categorization-svm-2/feature_reduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation on new data with Bubble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import sys\n",
    "import csv\n",
    "import time\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from featurize import tokenize_all\n",
    "from tqdm import tqdm_notebook\n",
    "from unidecode import unidecode\n",
    "\n",
    "from collections import defaultdict\n",
    "from collections import Counter\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from scipy.interpolate import spline\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import LeaveOneLabelOut\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_selection import SelectPercentile\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "source = 'data/product_nuts_with_usage_and_product_id.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data_pn_usage = []\n",
    "\n",
    "with open(source) as csvfile:\n",
    "    readCSV = csv.reader(csvfile, delimiter=',')\n",
    "    for row in tqdm_notebook(readCSV):\n",
    "        data_pn_usage.append(eval(row[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/unidecode/__init__.py:46: RuntimeWarning: Argument <type 'str'> is not an unicode object. Passing an encoded string will likely have unexpected results.\n",
      "  _warn_if_not_unicode(string)\n"
     ]
    }
   ],
   "source": [
    "id_tokens = tokenize_all(data_pn_usage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Remove doubles\n",
    "- Include only those nut products if the usage appears in at least 3 products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# removes doubles\n",
    "known = set()\n",
    "no_doubles = []\n",
    "\n",
    "for d in tqdm_notebook(id_tokens):\n",
    "    tok = ' '.join(d['tokens'])\n",
    "    if tok in known: \n",
    "        continue\n",
    "    no_doubles.append(d)\n",
    "    known.add(tok)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "no_doubles[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# makes a dict where each tuple has one usage and all the product_ids linked to it\n",
    "k = [{x['usage']: x['product_id']} for x in no_doubles]\n",
    "dd = defaultdict(list)\n",
    "\n",
    "for d in tqdm_notebook(k): \n",
    "    for key, value in d.iteritems():\n",
    "        dd[key].append(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# checks if a usage is linked to at least 3 products\n",
    "allowed_usage = []\n",
    "for x in tqdm_notebook(dd.items()):\n",
    "    if len(set(x[1])) > 2:\n",
    "        allowed_usage.append(x[0])\n",
    "        \n",
    "set_allowed_usage = set(allowed_usage)\n",
    "\n",
    "complete = []\n",
    "for x in tqdm_notebook(no_doubles):\n",
    "    if x['usage'] in set_allowed_usage:\n",
    "        complete.append(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LABELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = [x['product_id'] for x in complete]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "K_fold = int(len(set(labels)) / 6)\n",
    "\n",
    "one = set(list(set(labels))[:K_fold])\n",
    "two = set(list(set(labels))[K_fold:(K_fold * 2)])\n",
    "three = set(list(set(labels))[(2 * K_fold):(K_fold * 3)])\n",
    "four = set(list(set(labels))[(3 * K_fold):(K_fold * 4)])\n",
    "five = set(list(set(labels))[(4 * K_fold):(K_fold * 5)])\n",
    "six = set(list(set(labels))[(5 * K_fold):])\n",
    "\n",
    "labels_6 = []\n",
    "for label in tqdm_notebook(labels):\n",
    "    if label in one:\n",
    "        labels_6.append(1)\n",
    "    elif label in two:\n",
    "        labels_6.append(2)\n",
    "    elif label in three:\n",
    "        labels_6.append(3)\n",
    "    elif label in four:\n",
    "        labels_6.append(4)\n",
    "    elif label in five:\n",
    "        labels_6.append(5)\n",
    "    elif label in six:\n",
    "        labels_6.append(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "text_per_item_pn_TRAIN = [' '.join(tokens['tokens']) for tokens in complete if tokens['product_id'] not in six]\n",
    "text_per_item_pn_VALIDATE = [' '.join(tokens['tokens']) for tokens in complete if tokens['product_id'] in six]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(min_df=1, binary=True)\n",
    "X_TRAIN = vectorizer.fit_transform(text_per_item_pn_TRAIN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vocab = vectorizer.vocabulary_\n",
    "vectorizer = CountVectorizer(min_df=1, vocabulary=vocab)\n",
    "X_VALIDATE = vectorizer.fit_transform(text_per_item_pn_VALIDATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_TRAIN = [x['usage'] for x in complete if x['product_id'] not in six]\n",
    "Y_VALIDATE = [x['usage'] for x in complete if x['product_id'] in six]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels_TRAIN = []\n",
