ManualLabelsMetrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62d0b855-5eb9-4a35-8518-0d82368d7fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import json\n",
    "import glob\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "import os\n",
    "import numpy as np\n",
    "import math\n",
    "from sklearn.metrics import cohen_kappa_score, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8af372f9-68a0-45b2-bf7d-5136c3d9c5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_labels_path = '/dhc/groups/ocdetect/thesis/cleaned_labels'\n",
    "labels_windows_clean = '/dhc/groups/ocdetect/thesis/cleaned_windows'\n",
    "label_windows = '/dhc/groups/ocdetect/thesis/label_windows'\n",
    "extracted_labels = '/dhc/groups/ocdetect/thesis/extracted_labels'\n",
    "subjects = [\"01\", \"03\", \"04\", \"18\", \"20\", \"30\"]\n",
    "annotators = [\"a1\", \"a2\", \"a3\", \"a4\"]\n",
    "colors = ['#F6A800', '#8B9094', '#B1063A', '#5A6065', '#DD6108', \"#ABAFB2\", ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf3248a1-99dd-4a29-be66-a44226aa812e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Subject Annotator  Number of Set Labels  After Clean\n",
      "0       01        a1                   235          211\n",
      "1       01        a4                   195          190\n",
      "2       03        a1                   208          205\n",
      "3       03        a2                   212          209\n",
      "4       04        a2                   115          115\n",
      "5       04        a3                   127          123\n",
      "6       18        a1                   308          288\n",
      "7       18        a3                   347          312\n",
      "8       20        a3                   343          340\n",
      "9       20        a4                   323          323\n",
      "10      30        a2                   169          162\n",
      "11      30        a4                   124          121\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of set labels by annotator per subject \n",
    "def calculate_number_of_set_labels_by_subject_and_annotator():\n",
    "    data = []      \n",
    "    for subject in subjects:\n",
    "        for annotator in annotators:\n",
    "            path = os.path.join(extracted_labels, f\"{annotator}_subject_{subject}.csv\")\n",
    "            if os.path.exists(path):\n",
    "                num_labels = len(pd.read_csv(path))\n",
    "                clean_path = os.path.join(clean_labels_path,  f\"{annotator}_subject_{subject}.csv\")\n",
    "                after_clean = len(pd.read_csv(clean_path))\n",
    "                data.append([subject, annotator, num_labels, after_clean])\n",
    "\n",
    "    columns = [\"Subject\", \"Annotator\", \"Number of Set Labels\", \"After Clean\"]\n",
    "    set_labels_count_df = pd.DataFrame(data, columns=columns)\n",
    "    set_labels_count_df['Subject'] = set_labels_count_df['Subject'].astype(str)\n",
    "    return set_labels_count_df\n",
    "set_labels_count_df = calculate_number_of_set_labels_by_subject_and_annotator()\n",
    "print(set_labels_count_df)\n",
    "df = set_labels_count_df.sort_values(by='Subject')\n",
    "\n",
    "\n",
    "df['Deleted Labels'] = df['Number of Set Labels'] - df['After Clean']\n",
    "df['Subject/Annotator'] = df['Subject'] + '/' + df['Annotator']\n",
    "positions = []\n",
    "\n",
    "\n",
    "current_pos = 0\n",
    "positions.append(current_pos)\n",
    "\n",
    "\n",
    "for i in range(1, len(df)):\n",
    "    if i % 2 == 0: \n",
    "        current_pos += 2  \n",
    "    else:\n",
    "        current_pos += 1 \n",
    "    positions.append(current_pos)\n",
    "\n",
    "plt.figure(figsize=(14, 8))\n",
    "x = np.arange(len(df))\n",
    "width = 0.8\n",
    "\n",
    "\n",
    "plt.bar(x, df['After Clean'], width=width, color=colors[0], label='Labels After Cleaning')\n",
    "plt.bar(x, df['Deleted Labels'], bottom=df['After Clean'], width=width, color=colors[4], label='Deleted Labels')\n",
    "\n",
    "plt.xlabel('Subject/Annotator')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Number of Set Labels After Cleaning and Deleted Labels by Subject and Annotator')\n",
    "plt.xticks(x, df['Subject/Annotator'], rotation=0, ha='center')\n",
    "plt.legend()\n",
    "plt.grid(axis='y')\n",
