scoreRedditPosts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from tqdm import tqdm\n",
    "from sklearn.metrics import balanced_accuracy_score, accuracy_score\n",
    "from coherenceModel import *\n",
    "from pipe import dedup, groupby, where, select, sort, chain, traverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>content</th>\n",
       "      <th>subreddit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Well (and this is just speaking about our grou...</td>\n",
       "      <td>AskAcademia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I did this. I got my masters in mathematics th...</td>\n",
       "      <td>AskAcademia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I would say it's most definitely a possibility...</td>\n",
       "      <td>AskAcademia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I sort of did this, but from undergrad to grad...</td>\n",
       "      <td>AskAcademia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sure, it's called going to grad school ;-) I'm...</td>\n",
       "      <td>AskAcademia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>The weapons are not hitscan, but the bullet tr...</td>\n",
       "      <td>gaming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>a few friends of mine started playing rift. on...</td>\n",
       "      <td>gaming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>I like achievements and all, but it's rare tha...</td>\n",
       "      <td>gaming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>speaking form an independent developer standpo...</td>\n",
       "      <td>gaming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>wow, not one of you understands exchange rates...</td>\n",
       "      <td>gaming</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               content    subreddit\n",
       "0    Well (and this is just speaking about our grou...  AskAcademia\n",
       "1    I did this. I got my masters in mathematics th...  AskAcademia\n",
       "2    I would say it's most definitely a possibility...  AskAcademia\n",
       "3    I sort of did this, but from undergrad to grad...  AskAcademia\n",
       "4    Sure, it's called going to grad school ;-) I'm...  AskAcademia\n",
       "..                                                 ...          ...\n",
       "595  The weapons are not hitscan, but the bullet tr...       gaming\n",
       "596  a few friends of mine started playing rift. on...       gaming\n",
       "597  I like achievements and all, but it's rare tha...       gaming\n",
       "598  speaking form an independent developer standpo...       gaming\n",
       "599  wow, not one of you understands exchange rates...       gaming\n",
       "\n",
       "[600 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_df = pd.read_csv('reddit.csv').drop('Unnamed: 0', axis=1)\n",
