Reddit_v7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Executive Summary\n",
    "\n",
    "Reddit claims to be the front page of the internet, and that's because they are.  With an average of 542 million monthly visitors, of which 234 million are unique, Reddit is the third most visited site in the U.S. and ranked sixth in the world.  Reddit is subdivided into subreddits, which are themed discussion boards created and populated by Reddit users with links, text, videos and images.  These subreddits span an ostensibly endless array of interests including world news, sports, economics, movies, music, fitness, and really anything that anyone has ever thought about and wants to talk about.  Reddit members discuss proposed topics in the comments section, and the most popular comments are \"up-voted\" to the top of the discussion board an possibly even on the site's landing page.  In 2015, Reddit users submitted nearly 75 million posts and followed up with nearly three quarters of a billion comments.\n",
    "\n",
    "As such, Reddit is one of the most incredible internet platforms available to date.  Not only is it technologically accessible to anyone with an internet connection, but it is also philosophically accesible in that all people from all walks of life are free to engage in conversation with anyone else in the subreddit of their choosing (provided they follow Reddit's harrassment policies.) But with so many users, submissions, comments, and up-votes, it can also seem impossible to craft a post that will ever see the light of day.  \n",
    "\n",
    "But viral posts are not flukes.  In fact, they usually hue closely to established patterns and trends.  Once the trends are uncovered, anyone can write a popular post that will likely appeal to a wide audience and gain traction within the targeted discussion board. \n",
    "\n",
    "Using data science techniques like exploratory data analysis, predictive modeling, and natural language processing allows us to effiently search through thousands (and millions, if the project calls for it) data points to identify what matters and what doesn't - in other words, what sinks an article and what makes it swim.  Specifically, when analyzing text within titles, posts, and comments, we can vectorize words to identify which ones are more frequently used, and therefore appropriated.  Themes rise from posts when aggregating text in word clouds.  Comments can be analyzed for sentiment using prepackaged libraries to identify how users are engaging with the content.  \n",
    "\n",
    "With an endless supply of things to read on the internet, it seems impossible to write a post that anyone else but your mom will read.  Data science undeniably refutes this assumption.  Rather, with a few (hundred) keystrokes, a platform as wild as Reddit can be neatly distilled into a handful of targeted insights.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatic calling is: Smart\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
      "  warnings.warn(\"The twython library has not been installed. \"\n"
     ]
    }
   ],
   "source": [
    "# Import Packages\n",
    "\n",
    "# API\n",
    "import requests\n",
    "import time\n",
    "import datetime\n",
    "import json\n",
    "from bs4 import BeautifulSoup\n",
    "# import praw\n",
    "\n",
    "# Analysis\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Modeling\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, ExtraTreesClassifier\n",
    "\n",
    "# Plots\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "%matplotlib inline\n",
    "%autocall 1\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.externals.six import StringIO  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual, FloatSlider\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display, Markdown, Latex, Image\n",
    "\n",
    "# Natural Language Processing\n",
    "\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import nltk.data\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from nltk import sentiment\n",
    "import markovify as mk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# API Pull Set-Up\n",
    "\n",
    "# URL = \"http://www.reddit.com/hot.json\"\n",
    "\n",
    "# headers={'User-agent': 'jess-chace Bot 0.1'}\n",
    "\n",
    "# res = requests.get(URL, headers=headers)\n",
    "# soup = BeautifulSoup(res.content, 'lxml')\n",
    "# res.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# data = res.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# API Pull from Reddit for 5,000 posts\n",
    "\n",
    "# posts = []\n",
    "# after = None\n",
    "\n",
    "# for i in range(200):\n",
    "#     print(i)\n",
    "#     if after == None:\n",
    "#         params = {}\n",
    "#     else: \n",
    "#         params = {'after': after}\n",
    "#     url = URL\n",
    "#     res = requests.get(url, params=params, headers=headers)\n",
    "#     if res.status_code == 200:\n",
    "#         data = res.json()\n",
    "#         posts.extend(data['data']['children'])\n",
    "#         after = data['data']['after']\n",
    "        \n",
    "#     else:\n",
    "#         print(res.status_code)\n",
    "#         break\n",
    "#     time.sleep(3)\n",
    "    \n",
    "# print (datetime.datetime.utcnow())\n",
    "# print (time.time())\n",
    "# print (time.ctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store Results of API pull into variables\n",
    "\n",
    "# author = [post['data']['author'] for post in posts]\n",
    "# title = [post['data']['title'] for post in posts]\n",
    "# subreddit = [post['data']['subreddit'] for post in posts]\n",
    "# subreddit_subscribers = [post['data']['subreddit_subscribers'] for post in posts]\n",
    "# score = [post['data']['score'] for post in posts]\n",
    "# ups = [post['data']['ups'] for post in posts]\n",
    "# downs = [post['data']['downs'] for post in posts]\n",
    "# num_comments = [post['data']['num_comments'] for post in posts]\n",
    "# num_crossposts = [post['data']['num_crossposts'] for post in posts]\n",
    "# text = [post['data']['selftext'] for post in posts]\n",
    "# pinned = [post['data']['pinned'] for post in posts]\n",
    "# stickied = [post['data']['stickied'] for post in posts]\n",
    "# wls = [post['data']['wls'] for post in posts]\n",
    "# created_utc = [post['data']['created_utc'] for post in posts]\n",
    "# pulled_utc = 1527880293.3702872\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create DataFrame of Pull Results\n",
    "\n",
    "# Reddit = pd.DataFrame([post['data']['author'] for post in posts], \n",
    "#              columns=['Author'],\n",
    "#              index=[post['data']['id'] for post in posts])\n",
    "\n",
    "# Reddit['Title'] = [post['data']['title'] for post in posts]\n",
    "# Reddit['SubReddit'] = [post['data']['subreddit'] for post in posts]\n",
    "# Reddit['SubReddit_Subscribers'] = subreddit_subscribers\n",
    "# Reddit['Score'] = score\n",
    "# Reddit['Ups'] = ups\n",
    "# Reddit['Downs'] = downs\n",
    "# Reddit['Num_Comments'] = num_comments\n",
    "# Reddit['Num_Crossposts'] = num_crossposts\n",
    "# Reddit['Text'] = text\n",
    "# Reddit['Pinned'] = pinned\n",
    "# Reddit['Stickied'] = stickied\n",
    "# Reddit['WLS'] = wls\n",
    "# Reddit['Created_UTC'] = created_utc\n",
    "# Reddit['Pulled_UTC'] = 1527880293.3702872\n",
    "\n",
    "# Reddit['Time_Up'] = Reddit['Pulled_UTC'] - Reddit['Created_UTC']\n",
    "\n",
    "# Reddit.index.rename('Id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reddit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "focus": false,
    "id": "783fd153-28ac-47ab-bfca-27e7c1de95b4"
   },
   "outputs": [],
   "source": [
    "# Export to csv to avoid having to repull\n",
    "\n",
    "# Reddit.to_csv('./Reddit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "focus": false,
    "id": "588f9845-6143-4bcc-bfd1-85d45b79303d"
   },
   "outputs": [],
   "source": [
    "# Load Data pulled from Reddit\n",
    "\n",
    "Reddit = pd.read_csv('./Reddit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1950"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniquesubreddits = Reddit.groupby(['SubReddit']).sum()\n",
    "len(uniquesubreddits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.208488e+10</td>\n",
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       "      <td>-8.713784e+06</td>\n",
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       "      <th>pics</th>\n",
       "      <td>355289984</td>\n",
       "      <td>295713</td>\n",
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       "    <tr>\n",
       "      <th>OldSchoolCool</th>\n",
       "      <td>154313890</td>\n",
       "      <td>64992</td>\n",
       "      <td>64992</td>\n",
       "      <td>0</td>\n",
       "      <td>1519</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.833411e+10</td>\n",
       "      <td>1.832924e+10</td>\n",
       "      <td>-4.872863e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WritingPrompts</th>\n",
       "      <td>135494462</td>\n",
       "      <td>12389</td>\n",
       "      <td>12389</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.680642e+10</td>\n",
       "      <td>1.680180e+10</td>\n",
       "      <td>-4.617830e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>television</th>\n",
       "      <td>116745063</td>\n",
       "      <td>34058</td>\n",
       "      <td>34058</td>\n",
       "      <td>0</td>\n",
       "      <td>1903</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.222279e+10</td>\n",
       "      <td>1.221949e+10</td>\n",
       "      <td>-3.298862e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>science</th>\n",
       "      <td>112509647</td>\n",
       "      <td>47283</td>\n",
       "      <td>47283</td>\n",
       "      <td>0</td>\n",
       "      <td>2390</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.167057e+09</td>\n",
       "      <td>9.164620e+09</td>\n",
       "      <td>-2.437022e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SubReddit_Subscribers   Score     Ups  Downs  Num_Comments  \\\n",
       "SubReddit                                                                       \n",
       "funny                          412170105  113454  113454      0          3027   \n",
       "todayilearned                  395679314  221551  221551      0          5309   \n",
       "gaming                         382102366  140262  140262      0          8542   \n",
       "aww                            361566598  308835  308835      0          3888   \n",
       "pics                           355289984  295713  295713      0          7871   \n",
       "worldnews                      338186535  107341  107341      0          8093   \n",
       "AskReddit                      328170223   61156   61156      0         44513   \n",
       "gifs                           307343070  354599  354599      0          7986   \n",
       "videos                         302774183   48003   48003      0          5458   \n",
       "news                           273454058   54846   54846      0          5506   \n",
       "EarthPorn                      260702814   22178   22178      0           359   \n",
       "mildlyinteresting              257815278  212119  212119      0          5028   \n",
       "movies                         246890619   94450   94450      0          4710   \n",
       "Showerthoughts                 240470545   82434   82434      0          2115   \n",
       "Jokes                          177702767   48544   48544      0          1290   \n",
       "Art                            154606843   46639   46639      0           957   \n",
       "OldSchoolCool                  154313890   64992   64992      0          1519   \n",
       "WritingPrompts                 135494462   12389   12389      0           159   \n",
       "television                     116745063   34058   34058      0          1903   \n",
       "science                        112509647   47283   47283      0          2390   \n",
       "\n",
       "                   Num_Crossposts  Pinned  Stickied    WLS   Created_UTC  \\\n",
       "SubReddit                                                                  \n",
       "funny                          17     0.0       0.0  126.0  3.208498e+10   \n",
       "todayilearned                  20     0.0       0.0  126.0  3.208473e+10   \n",
       "gaming                         21     0.0       0.0  120.0  3.208490e+10   \n",
       "aww                            29     0.0       0.0  126.0  3.208488e+10   \n",
       "pics                           30     0.0       0.0  114.0  2.902901e+10   \n",
       "worldnews                      10     0.0       0.0  108.0  2.750131e+10   \n",
       "AskReddit                       5     0.0       0.0  102.0  2.597322e+10   \n",
       "gifs                           80     0.0       0.0  114.0  2.902896e+10   \n",
       "videos                          8     0.0       0.0  102.0  2.597334e+10   \n",
       "news                           10     0.0       0.0  102.0  2.597356e+10   \n",
       "EarthPorn                       5     0.0       0.0  102.0  2.597351e+10   \n",
       "mildlyinteresting              15     0.0       0.0  108.0  2.750115e+10   \n",
       "movies                          9     0.0       0.0   84.0  2.138991e+10   \n",
       "Showerthoughts                  2     0.0       0.0  102.0  2.597352e+10   \n",
       "Jokes                           1     0.0       0.0   78.0  1.986203e+10   \n",
       "Art                            10     0.0       0.0   72.0  1.833422e+10   \n",
       "OldSchoolCool                   0     0.0       0.0   72.0  1.833411e+10   \n",
       "WritingPrompts                  0     0.0       0.0   66.0  1.680642e+10   \n",
       "television                      4     0.0       0.0   48.0  1.222279e+10   \n",
       "science                        10     0.0       0.0   36.0  9.167057e+09   \n",
       "\n",
       "                     Pulled_UTC       Time_Up  \n",
       "SubReddit                                      \n",
       "funny              3.207617e+10 -8.809046e+06  \n",
       "todayilearned      3.207617e+10 -8.562389e+06  \n",
       "gaming             3.207617e+10 -8.730713e+06  \n",
       "aww                3.207617e+10 -8.713784e+06  \n",
       "pics               2.902130e+10 -7.716663e+06  \n",
       "worldnews          2.749386e+10 -7.455556e+06  \n",
       "AskReddit          2.596642e+10 -6.802315e+06  \n",
       "gifs               2.902130e+10 -7.663889e+06  \n",
       "videos             2.596642e+10 -6.914959e+06  \n",
       "news               2.596642e+10 -7.142325e+06  \n",
       "EarthPorn          2.596642e+10 -7.089040e+06  \n",
       "mildlyinteresting  2.749386e+10 -7.288445e+06  \n",
       "movies             2.138411e+10 -5.800554e+06  \n",
       "Showerthoughts     2.596642e+10 -7.100761e+06  \n",
       "Jokes              1.985668e+10 -5.355082e+06  \n",
       "Art                1.832924e+10 -4.979724e+06  \n",
       "OldSchoolCool      1.832924e+10 -4.872863e+06  \n",
       "WritingPrompts     1.680180e+10 -4.617830e+06  \n",
       "television         1.221949e+10 -3.298862e+06  \n",
       "science            9.164620e+09 -2.437022e+06  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniquesubreddits.sort_values(['SubReddit_Subscribers'],ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Shape of Data Set: (5000, 17)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 17 columns):\n",
      "Id                       5000 non-null object\n",
      "Author                   5000 non-null object\n",
      "Title                    5000 non-null object\n",
      "SubReddit                5000 non-null object\n",
      "SubReddit_Subscribers    5000 non-null int64\n",
      "Score                    5000 non-null int64\n",
      "Ups                      5000 non-null int64\n",
      "Downs                    5000 non-null int64\n",
      "Num_Comments             5000 non-null int64\n",
      "Num_Crossposts           5000 non-null int64\n",
      "Text                     416 non-null object\n",
      "Pinned                   5000 non-null bool\n",
      "Stickied                 5000 non-null bool\n",
      "WLS                      4316 non-null float64\n",
      "Created_UTC              5000 non-null float64\n",
      "Pulled_UTC               5000 non-null float64\n",
      "Time_Up                  5000 non-null float64\n",
      "dtypes: bool(2), float64(4), int64(6), object(5)\n",
      "memory usage: 595.8+ KB\n",
      "None\n",
      "       SubReddit_Subscribers          Score            Ups   Downs  \\\n",
      "count           5.000000e+03    5000.000000    5000.000000  5000.0   \n",
      "mean            1.486732e+06    1590.903400    1590.903400     0.0   \n",
      "std             4.301016e+06    6047.174081    6047.174081     0.0   \n",
      "min             3.710000e+02      25.000000      25.000000     0.0   \n",
