model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from Book_module.Book import Book\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  Story Perpetrator  Perp_Mention_Count  \\\n",
      "0                book_secret_garden.txt    Valentin                  50   \n",
      "1                book_eye_of_apollo.txt       Kevin                  18   \n",
      "2            book_soul_of_schoolboy.txt       Morty                  13   \n",
      "3  book_the_sins_of_prince_saradine.txt    Saradine                  33   \n",
      "4     book_sign_of_the_broken_sword.txt      Arthur                  29   \n",
      "\n",
      "   Perp_First_Mention  Perp_Sentiment  Perp_Rank  Total_Characters  \\\n",
      "0                   2        0.052329        1.0                10   \n",
      "1                 166        0.160821        4.0                 6   \n",
      "2                 532        0.072831        4.0                 6   \n",
      "3                   2        0.029970        2.0                 7   \n",
      "4                 350       -0.017970        1.0                 7   \n",
      "\n",
      "   Avg_Mention_Count  Crime_First_Introduction  Perp_Introduced_Before_Crime  \\\n",
      "0          21.700000                         1                         False   \n",
      "1          20.166667                        33                         False   \n",
      "2          13.000000                        79                         False   \n",
      "3          21.142857                        81                          True   \n",
      "4          15.000000                        11                         False   \n",
      "\n",
      "   Perp_Proximity_Score  \n",
      "0                    38  \n",
      "1                    30  \n",
      "2                     5  \n",
      "3                    24  \n",
      "4                    56  \n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "stories_folder = 'books'\n",
    "story_files = [f for f in os.listdir(stories_folder) if f.endswith('.txt')]\n",
    "\n",
    "#Dictionary mapping story filenames to perpetrators\n",
    "story_perpetrators = {\n",
    "    'book_bottomless_well.txt': 'Harrison',\n",
    "    'book_eye_of_apollo.txt': 'Kevin',\n",
    "    'book_face_in_the_target.txt' : 'Jenkins',\n",
    "    'book_fad_of_the_fisherman.txt' : 'Merivale',\n",
    "    'book_hole_in_the_wall.txt' : 'Haddow',\n",
    "    'book_secret_garden.txt' : 'Valentin',\n",
    "    'book_sign_of_the_broken_sword.txt' : 'Arthur',\n",
    "    'book_soul_of_schoolboy.txt' : 'Morty',\n",
    "    'book_temple_of_silence.txt' : 'Verner',\n",
    "    'book_the_blue_cross.txt' : 'Hercule',\n",
    "    'book_the_flying_stars.txt' : 'Hercule',\n",
    "    'book_the_hammer_of_god.txt' : 'Wilfred',\n",
    "    'book_the_queer_feet.txt' : 'Hercule',\n",
    "    'book_the_sins_of_prince_saradine.txt' : 'Saradine',\n",
    "    'book_the_wrong_shape.txt' : 'Harris',\n",
    "    'book_three_tools_of_death.txt' : 'Aaron',\n",
    "    'book_vanishing_prince.txt' : 'Wilson',\n",
    "    'book_vengeance_of_the_statue.txt' : 'Horne'\n",
    "}\n",
    "\n",
    "for story_file in story_files:\n",
    "    file_path = os.path.join(stories_folder, story_file)\n",
    "    book = Book(file_path)\n",
    "    book.pre_process()\n",
    "    book.feature_extraction()\n",
    "    \n",
    "    #Get the perp for this story\n",
    "    perpetrator = story_perpetrators.get(story_file)\n",
    "    if perpetrator is None:\n",
    "        print(f\"No perpetrator found for {story_file}. Skipping.\")\n",
    "        continue\n",
    "    \n",
    "    #Features related to the perp\n",
    "    perp_mention_count = book.character_mentions_all.get(perpetrator, 0)\n",
    "    perp_first_mention = book.character_mentions_first.get(perpetrator, -1)\n",
    "    perp_sentiment = book.character_sentiments.get(perpetrator, 0)\n",
    "    \n",
    "    #Rank of the perp by mentions\n",
    "    mentions_sorted = sorted(book.character_mentions_all.items(), key=lambda x: x[1], reverse=True)\n",
    "    perp_rank = next((rank for rank, (name, _) in enumerate(mentions_sorted, 1) if name == perpetrator), None)\n",
    "    \n",
    "    #Total number of characters\n",
    "    total_characters = len(book.names)\n",
    "    \n",
    "    #Average character mention count\n",
    "    avg_mention_count = sum(book.character_mentions_all.values()) / total_characters if total_characters > 0 else 0\n",
    "    \n",
    "    #Crime first introduction\n",
    "    crime_first_intro = book.crime_first_introduction\n",
    "    \n",
    "    #Is the perp introduced before the crime\n",
    "    perp_introduced_before_crime = perp_first_mention < crime_first_intro if crime_first_intro != -1 else False\n",
    "    \n",
    "    #Proximity features\n",
    "    perp_proximity_score = sum(count for (char1, char2), count in book.character_proximity.items() if perpetrator in (char1, char2))\n",
    "    \n",
    "    #Prepare the row data\n",
    "    row = {\n",
    "        'Story': story_file,\n",
    "        'Perpetrator': perpetrator,\n",