    "reddit_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_df[\"num_sent\"] = reddit_df.content.apply(lambda x : len(sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_df[\"num_words\"] = reddit_df.content.apply(lambda x : len(word_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AskAcademia', 'LawSchool', 'AskReddit', 'science', 'funny', 'gaming']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subreddits = list(reddit_df[\"subreddit\"].value_counts().keys())\n",
    "subreddits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>content</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>num_sent</th>\n",
       "      <th>num_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Well (and this is just speaking about our grou...</td>\n",
       "      <td>AskAcademia</td>\n",
       "      <td>23</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I did this. I got my masters in mathematics th...</td>\n",
       "      <td>AskAcademia</td>\n",
       "      <td>14</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I would say it's most definitely a possibility...</td>\n",
       "      <td>AskAcademia</td>\n",
       "      <td>8</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I sort of did this, but from undergrad to grad...</td>\n",
       "      <td>AskAcademia</td>\n",
       "      <td>7</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sure, it's called going to grad school ;-) I'm...</td>\n",
       "      <td>AskAcademia</td>\n",
       "      <td>5</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>The weapons are not hitscan, but the bullet tr...</td>\n",
       "      <td>gaming</td>\n",
       "      <td>6</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>a few friends of mine started playing rift. on...</td>\n",
       "      <td>gaming</td>\n",
       "      <td>6</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>I like achievements and all, but it's rare tha...</td>\n",
       "      <td>gaming</td>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>speaking form an independent developer standpo...</td>\n",
       "      <td>gaming</td>\n",
       "      <td>7</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>wow, not one of you understands exchange rates...</td>\n",
       "      <td>gaming</td>\n",
       "      <td>4</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               content    subreddit  num_sent   \n",
       "0    Well (and this is just speaking about our grou...  AskAcademia        23  \\\n",
       "1    I did this. I got my masters in mathematics th...  AskAcademia        14   \n",
       "2    I would say it's most definitely a possibility...  AskAcademia         8   \n",
       "3    I sort of did this, but from undergrad to grad...  AskAcademia         7   \n",
       "4    Sure, it's called going to grad school ;-) I'm...  AskAcademia         5   \n",
       "..                                                 ...          ...       ...   \n",
       "595  The weapons are not hitscan, but the bullet tr...       gaming         6   \n",