    "\n",
    "\n",
    "for i, (after_clean, deleted_labels) in enumerate(zip(df['After Clean'], df['Deleted Labels'])):\n",
    "    plt.text(i, after_clean / 2, f'{int(after_clean)}', ha='center', va='center', color='black', rotation=90)\n",
    "    plt.text(i, after_clean + deleted_labels / 2, f'{int(deleted_labels)}', ha='center', va='center', color='black', rotation=90)\n",
    "for i in range(1, len(df)//2):\n",
    "    plt.axvline(x=2*i - 0.5, color='grey', linestyle='--')\n",
    " \n",
    "plt.tight_layout()\n",
    "plt.savefig('number_of_labels.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "531f6bcd-51e9-4b2d-a79c-e222a688c7b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Subject  Number of Windows  After Clean\n",
      "0      01                362          348\n",
      "1      03                225          221\n",
      "2      04                130          130\n",
      "3      18                398          355\n",
      "4      20                366          366\n",
      "5      30                195          184\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_number_of_windows_labels_by_subject_and_annotator():\n",
    "    data = []      \n",
    "    for subject in subjects:\n",
    "        path = os.path.join(label_windows, f'subject_{subject}.csv')\n",
    "        if os.path.exists(path):\n",
    "            num_wwindows = len(pd.read_csv(path))\n",
    "            clean_path = os.path.join(labels_windows_clean,  f\"subject_{subject}.csv\")\n",
    "            after_clean = len(pd.read_csv(clean_path))\n",
    "            data.append([subject, num_wwindows, after_clean])\n",
    "\n",
    "    columns = [\"Subject\", \"Number of Windows\", \"After Clean\"]\n",
    "    set_labels_count_df = pd.DataFrame(data, columns=columns)\n",
    "    set_labels_count_df['Subject'] = set_labels_count_df['Subject'].astype(str)\n",
    "    return set_labels_count_df\n",
    "set_labels_count_df = calculate_number_of_windows_labels_by_subject_and_annotator()\n",
    "print(set_labels_count_df)\n",
    "df = set_labels_count_df.sort_values(by='Subject')\n",
    "\n",
    "\n",
    "df['Deleted Windows'] = df['Number of Windows'] - df['After Clean']\n",
    "df['Subject/Annotator'] = df['Subject']\n",
    "\n",
    "\n",
    "plt.figure(figsize=(14, 8))\n",
    "x = np.arange(len(df))\n",
    "width = 0.8\n",
    "\n",
    "\n",
    "plt.bar(x, df['After Clean'], width=width, color=colors[0], label='Windows After Cleaning')\n",
    "plt.bar(x, df['Deleted Windows'], bottom=df['After Clean'], width=width, color=colors[4], label='Deleted Windows')\n",
    "\n",
    "plt.xlabel('Subject')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Number of Windows After Cleaning and Deleted Windows by Subject')\n",
    "plt.xticks(x, df['Subject'], rotation=0, ha='center')\n",
    "plt.legend()\n",
    "plt.grid(axis='y')\n",
    "\n",
    "# Add text labels for each part of the bar\n",
    "for i, (after_clean, deleted_labels) in enumerate(zip(df['After Clean'], df['Deleted Windows'])):\n",
    "    plt.text(i, after_clean / 2, f'{int(after_clean)}', ha='center', va='center', color='black', rotation=90)\n",
    "    plt.text(i, after_clean + deleted_labels / 2, f'{int(deleted_labels)}', ha='center', va='center', color='black', rotation=90)\n",
    "\n",
    "# Display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5cd72a20-2454-4823-8caa-a8594aed5f10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matrix\n",
      "{'pp': 168, 'pn': 43, 'np': 22, 'nn': 115}\n",
      "matrix\n",
      "{'pp': 198, 'pn': 7, 'np': 11, 'nn': 5}\n",
      "matrix\n",
      "{'pp': 111, 'pn': 4, 'np': 12, 'nn': 3}\n",
      "matrix\n",
      "{'pp': 262, 'pn': 26, 'np': 50, 'nn': 17}\n",
      "matrix\n",
      "{'pp': 321, 'pn': 19, 'np': 2, 'nn': 24}\n",
      "matrix\n",
      "{'pp': 114, 'pn': 48, 'np': 7, 'nn': 15}\n",
      "  Subject Annotator pair  Cohens Kappa  Precision    Recall  F1 Score  \\\n",
      "0      01          a1/a4      0.618987   0.884211  0.796209  0.837905   \n",
      "1      03          a1/a2      0.314611   0.947368  0.965854  0.956522   \n",
      "2      04          a2/a3      0.215094   0.902439  0.965217  0.932773   \n",
      "3      18          a1/a3      0.189498   0.839744  0.909722  0.873333   \n",