    "        'Perp_Mention_Count': perp_mention_count,\n",
    "        'Perp_First_Mention': perp_first_mention,\n",
    "        'Perp_Sentiment': perp_sentiment,\n",
    "        'Perp_Rank': perp_rank,\n",
    "        'Total_Characters': total_characters,\n",
    "        'Avg_Mention_Count': avg_mention_count,\n",
    "        'Crime_First_Introduction': crime_first_intro,\n",
    "        'Perp_Introduced_Before_Crime': perp_introduced_before_crime,\n",
    "        'Perp_Proximity_Score': perp_proximity_score,\n",
    "    }\n",
    "    \n",
    "    data.append(row)\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Story Character  Label  Mention_Count  First_Mention  \\\n",
      "0  book_secret_garden.txt      Ivan      0             25             15   \n",
      "1  book_secret_garden.txt  Valentin      1             50              2   \n",
      "2  book_secret_garden.txt      Gina      0              6            240   \n",
      "3  book_secret_garden.txt     Simon      0             23            271   \n",
      "4  book_secret_garden.txt      Paul      0             24            292   \n",
      "\n",
      "   Sentiment  Rank  Proximity_Score  First_Mention_Exposition  \\\n",
      "0  -0.014336     4               57                         0   \n",
      "1   0.052329     1               38                         0   \n",
      "2   0.172943     9               29                         0   \n",
      "3  -0.081121     6               49                         0   \n",
      "4  -0.055629     5               52                         0   \n",
      "\n",
      "   First_Mention_Rising_Action  First_Mention_Climax  \\\n",
      "0                            1                     0   \n",
      "1                            1                     0   \n",
      "2                            0                     0   \n",
      "3                            0                     0   \n",
      "4                            0                     0   \n",
      "\n",
      "   First_Mention_Falling_Action  First_Mention_Resolution  \\\n",
      "0                             0                         0   \n",
      "1                             0                         0   \n",
      "2                             1                         0   \n",
      "3                             1                         0   \n",
      "4                             1                         0   \n",
      "\n",
      "   First_Mention_Unknown  Mentions_Exposition_Proportion  \\\n",
      "0                      0                             0.0   \n",
      "1                      0                             0.0   \n",
      "2                      0                             0.0   \n",
      "3                      0                             0.0   \n",
      "4                      0                             0.0   \n",
      "\n",
      "   Mentions_Rising Action_Proportion  Mentions_Climax_Proportion  \\\n",
      "0                           0.640000                         0.0   \n",
      "1                           0.591837                         0.0   \n",
      "2                           1.000000                         0.0   \n",
      "3                           0.458333                         0.0   \n",
      "4                           0.166667                         0.0   \n",
      "\n",
      "   Mentions_Falling Action_Proportion  Mentions_Resolution_Proportion  \\\n",
      "0                            0.320000                        0.000000   \n",
      "1                            0.306122                        0.000000   \n",
      "2                            0.000000                        0.000000   \n",
      "3                            0.250000                        0.291667   \n",
      "4                            0.500000                        0.208333   \n",
      "\n",
      "   Mentions_Unknown_Proportion  \n",
      "0                     0.040000  \n",
      "1                     0.102041  \n",
      "2                     0.000000  \n",
      "3                     0.000000  \n",
      "4                     0.125000  \n"
     ]
    }
   ],
   "source": [
    "character_data = []\n",
    "\n",
    "for story_file in story_files:\n",
    "    file_path = os.path.join(stories_folder, story_file)\n",
    "    book = Book(file_path)\n",
    "    book.pre_process()\n",
    "    book.feature_extraction()\n",
    "    \n",
    "    #Get the perp for this story\n",
    "    perpetrator = story_perpetrators.get(story_file)\n",
    "    if perpetrator is None:\n",
    "        print(f\"No perpetrator found for {story_file}. Skipping.\")\n",
    "        continue\n",
    "    \n",
    "    for character in book.names:\n",
    "        #Character-specific features\n",
    "        mention_count = book.character_mentions_all.get(character, 0)\n",
    "        first_mention = book.character_mentions_first.get(character, -1)\n",
    "        sentiment = book.character_sentiments.get(character, 0)\n",
    "        \n",
    "        #Rank of the character by mentions\n",
    "        mentions_sorted = sorted(book.character_mentions_all.items(), key=lambda x: x[1], reverse=True)\n",