      "25%             4.107400e+04      82.000000      82.000000     0.0   \n",
      "50%             1.309015e+05     191.000000     191.000000     0.0   \n",
      "75%             4.319662e+05     636.250000     636.250000     0.0   \n",
      "max             1.962717e+07  133613.000000  133613.000000     0.0   \n",
      "\n",
      "       Num_Comments  Num_Crossposts          WLS   Created_UTC    Pulled_UTC  \\\n",
      "count   5000.000000      5000.00000  4316.000000  5.000000e+03  5.000000e+03   \n",
      "mean      75.890400         0.22140     5.259731  1.527852e+09  1.527437e+09   \n",
      "std      378.211518         1.07546     1.798663  1.849306e+04  6.604855e-05   \n",
      "min        0.000000         0.00000     0.000000  1.527794e+09  1.527437e+09   \n",
      "25%        6.000000         0.00000     6.000000  1.527845e+09  1.527437e+09   \n",
      "50%       16.000000         0.00000     6.000000  1.527857e+09  1.527437e+09   \n",
      "75%       44.000000         0.00000     6.000000  1.527866e+09  1.527437e+09   \n",
      "max    20246.000000        32.00000     6.000000  1.527879e+09  1.527437e+09   \n",
      "\n",
      "             Time_Up  \n",
      "count    5000.000000  \n",
      "mean  -415584.158289  \n",
      "std     18493.063827  \n",
      "min   -442156.167889  \n",
      "25%   -429354.417889  \n",
      "50%   -420563.667889  \n",
      "75%   -408481.917889  \n",
      "max   -357029.167889  \n",
      " Total Null values: 5268\n",
      " Columns with Null Values: Text    4584\n",
      "WLS      684\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore Data\n",
    "\n",
    "print(f\" Shape of Data Set: {Reddit.shape}\")\n",
    "print(Reddit.info())\n",
    "print(Reddit.describe())\n",
    "print(f\" Total Null values: {Reddit.isnull().sum().sum()}\")\n",
    "null_values = Reddit.isnull().sum()\n",
    "columns_with_null_values = null_values[null_values > 0]\n",
    "print(f\" Columns with Null Values: {columns_with_null_values}\")\n",
    "Reddit_obj = Reddit.select_dtypes(exclude=[np.number])\n",
    "Reddit_num = Reddit.select_dtypes(include=[np.number])\n",
    "\n",
    "# Mean Num of Comments: 75.890400\n",
    "# Median Num of Comments: 16\n",
    "\n",
    "# Num_Comments:\n",
    "25%        6  \n",
    "50%       16          \n",
    "75%       44"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean Data\n",
    "\n",
    "Reddit.WLS.fillna(6, inplace=True)\n",
    "\n",
    "Reddit.Text.fillna(0, inplace=True)\n",
    "\n",
    "# Encode Boolean Columns\n",
    "\n",
    "Reddit['IsPinned'] = Reddit['Pinned'].map(lambda x: 1 if x == True else 0)\n",
    "\n",
    "Reddit['IsStickied'] = Reddit['Stickied'].map(lambda x: 1 if x == True else 0)\n",
    "\n",
    "# Drop Object Columns\n",
    "\n",
    "Reddit.drop(['Pinned', 'Stickied'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                       0\n",
       "Author                   0\n",
       "Title                    0\n",
       "SubReddit                0\n",
       "SubReddit_Subscribers    0\n",
       "Score                    0\n",
       "Ups                      0\n",
       "Downs                    0\n",
       "Num_Comments             0\n",
       "Num_Crossposts           0\n",
       "Text                     0\n",
       "WLS                      0\n",
       "Created_UTC              0\n",
       "Pulled_UTC               0\n",
       "Time_Up                  0\n",
       "IsPinned                 0\n",
       "IsStickied               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Reddit.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Engineering\n",
    "\n",
    "# Encode Hot Posts\n",
    "\n",
    "Reddit['HOT'] = Reddit['Num_Comments'].map(lambda x: 1 if x >= 13 else 0)\n",
    "\n",
    "# Encode Super Hot Posts\n",
    "\n",
    "Reddit['SUPER_HOT'] = Reddit['Num_Comments'].map(lambda x: 1 if x >= 1000 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train-Train-Split on Data Set\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
    "\n",
    "y = Reddit['HOT']\n",
    "X = Reddit.drop(['HOT', 'SUPER_HOT'], axis=1)\n",
    "\n",
    "# X = Reddit[not_target]\n",
    "# y = Reddit[target].values.ravel()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vectorize Words in Titles\n",
    "\n",
    "# CVEC fit\n",
    "  \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cvec = CountVectorizer(stop_words='english',max_features=20)\n",
    "cvec.fit(X_train['Title'])\n",
    "\n",
    "cvec_titles = pd.DataFrame(cvec.transform(X_train['Title']).todense(),\n",
    "                       columns=cvec.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cvec = CountVectorizer(stop_words='english',max_features=20)\n",
    "# cvec.fit(X_train['Title'])\n",
    "\n",
    "cvec_titles_test = pd.DataFrame(cvec.transform(X_test['Title']).todense(),\n",
    "                       columns=cvec.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.concat([X_train.reset_index(drop=True), cvec_titles],\n",
    "                    axis=1).select_dtypes(include=['number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SubReddit_Subscribers</td>\n",
       "      <td>0.024722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Score</td>\n",
       "      <td>0.086294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ups</td>\n",
       "      <td>0.072523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Downs</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Num_Comments</td>\n",
       "      <td>0.760779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Num_Crossposts</td>\n",
       "      <td>0.006181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WLS</td>\n",
       "      <td>0.002072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Created_UTC</td>\n",
       "      <td>0.016032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pulled_UTC</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Time_Up</td>\n",
       "      <td>0.020978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IsPinned</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IsStickied</td>\n",
       "      <td>0.000962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>best</td>\n",
       "      <td>0.000693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>day</td>\n",
       "      <td>0.001175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>don</td>\n",
       "      <td>0.000248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>got</td>\n",
       "      <td>0.000703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>happy</td>\n",
       "      <td>0.000677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>just</td>\n",
       "      <td>0.000753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>life</td>\n",
       "      <td>0.000164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>like</td>\n",
       "      <td>0.001016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>little</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>new</td>\n",
       "      <td>0.000510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>oc</td>\n",
       "      <td>0.000699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>old</td>\n",
       "      <td>0.000314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>people</td>\n",
       "      <td>0.000139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>post</td>\n",
       "      <td>0.000066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>time</td>\n",
       "      <td>0.000466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>today</td>\n",
       "      <td>0.000368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>trump</td>\n",
       "      <td>0.000471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ve</td>\n",
       "      <td>0.000445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>year</td>\n",
       "      <td>0.000169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>years</td>\n",
       "      <td>0.000253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        0         1\n",
       "0   SubReddit_Subscribers  0.024722\n",
       "1                   Score  0.086294\n",
       "2                     Ups  0.072523\n",
       "3                   Downs  0.000000\n",
       "4            Num_Comments  0.760779\n",
       "5          Num_Crossposts  0.006181\n",
       "6                     WLS  0.002072\n",
       "7             Created_UTC  0.016032\n",
       "8              Pulled_UTC  0.000000\n",
       "9                 Time_Up  0.020978\n",
       "10               IsPinned  0.000000\n",
       "11             IsStickied  0.000962\n",
       "12                   best  0.000693\n",
       "13                    day  0.001175\n",
       "14                    don  0.000248\n",
       "15                    got  0.000703\n",
       "16                  happy  0.000677\n",
       "17                   just  0.000753\n",
       "18                   life  0.000164\n",
       "19                   like  0.001016\n",
       "20                 little  0.000129\n",
       "21                    new  0.000510\n",
       "22                     oc  0.000699\n",
       "23                    old  0.000314\n",
       "24                 people  0.000139\n",
       "25                   post  0.000066\n",
       "26                   time  0.000466\n",
       "27                  today  0.000368\n",
       "28                  trump  0.000471\n",
       "29                     ve  0.000445\n",
       "30                   year  0.000169\n",
       "31                  years  0.000253"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Importances for Random Forest Classifier\n",
    "\n",
    "params = {'n_estimators' : [10, 15, 20],\n",
    "          'max_depth' : [None, 2, 3, 4, 5]}\n",
    "\n",
    "rf_gs = GridSearchCV(RandomForestClassifier(), param_grid=params, cv=5)\n",
    "\n",
    "rf_gs = rf_gs.fit(X_train, y_train)\n",
    "\n",
    "best_rf_gs = rf_gs.best_estimator_\n",
    "\n",
    "feat_imps = pd.DataFrame(list(zip(X_train.columns, rf_gs.best_estimator_.feature_importances_)))\n",
    "feat_imps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imps_df = pd.DataFrame(feat_imps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Num_Comments</td>\n",
       "      <td>0.760779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Score</td>\n",
       "      <td>0.086294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ups</td>\n",
       "      <td>0.072523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SubReddit_Subscribers</td>\n",
       "      <td>0.024722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Time_Up</td>\n",
       "      <td>0.020978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Created_UTC</td>\n",
       "      <td>0.016032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Num_Crossposts</td>\n",
       "      <td>0.006181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WLS</td>\n",
       "      <td>0.002072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>day</td>\n",
       "      <td>0.001175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>like</td>\n",
       "      <td>0.001016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IsStickied</td>\n",
       "      <td>0.000962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>just</td>\n",
       "      <td>0.000753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>got</td>\n",
       "      <td>0.000703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>oc</td>\n",
       "      <td>0.000699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>best</td>\n",
       "      <td>0.000693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>happy</td>\n",
       "      <td>0.000677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>new</td>\n",
       "      <td>0.000510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>trump</td>\n",
       "      <td>0.000471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>time</td>\n",
       "      <td>0.000466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ve</td>\n",
       "      <td>0.000445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>today</td>\n",
       "      <td>0.000368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>old</td>\n",
       "      <td>0.000314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>years</td>\n",
       "      <td>0.000253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>don</td>\n",
       "      <td>0.000248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>year</td>\n",
       "      <td>0.000169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>life</td>\n",
       "      <td>0.000164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>people</td>\n",
       "      <td>0.000139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>little</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>post</td>\n",
       "      <td>0.000066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IsPinned</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pulled_UTC</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Downs</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        0         1\n",
       "4            Num_Comments  0.760779\n",
       "1                   Score  0.086294\n",
       "2                     Ups  0.072523\n",
       "0   SubReddit_Subscribers  0.024722\n",
       "9                 Time_Up  0.020978\n",
       "7             Created_UTC  0.016032\n",
       "5          Num_Crossposts  0.006181\n",
       "6                     WLS  0.002072\n",
       "13                    day  0.001175\n",
       "19                   like  0.001016\n",
       "11             IsStickied  0.000962\n",
       "17                   just  0.000753\n",
       "15                    got  0.000703\n",
       "22                     oc  0.000699\n",
       "12                   best  0.000693\n",
       "16                  happy  0.000677\n",
       "21                    new  0.000510\n",
       "28                  trump  0.000471\n",
       "26                   time  0.000466\n",
       "29                     ve  0.000445\n",
       "27                  today  0.000368\n",
       "23                    old  0.000314\n",
       "31                  years  0.000253\n",
       "14                    don  0.000248\n",
       "30                   year  0.000169\n",
       "18                   life  0.000164\n",
       "24                 people  0.000139\n",
       "20                 little  0.000129\n",
       "25                   post  0.000066\n",
       "10               IsPinned  0.000000\n",
       "8              Pulled_UTC  0.000000\n",
       "3                   Downs  0.000000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imps_df.sort_values(1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.concat([X_test.reset_index(drop=True), cvec_titles_test], \n",
    "          axis=1).select_dtypes(include=['number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard Scaler\n",
    "\n",
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipelines Set-Up\n",
    "\n",
    "ss = StandardScaler()\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "SGD = SGDClassifier()\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "bNB = BernoulliNB()\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "\n",
    "rf = RandomForestClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipeline through Logistic Regression\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "logreg_pipe = Pipeline([\n",
    "    ('ss', ss),\n",
    "    ('logreg', logreg)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'logreg__penalty':['l1','l2'],\n",
    "    'logreg__solver':['liblinear'],\n",
    "    'logreg__C':np.logspace(-5,0,100)\n",
    "}\n",
    "\n",
    "logreg_gs = GridSearchCV(logreg_pipe, param_grid=params, cv=5)\n",
    "logreg_gs.fit(X_train, y_train)\n",
    "\n",
    "best_logreg_gs = logreg_gs.best_estimator_\n",
    "\n",
    "logreg_gs_train = best_logreg_gs.score(X_train, y_train)\n",
    "logreg_gs_test = best_logreg_gs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Pipeline through SGDClassifier\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "SGD_pipe = Pipeline([\n",
    "    ('ss', ss),\n",
    "    ('SGD', SGD)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'SGD__loss': ['hinge', 'log', 'modified_huber',\n",
    "    'squared_hinge', 'perceptron'],\n",
    "    'SGD__penalty': ['none', 'l2', 'l1', 'elasticnet'],\n",
    "    'SGD__learning_rate': ['constant', 'optimal', 'invscaling'],\n",
    "    'SGD__eta0' : [1]\n",
    "}\n",
    "\n",
    "SGD_gs = GridSearchCV(SGD_pipe, param_grid=params, cv=5)\n",
    "SGD_gs.fit(X_train, y_train)\n",
    "\n",
    "best_SGD_gs = SGD_gs.best_estimator_\n",
    "\n",
    "SGD_gs_train = best_SGD_gs.score(X_train, y_train)\n",
    "SGD_gs_test = best_SGD_gs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipeline through kNN Classification\n",
    "\n",
    "knn_pipe = Pipeline([\n",
    "    ('ss', ss),\n",
    "    ('knn', knn)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'knn__n_neighbors':[1,3,5,9,15,21],\n",
    "    'knn__weights':['uniform','distance'],\n",
    "    'knn__metric':['euclidean','manhattan']\n",
    "}\n",
    "\n",
    "knn_gs = GridSearchCV(knn_pipe, param_grid=params)\n",
    "knn_gs.fit(X_train, y_train)\n",
    "\n",
    "best_knn_gs = knn_gs.best_estimator_\n",
    "\n",
    "knn_gs_train = best_knn_gs.score(X_train, y_train)\n",
    "knn_gs_test = best_knn_gs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipeline through BernoulliNB\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "bNB_pipe = Pipeline([\n",
    "    ('ss', ss),\n",
    "    ('bNB', bNB)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'bNB__alpha': np.logspace(-5,0,100)\n",
    "}\n",
    "\n",
    "bNB_gs = GridSearchCV(bNB_pipe, param_grid=params, cv=5)\n",
    "bNB_gs.fit(X_train, y_train)\n",
    "\n",
    "best_bNB_gs = bNB_gs.best_estimator_\n",
    "\n",
    "bNB_gs_train = best_bNB_gs.score(X_train, y_train)\n",
    "bNB_gs_test = best_bNB_gs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipeline through Decision Tree Classifier\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "dt_pipe = Pipeline([\n",
    "    ('ss', ss),\n",
    "    ('dt', dt)\n",
    "])\n",
    "\n",
    "params = {'dt__max_depth': [3,4,7,10,None],\n",
    "         'dt__min_samples_split': range(2, 10)}\n",
    "\n",
    "dt_gs = GridSearchCV(dt_pipe, param_grid=params, cv=5)\n",
    "dt_gs.fit(X_train, y_train)\n",
    "\n",
    "best_dt_gs = dt_gs.best_estimator_\n",
    "\n",
    "dt_gs_train = best_dt_gs.score(X_train, y_train)\n",
    "dt_gs_test = best_dt_gs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipeline through Random Forest Classifier\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "rf_pipe = Pipeline([\n",
    "    ('ss', ss),\n",
    "    ('rf', rf)\n",
    "])\n",
    "\n",
    "params = {'rf__n_estimators' : [10, 15, 20],\n",
    "          'rf__max_depth' : [None, 2, 3, 4, 5]}\n",
    "\n",
    "rf_gs = GridSearchCV(rf_pipe, param_grid=params, cv=5)\n",
    "rf_gs.fit(X_train, y_train)\n",
    "\n",
    "best_rf_gs = rf_gs.best_estimator_\n",
    "\n",
    "rf_gs_train = best_rf_gs.score(X_train, y_train)\n",
    "rf_gs_test = best_rf_gs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42779999999999996"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Baseline Accuracy\n",
    "\n",
    "1 - sum(Reddit['HOT'])/len(Reddit['HOT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV across LogisticRegression:\n",
      "Best Parameters = {'logreg__C': 0.10974987654930568, 'logreg__penalty': 'l1', 'logreg__solver': 'liblinear'}\n",
      "Best CV Score = 0.8610666666666666\n",
      "Train Score = 0.8514666666666667\n",
      "Test Score = 0.8568\n",
      "\n",
      "GridSearchCV across SGDClassifier:\n",
      "Best Parameters = {'SGD__eta0': 1, 'SGD__learning_rate': 'optimal', 'SGD__loss': 'perceptron', 'SGD__penalty': 'l1'}\n",
      "Best CV Score = 0.9077333333333333\n",
      "Train Score = 0.9394666666666667\n",
      "Test Score = 0.936\n",
      "\n",
      "GridSearchCV across kNN Classification:\n",
      "Best Parameters = {'knn__metric': 'manhattan', 'knn__n_neighbors': 5, 'knn__weights': 'distance'}\n",
      "Best CV Score = 0.8117333333333333\n",
      "Train Score = 1.0\n",
      "Test Score = 0.788\n",
      "\n",
      "GridSearchCV across BernoulliNB:\n",
      "Best Parameters = {'bNB__alpha': 0.05462277217684343}\n",
      "Best CV Score = 0.6856\n",
      "Train Score = 0.6877333333333333\n",
      "Test Score = 0.6904\n",
      "\n",
      "GridSearchCV across Decision Tree:\n",
      "Best Parameters = {'dt__max_depth': 3, 'dt__min_samples_split': 2}\n",
      "Best CV Score = 1.0\n",
      "Train Score = 1.0\n",
      "Test Score = 1.0\n",
      "\n",
      "GridSearchCV across Random Forest:\n",
      "Best Parameters = {'rf__max_depth': None, 'rf__n_estimators': 10}\n",
      "Best CV Score = 1.0\n",
      "Train Score = 1.0\n",
      "Test Score = 1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Model Selection Executive Summary\n",
    "\n",
    "print('GridSearchCV across LogisticRegression:')\n",
    "print(f\"Best Parameters = {logreg_gs.best_params_}\")\n",
    "print(f\"Best CV Score = {logreg_gs.best_score_}\")\n",
    "print(f\"Train Score = {logreg_gs_train}\")\n",
    "print(f\"Test Score = {logreg_gs_test}\")\n",
    "print()\n",
    "\n",
    "print('GridSearchCV across SGDClassifier:')\n",
    "print(f\"Best Parameters = {SGD_gs.best_params_}\")\n",
    "print(f\"Best CV Score = {SGD_gs.best_score_}\")\n",
    "print(f\"Train Score = {SGD_gs_train}\")\n",
    "print(f\"Test Score = {SGD_gs_test}\")\n",
    "print()\n",
    "\n",
    "print('GridSearchCV across kNN Classification:')\n",
    "print(f\"Best Parameters = {knn_gs.best_params_}\")\n",
    "print(f\"Best CV Score = {knn_gs.best_score_}\")\n",
    "print(f\"Train Score = {knn_gs_train}\")\n",
    "print(f\"Test Score = {knn_gs_test}\")\n",
    "print()\n",
    "\n",
    "print('GridSearchCV across BernoulliNB:')\n",
    "print(f\"Best Parameters = {bNB_gs.best_params_}\")\n",
    "print(f\"Best CV Score = {bNB_gs.best_score_}\")\n",
    "print(f\"Train Score = {bNB_gs_train}\")\n",
    "print(f\"Test Score = {bNB_gs_test}\")\n",
    "print()\n",
    "\n",
    "print('GridSearchCV across Decision Tree:')\n",
    "print(f\"Best Parameters = {dt_gs.best_params_}\")\n",
    "print(f\"Best CV Score = {dt_gs.best_score_}\")\n",
    "print(f\"Train Score = {dt_gs_train}\")\n",
    "print(f\"Test Score = {dt_gs_test}\")\n",
    "print()\n",
    "\n",
    "print('GridSearchCV across Random Forest:')\n",
    "print(f\"Best Parameters = {rf_gs.best_params_}\")\n",
    "print(f\"Best CV Score = {rf_gs.best_score_}\")\n",
    "print(f\"Train Score = {rf_gs_train}\")\n",
    "print(f\"Test Score = {rf_gs_test}\")\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/jessicachace/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Set Model Results to DataFrame\n",
    "\n",
    "logreg_results = pd.DataFrame.from_dict(logreg_gs.cv_results_)\n",
    "\n",
    "SGD_results = pd.DataFrame.from_dict(SGD_gs.cv_results_)\n",
    "\n",
    "knn_results = pd.DataFrame.from_dict(knn_gs.cv_results_)\n",
    "\n",
    "BernoulliNB_results = pd.DataFrame.from_dict(bNB_gs.cv_results_)\n",
    "\n",
    "decisiontree_results = pd.DataFrame.from_dict(dt_gs.cv_results_)\n",
    "\n",
    "randomforest_results = pd.DataFrame.from_dict(rf_gs.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_dt__max_depth</th>\n",
       "      <th>param_dt__min_samples_split</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004634</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>{'dt__max_depth': 3, 'dt__min_samples_split': 2}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.004634      0.000606         0.000645        0.000042   \n",
       "\n",
       "  param_dt__max_depth param_dt__min_samples_split  \\\n",
       "0                   3                           2   \n",
       "\n",
       "                                             params  split0_test_score  \\\n",
       "0  {'dt__max_depth': 3, 'dt__min_samples_split': 2}                1.0   \n",
       "\n",
       "   split1_test_score  split2_test_score       ...         mean_test_score  \\\n",
       "0                1.0                1.0       ...                     1.0   \n",
       "\n",
       "   std_test_score  rank_test_score  split0_train_score  split1_train_score  \\\n",
       "0             0.0                1                 1.0                 1.0   \n",
       "\n",
       "   split2_train_score  split3_train_score  split4_train_score  \\\n",
       "0                 1.0                 1.0                 1.0   \n",
       "\n",
       "   mean_train_score  std_train_score  \n",
       "0               1.0              0.0  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decisiontree_results.sort_values('mean_test_score', ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6),\n",
    "          facecolor='w',\n",
    "          edgecolor='r')\n",
    "\n",
    "N = 6\n",
    "mean_test_score = (0.861067, 0.907733, 0.811733, 0.6856, 1.0, 1.0)\n",
    "\n",
    "std_test_score = (0.040278, 0.021345, 0.006183, 0.014183, 0.0, 0.0)\n",
    "\n",
    "ind = np.arange(N)    # the x locations for the groups\n",
    "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "p1 = plt.bar(ind, mean_test_score, width, yerr=std_test_score)\n",
    "\n",
    "plt.ylabel('Mean Test Scores')\n",
    "plt.title('Model Selection', )\n",
    "plt.xticks(np.arange(6),['LogReg: 0.861067', 'SGD: 0.907733', 'knn: 0.811733', 'bNB: 0.6856', \n",
    "                         'DT: 1.0', 'RF: 1.0'])\n",
    "plt.yticks(np.arange(0, 1.0, 0.1))\n",
    "plt.axhline(y=0.42779999999999996, linewidth=4, color='#FF4500')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Masking for Text Data in Posts\n",
    "\n",
    "WithText = Reddit[(Reddit['Text'] != 0)]\n",
    "\n",
    "WithText_Hot = Reddit[(Reddit['Text'] != 0) & (Reddit['HOT']==1)]\n",
    "\n",
    "WithText_Super_Hot = Reddit[(Reddit['Text'] != 0) & (Reddit['SUPER_HOT']==1)]\n",
    "\n",
    "WithText_NotHot = Reddit[(Reddit['Text'] != 0) & (Reddit['HOT']==0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Corpus of Hot Words in Posts with Text\n",
    "\n",
    "hot = []\n",
    "\n",
    "for row in WithText_Hot['Text']:\n",
    "        hot.append(row)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WordCloud for Hot Docs\n",
    "\n",
    "hotstring = \" \".join(hot)\n",
    "\n",
    "wordcloud = WordCloud(    colormap='magma',\n",
    "                          stopwords=STOPWORDS,\n",
    "                          background_color='white',\n",
    "                          width=2400,\n",
    "                          height=2000\n",
    "                         ).generate(hotstring)\n",
    "\n",
    "\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Corpus of Super Hot Words in Posts with Text\n",
    "\n",
    "superhot = []\n",
    "\n",
    "for row in WithText_Super_Hot['Text']:\n",
    "        superhot.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WordCloud for Super Hot Docs\n",
    "\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "superhotstring = \" \".join(superhot)\n",
    "\n",
    "wordcloud = WordCloud(    colormap='inferno',\n",
    "                          stopwords=STOPWORDS,\n",
    "                          background_color='white',\n",
    "                          width=2400,\n",
    "                          height=2000\n",
    "                         ).generate(superhotstring)\n",
    "\n",
    "\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Corpus of Cold Words in Posts with Text\n",
    "\n",
    "cold = []\n",
    "\n",
    "for row in WithText_NotHot['Text']:\n",
    "        cold.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WordCloud for Cold Docs\n",
    "\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "coldstring = \" \".join(cold)\n",
    "\n",
    "wordcloud = WordCloud(    colormap='viridis',\n",
    "                          stopwords=STOPWORDS,\n",
    "                          background_color='white',\n",
    "                          width=2400,\n",
    "                          height=2000\n",
    "                         ).generate(coldstring)\n",
    "\n",
    "\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Title</th>\n",
       "      <th>SubReddit</th>\n",
       "      <th>SubReddit_Subscribers</th>\n",
       "      <th>Num_Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>8nogl0</td>\n",
       "      <td>What celebrity has skeletons in their closet t...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304088</td>\n",
       "      <td>20246</td>\n",
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       "      <td>8nmvqb</td>\n",
       "      <td>What food has made you wonder \"how did our anc...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304127</td>\n",
       "      <td>4731</td>\n",
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       "    <tr>\n",
       "      <th>4963</th>\n",
       "      <td>8nlhuo</td>\n",
       "      <td>What's a shallow reason you wouldn't date some...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304150</td>\n",
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       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>8npdh5</td>\n",
       "      <td>One Piece: Chapter 906</td>\n",
       "      <td>OnePiece</td>\n",
       "      <td>167348</td>\n",
       "      <td>4186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>8nnoyh</td>\n",
       "      <td>My doctor charges me $65 for being fat</td>\n",
       "      <td>Wellthatsucks</td>\n",
       "      <td>691651</td>\n",
       "      <td>3761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4016</th>\n",
       "      <td>8nmewn</td>\n",
       "      <td>Trudeau responds to Trump Tariffs: \"We have to...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>18788144</td>\n",
       "      <td>3488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>8nr4pb</td>\n",
       "      <td>What's a good movie everyone should watch but ...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304083</td>\n",
       "      <td>3460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>8nneak</td>\n",
       "      <td>\"ARAB ANDY\" scares the shit out of a college c...</td>\n",
       "      <td>LivestreamFail</td>\n",
       "      <td>260629</td>\n",
       "      <td>3316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>8nneak</td>\n",
       "      <td>\"ARAB ANDY\" scares the shit out of a college c...</td>\n",
       "      <td>LivestreamFail</td>\n",
       "      <td>260629</td>\n",
       "      <td>3316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4569</th>\n",
       "      <td>8np3js</td>\n",
       "      <td>\"YE\" Album - First Thoughts Mega-Thread</td>\n",
       "      <td>Kanye</td>\n",
       "      <td>146302</td>\n",
       "      <td>2664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8nr18g</td>\n",
       "      <td>Two lions, two tigers and a jaguar have report...</td>\n",
       "      <td>news</td>\n",
       "      <td>16085493</td>\n",
       "      <td>2650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4158</th>\n",
       "      <td>8nnjqe</td>\n",
       "      <td>I texted my younger brother during graduation ...</td>\n",
       "      <td>pics</td>\n",
       "      <td>18699483</td>\n",
       "      <td>2543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4324</th>\n",
       "      <td>8nn95z</td>\n",
       "      <td>What's the creepiest video you've seen on the ...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304135</td>\n",
       "      <td>2459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3765</th>\n",
       "      <td>8nqgr5</td>\n",
       "      <td>Match Thread: England vs Pakistan at Headingle...</td>\n",
       "      <td>Cricket</td>\n",
       "      <td>71824</td>\n",
       "      <td>2450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>8nmhhi</td>\n",
       "      <td>First Image of Shia Labeouf as Shia Labeouf's ...</td>\n",
       "      <td>movies</td>\n",
       "      <td>17635011</td>\n",
       "      <td>2227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>8noo1r</td>\n",
       "      <td>Little cultural differences</td>\n",
       "      <td>PoliticalHumor</td>\n",
       "      <td>389478</td>\n",
       "      <td>2221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>8nmpzk</td>\n",
       "      <td>German Sheperd realizes owner isnt behind him ...</td>\n",
       "      <td>videos</td>\n",
       "      <td>17810231</td>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>8nrhq0</td>\n",
       "      <td>[FRESH ALBUM] Kanye West - ye</td>\n",
       "      <td>Kanye</td>\n",
       "      <td>146279</td>\n",
       "      <td>2175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3034</th>\n",
       "      <td>8nlanp</td>\n",
       "      <td>Amazon needs to get a handle on its counterfei...</td>\n",
       "      <td>technology</td>\n",
       "      <td>6207082</td>\n",
       "      <td>2153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>8nn6jy</td>\n",
       "      <td>LPT: If your friend owns a business, don't ask...</td>\n",
       "      <td>LifeProTips</td>\n",
       "      <td>14141470</td>\n",
       "      <td>2119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>8nn6jy</td>\n",
       "      <td>LPT: If your friend owns a business, don't ask...</td>\n",
       "      <td>LifeProTips</td>\n",
       "      <td>14141470</td>\n",
       "      <td>2119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3735</th>\n",
       "      <td>8nmtne</td>\n",
       "      <td>Father of four hears silence for the first time</td>\n",
       "      <td>gifs</td>\n",
       "      <td>16175955</td>\n",
       "      <td>2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>8nmow9</td>\n",
       "      <td>I got this PM because I said \"If you can't sta...</td>\n",
