predicting_helpfulness_small.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": [
    "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\"](http://cs229.stanford.edu/proj2014/Jordan%20Rodak,%20Minna%20Xiao,%20Steven%20Longoria,%20Predicting%20Helpfulness%20Ratings%20of%20Amazon%20Product%20Reviews.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 smaller such dataset, in which the swiss reviews had at least 10 helpful flags (votes) and totalling to 10729 reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"data/swiss-reviews-helpful-correct.txt\", 'r') as fp:\n",
    "    s_swiss_rev_help = fp.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10729"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s_swiss_rev_help)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"reviewerID\": \"A08719731S0E5PHVCG6EZ\", \"asin\": \"B005WFPLWA\", \"reviewerName\": \"Sophie Clouet\", \"helpful\": [0, 10], \"reviewText\": \"JE NE VOIS PAS ENCORE D\\'AM&Eacute;LIORATION CONTRE LA PERTE DES CHEVEUX.  JE VERRAI APR&Egrave;S LE TRAITEMENT SI C\\'EST BON OU NON\", \"overall\": 1.0, \"summary\": \"COMMENTAIRE\", \"unixReviewTime\": 1356739200, \"reviewTime\": \"12 29, 2012\"}\\n'"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n",
      "1000\n",
      "1500\n",
      "2000\n",
      "2500\n",
      "3000\n",
      "3500\n",
      "4000\n",
      "4500\n",
      "5000\n",
      "5500\n",
      "6000\n",
      "6500\n",
      "7000\n",
      "7500\n",
      "8000\n",
      "8500\n",
      "9000\n",
      "9500\n",
      "10000\n",
      "10500\n"
     ]
    }
   ],
   "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": 9,
   "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": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_features.drop(['reviewTime', 'reviewerName', 'unixReviewTime'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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",
       "      <th>rev_length</th>\n",
       "      <th>all_caps</th>\n",
       "      <th>sent_count</th>\n",
       "      <th>que_exc_ratio</th>\n",
       "      <th>ari_score</th>\n",
       "      <th>dev_mean_score</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B005WFPLWA</td>\n",
       "      <td>[0, 10]</td>\n",
       "      <td>-1.418073</td>\n",
       "      <td>JE NE VOIS PAS ENCORE D'AM&amp;Eacute;LIORATION CO...</td>\n",
       "      <td>A08719731S0E5PHVCG6EZ</td>\n",
       "      <td>COMMENTAIRE</td>\n",
       "      <td>-0.759382</td>\n",
       "      <td>13.618261</td>\n",
       "      <td>-0.777128</td>\n",
       "      <td>-0.323302</td>\n",
       "      <td>-0.690390</td>\n",
       "      <td>-0.746869</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00064FXH0</td>\n",
       "      <td>[17, 20]</td>\n",
       "      <td>-1.418073</td>\n",
       "      <td>I ordered this thinking it would speed up my r...</td>\n",
       "      <td>A1006UA0557GXP</td>\n",
       "      <td>Great idea...next year when the bugs are worke...</td>\n",
       "      <td>0.408628</td>\n",
       "      <td>-0.024824</td>\n",
       "      <td>0.938594</td>\n",
       "      <td>0.184259</td>\n",
       "      <td>-0.229376</td>\n",
       "      <td>-0.785152</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B003VAHYTG</td>\n",
       "      <td>[162, 174]</td>\n",
       "      <td>0.985577</td>\n",
       "      <td>I mainly got this set for music and some games...</td>\n",
       "      <td>A100JEV1I8ZHJ8</td>\n",
       "      <td>Quality 2.1 System for Music and Games</td>\n",
       "      <td>-0.422216</td>\n",
       "      <td>-0.046337</td>\n",
       "      <td>-0.575278</td>\n",
       "      <td>-0.323302</td>\n",
       "      <td>0.425492</td>\n",
       "      <td>0.780581</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         asin     helpful   overall  \\\n",
       "0  B005WFPLWA     [0, 10] -1.418073   \n",
       "1  B00064FXH0    [17, 20] -1.418073   \n",
       "2  B003VAHYTG  [162, 174]  0.985577   \n",
       "\n",
       "                                          reviewText             reviewerID  \\\n",
       "0  JE NE VOIS PAS ENCORE D'AM&Eacute;LIORATION CO...  A08719731S0E5PHVCG6EZ   \n",
       "1  I ordered this thinking it would speed up my r...         A1006UA0557GXP   \n",
       "2  I mainly got this set for music and some games...         A100JEV1I8ZHJ8   \n",
       "\n",
       "                                             summary  rev_length   all_caps  \\\n",
       "0                                        COMMENTAIRE   -0.759382  13.618261   \n",
       "1  Great idea...next year when the bugs are worke...    0.408628  -0.024824   \n",
       "2             Quality 2.1 System for Music and Games   -0.422216  -0.046337   \n",
       "\n",
       "   sent_count  que_exc_ratio  ari_score  dev_mean_score  Y  \n",
       "0   -0.777128      -0.323302  -0.690390       -0.746869  0  \n",
       "1    0.938594       0.184259  -0.229376       -0.785152  1  \n",
       "2   -0.575278      -0.323302   0.425492        0.780581  1  "
      ]
     },
     "execution_count": 242,
     "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": 12,
   "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": 13,
   "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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load mean rating scores for considered asins\n",
    "with open(\"data/asin_ratings_small.json\", \"r\") as fp:\n",
    "    s_asin_rev_ratings = fp.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_features['dev_mean_score'] = s_features.apply(get_dev_mean_score, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "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": [
       "10729"
      ]
     },
     "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": [
       "10693"
      ]
     },
     "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": [
       "(10693, 36391)"
      ]
     },
     "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": [
       "(10693, 36391)"
      ]
     },
     "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": [
       "<10693x36391 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 737288 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": [
       "(10693, 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": [
       "(10693, 36398)"
      ]
     },