    "        char_rank = next((rank for rank, (name, _) in enumerate(mentions_sorted, 1) if name == character), None)\n",
    "        \n",
    "        #Proximity features\n",
    "        proximity_score = sum(count for (char1, char2), count in book.character_proximity.items() if character in (char1, char2))\n",
    "        \n",
    "        #Label 1 if this character is the perp, 0 otherwise\n",
    "        label = 1 if character == perpetrator else 0\n",
    "\n",
    "        #Plot phase of First Mention\n",
    "        first_mention_phase = None\n",
    "        for phase, (start_idx, end_idx) in book.plot_structure.items():\n",
    "            if first_mention >= start_idx and first_mention <= end_idx:\n",
    "                first_mention_phase = phase\n",
    "                break\n",
    "        if first_mention_phase is None:\n",
    "            first_mention_phase = 'Unknown'\n",
    "\n",
    "        #One-hot encode the plot phase\n",
    "        plot_phase_features = {\n",
    "            'First_Mention_Exposition': int(first_mention_phase == 'Exposition'),\n",
    "            'First_Mention_Rising_Action': int(first_mention_phase == 'Rising Action'),\n",
    "            'First_Mention_Climax': int(first_mention_phase == 'Climax'),\n",
    "            'First_Mention_Falling_Action': int(first_mention_phase == 'Falling Action'),\n",
    "            'First_Mention_Resolution': int(first_mention_phase == 'Resolution'),\n",
    "            'First_Mention_Unknown': int(first_mention_phase == 'Unknown'),\n",
    "        }\n",
    "\n",
    "        #Mentions per plot phase\n",
    "        mentions_per_phase = {\n",
    "            'Exposition': 0,\n",
    "            'Rising Action': 0,\n",
    "            'Climax': 0,\n",
    "            'Falling Action': 0,\n",
    "            'Resolution': 0,\n",
    "            'Unknown': 0,\n",
    "        }\n",
    "\n",
    "        #Map sentence indices to plot phases\n",
    "        sentence_plot_phases = {}\n",
    "        for phase, (start_idx, end_idx) in book.plot_structure.items():\n",
    "            for idx in range(start_idx, end_idx + 1):\n",
    "                sentence_plot_phases[idx] = phase\n",
    "\n",
    "        #Count mentions per plot phase\n",
    "        for idx, sentence in enumerate(book.sentences):\n",
    "            phase = sentence_plot_phases.get(idx, 'Unknown')\n",
    "            if character in sentence:\n",
    "                mentions_per_phase[phase] += 1\n",
    "\n",
    "        #Add mentions per phase to the features\n",
    "        total_mentions = sum(mentions_per_phase.values())\n",
    "        if total_mentions > 0:\n",
    "            mentions_proportion = {\n",
    "                f'Mentions_{phase}_Proportion': count / total_mentions\n",
    "                for phase, count in mentions_per_phase.items()\n",
    "            }\n",
    "        else:\n",
    "            mentions_proportion = {\n",
    "                f'Mentions_{phase}_Proportion': 0\n",
    "                for phase in mentions_per_phase.keys()\n",
    "            }\n",
    "        \n",
    "        #Prepare the row data\n",
    "        row = {\n",
    "            'Story': story_file,\n",
    "            'Character': character,\n",
    "            'Label': label,\n",
    "            'Mention_Count': mention_count,\n",
    "            'First_Mention': first_mention,\n",
    "            'Sentiment': sentiment,\n",
    "            'Rank': char_rank,\n",
    "            'Proximity_Score': proximity_score,\n",
    "        }\n",
    "\n",
    "        #Add the plot phase features\n",
    "        row.update(plot_phase_features)\n",
    "        row.update(mentions_proportion)\n",
    "        \n",
    "        character_data.append(row)\n",
    "\n",
    "#Create the df\n",
    "character_df = pd.DataFrame(character_data)\n",
    "character_df.fillna(0, inplace=True)\n",
    "print(character_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.90      0.90        20\n",
      "           1       0.33      0.33      0.33         3\n",
      "\n",
      "    accuracy                           0.83        23\n",
      "   macro avg       0.62      0.62      0.62        23\n",
      "weighted avg       0.83      0.83      0.83        23\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Select features and target\n",
    "features = [\n",
    "    'Mention_Count', 'First_Mention', 'Sentiment', 'Rank', 'Proximity_Score',\n",
    "    'First_Mention_Exposition', 'First_Mention_Rising_Action', 'First_Mention_Climax',\n",
    "    'First_Mention_Falling_Action', 'First_Mention_Resolution', 'First_Mention_Unknown',\n",
    "    'Mentions_Exposition_Proportion', 'Mentions_Rising Action_Proportion',\n",
    "    'Mentions_Climax_Proportion', 'Mentions_Falling Action_Proportion',\n",
    "    'Mentions_Resolution_Proportion', 'Mentions_Unknown_Proportion',\n",
    "]\n",
    "\n",
    "x = character_df[features]\n",
    "y = character_df['Label']\n",
    "x = x.fillna(0)\n",
    "\n",
    "#Split the data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, stratify=y, random_state=1)\n",
    "\n",
