MarkovChains.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](https://a.thumbs.redditmedia.com/VHAsVfe-7uv-O_gcAwS9G0jE8o5r10YMadgzhf2GrK8.png)\n",
    "\n",
    "# Roast Me Generator\n",
    "\n",
    "A simple markov model implimented in python using [markovify](https://github.com/jsvine/markovify) and scaped reddit comments from [r/RoastMe](http://reddit.com/r/roastme).\n",
    "\n",
    "### Tools:\n",
    "- **Markovify**\n",
    "    - Markov model implimentation\n",
    "    - Weighted comments by number of upvotes\n",
    "- **Pandas**\n",
    "    - Data manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import markovify\n",
    "import pandas as pd\n",
    "\n",
    "# Import dataset\n",
    "file = pd.read_csv('comments.csv',sep='|')\n",
    "df = pd.DataFrame(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row on the dataset represents one comment on a reddit post. The dataset contains 247258 different comments that were on the \"top\" of r/RoastMe during the spring of 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub_id</th>\n",
       "      <th>sub_title</th>\n",
       "      <th>img_link</th>\n",
       "      <th>comment_score</th>\n",
       "      <th>comment_body</th>\n",
       "      <th>blankk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5nkbyc</td>\n",
       "      <td>We are hhproductions professional complainers ...</td>\n",
       "      <td>https://i.redd.it/78ckh6a43b9y.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>You are required to make a video on YouTube in...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5nkbyc</td>\n",
       "      <td>We are hhproductions professional complainers ...</td>\n",
       "      <td>https://i.redd.it/78ckh6a43b9y.jpg</td>\n",
       "      <td>36500</td>\n",
       "      <td>Your relationship is stable because thats wher...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5nkbyc</td>\n",
       "      <td>We are hhproductions professional complainers ...</td>\n",
       "      <td>https://i.redd.it/78ckh6a43b9y.jpg</td>\n",
       "      <td>16306</td>\n",
       "      <td>I turn on adblock for your videos   Edit thank...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5nkbyc</td>\n",
       "      <td>We are hhproductions professional complainers ...</td>\n",
       "      <td>https://i.redd.it/78ckh6a43b9y.jpg</td>\n",
       "      <td>16154</td>\n",
       "      <td>I thought roasting a couple Jews went out of s...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5nkbyc</td>\n",
       "      <td>We are hhproductions professional complainers ...</td>\n",
       "      <td>https://i.redd.it/78ckh6a43b9y.jpg</td>\n",
       "      <td>13846</td>\n",
       "      <td>The only reason youre posting here is for a vi...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub_id                                          sub_title  \\\n",
       "0  5nkbyc  We are hhproductions professional complainers ...   \n",
       "1  5nkbyc  We are hhproductions professional complainers ...   \n",
       "2  5nkbyc  We are hhproductions professional complainers ...   \n",
       "3  5nkbyc  We are hhproductions professional complainers ...   \n",
       "4  5nkbyc  We are hhproductions professional complainers ...   \n",
       "\n",
       "                             img_link  comment_score  \\\n",
       "0  https://i.redd.it/78ckh6a43b9y.jpg              1   \n",
       "1  https://i.redd.it/78ckh6a43b9y.jpg          36500   \n",
       "2  https://i.redd.it/78ckh6a43b9y.jpg          16306   \n",
       "3  https://i.redd.it/78ckh6a43b9y.jpg          16154   \n",
       "4  https://i.redd.it/78ckh6a43b9y.jpg          13846   \n",
       "\n",
       "                                        comment_body  blankk  \n",
       "0  You are required to make a video on YouTube in...     NaN  \n",
       "1  Your relationship is stable because thats wher...     NaN  \n",
       "2  I turn on adblock for your videos   Edit thank...     NaN  \n",
       "3  I thought roasting a couple Jews went out of s...     NaN  \n",
       "4  The only reason youre posting here is for a vi...     NaN  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markovify Dataset\n",
    "Some comments were incorrectly formatted, so we just excluded them from our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of comments: 246599\n",
      "Number of invalid comments: 659\n"
     ]
    }
   ],
   "source": [
    "model_list = []\n",
    "score_weights = []\n",
    "invalid_comment_count = 0\n",
    "\n",
    "# Iterate through dataset and add elements to a model\n",
    "for i in range(0,len(df['comment_body'])):\n",
    "    try:\n",
    "        # Make sure the comment body is a string\n",
    "        if type(df['comment_body'][i]) != 'str': \n",
    "            # Add both the comment text and scores to lists\n",
    "            model_list.append(markovify.Text(df['comment_body'][i], state_size=3))\n",
    "            score_weights.append(df['comment_score'][i]) \n",
    "    except:\n",
    "        # If comments are invalid they will throw an exception\n",
    "        invalid_comment_count += 1\n",
    "\n",
    "print('Number of comments: ' + str(len(score_weights)))\n",
    "print('Number of invalid comments: ' + str(invalid_comment_count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add score weights to model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_combo = markovify.combine(model_list,score_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Gravy is not a conditioner'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_combo.make_short_sentence(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**'First off cut your hair you look remarkably like John Travolta'**\n",
    "\n",
    "**'Someone has been lying to you to spare your feelings'**\n",
    "\n",
    "**'Gravy is not a conditioner'**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cool Markdown Stuff\n",
    "\n",
    "Some of the examples came from [here](http://www.unexpected-vortices.com/sw/rippledoc/quick-markdown-example.html)\n",
    "\n",
    "### Gifs\n",
    "![gif.gif](https://media.giphy.com/media/wpBCnSSBCbh3q/giphy.gif)\n",
    "\n",
    "### Math\n",
    "$$I = \\int \\rho R^{2} dV$$\n",
    "\n",
    "### Block quotes\n",
    "> WOW!\n",
    ">\n",
    "> They can span multiple paragraphs,\n",
    "\n",
    "### Lists\n",
    "Here's a numbered list:\n",
    "\n",
    " 1. first item\n",
    " 2. second item\n",
    " 3. third item\n",
    "\n",
    "### Bullets\n",
    "\n",
    "- Woah\n",
    "- Wow\n",
    "- Wowza\n",
    "\n",
    "# Matplotlib\n",
    "\n",
    "Graphs displayed beside code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ccnkJMVH89PrJlFY1snxDpQ8UGoIVYzD8yLnmdh5cXcbl47N8Or59bmEG104ZwaPrKqmq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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x217d87c1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = 1 + np.sin(2*np.pi*t)\n",
    "plt.plot(t, s)\n",
    "\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('voltage (mV)')\n",
    "plt.title('About as simple as it gets, folks')\n",
    "plt.grid(True)\n"
   ]
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}