       "596  a few friends of mine started playing rift. on...       gaming         6   \n",
       "597  I like achievements and all, but it's rare tha...       gaming         4   \n",
       "598  speaking form an independent developer standpo...       gaming         7   \n",
       "599  wow, not one of you understands exchange rates...       gaming         4   \n",
       "\n",
       "     num_words  \n",
       "0          533  \n",
       "1          275  \n",
       "2          226  \n",
       "3           98  \n",
       "4          150  \n",
       "..         ...  \n",
       "595        185  \n",
       "596        105  \n",
       "597         61  \n",
       "598        142  \n",
       "599        172  \n",
       "\n",
       "[600 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of all: 224.16833333333332\n",
      "SD of all: 206.7693723222943\n"
     ]
    },
    {
     "data": {
      "image/png": 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Yh4ACAACsQ0ABAADWCSugLF26VDfffLO6du2qlJQU3XnnnSotLQ1pc/bsWeXl5albt27q0qWLpkyZosrKypA2ZWVlmjhxohISEpSSkqJ58+apoaHh6mcDAADahLACyo4dO5SXl6c9e/aosLBQPp9POTk5OnPmTLDNww8/rPfee09vvfWWduzYoePHj2vy5MnB/Y2NjZo4caLq6+u1e/duvfLKK1qzZo0WLFgQuVkBAICo1iGcxhs3bgy5v2bNGqWkpKi4uFijR49WTU2NVq1apbVr1+rWW2+VJK1evVoDBgzQnj17NGrUKG3evFmHDh3Sli1blJqaqptuuklPPvmk5s+fr0WLFik+Pj5yswMAAFEprIDyQzU1NZKk5ORkSVJxcbF8Pp+ys7ODbfr3769evXqpqKhIo0aNUlFRkQYPHqzU1NRgm9zcXM2ZM0cHDx7UsGHDmjyO1+uV1+sN3q+trZUk+Xw++Xy+q5lCiEBfzhgTsT5bSqTqEOgnknWNNtTgHOpADSRqIFGDgEjUIZxjrzig+P1+PfTQQ/rpT3+qQYMGSZIqKioUHx+vpKSkkLapqamqqKgItvl+OAnsD+w7n6VLl2rx4sVNtm/evFkJCQlXOoULenK4P+J9NrcNGzZEtL/CwsKI9heNqME51IEaSNRAogYBV1OHurq6y257xQElLy9PBw4c0K5du660i8tWUFCg/Pz84P3a2lplZGQoJydHLpcrYo/j8/lUWFioJ/bFyOt3RKzflnBgUW5E+gnUYNy4cYqLi4tIn9GGGpxDHaiBRA0kahAQiToEngG5HFcUUObOnav169dr586d6tmzZ3C72+1WfX29qqurQ86iVFZWyu12B9t8/PHHIf0FrvIJtPkhp9Mpp9PZZHtcXFyzLBav3yFvY3QFlEjXoblqG02owTnUgRpI1ECiBgFXU4dwjgvrKh5jjObOnat33nlH27ZtU+/evUP2Z2VlKS4uTlu3bg1uKy0tVVlZmTwejyTJ4/Hos88+U1VVVbBNYWGhXC6XBg4cGM5wAABAGxXWGZS8vDytXbtW7777rrp27Rp8zUhiYqI6deqkxMREzZw5U/n5+UpOTpbL5dKDDz4oj8ejUaNGSZJycnI0cOBA3XvvvVq+fLkqKir0+OOPKy8v77xnSQAAQPsTVkBZuXKlJGnMmDEh21evXq377rtPkvTss88qJiZGU6ZMkdfrVW5url544YVg29jYWK1fv15z5syRx+NR586dNWPGDC1ZsuTqZgIAANqMsAKKMZe+/LZjx45asWKFVqxYccE2mZmZEb/qBAAAtB18Fg8AALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYJ0OrT0ARMa1j74fkX6csUbLR0iDFm2St9ERkT4v5ItlE5u1fwBA9OIMCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsE3ZA2blzpyZNmqT09HQ5HA6tW7cuZP99990nh8MRchs/fnxIm5MnT2ratGlyuVxKSkrSzJkzdfr06auaCAAAaDvCDihnzpzR0KFDtWLFigu2GT9+vMrLy4O3119/PWT/tGnTdPDgQRUWFmr9+vXauXOnZs+eHf7oAQBAmxT2pxlPmDBBEyZMuGgbp9Mpt9t93n2ff/65Nm7cqE8++UTDhw+XJD3//PO67bbb9Lvf/U7p6enhDgkAALQxzfIalO3btyslJUX9+vXTnDlzdOLEieC+oqIiJSUlBcOJJGVnZysmJkZ79+5tjuEAAIAoE/YZlEsZP368Jk+erN69e+vo0aN67LHHNGHCBBUVFSk2NlYVFRVKSUkJHUSHDkpOTlZFRcV5+/R6vfJ6vcH7tbW1kiSfzyefzxexsQf6csaYiPUZbQJzb4kaRPJ7F0mBcdk6vpZCHaiBRA0kahAQiTqEc2zEA8rdd98d/Hrw4MEaMmSI+vbtq+3bt2vs2LFX1OfSpUu1ePHiJts3b96shISEKx7rhTw53B/xPqNNS9Rgw4YNzf4YV6OwsLC1h2AF6kANJGogUYOAq6lDXV3dZbeNeED5oT59+qh79+46cuSIxo4dK7fbraqqqpA2DQ0NOnny5AVft1JQUKD8/Pzg/draWmVkZCgnJ0culytiY/X5fCosLNQT+2Lk9Tsi1m80ccYYPTnc3yI1OLAot1n7v1KBdTBu3DjFxcW19nBaDXWgBhI1kKhBQCTqEHgG5HI0e0D5+uuvdeLECaWlpUmSPB6PqqurVVxcrKysLEnStm3b5Pf7NXLkyPP24XQ65XQ6m2yPi4trlsXi9TvkbWyfASWgJWpg+w96c62vaEMdqIFEDSRqEHA1dQjnuLADyunTp3XkyJHg/WPHjqmkpETJyclKTk7W4sWLNWXKFLndbh09elS//e1vdd111yk399x/ywMGDND48eM1a9Ysvfjii/L5fJo7d67uvvturuABAACSruAqnn379mnYsGEaNmyYJCk/P1/Dhg3TggULFBsbq/379+v222/XDTfcoJkzZyorK0sfffRRyBmQ1157Tf3799fYsWN122236ZZbbtFLL70UuVkBAICoFvYZlDFjxsiYC1/hsWnTpkv2kZycrLVr14b70AAAoJ3gs3gAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYJ+yAsnPnTk2aNEnp6elyOBxat25dyH5jjBYsWKC0tDR16tRJ2dnZOnz4cEibkydPatq0aXK5XEpKStLMmTN1+vTpq5oIAABoO8IOKGfOnNHQoUO1YsWK8+5fvny5nnvuOb344ovau3evOnfurNzcXJ09ezbYZtq0aTp48KAKCwu1fv167dy5U7Nnz77yWQAAgDalQ7gHTJgwQRMmTDjvPmOMfv/73+vxxx/XHXfcIUn6n//5H6WmpmrdunW6++679fnnn2vjxo365JNPNHz4cEnS888/r9tuu02/+93vlJ6efhXTAQAAbUFEX4Ny7NgxVVRUKDs7O7gtMTFRI0eOVFFRkSSpqKhISUlJwXAiSdnZ2YqJidHevXsjORwAABClwj6DcjEVFRWSpNTU1JDtqampwX0VFRVKSUkJHUSHDkpOTg62+SGv1yuv1xu8X1tbK0ny+Xzy+XwRG3+gL2eMiVif0SYw95aoQSS/d5EUGJet42sp1IEaSNRAogYBkahDOMdGNKA0l6VLl2rx4sVNtm/evFkJCQkRf7wnh/sj3me0aYkabNiwodkf42oUFha29hCsQB2ogUQNJGoQcDV1qKuru+y2EQ0obrdbklRZWam0tLTg9srKSt10003BNlVVVSHHNTQ06OTJk8Hjf6igoED5+fnB+7W1tcrIyFBOTo5cLlfExu/z+VRYWKgn9sXI63dErN9o4owxenK4v0VqcGBRbrP2f6UC62DcuHGKi4tr7eG0GupADSRqIFGDgEjUIfAMyOWIaEDp3bu33G63tm7dGgwktbW12rt3r+bMmSNJ8ng8qq6uVnFxsbKysiRJ27Ztk9/v18iRI8/br9PplNPpbLI9Li6uWRaL1++Qt7F9BpSAlqiB7T/ozbW+og11oAYSNZCoQcDV1CGc48IOKKdPn9aRI0eC948dO6aSkhIlJyerV69eeuihh/Qf//Efuv7669W7d2898cQTSk9P15133ilJGjBggMaPH69Zs2bpxRdflM/n09y5c3X33XdzBQ8AAJB0BQFl3759+sUvfhG8H3jqZcaMGVqzZo1++9vf6syZM5o9e7aqq6t1yy23aOPGjerYsWPwmNdee01z587V2LFjFRMToylTpui5556LwHQAAEBbEHZAGTNmjIy58BUeDodDS5Ys0ZIlSy7YJjk5WWvXrg33oQEAQDvBZ/EAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwTofWHgDar2sffb+1h3Bezlij5SOkQYs2ydvoCNn3xbKJrTQqAGhfOIMCAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwTsQDyqJFi+RwOEJu/fv3D+4/e/as8vLy1K1bN3Xp0kVTpkxRZWVlpIcBAACiWLOcQbnxxhtVXl4evO3atSu47+GHH9Z7772nt956Szt27NDx48c1efLk5hgGAACIUh2apdMOHeR2u5tsr6mp0apVq7R27VrdeuutkqTVq1drwIAB2rNnj0aNGtUcwwEAAFGmWQLK4cOHlZ6ero4dO8rj8Wjp0qXq1auXiouL5fP5lJ2dHWzbv39/9erVS0VFRRcMKF6vV16vN3i/trZWkuTz+eTz+SI27kBfzhgTsT6jTWDu1OD8NYjkerNdYK7tac4/RA2ogUQNAiJRh3COdRhjIvqX6IMPPtDp06fVr18/lZeXa/Hixfrmm2904MABvffee7r//vtDwoYkjRgxQr/4xS/0X//1X+ftc9GiRVq8eHGT7WvXrlVCQkIkhw8AAJpJXV2d7rnnHtXU1Mjlcl20bcQDyg9VV1crMzNTzzzzjDp16nRFAeV8Z1AyMjL0t7/97ZITDIfP51NhYaGe2Bcjr98RsX6jiTPG6MnhfmpwgRocWJTbSqNqeYGfh3HjxikuLq61h9MqqAE1kKhBQCTqUFtbq+7du19WQGmWp3i+LykpSTfccIOOHDmicePGqb6+XtXV1UpKSgq2qaysPO9rVgKcTqecTmeT7XFxcc2yWLx+h7yN7fOPcwA1OH8N2uMvp+b6OYsm1IAaSNQg4GrqEM5xzf4+KKdPn9bRo0eVlpamrKwsxcXFaevWrcH9paWlKisrk8fjae6hAACAKBHxMyi/+c1vNGnSJGVmZur48eNauHChYmNjNXXqVCUmJmrmzJnKz89XcnKyXC6XHnzwQXk8Hq7gAQAAQREPKF9//bWmTp2qEydOqEePHrrlllu0Z88e9ejRQ5L07LPPKiYmRlOmTJHX61Vubq5eeOGFSA8DAABEsYgHlDfeeOOi+zt27KgVK1ZoxYoVkX5oAADQRvBZPAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6HVp7AEA0ufbR91t7CGH7YtnE1h4CAISNMygAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6HVp7AACa17WPvn9FxzljjZaPkAYt2iRvoyPCo7q4L5ZNbNHHA2AfzqAAAADrtOoZlBUrVujpp59WRUWFhg4dqueff14jRoxozSEBsMCVnvWJtHDOInHWB4isVjuD8uabbyo/P18LFy7U//3f/2no0KHKzc1VVVVVaw0JAABYotXOoDzzzDOaNWuW7r//fknSiy++qPfff18vv/yyHn300dYaFgBcEVvO+oQjGs/6tEadr/b1WNFYZxu0SkCpr69XcXGxCgoKgttiYmKUnZ2toqKiJu29Xq+8Xm/wfk1NjSTp5MmT8vl8ERuXz+dTXV2dOvhi1Ohv2RcF2qKD36iuzk8N2nkNJOogtf0aXPebP12yjTPG6PFhft3072/La0ENWuOP1tWug8ups432FowNuR/4G3nixAnFxcVdUZ+nTp2SJBljLt3YtIJvvvnGSDK7d+8O2T5v3jwzYsSIJu0XLlxoJHHjxo0bN27c2sDtq6++umRWiIrLjAsKCpSfnx+87/f7dfLkSXXr1k0OR+QSfW1trTIyMvTVV1/J5XJFrN9oQg2oQQB1oAYSNZCoQUAk6mCM0alTp5Senn7Jtq0SULp3767Y2FhVVlaGbK+srJTb7W7S3ul0yul0hmxLSkpqtvG5XK52vQglaiBRgwDqQA0kaiBRg4CrrUNiYuJltWuVq3ji4+OVlZWlrVu3Brf5/X5t3bpVHo+nNYYEAAAs0mpP8eTn52vGjBkaPny4RowYod///vc6c+ZM8KoeAADQfrVaQLnrrrv07bffasGCBaqoqNBNN92kjRs3KjU1tbWGJKfTqYULFzZ5Oqk9oQbUIIA6UAOJGkjUIKCl6+Aw5nKu9QEAAGg5fBYPAACwDgEFAABYh4ACAACsQ0ABAADWIaD8fytWrNC1116rjh07auTIkfr4449be0gRs3TpUt18883q2rWrUlJSdOedd6q0tDSkzZgxY+RwOEJuDzzwQEibsrIyTZw4UQkJCUpJSdG8efPU0NDQklO5YosWLWoyv/79+wf3nz17Vnl5eerWrZu6dOmiKVOmNHkjwWief8C1117bpA4Oh0N5eXmS2uY62LlzpyZNmqT09HQ5HA6tW7cuZL8xRgsWLFBaWpo6deqk7OxsHT58OKTNyZMnNW3aNLlcLiUlJWnmzJk6ffp0SJv9+/frZz/7mTp27KiMjAwtX768uad22S5WA5/Pp/nz52vw4MHq3Lmz0tPTNX36dB0/fjykj/OtnWXLloW0idYaSNJ9993XZH7jx48PaRPt60C6dB3O9/vB4XDo6aefDrZpsbUQkQ/XiXJvvPGGiY+PNy+//LI5ePCgmTVrlklKSjKVlZWtPbSIyM3NNatXrzYHDhwwJSUl5rbbbjO9evUyp0+fDrb5+c9/bmbNmmXKy8uDt5qamuD+hoYGM2jQIJOdnW0+/fRTs2HDBtO9e3dTUFDQGlMK28KFC82NN94YMr9vv/02uP+BBx4wGRkZZuvWrWbfvn1m1KhR5ic/+Ulwf7TPP6CqqiqkBoWFhUaS+fDDD40xbXMdbNiwwfz7v/+7efvtt40k884774TsX7ZsmUlMTDTr1q0zf/nLX8ztt99uevfubb777rtgm/Hjx5uhQ4eaPXv2mI8++shcd911ZurUqcH9NTU1JjU11UybNs0cOHDAvP7666ZTp07mD3/4Q0tN86IuVoPq6mqTnZ1t3nzzTfPXv/7VFBUVmREjRpisrKyQPjIzM82SJUtC1sb3f4dEcw2MMWbGjBlm/PjxIfM7efJkSJtoXwfGXLoO359/eXm5efnll43D4TBHjx4NtmmptUBAMcaMGDHC5OXlBe83Njaa9PR0s3Tp0lYcVfOpqqoyksyOHTuC237+85+bX//61xc8ZsOGDSYmJsZUVFQEt61cudK4XC7j9Xqbc7gRsXDhQjN06NDz7quurjZxcXHmrbfeCm77/PPPjSRTVFRkjIn++V/Ir3/9a9O3b1/j9/uNMW1/HfzwF7Lf7zdut9s8/fTTwW3V1dXG6XSa119/3RhjzKFDh4wk88knnwTbfPDBB8bhcJhvvvnGGGPMCy+8YK655pqQGsyfP9/069evmWcUvvP9Ufqhjz/+2EgyX375ZXBbZmamefbZZy94TLTXYMaMGeaOO+644DFtbR0Yc3lr4Y477jC33npryLaWWgvt/ime+vp6FRcXKzs7O7gtJiZG2dnZKioqasWRNZ+amhpJUnJycsj21157Td27d9egQYNUUFCgurq64L6ioiINHjw45I30cnNzVVtbq4MHD7bMwK/S4cOHlZ6erj59+mjatGkqKyuTJBUXF8vn84Wsgf79+6tXr17BNdAW5v9D9fX1evXVV/XP//zPIR+62dbXwfcdO3ZMFRUVId/7xMREjRw5MuR7n5SUpOHDhwfbZGdnKyYmRnv37g22GT16tOLj44NtcnNzVVpaqr///e8tNJvIqampkcPhaPKZZ8uWLVO3bt00bNgwPf300yFP7bWFGmzfvl0pKSnq16+f5syZoxMnTgT3tcd1UFlZqffff18zZ85ssq8l1kJUfJpxc/rb3/6mxsbGJu9gm5qaqr/+9a+tNKrm4/f79dBDD+mnP/2pBg0aFNx+zz33KDMzU+np6dq/f7/mz5+v0tJSvf3225KkioqK89YosM92I0eO1Jo1a9SvXz+Vl5dr8eLF+tnPfqYDBw6ooqJC8fHxTX4Zp6amBucW7fM/n3Xr1qm6ulr33XdfcFtbXwc/FBjz+eb0/e99SkpKyP4OHTooOTk5pE3v3r2b9BHYd8011zTL+JvD2bNnNX/+fE2dOjXkA+F+9atf6cc//rGSk5O1e/duFRQUqLy8XM8884yk6K/B+PHjNXnyZPXu3VtHjx7VY