')\n",
    "ax1.set_xlabel('Length')\n",
    "sns.histplot(data['abstract_length'], kde=True, ax=ax2)\n",
    "ax2.set_title('Abstract Lengths')\n",
    "ax2.set_xlabel('Length')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# We can see that there are some outliers in the data, short abstracts. What could be the reason?\n",
    "# We will investigate this further\n",
    "\n",
    "# We should also investigate for any correlations, this could help us in the future\n",
    "# It is always a good idea to know as much about the data as possible\n",
    "subject_correlation = data[subject_names].corr()\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(subject_correlation, annot=True, cmap='coolwarm', vmin=-1, vmax=1, center=0)\n",
    "plt.title('Correlation between Subjects')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# We see that CS and Physics have a correlation of -0.41\n",
    "# This means that if a paper is about CS, it is less likely to be about Physics\n",
    "# Nothing too interesting here, but it is good to know"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of short abstracts: 20\n",
      "\n",
      "First 10 short abstracts:\n",
      "\n",
      "Abstract 1:\n",
      "Title: Solution to the relaxation problem for a gas with a distribution function dependent on the velocity modulus\n",
      "Abstract: The...\n",
      "Length: 6\n",
      "Subjects: Physics\n",
      "\n",
      "Abstract 2:\n",
      "Title: Understanding the Impact of Early Citers on Long-Term Scientific Impact\n",
      "Abstract: This...\n",
      "Length: 7\n",
      "Subjects: Computer Science\n",
      "\n",
      "Abstract 3:\n",
      "Title: Measured Multiseries and Integration\n",
      "Abstract: A...\n",
      "Length: 4\n",
      "Subjects: Computer Science\n",
      "\n",
      "Abstract 4:\n",
      "Title: The distribution of symmetry of a naturally reductive nilpotent Lie group\n",
      "Abstract: We...\n",
      "Length: 5\n",
      "Subjects: Mathematics\n",
      "\n",
      "Abstract 5:\n",
      "Title: A Survey of Runtime Monitoring Instrumentation Techniques\n",
      "Abstract: Runtime...\n",
      "Length: 10\n",
      "Subjects: Computer Science\n",
      "\n",
      "Abstract 6:\n",
      "Title: Giant ripples on comet 67P/Churyumov-Gerasimenko sculpted by sunset thermal wind\n",
      "Abstract: Explaining...\n",
      "Length: 13\n",
      "Subjects: Physics\n",
      "\n",
      "Abstract 7:\n",
      "Title: Fundamental Limitations of Cavity-assisted Atom Interferometry\n",
      "Abstract: Atom...\n",
      "Length: 7\n",
      "Subjects: Physics\n",
      "\n",
      "Abstract 8:\n",
      "Title: Between-class Learning for Image Classification\n",
      "Abstract: In...\n",
      "Length: 5\n",
      "Subjects: Computer Science, Statistics\n",
      "\n",
      "Abstract 9:\n",
      "Title: Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning\n",
      "Abstract: In...\n",
      "Length: 5\n",
      "Subjects: Statistics\n",
      "\n",
      "Abstract 10:\n",
      "Title: Time-dependent focusing Mean-Field Games: the sub-critical case\n",
      "Abstract: We...\n",
      "Length: 5\n",
      "Subjects: Mathematics\n"
     ]
    }
   ],
   "source": [
    "# We come back to the question: Why do we have short abstracts?\n",
    "\n",
    "short_abstracts = data[data['ABSTRACT'].str.len() < 50]\n",
    "print(f\"\\nNumber of short abstracts: {len(short_abstracts)}\")\n",
    "print(\"\\nFirst 10 short abstracts:\")\n",
    "for i, (index, row) in enumerate(short_abstracts.iterrows()):\n",
    "    if i >= 10:\n",
    "        break\n",
    "    print(f\"\\nAbstract {i+1}:\")\n",
    "    print(f\"Title: {row['TITLE']}\")\n",
    "    print(f\"Abstract: {row['ABSTRACT']}\")\n",
    "    print(f\"Length: {len(row['ABSTRACT'])}\")\n",
    "    print(f\"Subjects: {', '.join([subj for subj in row.index[2:8] if row[subj] == 1])}\")\n",
    "\n",
    "# We find that the abstracts are short because the subject is obvious from the title.\n",
    "# Abstract 3 is an example of a short abstract that is garbage data (we avoid 'garbage in, gargabe out' at all costs)\n",
    "# So, we will remove all entries where the combined length of the title and abstract is less than some parameter\n",
    "# because that is too hard to interpret, even for a human"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 10 words in Computer Science:\n",
      "[('data', 2009), ('model', 1687), ('based', 1668), ('learning', 1523), ('problem', 1364), ('paper', 1356), ('network', 1280), ('algorithm', 1241), ('time', 1210), ('using', 1201)]\n",
      "\n",
      "Top 10 words in Physics:\n",
      "[('model', 880), ('using', 686), ('field', 684), ('results', 662), ('time', 639), ('data', 633), ('energy', 605), ('high', 605), ('based', 588), ('phase', 548)]\n",
      "\n",
      "Top 10 words in Mathematics:\n",
      "[('paper', 688), ('problem', 642), ('results', 528), ('prove', 484), ('model', 475), ('study', 459), ('mathbb', 459), ('function', 415), ('method', 414), ('space', 412)]\n",
      "\n",
      "Top 10 words in Statistics:\n",
      "[('data', 1757), ('learning', 1310), ('model', 1226), ('based', 908), ('models', 866), ('method', 820), ('methods', 735), ('using', 708), ('paper', 702), ('problem', 690)]\n",
      "\n",
      "Top 10 words in Quantitative Biology:\n",
      "[('data', 168), ('model', 150), ('networks', 118), ('network', 99), ('models', 92), ('based', 85), ('results', 84), ('different', 83), ('brain', 79), ('methods', 77)]\n",
      "\n",
      "Top 10 words in Quantitative Finance:\n",
      "[('model', 103), ('time', 44), ('data', 40), ('models', 35), ('financial', 31), ('approach', 30), ('volatility', 28), ('using', 26), ('risk', 26), ('market', 25)]\n",
      "\n",
      "\n",
      "\n",
      "Unique words in Computer Science:\n",
      "['030x', '03269', '03s', '05n', '079', '1007', '100s', '1020', '1024', '10e4', '10hz', '10s', '1100x', '11n', '1248', '1262', '12hz', '12n', '1382', '138mb']\n",
      "\n",
      "Unique words in Physics:\n",
      "['0001', '000k', '004', '005', '006', '007', '008', '009', '011006', '0125', '013402', '014', '016', '016403', '018', '018001', '021', '026', '029', '02e10']\n",
      "\n",
      "Unique words in Mathematics:\n",
      "['00002407', '00740', '01458', '02204', '02683', '02745v1', '032', '03294', '04144', '04424v2', '04651', '05770', '06432', '06498', '06589', '06712', '0737', '07397', '08101', '08364']\n",
      "\n",
      "Unique words in Statistics:\n",
      "['00001', '0015', '0033', '02647v1', '1266', '1300x', '167tm', '1708', '172hf', '173lu', '175yb', '1870', '1891', '1948', '1960', '228', '275h', '289', '30s', '30x']\n",
      "\n",
      "Unique words in Quantitative Biology:\n",
      "['0269', '03e', '0933', '163', '1713', '21kda', '229', '2296', '252', '2768', '307', '314', '347', '389', '442', '45000', '454', '468', '472', '486']\n",
      "\n",
      "Unique words in Quantitative Finance:\n",
      "['06739', 'accessibilities', 'actuarial', 'additively', 'afc', 'afi', 'afv', 'bachelier', 'bergomi', 'bielecki', 'brigo', 'brock', 'bsm', 'calendar', 'cev', 'collateralization', 'complementarities', 'confiscating', 'confiscations', 'crra']\n",
      "\n",
      "\n",
      "\n",
