notebooks/3.1_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e949085-8c73-4c17-a759-1466c229baf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "edd189db-1f01-4d6e-b162-3776eb04099e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"org_test_results_sorted.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f48e67c-e801-4e86-ac6c-da38ba129979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>password</th>\n",
       "      <th>best_pred_pass</th>\n",
       "      <th>avg_edit_dist</th>\n",
       "      <th>min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jinkies_0@hotmail.com</td>\n",
       "      <td>jinkies</td>\n",
       "      <td>jinkies</td>\n",
       "      <td>3.246154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>leonid-lebedev00@rambler.ru</td>\n",
       "      <td>leonid</td>\n",
       "      <td>leonid</td>\n",
       "      <td>3.622222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cheyen1@hotmail.fr</td>\n",
       "      <td>cheyen</td>\n",
       "      <td>cheyen</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>niknak_niknak@aol.com</td>\n",
       "      <td>niknak</td>\n",
       "      <td>niknak</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ali-sel@mail.ru</td>\n",
       "      <td>alisel</td>\n",
       "      <td>alisel</td>\n",
       "      <td>2.929825</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name password best_pred_pass  avg_edit_dist   \n",
       "0        jinkies_0@hotmail.com  jinkies        jinkies       3.246154  \\\n",
       "1  leonid-lebedev00@rambler.ru   leonid         leonid       3.622222   \n",
       "2           cheyen1@hotmail.fr   cheyen         cheyen       2.333333   \n",
       "3        niknak_niknak@aol.com   niknak         niknak       2.800000   \n",
       "4              ali-sel@mail.ru   alisel         alisel       2.929825   \n",
       "\n",
       "   min_distance  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "72aa02be-5b78-49c0-ad94-436716e1e1ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Username: jinkies_0\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "def get_username_from_email(email):\n",
    "    match = re.search(r'^([^@]+)@[^@]+$', email)\n",
    "    if match:\n",
    "        return match.group(1)\n",
    "    return None\n",
    "\n",
    "# Example usage\n",
    "email = \"jinkies_0@hotmail.com\"\n",
    "username = get_username_from_email(email)\n",
    "print(f'Username: {username}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5a783fb-0216-4ddb-97e7-8fa0ee858a75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting python-Levenshtein\n",
      "  Downloading python_Levenshtein-0.25.0-py3-none-any.whl (9.4 kB)\n",
      "Collecting Levenshtein==0.25.0\n",
      "  Downloading Levenshtein-0.25.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (177 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.4/177.4 KB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting rapidfuzz<4.0.0,>=3.1.0\n",
      "  Downloading rapidfuzz-3.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: rapidfuzz, Levenshtein, python-Levenshtein\n",
      "Successfully installed Levenshtein-0.25.0 python-Levenshtein-0.25.0 rapidfuzz-3.6.1\n"
     ]
    }
   ],
   "source": [
    "!pip install python-Levenshtein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37968f57-844c-43c4-ad1b-b924c9f0b918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from Levenshtein import distance as lev\n",
    "lev(\"123456\", \"123456\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dca800c4-65d2-49ed-9cdb-12d07c179fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ea99b9-476f-4d14-9016-5506c26106b3",
   "metadata": {},
   "source": [
    "## Finding editing distance between username and password"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "26f0eca1-d562-463a-abfa-ccb34b545a61",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.0100s.) Setting batch_size=2.\n",
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      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.0225s.) Setting batch_size=8.\n",
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      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.0384s.) Setting batch_size=16.\n",
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      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.0248s.) Setting batch_size=32.\n",
      "[Parallel(n_jobs=-1)]: Done 524 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.0372s.) Setting batch_size=64.\n",
      "[Parallel(n_jobs=-1)]: Done 908 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.0669s.) Setting batch_size=128.\n",
      "[Parallel(n_jobs=-1)]: Done 1644 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.1083s.) Setting batch_size=256.\n",
      "[Parallel(n_jobs=-1)]: Done 2924 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.1799s.) Setting batch_size=512.\n",
      "[Parallel(n_jobs=-1)]: Done 5612 tasks      | elapsed:    0.9s\n",
      "[Parallel(n_jobs=-1)]: Done 7913 tasks      | elapsed:    0.9s\n",
      "[Parallel(n_jobs=-1)]: Done 8396 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=-1)]: Done 8879 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=-1)]: Done 9408 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=-1)]: Done 10000 out of 10000 | elapsed:    1.0s finished\n"
     ]
    }
   ],
   "source": [
    "def avg_and_min_edit_dist(email, actual_pass):\n",
