predicting_helpfulness_big.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following notebook will create a model to predict ( or classify ) whether a given swiss product review is helpful or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook follows the same steps as the predicting_helpfulness_small notebook, the main diference being that this one uses a larger dataset of helpful reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The links towards the papers that inspired this work should be available here: [\"Predicting Amazon review helpfulness\"](https://cseweb.ucsd.edu/~jmcauley/cse255/reports/wi15/Shitij_Bhargava.pdf) and [\"Predicting Helpfulness Ratings of Amazon Product Reviews\"](https://cs229.stanford.edu/proj2014/Jordan%20Rodak,%20Minna%20Xiao,%20Steven%20Longoria,%20Predicting%20Helpfulness%20Ratings%20of%20Amazon%20Product%20Reviews.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the file containing the filtered helpful swiss reviews. This notebook uses the bigger such dataset, in which the swiss reviews had at least 5 helpful flags (votes) and totalling to 23755 reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"data/swiss-reviews-helpful-correct-bigger.txt\", 'r') as fp:\n",
    "    s_swiss_rev_help = fp.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23755"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s_swiss_rev_help)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"reviewerID\": \"A05423363IP01ZGDN77PM\", \"asin\": \"B006TIEY40\", \"reviewerName\": \"Francis C.\", \"helpful\": [4, 6], \"reviewText\": \"Have used it for several trips already. Very satisfied. Fits in overhead compartment. Excellent quality and so far durable. Great for overnight trips.\", \"overall\": 5.0, \"summary\": \"Perfect carry-on spinner\", \"unixReviewTime\": 1372032000, \"reviewTime\": \"06 24, 2013\"}\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_swiss_rev_help[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The ARI score is a measure of the readability of a piece of text and it represents one of the features in the later model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import nltk.data\n",
    "from nltk import wordpunct_tokenize\n",
    "\n",
    "# split into words by punctuations\n",
    "# remove punctuations and all '-' words\n",
    "RE = re.compile('[0-9a-z-]', re.I)\n",
    "s_sent = nltk.data.load('tokenizers/punkt/english.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_ARI_score(text):\n",
    "    words = list(filter(lambda w: RE.search(w) and w.replace('-', ''), wordpunct_tokenize(text)))\n",
    "    wordc = len(words)\n",
    "    charc = sum(len(w) for w in words)\n",
    "    sents = s_sent.tokenize(text)\n",
    "    sentc = len(sents)\n",
    "    return 4.71 * charc / (wordc + 0.1) + 0.5 * (wordc + 0.1) / (sentc + 0.1) - 21.43"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In the next step we convert the file containing the reviews into a pandas dataframe as it is easier to work with it in that format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convert_to_dict(line):\n",
    "    l = line.rstrip('\\n')\n",
    "    l = eval(l)\n",
    "    return l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "s_dict_list = []\n",
    "counter = 1\n",
    "for item in s_swiss_rev_help:\n",
    "    if(counter%500 == 0):\n",
    "        print(counter)\n",
    "    counter += 1\n",
    "    s_dict_list.append(convert_to_dict(item))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_features = pd.DataFrame(s_dict_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We remove the 'reviewTime', 'reviewerName', 'unixReviewTime' columns from the dataframe as they will not be used as features nor used to construct other features. As a note, in the papers which inspired the current work these columns were used to obtain features such as the age of a review and the time delay of the review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_features.drop(['reviewTime', 'reviewerName', 'unixReviewTime'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asin</th>\n",
       "      <th>helpful</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B006TIEY40</td>\n",
       "      <td>[4, 6]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Have used it for several trips already. Very s...</td>\n",
       "      <td>A05423363IP01ZGDN77PM</td>\n",
       "      <td>Perfect carry-on spinner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B006TIEZH6</td>\n",
       "      <td>[4, 6]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Have used it for several trips already. Very s...</td>\n",
       "      <td>A05423363IP01ZGDN77PM</td>\n",
       "      <td>Perfect carry-on spinner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B001F42MKG</td>\n",
       "      <td>[0, 6]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>dont buy this crap. hard to use. doesnt last l...</td>\n",
       "      <td>A05953714NT40ISVAAT5</td>\n",
       "      <td>dont buy this crap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         asin helpful  overall  \\\n",
       "0  B006TIEY40  [4, 6]      5.0   \n",
       "1  B006TIEZH6  [4, 6]      5.0   \n",
       "2  B001F42MKG  [0, 6]      1.0   \n",
       "\n",
       "                                          reviewText             reviewerID  \\\n",
       "0  Have used it for several trips already. Very s...  A05423363IP01ZGDN77PM   \n",
       "1  Have used it for several trips already. Very s...  A05423363IP01ZGDN77PM   \n",
       "2  dont buy this crap. hard to use. doesnt last l...   A05953714NT40ISVAAT5   \n",
       "\n",
       "                    summary  \n",
       "0  Perfect carry-on spinner  \n",
