2390 lines (2389 with data), 2.2 MB

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# EDA of the dataset",
   "id": "73df67546deed058"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T08:21:40.653466Z",
     "start_time": "2025-02-20T08:21:40.650328Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Import necessary libraries\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", \"\\nPyarrow\", DeprecationWarning)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "import re\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "from typing import Dict, List, Tuple, Set\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.util import ngrams\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from src.utils.logger import get_logger\n"
   ],
   "id": "f84381e87ddee2ea",
   "outputs": [],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T07:42:07.793557Z",
     "start_time": "2025-02-20T07:42:07.790582Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Configure logger\n",
    "logger = get_logger(__name__)\n",
    "\n",
    "# Set style for better visualization\n",
    "sns.set_theme()\n"
   ],
   "id": "fc4f4628005bfc1b",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Load the data",
   "id": "f367c1bbba678e2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T07:42:13.908061Z",
     "start_time": "2025-02-20T07:42:13.860547Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def load_data(file_path: str) -> pd.DataFrame:\n",
    "    \"\"\"Load the trials data from CSV file\"\"\"\n",
    "    try:\n",
    "        logger.info(f\"Loading data from {file_path}\")\n",
    "        df = pd.read_csv(file_path)\n",
    "        logger.info(f\"Successfully loaded {len(df)} records\")\n",
    "        return df\n",
    "    except Exception as e:\n",
    "        logger.error(f\"Error loading data: {str(e)}\")\n",
    "        raise\n",
    "\n",
    "# Load data\n",
    "file_path = Path('../data/trials.csv')\n",
    "df = load_data(str(file_path))\n"
   ],
   "id": "a616f379feb13015",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"timestamp\": \"2025-02-20T07:42:13.862637\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"213583333\", \"function\": \"load_data\", \"line\": 12, \"message\": \"Loading data from ../data/trials.csv\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:42:13.906604\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"213583333\", \"function\": \"load_data\", \"line\": 14, \"message\": \"Successfully loaded 1759 records\", \"taskName\": \"Task-4\"}\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Text length analysis",
   "id": "62e91313c16ae7b3"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T16:05:49.722535Z",
     "start_time": "2025-02-19T16:05:49.717343Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def analyze_text_length(df: pd.DataFrame) -> dict:\n",
    "    \"\"\"Analyze text length statistics for each class\"\"\"\n",
    "    # Calculate text length for each description\n",
    "    df['text_length'] = df['description'].str.len()\n",
    "    \n",
    "    # Calculate statistics per class\n",
    "    stats = df.groupby('label')['text_length'].agg([\n",
    "        'count',\n",
    "        'mean',\n",
    "        'std',\n",
    "        'min',\n",
    "        'max',\n",
    "        lambda x: x.quantile(0.25),\n",
    "        lambda x: x.quantile(0.75)\n",
    "    ]).round(2)\n",
    "    \n",
    "    stats.columns = ['Count', 'Mean', 'Std', 'Min', 'Max', 'Q1', 'Q3']\n",
    "    return stats\n",
    "\n",
    "def plot_length_distribution(df: pd.DataFrame, save_path: str = None):\n",
    "    \"\"\"Create visualizations for text length distribution\"\"\"\n",
    "    # Calculate text lengths\n",
    "    df['text_length'] = df['description'].str.len()\n",
    "    \n",
    "    # Create figure with multiple subplots\n",
    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 12))\n",
    "    \n",
    "    # 1. Boxplot\n",
    "    sns.boxplot(x='label', y='text_length', data=df, ax=ax1)\n",
    "    ax1.set_title('Text Length Distribution by Class (Boxplot)')\n",
    "    ax1.set_xlabel('Disease Class')\n",
    "    ax1.set_ylabel('Text Length (characters)')\n",
    "    ax1.tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    # 2. Violin plot with individual points\n",
    "    sns.violinplot(x='label', y='text_length', data=df, ax=ax2)\n",
    "    ax2.set_title('Text Length Distribution by Class (Violin Plot)')\n",
    "    ax2.set_xlabel('Disease Class')\n",
    "    ax2.set_ylabel('Text Length (characters)')\n",
    "    ax2.tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    # Adjust layout\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Save plot if path provided\n",
    "    if save_path:\n",
    "        plt.savefig(save_path)\n",
    "        logger.info(f\"Plot saved to {save_path}\")\n",
    "\n"
   ],
   "id": "8bf75e6efd6736ec",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T16:05:52.305871Z",
     "start_time": "2025-02-19T16:05:52.039717Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Calculate statistics\n",
    "stats = analyze_text_length(df)\n",
    "logger.info(\"\\nText length statistics per class:\")\n",
    "logger.info(\"\\n\" + str(stats))\n",
    "\n",
    "# Create plots\n",
    "plot_path = Path('figures/text_length_distribution.png')\n",
    "plot_path.parent.mkdir(exist_ok=True)\n",
    "plot_length_distribution(df, str(plot_path))\n",
    "\n",
    "# Additional insights\n",
    "total_mean = df['text_length'].mean()\n",
    "total_std = df['text_length'].std()\n",
    "logger.info(f\"\\nOverall statistics:\")\n",
    "logger.info(f\"Mean text length: {total_mean:.2f} characters\")\n",
    "logger.info(f\"Standard deviation: {total_std:.2f} characters\")\n",
    "\n",
    "# Find outliers\n",
    "z_scores = np.abs((df['text_length'] - total_mean) / total_std)\n",
    "outliers = df[z_scores > 3]\n",
    "logger.info(f\"\\nNumber of outliers (|z-score| > 3): {len(outliers)}\")\n"
   ],
   "id": "81b776d3356edc78",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"timestamp\": \"2025-02-19T16:05:52.049618\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1777717845\", \"function\": \"<module>\", \"line\": 3, \"message\": \"\\nText length statistics per class:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:05:52.052066\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1777717845\", \"function\": \"<module>\", \"line\": 4, \"message\": \"\\n                               Count     Mean      Std  Min    Max      Q1  \\\\\\nlabel                                                                        \\nALS                              368  1723.85  1720.49   38  13400  711.25   \\nDementia                         368  2414.52  2433.44   73  14976  899.50   \\nObsessive Compulsive Disorder    358  2212.34  2437.94    9  21765  853.75   \\nParkinson\\u2019s Disease              330  1740.59  1762.82   24  11737  618.00   \\nScoliosis                        335  1963.01  1929.05    9  11880  743.50   \\n\\n                                    Q3  \\nlabel                                   \\nALS                            2065.00  \\nDementia                       2930.00  \\nObsessive Compulsive Disorder  2564.50  \\nParkinson\\u2019s Disease            2219.75  \\nScoliosis                      2460.50  \", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:05:52.221403\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"2437702475\", \"function\": \"plot_length_distribution\", \"line\": 62, \"message\": \"Plot saved to figures/text_length_distribution.png\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:05:52.222016\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1777717845\", \"function\": \"<module>\", \"line\": 14, \"message\": \"\\nOverall statistics:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:05:52.222272\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1777717845\", \"function\": \"<module>\", \"line\": 15, \"message\": \"Mean text length: 2016.46 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:05:52.222584\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1777717845\", \"function\": \"<module>\", \"line\": 16, \"message\": \"Standard deviation: 2103.19 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:05:52.223488\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1777717845\", \"function\": \"<module>\", \"line\": 21, \"message\": \"\\nNumber of outliers (|z-score| > 3): 46\", \"taskName\": \"Task-4\"}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Different disease categories displayed unique text length patterns based on our observations. Most of the posts fell between 1,000 and 5,000 characters in length, yet each disease category had at least one very long post that stood out from the rest.\n",
    "\n",
    "Posts concerning dementia usually contained between 2000 and 3000 characters but extended past 10000 characters multiple times by different authors. The OCD category stood out because it contained a very lengthy post reaching 21,000 characters and multiple additional texts exceeding 10,000 to 15,000 characters.\n",
    "\n",
    "ALS and Parkinson's disease forum users consistently wrote posts which stayed within a standardised range and had fewer posts exceeding 10,000 characters. Taiwanese using the ALS forum published most of their posts in the middle range of character counts. The majority of scoliosis-related posts remained short, while other group categories did not include many very long posts.\n",
    "\n",
    "The results we obtained provide significant insights into building our analysis system. The analysis requires a solution for dealing with posts that exceed certain lengths either through truncation or normalisation techniques. The optimal treatment method for this issue could establish a maximum word count which includes 95% of all posts.\n",
    "\n",
    "We need to design a model strategy which addresses different text lengths properly. Our model requires padding modification for brief texts and division of extensive text into segments. The actual length of each post presents valuable information, which the model should take into account. Our predictions can benefit from length normalisation because we plan to normalise alternate measurements according to text length and group variables by their lengths."