    "    username = get_username_from_email(email)\n",
    "    return lev(str(username), str(actual_pass))\n",
    "\n",
    "results = Parallel(n_jobs=-1, verbose=10)(delayed(avg_and_min_edit_dist)(row['name'], row['password']) for index, row in df.iterrows())\n",
    "df['user_pass_min_dist'] = pd.DataFrame(results, index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ecbca0e-7c56-4cee-849a-24e8f8b853e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>password</th>\n",
       "      <th>best_pred_pass</th>\n",
       "      <th>avg_edit_dist</th>\n",
       "      <th>min_distance</th>\n",
       "      <th>user_pass_min_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jinkies_0@hotmail.com</td>\n",
       "      <td>jinkies</td>\n",
       "      <td>jinkies</td>\n",
       "      <td>3.246154</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>leonid-lebedev00@rambler.ru</td>\n",
       "      <td>leonid</td>\n",
       "      <td>leonid</td>\n",
       "      <td>3.622222</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cheyen1@hotmail.fr</td>\n",
       "      <td>cheyen</td>\n",
       "      <td>cheyen</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>niknak_niknak@aol.com</td>\n",
       "      <td>niknak</td>\n",
       "      <td>niknak</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ali-sel@mail.ru</td>\n",
       "      <td>alisel</td>\n",
       "      <td>alisel</td>\n",
       "      <td>2.929825</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name password best_pred_pass  avg_edit_dist   \n",
       "0        jinkies_0@hotmail.com  jinkies        jinkies       3.246154  \\\n",
       "1  leonid-lebedev00@rambler.ru   leonid         leonid       3.622222   \n",
       "2           cheyen1@hotmail.fr   cheyen         cheyen       2.333333   \n",
       "3        niknak_niknak@aol.com   niknak         niknak       2.800000   \n",
       "4              ali-sel@mail.ru   alisel         alisel       2.929825   \n",
       "\n",
       "   min_distance  user_pass_min_dist  \n",
       "0             0                   2  \n",
       "1             0                  10  \n",
       "2             0                   1  \n",
       "3             0                   7  \n",
       "4             0                   1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40253e67-11bf-47ed-a887-7826fa054dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_100_df = pd.read_csv(\"data/top_100_pass.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6edbdfe-8cc2-470b-b050-0e30304a03fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>password</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>123456</td>\n",
       "      <td>2034268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123456789</td>\n",
       "      <td>1214120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwerty</td>\n",
       "      <td>988396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>password</td>\n",
       "      <td>738200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12345</td>\n",
       "      <td>687856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    password    count\n",
       "0     123456  2034268\n",
       "1  123456789  1214120\n",
       "2     qwerty   988396\n",
       "3   password   738200\n",
       "4      12345   687856"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_100_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7ca6d5cf-2f2d-4ae4-9bb7-23f64051900d",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_100_pass = top_100_df['password'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "21ab8a18-27dd-4448-9b88-369bf4bacba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "[Parallel(n_jobs=-1)]: Done 10000 out of 10000 | elapsed:    1.9s finished\n"
     ]
    }
   ],
   "source": [
    "def avg_and_min_edit_dist(actual_pass):\n",
    "    edit_distances = [lev(str(actual_pass), str(top_pass)) for top_pass in top_100_pass]\n",
    "    avg_edit_dist = sum(edit_distances) / len(top_100_pass)\n",
    "    min_distance = min(edit_distances)\n",
    "    best_pred_pass = top_100_pass[edit_distances.index(min(edit_distances))]  \n",
    "    return (best_pred_pass, avg_edit_dist, min_distance)\n",
    "\n",
    "results = Parallel(n_jobs=-1, verbose=10)(delayed(avg_and_min_edit_dist)(row['password']) for index, row in df.iterrows())\n",
    "df[['top_100_best_pass', 'top_100_avg_edit_dist', 'top_100_min_distance']] = pd.DataFrame(results, index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2acc4f46-5068-4c65-9870-b4bb2a2d7526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>password</th>\n",
       "      <th>best_pred_pass</th>\n",
       "      <th>avg_edit_dist</th>\n",
       "      <th>min_distance</th>\n",
       "      <th>user_pass_min_dist</th>\n",
       "      <th>top_100_best_pass</th>\n",
       "      <th>top_100_avg_edit_dist</th>\n",
       "      <th>top_100_min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jinkies_0@hotmail.com</td>\n",
       "      <td>jinkies</td>\n",
       "      <td>jinkies</td>\n",
       "      <td>3.246154</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>tinkle</td>\n",
       "      <td>7.35</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>leonid-lebedev00@rambler.ru</td>\n",
       "      <td>leonid</td>\n",
       "      <td>leonid</td>\n",
       "      <td>3.622222</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>19weed</td>\n",