    "#Apply SMOTE to training data to try to make perp to non-perp even\n",
    "smote = SMOTE(random_state=42)\n",
    "x_resampled, y_resampled = smote.fit_resample(x_train, y_train)\n",
    "\n",
    "#Initialize the model\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42, class_weight='balanced')\n",
    "\n",
    "#Train the model\n",
    "model.fit(x_resampled, y_resampled)\n",
    "\n",
    "#Predict on the test set\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Testing to see how the model does on a book in the training set\n",
    "new_story_path = 'books/book_bottomless_well.txt'\n",
    "new_book = Book(new_story_path)\n",
    "\n",
    "#Process the book\n",
    "new_book.pre_process()\n",
    "new_book.feature_extraction()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract features\n",
    "new_character_data = []\n",
    "for character in new_book.names:\n",
    "    #Character-specific features\n",
    "    mention_count = new_book.character_mentions_all.get(character, 0)\n",
    "    first_mention = new_book.character_mentions_first.get(character, -1)\n",
    "    sentiment = new_book.character_sentiments.get(character, 0)\n",
    "\n",
    "    #Rank of the character by mentions\n",
    "    mentions_sorted = sorted(new_book.character_mentions_all.items(), key=lambda x: x[1], reverse=True)\n",
    "    char_rank = next((rank for rank, (name, _) in enumerate(mentions_sorted, 1) if name == character), None)\n",
    "\n",
    "    #Proximity features\n",
    "    proximity_score = sum(count for (char1, char2), count in new_book.character_proximity.items() if character in (char1, char2))\n",
    "\n",
    "    #Prepare the row data\n",
    "    row = {\n",
    "        'Character': character,\n",
    "        'Mention_Count': mention_count,\n",
    "        'First_Mention': first_mention,\n",
    "        'Sentiment': sentiment,\n",
    "        'Rank': char_rank,\n",
    "        'Proximity_Score': proximity_score,\n",
    "    }\n",
    "\n",
    "    #Plot phase of first mention\n",
    "    first_mention_phase = None\n",
    "    if hasattr(new_book, 'plot_structure'):\n",
    "        for phase, (start_idx, end_idx) in new_book.plot_structure.items():\n",
    "            if first_mention >= start_idx and first_mention <= end_idx:\n",
    "                first_mention_phase = phase\n",
    "                break\n",
    "    if first_mention_phase is None:\n",
    "        first_mention_phase = 'Unknown'\n",
    "\n",
    "    #One-hot encode the plot phase\n",
    "    plot_phase_features = {\n",
    "        'First_Mention_Exposition': int(first_mention_phase == 'Exposition'),\n",
    "        'First_Mention_Rising_Action': int(first_mention_phase == 'Rising Action'),\n",
    "        'First_Mention_Climax': int(first_mention_phase == 'Climax'),\n",
    "        'First_Mention_Falling_Action': int(first_mention_phase == 'Falling Action'),\n",
    "        'First_Mention_Resolution': int(first_mention_phase == 'Resolution'),\n",
    "        'First_Mention_Unknown': int(first_mention_phase == 'Unknown'),\n",
    "    }\n",
    "\n",
    "    #Mentions per plot phase\n",
    "    mentions_per_phase = {\n",
    "        'Exposition': 0,\n",
    "        'Rising Action': 0,\n",
    "        'Climax': 0,\n",
    "        'Falling Action': 0,\n",
    "        'Resolution': 0,\n",
    "        'Unknown': 0,\n",
    "    }\n",
    "\n",
    "    #Map sentence indices to plot phases\n",
    "    sentence_plot_phases = {}\n",
    "    if hasattr(new_book, 'plot_structure'):\n",
    "        for phase, (start_idx, end_idx) in new_book.plot_structure.items():\n",
    "            for idx in range(start_idx, end_idx + 1):\n",
    "                sentence_plot_phases[idx] = phase\n",
    "    else:\n",
    "        # If plot_structure is not available, assign 'Unknown' to all sentences\n",
    "        for idx in range(len(new_book.sentences)):\n",
    "            sentence_plot_phases[idx] = 'Unknown'\n",
    "\n",
    "    #Count mentions per plot phase\n",
    "    for idx, sentence in enumerate(new_book.sentences):\n",
    "        phase = sentence_plot_phases.get(idx, 'Unknown')\n",
    "        if character in sentence:\n",
    "            mentions_per_phase[phase] += 1\n",
    "\n",
    "    #Add mentions per phase to the features\n",
    "    total_mentions = sum(mentions_per_phase.values())\n",
    "    if total_mentions > 0:\n",
    "        mentions_proportion = {\n",
    "            f'Mentions_{phase}_Proportion': mentions_per_phase[phase] / total_mentions\n",
    "            for phase in mentions_per_phase.keys()\n",
    "        }\n",
    "    else:\n",
    "        mentions_proportion = {\n",
    "            f'Mentions_{phase}_Proportion': 0\n",
    "            for phase in mentions_per_phase.keys()\n",
    "        }\n",
    "\n",
    "    #Add the plot phase features\n",
    "    row.update(plot_phase_features)\n",
    "    row.update(mentions_proportion)\n",
    "\n",
    "    new_character_data.append(row)\n",
    "\n",
    "new_character_df = pd.DataFrame(new_character_data)\n",
    "\n",
    "features = [\n",
    "    'Mention_Count', 'First_Mention', 'Sentiment', 'Rank', 'Proximity_Score',\n",