      "Unique words in Computer Science:\n",
      "['030x', '03s', '05n', '100s', '10hz', '10s', '1100x', '11n', '12hz', '12n', '138mb', '13k', '13x', '13x13', '15hz', '15x', '15x15', '16gb', '16khz', '171m']\n",
      "\n",
      "Unique words in Physics:\n",
      "['000k', '03te', '07_', '07zn0', '09v', '0pm6', '100k', '100nm', '102b', '10b', '10d', '10khz', '10myr', '115in', '1195lb', '12b', '12co', '13bni', '13c', '13te']\n",
      "\n",
      "Unique words in Mathematics:\n",
      "['02745v1', '04424v2', '0_1', '0_3', '0th', '10_', '1950s', '1_', '1_n', '1_q', '1p', '1q', '200r', '25r', '2_', '2nm', '2u_j', '2w', '35x', '3p']\n",
      "\n",
      "Unique words in Statistics:\n",
      "['02647v1', '1300x', '167tm', '172hf', '173lu', '175yb', '275h', '30s', '30x', '4br', '60s', '6x', '700x', '90s', 'a2c', 'abadi', 'abctoolbox', 'abda', 'abnormally', 'abpackage']\n",
      "\n",
      "Unique words in Quantitative Biology:\n",
      "['21kda', '4es', '4s', 'abiotic', 'abound', 'accord', 'acetyl', 'acetylation', 'activates', 'acuity', 'aedes', 'afep', 'afterimages', 'agazzi', 'aggressiveness', 'akt', 'alchemical', 'alchemically', 'alchempmf', 'alga']\n",
      "\n",
      "Unique words in Quantitative Finance:\n",
      "['accessibilities', 'actuarial', 'additively', 'afc', 'afi', 'afv', 'bachelier', 'bergomi', 'bielecki', 'brigo', 'brock', 'bsm', 'calendar', 'cev', 'collateralization', 'complementarities', 'confiscating', 'confiscations', 'crra', 'dea']\n"
     ]
    }
   ],
   "source": [
    "# I think it's a good idea to see the most popular words\n",
    "# This could give us some insight into the data\n",
    "\n",
    "# Word Frequency Analysis\n",
    "def get_top_n_words(corpus, n=10):\n",
    "    vec = CountVectorizer(stop_words='english').fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "\n",
    "for subject in subject_names:\n",
    "    print(f\"\\nTop 10 words in {subject}:\")\n",
    "    top_words = get_top_n_words(data[data[subject] == 1]['ABSTRACT'], n=10)\n",
    "    print(top_words)\n",
    "print('\\n')\n",
    "\n",
    "# We can see that the most popular words are 'data', 'model'.\n",
    "# If we were to use other models than BERT, I would consider removing these words as they are not very informative\n",
    "\n",
    "# Viceversa, we should also explore for exclusive words, get more insight into the data\n",
    "def get_unique_words(data, subject_names, column='ABSTRACT'):\n",
    "    subject_words = defaultdict(set)\n",
    "    \n",
    "    # Get words for each subject\n",
    "    for subject in subject_names:\n",
    "        vec = CountVectorizer(stop_words='english').fit(data[data[subject] == 1][column])\n",
    "        subject_words[subject] = set(vec.get_feature_names_out())\n",
    "    \n",
    "    # Find unique words for each subject\n",
    "    unique_words = {}\n",
    "    for subject in subject_names:\n",
    "        other_subjects = set(subject_names) - {subject}\n",
    "        words_in_other_subjects = set.union(*[subject_words[s] for s in other_subjects])\n",
    "        unique_words[subject] = subject_words[subject] - words_in_other_subjects\n",
    "    \n",
    "    return unique_words\n",
    "\n",
    "unique_words_per_subject = get_unique_words(data, subject_names)\n",
    "\n",
    "for subject, words in unique_words_per_subject.items():\n",
    "    print(f\"\\nUnique words in {subject}:\")\n",
    "    print(sorted(list(words))[:20])\n",
    "print('\\n')\n",
    "\n",
    "# We can see that there are many unique numbers in the data, which are not very informative\n",
    "# It is only useful if the number has characters after it, like Hz, kda, mb, etc.\n",
    "# I will remove numbers from the data and check again\n",
    "def remove_standalone_numbers(text):\n",
    "    return re.sub(r'\\b\\d+(?:\\.\\d+)?(?:[eE][+-]?\\d+)?\\b(?!\\w)', '', text)\n",
    "\n",
    "data['NONUMBER_ABSTRACT'] = data['ABSTRACT'].apply(remove_standalone_numbers)\n",
    "unique_words_per_subject = get_unique_words(data, subject_names, 'NONUMBER_ABSTRACT')\n",
    "\n",
    "for subject, words in unique_words_per_subject.items():\n",
    "    print(f\"\\nUnique words in {subject}:\")\n",
    "    print(sorted(list(words))[:20])\n",
    "\n",
    "# Everything seems better now\n",
    "# For some optimizations it's a viable strategy to replace numbers with a special token, but I will not do that here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9062     connectivity properties of factorization poset...\n",
      "11887    infovae information maximizing variational aut...\n",
      "6931     the origin and early evolution of life in chem...\n",
      "8690     monadic second order logic with measure and ca...\n",
      "13788    optical reconfiguration and polarization contr...\n",
      "10364    providing effective realtime feedback in simul...\n",
      "15301    moments and cumulants of the twostage mannwhit...\n",
      "16343    multivariate central limit theorems for radema...\n",
      "5740     local and global boundary rigidity and the geo...\n",
      "13748    on training recurrent networks with truncated ...\n"
     ]
    }
   ],
   "source": [
    "# Settings to remove warnings, new pandas version unhappy for some reason\n",
    "pd.options.mode.copy_on_write = True\n",
    "\n",
    "class ScientificPaperDataset(Dataset):\n",
    "    def __init__(self, texts, labels):\n",
    "        self.texts = texts\n",
    "        self.labels = labels\n",
    "        # self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
    "        # SciBERT is a BERT model trained on scientific text, so we will use it to better fit our needs\n",
    "        self.tokenizer = AutoTokenizer.from_pretrained('allenai/scibert_scivocab_uncased')\n",
    "        # BERT's maximum sequence length 512, but for performance reasons we will use 64\n",
    "        self.max_length = config['data']['max_length']  \n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.texts)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        text = self.texts[idx]\n",
    "        labels = self.labels[idx]\n",
    "\n",
    "        encoding = self.tokenizer.encode_plus(\n",
    "            text,\n",
    "            add_special_tokens=True,\n",
    "            max_length=self.max_length,\n",
    "            return_token_type_ids=False,\n",
    "            padding='max_length',\n",
    "            truncation=True,\n",
    "            return_attention_mask=True,\n",
    "            return_tensors='pt',\n",
    "        )\n",
    "\n",
    "        return {\n",
    "            'input_ids': encoding['input_ids'].flatten(),\n",
    "            'attention_mask': encoding['attention_mask'].flatten(),\n",
    "            'labels': torch.FloatTensor(labels)\n",
    "        }\n",
    "\n",
    "def preprocess_data(data, is_test=False, test_size=0.2, random_state=12345):\n",
    "    # We combine TITLE + ABSTRACT\n",
    "    data['text'] = data['TITLE'] + ' ' + data['ABSTRACT']\n",
    "\n",
    "    # Remove entries with very short text\n",
    "    data = data[data['text'].str.len() > 35]\n",
    "\n",
    "    # Common text preprocessing\n",
    "    data['text'] = data['text'].str.lower()\n",
    "    data['text'] = data['text'].apply(remove_standalone_numbers)\n",