       "      <td>6.90</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cheyen1@hotmail.fr</td>\n",
       "      <td>cheyen</td>\n",
       "      <td>cheyen</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>)ryan</td>\n",
       "      <td>6.77</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>niknak_niknak@aol.com</td>\n",
       "      <td>niknak</td>\n",
       "      <td>niknak</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>)ryan</td>\n",
       "      <td>7.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ali-sel@mail.ru</td>\n",
       "      <td>alisel</td>\n",
       "      <td>alisel</td>\n",
       "      <td>2.929825</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>abc123</td>\n",
       "      <td>6.73</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name password best_pred_pass  avg_edit_dist   \n",
       "0        jinkies_0@hotmail.com  jinkies        jinkies       3.246154  \\\n",
       "1  leonid-lebedev00@rambler.ru   leonid         leonid       3.622222   \n",
       "2           cheyen1@hotmail.fr   cheyen         cheyen       2.333333   \n",
       "3        niknak_niknak@aol.com   niknak         niknak       2.800000   \n",
       "4              ali-sel@mail.ru   alisel         alisel       2.929825   \n",
       "\n",
       "   min_distance  user_pass_min_dist top_100_best_pass  top_100_avg_edit_dist   \n",
       "0             0                   2            tinkle                   7.35  \\\n",
       "1             0                  10            19weed                   6.90   \n",
       "2             0                   1             )ryan                   6.77   \n",
       "3             0                   7             )ryan                   7.00   \n",
       "4             0                   1            abc123                   6.73   \n",
       "\n",
       "   top_100_min_distance  \n",
       "0                     3  \n",
       "1                     5  \n",
       "2                     4  \n",
       "3                     5  \n",
       "4                     5  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cacb9da-70e2-44ab-958d-0ff4cd76b7fe",
   "metadata": {},
   "source": [
    "## Plotting minimum distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "424b24b0-6adb-4e43-a46c-f7e4f65bfe4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data/org_test_results_final.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "05c0d720-42bc-4e46-8057-d1de9acf8e5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'password', 'best_pred_pass', 'avg_edit_dist', 'min_distance',\n",
       "       'user_pass_min_dist', 'top_100_best_pass', 'top_100_avg_edit_dist',\n",
       "       'top_100_min_distance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c7188028-49bc-4b32-97ed-f47e6a163cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_distance</th>\n",
       "      <th>user_pass_min_dist</th>\n",
       "      <th>top_100_min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   min_distance  user_pass_min_dist  top_100_min_distance\n",
       "0             0                   2                     3\n",
       "1             0                  10                     5\n",
       "2             0                   1                     4\n",
       "3             0                   7                     5\n",
       "4             0                   1                     5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['min_distance','user_pass_min_dist','top_100_min_distance']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "228d224b-4f4d-49e1-a3f3-cbfb2041336b",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = df[['min_distance','user_pass_min_dist','top_100_min_distance']].apply(pd.Series.value_counts).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "56e592d4-409c-493d-8606-07fbea1d82ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_distance</th>\n",
       "      <th>user_pass_min_dist</th>\n",
       "      <th>top_100_min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>471.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>334.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>894.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1615.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1754.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>2033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2407.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1809.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>1267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1124.0</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>575.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>316.0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>176.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>120.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>105.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>39.0</td>\n",
       "      <td>511.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>40.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>22.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min_distance  user_pass_min_dist  top_100_min_distance\n",
       "0            6.0               153.0                 471.0\n",
       "1           41.0               103.0                 115.0\n",
       "2          128.0                64.0                 175.0\n",