   ],
   "id": "a34e988f5ba98319"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Analyze the outliers",
   "id": "ca4f1119badfae8d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T17:20:48.717736Z",
     "start_time": "2025-02-19T17:20:48.706713Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def analyze_outliers(df: pd.DataFrame, threshold: float = 1.5) -> Dict:\n",
    "    \"\"\"Analyze outliers in text length for each disease class\"\"\"\n",
    "    # Calculate text lengths\n",
    "    df['text_length'] = df['description'].str.len()\n",
    "    \n",
    "    outlier_analysis = {}\n",
    "    \n",
    "    for label in df['label'].unique():\n",
    "        class_data = df[df['label'] == label]['text_length']\n",
    "        \n",
    "        # Calculate quartiles and IQR\n",
    "        Q1 = class_data.quantile(0.25)\n",
    "        Q3 = class_data.quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        \n",
    "        # Define outlier boundaries\n",
    "        lower_bound = Q1 - threshold * IQR\n",
    "        upper_bound = Q3 + threshold * IQR\n",
    "        \n",
    "        # Identify outliers\n",
    "        outliers = df[\n",
    "            (df['label'] == label) & \n",
    "            (df['text_length'] > upper_bound)\n",
    "        ].sort_values('text_length', ascending=False)\n",
    "        \n",
    "        # Calculate statistics\n",
    "        outlier_analysis[label] = {\n",
    "            'total_samples': len(class_data),\n",
    "            'num_outliers': len(outliers),\n",
    "            'outlier_percentage': (len(outliers) / len(class_data)) * 100,\n",
    "            'max_length': outliers['text_length'].max() if len(outliers) > 0 else None,\n",
    "            'min_length': outliers['text_length'].min() if len(outliers) > 0 else None,\n",
    "            'Q1': Q1,\n",
    "            'Q3': Q3,\n",
    "            'IQR': IQR,\n",
    "            'upper_bound': upper_bound,\n",
    "            'outlier_examples': outliers.head(3)\n",
    "        }\n",
    "    \n",
    "    return outlier_analysis\n",
    "\n",
    "def plot_outlier_distribution(df: pd.DataFrame, outlier_analysis: Dict, save_path: str = None):\n",
    "    \"\"\"Create visualizations for outlier distribution\"\"\"\n",
    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 12))\n",
    "    \n",
    "    # Plot 1: Outlier percentages\n",
    "    labels = list(outlier_analysis.keys())\n",
    "    percentages = [analysis['outlier_percentage'] for analysis in outlier_analysis.values()]\n",
    "    \n",
    "    sns.barplot(x=labels, y=percentages, ax=ax1)\n",
    "    ax1.set_title('Percentage of Outliers by Disease Class')\n",
    "    ax1.set_xlabel('Disease Class')\n",
    "    ax1.set_ylabel('Percentage of Outliers')\n",
    "    ax1.tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    # Plot 2: Outlier severity\n",
    "    for label in labels:\n",
    "        data = df[df['label'] == label]['text_length']\n",
    "        upper_bound = outlier_analysis[label]['upper_bound']\n",
    "        outliers = data[data > upper_bound]\n",
    "        \n",
    "        if len(outliers) > 0:\n",
    "            ax2.scatter([label] * len(outliers), outliers, alpha=0.5)\n",
    "    \n",
    "    ax2.set_title('Outlier Severity by Disease Class')\n",
    "    ax2.set_xlabel('Disease Class')\n",
    "    ax2.set_ylabel('Text Length (characters)')\n",
    "    ax2.tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    if save_path:\n",
    "        plt.savefig(save_path)\n",
    "        logger.info(f\"Plot saved to {save_path}\")\n",
    "\n",
    "def print_outlier_insights(analysis: Dict):\n",
    "    \"\"\"Print detailed insights about outliers\"\"\"\n",
    "    for label, stats in analysis.items():\n",
    "        logger.info(f\"\\nDisease Class: {label}\")\n",
    "        logger.info(f\"Total samples: {stats['total_samples']}\")\n",
    "        logger.info(f\"Number of outliers: {stats['num_outliers']}\")\n",
    "        logger.info(f\"Outlier percentage: {stats['outlier_percentage']:.2f}%\")\n",
    "        logger.info(f\"Normal range (Q1-Q3): {stats['Q1']:.0f} - {stats['Q3']:.0f}\")\n",
    "        logger.info(f\"Outlier threshold: > {stats['upper_bound']:.0f}\")\n",
    "        \n",
    "        if stats['num_outliers'] > 0:\n",
    "            logger.info(\"\\nTop 3 outlier examples:\")\n",
    "            for _, row in stats['outlier_examples'].iterrows():\n",
    "                logger.info(f\"Length: {row['text_length']} characters\")\n",
    "                logger.info(f\"First 100 chars: {row['description'][:100]}...\")\n",
    "                logger.info(\"---\")\n"
   ],
   "id": "9bebdf54075e3266",
   "outputs": [],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T17:20:52.741782Z",
     "start_time": "2025-02-19T17:20:52.452361Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Analyze outliers\n",
    "outlier_analysis = analyze_outliers(df)\n",
    "\n",
    "# Create visualizations\n",
    "plot_path = 'figures/outlier_analysis.png'\n",
    "plot_outlier_distribution(df, outlier_analysis, plot_path)\n",
    "\n",
    "# Print insights\n",
    "print_outlier_insights(outlier_analysis)"
   ],
   "id": "f369bc9a66329b00",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"timestamp\": \"2025-02-19T17:20:52.598636\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"plot_outlier_distribution\", \"line\": 90, \"message\": \"Plot saved to figures/outlier_analysis.png\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.599422\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 95, \"message\": \"\\nDisease Class: ALS\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.599777\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 96, \"message\": \"Total samples: 368\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.600243\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 97, \"message\": \"Number of outliers: 22\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.600625\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 98, \"message\": \"Outlier percentage: 5.98%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.600858\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 99, \"message\": \"Normal range (Q1-Q3): 711 - 2065\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.601259\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 100, \"message\": \"Outlier threshold: > 4096\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.601605\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\nTop 3 outlier examples:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.602062\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 13400 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.602523\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: Introduction Amyotrophic lateral sclerosis (ALS) is a fatal disease with progressive muscle weakness...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.602856\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.603213\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 11963 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.603560\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: Amyotrophic lateral sclerosis (ALS) is a progressive, deadly, neurodegenerative disease which affect...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.603917\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.604235\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 11331 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.604538\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: Background||In the early 1990s Genervon hypothesized that Central Nervous System / Peripheral Nervou...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.604933\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.605305\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 95, \"message\": \"\\nDisease Class: Obsessive Compulsive Disorder\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.605733\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 96, \"message\": \"Total samples: 358\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.606076\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 97, \"message\": \"Number of outliers: 31\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.606377\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 98, \"message\": \"Outlier percentage: 8.66%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.606666\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 99, \"message\": \"Normal range (Q1-Q3): 854 - 2564\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.606906\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 100, \"message\": \"Outlier threshold: > 5131\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.607223\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\nTop 3 outlier examples:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.607584\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 21765 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.607840\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: RECRUITMENT:||Potential participants will be referred to our program by their treating clinicians, w...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.608066\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.608369\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 13899 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.608777\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: METHODOLOGY:||This study is planned as a placebo-controlled, interventional study of parallel groups...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.609032\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.609416\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 12319 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.609634\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: Background:||Obsessive-compulsive disorder (OCD) is a highly debilitating disorder with a lifetime p...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.610057\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.610302\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 95, \"message\": \"\\nDisease Class: Parkinson\\u2019s Disease\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.610634\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 96, \"message\": \"Total samples: 330\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.610834\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 97, \"message\": \"Number of outliers: 22\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.611095\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 98, \"message\": \"Outlier percentage: 6.67%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.611399\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 99, \"message\": \"Normal range (Q1-Q3): 618 - 2220\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.611883\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 100, \"message\": \"Outlier threshold: > 4622\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.612253\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\nTop 3 outlier examples:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.612671\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 11737 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.613085\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: Background The Parkinson's Disease Nurse Specialist (PDNS) can fulfill a pivotal role in the multidi...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.613425\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.613840\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 9658 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.614201\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: Parkinson's disease (PD) is a chronic and progressive movement disorder, mainly due to an altered mo...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.614456\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.614911\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 9318 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.615265\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: BACKGROUND||Basal Ganglia dysfunction in Parkinson's Disease (PD) induces gait impairment such as sh...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.615582\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.615962\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 95, \"message\": \"\\nDisease Class: Dementia\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.616277\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 96, \"message\": \"Total samples: 368\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.616497\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 97, \"message\": \"Number of outliers: 30\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.616774\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 98, \"message\": \"Outlier percentage: 8.15%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.617045\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 99, \"message\": \"Normal range (Q1-Q3): 900 - 2930\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.617247\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 100, \"message\": \"Outlier threshold: > 5976\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.617540\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\nTop 3 outlier examples:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.617964\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 14976 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.618341\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: Alzheimer's dementia (AD) is a major public health challenge for our century. Research into pharmace...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.618761\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.619105\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 14306 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.619437\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: Introduction Dementia The estimated prevalence of dementia in Denmark is 85.000 (2011) and the globa...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.619627\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.619865\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 12276 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.620201\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: The in-home respite intervention group will receive in-home respite care also called \\\"Baluchonnage\\\"....\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.620450\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.620832\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 95, \"message\": \"\\nDisease Class: Scoliosis\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.621230\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 96, \"message\": \"Total samples: 335\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.621560\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 97, \"message\": \"Number of outliers: 22\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.621803\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 98, \"message\": \"Outlier percentage: 6.57%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.622168\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 99, \"message\": \"Normal range (Q1-Q3): 744 - 2460\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.622398\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 100, \"message\": \"Outlier threshold: > 5036\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.622680\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\nTop 3 outlier examples:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.623111\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 11880 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.623373\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: Detailed description 1 Indications and Treatment of Adult Kypho- Scoliosis (INTRAKS Study) 2. Backgr...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.623711\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.624126\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 11462 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.624546\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: 1.1 Adult degenerative scoliosis. Scoliosis is a 3D spine deformity including frontal curves, verteb...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.624970\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.625377\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 105, \"message\": \"Length: 10980 characters\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.625658\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 106, \"message\": \"First 100 chars: By identifying the patients for whom the model predicts a significant progression the investigators ...\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:20:52.626094\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3437557200\", \"function\": \"print_outlier_insights\", \"line\": 107, \"message\": \"---\", \"taskName\": \"Task-4\"}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1200 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T17:28:00.082308Z",
     "start_time": "2025-02-19T17:28:00.074704Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def identify_outliers(df: pd.DataFrame, threshold: float = 1.5) -> pd.DataFrame:\n",
    "    \"\"\"Identify outlier descriptions based on text length\"\"\"\n",
    "    df['text_length'] = df['description'].str.len()\n",
    "    outliers = []\n",
    "    \n",
    "    for label in df['label'].unique():\n",
    "        class_data = df[df['label'] == label]['text_length']\n",
    "        Q1 = class_data.quantile(0.25)\n",
    "        Q3 = class_data.quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        upper_bound = Q3 + threshold * IQR\n",
    "        \n",
    "        class_outliers = df[\n",
    "            (df['label'] == label) & \n",
    "            (df['text_length'] > upper_bound)\n",
    "        ].copy()\n",
    "        outliers.append(class_outliers)\n",
    "    \n",
    "    return pd.concat(outliers)\n",
    "\n",
    "def analyze_section_patterns(text: str) -> Dict[str, bool]:\n",
    "    \"\"\"Analyze common section patterns in the text\"\"\"\n",
    "    sections = {\n",
    "        'inclusion_criteria': bool(re.search(r'inclusion criteria|eligibility criteria', text.lower())),\n",
    "        'exclusion_criteria': bool(re.search(r'exclusion criteria', text.lower())),\n",
    "        'study_design': bool(re.search(r'study design|trial design', text.lower())),\n",
    "        'background': bool(re.search(r'background|introduction', text.lower())),\n",
    "        'methodology': bool(re.search(r'methodology|methods|procedure', text.lower())),\n",
    "        'outcome_measures': bool(re.search(r'outcome measures|endpoints', text.lower())),\n",
    "        'statistical_analysis': bool(re.search(r'statistical analysis|statistics', text.lower()))\n",
    "    }\n",
    "    return sections\n",
    "\n",
    "def analyze_content_structure(text: str) -> Dict:\n",
    "    \"\"\"Analyze structural elements of the text\"\"\"\n",
    "    sentences = sent_tokenize(text)\n",
    "    return {\n",
    "        'num_sentences': len(sentences),\n",
    "        'avg_sentence_length': np.mean([len(s.split()) for s in sentences]),\n",