    "    'First_Mention_Exposition', 'First_Mention_Rising_Action', 'First_Mention_Climax',\n",
    "    'First_Mention_Falling_Action', 'First_Mention_Resolution', 'First_Mention_Unknown',\n",
    "    'Mentions_Exposition_Proportion', 'Mentions_Rising Action_Proportion',\n",
    "    'Mentions_Climax_Proportion', 'Mentions_Falling Action_Proportion',\n",
    "    'Mentions_Resolution_Proportion', 'Mentions_Unknown_Proportion',\n",
    "]\n",
    "\n",
    "#Handle missing features\n",
    "for feature in features:\n",
    "    if feature not in new_character_df.columns:\n",
    "        new_character_df[feature] = 0  # or appropriate default value\n",
    "\n",
    "x_new = new_character_df[features].fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Character  Perpetrator_Probability  Predicted_Label\n",
      "0     Boyle                     0.12                0\n",
      "1  Harrison                     0.93                1\n",
      "2     Horne                     0.11                0\n",
      "3       Tom                     0.10                0\n",
      "4    Grayne                     0.06                0\n"
     ]
    }
   ],
   "source": [
    "y_pred_proba = model.predict_proba(x_new)\n",
    "\n",
    "#Get the probability of each character being the perp\n",
    "new_character_df['Perpetrator_Probability'] = y_pred_proba[:, 1]\n",
    "\n",
    "#Predict class labels\n",
    "y_pred = model.predict(x_new)\n",
    "new_character_df['Predicted_Label'] = y_pred\n",
    "\n",
    "print(new_character_df[['Character', 'Perpetrator_Probability', 'Predicted_Label']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Character  Perpetrator_Probability  Predicted_Label\n",
      "1  Harrison                     0.93                1\n",
      "0     Boyle                     0.12                0\n",
      "2     Horne                     0.11                0\n",
      "3       Tom                     0.10                0\n",
      "4    Grayne                     0.06                0\n",
      "Actual Perpetrator: Harrison\n",
      "Model's Predicted Probability: 0.93\n",
      "Model's Predicted Label: 1\n"
     ]
    }
   ],
   "source": [
    "#Sort characters by predicted probability\n",
    "sorted_predictions = new_character_df.sort_values(by='Perpetrator_Probability', ascending=False)\n",
    "print(sorted_predictions[['Character', 'Perpetrator_Probability', 'Predicted_Label']])\n",
    "actual_perpetrator = 'Harrison'\n",
    "\n",
    "#Check if the model predicted correctly\n",
    "if actual_perpetrator in sorted_predictions['Character'].values:\n",
    "    predicted_prob = sorted_predictions.loc[sorted_predictions['Character'] == actual_perpetrator, 'Perpetrator_Probability'].values[0]\n",
    "    predicted_label = sorted_predictions.loc[sorted_predictions['Character'] == actual_perpetrator, 'Predicted_Label'].values[0]\n",
    "    print(f\"Actual Perpetrator: {actual_perpetrator}\")\n",
    "    print(f\"Model's Predicted Probability: {predicted_prob}\")\n",
    "    print(f\"Model's Predicted Label: {predicted_label}\")\n",
    "else:\n",
    "    print(f\"Actual Perpetrator {actual_perpetrator} not found among extracted characters.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Positives:\n",
      "Empty DataFrame\n",
      "Columns: [Character, Perpetrator_Probability]\n",
      "Index: []\n",
      "Actual Perpetrator's Prediction:\n",
      "  Character  Perpetrator_Probability  Predicted_Label\n",
      "1  Harrison                     0.93                1\n"
     ]
    }
   ],
   "source": [
    "#Identify false positives\n",
    "false_positives = sorted_predictions[(sorted_predictions['Predicted_Label'] == 1) & (sorted_predictions['Character'] != actual_perpetrator)]\n",
    "print(\"False Positives:\")\n",
    "print(false_positives[['Character', 'Perpetrator_Probability']])\n",
    "\n",
    "#Identify the actual perp's prediction\n",
    "actual_perp_prediction = sorted_predictions[sorted_predictions['Character'] == actual_perpetrator]\n",
    "print(\"Actual Perpetrator's Prediction:\")\n",
    "print(actual_perp_prediction[['Character', 'Perpetrator_Probability', 'Predicted_Label']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Perpetrator's Features:\n",
      "   Mention_Count  First_Mention  Sentiment  Rank  Proximity_Score  \\\n",
      "1             14            289   0.037757     5               39   \n",
      "\n",
      "   First_Mention_Exposition  First_Mention_Rising_Action  \\\n",
      "1                         0                            0   \n",
      "\n",
      "   First_Mention_Climax  First_Mention_Falling_Action  \\\n",
      "1                     0                             0   \n",
      "\n",
      "   First_Mention_Resolution  First_Mention_Unknown  \\\n",
      "1                         1                      0   \n",
      "\n",
      "   Mentions_Exposition_Proportion  Mentions_Rising Action_Proportion  \\\n",
      "1                             0.0                           0.071429   \n",
      "\n",
      "   Mentions_Climax_Proportion  Mentions_Falling Action_Proportion  \\\n",
