Hall Of Fame Usage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Hall Of Fame Usage**\n",
    "\n",
    "\n",
    "This notebook will higlights ``halloffame`` API and its main usage.\n",
    "\n",
    "\n",
    "The notebook is divided into five parts:\n",
    "* API / Connections\n",
    "* Scraping processes\n",
    "* Built-in statistics\n",
    "* Publishing / Editing\n",
    "* Furter statistics\n",
    "\n",
    "First, let's import the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from halloffame import HallOfFameAPI\n",
    "from halloffame.utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connect to the API\n",
    "\n",
    "## Warning !\n",
    "\n",
    "> To block scraping process and protect user's data, facebook uses dynamic CSS sheets. Thus, when a facebook page is refreshed or loaded, the HTML elements id change over time. To bypass this issue, this package uses a table of known elements, so the bot can be aware when an id changed in the CSS when a page refreshes.\n",
    "\n",
    "\n",
    "You will need to provide URLs to unique reactions. You can achieved that by creating a group, create multiple posts and associate for each a unique reaction (`LOVE`, `AHAH`, `LIKE` etc.). Then, click on the reaction and copy/paste its URL. Note that you should use the [mobile version of facebook](https://m.facebook.com) to retrive the URL and not the standard version of facebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change me!\n",
    "REACTION2HREF = {\n",
    "    \"LIKE\":  \"/ufi/reaction/profile/browser/?ft_ent_identifier=\",\n",
    "    \"LOVE\":  \"/ufi/reaction/profile/browser/?ft_ent_identifier=\",\n",
    "    \"CARE\":  \"/ufi/reaction/profile/browser/?ft_ent_identifier=\",\n",
    "    \"AHAH\":  \"/ufi/reaction/profile/browser/?ft_ent_identifier=\",\n",
    "    \"WOW\":   \"/ufi/reaction/profile/browser/?ft_ent_identifier=\",\n",
    "    \"SAD\":   \"/ufi/reaction/profile/browser/?ft_ent_identifier=\",\n",
    "    \"ANGER\": \"/ufi/reaction/profile/browser/?ft_ent_identifier=\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to `geckodriver.exe`\n",
    "EXECUTABLE_PATH = \"driver/geckodriver.exe\"\n",
    "\n",
    "\n",
    "api = HallOfFameAPI(executable_path=EXECUTABLE_PATH, reaction2href=REACTION2HREF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connect to the API with your credentials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "EMAIL = \"your_email\"\n",
    "PASSWORD = \"your_password\"\n",
    "\n",
    "api.login(EMAIL, PASSWORD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the reactions from the ``REACTION2HREF`` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.init_reactions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This ``init`` method will extract the corresponding class for each reaction. You can acess the table with ``class2reaction``attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sx_4760b1': 'LIKE',\n",
       " 'sx_b2274b': 'LOVE',\n",
       " 'sx_16145c': 'CARE',\n",
       " 'sx_b86f23': 'AHAH',\n",
       " 'sx_fedc67': 'WOW',\n",
       " 'sx_dae72a': 'SAD',\n",
       " 'sx_01fcdf': 'ANGER'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.class2reaction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scraping process\n",
    "\n",
    "This section will list the scraping methods:\n",
    "\n",
    "* ``get_posts`` (return a list): retrieve all posts from a group,\n",
    "* ``get_comments`` (return a list): retrieve all comments, replies and their reactions from a post,\n",
    "* ``get_reactions`` (return a list): retrieve reactions from either a post, comment or reply.\n",
    "\n",
    "## Extract a comments from a post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "GROUP_ID = \"your_group_id\"\n",
    "POST_ID = \"your_group_id\"\n",
    "\n",
    "comments = api.get_comments(GROUP_ID, POST_ID)\n",
    "comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "POST_ID = \"your_group_id\"\n",
    "\n",
    "reactions = api.get_reactions(POST_ID)\n",
    "reactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Retrieving Data: 100%|███████████████████████████████████████████████████████████████████| 2/2 [00:18<00:00,  9.32s/it]\n"
     ]
    }
   ],
   "source": [
    "GROUP_ID = \"your_group_id\"\n",
    "TOP_K = 10      # Max posts retrieved\n",
    "SCROLL_MAX = 5  # Max number of scroll the bot makes in the group page (the lower it is, the less posts are scraped)\n",
    "\n",
    "posts = api.get_posts(GROUP_ID, topk=TOP_K, scroll_max=SCROLL_MAX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should save the posts, in case your connection is breaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open(f\"posts.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(posts, file, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load your saved file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open(f\"posts.json\", \"r\", encoding=\"utf-8\") as file:\n",
    "    posts = json.load(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Built-in statistics\n",
    "\n",
    "You can use the built-in statistics to get an overview of the insights of your group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from halloffame import get_top_stats, get_user_stats\n",
    "\n",
    "user_stats = get_user_stats(posts)\n",
    "top_stats = get_top_stats(posts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply a template\n",
    "\n",
    "To apply statistics in a facebook post, you can use a template: it will fasten your workflow.\n",
    "Simply write the general structure of your text and wrap the elements that sill change (either stats or fonts) with `<< >>` tags.\n",
