W207_Final_Project_Baseline_v4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# W207 Final Project\n",
    "Erika, Jen Jen, Geoff, Leslie\n",
    "\n",
    "(In Python 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As of 3/35\n",
    "\n",
    "Outline:\n",
    "\n",
    "* Data Pre-Processing  \n",
    "* Simple Feature Selection\n",
    "* Basline Models\n",
    "* Possible Approaches\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Section 1 Loading and Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import Libraries #\n",
    "import json\n",
    "from pprint import pprint\n",
    "from pandas import *\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "# SK-learn library for importing the newsgroup data.\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "# General libraries\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# SK-learn libraries for feature extraction from text.\n",
    "from sklearn.feature_extraction.text import *\n",
    "\n",
    "# SK-learn libraries for pre/processing data\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from sklearn.decomposition import LatentDirichletAllocation as LDA\n",
    "\n",
    "# SK-learn libraries for learning\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# SK-learn libraries for evaluation\n",
    "from sklearn import cross_validation\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size of the normalized Data: (3040, 32)\n",
      "\n",
      "normalized data columns: ['giver_username_if_known', 'number_of_downvotes_of_request_at_retrieval', 'number_of_upvotes_of_request_at_retrieval', 'post_was_edited', 'request_id', 'request_number_of_comments_at_retrieval', 'request_text', 'request_text_edit_aware', 'request_title', 'requester_account_age_in_days_at_request', 'requester_account_age_in_days_at_retrieval', 'requester_days_since_first_post_on_raop_at_request', 'requester_days_since_first_post_on_raop_at_retrieval', 'requester_number_of_comments_at_request', 'requester_number_of_comments_at_retrieval', 'requester_number_of_comments_in_raop_at_request', 'requester_number_of_comments_in_raop_at_retrieval', 'requester_number_of_posts_at_request', 'requester_number_of_posts_at_retrieval', 'requester_number_of_posts_on_raop_at_request', 'requester_number_of_posts_on_raop_at_retrieval', 'requester_number_of_subreddits_at_request', 'requester_received_pizza', 'requester_subreddits_at_request', 'requester_upvotes_minus_downvotes_at_request', 'requester_upvotes_minus_downvotes_at_retrieval', 'requester_upvotes_plus_downvotes_at_request', 'requester_upvotes_plus_downvotes_at_retrieval', 'requester_user_flair', 'requester_username', 'unix_timestamp_of_request', 'unix_timestamp_of_request_utc']\n"
     ]
    }
   ],
   "source": [
    "## Get Data ##\n",
    "\n",
    "#reference on data: https://www.kaggle.com/c/random-acts-of-pizza/data\n",
    "# pull in the training and test data\n",
    "with open('/Users/Jen2/Desktop/W207/project/train.json', encoding='utf-8') as data_file:\n",
    "#with open('/home/levi/Documents/W207_Proj/data/train.json', encoding='utf-8') as data_file:\n",
    "    trainData = json.loads(data_file.read())   \n",
    "with open('/Users/Jen2/Desktop/W207/project/test.json', encoding='utf-8') as data_file:\n",
    "#with open('/home/levi/Documents/W207_Proj/data/train.json', encoding='utf-8') as data_file:\n",
    "    testData = json.loads(data_file.read())    \n",
    "\n",
    "# create a dev data set \n",
    "devData = trainData[0:1000]\n",
    "trainData = trainData[1000:]\n",
    "\n",
    "# show how the data looks in its original format\n",
    "#pprint(\"data in json format:\")\n",
    "#pprint(trainData[1])\n",
    "\n",
    "# create a normalized view\n",
    "allTData = json_normalize(trainData)\n",
    "print(\"\\nSize of the normalized Data:\", allTData.shape)\n",
    "print(\"\\nnormalized data columns:\", list(allTData))\n",
    "\n",
    "allDData = json_normalize(devData)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 1.1 Text data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3040, 1)\n"
     ]
    }
   ],
   "source": [
    "## Create subsets of data for analysis ###\n",
    "\n",
    "# create a flat dataset without the subreddits list\n",
    "flatData = allTData.drop('requester_subreddits_at_request', 1)\n",
    "# create a separate dataset with just subreddits, indexed on request id\n",
    "# we can creata a count vector on the words, run Naive Bayes against it, \n",
    "# and add the probabilities to our flat dataset\n",
    "subredTData = allTData[['request_id','requester_subreddits_at_request']]\n",
    "subredTData.set_index('request_id', inplace=True)\n",
    "\n",
    "subredDData= allDData[['request_id','requester_subreddits_at_request']]\n",
    "subredDData.set_index('request_id', inplace=True)\n",
    "\n",
    "# our training labels\n",
    "trainLabel = allTData['requester_received_pizza']\n",
    "\n",
    "devLabel = allDData['requester_received_pizza']\n",
    "\n",
    "# what do these look like?\n",
    "#print(list(flatData))\n",
    "print(subredTData.shape)\n",
    "#print(subredTData['requester_subreddits_at_request'][1])\n",
    "\n",
    "# create a corpus of subreddits to vectorize\n",
    "trainCorpus = []\n",
    "for index in range(len(subredTData)):\n",
    "    trainCorpus.append(' '.join(subredTData['requester_subreddits_at_request'][index]))\n",
    "\n",
    "devCorpus = []\n",
    "for index in range(len(subredDData)):\n",
    "    devCorpus.append(' '.join(subredDData['requester_subreddits_at_request'][index]))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"FoodstuffsAllAround IAmA RandomActsOfCookies RandomActsofCards RandomKindness Random_Acts_Of_Pizza comiccon cosplay cosplayers So it's been a while since it's happened, but yeah. Just got dumped by a girl I like... My brother has used up pretty much everything in the freezer. I would really appreciate a pizza right now... So it's been a while since it's happened, but yeah. Just got dumped by a girl I like... My brother has used up pretty much everything in the freezer. I would really appreciate a pizza right now... [Request] Just got dumped, no food in the freezer. Pizza?\", \"AskReddit Guitar Jazz Music NSFW_GIF Psychonaut RoomPorn StAugustine TwoXChromosomes WTF YouShouldKnow atheism aww bakedart catpictures cats crochet dubstep ents entwives food funny gonewild hiphopheads listentothis meetup offbeat pics realpics self sex tattoos treecomics treemusic trees videos vinyl zombies My boyfriend and I live in Saint Augustine, Florida and have been having a rough time financially the past few months.  In and out of various jobs, we've had to survive off of coscto sized ramen packs, and pasta and olive oil.  I applied for food stamps a couple days ago, and am waiting to hear back from them.  It's getting a little trite, and we're quite hungry tonight, a hot pizza would be a delight.  We'll happily pay it forward in the future.  