489pgkTJqioqEixsbHtbh1I0iuvvKKuXbtq8uTJIdtbai20+4DS3uTl5enAgQPatWtXyPbZs2cHvx48eLDS0tI0duxYHT16VH379m3pYUbchAkTgl8PGTJEI0eOVGZmpv70pz+pU6dOrTiy1rNq1SpNmDAh5GPP2/o6wMX5fD794z/+o4wxWrlyZci+/Pz84NdDhgxRfHy8/vVf/1VLly5tE28Bf/fddwe/Hjx4sIYMGaK+fftq+/btGjt2bCuOrPW8/PLLmjZtmjp27BiyvaXWQrt/iqd79+6KjY1tcsVGZWWl3G53K42qecydO1fr16/Xhx9+qJ49e1607ciRIyVJR44ckSS53e7z1iiwL9okJSXphhtu0JEjR+R2u1VfX6/q6uqQNt9fA21t/l9++aW2bNmif/mXf7lou7a+DgJjvtjPv9vtVlVVVcj+hoYGnTx5sk2tj0A4+fLLL1VYWBhy9uR8Ro4cqYaGBn3xxReS2kYNvq9Pnz7q3r17yNpvD+sg4KOPPlJpaeklf0dIzbcW2n1AiY+PV1ZWlrZu3Rrc5vf7tXXrVnk8nlYcWeQYYzR37ly988472rZtW5NTb+dTUlIiSUpLS5MkeTweffbZZyE/oIFfYgMHDmyWcTen06dP6+jRo0pLS1NWVpbi4uJC1kBpaanKysqCa6CtzX/16tVKSUnRxIkTL9qura+D3r17y+12h3zva2trtXfv3pDvfXV1tYqLi4Nttm3bJr/fHwxwHo9HO3fulM/nC7YpLCxUv379ouK0fiCcHD58WFu2bFG3bt0ueUxJSYliYmKCT3tEew1+6Ouvv9aJEydC1n5bXwfft2rVKmVlZWno0KGXbNtsayGsl9S2UW+88YZxOp1mzZo15tChQ2b27NkmKSkp5EqFaDZnzhyTmJhotm/fHnJZWF1dnTHGmCNHjpglS5aYffv2mWPHjpl3333X9OnTx4wePTrYR+Dy0pycHFNSUmI2btxoevToYfXlpd/3yCOPmO3bt5tjx46ZP//5zyY7O9t0797dVFVVGWPOXWbcq1cvs23bNrNv3z7j8XiMx+MJHh/t8/++xsZG06tXLzN//vyQ7W11HZw6dcp8+umn5tNPPzWSzDPPPGM+/fTT4BUqy5YtM0lJSebdd981+/fvN3fcccd5LzMeNmyY2bt3r9m1a5e5/vrrQy4vra6uNqmpqebee+81Bw4cMG+88YZJSEiw5vLSi9Wgvr7e3H777aZnz56mpKQk5HdE4CqM3bt3m2effdaUlJSYo0ePmldffdX06NHDTJ8+PfgY0VyDU6dOmd/85jemqKjIHDt2zGzZssX8+Mc/Ntdff705e/ZssI9oXwfGXPrnwZhzlwknJCSYlStXNjm+JdcCAeX/e/75502vXr1MfHy8GTFihNmzZ09rDyliJJ33tnr1amOMMWVlZWb06NEmOTnZOJ1Oc91115l58+aFvP+FMcZ88cUXZsKECaZTp06me/fu5pFHHjE+n68VZhS+u+66y6SlpZn4+Hjzox/9yNx1113myJEjwf3fffed+bd/+zdzzTXXmISEBPPLX/7SlJeXh/QRzfP/vk2bNhlJprS0NGR7W10HH3744XnX/4wZM4wx5y41fuKJJ0xqaqpxOp1m7NixTWpz4sQJM3XqVNOlSxfjcrnM/fffb06dOhXS5i9/+Yu55ZZbjNPpND/60Y/MsmXLWmqKl3SxGhw7duyCvyMC749TXFxsRo4caRITE03Hjh3NgAEDzFNPPRXyx9uY6K1BXV2dycnJMT169DBxcXEmMzPTzJo1q8k/qdG+Doy59M+DMcb84Q9/MJ06dTLV1dVNjm/JteAwxpjLP98CAADQ/Nr9a1AAAIB9CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsM7/A7iWwt7yqquuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_statistics(series, title):\n",
    "  print(f\"Mean of {title}: {np.mean(series.values)}\")\n",
    "  print(f\"SD of {title}: {np.sqrt(np.var(series.values))}\")\n",
    "  series.rename(title).hist(legend=True)\n",
    "  \n",
    "get_statistics(reddit_df.num_words, \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of AskAcademia: 12.98\n",
      "SD of AskAcademia: 9.95989959788752\n",
      "Mean of LawSchool: 12.97\n",
      "SD of LawSchool: 9.982439581585256\n",
      "Mean of AskReddit: 12.21\n",
      "SD of AskReddit: 10.085925837522304\n",
      "Mean of science: 8.95\n",
      "SD of science: 8.911088597921133\n",
      "Mean of funny: 7.71\n",
      "SD of funny: 7.783694495546443\n",
      "Mean of gaming: 9.04\n",
      "SD of gaming: 10.347869345908848\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for subreddit in subreddits:\n",
    "    get_statistics(reddit_df[reddit_df[\"subreddit\"] == subreddit].num_sent, subreddit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subreddit\n",