    "for x in enumerate(labels_6):\n",
    "    if x[1] != 6:\n",
    "        labels_TRAIN.append(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:  (21021, 26314) Y:  21021\n",
      "X:  (119050, 26314) Y:  119050 Labels:  119050\n"
     ]
    }
   ],
   "source": [
    "print 'X: ', X_VALIDATE.shape, 'Y: ', len(Y_VALIDATE)\n",
    "print 'X: ', X_TRAIN.shape, 'Y: ',len(Y_TRAIN), 'Labels: ', len(labels_TRAIN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Feature Reduction **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TRAIN = []\n",
    "\n",
    "percentages = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "for precentage in percentages:\n",
    "    TRAIN.append([precentage, SelectPercentile(percentile=precentage).fit_transform(X_TRAIN, Y_TRAIN)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TRAIN_SPECIFIC = []\n",
    "\n",
    "percentages = [71, 72, 73, 74, 75, 76, 77, 78, 79]\n",
    "for precentage in percentages:\n",
    "    TRAIN_SPECIFIC.append([precentage, SelectPercentile(percentile=precentage).fit_transform(X_TRAIN, Y_TRAIN)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross_Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compute_scores(X, Y, labels):\n",
    "    cv = LeaveOneLabelOut(labels_TRAIN)\n",
    "\n",
    "    clf = LinearSVC(random_state = 2, verbose = 1)\n",
    "    scores = cross_val_score(clf, X, Y, cv=cv, scoring='accuracy')\n",
    "    \n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "scores = [[train_x[0], compute_scores(train_x[1], Y_TRAIN, labels_TRAIN)] for train_x in TRAIN]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]done\n",
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]done\n",
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]done\n",
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]done\n",
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]done\n",
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]done\n",
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]done\n",
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]done\n",
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]done\n"
     ]
    }
   ],
   "source": [
    "scores_specific = []\n",
    "\n",
    "for train_x in TRAIN_SPECIFIC \n",
    "    scores_specific.append([train_x[0], compute_scores(train_x[1], Y_TRAIN, labels_TRAIN)])\n",
    "    print 'done'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Linear SVC 10: 0.53 (+/- 0.03)\n",
      "Accuracy Linear SVC 20: 0.59 (+/- 0.02)\n",
      "Accuracy Linear SVC 30: 0.61 (+/- 0.03)\n",
      "Accuracy Linear SVC 40: 0.62 (+/- 0.02)\n",
      "Accuracy Linear SVC 50: 0.63 (+/- 0.02)\n",
      "Accuracy Linear SVC 60: 0.63 (+/- 0.02)\n",
      "Accuracy Linear SVC 70: 0.63 (+/- 0.02)\n",
      "Accuracy Linear SVC 80: 0.64 (+/- 0.03)\n",
      "Accuracy Linear SVC 90: 0.64 (+/- 0.03)\n",
      "Accuracy Linear SVC 100: 0.64 (+/- 0.03)\n"
     ]
    }
   ],
   "source": [
    "for score in scores:\n",
    "    print \"Accuracy Linear SVC \" + str(score[0]) + \": %0.2f (+/- %0.2f)\" % (score[1].mean(), score[1].std() * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Linear SVC 71: 0.63 (+/- 0.02)\n",
      "Accuracy Linear SVC 72: 0.63 (+/- 0.02)\n",
      "Accuracy Linear SVC 73: 0.63 (+/- 0.02)\n",
      "Accuracy Linear SVC 74: 0.64 (+/- 0.03)\n",
      "Accuracy Linear SVC 75: 0.64 (+/- 0.03)\n",
      "Accuracy Linear SVC 76: 0.64 (+/- 0.03)\n",
      "Accuracy Linear SVC 77: 0.64 (+/- 0.03)\n",
      "Accuracy Linear SVC 78: 0.64 (+/- 0.03)\n",
      "Accuracy Linear SVC 79: 0.64 (+/- 0.03)\n"
     ]
    }
   ],
   "source": [
    "for score in scores_specific:\n",
    "    print \"Accuracy Linear SVC \" + str(score[0]) + \": %0.2f (+/- %0.2f)\" % (score[1].mean(), score[1].std() * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scores_all = scores_specific + scores\n",
    "scores_all = [[score[0], score[1].mean()] for score in scores_all]\n",
    "scores_all.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HBKgXvaGI3A0MAy5W1e8KU1C3bt0IhUK5bq1bt2bq1Km5tps9e3a+p+0HDhxI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\nc7FiZmZmpeZixczMzErt/wEHmVpLvCAOoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cb1f67d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CKLdk0SinGDTKSUREJIxGOYmIiIg0QAWNiIiIFD0VNJIXs2fPLnQTio4yC6Pc4lNmYZRb\nsqigkbyYOXNmoZtQdJRZGOUWnzILo9ySRZ2CY1CnYBERkTCtplOwmV1qZivNbKOZPWNmX2xg/fZm\ndqOZrTKzTWa2wswuyHj/JDN7zsw+MLP1ZvaCmZ2TtY82ZnZ9ettKM1tuZj9splMUERGRZrJDoRsA\nYGZnAD8FvgksAMYCj5pZf3evqGOzh4DdgVHAq8Ce1CzQ3gduAMqBT4DhwDQzW+Puf02vcxXwLeA8\noAw4FJhuZh+6+5QmPEURERFpRokoaIgKmF+5+70AZnYxcAIwGpiUvbKZHQd8Bejr7h+mF6/OXMfd\nn8ra7A4zOx8YDFQVNIOAOe7+l6p9mNnZwGGNPyURERHJl4LfcjKzdsAhwONVyzzq2PM3ooIjl+HA\n88CVZvaGmS0xs1vMbKd6jnMU0B/4R8bifwJHmVm/9DoHAV8G5jXilCSHUaNGFboJRUeZhVFu8Smz\nMMotWZJwhaYr0BZYk7V8DTCgjm36El2h2QScmN7HXcBngTFVK5lZZ+BNYEdgC3CJuz+RsZ+bgc5A\nuZltJSrwrnH33zXynCTL0KFDC92EoqPMwii3+JRZGOWWLEkoaEK0AbYBZ7v7egAz+z7wkJld4u4f\np9f7CDgI6AgcBdxmZisybkedAZwNnEnUh+bzwO1m9pa735e/02n5zjrrrEI3oegoszDKLT5lFka5\nJUvBbzkBFcBWoFvW8m7AO3Vs8zbwZlUxk7YYMKBn1QKPrHD3f7v7bcDDwNUZ20wCbnb3h9z9FXe/\nH7gta51ahg0bRiqVqvEaNGhQrUmWHnvssZxPY7300kuZOnVqjWWLFi0ilUpRUVGzD/S1117LxIkT\nayxbvXo1qVSK8vLyGssnT57MuHHjaiyrrKwklUoxf/78GstnzpyZ83LpGWecofPQeeg8dB46D51H\no85j5syZ1d+N3bt3J5VKMXbs2FrbNKVEzENjZs8Az7r799I/G1En3zvc/ZYc619EVHjs4e6V6WUj\niAqWjhlXaLK3mwr0cfevp3+uAP7X3e/OWOdq4Hx3H5hje81DIyIiEqC1zEPzM+AiMzvPzAYCvwQ6\nANMBzOwmM5uRsf4DRMOyp5lZiZkdSXS1ZWpVMWNmV5nZ0WbWx8wGmtkPgHOAzFtJfwR+aGbDzGwf\nMzuJaMTVH5r3dFuf7ApfGqbMwii3+JRZGOWWLIkoaNz9QeBy4DrgBeBA4Fh3fy+9Sndg74z1NwDH\nALsCzxEVKXOA72XsdhfgTuA/wHzgJGCku0/LWOcyoqs6dxL1oZlE1Ln4R017hjJpUq3R99IAZRZG\nucWnzMIot2RJxC2nYqFbTuEqKyvp0KFDoZtRVJRZGOUWnzILo9ziaS23nKSF0z/6+JRZGOUWnzIL\no9ySRQWNiIiIFD0VNCIiIlL0VNBIXmTPbyANU2ZhlFt8yiyMcksWFTSSF7169Sp0E4qOMguj3OJT\nZmGUW7JolFMMGuUkIiISRqOcRERERBqggkZERESKngoayYvsB6FJw5RZGOUWnzILo9ySRQWN5MUV\nV1xR6CYUHWUWRrnFp8zCKLdkUUEjeTFlypRCN6HoKLMwyi0+ZRZGuSWLChrJCw1vjE+ZhVFu8Smz\nMMotWVTQiIiISNFTQSMiIiJFTwWN5MXEiRML3YSio8zCKLf4lFkY5ZYsKmgkLyorKwvdhKKjzMIo\nt/iUWRjllix69EEMevSBiIhIGD36QERERKQBKmhERESk6KmgkbyoqKgodBOKjjILo9ziU2ZhlFuy\nqKCRvBg9enShm1B0lFkY5RafMguj3JJFBY3kxYQJEwrdhKKjzMIot/iUWRjlliwqaCQvNCosPmUW\nRrnFp8zCKLd4VqxY0az736FZ9y4iIiKtWkVFBeeNHkPl1uadJkYFjYiIiDSb80aP4egLv8u2LVv4\nx7w/NttxdMtJ8mLq1KmFbkLRUWZhlFt8yiyMcmvYP//5T+jQmd4DSpv9WCpoJC8WLWrySSFbPGUW\nRrnFp8zCKLeIu/P222/zxBNPMGXKFC655BK+9rWv0a1bN7785S+zV7+BeWmHHn0Qgx59ICIirZW7\n8/rrr1NWVkZZWRmLFy+u/vuHH34IQPv27enfvz+lpaWUlpbSqVMnnnj+JUZfezMrXvk34045Dprp\n0QfqQyMiIiLVtm7dysqVK2sVLYsXL2bDhg0A7LzzzpSUlFBaWsoJJ5xQXcD07duXHXaoWVr8LTWC\nVUvKmr3dKmhERERaoU8++YTly5fXKlyWLFnCxx9/DEDnzp0pLS3lgAMO4IwzzqguXHr16kWbNtvX\na+Xee6Zy3ugxbNiiUU4iIiISaOPGjSxZsqTW1ZZly5axZcsWAHbbbTdKS0sZNGgQY8aMobS0lJKS\nEvbaay/MrFHH79q1K/PmzuHhhx/mqT833ygnFTSSF6lUirlz5xa6GUVFmYVRbvEpszBJy+2jjz6i\nvLy8Vh+XFStWUNVfdq+99qKkpISjjz6a7373u9W3jXbfffdmb1/fvn2bdf8qaCQvLrvsskI3oego\nszDKLT5lFqZQuf33v/+tLlYyr7q8/vrr1evss88+lJaWcuKJJ1YXLSUlJey6664FaXM+aJRTDBrl\nJCIi+eDuvPvuuzlHFK1ZswaANm3asO+++1YXK1X9WwYMGEDHjh0LfAa1LVq0iEMOOQQ0yklERKRl\ncXfeeOONnCOK/vvf/wLQrl07+vfvT0lJCd/61reqC5d+/fqx0047FfgMkkMFjYiISDPbunUrq1at\nqlW0LF68mI8++giAnXbaiYEDB1JaWsrxxx9ffeVl3333pV27dgU+g+RTQSN5MXv2bE488cRCN6Oo\nKLMwyi0+ZRYmV26bN2/m1VdfrS5aqgqX8vJyNm3aBEDHjh2rr7Kceuqp1beL9tlnH9q2bVuIU2kR\nVNBIXsycOVP/wYxJmYVRbvEps/g2bdrEXXfdxaZNm2rcLlq2bBmbN28G4DOf+Qyf+9znOOywwzj/\n/POri5gePXo0eii01KZOwTGoU7CISOuyfv366qHQmbeLVqxYwbZt2wDo3r17rY65JSUl7LHHHipc\nMqhTsIiISDP74IMPqvu0ZN4uWr16dfU6vXr1oqSkhOHDh9coXD7zmc8UsOVSRQWNiIi0Cu7Oe++9\nV6toWbx4MW+//TYAZkbfvn0pLS3lrLPOqi5aBg4cSKdOnQp8BlIfFTQiItKiuDtvvvlmrRFFZWVl\nvP/++wDssMMO9OvXj9LSUi688MLq20X9+/dn5513LvAZSAgVNJIXo0aNYtq0aYVuRlFRZmGUW3zF\nmtm2bdt47bXXck4+VzUUescdd2TgwIGUlJRwzDHHVN8q2m+//Ro9FLpYc2upVNBIXgwdOrTQTSg6\nyiyMcosv6Zlt2bKleih0ZtFSXl7Oxo0bAdhll12qr7KcdNJJ1beK+vTp02xDoZOeW2ujUU4xaJST\niEjz+fjjj1m6dGmtPi5Lly6tHgq96667Vl9lyRxZ1LNnT9q0aVPgM5D6aJSTiIi0KBs2bKC8vLxW\nH5fly5dXD4XeY489KC0tZciQIVx88cXVBUy3bt00FFpyUkEjIiLNYu3atTk75q5atap6nZ49e9aY\n6r/qqstuu+1WuIZLUVJBI3kxf/58Bg8eXOhmFBVlFka5xVNWVsacOXMYMWIEpaWlQfuoqKioNQy6\nrKyMt956C4iGQvfp04eSkhJOO+206sJl4MCBdO7cuSlPJ6/0WUsWFTSSF5MmTdI//JiUWRjltn0q\nKio4b/QY2u+6Gy8teJZnFy/jkw/f5957ptK1a9da67s7b7/9dq2OuWVlZVRUVADQtm1b9ttvP0pL\nSxk1alT11ZYBAwbQoUOHfJ9is9NnLVnUKTgGdQoOV1lZ2SL/g9aclFkY5bZ9hqVGcPSF36X3gFI+\n3ljJjjt3YNWSMv766zu4a/LttQqXxYsXs3btWgDat2/PgAEDanXM7devH+3bty/wmeWPPmvxqFOw\ntAj6Rx+fMguj3Grbtm0b69evZ+3ataxbt44XX3wR79CJ3gOiW0w77hxl1ntAKWs/2UqfPn2AKMuq\nYiWVSlUXMH369GGHHfT1oc9asiTmE2lmlwKXA92Bl4DvuPtz9azfHrgWGJne5i3gOnefnn7/JOB/\ngf2AdsAy4Kfu/tus/ewFTASOBzqk1xvVHNWjiEgc7s6mTZuqC5HQPz/66COyr8aP+eENOY954BFf\n4ZjDD2HUqFH06tVLQ6GlaCSioDGzM4CfAt8EFgBjgUfNrL+7V9Sx2UPA7sAo4FVgTyDzX977wA1A\nOfAJMByYZmZr3P2v6ePuCvwf8DhwLFAB9AM+aNITFJFWZ/Pmzaxbt65RhcjatWvZsmVLncfYeeed\n6dy5M126dKnxZ/fu3XMur/qzoqKCX8/6Q859vr18Cd+9+nJ69+7dTMmINI9EFDREBcyv3P1eADO7\nGDgBGA1Myl7ZzI4DvgL0dfcP04tXZ67j7k9lbXaHmZ0PDAb+ml52FbDa3S/MWO+1Rp6L5DBu3Dhu\nueWWQjejqCizMI3Nrer2TGMLkaoZbHNp27YtXbp0qVVo7L333vUWIpl/durUqVH9VX75m6msWlJG\n7wGlzJh0Hedf8SNWLSlj89r3g0c7tTb6N5osBS9ozKwdcAjwk6pl7u5m9jdgUB2bDQeeB640s3OB\nDcBcYLy7b6rjOEcB/YF/ZO3nL2b2IDAEeBP4hbv/pnFnJdl69epV6CYUHWUWT9XtmV133ZWlS5cG\nFyK5bs9k6tSpU60CY7fddqNPnz4NFiJVf995550LPjncvfdM5bzRY2jXZTf++8F/mXHDNWxeG41y\nku2jf6PJUvBRTma2J1EhMcjdn81YPhE40t1rFTVm9mfgq0RXWq4DugJ3AU+4+5iM9Tqn970jsAW4\npKqPTfr9jYAT3e56GDgMuB34lrvfl+O4GuUk0gy2bNnS6Csi69atq54eP5eddtppu6581Pdnp06d\nWlyfkrKyMpYvX1493FqkuWiUU25tgG3A2e6+HsDMvg88ZGaXuPvH6fU+Ag4COgJHAbeZ2YqM21Ft\ngAXuPj7980tmtj9wMVCroBHJl7KyMpYtW0a/fv0S/SWzbds2NmzY0OhCpLKyss5jtG3bNmdx0aNH\nD0pLS7erEOncuXOrGk4cR9XIJZGi5+4FfRGNQNoMpLKWTwceqWOb6cDSrGUDga3AvvUc69fAnzN+\nXgXcnbXOxcDrdWx/MODdunXz4cOH13gdfvjh/sgjj3imRx991IcPH+7ZLrnkEv/Nb35TY9nChQt9\n+PDh/t5779VY/qMf/chvvvnmGstee+01Hz58uC9evLjG8jvuuMMvv/zyGss2bNjgw4cP96effrrG\n8gceeMAvuOCCWm07/fTTE3Uer7zyis+ePduff/75oj6PKg39Pt577z0/fnjKR5w7yi+5/hbfa5/e\nfvChX6zRjqY4j/Hjx/v111/v77zzji9dutSfe+45f+CBB/zQQw/1n/zkJ3777bf79ddf75dffrkf\nccQRPmDAAD/++OP9iCOO8P3339979OjhO+ywgxNd4azz1alTJ+/Ro4d37tzZ+/fv70OHDvXTTjvN\nx4wZ4yeffLL379/fb7vtNp86dao//PDD/thjj/kpp5zi119/vb/11lu+fv1637Ztm/596Dx0HkV2\nHg888ED1d2PVd+aRRx5Z9d+Gg70Z6onYt5zMrK+7r4hbODWwz2eAZ939e+mfjaiT7x3uXqvHlZld\nBNwG7OHulellI4huG3X0T6/QZG83Fejj7l9P/3w/0NPdh2SscxvwRXevNf2jbjmFKy8vZ+DAgdu9\nfuYspj36l/Lm0rJ6ZzFtKTInO3tjxTJ69u3HqiVl/O03dzBv7hzg09szjb1F09DtmcbcmunSpQsd\nO3akbdu2+YquWtzPmiizUMotniTeclpuZv8ApgIPex2dcGP6GTDdzBby6bDtDkRXYjCzm4C93P38\n9PoPAD8kGoY9gWj49iRgalUxY2ZXEXUcfpWoD80JwDlEV2Cq3Ab8n5ldDTwIfAm4ELioCc5JMlxx\nxRXMnTt3u9c/b/SY6i/2KquWlHHe6DHVX+wtTVlZGe133a36nO+75QauvmsGvQeUsm4L7L777lRW\nVtZ7e6YnVuEKAAAgAElEQVRNmzY5i4u99tqLkpKS6p8bGj2z44475uu0m1zcz5oos1DKLVlCCpqD\nieZ++RkwxcxmERUSC0Ib4e4PmllXog6+3YAXgWPd/b30Kt2BvTPW32BmxwCTgeeI5pyZBYzP2O0u\nwJ1AT2Aj0Xw0I9394Yz9PJ+egO/m9LYrge+5++9Cz0VymzJlynavm/3FXqX3gFLaddmNsrKy7b7n\nXyx9UdydRx55hG59+lcvu3D8jdV//9xhg9i7SwcOO+yweouRDh06FHz0TKHF+axJRJmFUW7JEjzK\nycx2AFLABcBxwFLgHuC+jEKkRdEtp/yYM2cOj728jKNOG1nrvT/ffw8v/nk2xx13HJ///Of5whe+\nQJ8+fWp9iRfLLavXXnuNe++9l+nTp7NixQq+fHyK79/2y1rrzbjhGm66+vJEF2UiIvVJ4i0nANx9\nC/AHM/sTcAlwE3Ar8JP0vC5XuvvbTdNMaU169OjB4um/zVnQrHj5JXbaaSd+85vf8M477wDQuXPn\n6uKm6nXFNT9k6EXfK+gtq7quDlVWVvLII48wbdo0nnjiCTp06MDpp5/O9OnT+cktt1ZPdpbZbk12\nJiJSv+CCxswOJZrJ90yiie1uJepX05PoGUtziOZ1Edluzz//PCNHjuT9tetYWf4KfQZ+rvq9VUvK\n2HHrxzz+9NMAvPPOO7z44ou88MILvPDCC/zpT3/i9ttvB2DQsSc0yS2rENlXh6b9fg4ff/g+3/32\nxTzyyCPMmjWLdevWMWTIEO655x5OPfVUOnbsCMB9JSXVk531HFDKG+liRpOdiYjUL3ZBk57vZRQw\nAJgHnAfMc/dt6VVWmtkFREOiRQCYOHEiV155ZZ3vb926lUmTJvGjH/2Igw46iBkzZnDdT26q94u9\ne/fuHHfccRx33HHVyz766COmTJnCa5/knvysR/8Sli9f3qwFTa4OzSsX/4eR559Lp5135Hvf+x7n\nn38+++67b61tu3btyry5cygrK2PixIncdPWVujITU0OfNalNmYVRbskScoXm20R9ZabXc0vpXWBM\nHe9JK1TfyJzVq1dz7rnn8vTTT3PVVVcxYcIE2rdvX/3Fvnz5cvY7edh2fbF36tSJESNG8L8335rz\n/UVPP8lLf5nN5s2bSaVStGvXLviccikrK6NNp11rXR3qU7I/X/ra0dwy/ir233//BvdTWlpK7969\nVcwEqO+zJrkpszDKLVkK/uiDYqJOwU1v5syZfPvb36Zz587cd999DBkypOGNtkPmfC5VVi0pY+ZN\n42ln8K9//Yvu3bszevRoLrzwQvr06VNrH9s7QmrTpk089dRTzJs3j1mzZnHChZcx7JzRtdZ7/KH7\nOfbA/qRSqSY5RxGRYpK4TsFmNgpY7+4PZS0/Dejg7jOaqnHScq1du5ZLL72U+++/nzPPPJO77rqL\nXXfdtcn2n/ngvcxbVn/+41y6du3Kyy+/zN13382dd97JTTfdxNChQ/nmN7/J8OHDWbt2ba0+MNkj\npFavXs28efOYN28ejz/+OJWVley9994ceeSRvLl0cc42vbGkjP1OHtZk5ygiIp8KueV0NdHkc9ne\nBe4GVNBIDdlXOubPn88555zDBx98wH333cfIkSObfO6UzL4ouW5ZHXDAAUyePJmJEycya9Ys7r77\nbk455RT23HNPduvWnVE/vqXW1Z0TTz2NL3/pMObNm8d//vMf2rZty+DBg7n22ms54YQTKC0txcwY\nlhqhkUoiInkW8uiDTcBAd1+Vtbw3sNjdd26qxiWNbjnFkzna57M99uH9N15jxX9e4pWXXuCII47g\nt7/9Lb179y50M6u99NJL3Hzzzbz24Xou//ndtd6f9J0LWb7oWVKpFMOGDePoo4/OeVWp6rxzdWiO\nMwdORUVFoubMKRbKLT5lFka5xZO4W05EV2IOpPYopoOIZuwVAWqO9rnp2+dz9V0zWLn4P9x7/dU8\n+eST7LBDsh72ftBBB3HmmWfy2MvLcr5/8OAhXHXJRZx44on17qehq0Pba/To0ZpWPYByi0+ZhVFu\nyZJ7bGv9ZgJ3mNnXzKxt+vV14HZAjwwQoPbjC8647AdANNqnd+mBLF26tJDNq1O/fv14c2lZzvfe\nXFZO//79c76XS2lpKalUKvg204QJE4K2a+2UW3zKLIxyS5aQgmY88CzwONEzkjYCjwFPAP/bdE2T\nYrZs2TJ69P/0i7zv5w6s/nvPAaUsX768EM1qUGlpKZ98+D6rltQsagrRB0a3NcMot/iUWRjlliyx\nr/m7+yfAGWY2nug200bgZXd/rakbJ8WrR48evDL13pyPL0j6aJ+6Rkhptl4RkeRqzLOclhI9kFKk\nhtdff50LL7yQN9e8y6ryV+id9fiCpI/2aao+MCIikj9BBY2Z9SR60nYvoH3me+7+/SZolxSpBQsW\nMGLECHbccUf+8OAsbrrlVtp12Y1169fTuWPHorrSUVpaWtBCZurUqYwZowm341Ju8SmzMMotWWL3\noTGzo4AlRI9A+AHwNaJnO40GPt+krZOi8uCDDzJkyBB69+7Ns88+y1e+8hXmzZ3DTVdfzs4fr+em\nqy9n3tw5Gua4nRYtavJRja2CcotPmYVRbskSMg/NAuDP7n6tmX1E1I/mXeB+4C/uflfTNzMZNA9N\nbu7OjTfeyPjx4znrrLO455572GmnnQrdLBERSZDmnocmZJRTCXBv+u9bgJ3dfT3wI0CPHW1lPv74\nY8477zzGjx/Pj3/8Y+6//34VMyIiknchfWg28Gm/mbeBfYFX0j/rXkIr8t5773HSSSfx/PPPM3Pm\nTM4888xCN0lERFqpkILmGWAwsBiYB/zUzA4ATk6/J61AWVkZ3/jGN9iwYQNPPvkkhx9+eKGbJCIi\nrVjILafvE02sB3At0QR7ZxA9CkHdvVugsrIy5syJhjEDPProowwaNIiOHTuyYMGC7SpmUqlUczez\nxVFmYZRbfMosjHJLllhXaMysLdAT+DeAu28ALm6GdkkCZD5cskf/Uqb9fg6ry8t4aeFzHHfcccyc\nOZPOnTtv174uu+yyZm5ty6PMwii3+JRZGOWWLKFP2y5x95XN06Tkam2jnIalRlQ/XLLKysX/Yeq1\n43h50ULatm1bwNaJiEgxSeIop/8AfZu6IZIs2Q+XrNKnZH/6HXgwS5YsKVDLREREagspaH4I3Gpm\n3zCzPc2sc+arqRsohbFs2TL22m9gzveS/HBJERFpnUIKmnlEk+nNBd4APki/Pkz/KUVs69atzJkz\nhxtuuIEX/u+pnOu8saSM/fbbL9Z+Z8+e3RTNa1WUWRjlFp8yC6PckiWkoPlaxuvrGa+qn6UIbdiw\ngTvvvJOBAwdy4okn0r59e7ZtWMuqJWU11gt9uOTMmTObsrmtgjILo9ziU2ZhlFuyxO4U3JoVc6fg\nsrIyli1bRr9+/WoUI2+88QZTpkzh7rvvZt26dZxyyimMHTuWww8/vHqUU7suu9FzQClvpIuZe++Z\nqucxiYhILM3dKTj2xHpmdmR977t77vsUUhC5hl5/8uH7/OB732XatGnMmjWLDh06cNFFF/Gd73yH\nffbZp3rbrl27