    "        'num_paragraphs': text.count('\\n\\n') + 1,\n",
    "        'has_lists': bool(re.search(r'\\n[-*•]|\\d+\\.', text)),\n",
    "        'has_numbers': bool(re.search(r'\\d+', text)),\n",
    "        'has_measurements': bool(re.search(r'\\d+\\s*(mg|kg|ml|cm|weeks|months)', text))\n",
    "    }\n",
    "\n",
    "def analyze_medical_content(text: str) -> Dict:\n",
    "    \"\"\"Analyze medical-specific content patterns\"\"\"\n",
    "    lower_text = text.lower()\n",
    "    return {\n",
    "        'has_dosage': bool(re.search(r'\\d+\\s*mg|\\d+\\s*kg', lower_text)),\n",
    "        'has_duration': bool(re.search(r'\\d+\\s*(weeks?|months?|years?)', lower_text)),\n",
    "        'has_medical_tests': bool(re.search(r'mri|ct scan|blood test|biopsy', lower_text)),\n",
    "        'has_procedures': bool(re.search(r'surgery|procedure|treatment|therapy', lower_text)),\n",
    "        'has_demographics': bool(re.search(r'age|gender|ethnicity|population', lower_text))\n",
    "    }\n",
    "\n",
    "def analyze_outlier_patterns(df: pd.DataFrame) -> Dict:\n",
    "    \"\"\"Analyze patterns in outlier descriptions\"\"\"\n",
    "    outliers = identify_outliers(df)\n",
    "    pattern_analysis = {label: [] for label in df['label'].unique()}\n",
    "    \n",
    "    for _, row in outliers.iterrows():\n",
    "        text = row['description']\n",
    "        label = row['label']\n",
    "        \n",
    "        # Analyze different aspects\n",
    "        sections = analyze_section_patterns(text)\n",
    "        structure = analyze_content_structure(text)\n",
    "        medical = analyze_medical_content(text)\n",
    "        \n",
    "        pattern_analysis[label].append({\n",
    "            'text_length': row['text_length'],\n",
    "            'sections': sections,\n",
    "            'structure': structure,\n",
    "            'medical': medical\n",
    "        })\n",
    "    \n",
    "    return pattern_analysis\n",
    "\n",
    "def print_outlier_insights(pattern_analysis: Dict):\n",
    "    \"\"\"Print insights about outlier patterns for each class\"\"\"\n",
    "    for label, analyses in pattern_analysis.items():\n",
    "        if not analyses:\n",
    "            continue\n",
    "            \n",
    "        logger.info(f\"\\n=== {label} Outlier Analysis ===\")\n",
    "        \n",
    "        # Section presence\n",
    "        section_stats = {\n",
    "            section: sum(a['sections'][section] for a in analyses) / len(analyses) * 100\n",
    "            for section in analyses[0]['sections'].keys()\n",
    "        }\n",
    "        logger.info(\"\\nCommon Sections (% presence):\")\n",
    "        for section, percentage in sorted(section_stats.items(), key=lambda x: x[1], reverse=True):\n",
    "            logger.info(f\"- {section}: {percentage:.1f}%\")\n",
    "        \n",
    "        # Structure statistics\n",
    "        avg_sentences = np.mean([a['structure']['num_sentences'] for a in analyses])\n",
    "        avg_sent_length = np.mean([a['structure']['avg_sentence_length'] for a in analyses])\n",
    "        list_presence = sum(a['structure']['has_lists'] for a in analyses) / len(analyses) * 100\n",
    "        \n",
    "        logger.info(\"\\nStructural Characteristics:\")\n",
    "        logger.info(f\"- Average sentences: {avg_sentences:.1f}\")\n",
    "        logger.info(f\"- Average sentence length: {avg_sent_length:.1f} words\")\n",
    "        logger.info(f\"- Presence of lists: {list_presence:.1f}%\")\n",
    "        \n",
    "        # Medical content\n",
    "        medical_stats = {\n",
    "            feature: sum(a['medical'][feature] for a in analyses) / len(analyses) * 100\n",
    "            for feature in analyses[0]['medical'].keys()\n",
    "        }\n",
    "        logger.info(\"\\nMedical Content (% presence):\")\n",
    "        for feature, percentage in sorted(medical_stats.items(), key=lambda x: x[1], reverse=True):\n",
    "            logger.info(f\"- {feature}: {percentage:.1f}%\")\n",
    "\n"
   ],
   "id": "9d04effd8aaf6ebc",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T17:28:03.112036Z",
     "start_time": "2025-02-19T17:28:02.905159Z"
    }
   },
   "cell_type": "code",
   "source": [
    "pattern_analysis = analyze_outlier_patterns(df)\n",
    "print_outlier_insights(pattern_analysis)"
   ],
   "id": "2e4e923fb9098dc5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"timestamp\": \"2025-02-19T17:28:03.081030\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\n=== ALS Outlier Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.081552\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 110, \"message\": \"\\nCommon Sections (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.081978\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- methodology: 36.4%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.082378\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- inclusion_criteria: 18.2%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.082745\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- exclusion_criteria: 13.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.082998\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- study_design: 13.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.083332\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- background: 13.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.083650\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- outcome_measures: 13.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.083895\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- statistical_analysis: 13.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.084387\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 119, \"message\": \"\\nStructural Characteristics:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.084748\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 120, \"message\": \"- Average sentences: 32.0\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.085018\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 121, \"message\": \"- Average sentence length: 35.1 words\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.085365\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 122, \"message\": \"- Presence of lists: 63.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.085560\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 129, \"message\": \"\\nMedical Content (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.085758\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_demographics: 86.4%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.086060\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_duration: 81.8%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.086312\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_procedures: 72.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.086665\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_dosage: 22.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.086903\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_medical_tests: 13.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.087238\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\n=== Obsessive Compulsive Disorder Outlier Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.087572\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 110, \"message\": \"\\nCommon Sections (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.087894\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- methodology: 74.2%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.088139\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- exclusion_criteria: 29.0%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.088317\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- background: 25.8%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.088575\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- study_design: 16.1%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.088811\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- outcome_measures: 16.1%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.089106\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- inclusion_criteria: 12.9%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.089355\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- statistical_analysis: 12.9%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.089735\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 119, \"message\": \"\\nStructural Characteristics:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.090015\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 120, \"message\": \"- Average sentences: 41.5\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.090328\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 121, \"message\": \"- Average sentence length: 32.8 words\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.090618\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 122, \"message\": \"- Presence of lists: 64.5%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.091005\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 129, \"message\": \"\\nMedical Content (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.091364\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_procedures: 93.5%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.091738\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_demographics: 87.1%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.091994\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_duration: 74.2%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.092165\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_medical_tests: 32.3%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.092598\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_dosage: 6.5%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.093069\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\n=== Parkinson\\u2019s Disease Outlier Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.093429\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 110, \"message\": \"\\nCommon Sections (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.093882\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- methodology: 40.9%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.094262\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- inclusion_criteria: 31.8%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.094571\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- background: 31.8%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.094878\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- statistical_analysis: 27.3%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.095185\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- outcome_measures: 22.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.095588\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- exclusion_criteria: 13.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.095894\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- study_design: 9.1%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.096263\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 119, \"message\": \"\\nStructural Characteristics:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.096535\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 120, \"message\": \"- Average sentences: 33.1\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.096944\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 121, \"message\": \"- Average sentence length: 35.8 words\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.097301\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 122, \"message\": \"- Presence of lists: 63.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.097579\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 129, \"message\": \"\\nMedical Content (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.098031\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_demographics: 86.4%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.098317\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_procedures: 77.3%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.098557\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_duration: 50.0%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.098814\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_medical_tests: 22.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.099166\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_dosage: 9.1%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.099386\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\n=== Dementia Outlier Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.099649\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 110, \"message\": \"\\nCommon Sections (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.099915\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- methodology: 66.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.100141\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- background: 56.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.100404\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- outcome_measures: 30.0%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.100758\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- statistical_analysis: 23.3%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.101077\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- inclusion_criteria: 16.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.101340\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- exclusion_criteria: 10.0%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.101656\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- study_design: 6.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.102015\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 119, \"message\": \"\\nStructural Characteristics:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.102366\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 120, \"message\": \"- Average sentences: 43.5\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.102617\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 121, \"message\": \"- Average sentence length: 32.4 words\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.103007\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 122, \"message\": \"- Presence of lists: 66.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.103273\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 129, \"message\": \"\\nMedical Content (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.103578\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_demographics: 100.0%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.103850\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_procedures: 90.0%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.104113\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_duration: 86.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.104338\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_medical_tests: 13.3%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.104711\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_dosage: 3.3%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.104937\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 103, \"message\": \"\\n=== Scoliosis Outlier Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.105263\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 110, \"message\": \"\\nCommon Sections (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.105524\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- methodology: 90.9%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.105869\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- exclusion_criteria: 31.8%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.106203\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- inclusion_criteria: 27.3%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.106739\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- background: 27.3%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.106980\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- outcome_measures: 22.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.107228\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- study_design: 13.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.107658\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 112, \"message\": \"- statistical_analysis: 13.6%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.108084\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 119, \"message\": \"\\nStructural Characteristics:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.108302\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 120, \"message\": \"- Average sentences: 38.9\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.108567\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 121, \"message\": \"- Average sentence length: 31.5 words\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.108908\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 122, \"message\": \"- Presence of lists: 72.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.109091\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 129, \"message\": \"\\nMedical Content (% presence):\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.109414\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_procedures: 100.0%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.109784\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_demographics: 100.0%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.110157\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_duration: 72.7%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.110595\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_dosage: 18.2%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T17:28:03.110936\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3678857319\", \"function\": \"print_outlier_insights\", \"line\": 131, \"message\": \"- has_medical_tests: 13.6%\", \"taskName\": \"Task-4\"}\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "We found special information in how extended posts were dispersed throughout the dialogue. The OCD post reached 21,000 characters as its longest post, whereas dementia contained the most extensive range of lengthy posts. Posts about scoliosis maintained an even distribution between short and long lengths. The distribution of lengthy posts in ALS and Parkinson's disease research was moderate, as these studies contained some long articles without reaching extreme lengths.\n",
    "\n",
    "Detailed medical information appeared as the main cause of lengthy posts, which included treatment steps together with clinical trial details as well as participant requirements and extensive explanations regarding the medical condition.\n",
    "\n",
    "The analysis requires us to consider truncating our posts to 5,000 characters to include at least 95% of our existing content. Extra-long messages will require breaking into smaller sections, or we should modify their distribution length. Shorter posts require additional padding to establish consistent length along with other sections."
   ],
   "id": "20578c2119d9d5fc"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Vocabulary size and distribution",
   "id": "b7ea831fd230d324"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T16:06:34.168983Z",