Much love. My boyfriend and I live in Saint Augustine, Florida and have been having a rough time financially the past few months.  In and out of various jobs, we've had to survive off of coscto sized ramen packs, and pasta and olive oil.  I applied for food stamps a couple days ago, and am waiting to hear back from them.  It's getting a little trite, and we're quite hungry tonight, a hot pizza would be a delight.  We'll happily pay it forward in the future.  Much love. [Request]  Saint Augustine, US.  Boyfriend and I have no money till next week, and are awaiting food stamps approval.\", \"Albany AskReddit Brooklyn Favors ImGoingToHellForThis Random_Acts_Of_Amazon Random_Acts_Of_Pizza YouShouldKnow cars cigars gaming pics shutupandtakemymoney ualbany upstate_new_york videos I seriously love buffalo chicken pizza. Like, straight up addicted. There's a local pizzeria that delivers and they make the best buffalo chicken pizza I've ever had; however if you dont feel safe with that or have a Giftcard to a chain I could care less, I just want a buffalo chicken pizza soooo bad!  I seriously love buffalo chicken pizza. Like, straight up addicted. There's a local pizzeria that delivers and they make the best buffalo chicken pizza I've ever had; however if you dont feel safe with that or have a Giftcard to a chain I could care less, I just want a buffalo chicken pizza soooo bad!  [Request] I'd love a Buffalo Chicken Puzza!\"]\n",
      "[0, 0, 1]\n",
      "---------------------------------------------------------------------------\n",
      "\n",
      " [' Hi I am in need of food for my 4 children we are a military family that has really hit hard times and we have exahusted all means of help just to be able to feed my family and make it through another night is all i ask i know our blessing is coming so whatever u can find in your heart to give is greatly appreciated Hi I am in need of food for my 4 children we are a military family that has really hit hard times and we have exahusted all means of help just to be able to feed my family and make it through another night is all i ask i know our blessing is coming so whatever u can find in your heart to give is greatly appreciated Request Colorado Springs Help Us Please', 'AskReddit Eve IAmA MontereyBay RandomKindness RedditBiography dubstep gamecollecting gaming halo i18n techsupport I spent the last money I had on gas today. Im broke until next Thursday :( I spent the last money I had on gas today. Im broke until next Thursday :( [Request] California, No cash and I could use some dinner', \" My girlfriend decided it would be a good idea to get off at Perth bus station when she was coming to visit me and has since had to spend all her money on a taxi to get to me here in Dundee. Any chance some kind soul would get us some pizza since we don't have any cash anymore? My girlfriend decided it would be a good idea to get off at Perth bus station when she was coming to visit me and has since had to spend all her money on a taxi to get to me here in Dundee. Any chance some kind soul would get us some pizza since we don't have any cash anymore? [Request] Hungry couple in Dundee, Scotland would love some pizza!\"]\n",
      "[0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "# combine all text sources into a single corpus\n",
    "fldTText = allTData[['request_id','request_text', 'request_text_edit_aware', 'request_title']]\n",
    "fldDText = allDData[['request_id','request_text', 'request_text_edit_aware', 'request_title']]\n",
    "\n",
    "trainCorpus = []\n",
    "for index in range(len(subredTData)):\n",
    "    a = ' '.join(subredTData['requester_subreddits_at_request'][index])\n",
    "    b = (a, fldTText['request_text'][index], fldTText['request_text_edit_aware'][index],\n",
    "        fldTText['request_title'][index])\n",
    "    trainCorpus.append(' '.join(b))\n",
    "\n",
    "devCorpus = []\n",
    "for index in range(len(subredDData)):\n",
    "    a = ' '.join(subredDData['requester_subreddits_at_request'][index])\n",
    "    b = (a, fldDText['request_text'][index], fldDText['request_text_edit_aware'][index],\n",
    "         fldDText['request_title'][index])\n",
    "    devCorpus.append(' '.join(b))\n",
    "\n",
    "# Print 3 examples  \n",
    "print (trainCorpus[:3])\n",
    "labels = trainLabel.astype(int)\n",
    "labels = list(labels)\n",
    "print(labels[:3])\n",
    "print('-'*75)\n",
    "\n",
    "print ('\\n' , devCorpus[:3])\n",
    "labels_dev = devLabel.astype(int)\n",
    "labels_dev = list(labels_dev)\n",
    "print(labels_dev[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "# #posts = subredTData\n",
    "\n",
    "# sentences2 = [\"Hello, thank you very much\", \"No, that's an awful idea\"]\n",
    "\n",
    "# sentences.extend(sentences2)\n",
    "# sid = SentimentIntensityAnalyzer()\n",
    "# for post in sentences:\n",
    "#     print(post)\n",
    "#     ss = sid.polarity_scores(post)\n",
    "#     for k in sorted(ss):\n",
    "#         print('{0}': '{1}', '.format(k, ss[k]), end='')\n",
    "#     print()\n",
    "    \n",
    "#for post in subredTData:\n",
    "#    print(post,\n",
    "#    sentiment = vaderSentiment(post))\n",
    "#    print(\"\\n\\t\" + str(sentiment))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 1.2 User status analysis (votes, flair)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## CREATE SUBSETS FOR USER STATUS ANALYSIS ##\n",
    "\n",
    "# subsets for upvotes-downvotes and upvotes+downvotes at time of request and retrieval\n",
    "diffTrequest = allTData['requester_upvotes_minus_downvotes_at_request']\n",
    "diffDrequest = allDData['requester_upvotes_minus_downvotes_at_request']\n",
    "diffTretrieval = allTData['requester_upvotes_minus_downvotes_at_retrieval']\n",
    "diffDretrieval = allDData['requester_upvotes_minus_downvotes_at_retrieval']\n",
    "totalvotesTrequest = allTData['requester_upvotes_plus_downvotes_at_request']\n",
    "totalvotesDrequest = allDData['requester_upvotes_plus_downvotes_at_request']\n",
    "totalvotesTretrieval = allTData['requester_upvotes_plus_downvotes_at_retrieval']\n",
    "totalvotesDretrieval = allDData['requester_upvotes_plus_downvotes_at_retrieval']\n",
    "\n",
    "# subsets for flair (training data only)\n",
    "Tflair = allTData['requester_user_flair'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "## CREATING NEW PARAMETER: the change in upvotes-downvotes from time of request to time of retrieval ##\n",
    "\n",
    "# user status with new parameter for training data\n",
    "allTData['request_to_retrieval_upvotes_minus_downvotes'] = diffTretrieval - diffTrequest\n",
    "diffTcombined = allTData[['requester_upvotes_minus_downvotes_at_request','requester_upvotes_minus_downvotes_at_retrieval',\n",
    "                          'request_to_retrieval_upvotes_minus_downvotes','requester_received_pizza']].copy()\n",
    "# rename column headings\n",