    "    data['text'] = data['text'].str.replace('[^a-zA-Z\\s]', '', regex=True)\n",
    "    data['text'] = data['text'].str.replace('\\s+', ' ', regex=True).str.strip()\n",
    "\n",
    "    if is_test:\n",
    "        # For test data, we don't split or create labels\n",
    "        dataset = ScientificPaperDataset(data['text'].tolist(), [[0]*6]*len(data))\n",
    "        return dataset, data['text'], data['ABSTRACT']\n",
    "    else:\n",
    "        # Prep labels\n",
    "        train_data, val_data = train_test_split(data, test_size=test_size, random_state=random_state, stratify=data[subject_names].sum(axis=1))\n",
    "\n",
    "        # Datasets\n",
    "        train_dataset = ScientificPaperDataset(train_data['text'].tolist(), train_data[subject_names].values.tolist())\n",
    "        val_dataset = ScientificPaperDataset(val_data['text'].tolist(), val_data[subject_names].values.tolist())\n",
    "\n",
    "        return train_dataset, val_dataset, data['text']\n",
    "\n",
    "def create_data_loaders(train_dataset, val_dataset, batch_size=32):\n",
    "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "    val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
    "    return train_loader, val_loader\n",
    "\n",
    "train_dataset, val_dataset, processed_texts = preprocess_data(data, test_size=config['data']['test_size'])\n",
    "train_loader, val_loader = create_data_loaders(train_dataset, val_dataset, batch_size=config['training']['batch_size'])\n",
    "\n",
    "# Let's see how the data looks like after preprocessing\n",
    "print(processed_texts.head(10).to_string())\n",
    "\n",
    "# Looking good, let's move on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Model, Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppress annoying warnings...\n",
    "# \"A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\" \n",
    "# \"A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\"\n",
    "from transformers import logging\n",
    "logging.set_verbosity_error()\n",
    "\n",
    "class BERTClassifier(nn.Module):\n",
    "    def __init__(self, num_labels):\n",
    "        super(BERTClassifier, self).__init__()\n",
    "        self.bert = AutoModel.from_pretrained(config['model']['pretrained_model'])\n",
    "        self.dropout = nn.Dropout(0.1) # default dropout\n",
    "        self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)  \n",
    "\n",
    "        # Could also consider different classifier implementations, but this is the simplest one\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
    "        pooled_output = outputs.pooler_output\n",
    "        pooled_output = self.dropout(pooled_output)\n",
    "        # outputs.last_hidden_state[:, 0, :] # Could be better than pooler_output\n",
    "        logits = self.classifier(pooled_output)\n",
    "        return logits\n",
    "\n",
    "def compute_class_weights(labels, device):\n",
    "    # Class imbalance correction\n",
    "    # It should be sufficient to use the inverse of the class frequencies\n",
    "    labels = np.array(labels)\n",
    "    class_counts = labels.sum(axis=0)\n",
    "    class_weights = 1.0 / class_counts\n",
    "    class_weights = class_weights / class_weights.sum() * len(class_weights)\n",
    "    return torch.FloatTensor(class_weights).to(device)\n",
    "\n",
    "def train_model(model, train_loader, num_epochs, device, criterion, lr=2e-5, max_grad_norm=1.0):\n",
    "    # AdamW optimizer is default \n",
    "    optimizer = AdamW(model.parameters(), lr=lr)\n",
    "    total_steps = len(train_loader) * num_epochs\n",
    "    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)\n",
    "    \n",
    "    # Variables to keep track of stats\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    train_f1_scores = []\n",
    "    val_f1_scores = []\n",
    "    val_accuracies = []\n",
    "\n",
    "    # Main training loop\n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "\n",
    "        # Again, more variables to keep track of epoch stats\n",
    "        train_loss = 0\n",
    "        train_preds = []\n",
    "        train_labels = []\n",
    "\n",
    "        for batch in tqdm(train_loader, desc=f\"Epoch {epoch + 1}/{num_epochs}\"):\n",
    "            input_ids = batch['input_ids'].to(device)\n",
    "            attention_mask = batch['attention_mask'].to(device)\n",
    "            labels = batch['labels'].to(device)\n",
    "            \n",
    "            # Standard training loop\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(input_ids, attention_mask)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=max_grad_norm)\n",
    "            optimizer.step()\n",
    "            scheduler.step()\n",
    "            \n",
    "            train_loss += loss.item()\n",
    "\n",
    "            # This is for stats\n",
    "            preds = torch.sigmoid(outputs).cpu().detach().numpy()\n",
    "            train_preds.extend(preds)\n",
    "            train_labels.extend(labels.cpu().numpy())\n",
    "        \n",
    "        # Save all the statistics\n",
    "        avg_train_loss = train_loss / len(train_loader)\n",
    "        train_losses.append(avg_train_loss)\n",
    "\n",
    "        train_f1 = f1_score((np.array(train_labels) > 0.5).astype(int), \n",
    "                            (np.array(train_preds) > 0.5).astype(int), \n",
    "                            average='micro')\n",
    "        train_f1_scores.append(train_f1)\n",
    "\n",
    "        \n",
    "        val_loss, val_f1, val_acc = evaluate_model(model, val_loader, criterion, device)\n",
    "        val_losses.append(val_loss)\n",
    "        val_f1_scores.append(val_f1)\n",
    "        val_accuracies.append(val_acc)\n",
    "\n",
    "        print(f\"Epoch {epoch + 1}/{num_epochs}\")\n",
    "        print(f\"Train Loss: {avg_train_loss:.4f}, Train F1: {train_f1:.4f}\")\n",
    "        print(f\"Val Loss: {val_loss:.4f}, Val F1: {val_f1:.4f}, Val Accuracy: {val_acc:.4f}\")\n",
    "\n",
    "    return model, train_losses, val_losses, train_f1_scores, val_f1_scores, val_accuracies\n",
    "\n",
    "def evaluate_model(model, data_loader, criterion, device):\n",
    "    # This is standard implementation of evaluation\n",
    "    model.eval()\n",
    "\n",
    "    # Stats variables\n",
    "    val_loss = 0\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for batch in data_loader:\n",
    "            input_ids = batch['input_ids'].to(device)\n",
    "            attention_mask = batch['attention_mask'].to(device)\n",
    "            labels = batch['labels'].to(device)\n",
    "            \n",
    "            # Get predictions\n",
    "            outputs = model(input_ids, attention_mask)\n",
    "            loss = criterion(outputs, labels)\n",
    "            val_loss += loss.item()\n",
    "            \n",
    "            # This is for stats\n",
    "            preds = torch.sigmoid(outputs).cpu().numpy()\n",
    "            all_preds.extend(preds)\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "    \n",
    "    # Calculate final stats\n",
    "    all_preds = np.array(all_preds)\n",
    "    all_labels = np.array(all_labels)\n",
    "    \n",