      "4      20          a3/a4      0.666087   0.993808  0.944118  0.968326   \n",
      "5      30          a2/a4      0.213553   0.942149  0.703704  0.805654   \n",
      "\n",
      "   Jaccard Index  \n",
      "0       0.813218  \n",
      "1       0.918552  \n",
      "2       0.876923  \n",
      "3       0.785915  \n",
      "4       0.942623  \n",
      "5       0.701087  \n"
     ]
    }
   ],
   "source": [
    "#calculate cohen's kappa, precision, recall, jaccard index, variance for number of labels\n",
    "\n",
    "\n",
    "def get_confusion_matrix(windows, file1, file2):\n",
    "    contigency_matrix = {\n",
    "    'pp': 0,\n",
    "    'pn': 0,\n",
    "    'np': 0,\n",
    "    'nn': 0\n",
    "    }\n",
    "    for index, row in windows_df.iterrows():\n",
    "        start_w = row['start']\n",
    "        end_w = row['end']\n",
    "        presence1 = file1[(file1['start'] >= start_w) & (file1['start'] <= end_w)].shape[0] > 0\n",
    "        presence2 = file2[(file2['start'] >= start_w) & (file2['start'] <= end_w)].shape[0] > 0\n",
    "    \n",
    "        if presence1 and presence2:\n",
    "            contigency_matrix['pp'] += 1\n",
    "        elif not presence2 and presence1:\n",
    "            contigency_matrix['pn'] += 1\n",
    "        elif not presence1 and presence2:\n",
    "            contigency_matrix['np'] += 1\n",
    "        else:\n",
    "            contigency_matrix['nn'] += 1\n",
    "    print('matrix')\n",
    "    print(contigency_matrix)\n",
    "    return contigency_matrix\n",
    "\n",
    "def calculate_jaccard_index(df, file_1, file_2):\n",
    "    table = get_confusion_matrix(df, file_1, file_2)\n",
    "    return (table['pp'] + table['nn'])/(table['pp'] + table['pn'] + table['np'] + table['nn'])\n",
    "\n",
    "def get_labels_binary(windows_df, file1, file2):\n",
    "    classification1 = []\n",
    "    classification2 = []\n",
    "\n",
    "    for index, row in windows_df.iterrows():\n",
    "        start_w = row['start']\n",
    "        end_w = row['end']\n",
    "        \n",
    "        presence1 = file1[(file1['start'] >= start_w) & (file1['start'] <= end_w)].shape[0] > 0\n",
    "        classification1.append(1 if presence1 else 0)\n",
    "\n",
    "        presence2 = file2[(file2['start'] >= start_w) & (file2['start'] <= end_w)].shape[0] > 0\n",
    "        classification2.append(1 if presence2 else 0)\n",
    "        \n",
    "    \n",
    "    return classification1, classification2\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "scores = []\n",
    "for subject in subjects:\n",
    "    kappa = 0\n",
    "    windows_path = os.path.join(labels_windows_clean, f'subject_{subject}.csv')\n",
    "    windows_df = pd.read_csv(windows_path)\n",
    "    windows_df['start'] = pd.to_datetime(windows_df['start']) # change later\n",
    "    windows_df['end'] = pd.to_datetime(windows_df['end'])\n",
    "    annotator_dfs = []\n",
    "    annotator_pair = []\n",
    "    for annotator in annotators:\n",
    "        index = 0\n",
    "        path = os.path.join(clean_labels_path, f'{annotator}_subject_{subject}.csv')\n",
    "\n",
    "        if os.path.exists(path):\n",
    "            file1 = pd.read_csv(path)\n",
    "            file1['start'] = pd.to_datetime(file1['start'])\n",
    "            file1['end'] = pd.to_datetime(file1['end'])\n",
    "            annotator_pair.append(annotator)\n",
    "            annotator_dfs.append(file1)\n",
    "\n",
    "            \n",
    "    annotator1, annotator2 = get_labels_binary(windows_df, annotator_dfs[0], annotator_dfs[1])\n",
    "    with np.errstate(divide='ignore', invalid='ignore'):\n",
    "        kappa = cohen_kappa_score(np.array(annotator1), np.array(annotator2))\n",
    "    # Treat annotator1 as the ground truth and annotator2 as predictions\n",
    "    precision = precision_score(annotator1, annotator2)\n",
    "    recall = recall_score(annotator1, annotator2)\n",
    "    f1 = f1_score(annotator1, annotator2)\n",
    "    jaccard_similarity = calculate_jaccard_index(windows_df, annotator_dfs[0], annotator_dfs[1])\n",
    "  \n",
    "\n",
    "    scores.append({'Subject': subject, \n",
    "                   'Annotator pair': f'{annotator_pair[0]}/{annotator_pair[1]}', \n",
    "                   'Cohens Kappa': kappa, \n",
    "                   'Precision': precision, \n",