Mm9uNP/M8uXL2e/kYQV9+rSIiEhdQmYKfjLHsszLPBrLmyDnjR6Tc+j1aWeNpMsu\nO3PrrbcyevRoOnXqVOc+SktLVciIiEiihRQ0n8n6uR3wBeB64JpGt0iaTH1Drw/76te5ZfzVHHDA\nAQVqnYiISNOJ3SnY3ddmvSrc/a9ET9qe1PRNlFBPPPEE3fr2z/nevgcezMqV+ZsbcdSoUXk7Vkuh\nzMIot/iUWRjlliwho5zqsgYY0IT7kwBbtmxh9uzZHH300XznO9/hP8/+M+d6IUOvG2Po0KF5O1ZL\noczCKLf4lFkY5ZYssQsaMzsw63WQmR0H/BJ4sembKNujoqKCm2++mX333ZeTTjqJyspK7r//fjq1\npcmGXjfGWWedlbdjtRTKLIxyi0+ZhVFuyRLSh+ZFok7AlrX8GWB0o1skOdU17Pr5559nypQp/O53\nvwPg7LPP5tJLL60aGsfQoUPrHHotIiLSUoQUNH2yft4GvOfum5qgPZIl17DrTf+tYMQJxzNjxgye\nffZZ9tlnH6677jpGjx5da34YDb0WEZHWIHZB4+6vNUdDJLe6hl1f881zOfTzBzJnzhxOOOGEBp98\nXeih1/Pnz2fw4MEFO34xUmZhlFt8yiyMckuWkD40d5jZZTmWX2ZmP2+aZgnUP+x60FFD+fnPf04q\nlWqwmEmCSZM0AC4uZRZGucWnzMIot2QJGeV0CjA/x/J/Aqc2rjmSadmyZezVL/dVld6fO7Connhd\n1cdHtp8yC6Pc4lNmYZRbsoQUNLsBH+VYvg7QA36akLvz8jNP53wv38OuG6tDhw6FbkLRUWZhlFt8\nyiyMckuWkIJmOXB8juXHAysa1xyp8sADD3D22Wez8pWXWbXklRrvFWLYtYiISJKFjHL6GTDFzHYH\nnkgvOwr4AfA/TdWw1uqTTz7h8ssvZ/LkyZx77rnccMMNXHzZdzTsWkREpB4hjz64h6h4GQP8Pf06\nB/i2u/+6aZvXurz11lt8/etf55e//CV33nknM2bMoFevXsybO4ebrr6cYw/sz01XX868uXNqDc9O\nunHjxhW6CUVHmYVRbvEpszDKLVlCrtDg7ncBd6Wv0mx09/VN26zW5+mnn+b000+nTZs2/OMf/2DQ\noEE13i/0sOvG6tWrV6GbUHSUWRjlFp8yC6PcksXcPd4GZn2AHdx9WdbyfsBmd1/VdM1LFjM7GFi4\ncOFCDj744CbZp7tz++23c/nllzN48GBmzZpFt27dmmTfIiIiSbFo0aKqWewPcfdFTb3/kE7B04Ev\n5Vj+pfR7sp02bNjA2WefzdixY/mf//kf/va3v6mYERERCRByy+kLwL9yLH8GmNK45rRsmc9jateu\nHSeffDIrV65k1qxZnH766YVunoiISNEKuULjQOccy7sAyZ+ytgAqKioYlhrB/958K4+9vIwf/PhG\njjhyCBs3bmTBggWtopgpLy8vdBOKjjILo9ziU2ZhlFuyhBQ0TwFXm1l18ZL++9XknkG41at6HtN5\n19zIUaeN5KLrbuWqu2bQd8DAou7oG8cVV1xR6CYUHWUWRrnFp8zCKLdkCbnldCVRUbPEzKqmsf0K\n0RWarzVVw1qKsrIy2nTaNefzmHb+7B6UlZW1iqJmyhTdjYxLmYVRbvEpszDKLVlC5qEpAw4EHgT2\nADoB9wL9m7ZpxWvbtm0888wzXH311Rx77LF075s7mp4DSovqeUyNoeGN8SmzMMotPmUWRrklS+g8\nNG8B/wtgZp2BM4G/AIfSCvrRrFixotaw7Y0bN/L4448zZ84c/vjHP7JmzRq6du3K4MGDeXNpWc79\nvLGkjP1OHpaPJouIiLRoIX1oADCzI81sBvAWcDnRjMGHN2J/l5rZSjPbaGbPmNkXG1i/vZndaGar\nzGyTma0wswsy3j/JzJ4zsw/MbL2ZvWBm59Szv6vMbJuZ/ayhtk6Zdi/DUiNYsmQJM2bM4OSTT6Zr\n164MHz6cJ598knPOOYennnqKd955h0ceeQTbuJ5VS2oWNXoek4iISNOJdYXGzLoDFxA99qAz0W2n\nHYET07eigpjZGcBPgW8CC4CxwKNm1t/dK+rY7CFgd2AU8CqwJzULtPeBG4By4BNgODDNzNa4+1+z\njv/F9LFf2p72fmPMpVibNgw6cggfvLuGww8/nPHjx5NKpSgpKcHMaqx/7z1TOW/0mFb9PKaJEydy\n5ZVXFroZRUWZhVFu8SmzMMotWba7oDGzPwJHAn8iegjlX9x9q5ld3ATtGAv8yt3vTR/rYuAEYDQw\nKUdbjiPqiNzX3T9ML16duY67P5W12R1mdj4wGKguaMysI/Bb4EJg/PY2uE/J/hz6la9yzXe+zZAh\nQ+pdt2vXrsybO4eysjKWL1/OficPa3VXZiorKwvdhKKjzMIot/iUWRjllizb/egDM9sC3AHclfnY\nAzPbDBwUeoXGzNoBlcAp7j43Y/l0oIu7n5RjmzuBfsBC4FxgAzAXGO/um+o4zlHAbGCEuz+RsXwG\n8J67X25mfwdecPfv17GPg4GFt/z+L/T93IE8/tD9HHtgf1KpVMipi4iItBrN/eiDOLecBhPdalpo\nZouB+4DfNUEbuhJ1JF6TtXwNMKCObfoSXaHZBJyY3sddwGfTbQSqOyy/SXRbbAtwSVYxcybweaLO\nzLGpU6+IiEgybHenYHd/xt0vIuqr8iuikU1vpfdxjJl1ap4m5tQG2Aac7e7Pu/tfgO8D55vZjhnr\nfQQcRFSwXAPcZmZHAphZT+DnwEh33xy3AerUKyIikhwh89BscPd73H0wcABRZ96rgHfNbG79W+dU\nAWwFsp/K2A14p45t3gbedPf1GcsWAwb0zGiru/sKd/+3u98GPEw0ozHAIUSdiheZ2eb0rbMhwPfM\n7BPL7tmb4ZqRJzLxmyPZ9snHpFIpUqkUgwYNYvbs2TXWe+yxx3Lejrr00kuZOrVmh+BFixaRSqWo\nqKjZB/raa69l4sSJNZatXr2aVCpVa9rtyZMnM27cuBrLKisrSaVSzJ9fcxLnmTNnMmrUqFptO+OM\nM5rlPMaNG9ciziOfv4+KiooWcR6Q39/Hiy++2CLOI5+/j4qKihZxHpDf30dFRUWLOA9o+t/HzJkz\nq78bu3fvTiqVYuzYsbW2aVLu3ugX0S2jE4G5gds/A9ye8bMBrwPj6lj/ImA90CFj2QhgM7BjPceZ\nCjyR/vsuQGnWawEwAyipY/uDAX/ooYdc4hk+fHihm1B0lFkY5RafMguj3OJZuHChEz0P8mBvgtoj\n+xU0sV6OomgrUYfb2Q2tW4efAdPNbCGfDtvuAEwHMLObgL3c/fz0+g8APyQahj2B6ErLJGCqu3+c\n3uYq4HmiId07Eo2aOge4ON3mDUCNjsxmtgF4390X19fYvn37Bp5m6zVhwoRCN6HoKLMwyi0+ZRZG\nuSVLkxQ0jeXuD5pZV+A6oltNLwLHuvt76VW6A3tnrL/BzI4BJgPPEc05M4uaw653Ae4kugW1kWg+\nmpHu/nB9TWmaM5Js2TMrS8OUWRjlFp8yC6PckiURBQ2Au/8C+EUd79W6WefuS4Fj69nfeGLMK5Pe\n5utx1hcREZFkCH70gYiIiEhSqKCRvMjulS8NU2ZhlFt8yiyMcksWFTSSF4sWNfmkkC2eMguj3OJT\nZmGUW7Js96MP5NNHHyxcuFCdwURERGJo7kcf6AqNiIiIFD0VNCIiIlL0VNCIiIhI0VNBI3mR65kk\nUj9lFka5xafMwii3ZFFBI3lx2WWXFboJRUeZhVFu8SmzMMotWTTKKQaNchIREQmjUU4iIiIiDVBB\nIyIiIkVPBY3kxezZswvdhKKjzMIot/iUWRjlliwqaCQvZs6cWegmFB1lFka5xafMwii3ZFGn4BjU\nKVhERCSMOgWLiIiINEAFjYiIiBQ9FTQiIiJS9FTQSF6MGjWq0E0oOsosjHKLT5mFUW7JooJG8mLo\n0KGFbkLRUWZhlFt8yiyMcksWjXKKQaOcREREwmiUk4iIiEgDVNCIiIhI0VNBI3kxf/78Qjeh6Ciz\nMMotPmUWRrkliwoayYtJkyYVuglFR5mFUW7xKbMwyi1Z1Ck4BnUKDldZWUmHDh0K3YyioszCKLf4\nlFkY5RaPOgVLi6B/9PEpszDKLT5lFka5JYsKGhERESl6KmhERESk6KmgkbwYN25coZtQdJRZGOUW\nnzILo9ySRQWN5EWvXr0K3YSio8zCKLf4lFkY5ZYsGuUUg0Y5iYiIhNEoJxEREZEGqKARERGRoqeC\nRvKivLy80E0oOsosjHKLT5mFUW7JooJG8uKKK64odBOKjjILo9ziU2ZhlFuyqKCRvJgyZUqhm1B0\nlFkY5RafMguj3JJFBY3khYY3xqfMwii3+JRZGOWWLCpoREREpOipoBEREZGip4JG8mLixImFbkLR\nUWZhlFt8yiyMcksWFTSSF5WVlYVuQtFRZmGUW3zKLIxySxY9+iAGPfpAREQkjB59ICIiItIAFTQi\nIiJS9FTQSF5UVFQUuglFR5mFUW7xKbMwyi1ZVNBIXowePbrQTSg6yiyMcotPmYVRbsmigkbyYsKE\nCYVuQtFRZmGUW3zKLIxySxYVNJIXGhUWnzILo9ziU2ZhlFuyqKARERGRoqeCRkRERIpeYgoaM7vU\nzFaa2UYze8bMvtjA+u3N7EYzW2Vmm8xshZldkPH+