       "3          334.0                73.0                 536.0\n",
       "4          894.0               108.0                1615.0\n",
       "5         1754.0               206.0                2033.0\n",
       "6         2407.0               520.0                2093.0\n",
       "7         1809.0               887.0                1267.0\n",
       "8         1124.0              1360.0                 698.0\n",
       "9          575.0              1274.0                 322.0\n",
       "10         316.0              1269.0                 195.0\n",
       "11         176.0              1048.0                 150.0\n",
       "12         120.0               807.0                 102.0\n",
       "13         105.0               655.0                  60.0\n",
       "14          39.0               511.0                  33.0\n",
       "15          40.0               331.0                  18.0\n",
       "16          18.0               210.0                  16.0\n",
       "17          22.0               136.0                   9.0\n",
       "18          13.0                90.0                  14.0\n",
       "19           9.0                60.0                   5.0\n",
       "20           7.0                38.0                  11.0\n",
       "21           9.0                30.0                   5.0\n",
       "22           5.0                14.0                   6.0\n",
       "23           9.0                12.0                   6.0\n",
       "24           5.0                 8.0                   5.0\n",
       "25           5.0                 6.0                  10.0\n",
       "26           4.0                 2.0                   2.0\n",
       "27           3.0                 3.0                   2.0\n",
       "28           2.0                 5.0                   4.0\n",
       "29           2.0                 1.0                   3.0\n",
       "30           3.0                 2.0                   3.0\n",
       "31           3.0                 0.0                   4.0\n",
       "32           1.0                 5.0                   1.0\n",
       "33           2.0                 2.0                   1.0\n",
       "34           2.0                 1.0                   2.0\n",
       "35           1.0                 0.0                   0.0\n",
       "36           2.0                 1.0                   3.0\n",
       "37           0.0                 1.0                   1.0\n",
       "38           1.0                 0.0                   1.0\n",
       "39           0.0                 0.0                   1.0\n",
       "40           2.0                 2.0                   0.0\n",
       "42           2.0                 2.0                   2.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bb4a1059-c451-460b-b0a8-ba181fd25c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting\n",
    "counts.plot(kind='bar', figsize=(15, 8))\n",
    "plt.title('Counts of Unique Values per Column')\n",
    "plt.xlabel('Unique Values')  # Corrected line\n",
    "plt.ylabel('Counts')\n",
    "plt.xticks(rotation=0)  # Keep the-tick labels readable\n",
    "plt.legend(title='Columns')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3e07b0ba-5d30-4435-aecf-f7f7aa851273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts.plot(kind='line', figsize=(15, 8))\n",
    "plt.title('Counts of Unique Values per Column')\n",
    "plt.xlabel('Unique Values')  # Corrected line\n",
    "plt.ylabel('Counts')\n",
    "plt.xticks(rotation=0)  # Keep the-tick labels readable\n",
    "plt.legend(title='Columns')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "edb81e66-313c-4625-a35b-e8d7dc6b1bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_edit_dist</th>\n",
       "      <th>top_100_min_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.246154</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.622222</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.800000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.929825</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_edit_dist  top_100_min_distance\n",
       "0       3.246154                     3\n",
       "1       3.622222                     5\n",
       "2       2.333333                     4\n",
       "3       2.800000                     5\n",
       "4       2.929825                     5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['avg_edit_dist','top_100_min_distance']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9b176f14-0035-4380-a992-b63e190cb057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model Comparison')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ['Model', 'Top 100']\n",
    "y = [df['avg_edit_dist'].mean(), df['top_100_min_distance'].mean()] \n",
    "\n",
    "plt.bar(x, y)\n",
    "plt.ylabel('Performance')\n",
    "plt.title('Model Comparison')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c06153d1-510c-4f5a-aa73-8e94e52d0484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.333333333333333"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['avg_edit_dist'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "54515aac-7330-45d3-9bf6-04cb33bf9947",
   "metadata": {},
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     "execution_count": 55,
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     "execution_count": 48,
     "metadata": {},
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