     "start_time": "2025-02-19T16:06:34.158236Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def preprocess_text(text: str) -> List[str]:\n",
    "    \"\"\"Preprocess text by cleaning and tokenizing\"\"\"\n",
    "    # Convert to lowercase\n",
    "    text = text.lower()\n",
    "    \n",
    "    # Remove special characters and numbers\n",
    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
    "    \n",
    "    # Tokenize\n",
    "    tokens = word_tokenize(text)\n",
    "    \n",
    "    # Remove stopwords\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    tokens = [token for token in tokens if token not in stop_words]\n",
    "    \n",
    "    # Remove single character tokens\n",
    "    tokens = [token for token in tokens if len(token) > 1]\n",
    "    \n",
    "    return tokens\n",
    "\n",
    "def analyze_vocabulary(df: pd.DataFrame) -> Tuple[Dict, pd.DataFrame]:\n",
    "    \"\"\"Analyze vocabulary statistics for each class\"\"\"\n",
    "    vocab_stats = {}\n",
    "    all_words = []\n",
    "    \n",
    "    # Analyze each class\n",
    "    for label in df['label'].unique():\n",
    "        class_docs = df[df['label'] == label]['description']\n",
    "        \n",
    "        # Process all documents in this class\n",
    "        class_tokens = []\n",
    "        for doc in class_docs:\n",
    "            tokens = preprocess_text(doc)\n",
    "            class_tokens.extend(tokens)\n",
    "            all_words.extend(tokens)\n",
    "        \n",
    "        # Calculate statistics\n",
    "        vocab_stats[label] = {\n",
    "            'unique_words': len(set(class_tokens)),\n",
    "            'total_words': len(class_tokens),\n",
    "            'avg_words_per_doc': len(class_tokens) / len(class_docs),\n",
    "            'top_words': Counter(class_tokens).most_common(10)\n",
    "        }\n",
    "    \n",
    "    # Create word frequency distribution\n",
    "    word_freq = pd.DataFrame(Counter(all_words).most_common(),\n",
    "                           columns=['word', 'frequency'])\n",
    "    \n",
    "    return vocab_stats, word_freq\n",
    "\n",
    "def plot_vocabulary_statistics(vocab_stats: Dict, word_freq: pd.DataFrame, save_path: str = None):\n",
    "    \"\"\"Create visualizations for vocabulary statistics\"\"\"\n",
    "    # Create figure with multiple subplots\n",
    "    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 16))\n",
    "    \n",
    "    # Extract data for plotting\n",
    "    labels = list(vocab_stats.keys())\n",
    "    unique_words = [stats['unique_words'] for stats in vocab_stats.values()]\n",
    "    avg_words = [stats['avg_words_per_doc'] for stats in vocab_stats.values()]\n",
    "    \n",
    "    # 1. Unique words per class\n",
    "    sns.barplot(x=labels, y=unique_words, ax=ax1)\n",
    "    ax1.set_title('Unique Words per Class')\n",
    "    ax1.set_xlabel('Disease Class')\n",
    "    ax1.set_ylabel('Number of Unique Words')\n",
    "    ax1.tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    # 2. Average words per document\n",
    "    sns.barplot(x=labels, y=avg_words, ax=ax2)\n",
    "    ax2.set_title('Average Words per Document')\n",
    "    ax2.set_xlabel('Disease Class')\n",
    "    ax2.set_ylabel('Average Word Count')\n",
    "    ax2.tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    # 3. Overall word frequency distribution (top 20)\n",
    "    sns.barplot(x='frequency', y='word', \n",
    "                data=word_freq.head(20), ax=ax3)\n",
    "    ax3.set_title('Top 20 Most Frequent Words')\n",
    "    ax3.set_xlabel('Frequency')\n",
    "    ax3.set_ylabel('Word')\n",
    "    \n",
    "    # 4. Word frequency distribution (log scale)\n",
    "    word_freq['rank'] = range(1, len(word_freq) + 1)\n",
    "    word_freq['log_freq'] = np.log10(word_freq['frequency'])\n",
    "    word_freq['log_rank'] = np.log10(word_freq['rank'])\n",
    "    \n",
    "    ax4.scatter(word_freq['log_rank'], word_freq['log_freq'], alpha=0.5)\n",
    "    ax4.set_title('Zipf\\'s Law Plot (Log-Log Scale)')\n",
    "    ax4.set_xlabel('Log(Rank)')\n",
    "    ax4.set_ylabel('Log(Frequency)')\n",
    "    \n",
    "    # Adjust layout\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Save plot if path provided\n",
    "    if save_path:\n",
    "        plt.savefig(save_path)\n",
    "        logger.info(f\"Plot saved to {save_path}\")\n",
    "\n",
    "def print_class_insights(vocab_stats: Dict):\n",
    "    \"\"\"Print insights about vocabulary statistics for each class\"\"\"\n",
    "    for label, stats in vocab_stats.items():\n",
    "        logger.info(f\"\\nClass: {label}\")\n",
    "        logger.info(f\"Unique words: {stats['unique_words']}\")\n",
    "        logger.info(f\"Total words: {stats['total_words']}\")\n",
    "        logger.info(f\"Average words per document: {stats['avg_words_per_doc']:.2f}\")\n",
    "        logger.info(\"Top 10 words:\")\n",
    "        for word, count in stats['top_words']:\n",
    "            logger.info(f\"  - {word}: {count}\")\n",
    "\n"
   ],
   "id": "f1850933f83a6831",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T16:08:08.085622Z",
     "start_time": "2025-02-19T16:08:06.514668Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Analyze vocabulary\n",
    "vocab_stats, word_freq = analyze_vocabulary(df)\n",
    "\n",
    "# Create plots\n",
    "plot_path = 'figures/vocabulary_distribution.png'\n",
    "plot_vocabulary_statistics(vocab_stats, word_freq, plot_path)\n",
    "\n",
    "# Print insights\n",
    "print_class_insights(vocab_stats)\n",
    "\n",
    "# Additional statistics\n",
    "logger.info(\"\\nOverall Vocabulary Statistics:\")\n",
    "logger.info(f\"Total unique words: {len(word_freq)}\")\n",
    "logger.info(f\"Total words: {word_freq['frequency'].sum()}\")\n",
    "logger.info(f\"Average word frequency: {word_freq['frequency'].mean():.2f}\")\n"
   ],
   "id": "3be71a16b364e162",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"timestamp\": \"2025-02-19T16:08:07.863661\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"plot_vocabulary_statistics\", \"line\": 123, \"message\": \"Plot saved to figures/vocabulary_distribution.png\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.864272\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 133, \"message\": \"\\nClass: ALS\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.864616\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 134, \"message\": \"Unique words: 8491\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.864970\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 135, \"message\": \"Total words: 56404\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.865240\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 136, \"message\": \"Average words per document: 153.27\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.865571\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 137, \"message\": \"Top 10 words:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.865768\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - als: 1315\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.866029\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - patients: 981\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.866285\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - study: 923\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.866581\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - disease: 498\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.866775\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - treatment: 418\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.867173\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - clinical: 355\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.867408\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - subjects: 317\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.867659\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - motor: 277\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.868032\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - months: 220\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.868326\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - lateral: 207\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.868653\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 133, \"message\": \"\\nClass: Obsessive Compulsive Disorder\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.869037\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 134, \"message\": \"Unique words: 9539\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.869558\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 135, \"message\": \"Total words: 69157\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.869815\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 136, \"message\": \"Average words per document: 193.18\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.870315\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 137, \"message\": \"Top 10 words:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.870600\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - treatment: 1103\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.870910\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - ocd: 917\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.871258\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - study: 865\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.871596\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - patients: 688\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.872042\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - participants: 445\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.872266\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - anxiety: 382\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.872576\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - disorder: 322\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.872862\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - clinical: 322\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.873168\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - weeks: 295\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.873426\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - therapy: 275\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.873625\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 133, \"message\": \"\\nClass: Parkinson\\u2019s Disease\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.873823\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 134, \"message\": \"Unique words: 8251\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.873995\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 135, \"message\": \"Total words: 50813\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.874174\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 136, \"message\": \"Average words per document: 153.98\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.874428\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 137, \"message\": \"Top 10 words:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.874575\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - pd: 889\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.874736\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - patients: 730\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.874892\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - study: 666\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.875157\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - disease: 636\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.875305\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - parkinsons: 498\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.875455\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - treatment: 313\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.875597\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - motor: 293\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.875754\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - clinical: 251\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.876030\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - participants: 240\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.876254\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - symptoms: 222\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.876499\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 133, \"message\": \"\\nClass: Dementia\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.876854\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 134, \"message\": \"Unique words: 10282\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.877314\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 135, \"message\": \"Total words: 77636\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.877718\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 136, \"message\": \"Average words per document: 210.97\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.878038\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 137, \"message\": \"Top 10 words:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.878250\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - dementia: 1475\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.878514\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - care: 877\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.878835\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - study: 859\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.879224\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - intervention: 725\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.879574\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - patients: 559\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.880039\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - group: 511\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.880488\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - participants: 494\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.880857\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - caregivers: 419\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.881200\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - cognitive: 409\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.881376\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - treatment: 349\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.881593\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 133, \"message\": \"\\nClass: Scoliosis\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.882001\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 134, \"message\": \"Unique words: 8902\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.882240\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 135, \"message\": \"Total words: 58099\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.882604\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 136, \"message\": \"Average words per document: 173.43\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.882996\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 137, \"message\": \"Top 10 words:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.883258\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - patients: 909\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.883473\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - study: 647\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.883709\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - scoliosis: 567\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.883933\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - surgery: 436\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.884205\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - spinal: 434\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.884412\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - treatment: 367\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.884599\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - pain: 324\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.884787\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - brace: 305\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.885124\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - group: 298\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.885423\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"4028163496\", \"function\": \"print_class_insights\", \"line\": 139, \"message\": \"  - spine: 286\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.885815\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3810756629\", \"function\": \"<module>\", \"line\": 12, \"message\": \"\\nOverall Vocabulary Statistics:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.886248\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3810756629\", \"function\": \"<module>\", \"line\": 13, \"message\": \"Total unique words: 26037\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.886849\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3810756629\", \"function\": \"<module>\", \"line\": 14, \"message\": \"Total words: 312109\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:08:07.887307\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3810756629\", \"function\": \"<module>\", \"line\": 15, \"message\": \"Average word frequency: 11.99\", \"taskName\": \"Task-4\"}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1600 with 4 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "We observed significant language variations between the posts. Posts about dementia trials employed the most extensive vocabulary set with 10,000 distinct words and contained average descriptions of 210 words. The evidence shows that writers about dementia trials required more intricate approaches to provide explanations.\n",
    "\n",
    "The descriptions for Parkinson's Disease alongside OCD and ALS and Scoliosis included 8,000 to 9,000 unique vocabulary items as well as relatively brief explanations throughout.\n",
    "\n",
    "The random occurrence of \"study,\" \"patients,\" and \"treatment\" words appeared throughout every clinical trial condition. A few words appear often throughout the text, whereas most words tend to be used rarely, according to Zipf's law, which linguists identify. The dataset contains everyday words that are presented repeatedly, but medical-specific terms seem to appear only two times throughout the information."
   ],
   "id": "58eeee1729abff03"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Specific vocabulary differences between classes",
   "id": "26969d53092271f6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T16:45:18.991491Z",
     "start_time": "2025-02-19T16:45:18.981549Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def analyze_class_specific_vocabulary(df: pd.DataFrame, min_freq: int = 10) -> Dict:\n",
    "    \"\"\"Analyze vocabulary differences between classes\"\"\"\n",
    "    # Initialize TF-IDF vectorizer\n",
    "    tfidf = TfidfVectorizer(\n",
    "        stop_words='english',\n",
    "        min_df=min_freq,\n",
    "        token_pattern=r'(?u)\\b[A-Za-z]+\\b'\n",
    "    )\n",
    "    \n",
    "    # Fit TF-IDF on all documents\n",
    "    tfidf_matrix = tfidf.fit_transform(df['description'])\n",
    "    feature_names = tfidf.get_feature_names_out()\n",
    "    \n",
    "    class_analysis = {}\n",
    "    \n",
    "    # Analyze each class\n",
    "    for label in df['label'].unique():\n",