     "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": [
       "(10693, 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": [
       "(10693,)"
      ]
     },
     "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": 134,
   "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": 53,
   "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": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_cllr = LogisticRegression(class_weight='balanced', n_jobs=-1, C=1e2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.850864890136\n",
      "Train set accuracy: 0.999181669394\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": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_clrf = RandomForestClassifier(n_estimators = 10, class_weight='balanced', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.854604955587\n",
      "Train set accuracy: 0.9957914426\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": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_clab = AdaBoostClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.826554464703\n",
      "Train set accuracy: 0.856675239654\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": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# C could be tuned for regularization, by default it is C=1.0\n",
    "s_clsvc = SVC(class_weight='balanced', kernel='linear', probability=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.84899485741\n",
      "Train set accuracy: 0.99894786065\n"
     ]
    }
   ],
   "source": [
    "s_clsvc.fit(x_tr, y_tr)\n",
    "print(\"Test set accuracy:\" , s_clsvc.score(x_te, y_te))\n",
    "print(\"Train set accuracy:\", s_clsvc.score(x_tr, y_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78127191956979192"
      ]
     },
     "execution_count": 62,
     "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": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shuffle = KFold(n_splits=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score mean -- Random Forest: 0.908334920568\n",
      "F1 score std -- Random Forest: 0.0075697501992\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": 75,
   "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": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score mean -- Logistic regression: 0.908059903298\n",
      "F1 score std -- Logistic regression: 0.00587383646152\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": 77,
   "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": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score mean -- AdaBoost: 0.90128576689\n",
      "F1 score std -- AdaBoost: 0.00710639811693\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 score mean -- AdaBoost:\" , f1s_ab.mean())\n",
    "print(\"F1 score std -- AdaBoost:\", f1s_ab.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f1s_svc = cross_val_score(s_clsvc, s_X, s_Y, cv=shuffle, scoring='f1', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score mean -- SVC: 0.907999089142\n",
      "F1 score std -- SVC: 0.00838618327585\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 score mean -- SVC:\" , f1s_svc.mean())\n",
    "print(\"F1 score std -- SVC:\", f1s_svc.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The F1 score is quite similar for all the classifiers, with the random forest model edging the logistic regression and svc models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can also take a look at the confusion matrix for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": 97,
   "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": 98,
   "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": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_y_pred_svc = cross_val_predict(s_clsvc, s_X, s_Y, cv=shuffle, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_cm_rf = confusion_matrix(s_Y, s_y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_cm_lr = confusion_matrix(s_Y, s_y_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_cm_ab = confusion_matrix(s_Y, s_y_pred_ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s_cm_svc = confusion_matrix(s_Y, s_y_pred_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[1369 1000]\n",
      " [ 615 7709]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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j+nTOOeNU9j/goIocMZ2pcgZfVU4cXqQC8UdR916rr4AN8/L8O+/1WKB2tMXG\nwDqSJuflaUPSz1tfIH4z7/UgklaxYgHc+3XReWfSbpll6qTtvf+B7NP7oObedbP68P0h9Nmn15xf\ntpecfzYAvfscxqVXXsdnn4zg0QcP5ttvv6F9++XZZLPuDHjqBdbpOvdS9NG/OYnp06ZxyflnMbGm\nhm4bbsS9jz5d57LzB+++Tf/LL2HKlO9Za52u/OnqG9n3F30W+PFa03r/3SHsu8euc86fC849C4A+\nhx7OtTfdxsn9zuCHqVM5/eQTmPTdRLb5yfY8+OgTtG7dek4Zl1z2Z1q2bMnRhx3EtOnT6LlrLy67\n6to6+7nlr3/j7NNPpvfPf0aLFi34+b7783+XX71Aj7W5PPLQAzw64ME6aZO++y6TutQO1ip1m0qR\n+X3Ekh4HJkTEr/LSTwFOjYg1JN0BLBMR++esvxrYJCJ2SV+PAq6OiGtz8rwHPBYRF0m6EdiM5NmR\n+Z/g1xExOX8/aR/wZxFxTE6ZewMPA4tHROTXI80zC9g/Ih7PSfseOCEi7k77pUcBm0bEh/W8L1Vz\nH7E1vWq6j9iaTlb3Ea9y6LW06bh2SdtOG/cp/7v3ZKiA+4gXhhbxcOCnBdK7AyMKpJfrXeBAkqD7\nfQnbbZ33elvgk0Zaw18DK9W+SPupl8zLsyiMCDczs2a2MAzWuglYV1J/SRtJWlfSacBBwJVNuJ97\ngQnA45K2l7S6pJ0kXSNp5Qa26yzpyrReBwMnAf0b2dcLwEmSNpW0BckxTs/LUznXVczM5oNU3lIp\nMm8RR8QoST2AS4F/Aa2BYcAvIuJfpRTVUFpE/JDu5zLgEWBp4H/A80BDk3DcDSwBDAZmklz+vq2R\nupwO/BV4BfgSOAXYvIj6mplVn3JGQVdQJM48EANExBDgZw2sP6pAWr+81/NMHBwRm+e9Hg/MU1ZD\n+wFmpKOmT6xnm34F0r4Cds9LXi5n/WjAnXhmZpTXwq2gOLxwBGIzM6teovTnEVdQHHYgboQvH5uZ\nNbNqbxEvDIO1FloRsUvOZB5mZtYM1EK0KHFRi/ojsaQL06mGc5eP8/J0k/R4On3x95LekrRqzvo2\nkm5Ip1SeLGmApA55ZbSXdK+Sp+/VSLpNUttSj9+B2MzMMtVMo6b/QzL1cKd02X7u/rQW8CrwMdAD\n2Ai4GPgxZ/v+wJ5A7zTPyiQDfXPdB3QjmeVxzzTfLaUevy9Nm5lZppppZq2ZDUw9fAnJlMjn5qSN\nmlN28lwABRGJAAAgAElEQVT5o4E+EfFymnYUMFTSVhExWFI3oBfJhCLvpXn6Ak9JOiMiin5WplvE\nZmaWqWZqEa8j6X+SPpN0j6TVkn1JJK3XTyQ9I2mcpDcl7ZOzbXeShurztQkRMRwYQzKpEyTPoK+p\nDcKp50jGFuVPBNUgB2IzM6s0b5I8na8XcBywBvBq2n/bAVgKOBt4mmRmx8eARyXtkG7fieRhPvlz\nTIxL19XmqfOczIiYRfKAopKeCuJL02ZmlqnGHms48T8vMPE/L9ZJmzVtSr35I2Jgzsv/SBoMjCaZ\n5viZNP3vOc8m+FDST0iC9qslH8B8ciA2M7NsNRKI22/Uk/Yb1X2G+NSvPuHTW48vqviI+E7SCGBt\nkqmOZwJD87INJXlWPSRP7mstqV1eq7hjuq42T/4o6pYkkzcV3T8MvjRtZmYZa+65piUtRRKEv4yI\nGcDbQNe8bOuStJoBhpAE6545ZXQFOpM8Cpf0/8tKyn08Xk+SuUbeKr52bhGbmVnGmnpmLUlXAE+Q\nBNZVgD8AM4AH0ixXAA9IehV4kWRK4r2AHQEiYpKk24GrJNUAk4FrgdcjYnCaZ5ikgcCtko4neU7C\ndcD9pYyYBgdiMzPLWDPMrLUqyT2+y5M8lvY1YJuI+AYgIv4u6Tjgt8A1JI/j3T8iBuWU0Q+YBQwA\n2pD0Lec/c+AQ4HqS0dKz07ynlHYkDsRmZpaxxgZr1bdNfSLi4Ma2j4g7gTsbWD8N6Jsu9eWZCBzW\n2L4a40BsZmaZqva5ph2IzcwsW1X+PGKPmjYzM8uQW8RmZpapZNR06dtUCgdiMzPLVFMP1lrUOBCb\nmVmmPFjLzMwsQ24Rm5mZZamMFnEldRI7EJuZWaaaeorLRY0DsZmZZcp9xGZmZhlyH7GZmVmGqj0Q\ne2YtMzOzDLlFbGZm2fKoaTMzs+yIMi5NV1AkdiA2M7NMedS0mZlZhqp9sJYDsZmZZcotYjMzswxJ\nooVbxGZmZtlwi7gIknYrtsCIeLb86piZWbXxXNPFeabIfAG0LLMuZmZmTU7SOcD/Af0j4rQ0rS1w\nGbAPsDwwCrg2Im7J2a4NcBVwENAGGAicEBHjc/K0B64H9gJmA48Ap0TElGLrV2wgXqLYAs3MzEoh\nQYtmujQtaUvgWOCDvFVXAzsBhwCjgd2AmyT9LyKeTPP0B3YHegOTgBtIAu0OOeXcB3QEegKtgTuB\nW4DDij2Woqa4jIhphRZgRoE0MzOzotXevlTqUkS5SwH3AMcAE/NWbwvcFRGvRsSYiLiNJFhvlW7b\nDjga6BcRL0fEe8BRwHaSavN0A3oBv4qIdyLiDaAv0EdSp2KPv+S5piW1kHSmpM+AHyWtmaZfKOmI\nUsszM7PqVjtYq9SlCDcAT0TECwXWvQHsLWnlpA7aGViH5PIzQHeSq8bP124QEcOBMSRBHGAboCYN\n0rWeI+mm3brY4y/noQ9nAyeSXG+fmZM+AjiujPLMzKyKqcz/GixT6gNsCpxbT5a+wFDgv5KmA08D\nJ0bE6+n6TsD0iJiUt924dF1tnvG5KyNiFvBtTp5GlXP70lHAsRHxrKT+OenvA+uVUZ6ZmVWxpu4j\nlrQqSf/urhExo55sJ5O0WvciaeX2AG6U9GU9LehmU04gXo2k9VtIm/moi5mZVaHGHvrw38ED+d/g\ngXXSZvzwfUNFdgdWBN7V3IJbAj0knQQsC1wK7BsR/0zX/0fSZsAZwAvAWKC1pHZ5reKO6TrS/3eo\ncyxSS2C5nDyNKicQDye5Pv55Xvp+wIdllGdmZlWssT7f1bbuxWpb96qTNnH0MF6+5PD6NnkO2Cgv\n7U6SS9F/IgnKiwGz8vLMYm6X7RCS7teewGNJPdUV6AwMSvMMApaVtFlOP3FPktuc36r/iOoqJxBf\nAtwiqUNa4T3Syv2aJBibmZkVrUUZU1w2lD+9h/fj3DRJU4BvImJo+vpl4EpJfUluX9oJOAI4NS1j\nkqTbgask1QCTgWuB1yNicJpnmKSBwK2Sjie5fek64P6IaL4WcUQMkDQRuJDk10J/kv7hA3Ka+GZm\nZsUpY4rLMqbWirzXBwF/JLm9aTmSYHxuRPwlJ08/klbyAJKu12dIBivnOoRkQo/nSCb0GACcUkrF\nypprOiKeS3eKJEVE/gGamZktNCJil7zX44FfNbLNNJLR1X0byDOREibvKKTshz5I2hDolv79cUR8\nND8VMTOz6pT0EZf69KVmqkwGSg7E6WwhfyPpkP4hTV5c0ovA4RHxVRPWz8zMKlzy0IfSt6kU5Uzo\ncRvQHtgsItpGRFtgc2AZ4NamrJyZmVW+2sFapS6VopxL0z2B7SNizgTaEfGBpBOAl5usZmZmVjUq\nJ6yWrpxA/GU96UEJNzCbmZnB3Ic+lLpNpSjn0vQ5wHXpYC1gzsCt/iTzUJuZmRWthcpbKkVRLWJJ\nX1H3Hqz2wAeSagdrLQFMB64BHm7SGpqZWUWr9hZxsZemf9+clTAzs+pWQXG1ZEUF4oi4pbkrYmZm\nVo3KntADQFKL/DIiYvp81cjMzKpKtV+aLnmwlqQlJF0paQxJv/APeYuZmVnRqn2wVjmjpv8I7A2c\nSxKIT0zTxgFHN13VzMysGtROcVnaknWtm045l6b3A46OiOcl3Qw8FxGfSvoM6A3c1aQ1NDOzildB\ncbVk5bSIVwA+Sf+eRHIrE8BLwM5NUCczM6si1T7FZTmBeBTQOf17OLB/+ncvksBsZmZWtNqHPpS0\nZF3pJlROIP4bsGX69xXAaZImATeQTOhhZmZWtNL7h0sfZb0wK7mPOCIuy/n7n+n0llsCn0bE4Kas\nnJmZVb7aVm6p21SK+bqPGCAiPmFun7GZmZmVoNi5po8ttsCI+Ev51TEzs2qjMgZfVeOl6T8UmS8A\nB2IzMyuaL00XISJWau6KWGHLtW1Nh3Ztsq6GLWLab3lS1lWwRdDsqV9nsl9RxhSXFTRuer77iM3M\nzOaHKP0WnsoJw+XdvmRmZtZkmnuKS0nnSJot6aq89IskfSlpqqR/SVo7b30bSTdImiBpsqQBkjrk\n5Wkv6V5J30mqkXSbpLalHL8DsZmZZao5H/ogaUvgWOCDvPSzgZPSdVsBU4CBklrnZOsP7EkyfXMP\nYGXgkbxd3Ad0A3qmeXsAJT062IHYzMwypTKCcDEtYklLAfcAxwAT81afAlwcEU9GxH+AI0gC7b7p\ntu1IHmTULyJejoj3gKOA7SRtlebpRjKr5K8i4p2IeAPoC/SR1KnY43cgNjOzTDXjzFo3AE9ExAt5\n+1sD6AQ8X5sWEZOAt4Bt06QtSMZR5eYZDozJybMNUJMG6VrPkdxBtHWxx1/WYK3018CxwFrAoRHx\npaQ+wOcR8WY5ZZqZWXVqQenPF26sFZnGpE1JAmq+TiTBclxe+rh0HUBHYHoaoOvL0wkYn7syImZJ\n+jYnT6NKDsSS9gYeBAaQ/CpYPF3VATgM2KvUMs3MzOoz9OUnGfbKU3XSpk2ZXG9+SauS9O/uGhEz\nmrd286+cFvGFwEkRcbukfXPSXwPObZpqmZlZtWhsQo/1d9qL9Xeq28Yb9+lH3H1q7/o26Q6sCLyr\nudewWwI9JJ0ErEdyB1RH6raKOwK1l5nHAq0ltctrFXdM19XmyR9F3RJYLidPo8rpI16PnGvmOSYy\n99nEZmZmRamd4rKUpZE+4ueAjUguTW+SLu+QDNzaJCJGkgTKnjl1aEfSr/tGmjQEmJmXpyvJY4AH\npUmDgGUlbZaz754kQf6tYo+/nBbxeGAN4PO89G1JnlVsZmZWtBaU3ipsKH9ETAE+zk2TNAX4JiKG\npkn9gd9J+pQknl0M/Bd4PC1jkqTbgask1QCTgWuB12ufNBgRwyQNBG6VdDzQGrgOuD8iim4RlxOI\n7wD6SzqCpLN7+fTXwJXA5WWUZ2ZmVWwBzTUddV5EXC5pSZJ7fpcFXgV2j4jpOdn6AbNIxkS1AZ4B\nTswr9xDgepJW+Ow07ymlVKycQHwJsBhJk3xx4E2S5vu1EXF1GeWZmVkVWxBPX4qIXQqk/R74fQPb\nTCO5L7hvA3kmkgxULlvJgTgiZgPnS/oT0BVYCvh3RNTMT0XMzKw6iTJaxM1Sk2yU/dCH9Br8u01Y\nFzMzq0KlTFmZu02lKOc+4qcbWh8Re5RfHTMzqzYL4tL0wqycFvHovNeLkQwRXxu4f75rZGZmVkXK\n6SM+vlC6pP+jsi7bm5nZArCARk0vtJryoQ93AL9uwvLMzKwKNOdjEBcFZQ/WKmBzYKGf09PMzBY+\nquILquUM1rovPwlYCdgOT+hhZmYlao6nLy1KymkR579ds4H3gasi4h/zXyUzM6smvn2pBOlTJa4G\nhkfEd81TJTMzqyqNP8Sh4DaVoqTWfUTMIpmPc/nmqY6ZmVWbah+sVc5l9o+B1Zq6ImZmVp1qb18q\ndakU5QTis4ArJe0qqb2k1rlLU1fQzMwqmyjjecQVNMq6nMFaA/P+n69lmXUxMzOrOuUE4t2bvBZm\nZla1PGq6SJIuAK6MiPpawmZmZiXzFJfFu5Dk2cNmZmZNpgUqa6kUpVyarpyjNjOzhUc5o6ArKCKV\n2kcczVILMzOrWu4jLs0ISQ0G44hYbj7qY2ZmVab2lqRSt6kUpQbiCwFPbWlmZk2qguJqyUoNxA9E\nxPhmqYmZmVWl5NJ0qS3i+tdJOg44Hlg9TfoIuCginpHUCriU5FbcNUkal88B50TEVzlltAGuAg4C\n2pDMnXFCbgyU1B64HtiL5AFIjwCnRMSUko6lhLzuHzYzs0XBF8DZwOZAd+AF4HFJ3YAlgU2BPwCb\nAfsBXYHH88roD+wJ9AZ6ACuTBNpc9wHdgJ5p3h7ALaVW1qOmzcwsU019H3FEPJWX9DtJxwPbRMQd\nQK+6Zekk4C1Jq0bEfyW1A44G+kTEy2meo4ChkraKiMFpUO8FdI+I99I8fYGnJJ0REWOLPZaiW8QR\n0cKXpc3MrKmJJBiVshQbtyW1kNSHpCU8qJ5sy5Jc9Z2Yvu5O0lB9vjZDRAwHxgDbpknbADW1QTj1\nXFrO1kVWDyhvikszM7MmozKeR9xYfkkbkgTexYHJwH4RMaxAvjbAn4D7IuL7NLkTMD0iJuVlH5eu\nq81Tp3EaEbMkfZuTpygOxGZmlinRcAt30MDHeXNg3S7cH77Pj5HzGAZsAiwD/AK4W1KP3GCcDtx6\nmKQVe0LpNW8aDsRmZpapxu4j3u5n+7Ldz/atk/b5sH9z/mF71LtNRMwERqYv35O0FXAKyWjq3CC8\nGrBLTmsYYCzQWlK7vFZxx3RdbZ4OufuU1BJYLidPUcp5HrGZmVmTUolLGVqQ3IaUG4TXBHpGRE1e\n3iHATJLR0KTbdAU6M7efeRCwrKTNcrbrmVbvrVIq5haxmZllSpQxarqhddL/Af8kGVy1NHAosCOw\nWxqEHyG5hWkvYDFJHdNNv42IGRExSdLtwFWSakj6mK8FXo+IwQARMUzSQODWdER2a+A64P5SRkyD\nA7GZmWWsGQZrdQDuAlYimbDjQ2C3iHhBUheSAAzwfm1xJP3EOwOvpGn9gFnAAJKW9DPAiXn7OYRk\nQo/nSCb0GEBy+bskDsRmZlZRIuKYBtaNBloWUcY0oG+61JdnInBYOXXM5UBsZmaZqr03uNRtKoUD\nsZmZZauMS9OV9JQIB2IzM8tUOSOhKycMOxCbmVnGkrmmSx2s1UyVyYADsZmZZcp9xGZmZllyH7GZ\nmVl23EdsZmaWoaaeWWtR40BsZmaZaoFoUWJoLTX/wqyS+rvNzMwWOW4Rm5lZtlTG2KvKaRA7EJuZ\nWbaU/lfqNpXCgdjMzDKlMlrEFXT3kgOxmZllq9oHazkQm5lZttxHbGZmlh1fmjYzM8tQMrNWqYO1\nKocDsZmZZaoF0KLEyFpJk2BU0rHYIu7LL7/k6CMPZ9VOK7BcuyXZavNNeO/dd+esf/zvj/HzPXqx\naqcVWLJ1C/794YfzlLFbz51YsnWLOUvbNi055aQTFuRhWDMb+uTvmTLk2nmWq84+AIApQ67l+3fm\nXX/K4bvMKaP1Yq24+pwD+eKFPzH+tSu574pfsWL7persZ9P1VuWJG0/ky5cvZ8wLf+S68/qw5OKt\nF+ixWnVwi9gWChMnTmSXHbdj55178o+nBrL8Civw2aef0L59+zl5pk6Zwnbb78AvDjiIE477dcFy\nJPGrY47lgj9cTEQAsOSSSy6QY7AFY7tDr6BlTvNpg3VW5skbT+KRZ5Mfbavv+ts6+XttvwE3XXAI\njz333py0K87sTa/t1ufgM29j8vc/0v/cA7n/ymPY9Vf9Aei0QjuevKkvDz/zDqf+6SHatV2cK8/8\nBbdedBiHnvXXBXCU1ab0+4gr6eK0A7EtFK68/E+stlpnbvrLbXPSunTpUifPwYceBsDo0aPnBNlC\nllhySVZcccXmqahl7tvvptR5vWePjRj53wm8/t5nAHxd832d9XvvvDEvv/MJY76qAWDptotz5D7b\ncMQ5d/DakE8BOPbCe3j/0d+xxQZdeOej0ey+w4ZMnzGTfpc9PKecvpc+wNsPncvqqyzP5//7pjkP\nsepU+2AtX5rOI2mUpJNL3KarpEGSfpD0buNbgKQjJdWUV8vK8/RTT7B59y049OAD6bJKR7bdcnPu\nuP22xjcs4MH772W1lVZki0034oLf/ZYffvihiWtrC4tWrVpw0B5bcNff3yi4fsX2S9Fr+w2487G5\n6zfrthqtWrbkxcEj5qR9Mno8X4ytYeuN1wCgTetWzJg5q05ZP06fAcBPNlurqQ+j6qnM/+otTzpX\n0mBJkySNk/SYpHUbyH+zpNn53/2S2ki6QdIESZMlDZDUIS9Pe0n3SvpOUo2k2yS1LeX4KyYQS7pD\n0qMF0ndM3+B2zbj7PwDfA+sAPUvYrv5mXZUZNXIkt95yE+uu25Unnn6WX//meE7vdzL33fO3ksrp\nc/Ch/PWuexj43Eucec5vue/ev3H0Lw9vplpb1vbZeROWWWoJ7nnirYLrD997GyZ9/yOPv/DBnLRO\nK7Rj+oyZTJ7yY52847+ZTMcVkq+Jl94eQcfll+bUw3vSqlULll16CS7uuzcRsNIKzflVUp1aqLyl\nATsA1wFbA7sCiwHPSloiP6Ok/dJ8/ytQTn9gT6A30ANYGXgkL899QDeS7/4903y3FH/01XNpurkD\n3lrAkxHx32beT8WaPXs2W2y5FRf+4WIANt5kEz766D/c+pebOeSw4gPpUb86Zs7f62+wASuttBK7\n79aTz0eNYvU11mjyelu2jthnWwa+/jHjvplccP3he2/DA0+/PU/rtjHDRo7l1xfcw2Wn789Fffdm\n5qxZ3Hj/y4z/djKzZ/v3c9Nr2j7iiNijTk7pl8B4oDvwWk76KsA1QC/g6bxt2gFHA30i4uU07Shg\nqKStImKwpG7ptt0j4r00T1/gKUlnRMTYYo6kYlrExZK0vaRXJE2VNFrSNZLqHc2TtqaPk/R0us1n\nknrnrgc2By6UNEvSBYVa4ZI2SdM6N+8RLpo6rbQS663XrU7aeut144svxsxXuVtsuRURwWeffTpf\n5djCZ7VO7dll667c8Wjhy9LbbbYW63TpwB2P1V0/dsIkWi/WiqXbLl4nvcPySzNuwqQ5rx8eOIQ1\ndzuPNXudxyo7n82lf3maFdsvxSj3Dze52j7iUpcSLEvSIPt27j4l4G7g8ogYWmCb7iSN1edrEyJi\nODAG2DZN2gaoqQ3CqefSfW1dbOWqIRDP+bgkrQX8E3gY2BA4CNiO5BJGQy5Kt9kYuBd4QFLXdF0n\n4GPgyvTvK9P0Qj+b/VO6Httuux0jRgyvkzZixHA6d+5SML+K/Ff4/nvvIYlOnVaa7zrawuWIfbZl\n/LeTeea1jwquP3LfbXlv6Bg+/uyrOunvDf2CmbNmsfNWc7sM1+nSgdU6teetD0fNU86Emu/54ccZ\nHNCrOz9Mm8Hzbw5r2gOxdEKP0peiyk6+LPoDr0XExzmrzgGmR8T19WzaKV0/KS99XLquNs/43JUR\nMYsk4HeiSJV2afrnkvKvUbXM+fsc4J6IqA28IyWdCrwk6fiImF5PuQ9FxB3p3xdI+inQFzgpIsZL\nmgl8HxFfQ/FBwubqe0o/dtlxO6647I/0/sWBDB78Fnf+9TZuuPnWOXlqamr4YswYvvzyf0QEw4cP\nIyLo2KkTHTt2ZNTIkTz4wH30+tkeLLf88vz7ww84+8zT2KHHjmyw4YYZHp01h8P33pq//eOtgiPo\nl267OPvtuhlnXZnfnQeTp/zInX8fxGWn70/NpB/4fuqP/PmsXzDo/ZG889HoOfl+c2AP3vxgJN//\nMI1dt+nGpafuw3n9H5+nb9nmXwuJFiV+b5aQ/0ZgfZJGFwCSugMnA5uVtNNmUmmB+AXgOOr+WNoG\nqB3xswmwkaTDctbX5l0DqNskm+vNvNeD0rKa3Vln9GOZZZapk3bAQQdzUJ+DF8TuF5juW2zBgwMe\n4/zfnsMfL72Y1ddYgyuvuoYDD+ozJ89TT/yDY485CklI4sjDkvfgvPMv5Le/u4DFWrfmheef44br\nrmHKlCmsutpq7N/7AM4+97ysDsuayS5br8eqHdtz9z8GFVz/i902B5LLy4WcdeWjzJ4d3HfFr2jT\nuhX/euNjTv3jQ3XybLFhF847bneWWqINwz8fx4kX3c+Dz7zTtAeSoVk1I5hV80mdtJhVX1ukeTXW\nwn32iQH868m6P6q+n5zfUC1QrnQ9sAewQ0TkXhrZHlgR+CKn4dQSuErSqRGxJjAWaC2pXV6ruGO6\njvT/+aOoWwLL5eRpvJ4N3Y+5KJF0B7BMROyfl74jSYBuTxJQnyXpnM//3MdExExJo4CrI+LadPvZ\nwBERcU9OmVcBm0REz/T1e8BjEXFR+noH4CVguYj4Lk3bAngLWCMixkg6Mt3PcvUcz+bAkDfeGsJm\nm29e9vti1an9lidlXQVbBM2e+jXTRzwEyeCjom7FnB+133N3/v0lum5QWttm+Ecf8Mt9d4J66poG\n4X2AHSNiZN669kB+f9WzJH3Gd0TEJ+kYn69JBms9lm7XFRgKbJMO1loP+AjYImew1m4kA79WLXaw\nVqW1iBvzLrB+RMzbEdSwbYB78l43dJJ+TRLoVwK+S9MWiksgZmYLpSbs0ZN0I3AwsDcwRVLHdNV3\nEfFjRNQANXnbzADGRsQnABExSdLtJK3kGmAycC3wekQMTvMMkzQQuFXS8UBrkjFH9xcbhKF6AnHt\nR3wZMEjSdcBtwBRgA2DXiOjbwPYHSBpCMuz9MGBLkmHt9fkU+AL4vaTfAV2B0+bvEMzMKlfpty81\n6DiSwbEv5aUfRdLqLaTQ5eF+wCxgANAGeAY4MS/PIcD1JKOlZ6d5TymlstUSiAMgIv6dXqq+FHiF\nJEB/BjyYnzfPhUAf4AbgK5JLFcPq2ya9xN0HuAn4AHgbOI9k5LWZmeVo6ikuI6LkO4LSfuH8tGkk\nA3PrbahFxESSBlrZKiYQR8RR9aS/TM7I6YgYAvysgXLm+TCALyOiVwPbzNOJGxGDgE3zknPrcRdw\nV31lmplVi1JuR8rdplJUTCA2M7NFVJVHYgfixlXGsHIzs4VUYw9xqG+bSuFA3IiIaNl4LjMzK1e1\nPwbRgdjMzDJXQXG1ZNUw17SZmdlCyy1iMzPLlgdrmZmZZceDtczMzDIkyhis1Sw1yYYDsZmZZarK\nr0w7EJuZWcaqPBI7EJuZWabcR2xmZpalMib0qKA47EBsZmbZqvIr0w7EZmaWsSqPxJ5Zy8zMLENu\nEZuZWaY8WMvMzCxDfvqSmZlZhqq8i9iB2MzMFgKVFFlL5EBsZmaZq6Q+31I5EJuZWaaqvY/Yty+Z\nmVmmVObSYJnSDpL+Iel/kmZL2rtAnm6SHpc0UdL3kt6StGrO+jaSbpA0QdJkSQMkdcgro72keyV9\nJ6lG0m2S2pZy/A7EZmaWreaIxNAWeB84AYh5dimtBbwKfAz0ADYCLgZ+zMnWH9gT6J3mWRl4JK+o\n+4BuQM80bw/glkZrl8OXps3MrOJExDPAMwBSwQvZlwBPRcS5OWmjav+Q1A44GugTES+naUcBQyVt\nFRGDJXUDegHdI+K9NE9f4ClJZ0TE2GLq6haxmZllSmX+V/b+ksC8J/CJpGckjZP0pqR9crJ1J2ms\nPl+bEBHDgTHAtmnSNkBNbRBOPUfSAt+62Po4EJuZWaZqB2uVusyHDsBSwNnA08BPgceARyXtkObp\nBEyPiEl5245L19XmGZ+7MiJmAd/m5GmUL02bmVnmGoqrTzz6EE889lCdtMmTvpuf3dU2Qv8eEdem\nf38o6SfAcSR9xwuMA7GZmWWrkcFXP+99ID/vfWCdtP98+B777rpduXucAMwEhualDwVqCx0LtJbU\nLq9V3DFdV5snfxR1S2C5nDyN8qVpMzPL1ILuI46IGcDbQNe8VesCo9O/h5AE655z6il1BToDg9Kk\nQcCykjbLKaMnyc+Kt4qtj1vEZmaWKVHGhB6NrU/u5V07J+uakjYBvo2IL4ArgAckvQq8COwO7AXs\nCBARkyTdDlwlqQaYDFwLvB4Rg9M8wyQNBG6VdDzQGrgOuL/YEdPgQGxmZhlrpoc+bEESYCNd/pym\n3wUcHRF/l3Qc8FvgGmA4sH9EDMopox8wCxgAtCG5HerEvP0cAlxPMlp6dpr3lFKOxYHYzMyy1QyR\nOL33t8Hu14i4E7izgfXTgL7pUl+eicBhDdemYe4jNjMzy5BbxGZmlqlyBl9V0tOaHIjNzCxb5UzQ\nUTlx2IHYzMyy1UyDtRYZDsRmZpapan8esQOxmZllrLrbxA7EZmaWKbeIzczMMlTd7WEHYjMzWwhU\nUgu3VA7EZmaWqWq/j9gza5mZmWXILWIzM8tWlXcSOxCbmVmmqjwOOxCbmVm2fPuSmZlZhpIWcamD\ntSqHA7GZmWWryq9NOxCbmVnmKiiulsyB2MzMMuU+YjMzswx5Qg8zMzPLjFvEZmaWKVHGpelmqUk2\n3FXpE3QAABRrSURBVCI2M7OKIqmFpIsljZQ0VdKnkn5XIN9Fkr5M8/xL0tp569tIukHSBEmTJQ2Q\n1KGp6+tAbGZmmaodrFXq0oBzgN8AJwDrAWcBZ0k6ae4+dTZwEnAssBUwBRgoqXVOOf2BPYHeQA9g\nZf6/vTuPt6qs9zj++QIqkoHTBUdwuISmopSKRg43LdKu2qQSTlho5HDJcMiuJkmmF42ch0RMcEDB\nEitSE7VMMRMoUxFJSyRUEJEIQfTwu388z9bFlplzzjrn7O/79eLl2Wutvc6zT6v13c+wngfurrcP\nnrlp2szMSrbmg7VW0Ti9LzAuIu7Lr2dI6ksK3IqBwJCI+BWApOOB14EvAndJag98HegTEb/Lx5wI\nTJW0d0Q8uYYFXiHXiM3MrFQNUCN+HDhIUtd0fu0O9ALG59fbA1sAEypviIh/AX8khTjAnqTKavGY\nacCMwjH1wjViMzMrVQNMrHUJ0B54XlIdqdL5vxExOu/fAghSDbjo9bwPoBOwJAf0io6pFw5iMzMr\n1yqSeMyddzD2rtHLbJs/f/7Kzng00BfoAzwH7AFcIWlWRIxax9LWOwexmZmValUTehx1dF+OOrrv\nMtv+PGUy++2754reMhS4OCLG5NfPStoOOBcYBbxGiv5OLFsr7gRMyT+/BqwvqX1VrbhT3ldv3Eds\nzdado+8ouwjWTNXNe6HsIlhBA/QRtwPqqrYtJWdeRPydFKYHfVAGtQd6kvqXASYB71Ud0w3oDExc\npw9cxUFszdaYOx3Etnbq5k0vuwjWsH4JnCfpUEldJH0JOAP4eeGYy/Mxh0naDRgJzATGwfuDt24C\nhkk6UNIngRHAY/U5YhrcNG1mZk1APc+UdRowBLgG6AjMAq7L2wCIiKGS2gE3ABsDjwKHRMSSwnnO\nINWsxwIbAPcBp9ZvUR3EZmZWtnoeNh0RC4Hv5H8rO24wMHgl+98BTs//GoyD2MzMSlXrqy85iJuu\ntgDTnp9adjmarPnz5zNl8uSyi9EkLX17TtlFaNKibon/RsuxdPG8yo9tG/P3Tnt+6hrHaku6Nyoi\nyi6DLUeeju22ssthZjXpmIi4vaF/iaTOwFTSKOe18Tawc0TMqL9SNT4HcRMlaTOgN/APYHG5pTGz\nGtEW2A64PyLmNsYvzGG8+Vq+/Y3mHsLgIDYzMyuVnyM2MzMrkYPYzMysRA5iMzOzEjmIzczMSuQg\nNjMzK5GD2GqWpI3LLoM1H9Iq1vsxW0sOYqtJkq4DHpO0RdllsaZL0s8kDQeIiHAYW0NwEFutupy0\nmsrtDmNbnrwyzzPA4ZIuA4exNQxP6GE1S9KOwG+BGUCfiHit5CJZEyOpA/A14IfALRExKG9X+OZp\n9cQ1Yqspkt6/5iPiReBzQBdgtGvGViGpNUBEzAdGA+cDJ0j6cd7umrHVGwex1ZSIWAogqaek/4iI\nvwEH4zC2goioA5DUMSLeAu7AYWwNxEFsNUXJfsBEoJ+kzXLN2GFsy5B0FPBPSbs7jK0hOYitRau+\nSUbyKDAEOIcUxpsXwnhb0gCurRu/tFaWYpdFwUPABODXkrpXhfHxki6FdE01XkmtJXIQW4slqWvx\nJlnp9wOIiAuAK0k31WLN+LPAPsD3Gru8Vg5JHSpdFoVtrSLiDaAv8GfggaowPg8YJOn0xi+xtTQe\nNW0tkqSngTnAwbn58BxgNnBHRCwuHHcBKXS/B4yKiNmStgJer/QTWssl6VFgckQMzK8HAGMj4o3K\nyGhJmwKjgB5A74j4a962P/BLXye2rhzE1uLkWsopEbFzft0auAo4CTgOuKcqjMcAvYDrgKtyrQdJ\nrX2TbbkkDQUOj4id8ustSY+zbQD0jIg3C2HcmdRUvRg4PiImF87TJiLeK+EjWAvhpmlrid4EOkjq\nLOki4NqIOAUYDtwEfElS28LxM4G3gE8B8ysbHcItXh0wDUDSIODzwNHAXNKsa5sWujbmAC8A2wMX\nFk/iELZ11absApjVpzw462ngceBhYAtgF4CI+FYelDM8H/pwRLwKbEmqKU+ujIL1AJya8DLwBUmP\nAJ8GOkfELEn9SE3Rf5C0X0TMBZaQAroX6foyqzdumrYWSdJY4AhgCnBsRLxQ2Hc18FVgFtAaWB/Y\nLSLey4N0li7vnNbySJoKdAZGAGdHxKL8Za4bcDOwHTAO2IN0rewTEXW+Tqw+OYitRZG0HqmP72pg\nEumRpPbAoKp+veNIjyq1BS7MIew+4RohaX3go8BTwB9IQTsauDEiZudj2gMXAx2BhcBJEfGuQ9jq\nm4PYmr3ijbG6WVnSl0iDtDakKoyrzuEQbuFWFqB5go7PA7cBwythnPe9f214YJY1BAexNWvF4JV0\nMrAXaWDNcxFxa97+ReBkUk35zIiY4n7g2lL1Ze1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa73fb9ce10>"