       "      <td>insanepeoplefacebook</td>\n",
       "      <td>501208</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>8nmow9</td>\n",
       "      <td>I got this PM because I said \"If you can't sta...</td>\n",
       "      <td>insanepeoplefacebook</td>\n",
       "      <td>501209</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>8nqles</td>\n",
       "      <td>Spain's prime minister is forced out of his jo...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>18788097</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4374</th>\n",
       "      <td>8nrijt</td>\n",
       "      <td>We're giving away a $10 eShop card, all you ne...</td>\n",
       "      <td>NintendoSwitch</td>\n",
       "      <td>626768</td>\n",
       "      <td>1857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8ns6ev</td>\n",
       "      <td>Give me an R rated Boba Fett movie with some r...</td>\n",
       "      <td>StarWars</td>\n",
       "      <td>884224</td>\n",
       "      <td>1801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>8nrb34</td>\n",
       "      <td>Conan at a pub in Belfast yesterday (x-post /r...</td>\n",
       "      <td>gifs</td>\n",
       "      <td>16175925</td>\n",
       "      <td>1762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4904</th>\n",
       "      <td>8nl606</td>\n",
       "      <td>He only loves his bed</td>\n",
       "      <td>BlackPeopleTwitter</td>\n",
       "      <td>1739855</td>\n",
       "      <td>1715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>8nm0fl</td>\n",
       "      <td>Quick reaction saves a life</td>\n",
       "      <td>nonononoyes</td>\n",
       "      <td>858931</td>\n",
       "      <td>1544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1512</th>\n",
       "      <td>8nm0fl</td>\n",
       "      <td>Quick reaction saves a life</td>\n",
       "      <td>nonononoyes</td>\n",
       "      <td>858932</td>\n",
       "      <td>1544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3422</th>\n",
       "      <td>8nlqwk</td>\n",
       "      <td>When you get out of school at 3, it felt very ...</td>\n",
       "      <td>Showerthoughts</td>\n",
       "      <td>14145327</td>\n",
       "      <td>1511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>8nq9js</td>\n",
       "      <td>How Scoliosis (Curvature of the Spine) Surgery...</td>\n",
       "      <td>educationalgifs</td>\n",
       "      <td>606241</td>\n",
       "      <td>1502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>8nm73x</td>\n",
       "      <td>Redditors of the US, how would you feel about ...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304131</td>\n",
       "      <td>1484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4004</th>\n",
       "      <td>8nm73x</td>\n",
       "      <td>Redditors of the US, how would you feel about ...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304131</td>\n",
       "      <td>1484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3796</th>\n",
       "      <td>8np2qv</td>\n",
       "      <td>What's a life lession that you learned from a ...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304130</td>\n",
       "      <td>1426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8nrndc</td>\n",
       "      <td>Fraction of Total Videogame Sales by Genre [OC]</td>\n",
       "      <td>dataisbeautiful</td>\n",
       "      <td>13056843</td>\n",
       "      <td>1417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>8nmwqa</td>\n",
       "      <td>Modern Icelandic houses are built with geother...</td>\n",
       "      <td>mildlyinteresting</td>\n",
       "      <td>14323054</td>\n",
       "      <td>1399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>8nmwqa</td>\n",
       "      <td>Modern Icelandic houses are built with geother...</td>\n",
       "      <td>mildlyinteresting</td>\n",
       "      <td>14323055</td>\n",
       "      <td>1399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4462</th>\n",
       "      <td>8nou82</td>\n",
       "      <td>[Post Game Thread] YOUR Golden State Warriors ...</td>\n",
       "      <td>warriors</td>\n",
       "      <td>104872</td>\n",
       "      <td>1355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8nqy5s</td>\n",
       "      <td>Cliffs of Moher</td>\n",
       "      <td>woahdude</td>\n",
       "      <td>1795252</td>\n",
       "      <td>1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8nssa4</td>\n",
       "      <td>I make paintings of cars. This is my latest wo...</td>\n",
       "      <td>pics</td>\n",
       "      <td>18699440</td>\n",
       "      <td>1313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8nsifi</td>\n",
       "      <td>Bless Online trailer plagiarized How to train ...</td>\n",
       "      <td>gaming</td>\n",
       "      <td>18195312</td>\n",
       "      <td>1298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>8nornq</td>\n",
       "      <td>Elon Musk Responds to Boeing's Claims It Will ...</td>\n",
       "      <td>Futurology</td>\n",
       "      <td>13033147</td>\n",
       "      <td>1271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>8nr3ju</td>\n",
       "      <td>My cat ... dont even know what to say 😅🤦🏼‍♂️</td>\n",
       "      <td>aww</td>\n",
       "      <td>17217424</td>\n",
       "      <td>1253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4092</th>\n",
       "      <td>8nlf5f</td>\n",
       "      <td>Lightning bolts colliding</td>\n",
       "      <td>gifs</td>\n",
       "      <td>16175956</td>\n",
       "      <td>1249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>8nqxw1</td>\n",
       "      <td>I did what any gamer would do to get their gf ...</td>\n",
       "      <td>gaming</td>\n",
       "      <td>18195333</td>\n",
       "      <td>1217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>8nr5lm</td>\n",
       "      <td>My husband and I are idiots. We've been bamboo...</td>\n",
       "      <td>personalfinance</td>\n",
       "      <td>12946527</td>\n",
       "      <td>1215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>8np6hz</td>\n",
       "      <td>ELI5 What's the reason behind of 666 being the...</td>\n",
       "      <td>explainlikeimfive</td>\n",
       "      <td>15598221</td>\n",
       "      <td>1189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>8nq5z6</td>\n",
       "      <td>Interesting thought</td>\n",
       "      <td>WhitePeopleTwitter</td>\n",
       "      <td>520534</td>\n",
       "      <td>1177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id                                              Title  \\\n",
       "420   8nogl0  What celebrity has skeletons in their closet t...   \n",
       "3009  8nmvqb  What food has made you wonder \"how did our anc...   \n",
       "4963  8nlhuo  What's a shallow reason you wouldn't date some...   \n",
       "633   8npdh5                             One Piece: Chapter 906   \n",
       "344   8nnoyh             My doctor charges me $65 for being fat   \n",
       "4016  8nmewn  Trudeau responds to Trump Tariffs: \"We have to...   \n",
       "157   8nr4pb  What's a good movie everyone should watch but ...   \n",
       "1098  8nneak  \"ARAB ANDY\" scares the shit out of a college c...   \n",
       "1682  8nneak  \"ARAB ANDY\" scares the shit out of a college c...   \n",
       "4569  8np3js            \"YE\" Album - First Thoughts Mega-Thread   \n",
       "31    8nr18g  Two lions, two tigers and a jaguar have report...   \n",
       "4158  8nnjqe  I texted my younger brother during graduation ...   \n",
       "4324  8nn95z  What's the creepiest video you've seen on the ...   \n",
       "3765  8nqgr5  Match Thread: England vs Pakistan at Headingle...   \n",
       "1447  8nmhhi  First Image of Shia Labeouf as Shia Labeouf's ...   \n",
       "435   8noo1r                        Little cultural differences   \n",
       "1353  8nmpzk  German Sheperd realizes owner isnt behind him ...   \n",
       "61    8nrhq0                      [FRESH ALBUM] Kanye West - ye   \n",
       "3034  8nlanp  Amazon needs to get a handle on its counterfei...   \n",
       "671   8nn6jy  LPT: If your friend owns a business, don't ask...   \n",
       "678   8nn6jy  LPT: If your friend owns a business, don't ask...   \n",
       "3735  8nmtne    Father of four hears silence for the first time   \n",
       "1199  8nmow9  I got this PM because I said \"If you can't sta...   \n",
       "1854  8nmow9  I got this PM because I said \"If you can't sta...   \n",
       "54    8nqles  Spain's prime minister is forced out of his jo...   \n",
       "4374  8nrijt  We're giving away a $10 eShop card, all you ne...   \n",
       "5     8ns6ev  Give me an R rated Boba Fett movie with some r...   \n",
       "34    8nrb34  Conan at a pub in Belfast yesterday (x-post /r...   \n",
       "4904  8nl606                              He only loves his bed   \n",
       "1498  8nm0fl                        Quick reaction saves a life   \n",
       "1512  8nm0fl                        Quick reaction saves a life   \n",
       "3422  8nlqwk  When you get out of school at 3, it felt very ...   \n",
       "99    8nq9js  How Scoliosis (Curvature of the Spine) Surgery...   \n",
       "3996  8nm73x  Redditors of the US, how would you feel about ...   \n",
       "4004  8nm73x  Redditors of the US, how would you feel about ...   \n",
       "3796  8np2qv  What's a life lession that you learned from a ...   \n",
       "18    8nrndc    Fraction of Total Videogame Sales by Genre [OC]   \n",
       "1551  8nmwqa  Modern Icelandic houses are built with geother...   \n",
       "1589  8nmwqa  Modern Icelandic houses are built with geother...   \n",
       "4462  8nou82  [Post Game Thread] YOUR Golden State Warriors ...   \n",
       "35    8nqy5s                                    Cliffs of Moher   \n",
       "1     8nssa4  I make paintings of cars. This is my latest wo...   \n",
       "3     8nsifi  Bless Online trailer plagiarized How to train ...   \n",
       "275   8nornq  Elon Musk Responds to Boeing's Claims It Will ...   \n",
       "84    8nr3ju       My cat ... dont even know what to say 😅🤦🏼‍♂️   \n",
       "4092  8nlf5f                          Lightning bolts colliding   \n",
       "1165  8nqxw1  I did what any gamer would do to get their gf ...   \n",
       "83    8nr5lm  My husband and I are idiots. We've been bamboo...   \n",
       "259   8np6hz  ELI5 What's the reason behind of 666 being the...   \n",
       "126   8nq5z6                                Interesting thought   \n",
       "\n",
       "                 SubReddit  SubReddit_Subscribers  Num_Comments  \n",
       "420              AskReddit               19304088         20246  \n",
       "3009             AskReddit               19304127          4731  \n",
       "4963             AskReddit               19304150          4456  \n",
       "633               OnePiece                 167348          4186  \n",
       "344          Wellthatsucks                 691651          3761  \n",
       "4016             worldnews               18788144          3488  \n",
       "157              AskReddit               19304083          3460  \n",
       "1098        LivestreamFail                 260629          3316  \n",
       "1682        LivestreamFail                 260629          3316  \n",
       "4569                 Kanye                 146302          2664  \n",
       "31                    news               16085493          2650  \n",
       "4158                  pics               18699483          2543  \n",
       "4324             AskReddit               19304135          2459  \n",
       "3765               Cricket                  71824          2450  \n",
       "1447                movies               17635011          2227  \n",
       "435         PoliticalHumor                 389478          2221  \n",
       "1353                videos               17810231          2183  \n",
       "61                   Kanye                 146279          2175  \n",
       "3034            technology                6207082          2153  \n",
       "671            LifeProTips               14141470          2119  \n",
       "678            LifeProTips               14141470          2119  \n",
       "3735                  gifs               16175955          2051  \n",
       "1199  insanepeoplefacebook                 501208          2001  \n",
       "1854  insanepeoplefacebook                 501209          2001  \n",
       "54               worldnews               18788097          1954  \n",
       "4374        NintendoSwitch                 626768          1857  \n",
       "5                 StarWars                 884224          1801  \n",
       "34                    gifs               16175925          1762  \n",
       "4904    BlackPeopleTwitter                1739855          1715  \n",
       "1498           nonononoyes                 858931          1544  \n",
       "1512           nonononoyes                 858932          1544  \n",
       "3422        Showerthoughts               14145327          1511  \n",
       "99         educationalgifs                 606241          1502  \n",
       "3996             AskReddit               19304131          1484  \n",
       "4004             AskReddit               19304131          1484  \n",
       "3796             AskReddit               19304130          1426  \n",
       "18         dataisbeautiful               13056843          1417  \n",
       "1551     mildlyinteresting               14323054          1399  \n",
       "1589     mildlyinteresting               14323055          1399  \n",
       "4462              warriors                 104872          1355  \n",
       "35                woahdude                1795252          1340  \n",
       "1                     pics               18699440          1313  \n",
       "3                   gaming               18195312          1298  \n",
       "275             Futurology               13033147          1271  \n",
       "84                     aww               17217424          1253  \n",
       "4092                  gifs               16175956          1249  \n",
       "1165                gaming               18195333          1217  \n",
       "83         personalfinance               12946527          1215  \n",
       "259      explainlikeimfive               15598221          1189  \n",
       "126     WhitePeopleTwitter                 520534          1177  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mask of Top 50 Most Commented on Posts\n",
    "\n",
    "Top50 = Reddit.sort_values(['Num_Comments'], ascending=False).head(50)\n",
    "Top50[['Id','Title', 'SubReddit', 'SubReddit_Subscribers', 'Num_Comments']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# API Pull Set-Up of Comments with Praw\n",
    "\n",
    "# reddit = praw.Reddit(user_agent='',\n",
    "#                      client_id='', client_secret='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Comments for Top 50 posts\n",
    "\n",
    "# comments = {}\n",
    "\n",
    "# for row in Top50['Id']:\n",
    "#     submission = reddit.submission(row)\n",
    "\n",
    "#     submission.comments.replace_more(limit=None)\n",
    "#     for comment in submission.comments.list():\n",
    "# #         print(row, comment.body)\n",
    "#             comments[row] = []\n",
    "    \n",
    "#     comments[row].append(comment.body)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Send pull to csv\n",
    "\n",
    "# comments_df = pd.DataFrame.from_dict(comments)\n",
    "# comments_df = comments_df.T\n",
    "\n",
    "# comments_df.to_csv('./comments')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in Comments Data\n",
    "\n",
    "comments = pd.read_csv('./comments')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8nl606</td>\n",
       "      <td>😂😂😂😂😂 you're just a dumb, impotent, angry litt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8nlanp</td>\n",
       "      <td>My only point is that recoup your losses is a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8nlf5f</td>\n",
       "      <td>No worries. If you sin then you need to repent...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8nlhuo</td>\n",
       "      <td>Opportunities are always presenting themselves...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8nlqwk</td>\n",
       "      <td>You just ignored the broader point of everythi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8nm0fl</td>\n",
       "      <td>No, I already have, doesn’t work. Did you know...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8nm73x</td>\n",
       "      <td>Apparently, my well rounded education seems to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8nmewn</td>\n",
       "      <td>Believing his lies seems to be one simple way.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8nmhhi</td>\n",
       "      <td>You are quickly becoming my favourite Reddit a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8nmow9</td>\n",