    "diffTcombined.columns = ['Request', 'Retrieval', 'Request-Retrieval', 'Got Pizza']\n",
    "# print(\"ADDING NEW PARAMETER - REQUEST-RETRIEVAL:  to represent change in counts from upvotes-downvotes from time of request to time of retrieval.\")\n",
    "# print(\"\\nSTATUS TRAINING DATA\")\n",
    "# display(diffTcombined.head())\n",
    "\n",
    "# user status with new parameter for training data\n",
    "allDData['request_to_retrieval_upvotes_minus_downvotes'] = diffDretrieval - diffDrequest\n",
    "diffDcombined = allDData[['requester_upvotes_minus_downvotes_at_request','requester_upvotes_minus_downvotes_at_retrieval',\n",
    "                          'request_to_retrieval_upvotes_minus_downvotes','requester_received_pizza']].copy()\n",
    "# rename columne headings\n",
    "diffDcombined.columns = ['Request', 'Retrieval', 'Request-Retrieval', 'Got Pizza']\n",
    "# print(\"\\n\")\n",
    "# print(\"STATUS DEVELOPMENT DATA\")\n",
    "# display(diffDcombined.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "## SUMMARY STATISTICS FOR UPVOTES-DOWNVOTES AT REQUEST AND AT RETRIEVAL ##\n",
    "\n",
    "# upvotes-downvotes at request, grouped by pizza success -------------------------\n",
    "# print(\"\\n\\nSUMMARY STATISTICS FOR UPVOTES-DOWNVOTES AT REQUEST AND AT RETRIEVAL, GROUPED BY PIZZA SUCCESS\")\n",
    "\n",
    "diffreq_pizzas = allTData[['requester_upvotes_minus_downvotes_at_request','requester_received_pizza']].copy()\n",
    "diffreq_pizzas.columns = ['Request', 'Got Pizza']\n",
    "pizza_diff_request = diffreq_pizzas.groupby(['Got Pizza'])\n",
    "# print(\"\\nGroup upvotes-downvotes at request by pizza success:\\n\")\n",
    "# print(round(pizza_diff_request.describe(), 2))\n",
    "\n",
    "# upvotes-downvotes at retrieval, grouped by pizza success -------------------------\n",
    "diffretrieve_pizzas = allTData[['requester_upvotes_minus_downvotes_at_retrieval','requester_received_pizza']].copy()\n",
    "diffretrieve_pizzas.columns = ['Retrieval', 'Got Pizza']\n",
    "pizza_diff_retrieval = diffretrieve_pizzas.groupby(['Got Pizza'])\n",
    "# print(\"\\n\")\n",
    "# print(\"-\"*100)\n",
    "# print(\"\\nGroup upvotes-downvotes at retrieval by pizza success:\\n\")\n",
    "# print(round(pizza_diff_retrieval.describe(), 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## USER FLAIR ##\n",
    "# None = no pizza\n",
    "#'shroom' = got pizza without 'pay it forward' (PIF)\n",
    "# PIF' = got pizza and paid forward\n",
    "\n",
    "flair = allTData[['requester_user_flair','requester_received_pizza']].copy()\n",
    "flair.columns = ['Flair', 'Got Pizza']\n",
    "# print(\"\\n\")\n",
    "# print(\"Types of Flair and Pizza Success\\n\")\n",
    "# print(flair.head(10))\n",
    "# pizza_flair = flair.groupby(['Got Pizza'])\n",
    "# print(\"\\n\")\n",
    "# print(\"-\"*100)\n",
    "# print(\"\\nGroup flair by pizza success:\\n\")\n",
    "# print(round(pizza_flair.describe(),2))\n",
    "\n",
    "# binarize categorical features in FLAIR\n",
    "def flair_numeric(x):\n",
    "    if x == 'None':\n",
    "        return 0\n",
    "    if x == 'shroom':\n",
    "        return 1\n",
    "    if x == 'PIF':\n",
    "        return 2\n",
    "bin_flair = flair['Flair'].replace(['None', 'shroom', 'PIF'], [-1, 0, 1])\n",
    "# bin_flair.value_counts()\n",
    "# print(\"\\nOut of 734 pizza recipients, 683 did not PIF and 51 did PIF.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## CONVERT UPVOTES-DOWNVOTES SUBSETS INTO Z-SCORES FOR STANDARDIZATION ##\n",
    "## CREATE FINAL SUBSET FOR UPVOTES-DOWNVOTES, Z-SCORES, AND FLAIR ##\n",
    "\n",
    "# Z-scores for training data\n",
    "statusTtrain = allTData[['requester_upvotes_minus_downvotes_at_request','requester_upvotes_minus_downvotes_at_retrieval',\n",
    "                          'request_to_retrieval_upvotes_minus_downvotes']].copy()\n",
    "statusTtrain.columns = ['Request', 'Retrieval', 'Request-Retrieval']\n",
    "for col in statusTtrain:\n",
    "    zscore_col = col + \" Z-score\"\n",
    "    statusTtrain[zscore_col] = (statusTtrain[col] - statusTtrain[col].mean() / statusTtrain[col].std(ddof=0))\n",
    "statusTtrain = (statusTtrain).join(allTData['requester_user_flair'])\n",
    "# display(statusTtrain.head())\n",
    "\n",
    "# Z-scores for development data\n",
    "statusDtrain = allDData[['requester_upvotes_minus_downvotes_at_request','requester_upvotes_minus_downvotes_at_retrieval',\n",
    "                          'request_to_retrieval_upvotes_minus_downvotes']].copy()\n",
    "statusDtrain.columns = ['Request', 'Retrieval', 'Request-Retrieval']\n",
    "\n",
    "for col in statusDtrain:\n",
    "    zscore_dev_col = col + \" Z-score\"\n",
    "    statusDtrain[zscore_dev_col] = (statusDtrain[col] - statusDtrain[col].mean() / statusDtrain[col].std(ddof=0))\n",
    "statusDtrain = (statusDtrain).join(allDData['requester_user_flair'])\n",
    "# display(statusDtrain.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 1.3 Time analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted unix timestamp to Date and Time:\n",
      "\n",
      "Indexed by Date and time:\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "from IPython.display import display\n",
    "\n",
    "# CHANGE UNIX TO DATETIME\n",
    "unix = allTData[['unix_timestamp_of_request_utc', 'requester_received_pizza']].copy()\n",
    "\n",
    "# unix --> datetime\n",
    "unix['Datetime'] = pandas.to_datetime(unix['unix_timestamp_of_request_utc'], unit='s')\n",
    "# just in case we wanted to add date columns to original dataframe\n",
    "unix['Date'] = unix['Datetime'].dt.date\n",
    "unix['Hour'] = unix['Datetime'].dt.hour\n",
    "print(\"Converted unix timestamp to Date and Time:\")\n",
    "# display(unix.head(5))\n",
    "\n",
    "# set datetime as index\n",
    "unix_index = unix.set_index('Datetime')\n",
    "unix_index = unix_index.drop(['unix_timestamp_of_request_utc', 'Date'], axis=1)\n",
    "print(\"\\nIndexed by Date and time:\")\n",
    "# display(unix_index.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To index by 1 date\n",
      "\n",
      "Summer pizzas:\n"
     ]
    }
   ],
   "source": [
    "# trial grouping by different dates or seasons\n",
    "\n",
    "# find Christmas\n",
    "# index by 1 date\n",
    "christmas2012_pizza = unix_index['2012-12-25']\n",
    "print(\"To index by 1 date\")\n",
    "# display(christmas2012_pizza)\n",
    "\n",
    "# to index by specific month and day\n",
    "# christmas_pizza = unix_index[unix_index.Date.dt.]\n",
    "\n",
    "# index a season, ex: summer\n",
    "summer_pizza = unix_index['2013-05-01':'2013-08-31']\n",
    "print(\"\\nSummer pizzas:\")\n",