       "1  Perfect carry-on spinner  \n",
       "2        dont buy this crap  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_features.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature gathering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature 1: Review length measured by the number of characters of the text.\n",
    "It will be noted here that after the creation of each feature it will also be standardized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Review length\n",
    "s_features[\"rev_length\"] = s_features['reviewText'].apply(lambda x: len(x))\n",
    "s_features[\"rev_length\"] = (s_features[\"rev_length\"] - \\\n",
    "                          s_features[\"rev_length\"].mean()) / s_features[\"rev_length\"].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature 2: The ratio of the number of caps to the total number of characters. +1 was added in the denominator to prevent division by 0 (apparently there are zero-length reviews)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Number of all caps\n",
    "def count_all_caps(text):\n",
    "    count = 0\n",
    "    for i in text:\n",
    "        if(i.isupper()):\n",
    "            count += 1\n",
    "    return count/(len(text) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_features[\"all_caps\"] = s_features['reviewText'].apply(count_all_caps)\n",
    "s_features[\"all_caps\"] = (s_features[\"all_caps\"] - \\\n",
    "                          s_features[\"all_caps\"].mean()) / s_features[\"all_caps\"].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature 3: Total number of sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sentence count\n",
    "s_features[\"sent_count\"] = s_features['reviewText'].apply(lambda x: \\\n",
    "                                                      len(nltk.tokenize.sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_features[\"sent_count\"] = (s_features[\"sent_count\"] - \\\n",
    "                          s_features[\"sent_count\"].mean()) / s_features[\"sent_count\"].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature 4: Question and exclamation marks ratio to the length of the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Question and exclamation marks ratio to characters\n",
    "def count_ques_exc_ratio(text):\n",
    "    count = 0\n",
    "    for i in text:\n",
    "        if(i == '!' or i == '?'):\n",
    "            count += 1\n",
    "    return count*1.0/(len(text)+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_features[\"que_exc_ratio\"] = s_features['reviewText'].apply(count_ques_exc_ratio)\n",
    "s_features[\"que_exc_ratio\"] = (s_features[\"que_exc_ratio\"] - \\\n",
    "                          s_features[\"que_exc_ratio\"].mean()) / s_features[\"que_exc_ratio\"].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature 5: ARI readability score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# ARI readability index\n",
    "s_features[\"ari_score\"] = s_features['reviewText'].apply(get_ARI_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_features[\"ari_score\"] = (s_features[\"ari_score\"] - \\\n",
    "                          s_features[\"ari_score\"].mean()) / s_features[\"ari_score\"].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature 6: The deviation of the review's rating from the mean rating score of the respective product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load mean rating scores for considered asins\n",
    "with open(\"data/asin_ratings_bigger.json\", \"r\") as fp:\n",
    "    s_asin_rev_ratings = fp.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_dict_list_ar = []\n",
    "for item in s_asin_rev_ratings:\n",
    "    s_dict_list_ar.append(convert_to_dict(item))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_dict_ar = dict([(key,d[key]) for d in s_dict_list_ar for key in d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_dev_mean_score(row):\n",
    "    if(row['asin'] in s_dict_ar.keys()):\n",
    "        return row['overall'] - s_dict_ar[row['asin']]\n",
    "    print(\"Should not get here\")\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_features['dev_mean_score'] = s_features.apply(get_dev_mean_score, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# should only be standardized after dev_mean_score has been computed\n",
    "s_features[\"overall\"] = (s_features[\"overall\"] - \\\n",
    "                          s_features[\"overall\"].mean()) / s_features[\"overall\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_features[\"dev_mean_score\"] = (s_features[\"dev_mean_score\"] - \\\n",
    "                          s_features[\"dev_mean_score\"].mean()) / s_features[\"dev_mean_score\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23755"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we remove duplicate ('asin', 'reviewText') pair values. There other approaches as well as removing duplicates only by the 'reviewText' field, but this would reduce the dataset considerably. Here we can make an interesting observation, regarding the fact that a lot of the reviews are copied in different products, without changing the content at the very least. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_features.drop_duplicates(subset=['asin','reviewText'], keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23675"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next part we create the labels by considering a review 'helpful' only if it has at least 0.6 helpful ratio (helpful votes / total votes). This approach was also used in the mentioned papers, the motivation being that the normal/intuitive threshold of 0.5 would be a little ambiguous and as such a more strict threshold was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def create_labels(pair):\n",