    "                  'Recall': recall, \n",
    "                   'F1 Score': f1,\n",
    "                  'Jaccard Index': jaccard_similarity})\n",
    "scores_df = pd.DataFrame(scores)\n",
    "print(scores_df)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7aed2cc1-9f88-4e23-99bc-d2d5afa69c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eio6ONv7+/kaS8fT0NB07djRvvfWWWbZsmYmPj7dcLjvfy2nnrGv3ZVq7zWYzn332mcO0efPmGZvNZjw9Pc0ff/zhMC2jc4ExxixZssTYbDYTFBRkVq9e7TBtx44dpmzZskaSWbVqlcO0Ll26GEmmbt266WJMSkoy3377rUNb2ucqq+83AABcjSI6AMAtZVVEL1u2rDl//ny65SZMmGAkmT59+ji0Z1VET0lJMWFhYUaS2bx5c4bzTJw40UgyI0aMcGhP+8f3a6+9lulykydPtthaRzNmzDCSzD333JPh9LNnz5rg4GDj6enpUARK+4f+O++8k631XE8RvW/fvkaS6dKlS4bLDRo0yEgyd9xxh0N72j/W69evn67wYYwxAwYMyHRfZqRt27ZGkhk+fHiG0++55x4jyTzxxBMO7S1btszwBxdjjOnUqZORZN5//317mzOPkazkVRG9SJEiJiYmJt1y8+bNM5LM7bff7tBudYx8+umnRpIJCwtz+CErzVdffWUvKF79I0Vu48hMXFyckWQ8PDwcfpS5mlURPbfbkZdF9LQfxaZMmZJpX1b9ZveVnSL6b7/9ZiSZ++67L9uxZFVEf/311zP8QSrN22+/bSSZypUrO7RXqFDBSDIffvhhumUuX75s/8HUqoju5eVlDhw4kO3tuFrae/LDDz84tKe97+XKlcvwx7e0H0QaNWqU7vyWlJRkSpQoYSQ5/BDx9NNPG0nmm2++yVWs18rs/bje83aVKlUsP2OZubqIntmrdu3aOeq3adOm6X48S0pKMqVKlTKSzNdff52tfpxxXjLGmPXr15uqVaum204PDw/TrFkzM2/evHTL5EURvVOnThku17Vr1wy/C62K6I0bNzaSzFdffZVhf19++aWRZLp27Wpv27Ztm5H+/VH777//ttyGq1FEBwDcKBjOBQBwQ2rbtq0KFy6crv3WW2+VpByPX/r7778rJiZGFStWVP369TOcJ+3W73Xr1mU43WpIlIYNG0qSJk6cqJIlS+qee+5RiRIlLGP54YcfJDne8n+1okWLqkGDBvrxxx+1adMm3XnnnYqNjdW2bdvk4eGhvn37WvadV1atWiVJlmOW9u3bV++//75++eUXpaSkqFChQg7T77nnngyHMcnp+5ecnKy1a9dmGcv3339vHys4Te/evfXLL79o9uzZevjhh+3tcXFx+uGHH+Tj46NHHnnE3u7MYyS/NGjQIMMHyOV0v6e9/w899JB8fHzSTe/SpYuKFy+uM2fOaMuWLWrevLlT4pD+N/ZwYGBguuMsK9e7HXmhZMmSkv63HTnVs2dPy2lpQzZlV9WqVeXv768ff/xRb775ph555BH7sxNyK20fW8XZt29fjRw5Uvv27VNMTIzCwsL0999/6+DBg5Lk8BlM4+3trW7dumnKlCmW661bt64qVKiQaWwxMTH64YcftHv3biUkJCg5OVmS9Oeff0qS9uzZo44dO6Zbrk2bNvL19U3XXrlyZe3YsUN33XVXuvObp6enypcvr9OnT9uHbpGkRo0a6YMPPtDzzz8vY4zuvPNOFS1aNNO4c+t6z9udOnXK8WfsWl27drXcvrR9cq39+/dryZIl2r9/v86ePWt/TkHaZ2bPnj2qVq2apH/H746Li1NQUJA6d+6co9jy8rwkSU2aNNGff/6p1atXa8mSJdq0aZO2bt2qhIQErVu3TuvWrdPixYstxyLPLavPWs+ePe1DwWXl5MmT+u233+Tn56d77703w3ky+r5bsmSJJOnuu+9WmTJlchY4AABujiI6AOCGZPWP7YCAAEnSpUuXctRfWsHmwIEDWY5RHRcXl2G71YPAWrdureeee06TJk1Sz549ZbPZVLlyZTVv3lz333+/7r33Xnl4/O9Z32mxPPbYY3rssceyFUt0dLQkKTQ0NFcPVsyptGKCVYGtYsWKkv59H06dOpXu4Wp59f6dOnXKPm9WsVxbAOnevbuGDBmin3/+WX///bfKli0rSfrss8+UlJSkBx98UMWLF7fP78xjJCtp6zPGZDpf2nSr+PJqv2f1/ttsNkVGRurMmTMZFp7y8vObkJDgsGxO5HY7cvp+ZCYt7jNnzmQr5mtlVoA7fPhwjoro/v7+ioqKUu/evfXSSy/ppZdeUmhoqJo0aaIOHTrokUceyXGBN6t9XKxYMZUoUUKnT5/W33//bS+iS/8+s8JqfVl9lrKa/uqrr+rNN9/M8KHOaRITEzNstzp+02K1mp42fvjVx/djjz2mZcuW6fPPP1fXrl1VqFAhVatWTS1atFC3bt10++23Z7odOXG95+3cnr+u9vbbb2e7n5SUFA0aNEgffvhhpp+lq9+ntIdn33LLLTl+3kRe5xWS5OHhoTZt2tgfbp6SkqL169frtdde07JlyzRnzhzdfffdeuCBB3LctxWr9zetPe3zlZlDhw7JGKOLFy9m+APj1a7+vkvb/1WrVs1uuAAA3DAoogMAbkhXF53zQmpqqiQpJCRE7du3z3Teax9GmsbPz89ymQkTJmjAgAFatGiRfv31V61du1ZRUVGKiopSw4YNtXLlShUpUsQhlg4dOqh06dKZxhIREZHpdHeV1+9fbhQpUkTdu3fXJ598orlz5+qFF16Q9L+i5LUPB3X2MZJVrJJ0/vz5TOc7d+6cJFkWHt1hv0t5G0faA3mtCp7OkFfvh/S/HwGu/sHGlbp27ap27dpp4cKF+uWXX7R27VotWLBACxYs0CuvvKJly5apZs2a+RJLZkXQrAqkmX3WvvnmG40dO1ZFixbV+++/r9tvv11hYWHy8/OTzWbTCy+8oPHjx1sWbrM6fnNyfHt4eOizzz7TCy+8oB9++EFr167V2rVrNWPGDM2YMUP33nuvFixYcN1XgOeF3J6/cmvKlCmaOXOmQkJCNHnyZDVr1kylS5e23wXwyCOP6P/+7/+y9WNVduTH+bFQoUJq0aKFFi9erEaNGmnr1q369ttvc1RET/suyq3s7K+0dRQtWtTyAbsAANxsKKIDACApPDxc0r9DK+T1rdVpypcvr8GDB2vw4MGSpE2bNunRRx/Vpk2bNHHiRL366qv2WHbv3q2+fftme/iPtCvojh07poSEBKdfjV6mTBkdOHBABw8eVI0aNdJNT7tq29fXN9Oha65XyZIl5ePjo8uXL+vgwYOqVauWZSwZ3Vreu3dvffLJJ5o9e7ZeeOEFbd26VTt27FDZsmV1xx13OMybH8eIlbT3d//+/ZnOt2/fPof5nSVtX6bt24wcOnTIYV5nSbtaNj4+PsMhKDKT2+1I278HDhyQMcayoJud9+PUqVOSlOUPZvkpMDDQ4U6Yo0ePavDgwfruu+80aNAgrV69Ott9lSlTRrt377bcxwkJCTp9+rR93qv/GxcXp/Pnz9t/tLja4cOHc7JJDr788ktJ0ptvvqn+/funm572vuWnatWqqVq1anr22WdljNGKFSv0yCOPaNGiRZo7d266H/Vyw13O29mV9j59+OGHuu+++9JNz+h9Svus7d27N9PPpqsVKlRIt99+u7Zu3aqTJ0/a2729vSVJZ8+ezXC5pKQkHTt2LNO+Dx06pNq1a6drT/vMpN11lZm07zubzaZPPvkk2z8wpO3/3bt3Z2t+AABuJO5xORIAAE6W9g/TtDFvr9WwYUMFBQVp165d9vFwna1hw4Z6+umnJUnbtm2zt991112S/ldAyI6QkBDVrl1bqamp+uSTT7K1TFb7JDNpY6FaFZPTYmjZsqU8PZ33m72np6datGiRrVjSbqe/WosWLVSlShXt27fPfneA9O/YsdcWDVxxjKRJG9Jh+fLl9iuXr3XmzBmtWLHCYX5nSXv/v/jiiwyHOFiwYIHOnDkjf39/y/Hj80pQUJDCw8NljMlx4Sa323HbbbfJ09NT8fHx9n2eka+++kpS5u/Hzp07Jcnp++l6hIeH23/ku/pcJWV9Hknbx3PmzMlwetrns3LlyvbieXh4uH3Ij//7v/9Lt8yVK1dyNEzNtdKK9hndyXPixAktW7Ys133nBZvNprZt29rHg792n+eWu5y3syuz9+nPP//McL80aNBAQUFBiouL07fffuvkCK1l52rvtGHYri5qlypVSt7e3jp9+rROnDiRbpmlS5dm+Z396aefZtg+d+5cSf87DjITFhamWrVq6ezZs/ZxzrOjQ4cOkqQff/xRMTEx2VrmenIRAADyE0V0AMBNIe0fqfv27ctwDFwvLy+NGTNGxhh17txZv/76a7p5UlJStGLFCm3YsCFH616wYIHWrFmT7hbspKQk+z9Ory4S9O/fXxEREZo/f76ee+65DK9Ii42N1UcffeTQNmbMGEnSiy++mGGBadeuXfrrr7/sf6f9Yz02NtZerMiuoUOHytPTU99++60+++wzh2k//fSTPvzwQ0nSyJEjc9RvbowYMUKSNGPGDC1fvtxh2uzZs7Vw4UJ5eXlp6NChGS6fdoXnzJkz9d///ldSxg/ec+YxkpVOnTrplltu0blz5/TYY4+lK6THx8fr0Ucf1fnz51W1alXdf//9ebr+az3wwAMqV66cYmJiNHz4cIfix6FDh+zvyeDBgzN8AGNeS/uBZP369TlaLrfbERISYn9434ABA7R3716HfpOTkzVmzBitX79evr6+lsdeQkKCdu3apaJFi6pRo0Y5it0Zfv/9d33xxRe6ePFiummLFi2SlL6gmXZutfph6YknnlBAQIC2bt2qcePGORQXf//9d73xxhuSpGeffdZhuSFDhkj697x29f5NTU3V6NGjdfTo0Zxunl3agyJnzZqlK1eu2NsTEhLUs2dPyx+qnGHu3LnasmVLuvazZ8/aHwCZV8N2udN5OzvS3qfp06c7fH8eO3ZMjz/+eIZFV09PT7344ouS/v0uXbNmTbp5Nm3alK1xwa/Hiy++qMGDB2vHjh3ppiUnJ+vDDz+0/8j20EMP2ad5eXnptttukyS99NJLDtu9fft2DRo0KMt1L1iwQPPmzXNo++qrr/T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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Interseption over Union aka Jaccard Index for start end times intersections\n",
    "def calculate_iou(labels1, labels2):\n",
    "    start1 = 0\n",
    "    start2 = 0\n",
    "    end1 = 0\n",
    "    end2 = 0\n",
    "    if len(labels1) == 1:\n",
    "        start1 = labels1.iloc[0]['start']\n",
    "        end1 = labels1.iloc[0]['end']\n",
    "    if len(labels2) == 1:\n",
    "        start2 = labels2.iloc[0]['start']\n",
    "        end2 = labels2.iloc[0]['end']\n",
    "    if start1 == 0 or start2 == 0:\n",
    "        return 0\n",
    "    intersection_start = max(start1, start2)\n",
    "    intersection_end = min(end1, end2)\n",
    "    intersection = max(0, (intersection_end - intersection_start).total_seconds())\n",
    "    union = (end1 - start1).total_seconds() + (end2 - start2).total_seconds() - intersection\n",
    "    iou = intersection / union if union != 0 else 0\n",
    "    return iou\n",
    "    \n",
    "\n",
    "all_results = []\n",
    "all_iou_values = []\n",
    "for subject in subjects:\n",
    "    results = []\n",
    "    annotator_dfs = []\n",
    "    path = os.path.join(labels_windows_clean, f\"subject_{subject}.csv\")\n",
    "    window_df = pd.read_csv(path)\n",
    "    window_df['start'] = pd.to_datetime(window_df['start'])\n",
    "    window_df['end'] = pd.to_datetime(window_df['end'])\n",
    "    \n",
    "    for annotator in annotators:\n",
    "        annotation_path = os.path.join(clean_labels_path, f\"{annotator}_subject_{subject}.csv\")\n",
    "        if os.path.exists(annotation_path):\n",
    "            df = pd.read_csv(annotation_path)\n",
    "            df['start'] = pd.to_datetime(df['start'])\n",
    "            df['end'] = pd.to_datetime(df['end'])\n",
    "            annotator_dfs.append(df)\n",
    " \n",
    "    for _, window_row in window_df.iterrows():\n",
    "        window_start = window_row['start']\n",
    "        window_end = window_row['end']\n",
    "        matching_labels_annotator1 = annotator_dfs[0][\n",
    "            (annotator_dfs[0]['start'] >= window_start) & \n",
    "            (annotator_dfs[0]['start'] <= window_end)\n",
    "        ]\n",
    "                \n",
    "        matching_labels_annotator2 = annotator_dfs[1][\n",
    "            (annotator_dfs[1]['start'] >= window_start) & \n",
    "            (annotator_dfs[1]['start'] <= window_end)\n",
    "        ]\n",
    "\n",
    "        if len(matching_labels_annotator1) > 1 or len(matching_labels_annotator2) > 1:\n",
    "            print(\"more than one label\")\n",
    "        elif len(matching_labels_annotator1) == 1 and len(matching_labels_annotator2) == 1:\n",
    "            iou = calculate_iou(matching_labels_annotator1, matching_labels_annotator2)\n",
    "            results.append({'subject': subject, \"iou\": iou})\n",
    "            all_iou_values.append(iou)\n",
    "    subject_df = pd.DataFrame(results)\n",
    "    all_results.append(subject_df)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Plotting\n",
    "fig, ax = plt.subplots()\n",
    "positions = []\n",
    "labels = []\n",
    "\n",
    "for i, subject_df in enumerate(all_results):\n",
    "    subject = subject_df['subject'].iloc[0]\n",