       "      <td>Periscopes are cool.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8nmpzk</td>\n",
       "      <td>&gt;Yes it's called being an adult.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8nmtne</td>\n",
       "      <td>The only thing you're contributing to is how p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8nmvqb</td>\n",
       "      <td>Probably so then!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8nmwqa</td>\n",
       "      <td>Okay sure but what about your constipation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8nn6jy</td>\n",
       "      <td>Let's put it this way.  An invoice is a contra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8nn95z</td>\n",
       "      <td>He's still speaking the truth with that tweet....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8nneak</td>\n",
       "      <td>Hypothetically, if i held a demonstration and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8nnjqe</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8nnoyh</td>\n",
       "      <td>Xddd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8nogl0</td>\n",
       "      <td>I find it quite funny that you’re resorting to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8noo1r</td>\n",
       "      <td>Haha yup you walked away from the argument jus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8nornq</td>\n",
       "      <td>Most titans of industry who acquired 10+ billi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8nou82</td>\n",
       "      <td>A. It was a shooting foul because Durant was g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8np2qv</td>\n",
       "      <td>Because... God is that conversation boring.  W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8np3js</td>\n",
       "      <td>Yikes by far I think it’s mainly because I saw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8np6hz</td>\n",
       "      <td>I said the writers of the bible choosing *our ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8npdh5</td>\n",
       "      <td>Thanks :)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8nq5z6</td>\n",
       "      <td>You don’t even own. Why the fuck are we even h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8nq9js</td>\n",
       "      <td>You are reusing the same two insults without e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8nqgr5</td>\n",
       "      <td>I see where there could be misunderstanding. I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8nqles</td>\n",
       "      <td>I would have react the same way if someone wou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8nqxw1</td>\n",
       "      <td>Superjail is pretty good. I still love Futuram...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8nqy5s</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8nr18g</td>\n",
       "      <td>After the formation of the Association of Zoos...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>8nr3ju</td>\n",
       "      <td>Don’t ask questions you don’t want to know the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8nr4pb</td>\n",
       "      <td>I literally admitted in an earlier post to you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8nr5lm</td>\n",
       "      <td>Selling an unsuitable product/breaching fiduci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>8nrb34</td>\n",
       "      <td>Whether it's \"fair\" or not, if you use that de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8nrhq0</td>\n",
       "      <td>Imagine getting so clowned on that you just st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>8nrijt</td>\n",
       "      <td>A\\nN\\nI\\nM\\nA\\nL\\n\\nC\\nR\\nO\\nS\\nS\\nI\\nN\\nG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>8nrndc</td>\n",
       "      <td>Sorry if I read between the lines unnecessaril...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>8ns6ev</td>\n",
       "      <td>“His start”\\n\\nOld canon he straight up told H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>8nsifi</td>\n",
       "      <td>Nice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>8nssa4</td>\n",
       "      <td>Back in my day we piston scum like you</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                            Comment\n",
       "0   8nl606  😂😂😂😂😂 you're just a dumb, impotent, angry litt...\n",
       "1   8nlanp  My only point is that recoup your losses is a ...\n",
       "2   8nlf5f  No worries. If you sin then you need to repent...\n",
       "3   8nlhuo  Opportunities are always presenting themselves...\n",
       "4   8nlqwk  You just ignored the broader point of everythi...\n",
       "5   8nm0fl  No, I already have, doesn’t work. Did you know...\n",
       "6   8nm73x  Apparently, my well rounded education seems to...\n",
       "7   8nmewn     Believing his lies seems to be one simple way.\n",
       "8   8nmhhi  You are quickly becoming my favourite Reddit a...\n",
       "9   8nmow9                              Periscopes are cool. \n",
       "10  8nmpzk                  >Yes it's called being an adult. \n",
       "11  8nmtne  The only thing you're contributing to is how p...\n",
       "12  8nmvqb                                  Probably so then!\n",
       "13  8nmwqa         Okay sure but what about your constipation\n",
       "14  8nn6jy  Let's put it this way.  An invoice is a contra...\n",
       "15  8nn95z  He's still speaking the truth with that tweet....\n",
       "16  8nneak  Hypothetically, if i held a demonstration and ...\n",
       "17  8nnjqe                                                  F\n",
       "18  8nnoyh                                               Xddd\n",
       "19  8nogl0  I find it quite funny that you’re resorting to...\n",
       "20  8noo1r  Haha yup you walked away from the argument jus...\n",
       "21  8nornq  Most titans of industry who acquired 10+ billi...\n",
       "22  8nou82  A. It was a shooting foul because Durant was g...\n",
       "23  8np2qv  Because... God is that conversation boring.  W...\n",
       "24  8np3js  Yikes by far I think it’s mainly because I saw...\n",
       "25  8np6hz  I said the writers of the bible choosing *our ...\n",
       "26  8npdh5                                          Thanks :)\n",
       "27  8nq5z6  You don’t even own. Why the fuck are we even h...\n",
       "28  8nq9js  You are reusing the same two insults without e...\n",
       "29  8nqgr5  I see where there could be misunderstanding. I...\n",
       "30  8nqles  I would have react the same way if someone wou...\n",
       "31  8nqxw1  Superjail is pretty good. I still love Futuram...\n",
       "32  8nqy5s                                                  K\n",
       "33  8nr18g  After the formation of the Association of Zoos...\n",
       "34  8nr3ju  Don’t ask questions you don’t want to know the...\n",
       "35  8nr4pb  I literally admitted in an earlier post to you...\n",
       "36  8nr5lm  Selling an unsuitable product/breaching fiduci...\n",
       "37  8nrb34  Whether it's \"fair\" or not, if you use that de...\n",
       "38  8nrhq0  Imagine getting so clowned on that you just st...\n",
       "39  8nrijt         A\\nN\\nI\\nM\\nA\\nL\\n\\nC\\nR\\nO\\nS\\nS\\nI\\nN\\nG\n",
       "40  8nrndc  Sorry if I read between the lines unnecessaril...\n",
       "41  8ns6ev  “His start”\\n\\nOld canon he straight up told H...\n",
       "42  8nsifi                                               Nice\n",
       "43  8nssa4             Back in my day we piston scum like you"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comments.rename(columns={'Unnamed: 0': 'Id', '0': 'Comment'}, inplace=True)\n",
    "comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'neg': 0.211, 'neu': 0.732, 'pos': 0.057, 'compound': -0.7744}\n",
      "{'neg': 0.146, 'neu': 0.692, 'pos': 0.163, 'compound': 0.2249}\n",
      "{'neg': 0.211, 'neu': 0.622, 'pos': 0.168, 'compound': -0.5423}\n",
      "{'neg': 0.023, 'neu': 0.736, 'pos': 0.241, 'compound': 0.9804}\n",
      "{'neg': 0.113, 'neu': 0.806, 'pos': 0.081, 'compound': -0.427}\n",
      "{'neg': 0.377, 'neu': 0.549, 'pos': 0.073, 'compound': -0.8519}\n",
      "{'neg': 0.0, 'neu': 0.718, 'pos': 0.282, 'compound': 0.6249}\n",
      "{'neg': 0.259, 'neu': 0.741, 'pos': 0.0, 'compound': -0.4215}\n",
      "{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
      "{'neg': 0.0, 'neu': 0.465, 'pos': 0.535, 'compound': 0.3182}\n",
      "{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
      "{'neg': 0.106, 'neu': 0.757, 'pos': 0.137, 'compound': 0.0601}\n",
      "{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
      "{'neg': 0.0, 'neu': 0.617, 'pos': 0.383, 'compound': 0.2732}\n",
      "{'neg': 0.152, 'neu': 0.791, 'pos': 0.057, 'compound': -0.765}\n",
      "{'neg': 0.163, 'neu': 0.769, 'pos': 0.068, 'compound': -0.4939}\n",
      "{'neg': 0.17, 'neu': 0.797, 'pos': 0.033, 'compound': -0.782}\n",
      "{'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}\n",
      "{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
      "{'neg': 0.172, 'neu': 0.45, 'pos': 0.378, 'compound': 0.6697}\n",
      "{'neg': 0.166, 'neu': 0.737, 'pos': 0.097, 'compound': -0.6966}\n",
      "{'neg': 0.053, 'neu': 0.624, 'pos': 0.324, 'compound': 0.9062}\n",
      "{'neg': 0.154, 'neu': 0.735, 'pos': 0.111, 'compound': -0.7512}\n",
      "{'neg': 0.028, 'neu': 0.782, 'pos': 0.19, 'compound': 0.8588}\n",
      "{'neg': 0.101, 'neu': 0.721, 'pos': 0.178, 'compound': 0.3818}\n",
      "{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n",
      "{'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.7096}\n",
      "{'neg': 0.226, 'neu': 0.774, 'pos': 0.0, 'compound': -0.5423}\n",
      "{'neg': 0.144, 'neu': 0.685, 'pos': 0.171, 'compound': 0.2023}\n",
      "{'neg': 0.048, 'neu': 0.952, 'pos': 0.0, 'compound': -0.2263}\n",
      "{'neg': 0.116, 'neu': 0.884, 'pos': 0.0, 'compound': -0.5267}\n",
      "{'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'compound': 0.9081}\n",
      "{'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}\n",
      "{'neg': 0.33, 'neu': 0.67, 'pos': 0.0, 'compound': -0.9779}\n",
      "{'neg': 0.0, 'neu': 0.885, 'pos': 0.115, 'compound': 0.0772}\n",
      "{'neg': 0.211, 'neu': 0.642, 'pos': 0.147, 'compound': -0.7783}\n",
      "{'neg': 0.203, 'neu': 0.678, 'pos': 0.119, 'compound': -0.3612}\n",
      "{'neg': 0.178, 'neu': 0.822, 'pos': 0.0, 'compound': -0.8442}\n",
      "{'neg': 0.137, 'neu': 0.863, 'pos': 0.0, 'compound': -0.4019}\n",
      "{'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}\n",
      "{'neg': 0.06, 'neu': 0.835, 'pos': 0.105, 'compound': 0.2401}\n",
      "{'neg': 0.107, 'neu': 0.738, 'pos': 0.156, 'compound': 0.2023}\n",
      "{'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.4215}\n",
      "{'neg': 0.0, 'neu': 0.762, 'pos': 0.238, 'compound': 0.3612}\n"
     ]
    }
   ],
   "source": [
    "# Sentiment Analysis of Top Comment in Top 50 Posts\n",
    "\n",
    "sid = SentimentIntensityAnalyzer()\n",
    "\n",
    "compound_scores = []\n",
    "\n",
    "for row in comments.Comment:\n",
    "    ss = sid.polarity_scores(row)\n",
    "    print(ss)\n",
    "    \n",
    "    compound_scores.append(ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make New DataFrames of Analyzed Comments, Then Join on Top50 for Full Analysis\n",
    "\n",
    "compound_scores = pd.DataFrame(compound_scores)\n",
    "\n",
    "comments_scores = pd.concat([comments, compound_scores], axis=1)\n",
    "\n",
    "joined = pd.merge(comments_scores, Top50, on='Id', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
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       "      <td>JamesOldie</td>\n",
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       "      <td>1.527827e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-390554.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8npdh5</td>\n",
       "      <td>Thanks :)</td>\n",
       "      <td>0.7096</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>Sakata_Kintoki</td>\n",
       "      <td>One Piece: Chapter 906</td>\n",
       "      <td>OnePiece</td>\n",
       "      <td>167348</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>**Chapter 906: \"The Holy Land Mary Geoise\"**\\n...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527830e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-393537.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8nogl0</td>\n",
       "      <td>I find it quite funny that you’re resorting to...</td>\n",
       "      <td>0.6697</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.378</td>\n",
       "      <td>ReeceInTheDarkness</td>\n",
       "      <td>What celebrity has skeletons in their closet t...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304088</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527821e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-384745.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8nm73x</td>\n",
       "      <td>Apparently, my well rounded education seems to...</td>\n",
       "      <td>0.6249</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.282</td>\n",
       "      <td>isometricLife</td>\n",
       "      <td>Redditors of the US, how would you feel about ...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304131</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527802e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-364975.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8nm73x</td>\n",
       "      <td>Apparently, my well rounded education seems to...</td>\n",
       "      <td>0.6249</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.282</td>\n",
       "      <td>isometricLife</td>\n",
       "      <td>Redditors of the US, how would you feel about ...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304131</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527802e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-364975.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>8nsifi</td>\n",
       "      <td>Nice</td>\n",
       "      <td>0.4215</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>Zouroark</td>\n",
       "      <td>Bless Online trailer plagiarized How to train ...</td>\n",
       "      <td>gaming</td>\n",
       "      <td>18195312</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527865e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-428712.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8np3js</td>\n",
       "      <td>Yikes by far I think it’s mainly because I saw...</td>\n",
       "      <td>0.3818</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.178</td>\n",
       "      <td>avayr44</td>\n",
       "      <td>\"YE\" Album - First Thoughts Mega-Thread</td>\n",
       "      <td>Kanye</td>\n",
       "      <td>146302</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Please remember :\\n\\n- **Do NOT downvote** jus...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527827e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-390793.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>8nssa4</td>\n",
       "      <td>Back in my day we piston scum like you</td>\n",
       "      <td>0.3612</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.238</td>\n",
       "      <td>automotiveartist</td>\n",
       "      <td>I make paintings of cars. This is my latest wo...</td>\n",
       "      <td>pics</td>\n",
       "      <td>18699440</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527867e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-430545.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8nmow9</td>\n",
       "      <td>Periscopes are cool.</td>\n",
       "      <td>0.3182</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.535</td>\n",
       "      <td>butter_otter</td>\n",
       "      <td>I got this PM because I said \"If you can't sta...</td>\n",
       "      <td>insanepeoplefacebook</td>\n",
       "      <td>501208</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527806e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-369143.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8nmow9</td>\n",
       "      <td>Periscopes are cool.</td>\n",
       "      <td>0.3182</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.535</td>\n",
       "      <td>butter_otter</td>\n",
       "      <td>I got this PM because I said \"If you can't sta...</td>\n",
       "      <td>insanepeoplefacebook</td>\n",
       "      <td>501209</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527806e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-369143.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8nmwqa</td>\n",
       "      <td>Okay sure but what about your constipation</td>\n",
       "      <td>0.2732</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.383</td>\n",
       "      <td>karljam</td>\n",
       "      <td>Modern Icelandic houses are built with geother...</td>\n",
       "      <td>mildlyinteresting</td>\n",