    "    if(pair[0]*1.0/pair[1] >= 0.6):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_features['Y'] = s_features['helpful'].apply(create_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To complete the set of features considered for the learning model we use tf-idf on the corpus of helpful reviews. In a little more detail we first compute the dictionary of the corpus and then attach to each word its tf-idf score. A given review will contain as features the vocabulary of words together with the tf-idf scores for its respective words contained in the review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "s_count_vect = CountVectorizer(stop_words='english')\n",
    "s_X_train_counts = s_count_vect.fit_transform(list(s_features['reviewText']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23675, 49576)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_X_train_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "s_tf_transformer = TfidfTransformer().fit(s_X_train_counts)\n",
    "s_X_train_tf = s_tf_transformer.transform(s_X_train_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23675, 49576)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_X_train_tf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to preserve the sparsity we scale by the maximum absolute value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale, maxabs_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<23675x49576 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 1396844 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxabs_scale(s_X_train_tf, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_Y = s_features.as_matrix(['Y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23675, 1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_X_train_else = s_features.as_matrix(['overall','rev_length','all_caps','sent_count','que_exc_ratio',\n",
    "                                   'ari_score', 'dev_mean_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s_X_train_else)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We combine the features constructed until now into the final feature matrix s_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.sparse import hstack\n",
    "s_X = hstack([s_X_train_tf, s_X_train_else])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.coo.coo_matrix"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23675, 49583)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23675, 1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_Y = s_Y.reshape((s_Y.shape[0],))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23675,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Time for some machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the classifier models to be tested: Random forest, Logistic regression, AdaBoost, SVC (from SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, KFold, StratifiedKFold\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the dataset into training and testing and see how the classifiers roughly behave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_tr, x_te, y_tr, y_te = train_test_split(s_X, s_Y, train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_cllr = LogisticRegression(class_weight='balanced', n_jobs=-1, C=1e2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.808658922914\n",
      "Train set accuracy: 0.999208025343\n"
     ]
    }
   ],
   "source": [
    "s_cllr.fit(x_tr, y_tr)\n",
    "print(\"Test set accuracy:\" , s_cllr.score(x_te, y_te))\n",
    "print(\"Train set accuracy:\", s_cllr.score(x_tr, y_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_clrf = RandomForestClassifier(n_estimators = 10, class_weight='balanced', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.823020063358\n",
      "Train set accuracy: 0.993505807814\n"
     ]
    }
   ],
   "source": [
    "s_clrf.fit(x_tr, y_tr)\n",
    "print(\"Test set accuracy:\" , s_clrf.score(x_te, y_te))\n",
    "print(\"Train set accuracy:\", s_clrf.score(x_tr, y_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_clab = AdaBoostClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.813305174234\n",
      "Train set accuracy: 0.820749736008\n"
     ]
    }
   ],
   "source": [
    "s_clab.fit(x_tr, y_tr)\n",
    "print(\"Test set accuracy:\" , s_clab.score(x_te, y_te))\n",
    "print(\"Train set accuracy:\", s_clab.score(x_tr, y_tr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Unfortunately the SVC (linear SVM) classifier takes a long time to compute and it was removed from the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75443505807814149"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_tr)/len(y_tr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy does not bring any meaningful observations as the 2 classes are highly imbalanced. The ratio of the positive (helpful) samples to the negative (unhelpful) samples is approximately 3:1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One alternative is to compute the F1 score for each classfier using cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shuffle = KFold(n_splits=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f1s_rf = cross_val_score(s_clrf, s_X, s_Y, cv=shuffle, scoring='f1', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score mean -- Random forest: 0.885844405397\n",
      "F1 score std -- Random forest: 0.00656460934811\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 score mean -- Random forest:\" , f1s_rf.mean())\n",