    "    subject_df['iou'] = (subject_df['iou'] * 100).astype(int)  \n",
    "    ax.boxplot(subject_df['iou'], positions=[i], labels=[subject])\n",
    "    positions.append(i)\n",
    "    labels.append(subject)\n",
    "\n",
    "ax.set_xlabel('Subject')\n",
    "ax.set_ylabel('Percent')\n",
    "ax.set_xticks(positions)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.title('Intersection over Union (IoU) for each subject in percent')\n",
    "plot_filename = 'iou_boxplot.png'\n",
    "plt.savefig(plot_filename)\n",
    "plt.show()\n",
    "\n",
    "#histogramm\n",
    "combined_results_df = pd.concat(all_results, ignore_index=True)\n",
    "\n",
    "all_ious = combined_results_df['iou'] * 100\n",
    "bin_width = 5\n",
    "bins = np.arange(0, 105, bin_width)  \n",
    "y_max = 0\n",
    "for subject_df in all_results:\n",
    "    counts, _ = np.histogram(subject_df['iou'], bins=bins)\n",
    "    y_max = max(y_max, max(counts))\n",
    "\n",
    "fig, axes = plt.subplots(3, 2, figsize=(15, 10))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, subject_df in enumerate(all_results):\n",
    "    subject = subject_df['subject'].iloc[0]\n",
    "    axes[i].hist(subject_df['iou'], bins=bins, color='skyblue', edgecolor='black', alpha=0.7)\n",
    "    axes[i].set_title(f'Subject: {subject}')\n",
    "    axes[i].set_xlim(0, 100)\n",
    "    axes[i].set_ylim(0, y_max+20)\n",
    "    axes[i].set_xlabel('IoU (%)')\n",
    "    axes[i].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.suptitle('Intersection over Union (IoU) Histograms for Each Subject', fontsize=16)\n",
    "plot_filename = 'iou_histograms.png'\n",
    "plt.savefig(plot_filename)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9492621-a074-49c8-add7-f6eda5c2aeb3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subject 01\n",
      "{'pp': 35, 'pn': 16, 'np': 94, 'nn': 23}\n",
      "subject 03\n",
      "{'pp': 40, 'pn': 46, 'np': 53, 'nn': 59}\n",
      "subject 04\n",
      "{'pp': 37, 'pn': 44, 'np': 16, 'nn': 14}\n",
      "subject 18\n",
      "{'pp': 17, 'pn': 208, 'np': 3, 'nn': 34}\n",
      "subject 20\n",
      "{'pp': 141, 'pn': 2, 'np': 168, 'nn': 10}\n",
      "subject 30\n",
      "{'pp': 14, 'pn': 54, 'np': 4, 'nn': 42}\n",
      "  subject  start_rmse  start_mean  start_tsd  start_variance   end_rmse  \\\n",
      "0      01   87.197918   52.613214  69.536513     4835.326575  37.059595   \n",
      "1      03   16.644243    9.386667  13.744865      188.921317  11.353557   \n",
      "2      04   34.193319   16.561802  29.914709      894.889799  17.918302   \n",
      "3      18   27.343972   15.043053  22.834171      521.399362  17.828431   \n",
      "4      20   18.786780    7.024735  17.424012      303.596179   5.788664   \n",
      "5      30   68.604662   34.313333  59.407027     3529.194826  16.417790   \n",
      "\n",
      "    end_mean    end_std  end_variance  \n",
      "0  16.908214  32.977657   1087.525868  \n",
      "1   5.548283   9.905545     98.119818  \n",
      "2   7.615856  16.219257    263.064289  \n",
      "3   7.824580  16.019641    256.628904  \n",
      "4   3.931589   4.248675     18.051240  \n",
      "5   7.749298  14.473845    209.492198  \n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"\\ncombined_results_df_new = combined_results_df\\ncombined_results_df_new['start'] = abs(combined_results_df_new['start'])\\ncombined_results_df_new['end'] = abs(combined_results_df_new['end'])\\n#histogramms\\n\\nall_start_times =  combined_results_df_new['start']\\nall_end_times = combined_results_df_new['end']\\n# Calculate the range of start and end times across all subjects\\nstart_range = np.ptp(all_start_times)\\nend_range = np.ptp(all_end_times)\\n\\n# Calculate the bin width based on the range and number of bins\\nbin_width = max(start_range, end_range) / 20\\n\\n# Plotting\\nfig, axes = plt.subplots(len(subjects), 2, figsize=(15, 20), sharex='col')\\n\\nfor i, subject in enumerate(subjects):\\n    subject_df = combined_results_df_new[combined_results_df_new['subject'] == subject]\\n    \\n    axes[i, 0].hist(subject_df['start'], bins=np.arange(min(all_start_times), max(all_start_times) + bin_width, bin_width), color='skyblue', edgecolor='black', alpha=0.7)\\n    axes[i, 0].set_title(f'Subject: {subject} Start Time')\\n    axes[i, 0].set_ylabel('Frequency')\\n    axes[i, 0].set_ylim(0, max_freq+100)\\n    \\n    axes[i, 1].hist(subject_df['end'], bins=np.arange(min(all_end_times), max(all_end_times) + bin_width, bin_width), color='salmon', edgecolor='black', alpha=0.7)\\n    axes[i, 1].set_title(f'Subject: {subject} End Time')\\n    axes[i, 1].set_ylabel('Frequency')\\n    axes[i, 1].set_ylim(0, max_freq+100)\\nplt.tight_layout()\\nplt.savefig('hisrogramm_distances.png')\\nplt.show()\\n\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate time differences between pairs of annotations\n",