       "      <td>14323054</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527808e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-370909.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8nmwqa</td>\n",
       "      <td>Okay sure but what about your constipation</td>\n",
       "      <td>0.2732</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.383</td>\n",
       "      <td>karljam</td>\n",
       "      <td>Modern Icelandic houses are built with geother...</td>\n",
       "      <td>mildlyinteresting</td>\n",
       "      <td>14323055</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527808e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-370909.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>8nrndc</td>\n",
       "      <td>Sorry if I read between the lines unnecessaril...</td>\n",
       "      <td>0.2401</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.105</td>\n",
       "      <td>divergentdata</td>\n",
       "      <td>Fraction of Total Videogame Sales by Genre [OC]</td>\n",
       "      <td>dataisbeautiful</td>\n",
       "      <td>13056843</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527858e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-421085.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8nlanp</td>\n",
       "      <td>My only point is that recoup your losses is a ...</td>\n",
       "      <td>0.2249</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.163</td>\n",
       "      <td>mixplate</td>\n",
       "      <td>Amazon needs to get a handle on its counterfei...</td>\n",
       "      <td>technology</td>\n",
       "      <td>6207082</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527795e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-358282.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>8ns6ev</td>\n",
       "      <td>“His start”\\n\\nOld canon he straight up told H...</td>\n",
       "      <td>0.2023</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.156</td>\n",
       "      <td>MattDropDead</td>\n",
       "      <td>Give me an R rated Boba Fett movie with some r...</td>\n",
       "      <td>StarWars</td>\n",
       "      <td>884224</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527862e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-425732.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>8nq9js</td>\n",
       "      <td>You are reusing the same two insults without e...</td>\n",
       "      <td>0.2023</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.171</td>\n",
       "      <td>YouKn0wN0thing</td>\n",
       "      <td>How Scoliosis (Curvature of the Spine) Surgery...</td>\n",
       "      <td>educationalgifs</td>\n",
       "      <td>606241</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527841e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-404534.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>8nr3ju</td>\n",
       "      <td>Don’t ask questions you don’t want to know the...</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.115</td>\n",
       "      <td>penguiin1337</td>\n",
       "      <td>My cat ... dont even know what to say 😅🤦🏼‍♂️</td>\n",
       "      <td>aww</td>\n",
       "      <td>17217424</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527852e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-415390.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8nmtne</td>\n",
       "      <td>The only thing you're contributing to is how p...</td>\n",
       "      <td>0.0601</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.137</td>\n",
       "      <td>OtherJaffa</td>\n",
       "      <td>Father of four hears silence for the first time</td>\n",
       "      <td>gifs</td>\n",
       "      <td>16175955</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527807e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-370312.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8nnoyh</td>\n",
       "      <td>Xddd</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NinkovichPlease</td>\n",
       "      <td>My doctor charges me $65 for being fat</td>\n",
       "      <td>Wellthatsucks</td>\n",
       "      <td>691651</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.527814e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-377627.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8nmhhi</td>\n",
       "      <td>You are quickly becoming my favourite Reddit a...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>BunyipPouch</td>\n",
       "      <td>First Image of Shia Labeouf as Shia Labeouf's ...</td>\n",
       "      <td>movies</td>\n",
       "      <td>17635011</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527804e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-367315.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8nmpzk</td>\n",
       "      <td>&gt;Yes it's called being an adult.</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>cainbackisdry</td>\n",
       "      <td>German Sheperd realizes owner isnt behind him ...</td>\n",
       "      <td>videos</td>\n",
       "      <td>17810231</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527806e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-369433.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8nnjqe</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Handsome_McAwesome</td>\n",
       "      <td>I texted my younger brother during graduation ...</td>\n",
       "      <td>pics</td>\n",
       "      <td>18699483</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527813e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-376319.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8np6hz</td>\n",
       "      <td>I said the writers of the bible choosing *our ...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Google_Knight</td>\n",
       "      <td>ELI5 What's the reason behind of 666 being the...</td>\n",
       "      <td>explainlikeimfive</td>\n",
       "      <td>15598221</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>Both biblical and anthropological,if posible.</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527828e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-391577.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8nqy5s</td>\n",
       "      <td>K</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>CodieCaldwell</td>\n",
       "      <td>Cliffs of Moher</td>\n",
       "      <td>woahdude</td>\n",
       "      <td>1795252</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527850e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-413640.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>8nrijt</td>\n",
       "      <td>A\\nN\\nI\\nM\\nA\\nL\\n\\nC\\nR\\nO\\nS\\nS\\nI\\nN\\nG</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Stalfo14</td>\n",
       "      <td>We're giving away a $10 eShop card, all you ne...</td>\n",
       "      <td>NintendoSwitch</td>\n",
       "      <td>626768</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Hey everyone, Talk Nintendo Podcast here with ...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527856e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-419748.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8nmvqb</td>\n",
       "      <td>Probably so then!</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Kingflares</td>\n",
       "      <td>What food has made you wonder \"how did our anc...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304127</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527807e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-370702.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8nqgr5</td>\n",
       "      <td>I see where there could be misunderstanding. I...</td>\n",
       "      <td>-0.2263</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.000</td>\n",
       "      <td>CricinfoBot</td>\n",
       "      <td>Match Thread: England vs Pakistan at Headingle...</td>\n",
       "      <td>Cricket</td>\n",
       "      <td>71824</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>###2nd Test, Pakistan tour of Ireland, England...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527844e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-407358.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>8nr5lm</td>\n",
       "      <td>Selling an unsuitable product/breaching fiduci...</td>\n",
       "      <td>-0.3612</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.119</td>\n",
       "      <td>gneiss_k</td>\n",
       "      <td>My husband and I are idiots. We've been bamboo...</td>\n",
       "      <td>personalfinance</td>\n",
       "      <td>12946527</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Ugh I'm so frustrated. I thought we were doing...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527853e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-416043.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>8nrhq0</td>\n",
       "      <td>Imagine getting so clowned on that you just st...</td>\n",
       "      <td>-0.4019</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.000</td>\n",
       "      <td>rdelamora1</td>\n",
       "      <td>[FRESH ALBUM] Kanye West - ye</td>\n",
       "      <td>Kanye</td>\n",
       "      <td>146279</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527856e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-419516.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8nmewn</td>\n",
       "      <td>Believing his lies seems to be one simple way.</td>\n",
       "      <td>-0.4215</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.000</td>\n",
       "      <td>ArrestedDevelopments</td>\n",
       "      <td>Trudeau responds to Trump Tariffs: \"We have to...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>18788144</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527803e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-366683.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8nlqwk</td>\n",
       "      <td>You just ignored the broader point of everythi...</td>\n",
       "      <td>-0.4270</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.081</td>\n",
       "      <td>razorbacks3129</td>\n",
       "      <td>When you get out of school at 3, it felt very ...</td>\n",
       "      <td>Showerthoughts</td>\n",
       "      <td>14145327</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527798e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-361572.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8nn95z</td>\n",
       "      <td>He's still speaking the truth with that tweet....</td>\n",
       "      <td>-0.4939</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.068</td>\n",
       "      <td>GoldPastures</td>\n",
       "      <td>What's the creepiest video you've seen on the ...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304135</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527810e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-373754.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8nqles</td>\n",
       "      <td>I would have react the same way if someone wou...</td>\n",
       "      <td>-0.5267</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.000</td>\n",
       "      <td>bbcnews</td>\n",
       "      <td>Spain's prime minister is forced out of his jo...</td>\n",
       "      <td>worldnews</td>\n",
       "      <td>18788097</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527846e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-409098.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8nq5z6</td>\n",
       "      <td>You don’t even own. Why the fuck are we even h...</td>\n",
       "      <td>-0.5423</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.000</td>\n",
       "      <td>GallowPlaceholder</td>\n",
       "      <td>Interesting thought</td>\n",
       "      <td>WhitePeopleTwitter</td>\n",
       "      <td>520534</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527840e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-403211.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8nlf5f</td>\n",
       "      <td>No worries. If you sin then you need to repent...</td>\n",
       "      <td>-0.5423</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.168</td>\n",
       "      <td>rosesarewet</td>\n",
       "      <td>Lightning bolts colliding</td>\n",
       "      <td>gifs</td>\n",
       "      <td>16175956</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527796e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-359191.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8noo1r</td>\n",
       "      <td>Haha yup you walked away from the argument jus...</td>\n",
       "      <td>-0.6966</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.097</td>\n",
       "      <td>UncleBojangle</td>\n",
       "      <td>Little cultural differences</td>\n",
       "      <td>PoliticalHumor</td>\n",
       "      <td>389478</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527823e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-386705.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8nou82</td>\n",
       "      <td>A. It was a shooting foul because Durant was g...</td>\n",
       "      <td>-0.7512</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.111</td>\n",
       "      <td>Robotsaur</td>\n",
       "      <td>[Post Game Thread] YOUR Golden State Warriors ...</td>\n",
       "      <td>warriors</td>\n",
       "      <td>104872</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>**[](/CLE) CLE**|**Min**|**FG**|**FT**|**3PT**...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527825e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-388261.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8nn6jy</td>\n",
       "      <td>Let's put it this way.  An invoice is a contra...</td>\n",
       "      <td>-0.7650</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.057</td>\n",
       "      <td>GerardWayNoWay</td>\n",
       "      <td>LPT: If your friend owns a business, don't ask...</td>\n",
       "      <td>LifeProTips</td>\n",
       "      <td>14141470</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527810e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-373118.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8nn6jy</td>\n",
       "      <td>Let's put it this way.  An invoice is a contra...</td>\n",
       "      <td>-0.7650</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.057</td>\n",
       "      <td>GerardWayNoWay</td>\n",
       "      <td>LPT: If your friend owns a business, don't ask...</td>\n",
       "      <td>LifeProTips</td>\n",
       "      <td>14141470</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527810e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-373118.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8nl606</td>\n",
       "      <td>😂😂😂😂😂 you're just a dumb, impotent, angry litt...</td>\n",
       "      <td>-0.7744</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.057</td>\n",
       "      <td>Benjamin_Franklean</td>\n",
       "      <td>He only loves his bed</td>\n",
       "      <td>BlackPeopleTwitter</td>\n",
       "      <td>1739855</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.527794e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-357338.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>8nr4pb</td>\n",
       "      <td>I literally admitted in an earlier post to you...</td>\n",
       "      <td>-0.7783</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.147</td>\n",
       "      <td>RevengeVagina</td>\n",
       "      <td>What's a good movie everyone should watch but ...</td>\n",
       "      <td>AskReddit</td>\n",
       "      <td>19304083</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527852e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-415752.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8nneak</td>\n",
       "      <td>Hypothetically, if i held a demonstration and ...</td>\n",
       "      <td>-0.7820</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.033</td>\n",
       "      <td>Intrilo</td>\n",
       "      <td>\"ARAB ANDY\" scares the shit out of a college c...</td>\n",
       "      <td>LivestreamFail</td>\n",
       "      <td>260629</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.527812e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-375058.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8nneak</td>\n",
       "      <td>Hypothetically, if i held a demonstration and ...</td>\n",
       "      <td>-0.7820</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.033</td>\n",
       "      <td>Intrilo</td>\n",
       "      <td>\"ARAB ANDY\" scares the shit out of a college c...</td>\n",
       "      <td>LivestreamFail</td>\n",
       "      <td>260629</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.527812e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-375058.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>8nrb34</td>\n",
       "      <td>Whether it's \"fair\" or not, if you use that de...</td>\n",