    "        class_mask = df['label'] == label\n",
    "        class_indices = np.where(class_mask)[0]  # Get indices where class_mask is True\n",
    "    \n",
    "        # Create the inverse mask using np.where\n",
    "        other_indices = np.where(~class_mask)[0]\n",
    "    \n",
    "        class_tfidf = tfidf_matrix[class_indices].mean(axis=0).A1\n",
    "        other_tfidf = tfidf_matrix[other_indices].mean(axis=0).A1  # Use other_indices\n",
    "    \n",
    "        # Get top words by TF-IDF score\n",
    "        top_indices = class_tfidf.argsort()[-20:][::-1]\n",
    "        top_words = [(feature_names[i], class_tfidf[i]) for i in top_indices]\n",
    "    \n",
    "        # Find distinctive words (high TF-IDF in this class vs others)\n",
    "        distinctiveness = class_tfidf - other_tfidf\n",
    "        distinctive_indices = distinctiveness.argsort()[-20:][::-1]\n",
    "        distinctive_words = [(feature_names[i], distinctiveness[i]) for i in distinctive_indices]\n",
    "    \n",
    "        class_analysis[label] = {\n",
    "            'top_words': top_words,\n",
    "            'distinctive_words': distinctive_words\n",
    "        }    \n",
    "\n",
    "    return class_analysis\n",
    "\n",
    "def plot_class_vocabulary_comparison(class_analysis: Dict, save_path: str = None):\n",
    "    \"\"\"Create visualizations for vocabulary differences between classes\"\"\"\n",
    "    n_classes = len(class_analysis)\n",
    "    fig, axes = plt.subplots(n_classes, 2, figsize=(20, 6*n_classes))\n",
    "    \n",
    "    for idx, (label, analysis) in enumerate(class_analysis.items()):\n",
    "        # Plot top words\n",
    "        words, scores = zip(*analysis['top_words'][:15])\n",
    "        sns.barplot(x=list(scores), y=list(words), ax=axes[idx, 0])\n",
    "        axes[idx, 0].set_title(f'Top Words - {label}')\n",
    "        axes[idx, 0].set_xlabel('TF-IDF Score')\n",
    "        \n",
    "        # Plot distinctive words\n",
    "        words, scores = zip(*analysis['distinctive_words'][:15])\n",
    "        sns.barplot(x=list(scores), y=list(words), ax=axes[idx, 1])\n",
    "        axes[idx, 1].set_title(f'Distinctive Words - {label}')\n",
    "        axes[idx, 1].set_xlabel('Distinctiveness Score')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    if save_path:\n",
    "        plt.savefig(save_path)\n",
    "        logger.info(f\"Plot saved to {save_path}\")\n",
    "\n",
    "def analyze_vocabulary_overlap(class_analysis: Dict) -> pd.DataFrame:\n",
    "    \"\"\"Analyze vocabulary overlap between classes\"\"\"\n",
    "    classes = list(class_analysis.keys())\n",
    "    overlap_matrix = np.zeros((len(classes), len(classes)))\n",
    "    \n",
    "    for i, class1 in enumerate(classes):\n",
    "        words1 = set(word for word, _ in class_analysis[class1]['top_words'])\n",
    "        for j, class2 in enumerate(classes):\n",
    "            words2 = set(word for word, _ in class_analysis[class2]['top_words'])\n",
    "            overlap = len(words1.intersection(words2)) / len(words1.union(words2))\n",
    "            overlap_matrix[i, j] = overlap\n",
    "    \n",
    "    return pd.DataFrame(overlap_matrix, index=classes, columns=classes)\n",
    "\n",
    "def print_vocabulary_insights(class_analysis: Dict):\n",
    "    \"\"\"Print insights about vocabulary differences between classes\"\"\"\n",
    "    for label, analysis in class_analysis.items():\n",
    "        logger.info(f\"\\nClass: {label}\")\n",
    "        \n",
    "        logger.info(\"\\nTop 10 Most Important Words:\")\n",
    "        for word, score in analysis['top_words'][:10]:\n",
    "            logger.info(f\"  - {word}: {score:.4f}\")\n",
    "        \n",
    "        logger.info(\"\\nTop 10 Most Distinctive Words:\")\n",
    "        for word, score in analysis['distinctive_words'][:10]:\n",
    "            logger.info(f\"  - {word}: {score:.4f}\")\n"
   ],
   "id": "97b9821eeb5ab887",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-19T16:45:22.708460Z",
     "start_time": "2025-02-19T16:45:21.149919Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Analyze vocabulary differences\n",
    "class_analysis = analyze_class_specific_vocabulary(df)\n",
    "\n",
    "# Create visualizations\n",
    "plot_path = 'figures/vocabulary_differences.png'\n",
    "plot_class_vocabulary_comparison(class_analysis, plot_path)\n",
    "\n",
    "# Analyze vocabulary overlap\n",
    "overlap_df = analyze_vocabulary_overlap(class_analysis)\n",
    "\n",
    "# Plot overlap heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(overlap_df, annot=True, cmap='YlOrRd', fmt='.2f')\n",
    "plt.title('Vocabulary Overlap Between Classes')\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/vocabulary_overlap.png')\n",
    "\n",
    "# Print insights\n",
    "print_vocabulary_insights(class_analysis)\n",
    "\n",
    "logger.info(\"\\nVocabulary Overlap Summary:\")\n",
    "logger.info(\"\\n\" + str(overlap_df))\n"
   ],
   "id": "9e8c81fb93e3d2a1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{\"timestamp\": \"2025-02-19T16:45:22.223422\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"2436038805\", \"function\": \"print_vocabulary_insights\", \"line\": 120, \"message\": \"  - home: 0.0207\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:45:22.223721\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"2436038805\", \"function\": \"print_vocabulary_insights\", \"line\": 120, \"message\": \"  - group: 0.0205\", \"taskName\": \"Task-4\"}\n",
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      "{\"timestamp\": \"2025-02-19T16:45:22.228350\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"2436038805\", \"function\": \"print_vocabulary_insights\", \"line\": 120, \"message\": \"  - pain: 0.0435\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:45:22.228563\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"2436038805\", \"function\": \"print_vocabulary_insights\", \"line\": 120, \"message\": \"  - ais: 0.0369\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:45:22.228865\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"2436038805\", \"function\": \"print_vocabulary_insights\", \"line\": 120, \"message\": \"  - fusion: 0.0331\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:45:22.229253\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"2436038805\", \"function\": \"print_vocabulary_insights\", \"line\": 120, \"message\": \"  - deformity: 0.0295\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:45:22.229418\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"2436038805\", \"function\": \"print_vocabulary_insights\", \"line\": 120, \"message\": \"  - surgical: 0.0289\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:45:22.229798\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1529995797\", \"function\": \"<module>\", \"line\": 21, \"message\": \"\\nVocabulary Overlap Summary:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-19T16:45:22.230951\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1529995797\", \"function\": \"<module>\", \"line\": 22, \"message\": \"\\n                                    ALS  Obsessive Compulsive Disorder  \\\\\\nALS                            1.000000                       0.111111   \\nObsessive Compulsive Disorder  0.111111                       1.000000   \\nParkinson\\u2019s Disease            0.250000                       0.250000   \\nDementia                       0.111111                       0.212121   \\nScoliosis                      0.081081                       0.081081   \\n\\n                               Parkinson\\u2019s Disease  Dementia  Scoliosis  \\nALS                                       0.250000  0.111111   0.081081  \\nObsessive Compulsive Disorder             0.250000  0.212121   0.081081  \\nParkinson\\u2019s Disease                       1.000000  0.250000   0.111111  \\nDementia                                  0.250000  1.000000   0.111111  \\nScoliosis                                 0.111111  0.111111   1.000000  \", \"taskName\": \"Task-4\"}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x3000 with 10 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "The language of fingerprints differs among each condition. The discussion about ALS mainly involves the impacts of the disease on nervous systems and body functions while utilising vocabulary such as \"sclerosis\" and ventilatory difficulties. The discussions about OCD emphasise behaviours together with therapeutic approaches where \"compulsive\" actions and cognitive behavioural therapy (CBT) receive numerous mentions.\n",
    "\n",
    "Parkinson's online discussions focus on movement difficulties and treatment approaches, which include regular discussions regarding \"motor\" issues and the use of levodopa. Posts about dementia feature information about memory deterioration and mental processing problems together with repeated \"cognitive\" decline notices and Alzheimer's disease descriptions. The discussion includes many fragments about the delivery of care to individuals. Scoliosis stands out the most because it employs precise terms about spinal curves and regularly describes surgical processes.\n",
    "\n",
    "The neurological conditions ALS Parkinson's and dementia use roughly 25% of overlapping terminology in their posts since their collective impacts affect shared body systems. The medical terms between OCD and scoliosis overlap only between 8-11% with other disease categories.\n",
    "\n",
    "Our analysis reveals an important finding that scoliosis will display the clearest symptoms to an automatic philtre because of its distinctive linguistic patterns. Brain-related conditions will require additional detailed evaluation since their language includes many shared elements, so context analysis becomes essential to separate them."
   ],
   "id": "e099876e75a022fd"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Class distribution",
   "id": "4a519f1baea490b5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T07:42:37.843776Z",
     "start_time": "2025-02-20T07:42:37.837188Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def analyze_class_distribution(df: pd.DataFrame) -> Tuple[Dict, pd.DataFrame]:\n",
    "    \"\"\"Analyze the distribution of disease classes in the dataset\"\"\"\n",
    "    # Calculate class distribution\n",
    "    class_counts = df['label'].value_counts()\n",
    "    total_samples = len(df)\n",
    "    \n",
    "    # Create distribution DataFrame\n",
    "    distribution_df = pd.DataFrame({\n",
    "        'count': class_counts,\n",
    "        'percentage': (class_counts / total_samples * 100).round(2)\n",
    "    }).sort_values('count', ascending=False)\n",
    "    \n",
    "    # Calculate statistics\n",
    "    stats = {\n",
    "        'total_samples': total_samples,\n",
    "        'num_classes': len(class_counts),\n",
    "        'largest_class': class_counts.index[0],\n",
    "        'largest_class_count': class_counts.iloc[0],\n",
    "        'smallest_class': class_counts.index[-1],\n",
    "        'smallest_class_count': class_counts.iloc[-1],\n",
    "        'imbalance_ratio': class_counts.iloc[0] / class_counts.iloc[-1]\n",
    "    }\n",
    "    \n",
    "    return stats, distribution_df\n",
    "\n",
    "def plot_class_distribution(df: pd.DataFrame, save_path: str = None):\n",
    "    \"\"\"Create visualizations for class distribution\"\"\"\n",
    "    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 16))\n",
    "    \n",
    "    # 1. Bar plot of class counts\n",
    "    sns.barplot(\n",
    "        data=df,\n",
    "        y='label',\n",
    "        x='count',\n",
    "        ax=ax1,\n",
    "        palette='viridis'\n",
    "    )\n",
    "    ax1.set_title('Disease Class Distribution')\n",
    "    ax1.set_xlabel('Number of Samples')\n",
    "    ax1.set_ylabel('Disease Class')\n",
    "    \n",
    "    # Add count labels on bars\n",
    "    for i, v in enumerate(df['count']):\n",
    "        ax1.text(v, i, f' {v}', va='center')\n",
    "    \n",
    "    # 2. Pie chart of class percentages\n",
    "    ax2.pie(\n",
    "        df['percentage'],\n",
    "        labels=df.index,\n",
    "        autopct='%1.1f%%',\n",
    "        startangle=90,\n",
    "        colors=sns.color_palette('viridis', n_colors=len(df))\n",
    "    )\n",
    "    ax2.set_title('Disease Class Distribution (%)')\n",
    "    \n",
    "    # 3. Normalized stacked bar\n",
    "    df_cumsum = df['percentage'].cumsum()\n",
    "    sns.barplot(\n",
    "        x=[1],\n",
    "        y=[100],\n",
    "        color='lightgrey',\n",
    "        ax=ax3\n",
    "    )\n",
    "    \n",
    "    colors = sns.color_palette('viridis', n_colors=len(df))\n",
    "    bottom = 0\n",
    "    \n",
    "    for idx, (label, percentage) in enumerate(df['percentage'].items()):\n",
    "        ax3.bar(\n",
    "            0,\n",
    "            percentage,\n",
    "            bottom=bottom,\n",
    "            color=colors[idx],\n",
    "            label=label\n",
    "        )\n",
    "        # Add percentage labels\n",
    "        ax3.text(\n",
    "            0,\n",
    "            bottom + percentage/2,\n",
    "            f'{percentage:.1f}%',\n",
    "            ha='center',\n",
    "            va='center'\n",
    "        )\n",
    "        bottom += percentage\n",
    "    \n",
    "    ax3.set_title('Normalized Class Distribution')\n",
    "    ax3.set_xlabel('')\n",
    "    ax3.set_ylabel('Percentage')\n",
    "    ax3.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    \n",
    "    # 4. Cumulative distribution\n",
    "    ax4.plot(\n",
    "        range(1, len(df) + 1),\n",
    "        df_cumsum,\n",
    "        marker='o'\n",
    "    )\n",
    "    ax4.set_title('Cumulative Class Distribution')\n",
    "    ax4.set_xlabel('Number of Classes')\n",
    "    ax4.set_ylabel('Cumulative Percentage')\n",
    "    ax4.grid(True)\n",
    "    \n",
    "    # Add percentage labels\n",
    "    for i, v in enumerate(df_cumsum):\n",
    "        ax4.text(i + 1, v, f'{v:.1f}%', ha='center', va='bottom')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    if save_path:\n",
    "        plt.savefig(save_path)\n",
    "        logger.info(f\"Plot saved to {save_path}\")\n",
    "\n",
    "def print_distribution_insights(stats: Dict, distribution_df: pd.DataFrame):\n",
    "    \"\"\"Print insights about class distribution\"\"\"\n",
    "    logger.info(\"\\n=== Class Distribution Analysis ===\")\n",
    "    logger.info(f\"Total number of samples: {stats['total_samples']}\")\n",
    "    logger.info(f\"Number of classes: {stats['num_classes']}\")\n",
    "    logger.info(f\"\\nClass distribution:\")\n",
    "    for idx, row in distribution_df.iterrows():\n",
    "        logger.info(f\"- {idx}: {row['count']} samples ({row['percentage']:.1f}%)\")\n",
    "    \n",
    "    logger.info(f\"\\nLargest class: {stats['largest_class']} ({stats['largest_class_count']} samples)\")\n",
    "    logger.info(f\"Smallest class: {stats['smallest_class']} ({stats['smallest_class_count']} samples)\")\n",
    "    logger.info(f\"Imbalance ratio: {stats['imbalance_ratio']:.2f}\")\n"
   ],
   "id": "3c21a804316f92cd",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T07:43:07.779035Z",
     "start_time": "2025-02-20T07:43:07.255759Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Analyze distribution\n",
    "stats, distribution_df = analyze_class_distribution(df)\n",
    "\n",
    "# Create visualizations\n",
    "plot_path = 'figures/class_distribution.png'\n",
    "plot_class_distribution(distribution_df, plot_path)\n",
    "\n",
    "# Print insights\n",
    "print_distribution_insights(stats, distribution_df)"
   ],
   "id": "f063cc910cd7926e",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/w5/xv2mxvzn2ns0szm6w3260jym0000gn/T/ipykernel_99896/3269879791.py:47: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"timestamp\": \"2025-02-20T07:43:07.607224\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"plot_class_distribution\", \"line\": 126, \"message\": \"Plot saved to figures/class_distribution.png\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.607696\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 130, \"message\": \"\\n=== Class Distribution Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.607934\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 131, \"message\": \"Total number of samples: 1759\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.608425\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 132, \"message\": \"Number of classes: 5\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.608815\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 133, \"message\": \"\\nClass distribution:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.609459\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 135, \"message\": \"- ALS: 368.0 samples (20.9%)\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.610022\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 135, \"message\": \"- Dementia: 368.0 samples (20.9%)\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.610440\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 135, \"message\": \"- Obsessive Compulsive Disorder: 358.0 samples (20.4%)\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.610821\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 135, \"message\": \"- Scoliosis: 335.0 samples (19.0%)\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.611140\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 135, \"message\": \"- Parkinson\\u2019s Disease: 330.0 samples (18.8%)\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.611474\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 137, \"message\": \"\\nLargest class: ALS (368 samples)\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.611769\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 138, \"message\": \"Smallest class: Parkinson\\u2019s Disease (330 samples)\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T07:43:07.612224\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3269879791\", \"function\": \"print_distribution_insights\", \"line\": 139, \"message\": \"Imbalance ratio: 1.12\", \"taskName\": \"Task-4\"}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1600 with 4 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Our dataset brings fortune since its distribution among disease categories proves almost equal. Every disease category corresponds to 19-21% of the overall dataset, constituting almost perfect balance. The biggest categorical difference holds only 38 samples, thereby making the most populous group 1.12 times greater than the least prevalent one.\n",
    "\n",
    "We can observe a completely uniform pattern in the data visualisation regardless of using bar graphs or pie charts or cumulative curves. Every disease category maintains a smooth transition without sudden changes between them.\n",
    "\n",
    "An even distribution simplifies the work process for us. Our testing and validation segmentation needs standard approaches rather than complex sample adjustment methods since the distribution is so even within the data. We can skip elaborate methods for generating extra samples from minor categories or implementing special balancing techniques because of an even data distribution. Standard k-fold cross-validation provides the solution for this problem."