    "def get_cont_matrix(df, subject):\n",
    "\n",
    "    contigency_matrix = {\n",
    "    'pp': 0,\n",
    "    'pn': 0,\n",
    "    'np': 0,\n",
    "    'nn': 0\n",
    "    }\n",
    "\n",
    "    for index, row in df.iterrows():\n",
    "        if row['start']>0 and row['end']>0:\n",
    "            contigency_matrix['pp'] += 1\n",
    "        elif row['start']>0 and row['end']<0:\n",
    "            contigency_matrix['pn'] += 1\n",
    "        elif row['start']<0 and row['end']>0:\n",
    "            contigency_matrix['np'] += 1\n",
    "        else:\n",
    "            contigency_matrix['nn'] += 1\n",
    "    print('subject', subject)\n",
    "    print(contigency_matrix)\n",
    "def calculate_rmse(differences):\n",
    "    return np.sqrt(np.mean(np.square(differences)))\n",
    "    \n",
    "def calculate_differences(annotator1, annotator2):\n",
    "    \n",
    "    start = (annotator2['start'] - annotator1['start']).total_seconds()\n",
    "    end = (annotator2['end'] - annotator1['end']).total_seconds()\n",
    "    \n",
    "    return start, end\n",
    "def calculate_statistics(differences):\n",
    "    new_diff = abs(differences)\n",
    "    mean = np.mean(new_diff)\n",
    "    variance = np.var(new_diff)\n",
    "    std = np.std(new_diff)\n",
    "    rmse = np.sqrt(np.mean(np.square(new_diff)))\n",
    "    return rmse, mean, std, variance\n",
    "\n",
    "all_results = []\n",
    "max_freq = 0\n",
    "for subject in subjects:\n",
    "    results = []\n",
    "    annotator_dfs = []\n",
    "    path = os.path.join(labels_windows_clean, f\"subject_{subject}.csv\")\n",
    "    window_df = pd.read_csv(path)\n",
    "    window_df['start'] = pd.to_datetime(window_df['start'])\n",
    "    window_df['end'] = pd.to_datetime(window_df['end'])\n",
    "    \n",
    "    for annotator in annotators:\n",
    "        annotation_path = os.path.join(clean_labels_path, f\"{annotator}_subject_{subject}.csv\")\n",
    "        if os.path.exists(annotation_path):\n",
    "            df = pd.read_csv(annotation_path)\n",
    "            df['start'] = pd.to_datetime(df['start'])\n",
    "            df['end'] = pd.to_datetime(df['end'])\n",
    "            annotator_dfs.append(df)\n",
    " \n",
    "    for _, window_row in window_df.iterrows():\n",
    "        window_start = window_row['start']\n",
    "        window_end = window_row['end']\n",
    "        matching_labels_annotator1 = annotator_dfs[0][\n",
    "            (annotator_dfs[0]['start'] >= window_start) & \n",
    "            (annotator_dfs[0]['start'] <= window_end)\n",
    "        ]\n",
    "                \n",
    "        matching_labels_annotator2 = annotator_dfs[1][\n",
    "            (annotator_dfs[1]['start'] >= window_start) & \n",
    "            (annotator_dfs[1]['start'] <= window_end)\n",
    "        ]\n",
    "\n",
    "        if len(matching_labels_annotator1) > 1 or len(matching_labels_annotator2) > 1:\n",
    "            print(\"more than one label\")\n",
    "        elif len(matching_labels_annotator1) == 1 and len(matching_labels_annotator2) == 1:\n",
    "            start, end = calculate_differences(matching_labels_annotator1.iloc[0], matching_labels_annotator2.iloc[0])\n",
    "            #temp\n",
    "            \n",
    "            results.append({'subject': subject, \"start\": start, \"end\": end})\n",
    "            \n",
    "    \n",
    "    \n",
    "    subject_df = pd.DataFrame(results)\n",
    "    get_cont_matrix(subject_df, subject)\n",
    "    all_results.append(subject_df)\n",
    "\n",
    "    # Calculate the maximum frequency for each subject\n",
    "    start_hist, _ = np.histogram(subject_df['start'], bins=20)\n",
    "    end_hist, _ = np.histogram(subject_df['end'], bins=20)\n",
    "    max_freq = max(max_freq, max(start_hist), max(end_hist))\n",