    "print(\"F1 score std -- Random forest:\", f1s_rf.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f1s_lr = cross_val_score(s_cllr, s_X, s_Y, cv=shuffle, scoring='f1', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score mean -- Logistic regression: 0.875015012955\n",
      "F1 score std -- Logistic regression: 0.00675991385759\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 score mean -- Logistic regression:\" , f1s_lr.mean())\n",
    "print(\"F1 score std -- Logistic regression:\", f1s_lr.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f1s_ab = cross_val_score(s_clab, s_X, s_Y, cv=shuffle, scoring='f1', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score mean -- AdaBoost: 0.878867782696\n",
      "F1 score std -- AdaBoost: 0.00774506029495\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 score mean -- AdaBoost:\" , f1s_ab.mean())\n",
    "print(\"F1 score std -- AdaBoost:\", f1s_ab.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the F1 score, the Random Forest classifier seems to be doing a little bit better than the Logistic Regression and AdaBoost classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can also take a look at the confusion matrix for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_y_pred_rf = cross_val_predict(s_clrf, s_X, s_Y, cv=shuffle, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_y_pred_lr = cross_val_predict(s_cllr, s_X, s_Y, cv=shuffle, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_y_pred_ab = cross_val_predict(s_clab, s_X, s_Y, cv=shuffle, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_cm_rf = confusion_matrix(s_Y, s_y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_cm_lr = confusion_matrix(s_Y, s_y_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_cm_ab = confusion_matrix(s_Y, s_y_pred_ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 3267  2563]\n",
      " [ 1545 16300]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zeeqqhres01ubbt2aD/S1cNHpO0nDCI8ieySWZ5iZPRD9fbOkAYQg/z3QmzD6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6d53e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot non-normalized confusion matrix -- Random forest\n",
    "plt.figure()\n",
    "plot_confusion_matrix(s_cm_rf, classes=['Unhelpful', 'Helpful'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 3725  2105]\n",
      " [ 2254 15591]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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yb2D/Uxn2+GM88dSzNG7ShKlTpwLQokULGjZsCMCsWbP4YfJkfv75J8yMiRO/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\nf0d+S1bbzvNPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6cca84a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot non-normalized confusion matrix -- Logistic regression\n",
    "plt.figure()\n",
    "plot_confusion_matrix(s_cm_lr, classes=['Unhelpful', 'Helpful'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 2901  2929]\n",
      " [ 1570 16275]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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yE5LYb7fta6Vfft1N7Lt/mhN/zOiRXHD2b/lm3BiW7dKVfQ84hJNPr/2w+fP/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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6d53eac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot non-normalized confusion matrix -- AdaBoost\n",
    "plt.figure()\n",
    "plot_confusion_matrix(s_cm_ab, classes=['Unhelpful', 'Helpful'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After preforming cross-validation to find the predictions of each model, we plotted the confusion matrix with respect to each model's predictions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are different measurements based on the confusion matrix, the F1 score (harmonic mean between precision and recall) computed before being one of them. One measurement which could be more useful is the false positive rate (FP / N; FP = false positives, N = total negatives). The intuition behind this is the following: classifying true helpful reviews as unhelpful is less significant than classifying true unhelpful reviews as helpful. We see that all the models perform well in identifying helpful reviews, yet some are better at handling the unhelpful reviews. The false positive rate focuses on finding which model is better or more sensitive for handling negative samples (unhelpful reviews)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tn_rf, fp_rf, fn_rf, tp_rf = s_cm_rf.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43962264150943398"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positive_rate_rf = fp_rf / (fp_rf + tn_rf)\n",
    "false_positive_rate_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tn_lr, fp_lr, fn_lr, tp_lr = s_cm_lr.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36106346483704976"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positive_rate_lr = fp_lr / (fp_lr + tn_lr)\n",
    "false_positive_rate_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tn_ab, fp_ab, fn_ab, tp_ab = s_cm_ab.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50240137221269299"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positive_rate_ab = fp_ab / (fp_ab + tn_ab)\n",
    "false_positive_rate_ab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression (again) provides the smallest false positive rate, suggesting that it is the better model for handling the negative cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next measurement step will be to analyze the roc curves for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# with StratifiedKFold the folds are made by preserving the percentage of samples for each class\n",
    "shuffle_5 = StratifiedKFold(n_splits=5, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import cycle\n",
    "colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy import interp\n",