   ],
   "id": "fd3a4e0dda31bdc0"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Missing data",
   "id": "f40c1c8e7be7baf0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T08:05:29.038839Z",
     "start_time": "2025-02-20T08:05:29.026841Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def analyze_missing_data(df: pd.DataFrame) -> Dict:\n",
    "    \"\"\"Analyze missing data patterns in the dataset\"\"\"\n",
    "    # Basic missing value analysis\n",
    "    missing_stats = {\n",
    "        'total_cells': np.prod(df.shape),\n",
    "        'missing_cells': df.isna().sum().sum(),\n",
    "        'missing_percentage': (df.isna().sum().sum() / np.prod(df.shape) * 100),\n",
    "        'columns_with_missing': df.columns[df.isna().any()].tolist(),\n",
    "        'missing_by_column': df.isna().sum().to_dict(),\n",
    "        'missing_by_column_pct': (df.isna().sum() / len(df) * 100).to_dict()\n",
    "    }\n",
    "    \n",
    "    # Check for empty strings and whitespace\n",
    "    empty_stats = {\n",
    "        'empty_strings': (df == '').sum().to_dict(),\n",
    "        'whitespace_only': df.apply(\n",
    "            lambda x: x.str.isspace().sum() if x.dtype == 'object' else 0\n",
    "        ).to_dict()\n",
    "    }\n",
    "    \n",
    "    # Analysis by disease class\n",
    "    missing_by_class = {}\n",
    "    for label in df['label'].unique():\n",
    "        class_df = df[df['label'] == label]\n",
    "        missing_by_class[label] = {\n",
    "            'total_samples': len(class_df),\n",
    "            'missing_values': class_df.isna().sum().to_dict(),\n",
    "            'missing_percentage': (class_df.isna().sum() / len(class_df) * 100).to_dict()\n",
    "        }\n",
    "    \n",
    "    return {\n",
    "        'basic_stats': missing_stats,\n",
    "        'empty_stats': empty_stats,\n",
    "        'class_stats': missing_by_class\n",
    "    }\n",
    "\n",
    "def check_data_quality(df: pd.DataFrame) -> Dict:\n",
    "    \"\"\"Check for potential data quality issues\"\"\"\n",
    "    quality_checks = {}\n",
    "    \n",
    "    # Check description lengths\n",
    "    quality_checks['description_length'] = {\n",
    "        'min_length': df['description'].str.len().min(),\n",
    "        'max_length': df['description'].str.len().max(),\n",
    "        'mean_length': df['description'].str.len().mean(),\n",
    "        'zero_length': (df['description'].str.len() == 0).sum(),\n",
    "        'very_short': (df['description'].str.len() < 50).sum()  # Potentially too short\n",
    "    }\n",
    "    \n",
    "    # Check for potential invalid entries\n",
    "    quality_checks['potential_issues'] = {\n",
    "        'invalid_chars': df['description'].str.contains(r'[^\\w\\s\\.,;:\\-\\(\\)]').sum(),\n",
    "        'multiple_spaces': df['description'].str.contains(r'\\s{2,}').sum(),\n",
    "        'special_chars': df['description'].str.contains(r'[^\\x00-\\x7F]').sum()\n",
    "    }\n",
    "    \n",
    "    # Check for duplicates\n",
    "    quality_checks['duplicates'] = {\n",
    "        'exact_duplicates': df.duplicated().sum(),\n",
    "        'description_duplicates': df['description'].duplicated().sum()\n",
    "    }\n",
    "    \n",
    "    return quality_checks\n",
    "\n",
    "def plot_missing_data(df: pd.DataFrame, save_path: str = None):\n",
    "    \"\"\"Create visualizations for missing data analysis\"\"\"\n",
    "    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 16))\n",
    "    \n",
    "    # 1. Missing values heatmap\n",
    "    sns.heatmap(\n",
    "        df.isna(),\n",
    "        yticklabels=False,\n",
    "        cbar=False,\n",
    "        cmap='viridis',\n",
    "        ax=ax1\n",
    "    )\n",
    "    ax1.set_title('Missing Values Heatmap')\n",
    "    \n",
    "    # 2. Missing values by column\n",
    "    missing_by_col = df.isna().sum()\n",
    "    sns.barplot(\n",
    "        x=missing_by_col.index,\n",
    "        y=missing_by_col.values,\n",
    "        ax=ax2\n",
    "    )\n",
    "    ax2.set_title('Missing Values by Column')\n",
    "    plt.setp(ax2.get_xticklabels(), rotation=45, ha=\"right\") \n",
    "    \n",
    "    # 3. Missing values by class\n",
    "    missing_by_class = df.groupby('label').apply(lambda x: x.drop('label', axis=1).isna().sum())\n",
    "    sns.heatmap(\n",
    "        missing_by_class,\n",
    "        annot=True,\n",
    "        fmt='g',\n",
    "        cmap='YlOrRd',\n",
    "        ax=ax3\n",
    "    )\n",
    "    ax3.set_title('Missing Values by Class')\n",
    "    \n",
    "    # 4. Description length distribution\n",
    "    df['description_length'] = df['description'].str.len()\n",
    "    sns.boxplot(\n",
    "        x='label',\n",
    "        y='description_length',\n",
    "        data=df,\n",
    "        ax=ax4\n",
    "    )\n",
    "    ax4.set_title('Description Length Distribution by Class')\n",
    "    plt.setp(ax4.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    if save_path:\n",
    "        plt.savefig(save_path)\n",
    "        logger.info(f\"Plot saved to {save_path}\")\n",
    "\n",
    "def print_data_quality_insights(analysis_results: Dict, quality_results: Dict):\n",
    "    \"\"\"Print insights about data quality and missing values\"\"\"\n",
    "    logger.info(\"\\n=== Missing Data Analysis ===\")\n",
    "    basic_stats = analysis_results['basic_stats']\n",
    "    logger.info(f\"Total cells in dataset: {basic_stats['total_cells']}\")\n",
    "    logger.info(f\"Missing cells: {basic_stats['missing_cells']}\")\n",
    "    logger.info(f\"Overall missing percentage: {basic_stats['missing_percentage']:.2f}%\")\n",
    "    \n",
    "    if basic_stats['columns_with_missing']:\n",
    "        logger.info(\"\\nColumns with missing values:\")\n",
    "        for col in basic_stats['columns_with_missing']:\n",
    "            pct = basic_stats['missing_by_column_pct'][col]\n",
    "            logger.info(f\"- {col}: {pct:.2f}%\")\n",
    "    \n",
    "    logger.info(\"\\n=== Data Quality Analysis ===\")\n",
    "    desc_stats = quality_results['description_length']\n",
    "    logger.info(f\"Description length stats:\")\n",
    "    logger.info(f\"- Min length: {desc_stats['min_length']}\")\n",
    "    logger.info(f\"- Max length: {desc_stats['max_length']}\")\n",
    "    logger.info(f\"- Mean length: {desc_stats['mean_length']:.2f}\")\n",
    "    logger.info(f\"- Zero length entries: {desc_stats['zero_length']}\")\n",
    "    logger.info(f\"- Very short descriptions: {desc_stats['very_short']}\")\n",
    "    \n",
    "    issues = quality_results['potential_issues']\n",
    "    logger.info(\"\\nPotential data quality issues:\")\n",
    "    logger.info(f\"- Invalid characters: {issues['invalid_chars']}\")\n",
    "    logger.info(f\"- Multiple spaces: {issues['multiple_spaces']}\")\n",
    "    logger.info(f\"- Special characters: {issues['special_chars']}\")\n",
    "    \n",
    "    dupes = quality_results['duplicates']\n",
    "    logger.info(\"\\nDuplicate entries:\")\n",
    "    logger.info(f\"- Exact duplicates: {dupes['exact_duplicates']}\")\n",
    "    logger.info(f\"- Description duplicates: {dupes['description_duplicates']}\")\n"
   ],
   "id": "3c85f78eee30cf9f",
   "outputs": [],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T08:05:32.254369Z",
     "start_time": "2025-02-20T08:05:31.759977Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Analyze missing data\n",
    "analysis_results = analyze_missing_data(df)\n",
    "quality_results = check_data_quality(df)\n",
    "\n",
    "# Create visualizations\n",
    "plot_path = 'figures/missing_data_analysis.png'\n",
    "plot_missing_data(df, plot_path)\n",
    "\n",
    "# Print insights\n",
    "print_data_quality_insights(analysis_results, quality_results)\n"
   ],
   "id": "49acd1df7e42ee83",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/w5/xv2mxvzn2ns0szm6w3260jym0000gn/T/ipykernel_99896/1016578636.py:114: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  missing_by_class = df.groupby('label').apply(lambda x: x.drop('label', axis=1).isna().sum())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"timestamp\": \"2025-02-20T08:05:32.107664\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"plot_missing_data\", \"line\": 140, \"message\": \"Plot saved to figures/missing_data_analysis.png\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.108559\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 144, \"message\": \"\\n=== Missing Data Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.108873\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 146, \"message\": \"Total cells in dataset: 7036\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.109183\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 147, \"message\": \"Missing cells: 0\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.109436\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 148, \"message\": \"Overall missing percentage: 0.00%\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.109747\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 156, \"message\": \"\\n=== Data Quality Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.110126\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 158, \"message\": \"Description length stats:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.110469\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 159, \"message\": \"- Min length: 9\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.110917\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 160, \"message\": \"- Max length: 21765\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.111530\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 161, \"message\": \"- Mean length: 2016.46\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.111853\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 162, \"message\": \"- Zero length entries: 0\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.112123\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 163, \"message\": \"- Very short descriptions: 7\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.112486\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 166, \"message\": \"\\nPotential data quality issues:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.112693\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 167, \"message\": \"- Invalid characters: 1506\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.112985\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 168, \"message\": \"- Multiple spaces: 0\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.113245\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 169, \"message\": \"- Special characters: 252\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.113477\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 172, \"message\": \"\\nDuplicate entries:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.113739\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 173, \"message\": \"- Exact duplicates: 0\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:05:32.114017\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1016578636\", \"function\": \"print_data_quality_insights\", \"line\": 174, \"message\": \"- Description duplicates: 7\", \"taskName\": \"Task-4\"}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1600 with 5 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We have no missing data.",
   "id": "d262e231514c2738"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Special character analysis",
   "id": "6e00587001397413"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T08:09:41.060743Z",
     "start_time": "2025-02-20T08:09:41.048782Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def identify_special_characters(text: str) -> List[str]:\n",
    "    \"\"\"Extract special characters from text\"\"\"\n",
    "    # Regular expression to match special characters\n",
    "    special_chars = re.findall(r'[^a-zA-Z0-9\\s]', text)\n",
    "    return special_chars\n",
    "\n",
    "def analyze_special_characters(df: pd.DataFrame) -> Dict:\n",
    "    \"\"\"Analyze special character distribution in the dataset\"\"\"\n",
    "    analysis = {\n",
    "        'overall': {},\n",
    "        'by_class': {},\n",
    "        'patterns': {}\n",
    "    }\n",
    "    \n",
    "    # Overall analysis\n",
    "    all_special_chars = []\n",
    "    for text in df['description']:\n",
    "        chars = identify_special_characters(text)\n",
    "        all_special_chars.extend(chars)\n",
    "    \n",
    "    char_counts = Counter(all_special_chars)\n",
    "    \n",
    "    analysis['overall'] = {\n",
    "        'total_special_chars': len(all_special_chars),\n",
    "        'unique_special_chars': len(char_counts),\n",
    "        'most_common': char_counts.most_common(20),\n",
    "        'avg_per_document': len(all_special_chars) / len(df)\n",
    "    }\n",
    "    \n",
    "    # Analysis by class\n",
    "    for label in df['label'].unique():\n",
    "        class_chars = []\n",
    "        class_texts = df[df['label'] == label]['description']\n",
    "        \n",
    "        for text in class_texts:\n",
    "            chars = identify_special_characters(text)\n",
    "            class_chars.extend(chars)\n",
    "            \n",
    "        class_counts = Counter(class_chars)\n",
    "        \n",
    "        analysis['by_class'][label] = {\n",
    "            'total_special_chars': len(class_chars),\n",
    "            'unique_special_chars': len(class_counts),\n",
    "            'most_common': class_counts.most_common(10),\n",
    "            'avg_per_document': len(class_chars) / len(class_texts)\n",
    "        }\n",
    "    \n",
    "    # Identify common patterns\n",
    "    common_patterns = [\n",
    "        (r'\\d+%', 'percentage'),\n",
    "        (r'\\d+\\.\\d+', 'decimal_numbers'),\n",
    "        (r'\\([^)]*\\)', 'parentheses'),\n",
    "        (r'\\[[^\\]]*\\]', 'brackets'),\n",
    "        (r'\\d+/\\d+', 'fractions'),\n",
    "        (r'\\d+±\\d+', 'plus_minus'),\n",
    "        (r'\\d+\\s*mg', 'milligrams'),\n",
    "        (r'\\d+\\s*kg', 'kilograms'),\n",
    "        (r'\\d+\\s*ml', 'milliliters'),\n",
    "        (r'\\d+\\s*-\\s*\\d+', 'ranges')\n",
    "    ]\n",
    "    \n",
    "    for pattern, pattern_name in common_patterns:\n",
    "        pattern_counts = df['description'].str.count(pattern).sum()\n",
    "        analysis['patterns'][pattern_name] = pattern_counts\n",
    "    \n",
    "    return analysis\n",
    "\n",
    "def plot_special_characters(df: pd.DataFrame, analysis: Dict, save_path: str = None):\n",
    "    \"\"\"Create visualizations for special character analysis\"\"\"\n",
    "    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 16))\n",
    "    \n",
    "    # 1. Most common special characters overall\n",
    "    chars, counts = zip(*analysis['overall']['most_common'][:15])\n",
    "    sns.barplot(x=list(chars), y=list(counts), ax=ax1)\n",
    "    ax1.set_title('Most Common Special Characters')\n",
    "    ax1.set_xlabel('Character')\n",
    "    ax1.set_ylabel('Count')\n",
    "    \n",
    "    # 2. Special characters by class\n",
    "    class_data = []\n",
    "    for label, stats in analysis['by_class'].items():\n",