       "      <td>-0.8442</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.000</td>\n",
       "      <td>unknown_human</td>\n",
       "      <td>Conan at a pub in Belfast yesterday (x-post /r...</td>\n",
       "      <td>gifs</td>\n",
       "      <td>16175925</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527854e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-417709.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8nm0fl</td>\n",
       "      <td>No, I already have, doesn’t work. Did you know...</td>\n",
       "      <td>-0.8519</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.073</td>\n",
       "      <td>medjarray7</td>\n",
       "      <td>Quick reaction saves a life</td>\n",
       "      <td>nonononoyes</td>\n",
       "      <td>858931</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527800e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-363614.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8nm0fl</td>\n",
       "      <td>No, I already have, doesn’t work. Did you know...</td>\n",
       "      <td>-0.8519</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.073</td>\n",
       "      <td>medjarray7</td>\n",
       "      <td>Quick reaction saves a life</td>\n",
       "      <td>nonononoyes</td>\n",
       "      <td>858932</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527800e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-363614.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>8nr18g</td>\n",
       "      <td>After the formation of the Association of Zoos...</td>\n",
       "      <td>-0.9779</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.000</td>\n",
       "      <td>ScaryChicken</td>\n",
       "      <td>Two lions, two tigers and a jaguar have report...</td>\n",
       "      <td>news</td>\n",
       "      <td>16085493</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.527851e+09</td>\n",
       "      <td>1.527437e+09</td>\n",
       "      <td>-414643.167889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                            Comment  compound  \\\n",
       "3   8nlhuo  Opportunities are always presenting themselves...    0.9804   \n",
       "37  8nqxw1  Superjail is pretty good. I still love Futuram...    0.9081   \n",
       "27  8nornq  Most titans of industry who acquired 10+ billi...    0.9062   \n",
       "29  8np2qv  Because... God is that conversation boring.  W...    0.8588   \n",
       "32  8npdh5                                          Thanks :)    0.7096   \n",
       "25  8nogl0  I find it quite funny that you’re resorting to...    0.6697   \n",
       "7   8nm73x  Apparently, my well rounded education seems to...    0.6249   \n",
       "8   8nm73x  Apparently, my well rounded education seems to...    0.6249   \n",
       "48  8nsifi                                               Nice    0.4215   \n",
       "30  8np3js  Yikes by far I think it’s mainly because I saw...    0.3818   \n",
       "49  8nssa4             Back in my day we piston scum like you    0.3612   \n",
       "11  8nmow9                              Periscopes are cool.     0.3182   \n",
       "12  8nmow9                              Periscopes are cool.     0.3182   \n",
       "16  8nmwqa         Okay sure but what about your constipation    0.2732   \n",
       "17  8nmwqa         Okay sure but what about your constipation    0.2732   \n",
       "46  8nrndc  Sorry if I read between the lines unnecessaril...    0.2401   \n",
       "1   8nlanp  My only point is that recoup your losses is a ...    0.2249   \n",
       "47  8ns6ev  “His start”\\n\\nOld canon he straight up told H...    0.2023   \n",
       "34  8nq9js  You are reusing the same two insults without e...    0.2023   \n",
       "40  8nr3ju  Don’t ask questions you don’t want to know the...    0.0772   \n",
       "14  8nmtne  The only thing you're contributing to is how p...    0.0601   \n",
       "24  8nnoyh                                               Xddd    0.0000   \n",
       "10  8nmhhi  You are quickly becoming my favourite Reddit a...    0.0000   \n",
       "13  8nmpzk                  >Yes it's called being an adult.     0.0000   \n",
       "23  8nnjqe                                                  F    0.0000   \n",
       "31  8np6hz  I said the writers of the bible choosing *our ...    0.0000   \n",
       "38  8nqy5s                                                  K    0.0000   \n",
       "45  8nrijt         A\\nN\\nI\\nM\\nA\\nL\\n\\nC\\nR\\nO\\nS\\nS\\nI\\nN\\nG    0.0000   \n",
       "15  8nmvqb                                  Probably so then!    0.0000   \n",
       "35  8nqgr5  I see where there could be misunderstanding. I...   -0.2263   \n",
       "42  8nr5lm  Selling an unsuitable product/breaching fiduci...   -0.3612   \n",
       "44  8nrhq0  Imagine getting so clowned on that you just st...   -0.4019   \n",
       "9   8nmewn     Believing his lies seems to be one simple way.   -0.4215   \n",
       "4   8nlqwk  You just ignored the broader point of everythi...   -0.4270   \n",
       "20  8nn95z  He's still speaking the truth with that tweet....   -0.4939   \n",
       "36  8nqles  I would have react the same way if someone wou...   -0.5267   \n",
       "33  8nq5z6  You don’t even own. Why the fuck are we even h...   -0.5423   \n",
       "2   8nlf5f  No worries. If you sin then you need to repent...   -0.5423   \n",
       "26  8noo1r  Haha yup you walked away from the argument jus...   -0.6966   \n",
       "28  8nou82  A. It was a shooting foul because Durant was g...   -0.7512   \n",
       "19  8nn6jy  Let's put it this way.  An invoice is a contra...   -0.7650   \n",
       "18  8nn6jy  Let's put it this way.  An invoice is a contra...   -0.7650   \n",
       "0   8nl606  😂😂😂😂😂 you're just a dumb, impotent, angry litt...   -0.7744   \n",
       "41  8nr4pb  I literally admitted in an earlier post to you...   -0.7783   \n",
       "22  8nneak  Hypothetically, if i held a demonstration and ...   -0.7820   \n",
       "21  8nneak  Hypothetically, if i held a demonstration and ...   -0.7820   \n",
       "43  8nrb34  Whether it's \"fair\" or not, if you use that de...   -0.8442   \n",
       "5   8nm0fl  No, I already have, doesn’t work. Did you know...   -0.8519   \n",
       "6   8nm0fl  No, I already have, doesn’t work. Did you know...   -0.8519   \n",
       "39  8nr18g  After the formation of the Association of Zoos...   -0.9779   \n",
       "\n",
       "      neg    neu    pos                Author  \\\n",
       "3   0.023  0.736  0.241            SenileNazi   \n",
       "37  0.000  0.392  0.608            JamesOldie   \n",
       "27  0.053  0.624  0.324                  mvea   \n",
       "29  0.028  0.782  0.190            Jackson160   \n",
       "32  0.000  0.000  1.000        Sakata_Kintoki   \n",
       "25  0.172  0.450  0.378    ReeceInTheDarkness   \n",
       "7   0.000  0.718  0.282         isometricLife   \n",
       "8   0.000  0.718  0.282         isometricLife   \n",
       "48  0.000  0.000  1.000              Zouroark   \n",
       "30  0.101  0.721  0.178               avayr44   \n",
       "49  0.000  0.762  0.238      automotiveartist   \n",
       "11  0.000  0.465  0.535          butter_otter   \n",
       "12  0.000  0.465  0.535          butter_otter   \n",
       "16  0.000  0.617  0.383               karljam   \n",
       "17  0.000  0.617  0.383               karljam   \n",
       "46  0.060  0.835  0.105         divergentdata   \n",
       "1   0.146  0.692  0.163              mixplate   \n",
       "47  0.107  0.738  0.156          MattDropDead   \n",
       "34  0.144  0.685  0.171        YouKn0wN0thing   \n",
       "40  0.000  0.885  0.115          penguiin1337   \n",
       "14  0.106  0.757  0.137            OtherJaffa   \n",
       "24  0.000  1.000  0.000       NinkovichPlease   \n",
       "10  0.000  1.000  0.000           BunyipPouch   \n",
       "13  0.000  1.000  0.000         cainbackisdry   \n",
       "23  0.000  0.000  0.000    Handsome_McAwesome   \n",
       "31  0.000  1.000  0.000         Google_Knight   \n",
       "38  0.000  0.000  0.000         CodieCaldwell   \n",
       "45  0.000  0.000  0.000              Stalfo14   \n",
       "15  0.000  1.000  0.000            Kingflares   \n",
       "35  0.048  0.952  0.000           CricinfoBot   \n",
       "42  0.203  0.678  0.119              gneiss_k   \n",
       "44  0.137  0.863  0.000            rdelamora1   \n",
       "9   0.259  0.741  0.000  ArrestedDevelopments   \n",
       "4   0.113  0.806  0.081        razorbacks3129   \n",
       "20  0.163  0.769  0.068          GoldPastures   \n",
       "36  0.116  0.884  0.000               bbcnews   \n",
       "33  0.226  0.774  0.000     GallowPlaceholder   \n",
       "2   0.211  0.622  0.168           rosesarewet   \n",
       "26  0.166  0.737  0.097         UncleBojangle   \n",
       "28  0.154  0.735  0.111             Robotsaur   \n",
       "19  0.152  0.791  0.057        GerardWayNoWay   \n",
       "18  0.152  0.791  0.057        GerardWayNoWay   \n",
       "0   0.211  0.732  0.057    Benjamin_Franklean   \n",
       "41  0.211  0.642  0.147         RevengeVagina   \n",
       "22  0.170  0.797  0.033               Intrilo   \n",
       "21  0.170  0.797  0.033               Intrilo   \n",
       "43  0.178  0.822  0.000         unknown_human   \n",
       "5   0.377  0.549  0.073            medjarray7   \n",
       "6   0.377  0.549  0.073            medjarray7   \n",
       "39  0.330  0.670  0.000          ScaryChicken   \n",
       "\n",
       "                                                Title             SubReddit  \\\n",
       "3   What's a shallow reason you wouldn't date some...             AskReddit   \n",
       "37  I did what any gamer would do to get their gf ...                gaming   \n",
       "27  Elon Musk Responds to Boeing's Claims It Will ...            Futurology   \n",
       "29  What's a life lession that you learned from a ...             AskReddit   \n",
       "32                             One Piece: Chapter 906              OnePiece   \n",
       "25  What celebrity has skeletons in their closet t...             AskReddit   \n",
       "7   Redditors of the US, how would you feel about ...             AskReddit   \n",
       "8   Redditors of the US, how would you feel about ...             AskReddit   \n",
       "48  Bless Online trailer plagiarized How to train ...                gaming   \n",
       "30            \"YE\" Album - First Thoughts Mega-Thread                 Kanye   \n",
       "49  I make paintings of cars. This is my latest wo...                  pics   \n",
       "11  I got this PM because I said \"If you can't sta...  insanepeoplefacebook   \n",
       "12  I got this PM because I said \"If you can't sta...  insanepeoplefacebook   \n",
       "16  Modern Icelandic houses are built with geother...     mildlyinteresting   \n",
       "17  Modern Icelandic houses are built with geother...     mildlyinteresting   \n",
       "46    Fraction of Total Videogame Sales by Genre [OC]       dataisbeautiful   \n",
       "1   Amazon needs to get a handle on its counterfei...            technology   \n",
       "47  Give me an R rated Boba Fett movie with some r...              StarWars   \n",
       "34  How Scoliosis (Curvature of the Spine) Surgery...       educationalgifs   \n",
       "40       My cat ... dont even know what to say 😅🤦🏼‍♂️                   aww   \n",
       "14    Father of four hears silence for the first time                  gifs   \n",
       "24             My doctor charges me $65 for being fat         Wellthatsucks   \n",
       "10  First Image of Shia Labeouf as Shia Labeouf's ...                movies   \n",
       "13  German Sheperd realizes owner isnt behind him ...                videos   \n",
       "23  I texted my younger brother during graduation ...                  pics   \n",
       "31  ELI5 What's the reason behind of 666 being the...     explainlikeimfive   \n",
       "38                                    Cliffs of Moher              woahdude   \n",
       "45  We're giving away a $10 eShop card, all you ne...        NintendoSwitch   \n",
       "15  What food has made you wonder \"how did our anc...             AskReddit   \n",
       "35  Match Thread: England vs Pakistan at Headingle...               Cricket   \n",
       "42  My husband and I are idiots. We've been bamboo...       personalfinance   \n",
       "44                      [FRESH ALBUM] Kanye West - ye                 Kanye   \n",
       "9   Trudeau responds to Trump Tariffs: \"We have to...             worldnews   \n",
       "4   When you get out of school at 3, it felt very ...        Showerthoughts   \n",
       "20  What's the creepiest video you've seen on the ...             AskReddit   \n",
       "36  Spain's prime minister is forced out of his jo...             worldnews   \n",
       "33                                Interesting thought    WhitePeopleTwitter   \n",
       "2                           Lightning bolts colliding                  gifs   \n",
       "26                        Little cultural differences        PoliticalHumor   \n",
       "28  [Post Game Thread] YOUR Golden State Warriors ...              warriors   \n",
       "19  LPT: If your friend owns a business, don't ask...           LifeProTips   \n",
       "18  LPT: If your friend owns a business, don't ask...           LifeProTips   \n",
       "0                               He only loves his bed    BlackPeopleTwitter   \n",
       "41  What's a good movie everyone should watch but ...             AskReddit   \n",
       "22  \"ARAB ANDY\" scares the shit out of a college c...        LivestreamFail   \n",
       "21  \"ARAB ANDY\" scares the shit out of a college c...        LivestreamFail   \n",
       "43  Conan at a pub in Belfast yesterday (x-post /r...                  gifs   \n",
       "5                         Quick reaction saves a life           nonononoyes   \n",
       "6                         Quick reaction saves a life           nonononoyes   \n",
       "39  Two lions, two tigers and a jaguar have report...                  news   \n",
       "\n",
       "    SubReddit_Subscribers    ...      Num_Crossposts  \\\n",
       "3                19304150    ...                   1   \n",
       "37               18195333    ...                   0   \n",
       "27               13033147    ...                   1   \n",
       "29               19304130    ...                   0   \n",
       "32                 167348    ...                   0   \n",
       "25               19304088    ...                   1   \n",
       "7                19304131    ...                   1   \n",
       "8                19304131    ...                   1   \n",
       "48               18195312    ...                   3   \n",
       "30                 146302    ...                   0   \n",
       "49               18699440    ...                   7   \n",
       "11                 501208    ...                   2   \n",
       "12                 501209    ...                   2   \n",
       "16               14323054    ...                   6   \n",
       "17               14323055    ...                   6   \n",
       "46               13056843    ...                   7   \n",
       "1                 6207082    ...                   3   \n",
       "47                 884224    ...                   0   \n",
       "34                 606241    ...                   5   \n",
       "40               17217424    ...                  20   \n",
       "14               16175955    ...                   8   \n",
       "24                 691651    ...                   9   \n",
       "10               17635011    ...                   4   \n",
       "13               17810231    ...                   4   \n",
       "23               18699483    ...                   7   \n",
       "31               15598221    ...                   2   \n",
       "38                1795252    ...                  15   \n",
       "45                 626768    ...                   0   \n",
       "15               19304127    ...                   0   \n",
       "35                  71824    ...                   0   \n",
       "42               12946527    ...                   1   \n",
       "44                 146279    ...                   0   \n",
       "9                18788144    ...                   0   \n",
       "4                14145327    ...                   0   \n",
       "20               19304135    ...                   1   \n",
       "36               18788097    ...                   1   \n",
       "33                 520534    ...                   1   \n",
       "2                16175956    ...                  32   \n",
       "26                 389478    ...                   4   \n",
       "28                 104872    ...                   0   \n",
       "19               14141470    ...                   5   \n",
       "18               14141470    ...                   5   \n",