      "1                         0.0                            0.714286   \n",
      "\n",
      "   Mentions_Resolution_Proportion  Mentions_Unknown_Proportion  \\\n",
      "1                        0.142857                     0.071429   \n",
      "\n",
      "   Perpetrator_Probability  Predicted_Label  \n",
      "1                     0.93                1  \n",
      "Top Predicted Characters' Features:\n",
      "  Character  Mention_Count  First_Mention  Sentiment  Rank  Proximity_Score  \\\n",
      "1  Harrison             14            289   0.037757     5               39   \n",
      "0     Boyle             29            231  -0.089571     2               85   \n",
      "2     Horne             49            313   0.110607     1              101   \n",
      "3       Tom             15            370   0.092940     4               46   \n",
      "4    Grayne             23            880  -0.011948     3               60   \n",
      "\n",
      "   First_Mention_Exposition  First_Mention_Rising_Action  \\\n",
      "1                         0                            0   \n",
      "0                         0                            0   \n",
      "2                         0                            0   \n",
      "3                         0                            0   \n",
      "4                         0                            0   \n",
      "\n",
      "   First_Mention_Climax  First_Mention_Falling_Action  \\\n",
      "1                     0                             0   \n",
      "0                     0                             1   \n",
      "2                     0                             0   \n",
      "3                     0                             0   \n",
      "4                     0                             0   \n",
      "\n",
      "   First_Mention_Resolution  First_Mention_Unknown  \\\n",
      "1                         1                      0   \n",
      "0                         0                      0   \n",
      "2                         1                      0   \n",
      "3                         0                      1   \n",
      "4                         0                      1   \n",
      "\n",
      "   Mentions_Exposition_Proportion  Mentions_Rising Action_Proportion  \\\n",
      "1                             0.0                           0.071429   \n",
      "0                             0.0                           0.085714   \n",
      "2                             0.0                           0.037037   \n",
      "3                             0.0                           0.000000   \n",
      "4                             0.0                           0.000000   \n",
      "\n",
      "   Mentions_Climax_Proportion  Mentions_Falling Action_Proportion  \\\n",
      "1                    0.000000                            0.714286   \n",
      "0                    0.000000                            0.771429   \n",
      "2                    0.018519                            0.814815   \n",
      "3                    0.000000                            0.933333   \n",
      "4                    0.000000                            0.956522   \n",
      "\n",
      "   Mentions_Resolution_Proportion  Mentions_Unknown_Proportion  \\\n",
      "1                        0.142857                     0.071429   \n",
      "0                        0.114286                     0.028571   \n",
      "2                        0.111111                     0.018519   \n",
      "3                        0.000000                     0.066667   \n",
      "4                        0.043478                     0.000000   \n",
      "\n",
      "   Perpetrator_Probability  Predicted_Label  \n",
      "1                     0.93                1  \n",
      "0                     0.12                0  \n",
      "2                     0.11                0  \n",
      "3                     0.10                0  \n",
      "4                     0.06                0  \n"
     ]
    }
   ],
   "source": [
    "#Features of the actual perp\n",
    "actual_perp_features = new_character_df[new_character_df['Character'] == actual_perpetrator]\n",
    "print(\"Actual Perpetrator's Features:\")\n",
    "print(actual_perp_features[features + ['Perpetrator_Probability', 'Predicted_Label']])\n",
    "\n",
    "#Features of top predicted perp\n",
    "top_predictions = sorted_predictions.head(5)\n",
    "print(\"Top Predicted Characters' Features:\")\n",
    "print(top_predictions[['Character'] + features + ['Perpetrator_Probability', 'Predicted_Label']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get feature importances from the model\n",
    "feature_importances = model.feature_importances_\n",
    "feature_names = features\n",
    "feat_importances = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
    "feat_importances = feat_importances.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "#Plot the feature importances\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=feat_importances)\n",