       "AskAcademia    88\n",
       "LawSchool      82\n",
       "AskReddit      81\n",
       "science        77\n",
       "gaming         68\n",
       "funny          55\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_subreddits = reddit_df[reddit_df[\"num_sent\"] >= 5]\n",
    "filtered_subreddits[\"subreddit\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_model = FFNN(5, device)\n",
    "basic_model.load_state_dict(torch.load('best_rnn.pt'))\n",
    "basic_model.eval()\n",
    "basic_model.to(device)\n",
    "wsize = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.downloader\n",
    "embed = gensim.downloader.load(\"glove-wiki-gigaword-50\")\n",
    "unk = np.mean(embed.vectors, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_windows(post, window_size):\n",
    "    sentences = get_paragraph_embedding_tup(embed, unk, post)\n",
    "    num_windows = len(sentences) - window_size + 1\n",
    "                \n",
    "    if num_windows < 0:\n",
    "        print(f\"WARNING: Post did not have enough sentences for window size {window_size}; returning None\")\n",
    "        return None\n",
    "    \n",
    "    return [tensor_of_tupled_par_embed(sentences[i:i+window_size]) for i in range(num_windows)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coherence(post):\n",
    "    windows_of_post = get_windows(post, wsize)\n",
    "    with torch.no_grad():\n",
    "        windows = [[s.to(device) for s in window] for window in windows_of_post]\n",
    "        scores = torch.sigmoid(basic_model(windows))\n",
    "        return torch.mean(scores).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18873/2578076136.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_subreddits['coherence'] = filtered_subreddits.content.apply(get_coherence)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 74,
     "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": [
    "filtered_subreddits['coherence'] = filtered_subreddits.content.apply(get_coherence)\n",
    "filtered_subreddits.coherence.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of AskAcademia: 0.5568155585364862\n",
      "SD of AskAcademia: 0.1255734131829495\n",
      "Mean of LawSchool: 0.5201205318657364\n",
      "SD of LawSchool: 0.11599824941637735\n",
      "Mean of AskReddit: 0.5238010141952538\n",
      "SD of AskReddit: 0.15214121443036013\n",
      "Mean of science: 0.5463853699046296\n",
      "SD of science: 0.17785123923051263\n",
      "Mean of funny: 0.5262214897708459\n",
      "SD of funny: 0.1533847597014325\n",
      "Mean of gaming: 0.5236281682463253\n",
      "SD of gaming: 0.1491866360461201\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for subreddit in subreddits:\n",
    "    get_statistics(filtered_subreddits[filtered_subreddits[\"subreddit\"] == subreddit].coherence, subreddit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>paragraph</th>\n",