SWb2nJl9YGbrzewFMzsnax9Xm9kCM1tnZmvM\n7BEz699Mp9iqTZ06tdBNKDrKLIxyi0+ZhVFuyZKIgsbMzgB+ClwLfAF4CXjUzLrWs9lDwNeAUUB/\n4CxgScb77wM3AIcDBwDTgGlmdkzGOl8BJgNfAo4G2gGPmdnOTXBakmHRoiafFLLFU2ZhlFt8yiyM\nckuWRDz6wMyeAZ519++lfzbgdeAOd5+UY/3jgAeAvu7+YYzjLAT+n7tfW8f7XYF3gSPdfX6O9/Xo\nAxERkQAt/tEHZtYOOAR4vGqZR1XW34BBdWw2HHgeuNLM3jCzJWZ2i5ntVM9xjiK6kvOPepqzK+DA\nf+OdhYiIiBTSDoVuANAVaAusyVq+BhhQxzZ9iW4XbQJOTO/jLuCzwJiqlcysM/AmsCOwBbjE3Z/I\ntcP0VaGfA/PdvSz0ZERERCT/klDQhGgDbAPOdvf1AGb2feAhM7vE3T9Or/cRcBDQETgKuM3MVrj7\nUzn2+QugFPhys7deREREmlTBbzkBFcBWoFvW8m7AO3Vs8zbwZlUxk7YYMKBn1QKPrHD3f7v7bcDD\nwNXZOzOzKcAw4Kvu/nZDDR42bBipVKrGa9CgQcyePbvGeo899hipVKrW9pdeemmt3vGLFi0ilUrV\nejbItddey8SJE2ssW716NalUivLy8hrLJ0+ezLhx42osq6ysJJVKMX9+zS5BM2fOZNSoUbXadsYZ\nZzTLeQwYMKBFnEc+fx+pVKpFnAfk9/dx9NFHt4jzyOfvI5VKtYjzgPz+PlKpVIs4D2j638fMmTOr\nvxu7d+9OKpVi7NixtbZpSknuFLyaqFPwLTnWvwi4DdjD3SvTy0YQFSwdM67QZG83Fejj7l/PWDYF\nGAEMcfcVDbRTnYIDPfbYYwwdOrTQzSgqyiyMcotPmYVRbvE0d6fgpBQ0pwPTgYuBBcBY4FRgoLu/\nZ2Y3AXu5+/np9XcByoBngAnA7sCvgb+7+8Xpda4i6jj8KlEfmhOAnwAXu/u09Dq/IBrunQKWZjRp\nrbtvytFOFTQiIiIBmrugSUQfGnd/MD1k+jqiW00vAse6+3vpVboDe2esvyE9n8xk4DmiOWdmAeMz\ndrsLcCfRLaiNQDkw0t0fzljnYqJRTU9mNWkUcG+TnJyIiIg0u0QUNADu/guijrm53qt1s87dlwLH\n1rO/8dQscHKtk4Q+RCIiItJI+kKXvMju8CYNU2ZhlFt8yiyMcksWFTSSFzNnzix0E4qOMguj3OJT\nZmGUW7IkolNwsVCnYBERkTAt/tEHIiIiIo2lgkZERESKngoaERERKXoqaCQvck2TLfVTZmGUW3zK\nLIxySxYVNJIXmh48PmUWRrnFp8zCKLdk0SinGDTKSUREJIxGOYmIiIg0QAWNiIiIFD0VNJIX8+fP\nL3QTio4yC6Pc4lNmYZRbsqigkbyYNGlSoZtQdJRZGOUWnzILo9ySRZ2CY1Cn4HCVlZV06NCh0M0o\nKsosjHKLT5mFUW7xqFOwtAj6Rx+fMguj3OJTZmGUW7KooBEREZGip4JGREREip4KGsmLcePGFboJ\nRUeZhVFu8SmzMMotWVTQSF706tWr0E0oOsosjHKLT5mFUW7JolFOMWiUk4iISBiNchIRERFpgAoa\nERERKXoqaCQvysvLC92EoqPMwii3+JRZGOWWLCpoJC+uuOKKQjeh6CizMMotPmUWRrkliwoayYsp\nU6YUuglFR5mFUW7xKbMwyi1ZVNBIXmh4Y3zKLIxyi0+ZhVFuyaKCRkRERIqeChoREREpeipoJC8m\nTpxY6CYUHWUWRrnFp8zCKLdkUUEjeVFZWVnoJhQdZRZGucWnzMIot2TRow9i0KMPREREwujRByIi\nIiINUEEjIiIiRU8FjeRFRUVFoZtQdJRZGOUWnzILo9ySRQWN5MXo0aML3YSio8zCKLf4lFkY5ZYs\nKmgkLyZMmFDoJhQdZRZGucWnzMIot2RRQSN5oVFh8SmzMMotPmUWRrkliwoaERERKXoqaERERKTo\nqaCRvJg6dWqhm1B0lFkY5RafMguj3JJFBY3kxaJFTT4pZIunzMIot/iUWRjllix69EEMevSBiIhI\nGD36QERERKQBKmhERESk6KmgERERkaKngkbyIpVKFboJRUeZhVFu8SmzMMotWVTQSF5cdtllhW5C\n0VFmYZRbfMosjHJLFo1yikGjnERERMJolJOIiIhIA1TQiIiISNFTQSN5MXv27EI3oegoszDKLT5l\nFka5JUtiChozu9TMVprZRjN7xsy+2MD67c3sRjNbZWabzGyFmV2Q8f5JZvacmX1gZuvN7AUzO6ex\nx5UwEydOLHQTio4yC6Pc4lNmYZRbsuxQ6AYAmNkZwE+BbwILgLHAo2bW390r6tjsIWB3YBTwKrAn\nNQu094EbgHLgE2A4MM3M1rj7XxtxXAmw++67F7oJRUeZhVFu8SmzMMotWZJyhWYs8Ct3v9fdy4GL\ngUpgdK6Vzew44CvAMHf/u7uvdvdn3f1fVeu4+1PuPsfdl7j7Sne/A/g3MDj0uCIiIpJMBS9ozKwd\ncAjweNUyj8aS/w0YVMdmw4HngSvN7A0zW2Jmt5jZTvUc5yigP/CPRhxXREREEigJt5y6Am2BNVnL\n1wAD6timL9EVmk3Aiel93AV8FhhTtZKZdQbeBHYEtgCXuPsTjTiuiIiIJFASCpoQbYBtwNnuvh7A\nzA3DzOkAAA4ZSURBVL4PPGRml7j7x+n1PgIOAjoCRwG3mdkKd38q8Lg7ASxevLhRjW+NFixYwKJF\nTT6PUoumzMIot/iUWRjlFk/Gd2edd1Mao+AzBadv/VQCp7j73Izl04Eu7n5Sjm2mA0e4e/+MZQOB\nV4D+7v5qHcf6NdDT3Y8PPO7ZwP0h5ykiIiIAjHT3B5p6pwW/QuPum81sIdEVlLkAZmbpn++oY7P/\nA041sw7uXpleNoDoqs0b9RyuDdHtp9DjPgqMBFYR3e4SERGR7bMT0Jvou7TJFfwKDYCZnQ5MJxpl\nVDV8+lRgoLu/Z2Y3AXu5+/np9XcByoBngAlEw7d/Dfzd3S9Or3MVUcfhV4mKmBOAnwAXu/u07Tlu\nc5+3iIiINI2CX6EBcPcHzawrcB3QDXgRODajqOgO7J2x/gYzOwaYDDxHNOfMLGB8xm53Ae4EegIb\nieajGenuD8c4roiIiBSBRFyhEREREWmMgs9DIyIiItJYKmhERESk6KmgySH9sMptOV6TM9a5zsze\nMrNKM/urme1XyDYXWkOZmdn0HO/NK3S7C83M2pjZ9emHq1aa2XIz+2GO9fR5S9uezMxsmj5vNZlZ\nRzP7efqBvpVmNt/MDs1aR5+zLA3lps8amNlXzGyumb2ZPv9UjnXq/WyZ2Y5mdqeZVZjZR2b2sJnt\nEacdKmhyO5SoI3LV6xjAgQcBzOxK4DKih1oeBmwgeqhl+4K0NhnqzSz99z8Tdb6uWues/Dczca4C\nvgVcAgwErgCuMLPLqlbQ562WBjNL0+etpqlE01KMBPYH/gr8zcz2BH3O6lFvbmmt/bO2C9GgmkuI\n/ltfw3Z+tn5ONBr5FOBIYC/g97Fa4e56NfBKB7004+e3gLEZP3cmGkl1eqHbmpRXjsymAX8odLuS\n9gL+CPw6a9nDwL0ZP+vzFj8zfd5q5rMTsBk4Lmv588B16b/rcxaWmz5rNbPZBqSyltX72Ur//DFw\nUsY6VXPLHba9x9YVmgakZxQeSVSlY2Z9iCrwzIdargOeRQ+1BGpnluGrZrbGzMrN7Bdm9tkCNC9p\n/gkcZWb9AMzsIODLwLz0z/q81VZvZhn0efvUDkTPrvs4a/lGYLA+Z3WqN7eMn/VZq8N2frYOJco6\nc50lwGpifP4SMQ9Nwp0EdAFmpH/uTnRJLddDLbvnsV1Jlp0ZRJdkfw+sBPYFbgLmmdkgT5fjrdTN\nRP93Um5mW4luA1/j7r9Lv6/PW20NZQb6vNXg7uvN7F/AeDMrJ/r8nE30ZbEMfc5y2o7cQJ+1hmzP\nZ6sb8Em60KlrnQapoGnYaODP7v5OoRtSRGpl5u4PZrz/ipm9TDSL81eBv+e3eYlyBtF/IM8kmv36\n88DtZvaWu99X0JYlV4OZ6fOW0/9v796DtarqMI5/Hy+keL/rpDgqpDnqMTExo5hMZZoJndTSyVsT\njTnTpI02OhqBSjNeSs2cckpFrVDJRM1S1MLJxmhsgBLRMBLREBQMBbmp5/z6Y61XNvu8twPn5ZwX\nns/MHs7ea+211157vfOud13YZwMTgAXAB8AM4B5gaF9mqg3ULTfXtf7DQ051SBoEnEB6rULFIkCk\nFmXRXjlss1ajzLqJiHnAEmBzX0VxPXBtRNwfEbMjYiJwE3B5Dnd9665RmXXj+pbKICI+R5rAuV9E\nHAsMAF7G9aymBuVWNT6beV0raaZuLQIGSNqxTpyG3KCp7+ukLq8Px+ZzZV1EmvUOQH4Iw0hj+5u7\nbmVWjaR9gd2AhRsjU/3YQKCzdKyL/Nl0fauqbplV4/q2VkSsiog3JO0CjAQecj1rrFq5VYvnurau\nJuvWdFLvVzHOwcAgYFqz1/KQUw2SBHwNuCsiukrBPwbGSJpLevP2eNJbvh/emHnsb2qVmdLLRMeR\nxpkXkX65XAe8RIveutpGHiHVpf8Cs4GjSC9Jvb0Qx/VtXXXLzPWtOkknkX4pzwGGkHq6XiC9oBdc\nz6qqV26ua0kuh8GkcgI4ME/W/19EvEaDuhURyyTdAdwoaSmwHPgJ8ExEPNt0Rvp6iVd/3Uj/j0on\nMLhG+JWkpWgrSRW3arzNaatVZqSlj1NIH/jVpK7aW4E9+jrPfb2RurFvJE0oXEGaaHgVsFUpnutb\nk2Xm+laz3L4MzCWt0FkA3AzsUIrjetaDcnNd+7CMRpB6STtL24Rm6xbwEdILp5eQGjT3A3v2JB9+\nOaWZmZm1Pc+hMTMzs7bnBo2ZmZm1PTdozMzMrO25QWNmZmZtzw0aMzMza3tu0JiZmVnbc4PGzMzM\n2p4bNGZmZtb23KAxs02CpG0lPSDpHUmdVV50V+/cLkkntzJ/ZtZabtCYtYikO/MXZaekNZL+Len7\nkvr9507SiJz3phsF/cB5wKeBY4F9ImJZOYKkcZJmbozMFMqwM/9b+fvqFlyjnZ6TWUv45ZRmrfUY\n6YWd2wBfAH4GrCG94K7HJG0dEe/3Wu7qXAoI1r5srh0cBLwYES82iLcx3/cSwMdI76apeLcX0++1\n5yRpq4j4YMOzZNY3+v0vRbM2tyYiFkfEaxHxC+CPwCmVQEnDJT0taaWk+ZJuljSwED5P0hhJd0t6\nB/h5Pv5RSfdKekvSu5KelfTJwnmnSJouaZWkuZLGStqyEN4labSkyZJWSHpJ0qgctj8wNUddmnsV\nJuSwkZL+ImmppCWSHpF0YPGGJR0naWa+9t8kjcrXO6IQ5zBJj0paLmmRpF9K2q1eQUo6TdLzklbn\ncrm4EPYUcAlQ6bGYWuX880hvRu4o9JacW4iyR7Xy2JA8Z4sj4s3CtrKQZqPnf7akv0taJmmhpImS\n9shh9Z7TPEkXlvI/U9LYwn6XpAskPSzpXeCKZu5T0umSnst5XiLpCUnbNlEOZi3lBo3ZxrUaGAAg\n6SBSD879wGHAGaQhk1tK51wC/AM4EhgvaTvgaWAf4IvA4cA15M+zpM8AdwM3AYcA3yQNx1xRSncs\ncF8+/1FgoqSdgdeA03KcIfk6F+X97YAbgKOA40lv1H2wkqCkHYDfAf8EPkFqQFxPoVdE0k7An4Dp\nOZ2RwJ7ApFqFJmloDr8nl9W4XBaVBsmXgNuAvwJ7AadWSWZSzvvsHGef0jVrlcd65bmY/Rr31Mzz\n3woYAxxBagjvD9yZw+o9p2aNAybn60+oc5+/yXnem/QMbifVrRH5/HbqybNNVV+/dtybt011I33x\nTC7snwCsAq7N+7cBt5bOGQ58AAzI+/OA35binA+8DexU47pPApeVjp0FLCjsdwFXFvYH5mMn5f0R\npMbKjg3ucfd83qF5/wLgzUr+87HROa0j8v73gMdK6eyb0xlc4zq/BqaUjl0HzCrs3wRMbZDfccCM\nKscblcf65HlEDl9GGnJanv/epdnnXyXNo3NZDqz3nHK9ubB0bCYwtnTPPyrFqXufpEZqJ7BfX3++\nvHkrb55DY9ZaoyQtB7Ym/YqdCFyVwzqAwyWdXYhf+aV7ADAn/z29lGYHMDMi3qlxzQ7gOEljCse2\nBAZI2iYiVudjsyqBEbFS0jLSr/GaJA0GrgaGkRozW5B6XwYBL5DmizwXEe8VTnuWdX/BdwDH53Ip\nCtI8mLlVLv1x4KHSsWeAiyQpInpjXky98lifPFfCh1OYNxMRSwtp1n3+uWdqXI67C2t71QcB/2r+\n1mqqVrfq3eeTpGGu5yU9DjxBanC/3Qt5MdsgbtCYtdZUUq/F+8DrEdFVCNueNCfmZrp32b9a+HtF\nKWxVg2tuTxo+mVwOKDRmyHlaJ5jGw9C/J/36/wbweo4/mzyM1qTtScNSl9L9vhf2IJ3eVq88NiTP\nr0SVFVc0eP55Ls0U0rDUV4HFpCGnKTQu764qaW5dJV65btW9z1x/T5T0KeAk4NvADyQNi4j5DfJk\n1lJu0Ji11oqImFcjbAZpqKZWeC3PAaMl7Vzjl/EM4OCIeLmH6RZVeliKE4l3JfXAjI6IZ/Kx4aXz\n5gBnad3VWMew7sqiGaQ5LvNLDbx6XiTNLykaDrzUw96Z9yjcUw+sT56bSbPm88+TqHcFLo+IBfnY\nMaVo3Z5Ttpg0p6aS1o6kXp9m8tTwPiNiGjBN0nhgPmkO04+bSN+sZTwp2KzvXEcaGrpFUoekwUqr\nk8qTgsvuBd4AHsorig6QdKqkYTn8auBcpZVNh0o6RNIZ+cunWfNJjZBRknbPE5GXAm8B50s6SNLx\npEm2xQbFPaQv19vydUeSJjVTiPdT0hf1fZKOlnSg0uqpCZJqTS69Afi80oqvIXnF0reAH/bgngBe\nAQ7I5b2bpGZ7ltYnz1B/smyj5/8qqcFyYX7GJ5MmCBdVe06QegbPyauoDgfuIs3N2aD7lHSMpMsl\nDZW0H2lS8u6k4UazPuUGjVkfiYhZpEmdQ0irlmYAVwILitGqnPc+cCJp8u0fSD02l5EmaxIRT5BW\nP51Imr8yDfgO6cu8ZrrFYxHxOmnuxrXAIuCW3BNyJjCUNN/kBuC7pbwtz9fuIE1CHc/aOUOrc5yF\npN6WLYDHc/5vBJbW6m2JiJnAV0grgWaRymlMRPyqWvw6HiAN2TxFKr8zy/devGzh+j3Oc510K2nW\nff4RsYT0fxidThrWu5S1jcNKGt2eUw66Bvgz8EjeHgT+0yhvTdznMuCzpHo3h9R4vjjXObM+pd6Z\nS2dmVp2ks4A7SKuy1vR1fsxs0+Q5NGbWqySdA7xM6mk4ktR7MMmNGTNrJTdozKy37U0aitiLtAJo\nEt3nfpiZ9SoPOZmZmVnb86RgMzMza3tu0JiZmVnbc4PGzMzM2p4bNGZmZtb23KAxMzOztucGjZmZ\nmbU9N2jMzMys7blBY2ZmZm3PDRozMzNre/8H+l0xVNfCse4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c01f6b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Feature Reduction')\n",
    "plt.xlabel('Percentage of the Features')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.52, 0.65])\n",
    "plt.grid(True)\n",
    "\n",
    "x = np.array([score[0] for score in scores_all])\n",
    "y = np.array([score[1] for score in scores_all])\n",
    "\n",
    "plt.plot(x, y, color='black', marker='o', linestyle='-', markerfacecolor='lightblue', markersize=5)\n",
    "plt.savefig('feature_reduction.png')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.title('Feature Reduction')\n",
    "plt.xlabel('Percentage of the Features')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.63, 0.64])\n",
    "plt.xlim([70, 100])\n",
    "plt.grid(True)\n",
    "\n",
    "x = np.array([score[0] for score in scores_all])\n",
    "y = np.array([score[1] for score in scores_all])\n",
    "\n",
    "plt.plot(x, y, color='black', marker='o', linestyle='-', markerfacecolor='lightblue', markersize=5)\n",
    "plt.savefig('feature_reduction_zoomed_in.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** GridSearch **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/sklearn/svm/base.py:920: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]Best parameters set found on development set: \n",
      "\n",
      "{'C': 0.1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n",
      "//anaconda/lib/python2.7/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n",
      "//anaconda/lib/python2.7/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[\"0.59 (+/-0.026) for {'C': 0.01}\",\n",
       " \"0.65 (+/-0.029) for {'C': 0.1}\",\n",
       " \"0.64 (+/-0.026) for {'C': 1}\",\n",
       " \"0.59 (+/-0.028) for {'C': 10}\",\n",
