Depression Analysis.ipynb

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    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "from IPython.core.display import display, HTML, Javascript\n",
    "display(HTML('<style>.container { width:90% !important; }</style>'))\n",
    "\n",
    "from collections import OrderedDict\n",
    "import csv\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.width',170, 'display.max_rows',20, 'display.max_columns',30)\n",
    "import sys\n",
    "from time import time\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "\n",
    "import h2o\n",
    "from h2o.estimators.glm import H2OGeneralizedLinearEstimator\n",
    "import lightgbm as lgb\n",
    "import pickle\n",
    "from scipy.sparse import save_npz\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from tensorflow.keras import backend, models, layers, utils\n",
    "\n",
    "from metrics import BinaryClassificationMetrics\n",
    "\n",
    "THOU = lambda x:'{:,}'.format(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Depression Analysis\n",
    "\n",
    "#### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NB_NAME': 'Depression Analysis',\n",
       " 'DIR': './',\n",
       " 'SEED': 123456,\n",
       " 'TRAIN_PCT': 80,\n",
       " 'NGRAM_RANGE': (1, 1),\n",
       " 'MIN_DF_PCT': 0.1,\n",
       " 'XVAL_FOLDS': 4,\n",
       " 'GLM_MAX_PRED': 200,\n",
       " 'NUM_THREAD': 8}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ignoring 10,044 words\n"
     ]
    }
   ],
   "source": [
    "param = dict(\n",
    "    NB_NAME = 'Depression Analysis',\n",
    "\n",
    "    DIR = './',\n",
    "\n",
    "    #seed for random number generator\n",
    "    SEED = 123456,\n",
    "\n",
    "    #percentage of dataset used in training\n",
    "    TRAIN_PCT = 80,\n",
    "\n",
    "    #range of ngrams to be included in model\n",
    "    NGRAM_RANGE = (1,1),\n",
    "\n",
    "    #minimum word frequency (as a percentage) in training data for word to be part of vocabulary\n",
    "    #.1 => 1 in 1000\n",
    "    MIN_DF_PCT = .1,\n",
    "\n",
    "    #number of folds in cross validation\n",
    "    XVAL_FOLDS = 4,\n",
    "\n",
    "    #max predictors\n",
    "    GLM_MAX_PRED = 200,\n",
    "\n",
    "    #number of threads to be used on the machine\n",
    "    NUM_THREAD = 8\n",
    ")\n",
    "param\n",
    "\n",
    "ignore_words = set(['adrienne_wf', 'amp', 'brookemiccio', 'casey', 'co', 'daniandkaceyfan', \n",
    "            'daniellecarolan', 'don', 'fuck', 'fucked', 'fucking', 'hairodynamic', 'https', 'https co', \n",
    "            'itscaitlinbea', 'jeanineamapola', 'kenzie', 'kenzieelizabeth', 'khoa_nguyen', 'la', \n",
    "            'lifeaseva', 'lyssryann', 'maddymcq', 'michaelnorco', 'michellexxo', 'missremiashten', \n",
    "            'mollybails', '_morganyates', 'nashville', 'nicholejacklyne', 'patheticutter', 're', \n",
    "            'sierra_furtado', 'tanamontana100', 'tastypinkbun', 'texas', 'theglenn_coco', 'this', 'to', \n",
    "            'via', 'video', 'vlog', 'xx', 'youtube', '000']) | set(map(str, range(1,10000)))\n",
    "print('Ignoring {:,} words'.format(len(ignore_words)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31,030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42,914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>73,944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       counts\n",
       "label        \n",
       "1      31,030\n",
       "0      42,914\n",
       "All    73,944"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Sample from dataset\n"
     ]
    },
    {
     "data": {
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       "            width:  600px;\n",
       "        }</style><table id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >label</th>        <th class=\"col_heading level0 col1\" >tweet</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1level0_row0\" class=\"row_heading level0 row0\" >6934</th>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row0_col1\" class=\"data row0 col1\" >I’m trying to not cut but it’s so hard not to</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1level0_row1\" class=\"row_heading level0 row1\" >55774</th>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row1_col1\" class=\"data row1 col1\" >@ingridnilsen I don't know what happened. I'm blaming the pan 😐🚣 #girlspan #wut</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1level0_row2\" class=\"row_heading level0 row2\" >25961</th>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row2_col0\" class=\"data row2 col0\" >1</td>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row2_col1\" class=\"data row2 col1\" >I'm the princess without a prince because I'm tough enough to get through life without someone holding my hand.</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1level0_row3\" class=\"row_heading level0 row3\" >59551</th>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row3_col1\" class=\"data row3 col1\" >Can't wait to get out and ride this baby tomorrow🏄 https://t.co/BvTCr3FM46</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1level0_row4\" class=\"row_heading level0 row4\" >28760</th>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row4_col0\" class=\"data row4 col0\" >1</td>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row4_col1\" class=\"data row4 col1\" >i think i’ll spend the day imagining the worst</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1level0_row5\" class=\"row_heading level0 row5\" >28398</th>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row5_col0\" class=\"data row5 col0\" >1</td>\n",