    "# display(summer_pizza.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zZrnRMFFrbeaCWAwWLHDvYVSHWnuxYHhLAgns5eW5MVTLmDDEYu7n\ntNNc9sHGpk6N/vj1KHv1VfdtKKqB3a8FNwIL7Dbr1JjkEsH96193k5Fg79T4oHOa55PZs937F/SK\nU6mqqfHnxnhggd2GOhrTusRM1XPPhX/9y/UP794drdmSuaasLNr39kT8GRkTSGC3r5LGpEbVtdwv\nvNDlpxGJ1rqwuSjKF8ZYLPmN82wE1mK3MezGpE4VXnwxul0IuaRLl7Br0Lrm91W8EFhgtxExxqQv\nqjf9cknUUgk01zj1gVcCC+z2ddKY9OVSGoGoinrsSWX2cboCC+w2DtcYY/ZVXe19l1sggb2szLpi\njDGmJQUFyRfgzrhMb4tr2Y4dlrLXGGNaUl/vVnfyUiDhtqTEJigZY0wy8+d7W16bgV1E/iIixSKy\nrNG2HiIyTURWichUETmotTJ27rQWuzHGJDNvnrflpRJuJwHfarZtHDBdVQcBM4G7WiugrMyGbRlj\nTDJr13pbXpuBXVXnAs1XabwYmBz/fTIwsrUybJEAY4xJrqLC29TMmXaQHKqqxQCquhU4tLWdo5oL\n2RhjoqCgoO0FV9Iqz6NyWk0YUFUV/UkCxhgTlljM22RgmY5VKRaRXqpaLCK9gS9a27mycnyj/42I\n/xhjjAGXG+jll4tYubLIk/JEU8jOJSIDgNdV9Svx/08ESlR1oojcCfRQ1XFJnqsiaknAjDGmFSef\nDAsX7v2/iKCqGSU8bzOwi8hzuCZ2T6AYuA94Bfgb0A9YD1ymqqVJnq+dO6ut2WiMMa3o0cPN+Unw\nNbBnS0S0a1e19KPGGNMKEZf0LTGZM5vAHsi0IZucZIwxrSsogM8+86gsb4oxxhiTjVgMVq70pqxA\nArvdODXGmNapwuLF3pQVSGC3MezGGNO2t9/2phwL7MYYExEff+xNOYEEdls9yRhj2lZS4s2iRIEE\ndls9yRhj2ibizRqoNirGGGMiwquRMYEEdsvFbowxbVOFJUuyL8cCuzHGRMjs2dmXYYHdGGMixIuF\nrQMJ7JJRtgNjjGl/tm/PflKn3Tw1xpiI2bgxu+dbSgFjjIkQ1exHxtg4dmOMiZBYDN5/P7syAgns\nu3cHcRRjjMkP2Y6MsVwxxhgTMcuWZff8QFZQAutkN8aYVHXsCHv2RHwFJWOMManLtpfDArsxxuQZ\nC+zGGBMx1mI3xhjThAV2Y4zJMxbYjTEmz1hgN8aYPGOB3Rhj8owFdmOMyTMW2I0xJs9kFdhF5DwR\n+VhEVovInV5VyhhjTOYyDuwiUgA8DnwLOBYYJSJf9qpiJlNFYVfA5IyisCtgfJJNi/0U4BNVXa+q\nu4EXgIu9qZbJXFHYFTA5oyjsChifZBPYDwc2NPr/xvi2LBRFaP9cLXudj2Wns2+Uyk53//ZS9jof\ny05nfz/LTnf/KJWduYjdPC2K0P65WvY6H8tOZ98olZ3u/u2l7HU+lp3O/n6Wne7+USo7cxnnYxeR\n04Dxqnpe/P/jAFXVic32s2TsxhiTgUzzsWcT2DsAq4CzgS3AQmCUqn6UUYHGGGM80THTJ6pqvYj8\nNzAN16XzFwvqxhgTPt+XxjPGGBOsiN08TU5EKtp4fJaInOBzHQ4XkVfiE7I+EZGHRSTptx4RuUVE\n9vepLq2+H0ERkZEiEhOR/wi7LglhnytROk/i5du5kkS+nis5E9iJxorY/wD+oar/AfwH0A14oJX9\nbwW6+FSXKLwfAFcAc4BR6TwpPsHNL2G/N1E6TyD89yPBzpV9+XOuqGpO/AAVwJnA6422PQZcE/99\nFnCCj8f/BlDUbFs3YBtwAPAQsBx4H/ghcDNQC3wAzPChPuXxP/B0YHH8OBfFH+sPrASeBD4E3gI6\n+1CHrri5DEcDH8e3nQnMBt4APgZ+3+xv+BCwFDg9H8+VqJ0ndq60z3Ml45unIVHCu8IeC7zXeIOq\nVojIBuB64AjgP1VVRaS7qpaKyG3ACFXd6VOdaoCRqlopIj2BBcBr8ceOBi5X1f8rIi8C3wGe8/j4\nFwNvqeqnIrJdRIbFt58MDAY+B6aKyH+p6j9wH+75qnqHx/VoSVjnShTPE7BzpTV5d67kUldMlJ0J\n/Enjl1xVLY1vl/iPXwSYICIf4Fpjh4nIofHH1qrq8vjv7wEDfDj+KFwqCYAXgdHx3xeqSzWhwPPA\nGfHt9bivnu1VWOdJ4hh2ruSOrM6VXGux7wE6NPq/bzecWrAS+G7jDSLSDXdVXRtgPRoOD1wF9ASG\nqWpMRNay9z2pbbRvPR6/VyLSA/dVcmh8EloHXKvnXy3snmgN7UqcqAEI61yJ2nkCdq60Je/OlVxq\nsSuwHhgiIp1EpDtuclQwB1edARwgIldBwwSt3wKTgKnAjfFtiRMZXN9moY/VKgS+iH9Qz8L1lyb4\n3QK8FHhGVQeq6pGq2h93Mn4NOFlE+sdvel2Ou2EWRJ0SQjtXInqegJ0ryeTluZITgT3+4mpVdRPw\nEu4mzwvAkka7BXF1vwS4TERW42727ALuBv6C6yNcJiJL2XvX/8/AWyIyw8tKxN+PGuBZ3AfjA1yL\nrPEEMb/fj8uBfzbb9g/cyIdFuJTOK4DPVPWVgOoUlXMlEucJ2LnSmnw+V3JigpKIHIfrbzot7LpE\nQZTfDxE5E/ixql4U0vEj+96EIcrvh50r/ol8i11EbsC1Nn4adl2iwN6P5Oy9acrej+Ty/b3JiRa7\nMcaY1EW+xW6MMSY9FtgjTkT6ishMEVkhIstF5Efx7T1EZJqIrBKRqSJyUHz7wfH9K0Tk0UblHCAi\nb4jIR/FyWpu2bHKQV+dK/LE3RWRpvJzfi0hQo1SMByywR98e4HZVPRYYDvxQ3KLh44DpqjoImAnc\nFd+/BvgZ8OMWyvqNqg4GhgFniMi3fK+9CZKX58qlqjpMVb8CHIobsmhyhAX2iFPVrar6fvz3Stww\ntb64KdqT47tNBkbG96lW1Xk0nXSCqu5S1dnx3/fghnT1DeRFmEB4da40ej4i0gnYj/CTZZk0WGDP\nISIyADgel+ejl6oWg/tA41pVqZbTHbgQ8HzctIkGL84VEXkL2IqbFPOyLxU1vrDAniNE5EDch+uW\neGuqeQsqpRZVfFLGc8AjqrrO00qaSPDqXFG3nnEfoDMuJYDJERbYc4C4xPsvA39V1Vfjm4tFpFf8\n8d7AFykW9ySwSlUf876mJmwenyuoah0uC+TFXtfV+McCe254Clipqr9rtO014Nr4798DXm3+JJrl\n2xCRXwKFqnqbH5U0kZD1uSIiXeMXgMSF4gLcdHeTI2yCUsSJyFeBt3EJ9xN5o+8GFuLyW/TDJTG6\nLJHaM565rxvuplcpcC5uQYENuBtqdfFyHlfVp4J8PcY/Hp4rJbjFL/bDNf5mAbepaizI12MyZ4Hd\nGGPyjHXFGGNMnrHAbowxecYCuzHG5BkL7MYYk2cssBtjTJ6xwG6MMXnGAruJPBGpF5ElIvJhPJXs\n7W2lkY0vkDyqtX1a2k9EThSRR7yotzFhscBuckGVqp6gqkOBc4DzgfvaeM5AYHQKZTfZT1XfU9Vb\nM66pMRFgE5RM5IlIuaoWNvr/QGCRqh4iIv2BvwJd4g//t6ouEJH5wJeBtbhUtY8BE4AzcUmtnlDV\nP7ew3/vAHap6oYjchwv8R+Jmbd4OnIa7sGwELlTVehE5Afh/QFdgO3BtIpuiMWGwFrvJOaq6FigQ\nkS8BxcA3VfUk4ApcAAe3uMSceEv/d8D3gVJVPRU4Bfi/8YtC8/2gafbDI4ERuCRY/wvMUNX/xC1S\ncUE8l8pjwHdU9WRgEmCrU5lQdQy7AsZkKNHHvh/wuIgcD9QDxyTZ/1zgKyKSWAmoML7v7jaO86aq\nxkRkOVCgqtPi25cDA4BBwFDg3/F+/wJgcwavxxjPWGA3OUdEjgT2qOq2eHfJVlX9z3iu+V3Jngbc\nrKr/blbWmW0crhZAVVVEGl8EYrjPjwAfqupXM3ktxvjBumJMLmicUvZLwB/Y2+VyELAl/vs1QIf4\n7xW4rIUJU4Gb4l0niMgxInJAC/ulVI9GVgFfEpHT4uV2FJEhKZZnjC+sxW5ywf4isgTX7bIbeEZV\nH44/9nvg7yJyDfAWUBXfvgyIichS4GlV/V18ubgl8S6TL3BrfzbZD3fzNJl9Rhqo6m4R+S7wmIgc\nhLuwPAKszOL1GpMVGxVjjDF5xrpijDEmz1hgN8aYPGOB3Rhj8owFdmOMyTMW2I0xJs9YYDfGmDxj\ngd0YY/KMBXZjjMkz/x/sck/PuIOJYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1215b61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XUrkHdPd2IsC1abOWjVJHhGyUOiJko9QRIRuljjTXr9Sk62nHzEapI0I2Sh0R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123c35e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# plot pizzas by month \n",
    "unix_index_base = unix_index.drop(['Hour'], axis=1)\n",
    "month_pizzas = unix_index_base.resample('M').sum()\n",
    "month_pizzas_area = month_pizzas.plot(kind='area')\n",
    "\n",
    "\n",
    "month_pizzas_bar = month_pizzas.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    24.00000\n",
      "mean     10.75000\n",
      "std       8.78858\n",
      "min       0.00000\n",
      "25%       3.50000\n",
      "50%       8.50000\n",
      "75%      16.00000\n",
      "max      32.00000\n",
      "Name: requester_received_pizza, dtype: float64\n",
      "Hour\n",
      "0     32.0\n",
      "1     23.0\n",
      "2     13.0\n",
      "3      8.0\n",
      "4      8.0\n",
      "5      5.0\n",
      "6      4.0\n",
      "7      2.0\n",
      "8      0.0\n",
      "9      1.0\n",
      "10     1.0\n",
      "11     1.0\n",
      "12     2.0\n",
      "13     4.0\n",
      "14     9.0\n",
      "15     8.0\n",
      "16    11.0\n",
      "17    13.0\n",
      "18    15.0\n",
      "19    15.0\n",
      "20    19.0\n",
      "21    19.0\n",
      "22    20.0\n",
      "23    25.0\n",
      "Name: requester_received_pizza, dtype: float64\n",
      "Pizza success by hour of the day\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123e622b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y6VgmPkdV69POtxzdyPaJN9dW5UIpZWaHEEbXTpjkdJd+RmBmE4EhwL5ACzAS\neBC4FzgIWAqc7+5rYsUYWxc/o1MJtdscsAQY2lG/rTdmdhLwNDCf8PvlwNWEWdv3kPLPkSbXiIik\niG5EioikiJK2iEiKKGmLiKSIkraISIooaYuIpIiStohIiihpS6aY2fodvr7YzG6OFY9IpSlpS9YU\nmnhQ9mSE/PZ6ItEpaUvdyC+r8GR+JbxpZjYgf3y8mZ3X6XXr8/+eYmZPm9kU4NVIYYt8RMl7RIok\nVF8zm5N/bsA+bF/35mZgvLvfaWaX5r8+t8A5Oo/MjwGOdPe3qxWwSG8oaUvWbMwvdwuEmjbw2fyX\nf8v2JH0H8IsizjdLCVuSROURqSdd1bbbyf8umJkBu3b63oZqByXSG0rakjXdbbk1E7gw//wi4Jn8\n8yXAcfnnZwO7VCUykQpQ0pas6a5T5HLgUjObB3wdGJ4/fhtwipnNBT6HRteSYFqaVUQkRTTSFhFJ\nESVtEZEUUdIWEUkRJW0RkRRR0hYRSRElbRGRFFHSFhFJESVtEZEU+X9rl9LvxxDqPAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123e67470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze pattern by hours\n",
    "hourly = unix_index['10/2012':'9/2013'].groupby('Hour')['requester_received_pizza'].sum()\n",
    "print(hourly.describe())\n",
    "print(hourly)\n",
    "print(\"Pizza success by hour of the day\")\n",
    "hourly.plot('area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Analysis by Days\n"
     ]
    },
    {
     "data": {