      ]
     },
     "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": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[1550  819]\n",
      " [ 748 7576]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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j4vCC9MHAcRGxuqQbgGUjYs+89ZcC60XENunrccClETE0L8/rwH0RcbakvwJ9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\nJfp/TFhWhXJBOKQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa74501e278>"
      ]
     },
     "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": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[1308 1061]\n",
      " [ 665 7659]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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8PWgwN117Jb0P2pclixezyaab8eizL9J+89RTR1YmX305moP226v0/Lnysv8D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\nm5mZlejfPxjgVyXuQmIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7445164a8>"
      ]
     },
     "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": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[1526  843]\n",
      " [ 725 7599]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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48ejD+O23X1mn+/oMfOSpWd5Tm3dd1n8A5//1bE46vi8//PA9nTsvxWGHH8lp\nf/5LrdsUtqbmn39+Xn/tFa69+krGVlXRoUNHNt1scwa/+CpLLLFEYx9CJu6/dyD333dPjbTx48Zl\nUpfcZK1yt2kq5oZAPB4o9q3YjqQbOWdqwfpg1q71uvIsDLwF7Musv6UaujUbRfbRqpKCzv7bxayx\n9rqzX6O5zEabbs6IHybVuv72+/5d7/ZffD+x3v20bNmSM879O2ec+/ey62jzhoUWWogLL76MCy++\nrORtPhz+vxqvO3XuzIOPPF5L7qap99770HvvfWqkvffuO2y+8fpzvC7NfbLW3BCIPwG2LZLeHfi0\nAffzDvBH4IeI+KWM7TYseL0x8Fk9reEfgM65F+k49YIFeeaFGeFmZtbI5obJWtcCq0rqL2lNSatK\nOhHYG7i0AfdzF/Aj8KikzSStIGlLSVdIWqqO7ZaTdGlar32AY4H+9ezreeBYSetIWo/kGKcU5Gk6\nP+fMzGaDVNnSVGTeIo6IEZJ6ABcAzwKtgeHAHyLi2XKKqistIn5N93MR8CCwCPAN8BxJ93htbgcW\nAIYC04B/RsSN9dTlJOBm4GXgW+B44Pcl1NfMrPmpZBZ0E4rEmQdigIh4G9i+jvWHFEnrV/C6S5E8\nvy94/T0wS1l17QeYms6aLnpLnsJ6pGnfATsUJC+Wt34kUHgNtJlZs1RJC7cJxeG5omvazMyaMVHB\ntcR1lSedk95YKX/5uCBPN0mPpjdr+kXSm5KWyVvfRtLV6Q2kJkh6QFKHgjLaS7pLybMGqiTdKGmh\nco/fgbhu7j42M2tkjTRG/BHJjZY6pctmM/enlYBXgI+BHsCawPnAb3nb9wd2AvZK8yxFMqyZ726g\nG8k9LXZK811f7vHPFV3Tc6uI2DrrOpiZNXVqIVq0KPPypfrzT6vjRkt/I7kB1Bl5aSNmlJ08Re9Q\noE9EvJSmHQIMk7RBRAyV1A3oBXSPiHfTPH2BJySdHBGjSz0Wt4jNzCxTjdQiXkXSN5I+l3SnpGWT\nfUkkrdfPJD0taYykNyTtlrdtd5KG6nO5hIj4BBhFcgkrJE/cq8oF4dRgkp7Uwste6+RAbGZmmWqE\nxyC+QfIsgl7AkcCKwCvp+G0Hkhs8nQY8SXIfi4eBhyRtnm7fieTWxYVX1IxJ1+XyfJ+/MiKmk9yO\nuRNlcNe0mZllqr4W7tiPXmDcf1+okTZ9cu131ouIQXkvP5I0FBhJclOnp9P0RyLiyvTvDyRtQhK0\nXym3/rPN1jzwAAAgAElEQVTLgdjMzOZq7dbYinZrbFUj7dfvPuPzm4peVTqLiBgn6VNgZZIbO00D\nCp8hP4zkyXyQPKegtaS2Ba3ijum6XJ7CWdQtSS5VLXl8GNw1bWZmGWvsxyBKWpgkCH8bEVNJHpe7\nWkG2VUlazQBvkwTrnnllrAYsR/LgH9L/t5OU/zCAniRXY71ZcuVwi9jMzLLWwHfWknQJ8BhJYF0a\n+CvJQ4Fyj5u6BLhH0ivACyQ3YNoZ2AIgIsZLugm4XFIVybPrrwRei4ihaZ7hkgYBN0g6iuSukAOA\ngeXMmAYHYjMzy1gj3FlrGZJrfBcneQjPq8BGEfETQEQ8IulI4M/AFSQPH9ozIobkldEPmA48QPK4\n3KeZ9Q6L+wJXkcyWrk7zHl/ekTgQm5lZxnJ31ip3m9pExD51rM7luRW4tY71k4G+6VJbnrHA/vXt\nqz4OxGZmlqnmfq9pB2IzM8tUuZOvcts0FQ7EZmaWKbeIzczMstTMn0fs64jNzMwy5BaxmZllKpk1\nXf42TYUDsZmZZcqTtczMzDLkyVpmZmYZcovYzMwsSxW0iJvSILEDsZmZZaqhb3E5r3EgNjOzTHmM\n2MzMLEMeIzYzM8tQcw/EvrOWmZlZhtwiNjOzbHnWtJmZWXZEBV3TTSgSOxCbmVmmPGvazMwsQ819\nspYDsZmZZcotYjMzswxJooVbxGZmZtlwi7gEkrYrtcCIeKby6piZWXPje02X5ukS8wXQssK6mJmZ\nNThJpwN/B/pHxIlp2kLARcBuwOLACODKiLg+b7s2wOXA3kAbYBBwdER8n5enPXAVsDNQDTwIHB8R\nE0utX6mBeIFSCzQzMyuHBC0aqWta0vrAEcD7Bav+CWwJ7AuMBLYDrpX0TUQ8nubpD+wA7AWMB64m\nCbSb55VzN9AR6Am0Bm4Frgf2L/VYSrrFZURMLrYAU4ukmZmZlSx3+VK5SwnlLgzcCRwGjC1YvTFw\nW0S8EhGjIuJGkmC9QbptW+BQoF9EvBQR7wKHAJtKyuXpBvQC/hQRb0XE60BfoI+kTqUef9n3mpbU\nQtIpkj4HfpPUJU0/R9KB5ZZnZmbNW26yVrlLCa4GHouI54usex3YVdJSSR20FbAKSfczQHeSXuPn\nchtExCfAKJIgDrARUJUG6ZzBJMO0G5Z6/JU89OE04BiS/vZpeemfAkdWUJ6ZmTVjqvC/OsuU+gDr\nAGfUkqUvMAz4WtIU4EngmIh4LV3fCZgSEeMLthuTrsvl+T5/ZURMB37Oy1OvSi5fOgQ4IiKekdQ/\nL/09oGsF5ZmZWTNW3xjxN0MH8c1/al6QM/XXX+ooT8uQjO9uExFTa8l2HEmrdWeSVm4P4BpJ39bS\ngm40lQTiZUlav8W0mY26mJlZM1TfQx+W2XB7ltlw+xppY0cO5+ULDqhtk+7AksA7mllwS6CHpGOB\ndsAFwO4R8VS6/iNJ6wInA88Do4HWktoWtIo7putI/9+hxrFILYHF8vLUq5Ku6U+Y2T+ebw/ggwrK\nMzOzZqwRxogHA2uSdE2vnS5vkUzcWpskKLcCphdsN52ZcfFtkuHXnjPrqdWA5YAhadIQoF0awHN6\nklzm/Gapx19Ji/hvwPWSOqQV3jGt3OEkwdjMzKxkLSq4xWVd+dNreD/OT5M0EfgpIoalr18CLpXU\nl+TypS2BA4ET0jLGS7oJuFxSFTABuBJ4LSKGpnmGSxoE3CDpKJLLlwYAAyOi5BZx2YE4Ih6QNBY4\nh+TXQn+S8eHeeU18MzOz0lRwi8sKbq0VBa/3Bv5B0kpejCQYnxER/8rL04+klfwAydDr0ySTlfPt\nS3JDj8EkN/R4ADi+nIpVdK/piBic7hRJiojCAzQzM5trRMTWBa+/B/5UzzaTSWZX960jz1jKuHlH\nMRU/9EHSGkC39O+PI+K/s1MRMzNrnpIx33KfvtRIlclA2YE4vVvIHSQD0r+myfNLegE4ICK+a8D6\nmZlZE5c89KH8bZqKSmZN3wi0B9aNiIUiYiHg98CiwA0NWTkzM2v6cpO1yl2aikq6pnsCm0XEjBto\nR8T7ko4GXmqwmpmZWbPRdMJq+SoJxN/Wkh6UcQGzmZkZzHzoQ7nbNBWVdE2fDgxIJ2sBMyZu9Se5\nD7WZmVnJWqiypakoqUUs6TtqXoPVHnhfUm6y1gLAFOAK4P4GraGZmTVpzb1FXGrX9LmNWQkzM2ve\nmlBcLVtJgTgirm/sipiZmTVHFd/QA0BSi8IyImLKbNXIzMyalebeNV32ZC1JC0i6VNIoknHhXwsW\nMzOzkjX3yVqVzJr+B7ArcAZJID4mTRsDHNpwVTMzs+Ygd4vL8pasa91wKuma3gM4NCKek3QdMDgi\n/ifpc2Av4LYGraGZmTV5TSiulq2SFvESwGfp3+NJLmUCeBHYqgHqZGZmzUhzv8VlJYF4BLBc+vcn\nwJ7p371IArOZmVnJcg99KGvJutINqJJAfAewfvr3JcCJksYDV5Pc0MPMzKxk5Y8Plz/Lem5W9hhx\nRFyU9/dT6e0t1wf+FxFDG7JyZmbW9OVaueVu01TM1nXEABHxGTPHjM3MzKwMpd5r+ohSC4yIf1Ve\nHTMza25UweSr5tg1/dcS8wXgQGxmZiVz13QJIqJzY1fEilts4TZ0XHT+rKth85j26x+bdRVsHlQ9\n6YdM9isquMVlE5o3PdtjxGZmZrNDlH8JT9MJw5VdvmRmZtZgGvsWl5JOl1Qt6fKC9PMkfStpkqRn\nJa1csL6NpKsl/ShpgqQHJHUoyNNe0l2SxkmqknSjpIXKOX4HYjMzy1RjPvRB0vrAEcD7BemnAcem\n6zYAJgKDJLXOy9Yf2Ink9s09gKWABwt2cTfQDeiZ5u0BlPXoYAdiMzPLlCoIwqW0iCUtDNwJHAaM\nLVh9PHB+RDweER8BB5IE2t3TbduSPMioX0S8FBHvAocAm0raIM3TjeSukn+KiLci4nWgL9BHUqdS\nj9+B2MzMMtWId9a6GngsIp4v2N+KQCfguVxaRIwH3gQ2TpPWI5lHlZ/nE2BUXp6NgKo0SOcMJrmC\naMNSj7+iyVrpr4EjgJWA/SLiW0l9gC8j4o1KyjQzs+apBeU/X7i+VmQak9YhCaiFOpEEyzEF6WPS\ndQAdgSlpgK4tTyfg+/yVETFd0s95eepVdiCWtCtwL/AAya+C3LU1HYD9gZ3LLdPMzKw2w156nOEv\nP1EjbfLECbXml7QMyfjuNhExtXFrN/sqaRGfAxwbETdJ2j0v/VXgjIaplpmZNRf13dBj9S13ZvUt\na7bxxvzvv9x+wl61bdIdWBJ4RzP7sFsCPSQdC3QluQKqIzVbxR2BXDfzaKC1pLYFreKO6bpcnsJZ\n1C2BxfLy1KuSMeKu5PWZ5xnLzGcTm5mZlSR3i8tylnrGiAcDa5J0Ta+dLm+RTNxaOyK+IAmUPfPq\n0JZkXPf1NOltYFpBntVIHgM8JE0aArSTtG7evnuSBPk3Sz3+SlrE3wMrAl8WpG9M8qxiMzOzkrWg\n/FZhXfkjYiLwcX6apInATxExLE3qD/xF0v9I4tn5wNfAo2kZ4yXdBFwuqQqYAFwJvJZ70mBEDJc0\nCLhB0lFAa2AAMDAiSm4RVxKIbwH6SzqQZLB78fTXwKXAxRWUZ2Zmzdgcutd01HgRcbGkBUmu+W0H\nvALsEBFT8rL1A6aTzIlqAzwNHFNQ7r7AVSSt8Oo07/HlVKySQPw3oBVJk3x+4A2S5vuVEfHPCsoz\nM7NmbE48fSkiti6Sdi5wbh3bTCa5LrhvHXnGkkxUrljZgTgiqoGzJF0IrAYsDHwYEVWzUxEzM2ue\nRAUt4kapSTYqfuhD2gf/TgPWxczMmqFyblmZv01TUcl1xE/WtT4idqy8OmZm1tzMia7puVklLeKR\nBa9bkUwRXxkYONs1MjMza0YqGSM+qli6pL/TtLrtzcxsDphDs6bnWg350IdbgMMbsDwzM2sGGvMx\niPOCiidrFfF7YK6/p6eZmc191Iw7VCuZrHV3YRLQGdgU39DDzMzK1BhPX5qXVNIiLny7qoH3gMsj\n4t+zXyUzM2tOfPlSGdKnSvwT+CQixjVOlczMrFmp/yEORbdpKspq3UfEdJL7cS7eONUxM7PmprlP\n1qqkm/1jYNmGroiZmTVPucuXyl2aikoC8anApZK2kdReUuv8paEraGZmTZuo4HnETWiWdSWTtQYV\n/L9QywrrYmZm1uxUEoh3aPBamJlZs+VZ0yWSdDZwaUTU1hI2MzMrm29xWbpzSJ49bGZm1mBaoIqW\npqKcrummc9RmZjb3qGQWdBOKSOWOEUej1MLMzJotjxGX51NJdQbjiFhsNupjZmbNTO6SpHK3aSrK\nDcTnAL61pZmZNagmFFfLVm4gvicivm+UmpiZWbOUdE2X2yJupMpkoJxZ0x4fNjOzuZ6kIyW9L2lc\nurwuaft03XySLpL0gaRfJH0j6TZJnQvKaCPpakk/Spog6QFJHQrytJd0V7qPKkk3Slqo3PqWE4ib\n0O8PMzObWzTCvaa/Ak4Dfg90B54HHpXUDVgQWAf4K7AusAewGvBoQRn9gZ2AvYAewFLAgwV57ga6\nAT3TvD2A68s9/pK7piOiKT2H2czM5hKi/Acf1BWHI+KJgqS/SDoK2CgibgF61ShLOhZ4U9IyEfG1\npLbAoUCfiHgpzXMIMEzSBhExNA3qvYDuEfFumqcv8ISkkyNidKnH4uBqZmaZUvo84nKXEstuIakP\nSUt4SC3Z2pEMv45NX3cnaag+l8sQEZ8Ao4CN06SNgKpcEE4NTsvZsLQjT1Ryr2kzM7MGI8of+6wv\nv6Q1SALv/MAEYI+IGF4kXxvgQuDuiPglTe4ETImI8QXZx6TrcnlqTF6OiOmSfs7LUxIHYjMzy1R9\n1xEPefoRhgyqOYQ76ZcJ9RU7HFgbWBT4A3C7pB75wVjSfMD9JK3YoyuqfANwIDYzs8zV1cLdZPvd\n2WT73WukjRj+IWftv2Ot20TENOCL9OW7kjYAjgeOghpBeFlg67zWMMBooLWktgWt4o7pulyewlnU\nLYHF8vKUxGPEZmaWKVHBrOnyd9MCaAM1gnAXoGdEVBXkfRuYRjIbmnSb1YDlmDnOPARoJ2ndvO16\npofzZjkVc4vYzMwyVc7kq/xt6lj3d+ApkslViwD7AVsA26VB+EGSS5h2BlpJ6phu+nNETI2I8ZJu\nAi6XVEUyxnwl8FpEDAWIiOGSBgE3pDOyWwMDgIHlzJgGB2IzM2t6OgC3AZ1Jbsv8AbBdRDwvaXmS\nAAzwXvp/kYwTbwW8nKb1A6YDD5C0pJ8GjinYz77AVSSzpavTvMeXW1kHYjMzy1QLyh8nrSt/RBxW\nx7qRQMv6yo+IyUDfdKktz1hg//rKqo8DsZmZZauCrumm9JQIB2IzM8tUY1xHPC9xIDYzs0wlM6HL\nnazVSJXJgAOxmZllqqHHiOc1DsRmZpYtjxGbmZllx2PEZmZmGcrdWavcbZoKB2IzM8tUC0SLMkNr\nufnnZk1pvNvMzGye4xaxmZllSxXMvWo6DWIHYjMzy5bS/8rdpqlwIDYzs0ypghZxE7p6yYHYzMyy\n1dwnazkQm5lZtjxGbGZmlh13TZuZmWUoubNWuZO1mg4HYjMzy1QLoEWZkbUp3QSjKR2LzcO6rrIi\nC7ZuMcvS7/i+TJs2jTPPOI31112LJdotTJfll+awQw7iu+++q1HGdj23rLHtQm1acvyxR2d0RNZY\nhj1+LhPfvnKW5fLTegPwr7/uP8u6hwccVaOMFZZenHsuPYyRz/2D0S9fwu0XHsKS7ReukWedrsvw\n2DXH8O1LFzPq+X8w4Mw+LDh/6zl2nNZ8uEVsc4XX3niL6dOnz3j9348+ZOcdtmOv3n9k0qRJfPD+\ne/z5rHNYc821qKqq4qR+x9F7z914dcjQGdtI4k+HHcHZfz2fiABgwQUXnOPHYo1r0/0uoWVe8+l3\nqyzF49ccy4PPvANABAx67WOOOOfOGd2Xk6dOm5F/gflb8fg1x/LBp1/T6/ArEHDuMbvw4BVH0uPA\nSwHotERbHr+2L/c//RYnXHgfbRean0tP+QM3nLc/+51685w61Gak/OuIm1LntAOxzRUWX3zxGq8v\nfvwxuqy0EptttjkAjz05qMb6f15xFT023ZCvv/6aZZZZZkb6AgsuyJJLLtn4FbbM/DxuYo3XO/VY\nky++/pHX3v18RtqUKdP4seqXottvvPZKLNd5MTbY+x9M+m0KAIedfQffvXQxW6y/Ki/951N22HwN\npkydRr+L7p+xXd8L7uE/953BCksvzpff/NQIR9Z8NffJWu6aLiBphKTjytxmNUlDJP0q6Z0StzlI\nUlVltWzapk6dyr0D7+KgQ/5Ua55x48YiiXbt2tVIv3fgXSzbeUnWW2dNzv7Ln/n1118bu7qWofnm\na8HeO67HbY+8XiN98/VW4cvBf+e9h/5C/zP+SPu2M3tG2rSejyCYMm1mK3nylGlUVwebrNtlRp6p\n06bXKPO3KVMB2GTdlRrrcJotVfhfU9FkArGkWyQ9VCR9C0nVkto24u7/CvwCrAL0LGO7aJzqzNse\nfeRhxo0bx/4HHFR0/eTJkznrz6ezd599WXjhmeN6ffbZj5tvu5NBg1/klNP/zN133cGhBx8wp6pt\nGdhtq7VZdOEFuPOxN2ekPfPafznsrDvY4YgrOfOKR9m8+yo8kjdGPPTDEUz8dQp/P3535m/TigXn\nb82FJ+5Bixai0xKLAvDifz6l4+KLcMIBPZlvvha0W2QBzu+7KxHQeYnG/CppnlqosqWpaC5d040d\n8FYCHo+Irxt5P83C7bfeTK/td6BTp06zrJs2bRr79ekNEldcdU2NdYf86bAZf6/+u9/RuXNndtiu\nJ1+OGMEKK67Y6PW2Oe/A3TZm0GsfM+anCTPSHnz23Rl/D/tiNB999i0fP3YOPdZbhZff+oyfxk5k\nv1Nv4so/783R+2zJ9Opq7nv6bd775Guqq5OviuFfjObws+/kopP25Ly+uzJt+nSuGfgS3/88YUYe\na0jNe4y4ybSISyVpM0kvS5okaaSkKyTVOqMnbU0fKenJdJvPJe2Vvx74PXCOpOmSzi7WCpe0dpq2\nXOMe4bxt1KhRPP/cYA750+GzrJs2bRr79unN1199xeNPPVOjNVzMeutvQETw+ef/a6zqWoaW7dSe\nrTdcjVseer3OfCO//Ykfx05kpWVnzh144c1PWHO381h269NZZqvTOfzsO1hqyUX58psfZ+S5f9Db\ndNnuTLr0OpOltzqNC/71JEu2X5gRHh9ucLkx4nKX2svTGZKGShovaYykhyWtWkf+69Lv5+MK0ttI\nulrSj5ImSHpAUoeCPO0l3SVpnKQqSTdKWqic428OgXjGxyVpJeAp4H5gDWBvYFNgQD1lnJdusxZw\nF3CPpNXSdZ2Aj4FL078vTdOL/Wz2T+l63H7rzXTo2JHtd9ixRnouCH/5xRc8+cxztG/fvt6y3nv3\nXSTRqVPnxqquZejA3Tbm+58n8PSr/60z39Id2rH4ogsx+sfxs6yrGj+JCRN/Y4v1V2XJ9gvz+Esf\nzpLnx6pf+PW3qfTu1Z1fJ0/luTeGN9gxWEIVLnXYnOR7fUNgG6AV8IykBWbZt7RHmu+bIuX0B3YC\n9gJ6AEsBDxbkuRvoRjIsuVOa7/q6q1dTU+ua3kXShIK0lnl/nw7cGRG5wPuFpBOAFyUdFRFTain3\nvoi4Jf37bEnbAn2BYyPie0nTgF8i4gdILqOx8kUEd9x+KwcceDAtWsz8jTht2jT2+eNevP/+ezz0\nyONMnTqVMWPGALDYYovRqlUrRnzxBffecze9tt+RxRZfnA8/eJ/TTjmRzXtswe/WWCOrQ7JGdMCu\nG3LHv9+ccakawILzt+bM/9uBR557jzE/jafLskvyt+N357OR3/PskI9n5Nt/lw35ZMRofqj6hY3W\n7sIlJ+/FlXc+z+ejfpiR5//+2IM33v+CX36dzDYbdeOCE3bjzP6PMmHib3P0OJuDFhItyvzerCt/\nRNT4JS/pYOB7oDvwal760sAVQC/gyYJt2gKHAn0i4qU07RBgmKQNImKopG7ptt0j4t00T1/gCUkn\nR8ToUo6lqQXi54EjqfljaSPgjvTvtYE1Je2ftz6Xd0Xgk1rKfaPg9ZC0rEZ36sn9WHTRRWuk9d57\nH/bus8+c2P0c9fxzg/n6q6848KBDaqR/8803PPnE4wBsuN46QBK0JTFo8AtstnkPWrVuzfPPDebq\nAVcwceJElll2WfbcqzennXHmHD8Oa3xbb9iVZTq25/Z/D6mRPr26mjVWWZp9d96QdosswHc/jOPZ\nIcM4/9onmDateka+VVfoyHl9d6V92wUZ+e1PXHjD01w98MUaZa23xvKceeQOLLxAGz75cgzHnDeQ\ne59+a04c3hwxvepTpld9ViMtptfWFmlcJbRwi25ThnYkPZI/z9g+aTHdDlwcEcOKNKC6k8TI53IJ\nEfGJpFHAxsBQkvhSlQvCqcHpvjYEHi2lck0tEE+MiBH5CZKWzXu5MEmXwRXM+jmOasB65P7F5++j\nVSUFXXzpP1n397+f/RrNA3pusy0TJ0+fJX355Zcvmp5vmWWW4ZnnXmykmtnc5vk3h7PwerNeZTh5\nyjR2O/aaIlvUdPaAf3P2gH/Xmefws++oc/28rmX7VWnZvuawafWkH5jy6X0Z1ahxpAG3P/BqRHyc\nt+p0YEpEXFXLpp3S9YVjGmPSdbk83+evjIjpkn7Oy1OvphaI6/MOsHphsC7BRsCdBa/rul74B5Ig\n3BkYl6atW+Y+zcyajzqauM889gDPPlZzaPaXCbOO+dfiGmB1kvlAya6k7sBxzCXfy80lEOc+4ouA\nIZIGADcCE4HfAdtERN86tu8t6W2SsYX9gfVJxg5q8z/gK+BcSX8BVgNOnL1DMDNruuq6fKnXLr3p\ntUvvGmnDP3qPg3ffsu4ypauAHYHNIyL/5vSbAUsCX+V1SbcELpd0QkR0AUYDrSW1LWgVd0zXkf6/\ncBZ1S2CxvDz1ag6zpiGdrRwRHwJbkNx442WSVu251JwtV2xm8zlAH+B9kkDcJyKG17ZNRExL83dN\ntzkF8GClmVkRDX35UlKmrgJ2A7aKiMKhx9tJroJZO2/5FriYZPIVwNvANPJu0pReLbMcyTwh0v+3\nk5Tfsu5J0vh7kxI1mRZxRBxSS/pL5M2cjoi3ge3rKKdLkeRvI6JXkfTcNrMM4kbEEGCdguT8etwG\n3FZbmWZmzUVDT9aSdA2wD7ArMFFSx3TVuIj4LSKqgKqCbaYCoyPiM4CIGC/pJpJWchUwAbgSeC0i\nhqZ5hksaBNwg6SigNcllUwNLnTENTSgQm5nZPKrhp00fSdJT+WJB+iEkreFiivWG9gOmAw8AbYCn\ngWMK8uwLXEUyW7o6zXt8nbUr4EBcP9+Ew8ysEVXyEIe68kdE2cOuxXpDI2IyyT0jap1DFBFjSYYs\nK+ZAXI+IaFl/LjMzq1RzfwyiA7GZmWWuCcXVsjWXWdNmZmZzJbeIzcwsW3PgHpdzMwdiMzPLVENP\n1prXOBCbmVmmRAWTtRqlJtlwIDYzs0w1855pB2IzM8tYM4/EDsRmZpYpjxGbmZllqYIbejShOOxA\nbGZm2WrmPdMOxGZmlrFmHol9Zy0zM7MMuUVsZmaZ8mQtMzOzDPnpS2ZmZhlq5kPEDsRmZjYXaEqR\ntUwOxGZmlrmmNOZbLgdiMzPLlMeIzczMMuQxYjMzsyw180jsG3qYmZllyIHYzMwypQr/q7NMaXNJ\n/5b0jaRqSbsWydNN0qOSxkr6RdKbkpbJW99G0tWSfpQ0QdIDkjoUlNFe0l2SxkmqknSjpIXKOX4H\nYjMzy1Rusla5Sz0WAt4DjgZi1n1qJeAV4GOgB7AmcD7wW162/sBOwF5pnqWABwuKuhvoBvRM8/YA\nri/n+D1