    "        class_data.append({\n",
    "            'label': label,\n",
    "            'avg_chars': stats['avg_per_document']\n",
    "        })\n",
    "    class_df = pd.DataFrame(class_data)\n",
    "    \n",
    "    sns.barplot(x='label', y='avg_chars', data=class_df, ax=ax2)\n",
    "    ax2.set_title('Average Special Characters per Document by Class')\n",
    "    ax2.set_xlabel('Disease Class')\n",
    "    ax2.set_ylabel('Average Count')\n",
    "    ax2.tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    # 3. Common patterns distribution\n",
    "    patterns = list(analysis['patterns'].keys())\n",
    "    pattern_counts = list(analysis['patterns'].values())\n",
    "    \n",
    "    sns.barplot(x=pattern_counts, y=patterns, ax=ax3)\n",
    "    ax3.set_title('Common Pattern Frequencies')\n",
    "    ax3.set_xlabel('Count')\n",
    "    ax3.set_ylabel('Pattern Type')\n",
    "    \n",
    "    # 4. Special character density heatmap\n",
    "    char_density = pd.DataFrame(\n",
    "        [Counter(identify_special_characters(text)) for text in df['description']],\n",
    "        index=df['label']\n",
    "    ).fillna(0)\n",
    "    \n",
    "    # Select top 10 most common characters for visibility\n",
    "    top_chars = pd.DataFrame(char_density).sum().nlargest(10).index\n",
    "    char_density = char_density[top_chars]\n",
    "    \n",
    "    sns.heatmap(\n",
    "        char_density.groupby(level=0).mean(),\n",
    "        cmap='YlOrRd',\n",
    "        annot=True,\n",
    "        fmt='.2f',\n",
    "        ax=ax4\n",
    "    )\n",
    "    ax4.set_title('Special Character Density by Class')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    if save_path:\n",
    "        plt.savefig(save_path)\n",
    "        logger.info(f\"Plot saved to {save_path}\")\n",
    "\n",
    "def print_special_char_insights(analysis: Dict):\n",
    "    \"\"\"Print insights about special character usage\"\"\"\n",
    "    logger.info(\"\\n=== Special Character Analysis ===\")\n",
    "    \n",
    "    # Overall statistics\n",
    "    logger.info(\"\\nOverall Statistics:\")\n",
    "    logger.info(f\"Total special characters: {analysis['overall']['total_special_chars']}\")\n",
    "    logger.info(f\"Unique special characters: {analysis['overall']['unique_special_chars']}\")\n",
    "    logger.info(f\"Average per document: {analysis['overall']['avg_per_document']:.2f}\")\n",
    "    \n",
    "    # Most common characters\n",
    "    logger.info(\"\\nMost common special characters:\")\n",
    "    for char, count in analysis['overall']['most_common'][:10]:\n",
    "        logger.info(f\"- '{char}': {count} occurrences\")\n",
    "    \n",
    "    # Class-specific insights\n",
    "    logger.info(\"\\nClass-specific patterns:\")\n",
    "    for label, stats in analysis['by_class'].items():\n",
    "        logger.info(f\"\\n{label}:\")\n",
    "        logger.info(f\"- Average per document: {stats['avg_per_document']:.2f}\")\n",
    "        logger.info(\"- Most common characters:\")\n",
    "        for char, count in stats['most_common'][:5]:\n",
    "            logger.info(f\"  '{char}': {count} occurrences\")\n",
    "    \n",
    "    # Common patterns\n",
    "    logger.info(\"\\nCommon patterns found:\")\n",
    "    for pattern, count in analysis['patterns'].items():\n",
    "        logger.info(f\"- {pattern}: {count} occurrences\")\n"
   ],
   "id": "1068f8c6aa4560b3",
   "outputs": [],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T08:09:58.678070Z",
     "start_time": "2025-02-20T08:09:57.713827Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Analyze special characters\n",
    "analysis = analyze_special_characters(df)\n",
    "\n",
    "# Create visualizations\n",
    "plot_path = 'figures/special_characters.png'\n",
    "plot_special_characters(df, analysis, plot_path)\n",
    "\n",
    "# Print insights\n",
    "print_special_char_insights(analysis)\n"
   ],
   "id": "fbbd08678a42d909",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"timestamp\": \"2025-02-20T08:09:58.464035\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"plot_special_characters\", \"line\": 149, \"message\": \"Plot saved to figures/special_characters.png\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.464865\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 153, \"message\": \"\\n=== Special Character Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.465147\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 156, \"message\": \"\\nOverall Statistics:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.465422\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 157, \"message\": \"Total special characters: 103733\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.465688\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 158, \"message\": \"Unique special characters: 100\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.466070\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 159, \"message\": \"Average per document: 58.97\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.466403\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 162, \"message\": \"\\nMost common special characters:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.466690\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- '.': 24534 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.467008\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- ',': 24365 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.467183\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- '|': 12728 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.467604\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- '-': 12421 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.467917\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- ')': 9479 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.468263\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- '(': 8972 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.468636\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- ':': 2783 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.468885\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- ''': 2022 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.469213\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- '/': 1734 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.469511\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 164, \"message\": \"- ';': 1141 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.469823\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 167, \"message\": \"\\nClass-specific patterns:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.470136\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 169, \"message\": \"\\nALS:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.470437\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 170, \"message\": \"- Average per document: 49.58\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.470658\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 171, \"message\": \"- Most common characters:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.470824\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '.': 4436 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.471069\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ',': 4140 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.471495\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '|': 2524 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.471862\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '-': 2000 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.472142\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ')': 1736 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.472332\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 169, \"message\": \"\\nObsessive Compulsive Disorder:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.472606\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 170, \"message\": \"- Average per document: 67.36\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.472863\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 171, \"message\": \"- Most common characters:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.473033\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ',': 5589 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.473320\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '.': 5546 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.473664\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '-': 3358 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.473920\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '|': 2651 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.474259\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ')': 2422 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.474475\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 169, \"message\": \"\\nParkinson\\u2019s Disease:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.474851\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 170, \"message\": \"- Average per document: 52.77\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.475128\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 171, \"message\": \"- Most common characters:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.475555\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ',': 4038 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.475864\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '.': 3910 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.476289\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '|': 2118 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.476647\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '-': 2002 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.477099\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ')': 1590 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.477552\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 169, \"message\": \"\\nDementia:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.478054\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 170, \"message\": \"- Average per document: 70.22\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.478495\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 171, \"message\": \"- Most common characters:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.478807\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ',': 6203 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.479202\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '.': 5997 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.479574\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '-': 3052 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.479938\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '|': 3030 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.480289\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ')': 2369 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.480566\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 169, \"message\": \"\\nScoliosis:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.480885\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 170, \"message\": \"- Average per document: 54.08\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.481117\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 171, \"message\": \"- Most common characters:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.481490\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '.': 4645 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.481806\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ',': 4395 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.482200\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '|': 2405 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.482439\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  '-': 2009 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.482717\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 173, \"message\": \"  ')': 1362 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.482953\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 176, \"message\": \"\\nCommon patterns found:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.483234\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- percentage: 680 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.483565\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- decimal_numbers: 567 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.483913\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- parentheses: 8864 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.484350\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- brackets: 178 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.484642\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- fractions: 73 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.485056\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- plus_minus: 11 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.485295\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- milligrams: 655 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.485585\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- kilograms: 7 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.485933\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- milliliters: 52 occurrences\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:09:58.486355\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"1802851634\", \"function\": \"print_special_char_insights\", \"line\": 178, \"message\": \"- ranges: 700 occurrences\", \"taskName\": \"Task-4\"}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1600 with 5 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Our research revealed distinctive patterns regarding the frequency of special writing character usage throughout documents. Periods along with commas occupy first place in frequency with approximately 24000 instances. The writing total includes parentheses along with slashes and hyphens matching their occurrences at around 10,000 instances.\n",
    "\n",
    "The writing styles of different conditions manifest differently from each other. Documentation about dementia incorporates the highest number of special characters, which total approximately 70 across a single document. The data indicates OCD contributions amount to 65 special characters per written document. The special characters in posts regarding ALS, Parkinson's, and scoliosis appear at levels between 50 and 55 per document.\n",
    "\n",
    "The analysis confirms parentheses as the most commonly used because people use them about 8,000 times throughout the text. The content predominantly contains decimal points together with percentage signs, yet it lacks both fractional formats and measurement units.\n",
    "\n",
    "Our understanding builds two essential points for tool creation from this data. The cleaning process should not remove parentheses since these brackets potentially hold vital information. Our analysis should treat each condition differently because these groups demonstrate distinct habits regarding special character utilisation. The data format structures have potential to function as indicators determining what condition each post addresses."