      "image/png": 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kdlkiCe8dfy/jfoFwALeuuxW+kC8eO60TOrQNtOXcz1Ts6d2DSfpJBY0aSaRY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\nEpErus9dAD5ERCtV7KrmEEJMJiJPdP1vN4DvAfg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12454b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0D/KMfwbiFJyGFPUth0gi/nL4L0YPo26Ip+IIxAOqPCd8cV/Ze2wsOIYWSwui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\niKhIQjsioiIJ7YiIinwFAXN29yS0RowAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123cc5860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze pattern by days\n",
    "print(\"\\nAnalysis by Days\")\n",
    "# Take a look at the highest frequency months (07/31/2012 and 4/30/2013)\n",
    "July2012 = unix_index['7/2012'].resample('D').mean()\n",
    "July2012_area = July2012.plot(kind='area')\n",
    "\n",
    "# Take a look at the lowest frequency months\n",
    "Nov2012 = unix_index['11/2012'].resample('D').mean()\n",
    "Nov2012_area = Nov2012.plot(kind='area')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Analysis by Month\n"
     ]
    },
    {
     "data": {
      "image/png": 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98fsy4NI615yQxlFERPYF/anZ+H0m/V02uYvG3TcC68ys+jPXEwF9PE9EpJVI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\nDuwCjk/Mo3Qb8D13H0v82+6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124775a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pKcE5x/fff5/4gi5V11xzDffdd1/ii73du3czffp0AM4991w2bdrE448/TjAY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\nuoiIRyjQRUQ8QoEuIuIR/x906Kw7r6ziqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1247de6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vfOMbANx888189tlndOvWjeOPP55//dd/5a9/3XsDUrXOpbFpCDppWmN0P3SR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\nwtGawYdmr7cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124bb12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+5ptvyMvLi7o2kVilVJ96Kmnbti0Aq1evZu3atTRt2hQI/LL7/X7OOeccANat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\njSjURUTSiEJdRCSNKNRFRNLI/wPSwyEFT3mOMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124c6f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze pattern by weeks\n",
    "\n",
    "print(\"\\nAnalysis by Month\")\n",
    "\n",
    "April2013 = unix_index['4/2013'].resample('W').mean()\n",
    "April2013_area = April2013.plot(kind='area')\n",
    "\n",
    "Jun2013 = unix_index['6/2013'].resample('W').mean()\n",
    "Jun2013_area = Jun2013.plot(kind='area')\n",
    "\n",
    "Jul2011 = unix_index['7/2011'].resample('W').mean()\n",
    "Jul2011_area = Jul2011.plot(kind='area')\n",
    "\n",
    "May2013 = unix_index['5/2013'].resample('W').mean()\n",
    "May2013_area = May2013.plot(kind='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Section 2. Simple Feature Selection and Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Simple Pre-Processing\n",
    "\n",
    "def data_preprocessor(s):\n",
    "    \"\"\"\n",
    "    Note: this function pre-processors data:\n",
    "    (1) removes non-alpha characters\n",
    "    (2) converts digits to 'number'\n",
    "    (3) regularizes spaces (although CountVectorizer ignores this unless they are part of words)\n",
    "    (4) reduces word size to n\n",
    "    \"\"\"\n",
    "\n",
    "    s = [re.sub(r'[?|$|.|!|@|\\n|(|)|<|>|_|-|,|\\']',r' ',s) for s in s] # strip out non-alpha numeric char, replace with space\n",
    "    s = [re.sub(r'\\d+',r'number ',s) for s in s] # convert digits to number\n",
    "    s = [re.sub(r' +',r' ',s) for s in s] # convert multiple spaces to single space\n",
    "    \n",
    "    # This sets word size to n=5\n",
    "    num = 5\n",
    "    def set_word(s):\n",
    "        temp = []\n",
    "        for s in s:\n",
    "            x = s.split()\n",
    "            z = [elem[:num] for elem in x]\n",
    "            z = ' '.join(z)\n",
    "            temp.append(z)       \n",
    "        return temp\n",
    "    \n",
    "    s = set_word(s)\n",
    "    \n",
    "    return s\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Raw data:\n",
      "The size of the vocabulary for the training text data is 17213\n",
      "First 5 feature Names: ['000', '0000', '0011011001111000', '00243364', '00pm'] \n",
      "\n",
      "\n",
      "Pre-Processed data:\n",
      "The size of the vocabulary for the training text data is 10491\n",
      "First 5 feature Names: ['aaaaa', 'aan', 'ab', 'aback', 'aband'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Set up the data with CountVectorizer\n",
    "\n",
    "#vectorizer = CountVectorizer(lowercase=True, strip_accents='unicode',stop_words='english')\n",
    "\n",
    "vectorizer = CountVectorizer(min_df=1,lowercase=True)\n",
    "tVector = vectorizer.fit_transform(trainCorpus)\n",
    "dVector = vectorizer.transform(devCorpus)\n",
    "\n",
    "print ('\\nRaw data:')\n",
    "print (\"The size of the vocabulary for the training text data is\", tVector.shape[1])\n",
    "print (\"First 5 feature Names:\", vectorizer.get_feature_names()[1:6], \"\\n\")\n",
    "\n",
    "tVector_p = vectorizer.fit_transform(data_preprocessor(trainCorpus))\n",
    "dVector_p = vectorizer.transform(data_preprocessor(devCorpus))\n",
    "\n",
    "print ('\\nPre-Processed data:')\n",
    "print (\"The size of the vocabulary for the training text data is\", tVector_p.shape[1])\n",
    "print (\"First 5 feature Names:\", vectorizer.get_feature_names()[1:6], \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Section 3. Baseline Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group scoring functions\n",
    "\n",
    "def roc_curve1(y_true, y_pred_prob):\n",
    "    \"\"\"This function plots the ROC curve\n",
    "    Inputs: y_true, correct label\n",
    "            y_pred_prob, predicted probabilities\n",
    "    \"\"\"\n",
    "    fpr, tpr, thr = roc_curve(y_true, y_pred_prob)\n",
    "   \n",
    "    plt.figure()\n",
    "    plt.plot(fpr,tpr)\n",
    "    plt.xlabel(\"False positive rate\")\n",
    "    plt.ylabel(\"True positive rate\")\n",
    "    plt.title(\"ROC Curve\")\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "def score_rep(y_true, y_pred, desc):\n",
    "    \"\"\"Function to print out comprehensive report for classification test\n",
    "    Inputs: y_true, correct label\n",
    "            y_pred, predicted label from model\n",
    "            desc, description of model\n",
    "    Output: classification report\n",
    "    \"\"\"\n",
    "    print(desc)\n",
    "    print(\"-\"*75)\n",
    "    print(\"Accuracy: \", metrics.accuracy_score(y_true, y_pred))\n",
    "    print(\"Area under curve of ROC: \", metrics.roc_auc_score(y_true, y_pred))\n",
    "    print(\"Classification report:\\n\")\n",
    "    print(metrics.classification_report(y_true, y_pred))\n",
    "    print(\"-\"*75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Subreddit Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3.1a Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C =  0.01 Logistic regression accuracy: 0.736\n",