GbGZmmWvoId+IeBp4GkAqGrb/BjwREWfkpY2YUR+pLXAo0CciXkrTDgGGSdogIoZK6gb0\nArpHxLtpnr7AE5JOjojRpdTVLWIzM8uWKlwq3V0SmHcCPpP0tKQxkt6QtFtetu4kjdXncgkR8Qkw\nCtg4TdoIqMoF4dRgkhb4hqXWx4HYzMwy1RhjxPXoACwMnAY8CWwLPAw8JGnzNE8nYEpEjC/Ydky6\nLpfn+/yVETEd+DkvT73cNW1mZpkSdY/5/vuhe3nsoftrpE0YP252dplrhD4SEVemf38gaRPgSJKx\n4znGgdjMzDJVX0/zbnvuzW577l0j7aP332WXbTapdJc/AtOAYQXpw4BN079HA60ltS1oFXdM1+Xy\nFM6ibgkslpenXu6aNjOzbM3hMeKImAr8B1itYNWqwMj077dJgnXPGdWUVgOWA4akSUOAdpLWzSuj\nZ1q7N0utj1vEZmbW5KTX8q7MzJDdRdLawM8R8RVwCXCPpFeAF4AdgJ2BLQAiYrykm4DLJVUBE4Ar\ngdciYmiaZ7ikQcANko4CWgMDgIGlzpgGB2IzM8tYJZOvSsi/HkmAjXS5LE2/DTg0Ih6RdCTwZ+AK\n4BNgz4gYkldGP2A68ADQhuRyqGMK9rMvcBXJbOnqNO/x5RyLA7GZmWWrgqcv1ReH02t/6xx+jYhb\ngVvrWD8Z6JsuteUZC+xfd23q5kBsZmaZaubPfHAgNjOzbPl5xGZmZplq3m1iB2IzM8uUW8RmZmYZ\nat7tYQdiMzObCzSlFm65HIjNzCxTjXQd8TzDt7g0MzPLkFvEZmaWrWY+SOxAbGZmmWrmcdiB2MzM\nsuXLl8zMzDKUtIjLnazVdDgQm5lZtpp537QDsZmZZa4JxdWyORCbmVmmPEZsZmaWId/Qw8zMzDLj\nFrGZmWVKVNA13Sg1yYZbxGZmZhlyi9jMzDLlyVpmZmaZKn+yVlPqnHYgNjOzTDX3FrHHiM3MLFOq\ncKm1PKmFpPMlfSFpkqT/SfpLkXznSfo2zfOspJUL1reRdLWkHyVNkPSApA4Nccz5HIjNzCxbDR2J\n4a0TkW4AABPsSURBVHTg/4Cjga7AqcCpko6dsUvpNOBY4AhgA2AiMEhS67xy+gM7AXsBPYClgAdn\n51CLcde0mZllqhFu6LEx8GhEPJ2+HiVpX5KAm3M8cH5EPA4g6UBgDLA7cJ+ktsChQJ+IeCnNcwgw\nTNIGETG0rArXwS1im2fde8/ArKtg86jpVZ9mXQXLkxsjLnepw+tAT0mrJOVrbWBT4Mn09YpAJ+C5\n3AYRMR54kySIA6z3/+3debxVZb3H8c8XUBEHcrjgiKYRmori1bAcb1pk3bTMAWdMNJyuFQ7ZS3NO\nLxo5p4maooYeK7GycL6Z4TWBNBWR1ERCBRG5hCBy+N0/nmfrYnsYz7DO2fv79nVenr3W2ms/+7jc\n3/0M63lIldXiMZOAKYVjWoSD2DqshrscxLZyGmdNLrsI1rouBe4CXpS0ABgHXBERo/L+DYAg1YCL\n3sr7AHoCC3JAL+mYFuGmaTMzK93SKrh33/ULGu4atdi22bPfXdrpDgEOAwYCLwA7AFdKmhYRI5tZ\n1BbnIDYzs3ItY/DVwQMP5eCBhy62bcKE8ezWf6clPWUYcElENOTHz0vaHDgLGAm8mV+xJ4vXinsC\nE/LvbwKrSlq7qlbcM+9rMW6aNjOzUmkl/1mKbkBj1bZF5MyLiFdJYbr3h2VIg7P6k/qXITVnL6w6\npg/QCxjbvHe8ONeI26+uAJNenFh2Odqt2bNnM2H8+LKL0S4tem9G2UVo16Jxgf9GTVg0f1bl165t\n+bqTXpy4wvNkLeOz8TfA2ZKmAs8DOwLfBUYUjrkiH/N34B/AhcBUYDSkwVuSbgKGS5oFzAGuAp5o\nyRHT5BfzTzv8IfVvhH/84x//lPBzWBt9zvUi3b+7suWcC/Rq4rxrAMOBV/Mxk4HzgS5Vx50HTAPe\nA8YAn6ravxpwNfA2KYgbgB4t/XdQfjFrZyStBwwgfVObX25pzKxOdAU2B8ZExMy2eEFJvYD1V/Lp\nb0fElJYsTxkcxGZmZiXyYC0zM7MSOYjNzMxK5CA2MzMrkYPYzMysRA5iMzOzEjmIrW5J+kTZZbCO\nQ1rGej9mK8lBbHVJ0k+BJyS16CoqVlsk/VzSCICICIextQYHsdWrK0iz5tzpMLamSOoGPAfsJ+ly\ncBhb6/CEHla3JG0JPEha6HtgRLToiirW8UnqDhwKXATcGhFD83aFPzythbhGbHVF0ofXfES8DHwJ\n2AwY5ZqxVUjqDBARs4FRwDnA0ZJ+nLe7ZmwtxkFsdSUiFgFI6i/p3yLi78A+OIytICIaAST1iIh3\ngV/gMLZW4iC2uqJkd9J6ooMkrZdrxg5jW4ykg4F/StreYWytyUFsNa36QzKSx0lrj55JCuP1C2G8\nKWkA18ZtX1orS7HLouAR4GHgd5L6VoXxUZIug3RNtV1JrRY5iK1mSepd/JCs9PsBRMS5pEW+z2Hx\nmvEXgV2AH7R1ea0ckrpXuiwK2zpFxNukdcH/CjxQFcZnA0MlndL2JbZa41HTVpMkPQvMAPbJzYdn\nAtOBX0TE/MJx55JC9wfAyIiYLmkj4K1KP6HVLkmPA+Mj4tT8eAhwT0S8XRkZLWldYCTQDxgQEX/L\n2/YAfuPrxJrLQWw1J9dSToyIrfPjzsDVwHHAkcC9VWHcAOwK/BS4Otd6kNTZH7K1S9IwYL+I2Co/\n3pB0O9tqQP+IeKcQxr1ITdXzgaMiYnzhPF0iYmEJb8FqhJumrRa9A3SX1EvSxcB1EXEiMAK4CfiG\npK6F46cC7wKfB2ZXNjqEa14jMAlA0lDgy8AhwEzSrGvrFro2ZgAvAZ8ELiiexCFszdWl7AKYtaQ8\nOOtZ4M/Ao8AGwDYAEXFCHpQzIh/6aES8AWxIqimPr4yC9QCcuvAa8FVJjwG7Ab0iYpqkQaSm6D9J\n2j0iZgILSAG9K+n6Mmsxbpq2miTpHmB/YAJwRES8VNh3DXAgMA3oDKwKbBcRC/MgnUVNndNqj6SJ\nQC/gZuCMiJiXv8z1AW4BNgdGAzuQrpVdIqLR14m1JAex1RRJq5D6+K4BxpFuSVobGFrVr3ck6Val\nrsAFOYTdJ1wnJK0KrAU8DfyJFLSjgBsjYno+Zm3gEqAHMBc4LiI+cAhbS3MQW4dX/GCsblaW9A3S\nIK3VqQrjqnM4hGvc0gI0T9DxZeAOYEQljPO+D68ND8yy1uAgtg6tGLySjgd2Jg2seSEibs/bvw4c\nT6opnxYRE9wPXF+qvqztB2xC6iN+NSJeyNsvB/YFbieF8Yyqc/iasVbhILYOqyqELwC+C9wPbAR8\nGrgvIo7L+/cHBpOmsTyw2Gds9UPSpcBJwMuka+FvpPvHb8z7h5Fqxr8FhlVuZTNrTR41bR1SsYlQ\n0k7AdsBXI+KPeem6fYEbJM2PiFMiYnTuF+xP+hC2OlDVrLwTaea0ARHxZ0n9gCHAkHydjIyIMySt\nRRrANXvJZzZrOa4RW4ci6VBgVKEmfBQwCOgOfCUi3srbVwOOAk4DDo+Ip6vO4z7hGiZpm4h4vvD4\nTGArUvfEkYVw/gxwHhDA0ZWJXgoTebg52lqdJ/SwDiOPdL4YOL+wmMNcYD1gW+BzlWMj4n3gSdKI\n1/Wrz+UQrl2ShgO3StqzsHl14GjSNbJJZWPuH74H+CawcWG7Q9jajIPYOpLfAQ2k5sUL8wflL0l9\nw88Bx0v6QuH4aaSBW93avKRWputJ9/x+v3I9RMR5pL7hzYBjJBW/nL0CTAZWKZ7EIWxtxU3T1iFU\nRr1K+gRpgYY9SEvUnZ1rL18lraTUiXQ/6DTgcGBL0mQdrgHXgUqXg6QtgF8DbwKXRcRDef/pwH+T\nVt66j/RFbRip1aS/7w+2MjiIrd0r9NdVPmTX4aMwfoiPwvjLpAkYtieNeh0PXJif4z7hOlG4TrYE\nfkUK42ER8XDefxopfAFuA9YEBnpmNSuLR01bu1b8YMwfrl0jYlZezKGR1EyNpLMj4g+SFpL6kacD\nD1emIwT84VrDmrhOOkXEy5IOInVnnCmJiHg4Ii6X9C/gOtIUqD/3zGpWJgextVtVkzCcTFoPto+k\n20mLs18ICNgbCEnnRMRDkrqRaswnSVolIh4p6S1YG6i6Tg4BegOrSxodEU/l2dV+DZxRCOPrczfH\nT4DOkm6JiFnlvQurZw5ia7cKH66Xkm5Fuhl4nlST6UsafHNJPvwLwNqSTo2I+yTNJ/UDHiFpbETM\na/M3YG2icJ1cBhxEquXOBZ6UdERE3JnD+JfAUEmrRcT9EXGppAXA5cACSdd6gJaVwUFs7ZqkXUkf\nrl/PtZt+edfY/KH5rqQfkQbgrE4aLbswIh6QdBLwskO4dhUG8R0AHEa6Tv6SB+8dVjkuIl7JzdSP\nA/tIGhMRjRExXNIHwCMOYSuLg9jau27A1BzCBwM3ASdGxMi8Os42ETE2j4b9Vx601SUiFlYG51jt\nkTQAeKrQnLwJ8EAO4QNJSxgOybXh7sA6EfF3STsDbxQH8EXE1SW9DTPA9xFb+9cJ2FjSMcDPSGvG\nXp/37UEahLN5RMwpTMLg1XFqWJ6CcjgwPvfzQvrCtm4O4crawj/L+/Yn3VP8iYiY6lH01t44iK1d\nUFpH+GO/A08AE4ERwPCI+Gk+pitpRaV5wJTKwW5erH0RMYfUXTEDGJtvZ3uYND/0SOC8wnWyJnAw\nsIDC3NEOYWtP3DRtparMCRwRH+THJwL9JL0D3BwRkyTdQJpw4UuSngHWAQaSpiTsl/sIff9nHSj8\nd36JFMZ3kybm+E9gNNAT6CppG2Bt4IfABqS+Y09bae2Sa8RWmuo5gSWdRZpooQtwHHCzpP0j4rfA\nRaTZsm4DjgVmAjsW7v90CNcwSetBGiEtadU8BuA14B1gV+APpFuRbgMOIC1vOJw0eO+zhevEIWzt\njmfWstJI+jRwF2nyjatIU1JeGxFP5H7ABlKtZlhE3JufsynwBtBYHJhVzjuwtiBpd+AC4NyI+GNh\newPQhzTX+I+B+aS1hOcDOwOvkQb6LfJ1Yu2Zg9hKUTUn8L2kGm434KCImJKP6cFHUxBeDTQUa75u\nZqwPkvoAN5DuDf5hRIyTdA9pWcN9I+J1SVsDd5IneImImYXnu9vC2jUHsZWmKoxHAduR1oq9p3DM\n+sCtwBbAyb4lqT5J6k1qNWkkrT29BnBARPyjcMxWwIPA/0TEEWWU02xlOIitTS2pdiLpk6QJ+mcC\nF0XEY4V9PYEzSLekeLRrncphfB2p2fm4iGjI24tTXG5Gao72dWIdhoPY2kyxKVnSQGBz4EXg6YiY\nmj9o7yGtlnNJMYwL5/D9n3Usr6h0LWkRjx9FxJ/y9k5V3Ra+TqzDcBBbm6gK4WHAINIgrVWAZ0gD\ncSbmMG4gjZC+MiLGlFRka6cKzdSQWk+eKLM8Zs3l25esTUnaDvgkaXRrX9IKSusCP5H0mYiYTLo/\ntB8woLSCWruVr5H/IvUXXyGpb8lFMmsW14itVUnaizR4JiQdSboHeA5pdPT8fMzBpFmyPgC+l2vG\nmwLT3LxoS5JHSg8GTveoaOvIHMTWaiRtQJqi8u2I6C/p28BpwKrADoUJ+8kr4wwm1Y4PqoyGdV+f\nLQ/fomQdmZumrTXNBE4lLdL+u4i4Afg+8D5wtaQNKwfmEbB3An9m8bmjHcK2TA5h68hcI7ZWUVgn\ntguwD3AF8EpEfEXS4aQ+vonA9yPizSU9v21LbWbW9lwjthaVp6aszAncKU8r+BBpGsItJN0fEXeQ\nbkHpA1wsaePq8ziEzaxeOIitxUj6InBNHhldHcYPAt8BNpN0c0TcRpoxazfSrUxmZnXJQWwt6R3g\ns8BJeRm66jB+jLRCzs6SekfE9cDZwKVlFdjMrGwOYmsxETEOOJQ0BeF3qsK4c75daQywJbBD3teQ\n55vuXFa5zczK5CC2FhURfyXdhrQjcGohjCujnzsD44DXq57n0dFmVpccxNbiImICKYz/HThDUj/4\ncCWlK0jzBD9VXgnNzNoP375krSZPPXgVaUrLKaS1YgXsFREf+BYlMzMHsbWyPGnH3sDWwGRgZO4T\n7pIHcJmZ1TUHsbU5T1tpZvYRB7GZmVmJPFjLzMysRA5iMzOzEjmIzczMSuQgNjMzK5GD2MzMrEQO\nYjMzsxI5iM3MzErkIDYzMyuRg9isBJI2k7Qoz8eNpD0lNUpau4SyPCpp+FL2nytpwgqec5Gk/ZpZ\nrlsk/ao55zDrCBzEZln+4F+UA/F9SZMlnSOptf4/KU5r9wSwYUT83/I8cVnh2Qo8BZ9ZK+lSdgHM\n2pnfA4OArsC+wHXA+8Cw6gNzQEes/DyxqvySF8CYvpLnMbMOzDVis8W9HxEzIuL1iPgZ8BCwP4Ck\nQZJmSfqapOeB+cCmed9gSS9Impf/fULxpJI+K2l83v8U0I9CLTM3TS8qNk1L2jXXfOdKekfS7yV1\nl3QLsCdwaqEG3ys/Z1tJ90uaI+lNSbdJWq9wzm552xxJ/5T0vRX9A0naSdIDkmZIelfSY5U1p6ts\nlMvynqSXJX2z6jybSLor/01nSrpX0mYrWh6zjs5BbLZ084FV8+8BdAPOAI4FtgGmSzocOA84C9gK\n+AFwgaQjASStAfwGeA7YMR97eROvVQzmHUhfAp4DdgE+B4wGOgOnAmOBG4GewIbA65K6Aw8D4/Lr\nDAB6AHcXXuNyYHfga8CXgL3ysStiLeDnwOeB/sBLwP35fRZdADQAfYE7gFGS+uT31wUYA8wGds3n\nmgP8Ie8zqxu+4M2WQNI+pDC7srC5C3BCRDxXOO48YGhEjM6bXpO0DfBtYCRwOKkZenBELAAmStqU\n1Oy9JKcDf4mIUwrbJhVecwHwXkTMKGw7GRgfEecUtg0Gpkj6FPAG8C3gsIh4LO8/Gpi6HH+OD0XE\no8XHkoYAh5Bq6fcXdt0dEbfk338o6YvAKcDJwEDS6m/HF85zLDCL9OXgoRUpk1lH5iA2W9zXJM0B\nViGF5x3A+YX9C6pCuBuwJXCTpBGF47qQQgVSLfnZHMIVY5dRjh1YvCa7PLYHvpDLXxS5jN1I7+up\nD3dEzJI0iRUgqQdwMSl4e5Bq6asDvaoOfbLq8dhcRki15N5NlHW1XFYHsdUNB7HZ4h4BhgAfANMi\nYlHV/nlVj9fM/x5MIeCyxmaUo/p1lseawH2kpnNV7XsD6N2M8hTdBqxDqt1OIQ1me5KPmvCXx5rA\n08BhfLysMz5+uFntch+x2eLmRsSrETG1iRD+mIiYDkwDtoyIV6p+XsuHTQT6SioG1eeWcepngb2X\nsn8BqSZaNJ7Ub/1aE2WZB7wMLCT16wIgaR3g08t6n1U+D1wVEWMiYiLpS8v6TRy3SxOPJxbK2huY\n0URZq2vJZjXNQWzWfOcCZ0k6RVLvPHJ5kKTv5v13kpqHR0jaWtJXgKFNnKdYM7wE2FnStZK2k7SV\npCGS1s37/wH0zxODVEZFXwusSxoUtZOkLSQNkHSzJEXEXOAm4DJJ/yFpW+AWVrzmPhk4MpepP3A7\n8F4Txx0k6Zj8Nzkf2Bm4Ju+7A3gbGC1pN0mbS9pL0pWSNlrB8ph1aA5is2aKiJtITdPHkGqyjwFH\nA6/k/XNJo5S3JdUELyQ1H3/sVIVzTiaNau4L/C9pwo/9SDVaSKOfG4EXSCO3e0XEG6QRyJ1II5Kf\nBYYDswr3Op8OPE5qwn4g/z5uBd/yt0hN0+OAW0mD2arvgQ7SF5SBwDPAEcDAiHgxv795wB6kpu1f\n5vdxI6mPeLkmNTGrFVr5uQjMzMysuVwjNjMzK5GD2MzMrEQOYjMzsxI5iM3MzErkIDYzMyuRg9jM\nzKxEDmIzM7MSOYjNzMxK5CA2MzMrkYPYzMysRA5iMzOzEv0/lvtCi7XhB+0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa74453b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot non-normalized confusion matrix -- SVC\n",
    "plt.figure()\n",
    "plot_confusion_matrix(s_cm_svc, 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": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tn_rf, fp_rf, fn_rf, tp_rf = s_cm_rf.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42211903756859437"
      ]
     },
     "execution_count": 119,
     "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": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tn_lr, fp_lr, fn_lr, tp_lr = s_cm_lr.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34571549176867877"
      ]
     },
     "execution_count": 116,
     "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": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tn_ab, fp_ab, fn_ab, tp_ab = s_cm_ab.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44786829886027862"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positive_rate_ab = fp_ab / (fp_ab + tn_ab)\n",
    "false_positive_rate_ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tn_svc, fp_svc, fn_svc, tp_svc = s_cm_svc.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35584634867032505"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positive_rate_svc = fp_svc / (fp_svc + tn_svc)\n",
    "false_positive_rate_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression 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": 196,
   "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": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import cycle\n",
    "colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zOVF2F8rmRNk0NocTlIvA0kLO3LwGlx1cNoV22KyfCpfdhsuu2NqlPYVBftjsCpsdlM2G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kJgbW0NPT092NvbY/jw4Th06JDacS5ZsgRbtmzB22+/jdTUVIwaNeqJ5qsOxhgYU7+7wa5duxAc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\n3f8D4J7Vs/euxg0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa73fb0f978>"
      ]
     },
     "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": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G8tErUevL4D1glPV4Z35rDpS15t625V1HtYlFKaUaFw1AVKPgaXrZscN6nJoK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EAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa73fb28518>"
      ]
     },
     "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": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TIv9gkuJ6MG93OJt0K/Z6tiChf0tadY2kdbcoWnWNom3PaOI7upoBpNz27XDN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\nwzkAMTMzs4ZzAGJmZmYN939Pb8ZR7gaq2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa73e924c18>"
      ]
     },
     "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": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h5c5cX5LXnU7TgA5c3aU/LzwFE98u3M7PXFiQgNQHTiv4k9OBOjBh9KPMHV94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\npfsHEPzh9iJb8N4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa73e8ab588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve(s) for SVC\n",
    "plt.figure()\n",
    "plot_roc_cv(s_clsvc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Based on the above results, logistic regression provides the best mean roc area under curve, followed closely by the svc classifier."
   ]
  },
  {
   "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) logistic regression is the more robust model out of the trained classifiers."
   ]
  },
  {
   "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": 237,
   "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": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s_feature_names) == s_X.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "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": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qF2U4yfi1JGe11v7UfuJdo6qOTTKf4ar2r2cI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\nbwYjw2Wlzs8rAETE2iy8VFHvObI98O2I2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa73e8a3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature dev_mean_score (0.054988)\n",
      "2. feature overall (0.032803)\n",
      "3. feature rev_length (0.026713)\n",
      "4. feature ari_score (0.009627)\n",
      "5. feature sent_count (0.008342)\n",
      "6. feature all_caps (0.007921)\n",
      "7. feature little (0.005769)\n",
      "8. feature easy (0.005595)\n",
      "9. feature use (0.004540)\n",
      "10. feature works (0.004311)\n",
      "11. feature buy (0.004005)\n",
      "12. feature just (0.003755)\n",
      "13. feature great (0.003749)\n",
      "14. feature perfect (0.003410)\n",
      "15. feature like (0.003393)\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
}