    "    val_loss /= len(data_loader)\n",
    "    f1 = f1_score(all_labels, (all_preds > 0.5).astype(int), average='micro')\n",
    "    accuracy = accuracy_score(all_labels, (all_preds > 0.5).astype(int))\n",
    "    \n",
    "    return val_loss, f1, accuracy\n",
    "\n",
    "def predict(model, data_loader, device):\n",
    "    # For predicting validation data\n",
    "    model.eval()\n",
    "\n",
    "    # Stats variables\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for batch in tqdm(data_loader, desc=\"Predicting\"):\n",
    "            input_ids = batch['input_ids'].to(device)\n",
    "            attention_mask = batch['attention_mask'].to(device)\n",
    "            labels = batch['labels']\n",
    "            \n",
    "            # Get predictions\n",
    "            outputs = model(input_ids, attention_mask)\n",
    "            preds = torch.sigmoid(outputs).cpu().numpy()\n",
    "\n",
    "            # This is for stats\n",
    "            all_preds.extend(preds)\n",
    "            all_labels.extend(labels.numpy())\n",
    "    \n",
    "    return np.array(all_preds), np.array(all_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using: cuda\n",
      "numpy: 1.24.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/3:   0%|          | 0/338 [00:00<?, ?it/s]c:\\Users\\gedas\\anaconda3\\envs\\cybercare\\Lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:439: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at ..\\aten\\src\\ATen\\native\\transformers\\cuda\\sdp_utils.cpp:263.)\n",
      "  attn_output = torch.nn.functional.scaled_dot_product_attention(\n",
      "Epoch 1/3: 100%|██████████| 338/338 [01:08<00:00,  4.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "Train Loss: 0.1220, Train F1: 0.3768\n",
      "Val Loss: 0.0804, Val F1: 0.6119, Val Accuracy: 0.4952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2/3: 100%|██████████| 338/338 [01:13<00:00,  4.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/3\n",
      "Train Loss: 0.0738, Train F1: 0.6658\n",
      "Val Loss: 0.0760, Val F1: 0.6593, Val Accuracy: 0.5056\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3/3: 100%|██████████| 338/338 [01:09<00:00,  4.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/3\n",
      "Train Loss: 0.0570, Train F1: 0.7472\n",
      "Val Loss: 0.0750, Val F1: 0.7315, Val Accuracy: 0.5899\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "# device = torch.device('cpu')\n",
    "\n",
    "print(f\"Using: {device}\")\n",
    "print(\"numpy: \" + np.__version__) # doesn't work with 2.0.1 numpy\n",
    "\n",
    "num_labels = config['model']['num_labels'] # CS, Physics, Math, Stats, QB, QF\n",
    "model = BERTClassifier(num_labels).to(device)\n",
    "\n",
    "class_weights = compute_class_weights(train_dataset.labels, device)\n",
    "# We use BCEWithLogitsLoss because it combines sigmoid and binary cross entropy loss\n",
    "criterion = nn.BCEWithLogitsLoss(pos_weight=class_weights.to(device))\n",
    "num_epochs = config['training']['num_epochs']\n",
    "\n",
    "# Train and save the model\n",
    "trained_model, train_losses, val_losses, train_f1_scores, val_f1_scores, val_accuracies = train_model(model, train_loader, num_epochs, device, criterion, lr=float(config['training']['learning_rate']))\n",
    "torch.save(trained_model.state_dict(), config['model']['save_path'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.0743\n",
      "Validation F1 Score: 0.7214\n",
      "Validation Accuracy: 0.5744\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Predicting: 100%|██████████| 85/85 [00:05<00:00, 14.63it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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hRjc/L08PeXl5KvVcus301LR01a5aVj7eFxoRU89lWOcZY3TuXIZqVykr6cKtXcYYpV1UJjUtQ5mZxloGyGvJ/10YzicoOPgKZZIVEBBA43g+YrFYcv0FxyK785+gQD9lZmYq8b+zki7kdHr6eZs7us6mXRhaJSun9x44ouOnkhXTurYKeHnK16eAOrWupV1/HtZfh046ficA5FvOzu4tW7aoatWqqlq1qiSpb9++qlq1qgYOHChJevXVV9WzZ0917dpVNWrUUHJyspYsWSJfX1/rOuLi4nTHHXeoUaNGat68uerWratp06ZZ5wcHB2vp0qWKj49XtWrV9NJLL2ngwIHq2jV7T2BXRHbnLwW8PNW5TR0l/pdiM6xZWGigJg94Qp0HzFbKRUOiAfnB8KFDVL9+A91bq7azqwI7ODu716xZo5YtWyoiIkIWi0ULFiywmW+M0cCBA1W8eHH5+fkpOjpa+/btsylz8uRJdejQQUFBQQoJCVHnzp2VnGx7x82vv/6qevXqydfXVyVLltTIkSOv+Vjliwbyt956K9vOSVJKSkq2p5ojdySnpOmnX/5U/y7NVLxosDw8LGrXvIZqVo5SeJEg7TmQoIOHT+rtng8pJNBPBbw89VKnaJUIL6TwIhcaIjf9dkBnzp7TsBdbyc+3gAr6euudvg/Ly8tT4UWCnLyHcAeZmZkaOWK4qlS9W7fddnuOZU6dOqlpUyer7aOPO7h2uCJLHrzgUGR3/uLj7aWhvVpp3pKt1ru6Vm/ao2KFg9SnYyMV8PJUSKCfhvZqJUkKL3ohy5NT0tSkyzg90byGTv00RsfXvacHapdX6x6Tdf48w6sAuIiTs7thw4YyxmR7zZw580L1LBYNGTJECQkJSk1N1fLly3X77bZ/H4aGhmrOnDn677//lJSUpOnTpysgIMCmTOXKlfXjjz8qNTVV//zzj/r163dtFc3HyO78oVm9O3Vs3XtK3DhGPZ+8Tw8+P1EnEs9Y508b8qQ+mL9W2/73bDAgv/ju28XatWunevV5ydlVgb2cnN1nzpzRXXfdpUmTJuU4f+TIkRo/frymTp2qjRs3yt/fX02aNFFqaqq1TIcOHbRjxw4tW7ZMixYt0po1a2wuXJ8+fVqNGzdWZGSktm7dqlGjRmnw4ME2F8DtkS+6UxpjcrwK8csvvyg0NPSKy6alpSktLc12fZnnZfHwzNU63oyeeXO23h/cQX8uHaaMjPPavvtvzVuyRVXLl1JGRqbavfSBpgzqoMNrRikj47xWbtyjJWt3KOtHdfxUsjq8+pHGv/64XniigTIzjeYt2aptOw8qk/HH4QDDh76lP/bt08yP5+Q4Pzk5WT26PacyZcvq+Reyj3kK4PqR3fmHl5eHPhnZ+cL4sf+75VqSdv2ZoC4DP9Y7L7XRkJ4P6XxmpiZ/+oMSjp+WybzQ+O3rU0BTB3XQhl/+VEz/GfL09FDvjo305fhuqvvkKKWmpV9uswAAF0N25w8/bN6rmu1iVSQkQE+3qa1PRj6j+k+9q2OnkvXCEw0UWNBXo6YvdXY1ARsJhw9r5DvD9P4H03Pl4cpwD82aNVOzZs1ynGeM0dixY/Xmm2+qVasLnXhmz56tYsWKacGCBWrXrp127dqlJUuWaPPmzapevbokacKECWrevLneffddRUREKC4uTufOndP06dPl7e2tihUravv27Ro9evQ13QHm1AbyQoUKWbvo33777TZhff78eSUnJ+v555+/4jpiY2OzXe32LFZDBYrfkyd1vpnE/3NcjZ8dp4K+3goK8FXC8dP6+J2nFf/vhfFGf971t+5t946CAnzlXcBLx08la83sl7X1oivZK37arYoPvaXCIf7KyMhUUvJZxS8brgPfb3XWbsFNDB86RGt+WK3psz5RsfDwbPPPnEnWC889K39/f40ZP8n6oCXkDwyJ4rrI7vzFy8tDcSM6q1TxQmrWdYK193iWuUu2aO6SLQoLDdSZs2kyRur15P2K/+eEJOnxZtVVKiJUDWL+/+HaMf1n6vCakWrZsLI+J88B/A/Z7brI7vwlJfWc/vz7uP78+7g2/XZAv309UDEP19a705eqYY3bVbNylJI2jrVZZl3cq/rsuy3qMvBj51Qabm/nzh06eeKE2j3axjrt/Pnz2rplsz77NE6bf/5Nnp5cMMtv8nN2x8fHKyEhQdHR0dZpwcHBqlmzpjZs2KB27dppw4YNCgkJsTaOS1J0dLQ8PDy0ceNGPfzww9qwYYPq168vb29va5kmTZpoxIgROnXqlN0P2XZqA/nYsWNljNEzzzyjt956S8EXjSHs7e2t0qVLq1atWldcR//+/dW3b1+baWH1bp7b4BwhJfWcUlLPKSTQT9G1y+uNsV/bzD+dfOHLdtlSRXV3hVJ6a/KibOvIuiWsQY3bFRYaoEU//Jb3FYdbMsYodtjbWrlimT6a+bFKlCiZrUxycrK6de0sb29vjZs4hSvc+VB+DmpcGdmdf2Q1jpctVVRNu47XyaQzly179OSF5zV0bHWvUs+la8VPuyVJBX29lZlpbMYpzzRGxkgefE4BXITsdl1kd/7mYbHIp8CFppmXRs7X4En//327eNFgLZrSQ0+9NkObfzvgpBoCUs1779X8BQttpg16o79Klymjpzt3oXE8n8qL7M7pjiIfH59rbndJSEiQJBUrVsxmerFixazzEhISFBYWZjPfy8tLoaGhNmWioqKyrSNrnks0kMfExEiSoqKiVLt27evq4ZnTD4HbvOwTXau8LBZp74GjKluyqIb3aa298Uc0+5sNkqQ20VV17FSy/k44qTtvi9C7rzyihat/tX6plqSnHrpXe+ITdOxUsmpWjtK7rzyiCXGrtO+vo87aLdzkhr/9lr77dpHGTpgs/4L+On7smCQpIDBQvr6+F3rAdHlGqalnNfydUTqTnKwz/xtrsVBoKMEN3CCy23H8/bxVtmRR6/vStxRW5dtv0anTKTp8PElzRj2rqneUVJsXp8rTw6JihQMlSSeTUpSecV6S9Pzj9fXTL38qOeWcGt17h4b3bq0BE75WUvKFB3mu+Gm3hvdurbH9H9OUz36Qh8Wil59urIzz5/XDlr2O32nclFLOnNHBg/9/B+K///yj3bt2KTg4WMUjIpSUmKjDhw/r2LELfz8eOBAvSSpSpIiKFC2a4zoB2I/sdpwrZfeJxDPq92wTLf7hNyUcT1LhkAA991h9RYSF6Mtl2yRJfyecsllfcsqFRqg//z6mf48mWqffUSZc3l6eKhTsr8CCPqp8+y2SpF8vetgnkJv8/QOyPffLr2BBhQSHXPZ5YLg55XRH0aBBgzR48GDnVCiXOK2B/PTp0woKuvAgx6pVq+rs2bM6e/ZsjmWzyiF3BQf4akjPh3RLsRCdTErR1yu2a9CkhcrIuDAuaXjRII14qY3CCgcq4fhpxS3aqNhpS2zWcXvpMA3p+ZBCgwvqr0MnNfKj7zX+k5XO2B24iXlzP5Ukde70lM30IUNj1erhNtq1c4d++/UXSdKDzR6wKfPt0hW65ZYSjqkorohOaK6J7HasuytEaumHL1rfj3y5rSTp429