      "For C =  0.01 Logistic regression (processed data) accuracy: 0.73\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "C = 0.01 #(For now)\n",
    "\n",
    "modelLogit = LogisticRegression(penalty='l2', C=C)\n",
    "modelLogit.fit(tVector,trainLabel)\n",
    "logitScore = round(modelLogit.score(dVector, devLabel), 4)\n",
    "print(\"For C = \", C, \"Logistic regression accuracy:\", logitScore)\n",
    "\n",
    "modelLogit.fit(tVector_p,trainLabel)\n",
    "logitScore = round(modelLogit.score(dVector_p, devLabel), 4)\n",
    "print(\"For C = \", C, \"Logistic regression (processed data) accuracy:\", logitScore)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3.1b Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bernoulli NB using raw data with alpha = 0.010: 0.719\n",
      "Bernoulli NB using processed data  with alpha = 0.010: 0.708\n"
     ]
    }
   ],
   "source": [
    "# Multinomial NB\n",
    "\n",
    "alpha = 0.01\n",
    "\n",
    "clf = BernoulliNB(alpha=alpha)\n",
    "clf.fit(tVector, trainLabel)\n",
    "test_predicted_labels = clf.predict(dVector) \n",
    "print ('Bernoulli NB using raw data with alpha = %1.3f:' %alpha, metrics.accuracy_score(devLabel,test_predicted_labels) )\n",
    "\n",
    "clf.fit(tVector_p, trainLabel)\n",
    "test_predicted_labels = clf.predict(dVector_p) \n",
    "print ('Bernoulli NB using processed data  with alpha = %1.3f:' %alpha, metrics.accuracy_score(devLabel,test_predicted_labels) )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3.1c Logistic Regression More Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C =  0.0001 Logistic regression accuracy: 0.74\n",
      "For C =  0.001 Logistic regression accuracy: 0.74\n",
      "For C =  0.01 Logistic regression accuracy: 0.74\n",
      "For C =  0.1 Logistic regression accuracy: 0.734\n",
      "For C =  0.5 Logistic regression accuracy: 0.673\n",
      "For C =  1.0 Logistic regression accuracy: 0.653\n",
      "For C =  2.0 Logistic regression accuracy: 0.652\n",
      "For C =  6.0 Logistic regression accuracy: 0.645\n",
      "For C =  10.0 Logistic regression accuracy: 0.646\n",
      "1.33221597834\n",
      "\n",
      "Top 5 Weighted Features:\n",
      "4411\n",
      "impre 0.816549801383\n",
      "6543\n",
      "orang 0.838660820545\n",
      "4292\n",
      "hurti 0.993686027347\n",
      "3753\n",
      "gpa 1.07987259785\n",
      "10175\n",
      "wiiu 1.33221597834\n"
     ]
    }
   ],
   "source": [
    "# get the best regularization\n",
    "regStrength = [0.0001, 0.001, 0.01, 0.1, 0.5, 1.0, 2.0, 6.0, 10.0]\n",
    "\n",
    "\n",
    "for c in regStrength:\n",
    "    modelLogit = LogisticRegression(penalty='l1', C=c)\n",
    "    modelLogit.fit(tVector_p, trainLabel)\n",
    "    logitScore = round(modelLogit.score(dVector_p, devLabel), 4)\n",
    "    print(\"For C = \", c, \"Logistic regression accuracy:\", logitScore)\n",
    "\n",
    "# although the best score comes from c=.001, the bet F1-score \n",
    "# comes from c=.5, and this gives better weight options\n",
    "modelLogit = LogisticRegression(penalty='l1', C=.5, tol = .1)\n",
    "modelLogit.fit(tVector_p, trainLabel)\n",
    "\n",
    "print(max(modelLogit.coef_[0]))\n",
    "numWeights = 5\n",
    "\n",
    "sortIndex = np.argsort(modelLogit.coef_)\n",
    "iLen = len(sortIndex[0])\n",
    "print(\"\\nTop\", numWeights, \"Weighted Features:\")\n",
    "\n",
    "for index in range((iLen - numWeights) , iLen):\n",
    "    lookup = sortIndex[0][index]\n",
    "    print(lookup)\n",
    "    weight =  modelLogit.coef_[0][lookup]\n",
    "    print(vectorizer.get_feature_names()[sortIndex[0][index]], weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 LR models for Status/Time features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2a LR model for Status Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For model with Request-Retrieval Z-Score only:\n",
      "Accuracy: 0.738\n",
      "F1 score: 0.0\n",
      "\n",
      "Z-scores for Request, Retrieval, Request-Retrieval Z-Score and Flair:\n",
      "Accuracy: 1.0\n",
      "F1 score: 1.0\n",
      "\n",
      "Z-scores for Request, Retrieval, Request-Retrieval Z-Score:\n",
      "Accuracy: 0.737\n",
      "F1 score: 0.0\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore') \n",
    "\n",
    "# LOGISTIC REGRESSION MODELS\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression(penalty='l2')\n",
    "\n",
    "# create training labels for pizza success\n",
    "train_labels = np.asarray(allTData['requester_received_pizza'])\n",
    "dev_labels = np.asarray(allDData['requester_received_pizza'])\n",
    "\n",
    "# Z-scored for Request-Retrieval Z-score\n",
    "diff_combined = np.asarray(statusTtrain[['Request-Retrieval Z-score']].copy())\n",
    "diff_dev_combined = np.asarray(statusDtrain[['Request-Retrieval Z-score']].copy())\n",
    "train_combine_model = lr.fit(diff_combined, train_labels)\n",
    "dev_combine_labels = train_combine_model.predict(diff_dev_combined)\n",
    "combine_score = train_combine_model.score(diff_dev_combined, dev_labels)\n",
    "combine_F1score = metrics.f1_score(dev_combine_labels, dev_labels)\n",
    "\n",
    "# Z-scores for Request, Retrieval, and Request-Retrieval Z-scores and Flair\n",
    "zcomb_train = statusTtrain[['Request Z-score','Retrieval Z-score','Request-Retrieval Z-score', 'requester_user_flair']]\n",
    "zcomb_train['requester_user_flair'] = zcomb_train['requester_user_flair'].apply(flair_numeric).fillna(0)\n",
    "\n",
    "zcomb_dev = statusDtrain[['Request Z-score','Retrieval Z-score','Request-Retrieval Z-score', 'requester_user_flair']]\n",
    "zcomb_dev['requester_user_flair'] = zcomb_dev['requester_user_flair'].apply(flair_numeric).fillna(0)\n",
    "\n",
    "zcomb_model = lr.fit(zcomb_train, train_labels)\n",
    "zcomb_labels = zcomb_model.predict(zcomb_dev)\n",
    "zcomb_score = zcomb_model.score(zcomb_dev, dev_labels)\n",
    "zcomb_F1score = metrics.f1_score(zcomb_labels, dev_labels)\n",
    "\n",
    "# Z-scores for Request, Retrieval, and Request-Retrieval Z-scores\n",
    "z_status_train = statusTtrain[['Request Z-score','Retrieval Z-score','Request-Retrieval Z-score']]\n",
    "z_status_dev = statusDtrain[['Request Z-score','Retrieval Z-score','Request-Retrieval Z-score']]\n",
    "\n",
    "z_status_model = lr.fit(z_status_train, train_labels)\n",
    "z_status_labels = z_status_model.predict(z_status_dev)\n",
    "z_status_score = z_status_model.score(z_status_dev, dev_labels)\n",
    "z_status_F1score = metrics.f1_score(z_status_labels, dev_labels)\n",
    "\n",
    "                       \n",
    "print(\"\\nFor model with Request-Retrieval Z-Score only:\")\n",
    "print(\"Accuracy:\", combine_score)\n",
    "print(\"F1 score:\", combine_F1score)\n",
    "\n",
    "print(\"\\nZ-scores for Request, Retrieval, Request-Retrieval Z-Score and Flair:\")\n",