       "0                 1739855    ...                   3   \n",
       "41               19304083    ...                   0   \n",
       "22                 260629    ...                   7   \n",
       "21                 260629    ...                   7   \n",
       "43               16175925    ...                   0   \n",
       "5                  858931    ...                   5   \n",
       "6                  858932    ...                   5   \n",
       "39               16085493    ...                   5   \n",
       "\n",
       "                                                 Text  WLS   Created_UTC  \\\n",
       "3                                                   0  6.0  1.527796e+09   \n",
       "37                                                  0  6.0  1.527850e+09   \n",
       "27                                                  0  6.0  1.527824e+09   \n",
       "29                                                  0  6.0  1.527827e+09   \n",
       "32  **Chapter 906: \"The Holy Land Mary Geoise\"**\\n...  6.0  1.527830e+09   \n",
       "25                                                  0  6.0  1.527821e+09   \n",
       "7                                                   0  6.0  1.527802e+09   \n",
       "8                                                   0  6.0  1.527802e+09   \n",
       "48                                                  0  6.0  1.527865e+09   \n",
       "30  Please remember :\\n\\n- **Do NOT downvote** jus...  6.0  1.527827e+09   \n",
       "49                                                  0  6.0  1.527867e+09   \n",
       "11                                                  0  6.0  1.527806e+09   \n",
       "12                                                  0  6.0  1.527806e+09   \n",
       "16                                                  0  6.0  1.527808e+09   \n",
       "17                                                  0  6.0  1.527808e+09   \n",
       "46                                                  0  6.0  1.527858e+09   \n",
       "1                                                   0  6.0  1.527795e+09   \n",
       "47                                                  0  6.0  1.527862e+09   \n",
       "34                                                  0  6.0  1.527841e+09   \n",
       "40                                                  0  6.0  1.527852e+09   \n",
       "14                                                  0  6.0  1.527807e+09   \n",
       "24                                                  0  1.0  1.527814e+09   \n",
       "10                                                  0  6.0  1.527804e+09   \n",
       "13                                                  0  6.0  1.527806e+09   \n",
       "23                                                  0  6.0  1.527813e+09   \n",
       "31     Both biblical and anthropological,if posible.   6.0  1.527828e+09   \n",
       "38                                                  0  6.0  1.527850e+09   \n",
       "45  Hey everyone, Talk Nintendo Podcast here with ...  6.0  1.527856e+09   \n",
       "15                                                  0  6.0  1.527807e+09   \n",
       "35  ###2nd Test, Pakistan tour of Ireland, England...  6.0  1.527844e+09   \n",
       "42  Ugh I'm so frustrated. I thought we were doing...  6.0  1.527853e+09   \n",
       "44                                                  0  6.0  1.527856e+09   \n",
       "9                                                   0  6.0  1.527803e+09   \n",
       "4                                                   0  6.0  1.527798e+09   \n",
       "20                                                  0  6.0  1.527810e+09   \n",
       "36                                                  0  6.0  1.527846e+09   \n",
       "33                                                  0  6.0  1.527840e+09   \n",
       "2                                                   0  6.0  1.527796e+09   \n",
       "26                                                  0  6.0  1.527823e+09   \n",
       "28  **[](/CLE) CLE**|**Min**|**FG**|**FT**|**3PT**...  6.0  1.527825e+09   \n",
       "19                                                  0  6.0  1.527810e+09   \n",
       "18                                                  0  6.0  1.527810e+09   \n",
       "0                                                   0  5.0  1.527794e+09   \n",
       "41                                                  0  6.0  1.527852e+09   \n",
       "22                                                  0  1.0  1.527812e+09   \n",
       "21                                                  0  1.0  1.527812e+09   \n",
       "43                                                  0  6.0  1.527854e+09   \n",
       "5                                                   0  6.0  1.527800e+09   \n",
       "6                                                   0  6.0  1.527800e+09   \n",
       "39                                                  0  6.0  1.527851e+09   \n",
       "\n",
       "      Pulled_UTC        Time_Up  IsPinned  IsStickied  HOT  SUPER_HOT  \n",
       "3   1.527437e+09 -359731.167889         0           0    1          1  \n",
       "37  1.527437e+09 -413542.167889         0           0    1          1  \n",
       "27  1.527437e+09 -387557.167889         0           0    1          1  \n",
       "29  1.527437e+09 -390554.167889         0           0    1          1  \n",
       "32  1.527437e+09 -393537.167889         0           0    1          1  \n",
       "25  1.527437e+09 -384745.167889         0           0    1          1  \n",
       "7   1.527437e+09 -364975.167889         0           0    1          1  \n",
       "8   1.527437e+09 -364975.167889         0           0    1          1  \n",
       "48  1.527437e+09 -428712.167889         0           0    1          1  \n",
       "30  1.527437e+09 -390793.167889         0           1    1          1  \n",
       "49  1.527437e+09 -430545.167889         0           0    1          1  \n",
       "11  1.527437e+09 -369143.167889         0           0    1          1  \n",
       "12  1.527437e+09 -369143.167889         0           0    1          1  \n",
       "16  1.527437e+09 -370909.167889         0           0    1          1  \n",
       "17  1.527437e+09 -370909.167889         0           0    1          1  \n",
       "46  1.527437e+09 -421085.167889         0           0    1          1  \n",
       "1   1.527437e+09 -358282.167889         0           0    1          1  \n",
       "47  1.527437e+09 -425732.167889         0           0    1          1  \n",
       "34  1.527437e+09 -404534.167889         0           0    1          1  \n",
       "40  1.527437e+09 -415390.167889         0           0    1          1  \n",
       "14  1.527437e+09 -370312.167889         0           0    1          1  \n",
       "24  1.527437e+09 -377627.167889         0           0    1          1  \n",
       "10  1.527437e+09 -367315.167889         0           0    1          1  \n",
       "13  1.527437e+09 -369433.167889         0           0    1          1  \n",
       "23  1.527437e+09 -376319.167889         0           0    1          1  \n",
       "31  1.527437e+09 -391577.167889         0           0    1          1  \n",
       "38  1.527437e+09 -413640.167889         0           0    1          1  \n",
       "45  1.527437e+09 -419748.167889         0           0    1          1  \n",
       "15  1.527437e+09 -370702.167889         0           0    1          1  \n",
       "35  1.527437e+09 -407358.167889         0           0    1          1  \n",
       "42  1.527437e+09 -416043.167889         0           0    1          1  \n",
       "44  1.527437e+09 -419516.167889         0           1    1          1  \n",
       "9   1.527437e+09 -366683.167889         0           0    1          1  \n",
       "4   1.527437e+09 -361572.167889         0           0    1          1  \n",
       "20  1.527437e+09 -373754.167889         0           0    1          1  \n",
       "36  1.527437e+09 -409098.167889         0           0    1          1  \n",
       "33  1.527437e+09 -403211.167889         0           0    1          1  \n",
       "2   1.527437e+09 -359191.167889         0           0    1          1  \n",
       "26  1.527437e+09 -386705.167889         0           0    1          1  \n",
       "28  1.527437e+09 -388261.167889         0           0    1          1  \n",
       "19  1.527437e+09 -373118.167889         0           0    1          1  \n",
       "18  1.527437e+09 -373118.167889         0           0    1          1  \n",
       "0   1.527437e+09 -357338.167889         0           0    1          1  \n",
       "41  1.527437e+09 -415752.167889         0           0    1          1  \n",
       "22  1.527437e+09 -375058.167889         0           0    1          1  \n",
       "21  1.527437e+09 -375058.167889         0           0    1          1  \n",
       "43  1.527437e+09 -417709.167889         0           0    1          1  \n",
       "5   1.527437e+09 -363614.167889         0           0    1          1  \n",
       "6   1.527437e+09 -363614.167889         0           0    1          1  \n",
       "39  1.527437e+09 -414643.167889         0           0    1          1  \n",
       "\n",
       "[50 rows x 24 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort Descending on Best Compound Scores on Top Comment of Top50 Posts\n",
    "\n",
    "bringittogether = joined.sort_values('compound', ascending=False)\n",
    "bringittogether"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>46</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>8nrndc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comment</th>\n",
       "      <td>Sorry if I read between the lines unnecessaril...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compound</th>\n",
       "      <td>0.2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neg</th>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neu</th>\n",
       "      <td>0.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos</th>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Author</th>\n",
       "      <td>divergentdata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <td>Fraction of Total Videogame Sales by Genre [OC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SubReddit</th>\n",
       "      <td>dataisbeautiful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SubReddit_Subscribers</th>\n",
       "      <td>13056843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <td>18918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ups</th>\n",
       "      <td>18918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Downs</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num_Comments</th>\n",
       "      <td>1417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num_Crossposts</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Text</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WLS</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Created_UTC</th>\n",
       "      <td>1.52786e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulled_UTC</th>\n",
       "      <td>1.52744e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_Up</th>\n",
       "      <td>-421085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsPinned</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsStickied</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOT</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUPER_HOT</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      46\n",
       "Id                                                                8nrndc\n",
       "Comment                Sorry if I read between the lines unnecessaril...\n",
       "compound                                                          0.2401\n",
       "neg                                                                 0.06\n",
       "neu                                                                0.835\n",
       "pos                                                                0.105\n",
       "Author                                                     divergentdata\n",
       "Title                    Fraction of Total Videogame Sales by Genre [OC]\n",
       "SubReddit                                                dataisbeautiful\n",
       "SubReddit_Subscribers                                           13056843\n",
       "Score                                                              18918\n",
       "Ups                                                                18918\n",
       "Downs                                                                  0\n",
       "Num_Comments                                                        1417\n",
       "Num_Crossposts                                                         7\n",
       "Text                                                                   0\n",
       "WLS                                                                    6\n",
       "Created_UTC                                                  1.52786e+09\n",
       "Pulled_UTC                                                   1.52744e+09\n",
       "Time_Up                                                          -421085\n",
       "IsPinned                                                               0\n",
       "IsStickied                                                             0\n",
       "HOT                                                                    1\n",
       "SUPER_HOT                                                              1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example post to pitch given neutrality of comment, relevant subreddit, amount of subscribers, and high score\n",
    "\n",
    "dataisbeautiful = joined[joined['SubReddit']=='dataisbeautiful']\n",
    "dataisbeautiful.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sentiment Analysis Plot\n",
    "\n",
    "# import plotly\n",
    "# import plotly.graph_objs as go\n",
    "# import plotly.plotly as py\n",
    "\n",
    "# layout = go.Layout(\n",
    "#     autosize=True,\n",
    "#     title='Sentiment Analysis of Top Comments',\n",
    "#     titlefont = {'family':'Georgia',\n",
    "#                  'size': 24},\n",
    "#     xaxis={'title':'Negative Score'}, \n",
    "#     yaxis={'title':'Positive Score'},\n",
    "#     hovermode= 'closest')\n",
    "\n",
    "# x0=comments_scores['neg']\n",
    "# y0=comments_scores['pos']\n",
    "\n",
    "# comments_scores['text'] = comments_scores['compound']\n",
    "\n",
    "# trace1 = go.Scatter(x=x0,\n",
    "#                     y=y0,\n",
    "#                     mode = 'markers',\n",
    "#                     marker={'color' : comments_scores['compound'],\n",
    "#                             'colorscale' : 'Jet',\n",
    "#                             'size' : 16,\n",
    "#                             'showscale' : True,\n",
    "#                             'colorbar' : {'title' : 'Compound Score'}},\n",
    "#                     hovertext=comments_scores['text'],\n",
    "#                    text = comments_scores['compound'],\n",
    "#                    textposition = 'middle right')\n",
    "\n",
    "# data = [trace1]\n",
    "\n",
    "# fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "# py.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"hotdataframe\", encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "the = f.read()\n",
    "\n",
    "text_model = mk.NewlineText(the)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will be a jack wagon for some reason.\n",
      "\n",
      "I can't remember which grocery store it was only a matter of time it took them — for example — to get in trouble over what I talk about is; those rubberbands, laggish &amp; teleporting thralls &amp; NPC's is basically an ICMP protocol ping where the later one is ingame\\-protocol based ping.\n",
      "\n",
      "I'm not even entirely sure what her job is there, but it had no idea how little my body and started to shut. The crowd was completely silent\\- not a GREYHOUND or AMTRAK uniform. The cafe is about the amount of reaper kills assigned for Solak has been lowered from 45,000 to 25,000.\n",
      "\n",
      "Why do tiny particles inside atoms?*\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    print(text_model.make_sentence_with_start(beginning='I will', strict=False))\n",
    "print()\n",
    "for i in range(1):\n",
    "    print(text_model.make_sentence_with_start(beginning=\"I can't\", strict=False))\n",
    "print()\n",
    "for i in range(1):\n",
    "    print(text_model.make_sentence_with_start(beginning=\"I'm not\", strict=False))\n",
    "print()\n",
    "for i in range(1):\n",
    "    print(text_model.make_sentence_with_start(beginning=\"Why\", strict=False))\n",
    "print()\n",
    "for i in range(1):\n",
    "    print(text_model.make_sentence_with_start(beginning=\"Women\", strict=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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