+0tCp36plw8qSpE1z+9ss1/jZcfpx64Wnv1e/M1JvPt9CAQW9tefAEfUY9qk+XbzZWnbvgSNq++L7euO5Zlo96yVlZhr9svsfteo+WQnHT+f1LsJN7Njxu559uqP1/bsjYyVJD7V6WG8Pf0erV63UwDf//zzu93IfSdLzL/RQt+49HVtZXBbZ7ZrIbse6Unb3HPaZypUupidb1lThEH+dTErRlh1/KfqZMdr1Z8I1bWfBhG6KjChsfb/xf38L+FXluUsA/l9eZHdOdxRdz1374f8bKvfIkSMqXry4dfqRI0dUpUoVa5mjR2074GZkZOjkyZPW5cPDw3XkyBGbMlnvw3MYjvdyLMY452mKnp6eOnz4sMLCwuTh4ZFjt/+sh4icP3/+mtZNKMBVnNo80dlVAK7KNw8upd768ne5vs797+b88A/kHrIbILvhGshuZCG7AbIbruFmz26LxaKvvvpKrVu3lnQheyIiIvTyyy/rpZdeknThom5YWJhmzpxpfUhnhQoVtGXLFlWrVk2StHTpUjVt2lT//POPIiIiNGXKFL3xxhs6cuSI9Q6p119/XV9++aV2796dY11y4rQe5CtXrrQ+KXvVqlXOqgYAwAkYx9Q1kd0A4L7IbtdEdgOA+3J2dicnJ2v//v3W9/Hx8dq+fbtCQ0NVqlQp9e7dW0OHDtVtt92mqKgoDRgwQBEREdZG9PLly6tp06bq0qWLpk6dqvT0dPXo0UPt2rVTRESEJKl9+/Z666231LlzZ/Xr10+///67xo0bpzFjxlxTXZ3WQN6gQYMc/w0AuPnxHds1kd0A4L7IbtdEdgOA+3J2dm/ZskX33Xef9X3W0CwxMTGaOXOmXn31VZ05c0Zdu3ZVYmKi6tatqyVLlsjX19e6TFxcnHr06KFGjRrJw8NDbdu21fjx463zg4ODtXTpUnXv3l3VqlVTkSJFNHDgQHXt2vWa6urUh3RmWbJkiQICAlS3bl1J0qRJk/TBBx+oQoUKmjRpkt1PHAUAAI5BdgMA4FrIbgCAIzVs2FBXGtnbYrFoyJAhGjJkyGXLhIaGas6cOVfcTuXKlfXjjz9edz0lyeOGls4lr7zyik6fvvAgqN9++019+/ZV8+bNFR8fn23gdwCA67NYLLn+gmOR3QDgXshu10d2A4B7Ibvtly96kMfHx6tChQqSpC+++EItW7bU8OHDtW3bNjVv3tzJtQMA5LabOFfdBtkNAO6F7HZ9ZDcAuBey2375oge5t7e3UlJSJEnLly9X48aNJV3oRp91hRsAAOQfZDcAAK6F7AYAIGf5ogd53bp11bdvX9WpU0ebNm3S3LlzJUl79+5ViRIlnFw7AEBu8/DgUrarI7sBwL2Q3a6P7AYA90J22y9f9CCfOHGivLy8NH/+fE2ZMkW33HKLJOm7775T06ZNnVw7AABwKbIbAADXQnYDAJCzfNGDvFSpUlq0aFG26WPGjHFCbQAAeY2x0Fwf2Q0A7oXsdn1kNwC4F7LbfvmigVySzp8/rwULFmjXrl2SpIoVK+qhhx6Sp6enk2sGAMhtN/PTr90J2Q0A7oPsvjmQ3QDgPshu++WLBvL9+/erefPm+vfff1WuXDlJUmxsrEqWLKnFixerbNmyTq4hAAC4GNkNAIBrIbsBAMhZvhiDvFevXipbtqz+/vtvbdu2Tdu2bdPBgwcVFRWlXr16Obt6AIBcZrHk/guORXYDgHshu10f2Q0A7oXstl++6EH+ww8/6KefflJoaKh1WuHChfXOO++oTp06TqwZAADICdkNAIBrIbsBAMhZvmgg9/Hx0X///ZdtenJysry9vZ1QIwBAXmIsNNdHdgOAeyG7XR/ZDQDuhey2X74YYuXBBx9U165dtXHjRhljZIzRTz/9pOeff14PPfSQs6sHAMhlFosl119wLLIbANwL2e36yG4AcC9kt/3yRQP5+PHjdeutt6p27dry9fWVr6+v6tSpo1tvvVXjxo1zdvUAAMAlyG4AAFwL2Q0AQM6cOsRKZmamRo0apW+++Ubnzp1T69atFRMTI4vFovLly+vWW291ZvUAAHnkJr7wfNMjuwHAPZHdrovsBgD3RHbbz6kN5MOGDdPgwYMVHR0tPz8/ffvttwoODtb06dOdWS0AQB67mW/NutmR3QDgnshu10V2A4B7Irvt59QhVmbPnq3Jkyfr+++/14IFC7Rw4ULFxcUpMzPTmdUCAACXQXYDAOBayG4AAK7MqQ3kBw8eVPPmza3vo6OjZbFYdOjQISfWCgCQ1yyW3H/BMchuAHBPZLfrIrsBwD2R3fZzagN5RkaGfH19baYVKFBA6enpTqoRAAC4ErIbAADXQnYDAHBlTh2D3BijTp06ycfHxzotNTVVzz//vPz9/a3TvvzyS2dUDwCQRxgLzXWR3QDgnshu10V2A4B7Irvt59QG8piYmGzTnnzySSfUBADgSOS06yK7AcA9kd2ui+wGAPdEdtvPqQ3kM2bMcObmAQDANSK7AQBwLWQ3AABX5tQGcgCAe+JWLwAAXAvZDQCAayG77UcDOQDA4chpAABcC9kNAIBrIbvt5+HsCgAAAAAAAAAA4Az0IAcAOBy3egEA4FrIbgAAXAvZbT96kAMAAAAAAAAA3BI9yAEADseFbAAAXAvZDQCAayG77UcDOQDA4bjVCwAA10J2AwDgWshu+zHECgAAAAAAAADALdGDHADgcFzIBgDAtZDdAAC4FrLbfjSQAwAcjlu9AABwLWQ3AACuhey2H0OsAAAAAAAAAADcEj3IAQAOx4VsAABcC9kNAIBrIbvtRw9yAAAAAAAAAIBbogc5AMDhGAsNAADXQnYDAOBayG770UAOAHA4ghoAANdCdgMA4FrIbvsxxAoAAAAAAAAAwC3RgxwA4HBcyAYAwLWQ3QAAuBay2340kAMAHI5bvQAAcC1kNwAAroXsth9DrAAAAAAAAAAA3BI9yAEADseFbAAAXAvZDQCAayG77UcPcgAAAAAAAACAW6IHOQDA4RgLDQAA10J2AwDgWshu+9FADgBwOHIaAADXQnYDAOBayG77McQKAAAAACDfOn/+vAYMGKCoqCj5+fmpbNmyevvtt2WMsZYxxmjgwIEqXry4/Pz8FB0drX379tms5+TJk+rQoYOCgoIUEhKizp07Kzk52dG7AwAA8hl6kAMAHM6DS9kAALgUZ2b3iBEjNGXKFM2aNUsVK1bUli1b9PTTTys4OFi9evWSJI0cOVLjx4/XrFmzFBUVpQEDBqhJkybauXOnfH19JUkdOnTQ4cOHtWzZMqWnp+vpp59W165dNWfOHKftGwAAeYXv3fajgRwAAAAAkG+tX79erVq1UosWLSRJpUuX1qeffqpNmzZJutB7fOzYsXrzzTfVqlUrSdLs2bNVrFgxLViwQO3atdOuXbu0ZMkSbd68WdWrV5ckTZgwQc2bN9e7776riIgI5+wcAABwOoZYAQA4nMWS+y8AAJB38iK709LSdPr0aZtXWlpatm3Xrl1bK1as0N69eyVJv/zyi9auXatmzZpJkuLj45WQkKDo6GjrMsHBwapZs6Y2bNggSdqwYYNCQkKsjeOSFB0dLQ8PD23cuDEvDx0AAE7B92770UAOAHA4i8WS6y8AAJB38iK7Y2NjFRwcbPOKjY3Ntu3XXntN7dq10x133KECBQqoatWq6t27tzp06CBJSkhIkCQVK1bMZrlixYpZ5yUkJCgsLMxmvpeXl0JDQ61lAAC4mfC9234MsQIAAAAAcLj+/furb9++NtN8fHyylZs3b57i4uI0Z84cVaxYUdu3b1fv3r0VERGhmJgYR1UXAADcpGggBwA4nMfNe+EZAICbUl5kt4+PT44N4pd65ZVXrL3IJalSpUr666+/FBsbq5iYGIWHh0uSjhw5ouLFi1uXO3LkiKpUqSJJCg8P19GjR23Wm5GRoZMnT1qXBwDgZsL3bvsxxAoAwOG41QsAANfizOxOSUmRh4ftV1dPOiL6wQAAUBRJREFUT09lZmZKkqKiohQeHq4VK1ZY558+fVobN25UrVq1JEm1atVSYmKitm7dai2zcuVKZWZmqmbNmjdyaAAAyJecmd3nz5/XgAEDFBUVJT8/P5UtW1Zvv/22jDHWMsYYDRw4UMWLF5efn5+io6O1b98+m/WcPHlSHTp0UFBQkEJCQtS5c2clJyfn2jHKQgM5AAAAACDfatmypYYNG6bFixfrwIED+uqrrzR69Gg9/PDDki40APTu3VtDhw7VN998o99++00dO3ZURESEWrduLUkqX768mjZtqi5dumjTpk1at26devTooXbt2ikiIsKJewcAwM1nxIgRmjJliiZOnKhdu3ZpxIgRGjlypCZMmGAtM3LkSI0fP15Tp07Vxo0b5e/vryZNmig1NdVapkOHDtqxY4eWLVumRYsWac2aNeratWuu15cGcgCAw1mc/DTtf//9V08++aQKFy4sPz8/VapUSVu2bLHOz09XsgEAyA+cmd0TJkzQI488ohdeeEHly5fXyy+/rOeee05vv/22tcyrr76qnj17qmvXrqpRo4aSk5O1ZMkS+fr6WsvExcXpjjvuUKNGjdS8eXPVrVtX06ZNy83DBABAvuHM7F6/fr1atWqlFi1aqHTp0nrkkUfUuHFjbdq0SdKF79xjx47Vm2++qVatWqly5cqaPXu2Dh06pAULFkiSdu3apSVLlujDDz9UzZo1VbduXU2YMEH/1959x0dV5u0fvyaVdAiSBBRikK6oSBQCCApZIsVHF1YXRUVlYY2hC0KsGJSAK0VURFARVooFGyiLgIAiERARKVIUJCuQgJQECOnz+4Mfs4yhDMmZcmY+b1/zvJJzzpx8JztPLuZ77nPf8+fP1/79+w39XdEgBwD4lKNHj6pt27YKDAzU4sWLtW3bNk2YMEE1atSwHeNJV7IBAPB1ERERmjx5svbu3atTp07p119/1fPPP6+goCDbMRaLRRkZGcrJyVFhYaGWLVumRo0a2Z0nOjpac+fO1fHjx5WXl6e3335b4eHhrn45AACYVlFRkfLz8+0eRUVFFY5r06aNli9frp07d0qSNm3apNWrV6tLly6SpD179ignJ0fJycm250RFRalVq1bKysqSJGVlZal69epKTEy0HZOcnCw/Pz+tXbvW0NfFIp0AAJezyH1zho8fP15169bVzJkzbdsSEhJsX//5SrYkzZ49W7Gxsfrkk0/Uq1cv25Xs9evX28L6lVdeUdeuXfXSSy9xqzYAwOu4M7sBAMClc0Z2Z2Zm6rnnnrPb9uyzz2r06NF220aNGqX8/Hw1adJE/v7+Kisr0wsvvKDevXtLknJyciRJsbGxds+LjY217cvJyVFMTIzd/oCAAEVHR9uOMQojyAEALudnMf7h6JXszz77TImJibrrrrsUExOjFi1aaMaMGbb9nnYlGwAAT+CM7AYAAM7jjOxOT09XXl6e3SM9Pb3Cz37//fc1Z84czZ07Vz/88INmzZqll156SbNmzXLDb+LiaJADALxCZmamoqKi7B6ZmZkVjtu9e7def/11NWzYUEuWLFFqaqoGDRpkC2pPu5INAAAAAIAnCA4OVmRkpN0jODi4wnEjRozQqFGj1KtXLzVv3lz333+/hg4davuMHhcXJ0nKzc21e15ubq5tX1xcnA4ePGi3v7S0VEeOHLEdYxSmWAEAuJzlUlfVdEB6erqGDRtmt+1cQV1eXq7ExESNHTtWktSiRQtt2bJF06ZNU58+fQyvCwAAb+CM7AYAAM7jzuwuKCiQn5/9uGx/f3+Vl5dLOj3NaVxcnJYvX67rr79ekpSfn6+1a9cqNTVVkpSUlKRjx45pw4YNatmypSTpq6++Unl5uVq1amVovTTIAQAu54ycDg4OPmdD/M9q166tZs2a2W1r2rSpFixYIMn+Snbt2rVtx+Tm5tqC25VXsgEA8AT0xwEAMBd3Zvftt9+uF154QfXq1dPVV1+tjRs3auLEiXr44Yf/f20WDRkyRM8//7waNmyohIQEPf3006pTp47uvPNOSac/p992223q16+fpk2bppKSEg0YMEC9evUyfN0vplgBAPiUtm3baseOHXbbdu7cqfj4eEn2V7LPOHMlOykpSZL9lewznHUlGwAAAAAAM3nllVf0t7/9TY8++qiaNm2q4cOH65///KfGjBljO+bxxx/XwIED1b9/f9144406ceKE/vOf/6hatWq2Y+bMmaMmTZqoU6dO6tq1q9q1a6fp06cbXi8jyAEALufnxkvZQ4cOVZs2bTR27FjdfffdWrdunaZPn24LWU+7kg0AgCdwZ3YDAIBL587sjoiI0OTJkzV58uTzHmOxWJSRkaGMjIzzHhMdHa25c+c6oUJ7NMgBAD7lxhtv1Mcff6z09HRlZGQoISFBkydPVu/evW3HPP744zp58qT69++vY8eOqV27due8kj1gwAB16tRJfn5+6tmzp6ZMmeKOlwQAAAAAACqJBjkAwOXcPQite/fu6t69+3n3e9KVbAAAPIG7sxsAAFwasttxNMgBAC7nztW0AQDApSO7AQAwF7LbcQ43yPPz8x0+aWRkZKWKAQAAxiG7AQAwF7IbAADXc7hBXr16dYevPJSVlVW6IACA9+NCtmuQ3QAAo5DdrkF2AwCMQnY7zuEG+YoVK2xf//bbbxo1apQefPBBJSUlSZKysrI0a9YsZWZmGl8lAMCruHM1bV9CdgMAjEJ2uwbZDQAwCtntOIcb5B06dLB9nZGRoYkTJ+qee+6xbfu///s/NW/eXNOnT1efPn2MrRIAAFwyshsAAHMhuwEAcD2/yjwpKytLiYmJFbYnJiZq3bp1VS4KAODdLE544MLIbgBAVZDdrkd2AwCqgux2XKUa5HXr1tWMGTMqbH/zzTdVt27dKhcFAACMRXYDAGAuZDcAAK7h8BQrZ5s0aZJ69uypxYsXq1WrVpKkdevWadeuXVqwYIGhBQIAvI+ji0/BOGQ3AKAqyG7XI7sBAFVBdjuuUiPIu3btqp07d+r222/XkSNHdOTIEd1+++3auXOnunbtanSNAAAv42cx/oELI7sBAFVBdrse2Q0AqAqy23GVGkEunb7da+zYsUbWAgAAnIjsBgDAXMhuAACcr1IjyCXpm2++0X333ac2bdpo3759kqR///vfWr16tWHFAQC8k8ViMfyBiyO7AQCVRXa7B9kNAKgssttxlWqQL1iwQCkpKQoJCdEPP/ygoqIiSVJeXh5XtwEAF2WxGP/AhZHdAICqILtdj+wGAFQF2e24SjXIn3/+eU2bNk0zZsxQYGCgbXvbtm31ww8/GFYcAAAwBtkNAIC5kN