    "print(\"Accuracy:\", zcomb_score)\n",
    "print(\"F1 score:\", zcomb_F1score)\n",
    "                        \n",
    "print(\"\\nZ-scores for Request, Retrieval, Request-Retrieval Z-Score:\")\n",
    "print(\"Accuracy:\", z_status_score)\n",
    "print(\"F1 score:\", z_status_F1score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2b LR model for Status + Hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scoring for Request-retrieval Z-scores and Hour\n",
      "Accuracy: 0.74\n",
      "F1 score: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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YWO/ZZ5OORkQKkRJEPTR7NvTvD7NmwciRsN9+SUckIoVICaKeuf/+MLfS+vVh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\nAZKvusygrCe6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12544a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression, C = 0.01\n",
      "---------------------------------------------------------------------------\n",
      "Accuracy:  0.74\n",
      "Area under curve of ROC:  0.5\n",
      "Classification report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "      False       0.74      1.00      0.85       740\n",
      "       True       0.00      0.00      0.00       260\n",
      "\n",
      "avg / total       0.55      0.74      0.63      1000\n",
      "\n",
      "---------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore') \n",
    "\n",
    "# FOR STATUS AND TIME BY HOUR\n",
    "lr = LogisticRegression(penalty='l2', C=0.01)\n",
    "\n",
    "train_labels = np.asarray(allTData['requester_received_pizza'])\n",
    "dev_labels = np.asarray(allDData['requester_received_pizza'])\n",
    "\n",
    "# setup training and development datasets\n",
    "# train\n",
    "stathourTData = pandas.concat([statusTtrain['Request-Retrieval Z-score'], allTData['unix_timestamp_of_request_utc']], axis=1, \n",
    "                              keys=['Request-Retrieval Z-score', 'unix_timestamp_of_request_utc'])\n",
    "stathourTData['Datetime'] = pandas.to_datetime(stathourTData['unix_timestamp_of_request_utc'], unit='s')\n",
    "stathourTData['Hour'] = stathourTData['Datetime'].dt.hour\n",
    "stathourTData_ = stathourTData.drop(['Datetime', 'unix_timestamp_of_request_utc'], axis=1)\n",
    "# display(stathourTData.head(5))\n",
    "\n",
    "# development\n",
    "stathourDData = pandas.concat([statusDtrain['Request-Retrieval Z-score'], allDData['unix_timestamp_of_request_utc']], axis=1, \n",
    "                              keys=['Request-Retrieval Z-score', 'unix_timestamp_of_request_utc'])\n",
    "stathourDData['Datetime'] = pandas.to_datetime(stathourDData['unix_timestamp_of_request_utc'], unit='s')\n",
    "stathourDData['Hour'] = stathourDData['Datetime'].dt.hour\n",
    "stathourDData_ = stathourDData.drop(['Datetime', 'unix_timestamp_of_request_utc'], axis=1)\n",
    "# display(stathourDData.head(5))\n",
    "\n",
    "# fit LR models\n",
    "train_stathour_model = lr.fit(stathourTData_, train_labels)\n",
    "dev_stathour_labels = train_stathour_model.predict(stathourDData_)\n",
    "stathour_score = train_stathour_model.score(stathourDData_, dev_labels)\n",
    "stathour_F1score = metrics.f1_score(dev_stathour_labels, dev_labels)\n",
    "\n",
    "print(\"\\nScoring for Request-retrieval Z-scores and Hour\")\n",
    "print(\"Accuracy:\", stathour_score)\n",
    "print(\"F1 score:\", stathour_F1score)\n",
    "roc_curve1(dev_labels, train_stathour_model.predict_proba(stathourDData_)[:,0])\n",
    "score_rep(dev_labels, dev_stathour_labels,\"Logistic Regression, C = 0.01\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2c LR model for Status and Bucketed Hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scoring for Request-retrieval Z-scores and bucketed Hour:\n",
      "Accuracy: 0.738\n",
      "F1 score: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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lr76C+++HESPg229hp53gRz+q+tG8edJRi0gZ9WKSeuUOr7wSksKzz4YFee66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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123efb898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression, C = 0.01\n",
      "---------------------------------------------------------------------------\n",
      "Accuracy:  0.738\n",
      "Area under curve of ROC:  0.498648648649\n",
      "Classification report:\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "      False       0.74      1.00      0.85       740\n",
      "       True       0.00      0.00      0.00       260\n",
      "\n",
      "avg / total       0.55      0.74      0.63      1000\n",
      "\n",
      "---------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# FOR STATUS AND BUCKETED HOUR\n",
    "lr = LogisticRegression(penalty='l2')\n",
    "\n",
    "# Bucket hours\n",
    "def bucket(x):\n",
    "    if x >= 6 and x <= 13:\n",
    "        return 0\n",
    "    if x >= 0 and x <=2:\n",
    "        return 1\n",
    "    if x >= 16 and x <=23:\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "\n",
    "bucketTData = stathourTData_\n",
    "bucketTData['Hour'] = bucketTData['Hour'].apply(bucket)\n",
    "bucketDData = stathourDData_\n",
    "bucketDData['Hour'] = bucketDData['Hour'].apply(bucket)\n",
    "\n",
    "# fit LR models\n",
    "train_buckethr_model = lr.fit(bucketTData, train_labels)\n",
    "dev_buckethr_labels = train_buckethr_model.predict(bucketDData)\n",
    "buckethr_score = train_buckethr_model.score(bucketDData, dev_labels)\n",
    "buckethr_F1score = metrics.f1_score(dev_buckethr_labels, dev_labels)\n",
    "\n",
    "print(\"\\nScoring for Request-retrieval Z-scores and bucketed Hour:\")\n",
    "print(\"Accuracy:\", buckethr_score)\n",
    "print(\"F1 score:\", buckethr_F1score)\n",
    "roc_curve1(dev_labels, train_buckethr_model.predict_proba(bucketDData)[:,0])\n",
    "score_rep(dev_labels, dev_buckethr_labels,\"Logistic Regression, C = 0.01\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2c LR model with Status + Hour + Text probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-8e034c5ae0c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# fit LR models\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mtrain_comb_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombinedTData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mdev_comb_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_train_comb_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombinedDData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mcomb_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_comb_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombinedDData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdev_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Jen2/anaconda/lib/python3.5/site-packages/sklearn/linear_model/logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m         X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64, \n\u001b[0;32m-> 1142\u001b[0;31m                          order=\"C\")\n\u001b[0m\u001b[1;32m   1143\u001b[0m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1144\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Jen2/anaconda/lib/python3.5/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    508\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    509\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    511\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m/Users/Jen2/anaconda/lib/python3.5/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    396\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    397\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Jen2/anaconda/lib/python3.5/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     52\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     53\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 54\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "# retrieve probabilities from text model where training label is True\n",