    "\n",
    "For example, to apply a bold font on the text ``this is a text``, simply use ``<<BOLD>>this is a text<<BOLD>>``.\n",
    "Same for statistics: ``<<TOP1-BEST-POST-REACTION>>``. Note that ``BEST-POST-REACTION`` is a list, so to get the first user add the token ``TOP1`` (and ``TOP2`` for the second etc.).\n",
    "You can also use both together: ``<<BOLD>><<TOP1-BEST-POST-REACTION>><<BOLD>>``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEMPLATE = \"\"\"👑 <<BOLD-SERIF>>Hall Of Fame<<BOLD-ITALIC>> 👑  \n",
    "\n",
    "Here is a template example for a meme group.\n",
    "\n",
    "🔥 <<BOLD>>Rank<<BOLD>> 🔥  \n",
    "\n",
    "🏅 <<BOLD-ITALIC>>Best Memes<<BOLD-ITALIC>>  \n",
    "🥇 <<TOP1-BEST-POST-REACTION>>  \n",
    "🥈 <<TOP2-BEST-POST-REACTION>>   \n",
    "🥉 <<TOP3-BEST-POST-REACTION>>  \n",
    "\n",
    "𝑒𝑡𝑐...\n",
    "\n",
    "🔥 <<BOLD>>Honors<<BOLD>> 🔥\n",
    "\n",
    "📈 <<BOLD-ITALIC>>Most Active<<BOLD-ITALIC>>      \n",
    "🥇 <<TOP1-POST-COUNT>>  \n",
    "🥈 <<TOP2-POST-COUNT>>  \n",
    "🥉 <<TOP3-POST-COUNT>>   \n",
    "\n",
    "𝑒𝑡𝑐...\n",
    "\n",
    "🔥 <<BOLD>>Reactions<<BOLD>> 🔥  \n",
    "\n",
    "😆 <<BOLD-ITALIC>>Funniest<<BOLD-ITALIC>>  \n",
    "🥇 <<TOP1-REACTION-AHAH>>  \n",
    "🥈 <<TOP2-REACTION-AHAH>>  \n",
    "🥉 <<TOP3-REACTION-AHAH>>  \n",
    "\n",
    "𝑒𝑡𝑐...\n",
    "\n",
    "🕙 Message generated at <<DATE-NOW>>\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from halloffame import apply_template\n",
    "\n",
    "message = apply_template(template, top_stats)\n",
    "message"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Publish / Edit a post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_id = api.publish_post(GROUP_ID, message)\n",
    "post_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.edit_post(GROUP_ID, post_id, \"This message was updated\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further statistics\n",
    "\n",
    "This section present simple graphs that you can create from the list of posts previoulsy retrieved. \n",
    "\n",
    "## Time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2298dcce5e0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "\n",
    "posts_sorted = sorted(posts, key=lambda post: datetime.fromisoformat(post[\"date\"]).timestamp(), reverse=False)\n",
    "timeseries_reactions = [len(post[\"reactions\"]) for post in posts_sorted]\n",
    "\n",
    "plt.plot(timeseries_reactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "posts_sorted = sorted(posts, key=lambda post: datetime.fromisoformat(post[\"date\"]).timestamp(), reverse=False)\n",
    "dates = [datetime.fromisoformat(post[\"date\"]) for post in posts_sorted]\n",
    "timeseries_reactions = [len(post[\"reactions\"]) for post in posts_sorted]\n",
    "timeseries_comments = [len(post[\"comments\"]) for post in posts_sorted]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\"reaction\": timeseries_reactions, \"comment\": timeseries_comments}, index=pd.to_datetime(dates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>reaction</th>\n",
       "      <th>comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-01 13:32:00</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-02 12:54:00</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-02 13:29:00</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-02 19:35:00</th>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-02 19:49:00</th>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-02 20:56:00</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-03 10:42:00</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-03 21:45:00</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-04 18:59:00</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-05 17:34:00</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     reaction  comment\n",
       "2020-12-01 13:32:00         9        1\n",
       "2020-12-02 12:54:00        29        0\n",
       "2020-12-02 13:29:00        21        2\n",
       "2020-12-02 19:35:00        40        5\n",
       "2020-12-02 19:49:00        48        2\n",
       "2020-12-02 20:56:00        37        0\n",
       "2020-12-03 10:42:00        19        4\n",
       "2020-12-03 21:45:00        25        0\n",
       "2020-12-04 18:59:00        16        2\n",
       "2020-12-05 17:34:00        32        1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weekly mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "logic = {\n",
    "    'reaction'  : 'mean',\n",
    "     'comment'  : 'mean'\n",
    "}\n",
    "\n",
    "offset = pd.offsets.timedelta(days=6)\n",
    "\n",
    "df_week = df.resample('W', loffset=offset).apply(logic)\n",
    "# Interpolate missing data\n",
    "df_week = df_week.interpolate(method='quadratic', axis=0).ffill().bfill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x229a8155f10>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_week[\"reaction\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sliding average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "N = 60  # Mean over N points\n",
    "mean = np.convolve(timeseries_reactions, np.ones(N)/N, mode='valid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(344, 344, 14)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(timeseries_reactions[N-1:]), len(mean), len(timeseries_reactions[::30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.plot(dates[N-1:], mean, color=\"skyblue\", alpha=1)\n",
    "plt.scatter(dates[N-1:], timeseries_reactions[N-1:], color=\"skyblue\", alpha=0.5, s=1)\n",
    "ax.set_xlim(dates[N-1], dates[-1])\n",
    "\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}