0AALhGpeYg37Fjh9q3b19he1RUlI4dO1bVmgAAXs6bb83yVGQ3AKAqyG7XI7sBAFVBdjuuUiPI4+Li9Msvv1TYvnr1atWvX7/KRQEAAGOR3QAAmAvZDQCAa1SqQd6vXz8NHjxYa9eulcVi0f79+zVnzhwNHz5cqampRtcIAPAyfhbjH7gwshsAUBVkt+uR3QCAqiC7HVepKVZGjRql8vJyderUSQUFBWrfvr2Cg4M1fPhwDRw40OgaAQBehlu9XI/sBgBUBdntemQ3AKAqyG7HVapBbrFY9OSTT2rEiBH65ZdfdOLECTVr1kzh4eFG1wcAAAxAdgMAYC5kNwAArlGpKVYefvhhHT9+XEFBQWrWrJluuukmhYeH6+TJk3r44YeNrhEA4GUsTnjgwshuAEBVkN2uR3YDAKqC7HZcpRrks2bN0qlTpypsP3XqlGbPnl3logAAgLHIbgAAzIXsBgDANS5pipX8/HxZrVZZrVYdP35c1apVs+0rKyvTF198oZiYGMOLBAB4Fz/mQnMZshsAYASy23XIbgCAEchux11Sg7x69eqyWCyyWCxq1KhRhf0Wi0XPPfecYcUBALwTOe06ZDcAwAhkt+uQ3QAAI5DdjrukBvmKFStktVrVsWNHLViwQNHR0bZ9QUFBio+PV506dQwvEgAAVA7ZDQCAuZDdAAC41iU1yDt06CBJ2rNnj+rVqycLlyIAAJVAfrgO2Q0AMAL54TpkNwDACOSH4yq1SOdXX32lDz/8sML2Dz74QLNmzapyUQAA72axGP/AhZHdAICqILtdj+wGAFQF2e24SjXIMzMzddlll1XYHhMTo7Fjx1a5KAAAYCyyGwAAcyG7AQBwjUuaYuWM7OxsJSQkVNgeHx+v7OzsKhcFAPBurKbtemQ3AKAqyG7XI7sBAFVBdjuuUiPIY2Ji9NNPP1XYvmnTJtWsWbPKRQEAAGOR3QAAmAvZDQCAa1RqBPk999yjQYMGKSIiQu3bt5ckrVq1SoMHD1avXr0MLRAA4H24kO16ZDcAoCrIbtcjuwEAVUF2O65SDfIxY8bot99+U6dOnRQQcPoU5eXleuCBB5gLDQBwUaym7XpkNwCgKshu1yO7AQBVQXY7rlIN8qCgIL333nsaM2aMNm3apJCQEDVv3lzx8fFG1wcAAAxAdgMAYC5kNwAArlGpBvkZjRo1UqNGjYyqxTBbv3zJ3SUADvntUIG7SwAuqkntUMPPWakFMGAIT83uI+tedXcJgEPKrVZ3lwA4wPgRY2S3+3hqdh9dT3YDgCcjux3ncIN82LBhGjNmjMLCwjRs2LALHjtx4sQqFwYA8F7c6uUaZDcAwChkt2uQ3QAAo5DdjnO4Qb5x40aVlJTYvj4ffvkAAHgGshsAAHMhuwEAcD2HG+QrVqw459cAAFwqPz7TuQTZDQAwCtntGmQ3AMAoZLfjmI4GAAAAAAAAAOCTHB5B3qNHD4dP+tFHH1WqGACAb+BKtmuQ3QAAo5DdrkF2AwCMQnY7zuEGeVRUlO1rq9Wqjz/+WFFRUUpMTJQkbdiwQceOHbukQAcA+CbmzXQNshsAYBSy2zXIbgCAUchuxzncIJ85c6bt65EjR+ruu+/WtGnT5O/vL0kqKyvTo48+qsjISOOrBAAAl4zsBgDAXMhuAABcz2K1Wq2X+qRatWpp9erVaty4sd32HTt2qE2bNjp8+LBhBVbG7kOFbv35gKOKS8vdXQJwUU1qhxp+zhGLdhh+zn91b3zxg3yYp2f3qRK3/njAYVZd8j+dAZcLDTR+xBjZ7Xqent2FpW798QDgVao5PITZcWS34yq1SGdpaam2b99eYfv27dtVXk7DDwBwYRaL8Q9cGNkNAKgKstv1yG4AQFWQ3Y6r1PWJhx56SH379tWvv/6qm266SZK0du1ajRs3Tg899JChBQIAgKojuwEAMBeyGwAA16hUg/yll15SXFycJkyYoAMHDkiSateurREjRuixxx4ztEAAgPfx8+ZLzx6K7AYAVAXZ7XpkNwCgKshux1VqDvKz5efnS5JHLRLCHOQwC+Yghxk4Yw7yUV/sNPyc47o2Mvyc3soTs5s5yGEWzEEOM3DGHORkt3t5YnYzBzkAGMcZc5CT3Y6r1Bzk0un50JYtW6Z58+bJ8v+vSOzfv18nTpwwrDgAgHfyc8IDF0d2AwAqy93ZvW/fPt13332qWbOmQkJC1Lx5c33//fe2/VarVc8884xq166tkJAQJScna9euXXbnOHLkiHr37q3IyEhVr15dffv29fgMJLsBAJXl7uw2k0pdn9i7d69uu+02ZWdnq6ioSH/5y18UERGh8ePHq6ioSNOmTTO6TgCAF+FOL9cjuwEAVeHO7D569Kjatm2rW2+9VYsXL1atWrW0a9cu1ahRw3bMiy++qClTpmjWrFlKSEjQ008/rZSUFG3btk3VqlWTJPXu3VsHDhzQ0qVLVVJSooceekj9+/fX3Llz3fXSLojsBgBUBZ+7HVep5v/gwYOVmJioo0ePKiQkxLb9r3/9q5YvX25YcQAAwBhkNwDArMaPH6+6detq5syZuummm5SQkKDOnTvrqquuknR69PjkyZP11FNP6Y477tC1116r2bNna//+/frkk08kST///LP+85//6M0331SrVq3Url07vfLKK5o/f77279/vxld3fmQ3AACuUakR5N98843WrFmjoKAgu+1XXnml9u3bZ0hhAADvxWIhrkd2AwCqwhnZXVRUpKKiIrttwcHBCg4Ottv22WefKSUlRXfddZdWrVqlyy+/XI8++qj69esnSdqzZ49ycnKUnJxse05UVJRatWqlrKws9erVS1lZWapevboSExNtxyQnJ8vPz09r167VX//6V8NfX1WR3QCAquBzt+MqNYK8vLxcZWVlFbb//vvvioiIqHJRAADvZrEY/8CFkd0AgKpwRnZnZmYqKirK7pGZmVnhZ+/evVuvv/66GjZsqCVLlig1NVWDBg3SrFmzJEk5OTmSpNjYWLvnxcbG2vbl5OQoJibGbn9AQICio6Ntx3gashsAUBXu/txtpvVDKtUg79y5syZPnmz73mKx6MSJE3r22WfVtWtXo2oDAAAGIbsBAJ4mPT1deXl5do/09PQKx5WXl+uGG27Q2LFj1aJFC/Xv31/9+vXz+jm4yW4AgFmdWT8kMDBQixcv1rZt2zRhwoRzrh8ybdo0rV27VmFhYUpJSVFhYaHtmN69e2vr1q1aunSpFi1apK+//lr9+/c3vN5KTbHy0ksv6bbbblOzZs1UWFioe++9V7t27dJll12mefPmGV0jAMDL+DHi2+XIbgBAVTgju881ncq51K5dW82aNbPb1rRpUy1YsECSFBcXJ0nKzc1V7dq1bcfk5ubq+uuvtx1z8OBBu3OUlpbqyJEjtud7GrIbAFAV7vzcffb6IWckJCTYvv7z+iGSNHv2bMXGxuqTTz5Rr169bOuHrF+/3jZF2iuvvKKuXbvqpZdeUp06dQyrt1IjyOvWratNmzbpySef1NChQ9WiRQuNGzdOGzdurHDbGgAAcD+yGwBgVm3bttWOHTvstu3cuVPx8fGSTn/gjouLs1u4Mj8/X2vXrlVSUpIkKSkpSceOHdOGDRtsx3z11VcqLy9Xq1atXPAqLh3ZDQAwq88++0yJiYm66667FBMToxYtWmjGjBm2/RdbP0TSRdcPMdIljyAvKSlRkyZNtGjRIvXu3Vu9e/c2tCAAgPdjsRDXIrsBAFXlzuweOnSo2rRpo7Fjx+ruu+/WunXrNH36dE2fPl3S6alHhgwZoueff14NGzZUQkKCnn76adWpU0d33nmnpNMjzm+77Tbb1CwlJSUaMGCAevXqZegINKOQ3QCAqnLnAttn1g8ZNmyYnnjiCa1fv16DBg1SUFCQ+vTp43Hrh1zyCPLAwEC7uWAAALhU7l4sxNeQ3QCAqnJndt944436+OOPNW/ePF1zzTUaM2aMJk+ebNc0fvzxxzVw4ED1799fN954o06cOKH//Oc/qlatmu2YOXPmqEmTJurUqZO6du2qdu3a2ZrsnobsBgBUlTsX2Dbb+iGVmmIlLS1N48ePV2lpqdH1AAAAJyC7AQBm1r17d23evFmFhYX6+eef1a9fP7v9FotFGRkZysnJUWFhoZYtW6ZGjRrZHRMdHa25c+fq+PHjysvL09tvv63w8HBXvoxLQnYDADyNowtsn2/9kOzsbEn264ecLTc317bPleuHVGqRzvXr12v58uX68ssv1bx5c4WFhdnt/+ijjwwpDgDgnVik0/XIbgBAVZDdrkd2AwCqwp0LbF/K+iFnFtQ+s35IamqqJPv1Q1q2bCnJeeuHVKpBXr16dfXs2dPQQgAAgPOQ3QAAmAvZDQAwK7OtH3JJDfLy8nL961//0s6dO1VcXKyOHTtq9OjRCgkJMbQoAIB3s4hhaK5CdgMAjEB2uw7ZDQAwgjuz+8z6Ienp6crIyFBCQsI51w85efKk+vfvr2PHjqldu3bnXD9kwIAB6tSpk/z8/NSzZ09NmTLF8HovqUH+wgsvaPTo0UpOTlZISIimTJmiQ4cO6e233za8MACA9+I2bdchuwEARiC7XYfsBgAYwd3Z3b17d3Xv3v28+8+sH5KRkXHeY86sH+Jsl7RI5+zZszV16lQtWbJEn3zyiRYuXKg5c+aovLzcWfUBAIAqILsBADAXshsAANe6pAZ5dna2unbtavs+OTlZFotF+/fvN7wwAID38rMY/8C5kd0AACOQ3a5DdgMAjEB2O+6SplgpLS21mwdGkgIDA1VSUmJoUQAA72axeHGyehiyGwBgBLLbdchuAIARyG7HXVKD3Gq16sEHH1RwcLBtW2FhoR555BGFhYXZtn300UfGVQgAACqN7AYAwFzIbgAAXOuSGuR9+vSpsO2+++4zrBgAgG/w5luzPA3ZDQAwAtntOmQ3AMAIZLfjLqlBPnPmTGfVAQAAnIDsBgDAXMhuAABc65Ia5AAAGIGp0AAAMBeyGwAAcyG7HUeDHADgcn4kNQAApkJ2AwBgLmS34/zcXQAAAAAAAAAAAO7ACHIAgMuxWAgAAOZCdgMAYC5kt+NokAMAXI47vQAAMBeyGwAAcyG7HccUKwAAAAAAAAAAn8QIcgCAy/mJS9kAAJgJ2Q0AgLmQ3Y5jBDkAAAAAAAAAwCfRIAcAuJzFYvyjssaNGyeLxaIhQ4bYthUWFiotLU01a9ZUeHi4evbsqdzcXLvnZWdnq1u3bgoNDVVMTIxGjBih0tLSyhcCAIAH86TsBgAAF0d2O44pVgAALucpq2mvX79eb7zxhq699lq77UOHDtXnn3+uDz74QFFRURowYIB69Oihb7/9VpJUVlambt26KS4uTmvWrNGBAwf0wAMPKDAwUGPHjnXHSwEAwKk8JbsBAIBjyG7HMYIcAOCTTpw4od69e2vGjBmqUaOGbXteXp7eeustTZw4UR07dlTLli01c+ZMrVmzRt99950k6csvv9S2bdv07rvv6vrrr1eXLl00ZswYvfbaayouLnbXSwIAAAAAAJeIBjkAwOX8LBbDH0VFRcrPz7d7FBUVnbeGtLQ0devWTcnJyXbbN2zYoJKSErvtTZo0Ub169ZSVlSVJysrKUvPmzRUbG2s7JiUlRfn5+dq6davBvy0AANzPGdkNAACch+x2HA1yAIDLOWMutMzMTEVFRdk9MjMzz/nz58+frx9++OGc+3NychQUFKTq1avbbY+NjVVOTo7tmLOb42f2n9kHAIC3YR5TAADMhex2HHOQAwC8Qnp6uoYNG2a3LTg4uMJx//3vfzV48GAtXbpU1apVc1V5AAAAAADAA9EgBwC4nDNuzQoODj5nQ/zPNmzYoIMHD+qGG26wbSsrK9PXX3+tV199VUuWLFFxcbGOHTtmN4o8NzdXcXFxkqS4uDitW7fO7ry5ubm2fQAAeBtvvq0aAABvRHY7jilWAAA+pVOnTtq8ebN+/PFH2yMxMVG9e/e2fR0YGKjly5fbnrNjxw5lZ2crKSlJkpSUlKTNmzfr4MGDtmOWLl2qyMhINWvWzOWvCQAAAAAAVA4jyAEALufOC9kRERG65ppr7LaFhYWpZs2atu19+/bVsGHDFB0drcjISA0cOFBJSUlq3bq1JKlz585q1qyZ7r//fr344ovKycnRU089pbS0NIdGsQMAYDYMQgMAwFzIbsfRIAcAuJyn3740adIk+fn5qWfPnioqKlJKSoqmTp1q2+/v769FixYpNTVVSUlJCgsLU58+fZSRkeHGqgEAcB5Pz24AAGCP7HYcDXIAgM9buXKl3ffVqlXTa6+9ptdee+28z4mPj9cXX3zh5MoAAAAAAIAz0SAHALichXu9AAAwFbIbAABzIbsdR4McAOByxDQAAOZCdgMAYC5kt+OYjgYAAAAAAAAA4JMYQQ4AcDk/bvUCAMBUyG4AAMyF7HYcI8gBAAAAAAAAAD6JEeQAAJfjOjYAAOZCdgMAYC5kt+NokAMAXI47vQAAMBeyGwAAcyG7HccUKwAAAAAAAAAAn8QIcgCAy1m4lA0AgKmQ3QAAmAvZ7ThGkAMAAAAAAAAAfBIjyAEALsfVWQAAzIXsBgDAXMhux9EgBwC4HLd6AQBgLmQ3AADmQnY7josJAAAAAAAAAACfxAhyAIDLcR0bAABzIbsBADAXsttxNMgBAC7HrV4AAJgL2Q0AgLmQ3Y5jihUAAAAAAAAAgE9iBDkAwOW4OgsAgLmQ3QAAmAvZ7Th+VwAAAAAAAAAAn8QIcgCAyzEXGgAA5kJ2AwBgLmS342iQAwBcjpgGAMBcyG4AAMyF7HYcU6wAAAAAAAAAAHwSI8gBAC7HnV4AAJgL2Q0AgLmQ3Y7zmBHk33zzje677z4lJSVp3759kqR///vfWr16tZsrAwAYzU8Wwx9wPbIbAHwH2e0dyG4A8B1kt+M8okG+YMECpaSkKCQkRBs3blRRUZEkKS8vT2PHjnVzdQAA4M/IbgAAzIXsBgDg3DyiQf78889r2rRpmjFjhgIDA23b27Ztqx9++MGNlQEAnMFiMf4B1yK7AcC3eFJ2jxs3ThaLRUOGDLFtKywsVFpammrWrKnw8HD17NlTubm5ds/Lzs5Wt27dFBoaqpiYGI0YMUKlpaWVL8RkyG4A8C2elN2eziMa5Dt27FD79u0rbI+KitKxY8dcXxAAALggshsA4A7r16/XG2+8oWuvvdZu+9ChQ7Vw4UJ98MEHWrVqlfbv368ePXrY9peVlalbt24qLi7WmjVrNGvWLL3zzjt65plnXP0S3IbsBgDg3DyiQR4XF6dffvmlwvbVq1erfv36bqgIAOBMFif8B9ciuwHAt3hCdp84cUK9e/fWjBkzVKNGDdv2vLw8vfXWW5o4caI6duyoli1baubMmVqzZo2+++47SdKXX36pbdu26d1339X111+vLl26aMyYMXrttddUXFxs2O/Jk5HdAOBbPCG7z/D0u788okHer18/DR48WGvXrpXFYtH+/fs1Z84cDR8+XKmpqe4uDwBgMG71Mj+yGwB8izOyu6ioSPn5+XaPM/Nin0taWpq6deum5ORku+0bNmxQSUmJ3fYmTZqoXr16ysrKkiRlZWWpefPmio2NtR2TkpKi/Px8bd261eDflmciuwHAt3jK524z3P0VYPgZK2HUqFEqLy9Xp06dVFBQoPbt2ys4OFjDhw/XwIED3V0eAAD4E7IbAFBVmZmZeu655+y2Pfvssxo9enSFY+fPn68ffvhB69evr7AvJydHQUFBql69ut322NhY5eTk2I45uzl+Zv+Zfb6A7AYAuNrZd389//zztu1n7v6aO3euOnbsKEmaOXOmmjZtqu+++06tW7e23f21bNkyxcbG6vrrr9eYMWM0cuRIjR49WkFBQYbV6REjyC0Wi5588kkdOXJEW7Zs0XfffadDhw5pzJgx7i4NAOAEfrIY/oBrkd0A4Fuckd3p6enKy8uze6Snp1f42f/97381ePBgzZkzR9WqVXPDq/cOZDcA+BZP+Nxtlru/PGIE+bvvvqsePXooNDRUzZo1c3c5AAAnY0oU8yO7AcC3OCO7g4ODFRwcfNHjNmzYoIMHD+qGG26wbSsrK9PXX3+tV199VUuWLFFxcbGOHTtmN4o8NzdXcXFxkk7Pv71u3Tq7856Z5/TMMd6O7AYA3+KM7C4qKqowHdr58txMd395xAjyoUOHKiYmRvfee6+++OILlZWVubskAABwAWQ3AMBVOnXqpM2bN+vHH3+0PRITE9W7d2/b14GBgVq+fLntOTt27FB2draSkpIkSUlJSdq8ebMOHjxoO2bp0qWKjIz0mWYx2Q0AqKrMzExFRUXZPTIzMyscZ7a7vzyiQX7gwAHNnz9fFotFd999t2rXrq20tDStWbPG3aUBAJzAUxYLQeWR3QDgW9yZ3REREbrmmmvsHmFhYapZs6auueYaRUVFqW/fvho2bJhWrFihDRs26KGHHlJSUpJat24tSercubOaNWum+++/X5s2bdKSJUv01FNPKS0tzaFR7N6A7AYA3+KM7HZ0erSz7/4KCAhQQECAVq1apSlTpiggIECxsbG2u7/O9ue7v87c7XX2/jP7jOQRDfKAgAB1795dc+bM0cGDBzVp0iT99ttvuvXWW3XVVVe5uzwAAPAnZDcAwJNMmjRJ3bt3V8+ePdW+fXvFxcXpo48+su339/fXokWL5O/vr6SkJN1333164IEHlJGR4caqXYvsBgBUVXBwsCIjI+0e57rQbLa7vzxiDvKzhYaGKiUlRUePHtXevXv1888/u7skAIDBLCyq6VXIbgDwfp6W3StXrrT7vlq1anrttdf02muvnfc58fHx+uKLL5xcmTmQ3QDg/dyZ3Wfu/jrb2Xd/SbLd/RUdHa3IyEgNHDjwvHd/vfjii8rJyXHa3V8e0yAvKCjQxx9/rDlz5mj58uWqW7eu7rnnHn344YfuLg0AYDA/z/qMjUoiuwHAd5Dd3oHsBgDf4enZPWnSJPn5+alnz54qKipSSkqKpk6datt/5u6v1NRUJSUlKSwsTH369HHK3V8Wq9VqNfysl6hXr15atGiRQkNDdffdd6t379624fSVsftQoYHVAc5TXFru7hKAi2pSO9Twcy7f/ofh5+zU5DLDz4nzMzq7T5UYWBzgRFa5/Z/OwEWFBhr/iZjsNj+js7uw1MDiAMDHVXPCEGay23EeMYLc399f77//vlJSUuTv7+/ucgAATuZpt2nj0pHdAOBbyG7zI7sBwLeQ3Y7ziAb5nDlz3F0CAMCFLOS06ZHdAOBbyG7zI7sBwLeQ3Y5zW4N8ypQp6t+/v6pVq6YpU6Zc8NhBgwa5qCoAAHA+ZDcAAOZCdgMAcHFum4M8ISFB33//vWrWrKmEhITzHmexWLR79+5LOjdzkMMsmIMcZuCMOchX7jhi+DlvaRxt+Dlhz5nZzRzkMAvmIIcZOGMOcrLbnJyZ3cxBDgDGccYc5GS349w2gnzPnj3n/BoAAHgmshsAAHMhuwEAuDg/dxcgSRkZGSooKKiw/dSpU8rIyHBDRQAAZ/KzGP+Aa5HdAOBbyG7zI7sBwLeQ3Y5z2xQrZ/P399eBAwcUExNjt/3w4cOKiYlRWVnZJZ2PKVZgFkyxAjNwxhQr3+w8avg5b25Uw/Bz4vyMzm6mWIFZMMUKzMAZU6yQ3eZndHYzxQoAGMcZU6yQ3Y5z2xQrZ7NarbKcY2nVTZs2KTraO+e28QSbf9ygD+e+o192/Kwjhw/p6bGT1KZ9R9v+CS88rWWLP7N7Tsub2uj5ia/bvj+en6epk8Zp7ber5Ofnp7YdOumRwSMVEmp8Qw2+aeumDfp4/mz9snObjh7+Q+ljJqr1zbfa9p8qKNDs6VO0dvUKHc/PU0ztOure4x51ueMu2zHFRUV6+/WJWv3VEpUUF6vFTUl6ZMgTqh5d0x0vCfAKZLdn2PD9es2a+ZZ+3rZFhw4d0sSXX1PHTsm2/Yf/+EOTJ72k79as1vHjx3VDy0SNfOJpxcdf6b6i4XPemvGGvlq2VL/t2a3gatV03fUtNHjoY7oyob7tmOefe0Zrs7J06NBBhYSG/v9jhiuhfv0LnBnApSC7PdP78+fq/ffmaf++fZKkqxo01D9TH1W7mzu4uTLgf3ifwtu5tUFeo0YNWSwWWSwWNWrUyC6sy8rKdOLECT3yyCNurNC7FZ46pfoNGqtztzv1/JPDznlMYqu2GvrE/263CwwMstv/4nPpOnL4D42dNE2lpaWalPmspryYoZGjxzm1dviOwsJTuvKqRurU9Q6Ne/qxCvvfnjpBP/2wXkOffEExcXX04/dZmjYpU9GX1VKrtrdIkt567SV9/91qPT76RYWGhWv6y+OU+cxjGv/qO659MbA5x2czmATZ7VlOnSpQo8aNdedfe2rYkAF2+6xWq4YOTlNAQIAmTZmq8PBw/Xv2O3rkHw/po08/52I2XOaH79fr7/fcq6uvaa7S0jK9+vIkpfb/hz76dJHtfdi02dXq0u121a5dW3l5eZo29VU92r+vFi1ZJn9/fze/Akhkt5mR3Z4tJjZOg4cOV734eFmtVi389BMNHpCm9xZ8rAYNGrq7PEAS71OzIrsd59YG+eTJk2W1WvXwww/rueeeU1RUlG1fUFCQrrzySiUlJbmxQu92Y1I73ZjU7oLHBAYFKbrmZefcl/3bbn2/9lu9/OZcNWpytSQpdcgoPTMiTf8YMEw1L4s55/OAS9GyVTu1bHX+9+n2LZvU8bbuat4iUZKUcntPLVm4QLt+3qpWbW/RyRPHteyLTzTsqbG69oabJEmDRj6ntD49tGPrT2p89bUueR2AtyC7PUu7mzucd+RO9t7f9NOmH/XhJ4tsH1yefHq0Ot3SVou/+Fw9/nbXOZ8HGO21N960+/65FzLVqX0bbdu2VS0Tb5Qk9bzr77b9dS6/QmkDh+jvPe/Q/n37VLdePZfWC3gbstuz3XJrR7vvBw4eqvfnz9NPm36k8QiPwfsU3s6tDfI+ffpIkhISEtSmTRsFBga6sxycw08bv1ev7rcoPCJS17W8SX36DVBkVHVJ0s9bNik8PMLWHJekFomtZPHz0/atm9W2Qyc3VQ1f0uSa67Tu21VK7nKnoi+rpc0/fq99/92rvmmnR5v/uvNnlZaW6rqWrW3PuSI+QbVi47R9Gw1yd+FCtnmR3eZRXFwsSQoOCrZt8/PzU1BgkDZu3ECDHG5z4sRxSbJr0p3tVEGBPvvkI11+xRWKqx3nytJwAWS3eZHd5lFWVqYvl/xHp04V6LrrWri7HOCceJ+aB9ntOI+Yg7xDh/+NfCosLLR9oDsjMjLS1SVBUstWbdS2QyfF1r5cB/b9V+9Mf0VPD39UE6f9W/7+/jp65LCiatjPVecfEKCIiEgdPXLYTVXD1/QfNFKvTRijh+9Kkb9/gCx+FqUNf1pXX9dSknT0yGEFBAYqPCLC7nnVa9TUMd6nbuPHvV6mR3Z7visT6qt27Tqa8vIEPf1MhkJCQ/Tu7HeUm5ujPw4dcnd58FHl5eV6adxYXd/iBjVo2Mhu3/vz52ryhJd06lSBrkxI0OvT364wvR/ch+w2P7Lbc+3auUP339tLxcVFCg0N1aQpr+mqBg3cXRZgh/ep+ZDdjvOIBnlBQYEef/xxvf/++zp8uGLD6kKraRcVFamoqOhP26wKDg4+zzPgqFuSu9i+TriqoRKuaqSH/95NP238Xi0SW7mxMuB/Fn00Xzu2bdaTYycrJra2tm76QW9MHqfomrV0fWLri58AQKUYnd3lfsFkt8ECAwM1YfIrGv3Mk2rf9ib5+/urVesktb25vWS1urs8+KjM5zP0yy+7NHP23Ar7unS7Xa2S2uiPQ4c0+523NXL4EM389zz+NgAGMTq7rf5kt1GuvDJB7y/4RCdOHNfSL5fo6SdG6q133qX5CI/C+xTezM/dBUjSiBEj9NVXX+n1119XcHCw3nzzTT333HOqU6eOZs+efcHnZmZmKioqyu4x7eV/uahy31L78isUWb2GDvyeLUmqEV1TeUeP2B1TVlqq48fzVSO6pjtKhI8pKirUu2++or6PPqab2nTQlVc1UrcevdTu1s765L1/Szr9Pi0tKdGJ48ftnnvs6GFV533qNhYnPOBaRmf3v8Znuqhy39Ls6mv0/oJP9U3W91q6YrWmvvGW8o4d0+VX1HV3afBB417I0DerVmrG27MVG1dx6pSIiAjFx1+plok36qVJL2vPnj36avlSN1SKcyG7zY/s9lyBQUGqFx+vZldfo8FDH1Ojxk00590L/28CuBrvU/Mhux3nESPIFy5cqNmzZ+uWW27RQw89pJtvvlkNGjRQfHy85syZo969e5/3uenp6Ro2bJjdtn35jIpyhkMHc3U875iiL6slSWp6zXU6ceK4dm3fpoZNmkmSfvxhnazl5WpydXN3lgofUVZaqtLSUln87P9M+/v7y2otlyRd1aipAgIC9NMPa9WmQ7Ik6ffs33QoN0dNmjH/uNt4c7L6CKOzu9yPEWjOFPH/p5nau/c3bdu6RY8OGOzmiuBLrFarxo8do6+WL9OMmbN1+RVXOPCc0/+n5E9TQMCNyG7TMzq7rf5kt7OUl5fz9w8ej/epCZDdDvOIBvmRI0dUv359SafnPTty5PSo5Hbt2ik1NfWCzw0Ornhb1x9Fhc4p1MucKijQ/n3Ztu9zD+zTr7u2KyIiShGRUZozc5radkhWdM2a2r/vd709dZLqXF5XN9zURpJU78r6SmzVVi+/+JwGDn9KpaWlen1ipjp0uk01L4tx18uClzlVUKAD+/5r+z43Z59279qhiMhI1YqtrWuua6l3Xp+soKBqiomrrS0/btCKJYv0cNrpf8CHhUcoueudenvqBIVHRik0NEzTp4xX46uvZYFOoAqMzu5TJc6p09sVFJxUdvb/snzfvt+1ffvPioqKUu3adfTlksWqUSNatWvX0a5dO/TiuLG6tWOy2rRt58aq4Wsyn8/Q4i8WadKU1xQWFqY//jg9B354eISqVaum3//7Xy35zxdKatNWNaKjlZuTo5lvzVBwcLDa3dzhImcH4Cijs7uw1Dl1+pqXJ01Qu5vbK652bRWcPKkvPl+k79ev0+vT33J3aYAN71N4O49okNevX1979uxRvXr11KRJE73//vu66aabtHDhQlWvXt3d5XmtXdu3auSgf9i+n/7KS5Kk5C7/pwHDn9SeX3dq2eLPdPLEcUVfFqMbbkzSA/3SFBT0v8WSHn82U1MnZip9cH9Z/PzUtkMnpQ4Z5fLXAu/1y45tempoP9v3b782QZLUMeV2DU7P0PBnxmn2jFc08YUndCI/X7Via+u+f6Tptv+7y/acvmnDZfHz0/hnhqukpFgtbmyjR4aku/y14H8sXMo2PbLbM2zdskX9Hn7A9v2EF0/f7n77HX/VmBfG6Y9DhzThxXE6fPiwatWqpe7/d4f6P/Kou8qFj/rgvXmSpH4PPWC3/bnnx+r/7uyhoOAgbfxhg+b+e7by8/NVs2ZN3ZCYqHfenafomkyH5inIbvMjuz3TkSOH9VT6SB06dFDhERFq1KixXp/+lpLatHV3aYAN71NzIrsdZ7Fa3b9K06RJk+Tv769BgwZp2bJluv3222W1WlVSUqKJEydq8OBLuw149yFGkMMcikvL3V0CcFFNaocafs61v+YZfs5WV0UZfk6cn9HZzQhymIVVbv+nM3BRoYHGfyAmu83P6OxmBDkAGKeaE4Ywk92O84gG+Z/t3btXGzZsUIMGDXTttZc+BQINcpgFDXKYgTMa5Ot2Gx/UN9X3zqA2i6pmNw1ymAUNcpiBMxrkZLf3qWp20yAHAOM4o0FOdjvOI6ZY+bP4+HjFx8e7uwwAgJNwo5f3IbsBwLuR3d6H7AYA70Z2O84jGuRTpkw553aLxaJq1aqpQYMGat++vfz9/V1cGQAAOBeyGwAAcyG7AQA4N49okE+aNEmHDh1SQUGBatSoIUk6evSoQkNDFR4eroMHD6p+/fpasWKF6tat6+ZqAQBVxqVs0yO7AcDHkN2mR3YDgI8hux3m5+4CJGns2LG68cYbtWvXLh0+fFiHDx/Wzp071apVK7388svKzs5WXFychg4d6u5SAQA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      "text/plain": [
       "<Figure size 1500x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "                      precision    recall  f1-score   support\n",
      "\n",
      "    Computer Science       0.96      0.44      0.60       536\n",
      "             Physics       0.97      0.78      0.86       406\n",
      "         Mathematics       0.96      0.63      0.76       373\n",
      "          Statistics       0.89      0.55      0.68       330\n",