    "text_model = modelLogit.fit(tVector_p, trainLabel)\n",
    "prob = text_model.predict_proba(dVector_p)\n",
    "prob_true = [float(y) for x,y in prob]\n",
    "prob_true = pandas.DataFrame(prob_true)\n",
    "prob_false = [float(x) for x,y in prob]\n",
    "prob_false = pandas.DataFrame(prob_false)\n",
    "\n",
    "# add as a parameter to status/hour dataframe\n",
    "combinedTData = pandas.concat([stathourTData_, prob_true, prob_false], axis = 1)\n",
    "combinedDData = stathourDData_\n",
    "\n",
    "# fit LR models\n",
    "train_comb_model = lr.fit(combinedTData, train_labels)\n",
    "dev_comb_labels = train_train_comb_model.predict(combinedDData)\n",
    "comb_score = train_comb_model.score(combinedDData, dev_labels)\n",
    "comb_F1score = metrics.f1_score(dev_comb_labels, dev_labels)\n",
    "\n",
    "print(\"\\nScoring for Request-retrieval Z-scores and bucketed Hour:\")\n",
    "print(\"Accuracy:\", comb_score)\n",
    "print(\"F1 score:\", comb_F1score)\n",
    "roc_curve1(dev_labels, train_comb_model.predict_proba(combinedDData)[:,0])\n",
    "score_rep(dev_labels, dev_comb_labels,\"Logistic Regression, C = 0.01\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2d LR model for Status + Hour + Month half"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# FOR STATUS, HOUR, AND HALF OF THE MONTH\n",
    "\n",
    "totalTData = concat([stathourTData_, stathourTData['Datetime']], axis=1)\n",
    "totalDData = concat([stathourDData_, stathourDData['Datetime']], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vader Sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'vaderSentiment'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-cbef269f45c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Testing our Vader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mvaderSentiment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvaderSentiment\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSentimentIntensityAnalyzer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0manalyser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSentimentIntensityAnalyzer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"It was one of the worst movies I've seen, despite good reviews.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named 'vaderSentiment'"
     ]
    }
   ],
   "source": [
    "#Testing our Vader\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "\n",
    "analyser = SentimentIntensityAnalyzer()\n",
    "test = \"It was one of the worst movies I've seen, despite good reviews.\"\n",
    "\n",
    "def print_sentiment_scores(sentence):\n",
    "    snt = analyser.polarity_scores(sentence)\n",
    "    print(\"{:-<40} {}\".format(sentence, str(snt)))\n",
    "    print(snt['compound'])\n",
    "\n",
    "print_sentiment_scores(\"It was one of the worst movies I've seen, despite good reviews.\")\n",
    "\n",
    "#sentences = \"VADER is smart, handsome, and funny.\"\n",
    "\n",
    "#print_sentiment_scores(sentences)\n",
    "\n",
    "sentences = [\"VADER is smart, handsome, and funny.\", \"VADER is silly, ugly, and rude!\"]\n",
    "\n",
    "for sentence in sentences:\n",
    "    print(\"\\n\")\n",
    "    print(sentence)\n",
    "    vs = print_sentiment_scores(sentence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Setting up for Titles\n",
    "title = allTData[['request_title', 'requester_received_pizza']].copy()\n",
    "title.columns = ['Title', 'Got Pizza']\n",
    "print(\"\\n\")\n",
    "print(\"Titles and Pizza Success\\n\")\n",
    "print(title.head(10))\n",
    "pizza_title = title.groupby(['Got Pizza'])\n",
    "print(\"\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "titles = allTData['request_title']\n",
    "\n",
    "for title in titles:\n",
    "    print(\"\\n\")\n",
    "    print_sentiment_scores(title)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# I think I have the wrong approach here\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "def score_rep(y_true, y_pred, desc):\n",
    "    \"\"\"Function to print out comprehensive report for classification test\n",
    "    Inputs: y_true, correct label\n",
    "            y_pred, predicted label from model\n",
    "            desc, description of model\n",
    "    Output: classification report\n",
    "    \"\"\"\n",
    "    print(desc)\n",
    "    print(\"-\"*75)\n",
    "    print(\"Accuracy: \", metrics.accuracy_score(y_true, y_pred))\n",
    "    print(\"Area under curve of ROC: \", metrics.roc_auc_score(y_true, y_pred))\n",
    "    print(\"Classification report:\\n\")\n",
    "    print(metrics.classification_report(y_true, y_pred))\n",
    "    print(\"-\"*75)\n",
    "    \n",
    "C = 100\n",
    "\n",
    "modelLogit = LogisticRegression(penalty = 'l2', C = C)\n",
    "\n",
    "trainLabel = allTData['requester_received_pizza']\n",
    "\n",
    "devLabel = allDData['requester_received_pizza']\n",
    "\n",
    "snt = analyser.polarity_scores(sentence)\n",
    "\n",
    "title = snt['compound']\n",
    "\n",
    "modelLogit.fit(tVector,trainLabel)\n",
    "score_rep(devLabel,modelLogit.predict(dVector),'Logistic Regression, C = 0.001')\n",
    "\n",
    "modelLogit.fit(got_pizza,trainLabel)\n",
    "score_rep(devLabel,modelLogit.predict(dVector_p),'Logistic Regression, C = 0.001')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Setting up for Body Copy\n",
    "title = allTData[['request_text', 'requester_received_pizza']].copy()\n",
    "title.columns = ['Text', 'Got Pizza']\n",
    "print(\"\\n\")\n",
    "print(\"Text and Pizza Success\\n\")\n",
    "print(title.head(10))\n",
    "pizza_title = title.groupby(['Got Pizza'])\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "text = allTData['request_text']\n",
    "\n",
    "for copy in text:\n",
    "    print(\"\\n\")\n",
    "    print_sentiment_scores(copy)\n",
    "    "
   ]
  }
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