    "    \n",
    "combined_results_df = pd.concat(all_results, ignore_index=True)\n",
    "\n",
    "statistics_results = []\n",
    "for subject in subjects:\n",
    "    subject_df = combined_results_df[combined_results_df['subject'] == subject]\n",
    "    start_rmse, start_mean, start_std, start_variance = calculate_statistics(subject_df['start'])\n",
    "    end_rmse, end_mean, end_std, end_variance = calculate_statistics(subject_df['end'])\n",
    "    statistics_results.append({\n",
    "        'subject': subject,\n",
    "        'start_rmse': start_rmse, 'start_mean': start_mean, 'start_tsd': start_std, 'start_variance': start_variance,\n",
    "        'end_rmse': end_rmse, 'end_mean': end_mean, 'end_std': end_std, 'end_variance': end_variance\n",
    "    })\n",
    "\n",
    "statistics_df = pd.DataFrame(statistics_results)\n",
    "print(statistics_df)\n",
    "\n",
    "# Plotting\n",
    "rows = 3\n",
    "cols = 2\n",
    "fig, axes = plt.subplots(rows, cols, figsize=(15, 10), sharex=True, sharey=True)\n",
    "fig.suptitle('Start and End Time Differences Across Subjects', fontsize=16)\n",
    "\n",
    "all_start_diffs = combined_results_df['start'].abs() \n",
    "all_end_diffs = combined_results_df['end'].abs()  \n",
    "x_limits = (0, max(all_start_diffs) + 10)  \n",
    "y_limits = (0, max(all_end_diffs) + 10)\n",
    " \n",
    "\n",
    "for i, subject in enumerate(subjects):\n",
    "    row = i // cols\n",
    "    col = i % cols\n",
    "    subject_df = combined_results_df[combined_results_df['subject'] == subject]\n",
    "    \n",
    "    axes[row, col].scatter(abs(subject_df['start']), abs(subject_df['end']))\n",
    "    axes[row, col].set_title(f'Subject: {subject}')\n",
    "    axes[row, col].set_xlim(x_limits)\n",
    "    axes[row, col].set_ylim(y_limits)\n",
    "    #axes[row, col].legend()\n",
    "     # Set major and minor ticks\n",
    "plt.subplots_adjust(hspace=200, wspace=400)\n",
    "for ax in axes.flat:\n",
    "    ax.label_outer()   \n",
    "    ax.set_xlabel('Start Time Difference (s)')\n",
    "    ax.set_ylabel('End Time Difference (s)')\n",
    "    ax.xaxis.set_tick_params(which='both', labelbottom=True)\n",
    "    ax.yaxis.set_tick_params(which='both', labelleft=True)\n",
    "    \n",
    "\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])  \n",
    "plt.savefig('start_end_time_differences.png')\n",
    "plt.show()\n",
    "'''\n",
    "combined_results_df_new = combined_results_df\n",
    "combined_results_df_new['start'] = abs(combined_results_df_new['start'])\n",
    "combined_results_df_new['end'] = abs(combined_results_df_new['end'])\n",
    "#histogramms\n",
    "\n",
    "all_start_times =  combined_results_df_new['start']\n",
    "all_end_times = combined_results_df_new['end']\n",
    "# Calculate the range of start and end times across all subjects\n",
    "start_range = np.ptp(all_start_times)\n",
    "end_range = np.ptp(all_end_times)\n",
    "\n",
    "# Calculate the bin width based on the range and number of bins\n",
    "bin_width = max(start_range, end_range) / 20\n",
    "\n",
    "# Plotting\n",
    "fig, axes = plt.subplots(len(subjects), 2, figsize=(15, 20), sharex='col')\n",
    "\n",
    "for i, subject in enumerate(subjects):\n",
    "    subject_df = combined_results_df_new[combined_results_df_new['subject'] == subject]\n",
    "    \n",
    "    axes[i, 0].hist(subject_df['start'], bins=np.arange(min(all_start_times), max(all_start_times) + bin_width, bin_width), color='skyblue', edgecolor='black', alpha=0.7)\n",
    "    axes[i, 0].set_title(f'Subject: {subject} Start Time')\n",
    "    axes[i, 0].set_ylabel('Frequency')\n",
    "    axes[i, 0].set_ylim(0, max_freq+100)\n",
    "    \n",
    "    axes[i, 1].hist(subject_df['end'], bins=np.arange(min(all_end_times), max(all_end_times) + bin_width, bin_width), color='salmon', edgecolor='black', alpha=0.7)\n",
    "    axes[i, 1].set_title(f'Subject: {subject} End Time')\n",
    "    axes[i, 1].set_ylabel('Frequency')\n",
    "    axes[i, 1].set_ylim(0, max_freq+100)\n",
    "plt.tight_layout()\n",
    "plt.savefig('hisrogramm_distances.png')\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d543dd0-bbd0-450a-bb00-ce569ae2b64e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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