    "plt.title('Feature Importances')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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84id/zf8+uCTtT5+Ln/2iuhw6ZVTGDK/M4dNGZ8iWrsk3rU0e+3Xys39O1i3tmDZhv+S11yTj901K7cvZsZaubcxXf/NYvnf3/DS3dtwXcMoBu+Qjr9qnR/cFrFjXlP/5w/x85TePZcPTY7Fesue4/Ptr98+uo55tSN3VPcdnHLZrZr98z367t3fVhub86M8L84VfPuc+jmmj8rlXTszUG09N1izsaNZx5D8lM9+dDO98zzwwsJW0t7e392bFJUuW5J//+Z9z2223ZenSpXn+y7S2tvZJgc915JFH5kUvelG+/OUvJ+nojF9XV5f3vve9+fCHP9xp+bPPPjvr16/PT3/6003TjjrqqBx88MH52te+lvb29kyaNCkf+MAH8s///M9Jkvr6+kyYMCHf+ta38oY3vGGr6lqzZk1GjhyZ+vr61NTU9MGWFtjSecl3XpM/v/LmnP7fj3eaPayyLD9//0syZXT3T2SYv3J9Tr7ijk07zWeUlCQ3v/uYHFRXu2na2saWfOSH9+fH9y3q9DqnHrhLLnvdARnRyxsEt8W6xo352I8eyE33Luw075X7T8xnzjiwzy6us+1kuHt3PbY85/znPZtN+9wpk/PaRz+U0pKyXDv93/Ovv9z83/l/vuXwXHLDfVm9oaXT633vbUfmmN3Hbtea+8KTy9fnpCv+L00b2zabXlqS/PS9L86+k/w7GWjkuHvL1jbmvG/+MQ8+fRP9c/3zK/bMO4+bsU3dku+dvyqv/cqdnaYPryrPz9734i3u87Pkr8nXjk3aN89ayquTf7qzo4HHFrS2tec7dz2ZT/zkwU7zZowblre/ZLd8+Mb7N00rKy3Jzy46NntN9G9kIJLjzpasacwbrrl700CN5/rUa/bLuUdO3XJjnC60t7fnxrkL88/X39dp3q6jhuSGd83MxF40w/jHqg151VW/S31D5/3/tW8/MkfP6Pn+v7GlNZ//5cP5r9890WnekbuNzlfPPbR3jUuWP5J89eik9Xm1lpYn7/p9Mn7vnr8mSXbiHC95MPmPY5O2510HKatM3n1PHmgck1d/+Xd5/tWXIRVl+cX7X/yCzXLufGx53vi8Y/IkGTmkIrdcdOxmF5j7wqL6hrz+q3dl4eqGTvMuP+ugvO7QXbfqdVZvaM7F1/05tz+8rNO8c15Ul4+eum+GVmmGN5DstBmGQUSOB56m5tZ8664nc9nP53WaN2PcsHzjrYdn6hiNO3iWHA88/1i5Lpfc8EDuenxFp3nnHjkls0/cPWNGeKowz5LjLXt82bqc/pXfbxpA+VzfueCIvGTPwfFkr6dWN+Q1X/59lq1r6jTvv847PCfuM6EfqmJryPAL+8MTK3LWf9zdafqwyrL8eNaxmTF+cBzfPvSPFXnV1Xen7XnX9KrKS/OL987M9Am1/VIXL0yOt96K1fX555sezO2PrOw075zDdsmHT94zI0cUK9P3PL4iZ1/T+T1qeFV5fjTrmMzQPLMQ5Hjg+cfK9Zl9/V/yhyc6v1+8ZebUXHzCjIwa7tyYZ8lx9x5dsjanful3XY5L/NF7jskBu9b2yc95cvn6nHzl/6WxZfOfU1KS/GTWsdl/8jY256pfmHz9xGTNU53nvf4byf5nJOl4yMOZ/3FnFqzs/Nnzp884IGcfXrddHlLBtpHhF3b348vzhms6j+MYXlWeH886ZtA0bX/wqTU59Ut3dHlu/Mv3vyTTxmrcNVAN2hw/cGNywwWdp9dMSi78VTJy8la9zBPL1+XkK+7otD8uKy3JLRcdm727GWP8wML6bRoDtr10dy5cM6Q8P5l1bM79r3vyj1Wd98Wff/2BOeOwXe2LB6hBm+M+cstfnsp7rr230/QJNVW5+d3HZJfa3p2jLlnTmDf+5915bFnncduXvnrfvGXmtC0+xLwvrVjXlHf+95z86e+rOs077+hpWbmuKe9+2e7ZZ5ct/Pv4+13JN0/uPL16ZPLOO5JRU/uwYp5PjjfX0LIxl/1sXr5z1987zTt297G56pxDtqqxY2trW75555P5/255qNO8PcYPz/fedmTG11RnTUNL/uWm+3PL/Z3vOX7NwZPy/52+/w6/57i9vT0/vHdhZv+g830ck2uH5MZT2jPxptc9O/HFH0iO+1BSvmMbBgHbptd3aL71rW/N3Llz89GPfjQ33HBDbrrpps2++lpzc3PmzJmTE088cdO00tLSnHjiibnrrru6XOeuu+7abPkkOemkkzYt/8QTT2Tx4sWbLTNy5MgceeSR3b4mW7CxMbnr6qw54Lx87s7OB4VJsr65NT/7S+ed3TPa29vzk/sWdWrY0TEv+cKtj2Rt47M31y1f15yfdPN6t9y/aFOnyB1t+bqm3Pznzg07kuTnDyzO8i4GaMFAs2p9c6cbCspLS3LUuJaUL7g7Sw6elSt+v/kNeHtNGJHHlq7rsmFHklz284eycn3/5HJrtba25fo/Leh0IS5J2tqTq257NOubOg8mhYFqUX1jlw07kuRrv308S57u9twbaxpa8rlfPtzlvHVNG/PzLk5wN2len/zf5zs37Eg6jinmfCtp3XLWlqxpzFW3PdrlvMeWrc/QirLUVD97M3BrW3u+8pvH0tAiwxTDk8vXd9mwI0mu+NWjWbK25/ldsqYpn+8mt/9Y1ZBHlqzr8WsmyR2PLO+yYUeSfPrn87KqF/v/ZWub8t93d74wlyT3PL5y05MqemRjU3L3f3Ru2JEkbRuTO7+UtPT+fZGdUEtD8rsrOjfsSJLW5qxb+NdcedsjnT6sTZKGltbcOHdhWp8/uuE5Vq1vzme6uMk3SeobWnLHI8t7WXj3HlmytsuGHUnyuV8+vOlJES9kxbrmLht2JMkP5vyjyxuXAGCwWVjfkK/99rEu5z22bH2XA5WBgaW+obXLhh1J8oM/LciqLhoPAN379cNLu2zYkSRfvPWRLFzZ9bWworl3/qpuz3sv+9k8nxVTWIvqG3LFr7r+XGZ9c2t+8pcubtgroA0bNuTq3zze6aakJGna2Jb/uefJbGzp+no4FMmqhtYuG3YkyQ/uXZxV3Yz7GKiWrW3KF3/1SJfz1jVtzM+29Nk1sEWrGjZ22bAjSa7744Ks2ODcGLZGU0trvnfP/G7HJX7lN49l9YZtH1vZ1taem+5d2KlhR9IxDvvK2x7JuqZt3M8vvr/rhh1J8qtPJGsXJ0keW7au2+vgl//vI9s0bg36y+JV6/LFW7s+N17XtDE/f2BwHHeub9qYL/360W7PjX/wpwVpbe1i7CdsL2sXd+xjurLmqY5901bY2NqW/+lmf9za1p6rf/23bGjufHy7rqllm8aAbS+r1jfn07/oemxZZVlp5i1a22XDjiT5/P8+kiVrXKumeJauaez23/2SNU154Kn6Xr/2/JUbumzYkXTcR7Qjj1+Xrm3qsmFHknz/D/NzyoG75Orbu37PSpI0rEp+9fGu5zXWJ4/8om8Kha20bE1z/ucP87uc97u/Lc+Krfz8dMnapnzp13/rct6jS9dlwdP7vRXrm/Ozbo7Nf3zfU/1yz/GSNY3d3n+1cHVDHm4emwx/zsMf7v7KpvNroDh6/UjT3/3ud7njjjty8MEH92E53Vu+fHlaW1szYcLmT52ZMGFC5s3r+mBr8eLFXS6/ePHiTfOfmdbdMl1pampKU9OzO4I1a7q+EXan07Q+eWpONhx2SR68p/ub/e55YmUuOHZ6Kss794xp3tiWP3bzAU+S/HVhfdY3tW7qZFXf0NzliW/ScXG54wL2ju9YubqhpcuLVJvmF+yD7cFGhrdO48bWzFu0drNpI4dUpGLtgo75lWOyfN3mNwlOGzssDy3u/vc5b9HaNLV0cUPjANKwsbXbk9skeeCp+qxv2phhngrer+R46z22tPt98rqmjV02ytpaG5o35sFF3f/u73liZc4/put9fprXJ4v+3P2LL7gnaVmflHX/ZIeGltYtDhZ7Yvn6TKipzprGZ38Hf/lHfdY3tmZIhQz3Nzl+YVvK14r1zWnoRX6bNrZu8Yb7+/9R36snmt71ePeNA+YtWpvGjT2vdW1jS5cfjj1j4eqG7NVNV/tuNa9PFv6p+/lPzUma1yUV1T173Z2UHCdpWpcsmtvt7PVt5XlgYfe/lz8+uTKNLa3dHls2bmzNvMVru5yXdGTvnCOnbH29W+EvC7r/0GpRfeNWH89vqbFOa1t71jQ6L+5vMgzFJ8cDX+MLnLc+uGhNjt2j58ffDB5yPPA91U1DuyRpaW3vtoElOw853nptbW259++ru53/0OI1aW7d8YOat4c/Ptn950yPLVuXpi5unKJ/yHDPNLZs+VrVfQvqs6ahOTVDXvhJaAPZ+qbm3L+o+8/X5izckIbGpoyo2LFPYKNrctx7y7fQHL61rT1rCnasu6F5Yx5a1P171J8XrE5D88YMqfQ58UAjxwPfP1Zt6HZe08a2rG3UtGNnJ8dbZ21jS+5f2P1nsQ8uWpO1jS2pHbptx9ONG1vzpye7H4f9wMI1Wd/UmuFV23A8+48/dj9v9d87HtiU5IEtbO/StU1dNhZhx5Phnmnc2L7FcdL3zl+dhqbmDKkq9rnxhuaNW3zPmvP3VdnQ0poRZb1+hjB9aKfI8cbGjn1Md/7xx2Svk1/wZTY0t2bO/NXdzr9/Ycd9AkOfd+64vql1m8aAbS+NG1vzUDfjTHcdNTT3L1zd7bqL1zT2alwn28dOkeM+0rSxbYsPSLl3/uq8fN+JvXrteVvYx6/a0JKGHXgv1IKVWz4Xb2/v/j0rSccD6ZY80P0PePKO5EVvT0rty/uKHG/Z2qaWtGzhs+DF9Y3ZY8KIF3ydDc2tWxyn8cjitTls6qjUb9jyPcf1DTv+nuOmjW1ZVN/9dfm/LGnOcbV1ybolHRNaGjruawAKpdd71rq6urR39841yF122WUZOXLkpq+6urr+LmlgqKhOaqemcv1TmTxqSLeL7T5+eCrKSrp+ibLSzBjf/Q5v8qghqXrOjb/DXuCD1OH9dFP9C/1cN/v3LxneOuWlJZ2yvK5pY1qHjU+SVLY3ZGhl2Wbzl69ryuTa7vM/edSQlJV2nf+Boqq8LNPHdv8+tOuoIamuKOt2PjuGHG+9SVvIZEVZyTb9e64sL9ti5vfYwj4/5dVJ7RZuMh49Iynv/rWTpKq8NNUV3R/OThw5JKue9wSKulFDUrWFddhx5PiFTR09tNt5QyrKNjsu