       " \"0.53 (+/-0.029) for {'C': 100}\"]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'C':[0.01, 0.1, 1, 10, 100]} \n",
    "\n",
    "svr = LinearSVC(random_state = 2, verbose = 1)\n",
    "cv = LeaveOneLabelOut(labels_TRAIN)\n",
    "X = TRAIN_SPECIFIC[5][1]\n",
    "\n",
    "clf = GridSearchCV(svr, parameters, cv=cv, scoring='accuracy')\n",
    "clf.fit(X, Y_TRAIN)\n",
    "\n",
    "print(\"Best parameters set found on development set: \\n\")\n",
    "print(clf.best_params_)\n",
    "\n",
    "grid_scores = []\n",
    "means = [y[1] for y in clf.grid_scores_]\n",
    "stds = [np.std(y[2]) for y in clf.grid_scores_]\n",
    "params = [a[0] for a in clf.grid_scores_]\n",
    "\n",
    "for mean, std, params in zip(means, stds, params):\n",
    "    grid_scores.append(\"%0.2f (+/-%0.03f) for %r\" % (mean, std * 2, params))\n",
    "    \n",
    "grid_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid_scores = [\"0.47 (+/-0.032) for {'C': 0.001}\"] + grid_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.001, 0.47),\n",
       " (0.01, 0.59),\n",
       " (0.1, 0.65),\n",
       " (1.0, 0.64),\n",
       " (10.0, 0.59),\n",
       " (100.0, 0.53)]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co = [(float(grid.split(':')[-1][:-1].strip()),float(grid.split(' ')[0].strip())) for grid in grid_scores]\n",
    "co.sort()\n",
    "co"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wjLvf7e6L3P0hd3+waFV398Xu/nr6WlzVvQho6dKlfHrIp9nyx1ty6cJL+fZL3+bShZey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\nD+q/D9ZYD/aeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c91089d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i[0] for i in co]\n",
    "y = [i[1] for i in co]\n",
    "\n",
    "plt.plot(x, y, 'm-', marker='o')\n",
    "plt.grid(True)\n",
    "plt.ylim([0.4, 0.7])\n",
    "plt.xlim([0, 1])\n",
    "plt.title('GridSearch on C value')\n",
    "plt.xlabel('Value of C')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Validate **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# transform validation set to only contain selected features\n",
    "selector = SelectPercentile(percentile=76)\n",
    "selector.fit_transform(X_TRAIN, Y_TRAIN)\n",
    "X_VAL = selector.transform(X_VALIDATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear]"
     ]
    }
   ],
   "source": [
    "clf = LinearSVC(random_state = 2, verbose = 1, C = 0.1)\n",
    "clf.fit(TRAIN_SPECIFIC[5][1], Y_TRAIN)\n",
    "predictions = clf.predict(X_VAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "def classifaction_report_csv(report):\n",
    "    report_data = []\n",
    "    lines = report.encode('ascii', 'ignore').split('\\n')\n",
    "    for line in (lines[2:-3] + [lines[-2]]):\n",
    "        row = {}\n",
    "        row_data = line.strip().split('  ')\n",
    "        row_data = [x for x in row_data if x != '']\n",
    "        row['class'] = row_data[0]\n",
    "        row['precision'] = row_data[1]\n",
    "        row['recall'] = row_data[2]\n",
    "        row['f1_score'] = row_data[3]\n",
    "        row['support'] = row_data[4]\n",
    "        report_data.append(row)\n",
    "    dataframe = pd.DataFrame.from_dict(report_data)\n",
    "    return dataframe\n",
    "\n",
    "report = classification_report(Y_VALIDATE, predictions)\n",
    "df = classifaction_report_csv(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "macro recall score:  0.546890774734\n",
      "micro recall score:  0.739974311403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print 'macro recall score: ',recall_score(Y_VALIDATE, predictions, average = 'macro')\n",
    "print 'micro recall score: ',recall_score(Y_VALIDATE, predictions, average = 'micro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Reliable Recall Macro Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "macro recall score:  0.600547619048\n"
     ]
    }
   ],
   "source": [
    "report_data = []\n",
    "lines = report.encode('ascii', 'ignore').split('\\n')\n",
    "for line in (lines[2:-3] + [lines[-2]]):\n",
    "    row = {}\n",
    "    row_data = line.strip().split('  ')\n",
    "    row_data = [x for x in row_data if x != '']\n",
    "    row['class'] = row_data[0]\n",
    "    row['precision'] = row_data[1]\n",
    "    row['recall'] = row_data[2]\n",
    "    row['f1_score'] = row_data[3]\n",
    "    row['support'] = row_data[4]\n",
    "    report_data.append(row)\n",
    "    \n",
    "recall = []\n",
    "for d in report_data:\n",
    "    if d['support'].strip() != '0':\n",
    "        recall.append(d['recall'])\n",
    "        \n",
    "n_recall = [float(x) for x in recall]\n",
    "print 'macro recall score: ',sum(n_recall) / len(recall)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
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