      "Quantitative Biology       0.68      0.55      0.61        42\n",
      "Quantitative Finance       0.43      0.50      0.46         6\n",
      "\n",
      "           micro avg       0.94      0.59      0.72      1693\n",
      "           macro avg       0.81      0.57      0.66      1693\n",
      "        weighted avg       0.94      0.59      0.71      1693\n",
      "         samples avg       0.73      0.65      0.67      1693\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\gedas\\anaconda3\\envs\\cybercare\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\gedas\\anaconda3\\envs\\cybercare\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\gedas\\anaconda3\\envs\\cybercare\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in samples with no true nor predicted labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "# I will show the standard plotting and statistics\n",
    "# But they are not worth discussing too much because we are making a model that is not very good from the beginning\n",
    "# It is not good due to performance reasons (it is not my priority), I wanted to show the ML piepline\n",
    "\n",
    "def plot_training_progress(train_losses, val_losses, train_f1_scores, val_f1_scores, val_accuracies):\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    \n",
    "    epochs = range(1, len(train_losses) + 1)\n",
    "\n",
    "    # Plot for Loss\n",
    "    plt.subplot(1, 3, 1)\n",
    "    plt.plot(epochs, train_losses, label='Train Loss')\n",
    "    plt.plot(epochs, val_losses, label='Val Loss')\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.xticks(epochs)\n",
    "    \n",
    "    # Plot for F1 Score\n",
    "    plt.subplot(1, 3, 2)\n",
    "    plt.plot(epochs, train_f1_scores, label='Train F1')\n",
    "    plt.plot(epochs, val_f1_scores, label='Val F1')\n",
    "    plt.title('Training and Validation F1 Score')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('F1 Score')\n",
    "    plt.legend()\n",
    "    plt.xticks(epochs)\n",
    "    \n",
    "    # Plot for Accuracy\n",
    "    plt.subplot(1, 3, 3)\n",
    "    plt.plot(epochs, val_accuracies, label='Val Accuracy')\n",
    "    plt.title('Validation Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "    plt.xticks(epochs)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, labels):\n",
    "    cm = multilabel_confusion_matrix(y_true, y_pred)\n",
    "    fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
    "    axes = axes.ravel()\n",
    "    \n",
    "    for i, (ax, matrix, label) in enumerate(zip(axes, cm, labels)):\n",
    "        # Rearrange matrix to standard format (for me)\n",
    "        matrix = matrix[::-1, ::-1]  # Flip\n",
    "        matrix = np.array([[matrix[1,1], matrix[1,0]],  # TP, FP\n",
    "                           [matrix[0,1], matrix[0,0]]])  # FN, TN\n",
    "\n",
    "        sns.heatmap(matrix, annot=True, fmt='d', cmap='Blues', ax=ax)\n",
    "        ax.set_title(f'Confusion Matrix for {label}')\n",
    "        ax.set_ylabel('Predicted')\n",
    "        ax.set_xlabel('True')\n",
    "        ax.xaxis.set_ticklabels(['Positive', 'Negative'])\n",
    "        ax.yaxis.set_ticklabels(['Positive', 'Negative'])\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Plotting\n",
    "\n",
    "val_loss, val_f1, val_accuracy = evaluate_model(trained_model, val_loader, criterion, device)\n",
    "print(f\"Validation Loss: {val_loss:.4f}\")\n",
    "print(f\"Validation F1 Score: {val_f1:.4f}\")\n",
    "print(f\"Validation Accuracy: {val_accuracy:.4f}\")\n",
    "plot_training_progress(train_losses, val_losses, train_f1_scores, val_f1_scores, val_accuracies)\n",
    "\n",
    "# Loss is going down, F1 score is going up, Accuracy is going up\n",
    "# This is expected because the model is learning\n",
    "# If we had more epochs, we would see better results (hopefully)\n",
    "\n",
    "val_preds, val_true = predict(trained_model, val_loader, device)\n",
    "val_preds_binary = (val_preds > 0.5).astype(int)\n",
    "val_true_binary = (val_true > 0.5).astype(int)\n",
    "plot_confusion_matrix(val_true_binary, val_preds_binary, subject_names)\n",
    "\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(val_true_binary, val_preds_binary, target_names=subject_names))\n",
    "\n",
    "# CM shows struggle to learn the patterns for the minority class because of low recall\n",
    "# Expected poor results because Dataset max_length=64, should be 512\n",
    "# Low epochs contribute to poor results\n",
    "# No hyperparameter fine-tuning contribue to poor results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Predict test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Predicting: 100%|██████████| 263/263 [00:18<00:00, 14.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have been saved to predictions.parquet\n"
     ]
    }
   ],
   "source": [
    "# Load and preprocess the test data\n",
    "test_data = pd.read_parquet('cc_test.parquet')\n",
    "test_dataset, processed_texts, abstracts = preprocess_data(data=test_data, is_test=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=config['training']['batch_size'])\n",
    "\n",
    "# Load the trained model\n",
    "model = BERTClassifier(num_labels=config['model']['num_labels']).to(device)\n",
    "model.load_state_dict(torch.load(config['model']['save_path']))\n",
    "model.eval()\n",
    "\n",
    "# Predict\n",
    "all_preds = []\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(test_loader, desc=\"Predicting\"):\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        attention_mask = batch['attention_mask'].to(device)\n",
    "        \n",
    "        outputs = model(input_ids, attention_mask)\n",
    "        preds = torch.sigmoid(outputs).cpu().numpy()\n",
    "        all_preds.extend(preds)\n",
    "predictions_df = pd.DataFrame(all_preds, columns=subject_names)\n",
    "\n",
    "# I did not see any instructions about the format of the output file\n",
    "# So, we will create an ID column, ABSTRACT column for easier identification\n",
    "# And, a list of predicted subjects column (threshold-based) and top 2 predicted subjects column\n",
    "# Threshold-based because these are high confidence predictions, top 2 because it is useful to know the top 2 subjects (sometimes the model predicts empty list)\n",
    "# And also have 6 columns with predicted subjects probabilities\n",
    "predictions_df['ID'] = range(len(predictions_df))\n",
    "predictions_df['Abstract'] = abstracts\n",
    "\n",
    "predictions_df['Subjects_Threshold'] = predictions_df[subject_names].apply(\n",
    "    lambda row: [subject for subject, value in row.items() if value > 0.5], axis=1\n",
    ")\n",
    "predictions_df['Subjects_Top2'] = predictions_df[subject_names].apply(\n",
    "    lambda row: [subject for subject, value in sorted(row.items(), key=lambda x: x[1], reverse=True)[:2]], axis=1\n",
    ")\n",
    "\n",
    "predictions_df = predictions_df[['ID', 'Abstract', 'Subjects_Threshold', 'Subjects_Top2'] + subject_names]\n",
    "\n",
    "# Export\n",
    "predictions_df.to_parquet('predictions.parquet')\n",
    "print(\"Predictions have been saved to predictions.parquet\")"
   ]
  }
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