       "      <th>is_coherent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The preflight inspection of the fuel tanks by ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The pilot reported that he was cleared to 4,00...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The instrument-rated private pilot lost contro...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The non-instrument rated private pilot was rec...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The commercial pilot reported a partial power ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Personnel at the airport reported that the bat...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>The student pilot reported that he was practic...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>The commercial certificated pilot was maneuver...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>The private pilot of the amateur built experim...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Approximately 5 minutes after takeoff, at an a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              paragraph  is_coherent\n",
       "0     The preflight inspection of the fuel tanks by ...            1\n",
       "1     The pilot reported that he was cleared to 4,00...            1\n",
       "2     The instrument-rated private pilot lost contro...            1\n",
       "3     The non-instrument rated private pilot was rec...            1\n",
       "4     The commercial pilot reported a partial power ...            1\n",
       "...                                                 ...          ...\n",
       "1995  Personnel at the airport reported that the bat...            1\n",
       "1996  The student pilot reported that he was practic...            1\n",
       "1997  The commercial certificated pilot was maneuver...            1\n",
       "1998  The private pilot of the amateur built experim...            1\n",
       "1999  Approximately 5 minutes after takeoff, at an a...            1\n",
       "\n",
       "[2000 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aviation_df = pd.read_csv('moreAviationPerms.csv')\n",
    "only_coherent_aviation = aviation_df[aviation_df['is_coherent'] == 1]\n",
    "only_coherent_aviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18873/217935567.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  only_coherent_aviation['coherence'] = only_coherent_aviation.paragraph.apply(get_coherence)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 78,
     "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": [
    "only_coherent_aviation['coherence'] = only_coherent_aviation.paragraph.apply(get_coherence)\n",
    "only_coherent_aviation.coherence.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_coherent_aviation.to_csv('aviationWithCoherence.csv')\n",
    "filtered_subreddits.to_csv('subredditsWithCoherence.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "dec0f04a0c497e8ad4a75383cb1718df73f989e41df9817b9ead081e3efac1a8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}