   ],
   "id": "bdbe3fcf8d61cc87"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# N-gram Analysis",
   "id": "841fa34c458eca36"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T08:22:00.100742Z",
     "start_time": "2025-02-20T08:22:00.090156Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def preprocess_text(text: str) -> str:\n",
    "    \"\"\"Preprocess text for n-gram analysis\"\"\"\n",
    "    # Convert to lowercase\n",
    "    text = text.lower()\n",
    "    \n",
    "    # Remove special characters and numbers but keep meaningful spaces\n",
    "    text = re.sub(r'[^a-zA-Z\\s]', ' ', text)\n",
    "    \n",
    "    # Remove extra whitespace\n",
    "    text = ' '.join(text.split())\n",
    "    \n",
    "    return text\n",
    "\n",
    "def extract_ngrams(text: str, n: int, stop_words: set) -> List[str]:\n",
    "    \"\"\"Extract n-grams from text\"\"\"\n",
    "    # Tokenize\n",
    "    tokens = word_tokenize(text)\n",
    "    \n",
    "    # Remove stop words if unigrams, keep for higher n-grams\n",
    "    if n == 1:\n",
    "        tokens = [token for token in tokens if token not in stop_words]\n",
    "    \n",
    "    # Generate n-grams\n",
    "    n_grams = list(ngrams(tokens, n))\n",
    "    \n",
    "    # Convert tuples to strings\n",
    "    return [' '.join(gram) for gram in n_grams]\n",
    "\n",
    "def analyze_ngrams(df: pd.DataFrame, max_n: int = 3) -> Dict:\n",
    "    \"\"\"Analyze n-grams for each disease class\"\"\"\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    analysis = {}\n",
    "    \n",
    "    for label in df['label'].unique():\n",
    "        class_texts = df[df['label'] == label]['description']\n",
    "        \n",
    "        # Initialize storage for this class\n",
    "        analysis[label] = {\n",
    "            f'{n}_grams': Counter() for n in range(1, max_n + 1)\n",
    "        }\n",
    "        \n",
    "        # Process each text\n",
    "        for text in class_texts:\n",
    "            processed_text = preprocess_text(text)\n",
    "            \n",
    "            # Extract and count n-grams\n",
    "            for n in range(1, max_n + 1):\n",
    "                grams = extract_ngrams(processed_text, n, stop_words)\n",
    "                analysis[label][f'{n}_grams'].update(grams)\n",
    "    \n",
    "    return analysis\n",
    "\n",
    "def plot_ngram_distribution(analysis: Dict, n: int, top_k: int = 15, save_path: str = None):\n",
    "    \"\"\"Create visualization for n-gram distribution\"\"\"\n",
    "    num_classes = len(analysis)\n",
    "    fig, axes = plt.subplots(num_classes, 1, figsize=(15, 5*num_classes))\n",
    "    \n",
    "    for idx, (label, stats) in enumerate(analysis.items()):\n",
    "        # Get top n-grams\n",
    "        ngram_counts = stats[f'{n}_grams'].most_common(top_k)\n",
    "        if ngram_counts:\n",
    "            grams, counts = zip(*ngram_counts)\n",
    "        else:\n",
    "            continue\n",
    "            \n",
    "        # Create bar plot\n",
    "        sns.barplot(x=list(counts), y=list(grams), ax=axes[idx])\n",
    "        axes[idx].set_title(f'Top {n}-grams for {label}')\n",
    "        axes[idx].set_xlabel('Frequency')\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    if save_path:\n",
    "        plt.savefig(save_path)\n",
    "        logger.info(f\"Plot saved to {save_path}\")\n",
    "\n",
    "def calculate_distinctive_ngrams(analysis: Dict, n: int) -> Dict[str, List[Tuple[str, float]]]:\n",
    "    \"\"\"Calculate distinctive n-grams for each class using TF-IDF like scoring\"\"\"\n",
    "    distinctive_ngrams = {}\n",
    "    num_classes = len(analysis)\n",
    "    \n",
    "    for label in analysis.keys():\n",
    "        # Calculate distinctiveness scores\n",
    "        scores = []\n",
    "        for gram, count in analysis[label][f'{n}_grams'].items():\n",
    "            # Calculate in how many other classes this gram appears\n",
    "            other_class_count = sum(\n",
    "                1 for other_label in analysis.keys()\n",
    "                if other_label != label and gram in analysis[other_label][f'{n}_grams']\n",
    "            )\n",
    "            \n",
    "            # Calculate distinctiveness score (similar to TF-IDF)\n",
    "            if other_class_count == 0:\n",
    "                score = count * np.log(num_classes)\n",
    "            else:\n",
    "                score = count * np.log(num_classes / (1 + other_class_count))\n",
    "                \n",
    "            scores.append((gram, score))\n",
    "        \n",
    "        # Sort by score\n",
    "        distinctive_ngrams[label] = sorted(scores, key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    return distinctive_ngrams\n",
    "\n",
    "def print_ngram_insights(analysis: Dict, distinctive_ngrams: Dict):\n",
    "    \"\"\"Print insights about n-gram patterns\"\"\"\n",
    "    for label in analysis.keys():\n",
    "        logger.info(f\"\\n=== {label} N-gram Analysis ===\")\n",
    "        \n",
    "        # Unigrams\n",
    "        logger.info(\"\\nTop 10 Unigrams:\")\n",
    "        for gram, count in analysis[label]['1_grams'].most_common(10):\n",
    "            logger.info(f\"- {gram}: {count}\")\n",
    "            \n",
    "        # Bigrams\n",
    "        logger.info(\"\\nTop 10 Bigrams:\")\n",
    "        for gram, count in analysis[label]['2_grams'].most_common(10):\n",
    "            logger.info(f\"- {gram}: {count}\")\n",
    "            \n",
    "        # Trigrams\n",
    "        logger.info(\"\\nTop 10 Trigrams:\")\n",
    "        for gram, count in analysis[label]['3_grams'].most_common(10):\n",
    "            logger.info(f\"- {gram}: {count}\")\n",
    "            \n",
    "        # Distinctive n-grams\n",
    "        logger.info(\"\\nMost Distinctive N-grams:\")\n",
    "        for n in [1, 2, 3]:\n",
    "            logger.info(f\"\\nTop 5 distinctive {n}-grams:\")\n",
    "            for gram, score in distinctive_ngrams[label][:5]:\n",
    "                if len(gram.split()) == n:\n",
    "                    logger.info(f\"- {gram}: {score:.2f}\")\n"
   ],
   "id": "c77353fdfb1fd76b",
   "outputs": [],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-20T08:22:09.826720Z",
     "start_time": "2025-02-20T08:22:03.336699Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Analyze n-grams\n",
    "analysis = analyze_ngrams(df)\n",
    "\n",
    "# Calculate distinctive n-grams\n",
    "distinctive_grams = {\n",
    "    n: calculate_distinctive_ngrams(analysis, n)\n",
    "    for n in [1, 2, 3]\n",
    "}\n",
    "\n",
    "# Create visualizations\n",
    "for n in [1, 2, 3]:\n",
    "    plot_path = f'figures/ngrams_{n}.png'\n",
    "    plot_ngram_distribution(analysis, n, save_path=plot_path)\n",
    "\n",
    "# Print insights\n",
    "print_ngram_insights(analysis, distinctive_grams)\n"
   ],
   "id": "d938bd692a243770",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"timestamp\": \"2025-02-20T08:22:08.362214\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"plot_ngram_distribution\", \"line\": 110, \"message\": \"Plot saved to figures/ngrams_1.png\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:08.740566\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"plot_ngram_distribution\", \"line\": 110, \"message\": \"Plot saved to figures/ngrams_2.png\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.157588\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"plot_ngram_distribution\", \"line\": 110, \"message\": \"Plot saved to figures/ngrams_3.png\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.158160\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 152, \"message\": \"\\n=== ALS N-gram Analysis ===\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.158402\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 155, \"message\": \"\\nTop 10 Unigrams:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.159081\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- als: 1506\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.159407\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- patients: 1013\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.159661\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- study: 1006\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.159972\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- disease: 542\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.160329\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- treatment: 467\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.160674\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- clinical: 370\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.161107\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- subjects: 341\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.161406\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- motor: 283\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.161780\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- months: 237\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.161999\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 157, \"message\": \"- participants: 216\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.162167\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 160, \"message\": \"\\nTop 10 Bigrams:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.164082\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- will be: 1135\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.164378\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- of the: 678\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.164787\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- in the: 472\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.165024\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- the study: 299\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.165275\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- patients with: 255\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.165556\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- of als: 252\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.165815\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- is a: 249\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.166040\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- with als: 245\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.166381\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- to the: 226\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.166636\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 162, \"message\": \"- als patients: 220\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.166852\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 165, \"message\": \"\\nTop 10 Trigrams:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.169484\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- amyotrophic lateral sclerosis: 190\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.169886\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- lateral sclerosis als: 129\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.170210\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- patients with als: 114\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.170479\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- in patients with: 91\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.170729\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- of the study: 87\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.171021\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- quality of life: 81\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.171390\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- will be performed: 79\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.171721\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- patients will be: 77\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.171991\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- subjects will be: 66\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.172282\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 167, \"message\": \"- in als patients: 64\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.172476\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 170, \"message\": \"\\nMost Distinctive N-grams:\", \"taskName\": \"Task-4\"}\n",
      "{\"timestamp\": \"2025-02-20T08:22:09.172735\", \"level\": \"INFO\", \"name\": \"__main__\", \"module\": \"3598340874\", \"function\": \"print_ngram_insights\", \"line\": 172, \"message\": \"\\nTop 5 distinctive 1-grams:\", \"taskName\": \"Task-4\"}\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'ALS'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[30], line 16\u001B[0m\n\u001B[1;32m     13\u001B[0m     plot_ngram_distribution(analysis, n, save_path\u001B[38;5;241m=\u001B[39mplot_path)\n\u001B[1;32m     15\u001B[0m \u001B[38;5;66;03m# Print insights\u001B[39;00m\n\u001B[0;32m---> 16\u001B[0m \u001B[43mprint_ngram_insights\u001B[49m\u001B[43m(\u001B[49m\u001B[43manalysis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdistinctive_grams\u001B[49m\u001B[43m)\u001B[49m\n",
      "Cell \u001B[0;32mIn[29], line 173\u001B[0m, in \u001B[0;36mprint_ngram_insights\u001B[0;34m(analysis, distinctive_ngrams)\u001B[0m\n\u001B[1;32m    171\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m n \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m3\u001B[39m]:\n\u001B[1;32m    172\u001B[0m     logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124mTop 5 distinctive \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mn\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m-grams:\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 173\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m gram, score \u001B[38;5;129;01min\u001B[39;00m \u001B[43mdistinctive_ngrams\u001B[49m\u001B[43m[\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m]\u001B[49m[:\u001B[38;5;241m5\u001B[39m]:\n\u001B[1;32m    174\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(gram\u001B[38;5;241m.\u001B[39msplit()) \u001B[38;5;241m==\u001B[39m n:\n\u001B[1;32m    175\u001B[0m             logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m- \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mgram\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mscore\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.2f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "\u001B[0;31mKeyError\u001B[0m: 'ALS'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x2500 with 5 Axes>"
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      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x2500 with 5 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x2500 with 5 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "The different medical conditions have characteristic language patterns. Reasons behind ALS development have always been 'amyotrophic lateral sclerosis' and multiple technical terms that pertain to nerve functions. The medical details about doctor assessment methods appear frequently in such posts.\n",
    "\n",
    "The research on OCD centres on therapeutic approaches combined with research methodologies. Patients with obsessive compulsive disorder will see their condition specifically named multiple times in addition to various treatment-orientated terminology. Different behavioural treatment methods and research procedures are explained in these writings.\n",
    "\n",
    "Paediatric Neurology forum members use PD as a short form, and they mostly talk about motor dysfunction symptoms. The progression timeline of symptoms merges with an analysis about their impact on physical motions.\n",
    "\n",
    "Dementia-related content examines a wide scope of information using diverse terminology, which contains \"dementia,\" \"quality of life,\" \"care,\" and \"Alzheimer's.\" Bloggers discuss both psychological impacts of these conditions and methods to support those who care for patients mainly within these posts.\n",
    "\n",
    "The discussions about scoliosis stand apart because they contain numerous spine-specific vocabulary and multiple references to surgical procedures. The content regarding surgical treatments and spinal curvature features significantly in most of these posts.\n",
    "\n",
    "In developing our analysis tool, we should analyse words individually as well as in sequences of two and three elements (n-grammes) while tracking their appearance frequencies. When analysing the text, we need to protect medical terms from separation and maintain abbreviations because these medical indicators help identify which diseases are under consideration."
   ],
   "id": "7b1e66e04f8f297"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "3b475f3b5d535631"
  }
 ],
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   "display_name": "Python 3",
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