3loVZaWpqe7+WHTa2O5/5pZsqXnG5FFDUt6L/f/QyvKUbGG1scOrevyaqahORk/vfv6oaUnFlt97eJYcp+Pfy8ip3c6uTssW95W7jR3WdXOrp5WXlmxxX77nVlyg7qnp47o/Fq6pLk/FVg64qB265UFeL3Q+z/Ynw1B8cjzwvdB569TRO77hNAOLHA98Y0d0f+5ZUtJ/n0MxcMjx1istLd3itafJtUMzwD9C2mp7jB/e7bxxI6pS3t11c3Y4Ge6ZitLSTKrtvuHx1DFDM3QQfI5aXVGeui1s5/TRVakq+M1Xg4kc997IoVv+nGVowY51K8pKs+sWxqtNHT20V5+tsf3J8cA37gXOjYdVFn//z7aR460ztLI8daO2dF48pOub7Xqosqx0i+MfJ9UO2fZ94tg9u583dHRS2vFZ8bQt1DG8qnyLn5Wz48hwz5SXJrvWdp/laWOGFb5hR9IxlnpLx9fTxgxLVbljgIFip8hxaWXHPqY7Y/fYqpeprijLtDHdZ7i7+wSqy0u3aQzY9lJeWprJ3bwnLV/XtMV98Yiq8lRqvDNg7BQ57iMVZSUZtYWxibuN6/4zmhcyZQvH69UVpTv02tL4mu7PxUtLOn4PdVu6t6m0Iqndwr+jcfto2NHH5HjLXuh8d/TwrTuGri7fchZ3Hd2xv36he3f7YwzzC93HMX1UebL+OQ9SLS1zXwMUUK/3rldccUU+/OEP58knn+zDcro3duzYlJWVZcmSJZtNX7JkSSZO7LoD2sSJE7e4/DP/7clrJsm//Mu/pL6+ftPXggULerw9g1LlsOTYizP6/v/K+44c2eUipSXJWYfvmpJu7r4rLS3JG140pdsBWe87YY+MGvbsTnjM8MocMqW2y2UP2nVkxmzlDruvjRlWmRdNG9XlvP0n12RsP9VFBxneOuNGVOe9x+++2bSmjW15eENNUjs14/76zbz50LGbzZ87f1VeNG10tzfpv/dlu2d8zcB+cn1FWWnOO3patzcJz3rZ7qkZ4qlJ/U2Ot96U0UMzvpsBFKcfPHmb9kmjh1XmfSd0faG7tCQ5cwv7/FTXJC+5pPsXP+qfkvIt1zZuRFXOOaLrxh+1QysyrLIsy9dt3rTj3S/bPSOqZXggkOMXttfEmozsZp/zpqOmbHFwVHfGjajK+cd03bRieFV5Dqqr7fFrJskpB+zSbWOO9x6/e8aN6Pn+f8zwyrxi3wldzps6Zmgm9OaYomJocvRF3c8/9uKO8xq2ihwnqRqevOQD3c4eOXFGLupmX1lSkrx55tQtNsHo6pj8GeWlJXnVgZN6Vu9WOLiuttvBlRccO73b44rnGzO8Knt101TkmBljMnqY8+L+JsNQfHI88I0bXpEzD9u1y3m1Qyuyx4TeDxZhcJDjgW/00Irs1s2AxuP2GJcRVQYl7+zkuGdOO2hSt9eQ3nncbpk6dnDsG1+8x9huB4u952W7b/W5NdufDPfMrqOH5l3HzehyXsdYkLqUD4IbdkYMH573HNd9o97zj56aykrXtgYKOe690UPKtnANd1RGDSlWnifVDsk7X9L9e9SZh9el1I0IA5IcD3xjh1Vmajc3Nh6/1/jUVBfr/YK+J8dbZ2hVec4/Zlq389953IyM6c3DS56nvKw0b545tdvxjxedsEdqh27j8eyUozrGX3Rl5kXJiI4x+PtNqsmIbm6UOm/m1Iwb4bh6IJDhntl1zIi887iux16VlZbkjMMm7+CKto+aIRV57/HdN0F46zHTNN4ZQHaKHA+f0LGP6UrF0GTKzK16mcry0lzQzfjJJJl1/B5djjEeObRym8aAbS/jRlTlom7Gli2ub8whU0Z124D+/GOm9WoMKtvHTpHjPjJuRHW316mrK0pzxPSu7+XbGrtPGNHtw8rOOWJKxvdiLHRv7TJySLcNtF6+74Tc8ejyvGdL90UMH5e85INdzystSw48q48q5RlyvGVjhlXm+L3GdTlvxrhhGbeV58NjR1Tl7Bd13RBlzLDKzHi6cc+Y4ZU5aNeu728+pK62X+45Hj+iKhce2/VxyLDKshxU25ysevLZifucnjz94HWgOHp9VnD22WfnN7/5TWbMmJERI0Zk9OjRm331tcrKyhx22GG57bbbNk1ra2vLbbfdlpkzuz7Bmjlz5mbLJ8mtt966afnp06dn4sSJmy2zZs2a3HPPPd2+ZpJUVVWlpqZmsy+eNnavZOZ7cljTPbno2AmbNd8YUlGWr73psC12XU06nsT9lXMPy5DndHsrKy3J+0/YI4dO2fzgefSwqlz1hkOy98TNP0Tea8KIfPmNh2b0sP45iRw1rDKXn3Vw9t1l838be04Ynq+ce1ifXFin92R46x2357hceOzmDSwuu2NVGs++LpVL/5ILZqzJyfs8+57f3p7cOOcfuebNh292gaekpOPCzsv2LsbB4pTRQ3PF2QdvNqCyvLQkHz55r+w/ueuDdnYsOd56u9QOyX9feGR2Gbn5RaIX7zE2H3jFXtv8hIYXTRuV9x6/+2b7/KGVZfmPNx2WyVvoNpskmbBf8op/T0qfU0N5dfLa/0hG7/aCP7uqvCzvOm5GTtxn8/eWcSOq8l9vOTxf+c3fNk2rKCvJR0/dJ/vs4t/KQCHHL2xSbXWufduRnS5CvWLfCXn7i3dLZS8GP1eUlebcI6fktIM2v9F/9LDKfO9tR2aXkb3rxjppZHW+8dYXddr/X3jstBy3Z9cX2F7IiOqKXPrq/XLY1NrNpk8ZPTTffOuLete0I0nG7J68+qqk7DkX28oqklM+n4zbu3evuZOS46eN3zc5+TMd/46eUV6VvOYrSe2UHLjryHzwpL1S9pydZXVFaa56wyFb7Mz+jK6OyYdXlecbb31RJm/hiZ+9tcvIIfne24/q1FTjtIMm5dwjp6R8Kz9gHjeiKte85bDMGLf5DY4H7joyn339gds+EIxtJsNQfHI88NUMrc7bX7Jbjt9782PicSOq8u3zj8iUWteJd3ZyPPBNHTs8X3vzYZ2eunZwXW0+ftp+mTRK48ednRz3zMQRlfnKuYdu1iyytKTjRp2jd+v7MQb9ZZeRQ/KdC49IzZDNr7+fc0RdTj1gl+6bXbPDyXDPHVxXm3cdt1unsSBXvOGQTNjCk/+KZt+Jw/OvJ+22WaOhqvLSXH7G3pn6AmNe2LHkuPcmjKnNf5x7YKdruAdMrslnXrtfxtQWb2zEi6aNyjteslunz66/dM6hW3wiMv1Ljge+aWOH55o3H54pozc/Nz50Sm0+9up9M7HWufHOTo63Xt2oIfn06w7oNC7xkpP2yj4T+66R5ZRRQ3PVGw5JdcWzP6fs6Z/T3U1LPVIzOXnLj5Ihz7sh8qBzkkPO7bgJMMmkkUPyvbcfmTHP++z5lAMm5ryjp6WiTNOfgUCGe+5FU2rz9hdP73zc+YaDM3H4jn9i9/ay36Sa/Msr9+50bnzl2Qd3Oi6gf+0UOS4rSw55Y8e+5rmGjOrYJ9VsfcOcqWOH5fKzDuq0P/7XV+3T6b6k59rWMWDby7F7jO10Ljyssiz/dd7hmTSyOt9725GdxoG9+sBd8qZ+ajRC13aKHPeRstKSvO7QyXn9oZvnvnZoRb73tqMyqZfjoJNsyszzG9qcuM/4vOu4GTu0YdWEmup86/wjUjd68+05fOqonP2iukwbM6zTPYudTD8uOeb9Sclz6q4cnrzhf5KRXTc9oPfkeMtqhlTk/3vtATlkSu1m06eNGZr/Ou/wrX5AeHVFWd7zst1z/F6b30M0oaYq333bs/dNjR5WlS+98dBOjav3njgiV51zSL/cc1xeVpo3dnMfx7VvnJFJd3zo2YlTj0le8amOh0sChVLS3t7e3psVv/3tb29x/nnnndergrbkuuuuy3nnnZf/+I//yBFHHJErrrgiP/jBDzJv3rxMmDAhb3nLWzJ58uRcdtllSZI777wzxx13XD796U/nVa96Vb7//e/n3//93zN37tzsv//+SZLPfOYz+fSnP51vf/vbmT59ej760Y/mL3/5Sx588MFUV2/dm/2aNWsycuTI1NfX26EmSfOGZP3SrFu/ISvKx+XxlS2pqqzIlDHDMn5E1VbdXNi8sTVL1zbl7ys2pHljW3YbNyxjhld12+Vx2dqmLF3TmEX1jZk4sjoTaqp69TTvvvb8usbXVO3QznpsHRnesrWNLVmxrjmPL1+XoRXlqRs9JONHVKdiw5JkzaKsLhmWZSVj8/dVzRk1vCqTRg7J2OGVWbq2OQtWbciG5o2ZPnZ4xg6v7L6L4wDU1PL0+9DKDdnY2pbdnt6God28D9G/5PiFLapvyOL6xqxY15y60UMzbkRVnz3hfl3TxqxY15THl61PVUVppoweutX7/DSvT9YvS5b/reMD29G7dXTErtj6/eWq9c1Zvq4jr6OHVmaX2uqMGlKRZeua8+SK9Wlra8/0scMydkTVNjcpYfuR4661t7dncX1jnqpvzKoNzZn6dH639Wb31Ruas3xdc/6+Yn1GDqnIpNohmVhTndJunnS6NVo2tmXp2sYsWNmQDS0bs9vY4RnTB/v/FeuasnRtUxauasi4EVVPH+9v4zF1S0Oybmmy4rEk7cmYGR2daCt9oLwtduocP30enBV/S0rKktHTn96fdXxgsv6ZfeXy9SkvK83U0UMzoWYr95XZwjH5dvoQqK2tPYvXNGbh6oasaWjJ1DHDMnZ4Za/ee5auacziNY1ZuqYpu9R25HesRpYD0k6dYRgk5Hjg+sfKdalvbM0Ty9dnzLDKTKypTl1tVcorinOtjB1DjgeuJ5evy7J1zVmypjG7jhqS0UM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",
      "text/plain": [
       "<Figure size 4371.11x4250 with 306 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compare feature distributions, AKA supergraph\n",
    "sns.pairplot(new_character_df, vars=features, hue='Predicted_Label')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nlp",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}