    "def plot_roc_cv(classifier):\n",
    "    i = 0\n",
    "    mean_tpr = 0.0\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "    for (train, test), color in zip(shuffle_5.split(s_X, s_Y), colors):\n",
    "        classifier.fit(s_X.tocsr()[train], s_Y[train])\n",
    "        probas_ = classifier.predict_proba(s_X.tocsr()[test])\n",
    "        # Compute ROC curve and area under the curve\n",
    "        fpr, tpr, thresholds = roc_curve(s_Y[test], probas_[:, 1])\n",
    "        mean_tpr += interp(mean_fpr, fpr, tpr)\n",
    "        mean_tpr[0] = 0.0\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr, lw=2, color=color, label='ROC fold %d (area = %0.4f)' % (i, roc_auc))\n",
    "        i += 1\n",
    "        \n",
    "    mean_tpr /= shuffle_5.get_n_splits()\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "    plt.plot(mean_fpr, mean_tpr, color='r', linestyle='--', label='Mean ROC (area = %0.4f)' % mean_auc, lw=2)\n",
    "    \n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dlcji3wJYkeaD5yWRqVx8LAHZHRzKpu4BhA9Opl6PA1TNYZ4vY2ogcgW2paMd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\nzgmImZmZFe5fQyRvV+1LLyAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6cf2db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve(s) for Random Forest\n",
    "plt.figure()\n",
    "plot_roc_cv(s_clrf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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urajrrDRjKy9wDwNZQBCZRe73+XvPZ2332mTl2li+PY6eL19GZq71cdFNTqPP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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6cf35208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve(s) for Logistic Regression\n",
    "plt.figure()\n",
    "plot_roc_cv(s_cllr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V+2iERATQ78qO9BvWgWZdEvVOh1JHGQ1AlDqOFBfD7Nl2dtEHH4Tly33nc7mg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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6cd8f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve(s) for AdaBoost\n",
    "plt.figure()\n",
    "plot_roc_cv(s_clab)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Based on the results above, random forest provides the best mean roc area under curve, followed by the logistic regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After observing the results of the F1 score, FPR (false positive rate) and the area under the ROC curve (AUC) both the random forest and the logistic regression models seem perform better than AdaBoost, however between the two of them it is hard to say which is better as their results are quite similar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Most relevant features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last part of the analysis we try to get an intuition of the most relevant features when training the random forest. For the other classifiers identifying the most important features it is not as straight forward as for a random forest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "s_feature_names stores the name of the features (the vocabulary together with the constructed features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_words_list = s_count_vect.get_feature_names()\n",
    "s_other_feats = ['overall','rev_length','all_caps','sent_count','que_exc_ratio',\n",
    "                                   'ari_score', 'dev_mean_score']\n",
    "s_feature_names = s_words_list + s_other_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s_feature_names) == s_X.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# the number of the most relevant features to be plotted\n",
    "number_of_features = 15\n",
    "importances = s_clrf.feature_importances_\n",
    "# sort the indices based on the importance values\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# the names of the features based on the indices and how many to select\n",
    "temp_names = []\n",
    "for i in range(number_of_features):\n",
    "    temp_names.append(s_feature_names[indices[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lcwkKDfJx8Egza+F7UBim+4C9vPfv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\nXq8AywA7AQt2yp1pQZUSXr8vJw2OiBW78Cb1CuU52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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f6cc91048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature overall (0.041733)\n",
      "2. feature dev_mean_score (0.036224)\n",
      "3. feature rev_length (0.012483)\n",
      "4. feature sent_count (0.011424)\n",
      "5. feature ari_score (0.009004)\n",
      "6. feature all_caps (0.007740)\n",
      "7. feature great (0.007492)\n",
      "8. feature little (0.007119)\n",
      "9. feature que_exc_ratio (0.005137)\n",
      "10. feature easy (0.004543)\n",
      "11. feature time (0.003565)\n",
      "12. feature mouse (0.003296)\n",
      "13. feature use (0.003090)\n",
      "14. feature bit (0.002937)\n",
      "15. feature love (0.002801)\n"
     ]
    }
   ],
   "source": [
    "# Plot the feature importances of the random forest\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Feature importances\", fontsize=20)\n",
    "plt.bar(range(number_of_features), importances[indices[:number_of_features]],\n",
    "       color=\"r\", align=\"center\")#, yerr=std[indices])\n",
    "plt.xticks(range(number_of_features), temp_names, fontsize=20, rotation=45)\n",
    "plt.xlim([-1, number_of_features])\n",
    "plt.show()\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(number_of_features):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1,  s_feature_names[indices[f]], importances[indices[f]]))\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}