       "                        <td id=\"T_af0f8a54_7439_11e9_b52b_54bf6432fbb1row5_col1\" class=\"data row5 col1\" >not having a crush on anyone is going to kill me</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x289e0c77438>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depr_pd = pd.read_table(param['DIR']+'/depressed_tweets.csv', delimiter='|', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "depr_pd['label'] = 1\n",
    "\n",
    "norm_pd = pd.read_table(param['DIR']+'/normal_tweets.csv', delimiter='|', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "norm_pd['label'] = 0\n",
    "\n",
    "data_pd = pd.concat([depr_pd,norm_pd])[['label','Tweet']].reset_index(drop=True)\n",
    "data_pd.columns = map(str.lower, data_pd.columns)\n",
    "pd.pivot_table(data_pd.groupby('label').size().reset_index(name='counts'),\n",
    "               values='counts', index=['label'], aggfunc=np.sum, margins=True).loc[[1,0,'All']].applymap(THOU)\n",
    "\n",
    "print('\\n','Sample from dataset')\n",
    "data_pd.sample(6, random_state=123456).style.set_properties(subset=['tweet'], **{'width': '600px'})\n",
    "\n",
    "del depr_pd, norm_pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pre-process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Replace all unicode characters\n",
      "\n",
      "Split into Train 80% and Test 20%\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>train</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6,284</td>\n",
       "      <td>24,746</td>\n",
       "      <td>31,030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8,840</td>\n",
       "      <td>34,074</td>\n",
       "      <td>42,914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>15,124</td>\n",
       "      <td>58,820</td>\n",
       "      <td>73,944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "train   False    True     All\n",
       "label                        \n",
       "1       6,284  24,746  31,030\n",
       "0       8,840  34,074  42,914\n",
       "All    15,124  58,820  73,944"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Split Train into folds (test data is in fold 0)\n"
     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>fold</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6,284</td>\n",
       "      <td>6,098</td>\n",
       "      <td>6,196</td>\n",
       "      <td>6,268</td>\n",
       "      <td>6,184</td>\n",
       "      <td>31,030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8,840</td>\n",
       "      <td>8,544</td>\n",
       "      <td>8,592</td>\n",
       "      <td>8,475</td>\n",
       "      <td>8,463</td>\n",
       "      <td>42,914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>15,124</td>\n",
       "      <td>14,642</td>\n",
       "      <td>14,788</td>\n",
       "      <td>14,743</td>\n",
       "      <td>14,647</td>\n",
       "      <td>73,944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fold        0       1       2       3       4     All\n",
       "label                                                \n",
       "1       6,284   6,098   6,196   6,268   6,184  31,030\n",
       "0       8,840   8,544   8,592   8,475   8,463  42,914\n",
       "All    15,124  14,642  14,788  14,743  14,647  73,944"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sample from dataset\n"
     ]
    },
    {
     "data": {
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       "        }</style><table id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >label</th>        <th class=\"col_heading level0 col1\" >tweet</th>        <th class=\"col_heading level0 col2\" >train</th>        <th class=\"col_heading level0 col3\" >fold</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1level0_row0\" class=\"row_heading level0 row0\" >6934</th>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row0_col1\" class=\"data row0 col1\" >I m trying to not cut but it s so hard not to</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row0_col2\" class=\"data row0 col2\" >True</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row0_col3\" class=\"data row0 col3\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1level0_row1\" class=\"row_heading level0 row1\" >55774</th>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row1_col1\" class=\"data row1 col1\" >@ingridnilsen I don't know what happened. I'm blaming the pan   #girlspan #wut</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row1_col2\" class=\"data row1 col2\" >True</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row1_col3\" class=\"data row1 col3\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1level0_row2\" class=\"row_heading level0 row2\" >25961</th>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row2_col0\" class=\"data row2 col0\" >1</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row2_col1\" class=\"data row2 col1\" >I'm the princess without a prince because I'm tough enough to get through life without someone holding my hand.</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row2_col2\" class=\"data row2 col2\" >True</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row2_col3\" class=\"data row2 col3\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1level0_row3\" class=\"row_heading level0 row3\" >59551</th>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row3_col1\" class=\"data row3 col1\" >Can't wait to get out and ride this baby tomorrow  https://t.co/BvTCr3FM46</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row3_col2\" class=\"data row3 col2\" >True</td>\n",
       "                        <td id=\"T_afbc60be_7439_11e9_866f_54bf6432fbb1row3_col3\" class=\"data row3 col3\" >4</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x289e0c56fd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\\nReplace all unicode characters')\n",
    "data_pd['tweet'] = data_pd['tweet'].replace({r'[^\\x00-\\x7F]+':' '}, regex=True)\n",
    "\n",
    "print('\\nSplit into Train %.0f%% and Test %.0f%%'%(param['TRAIN_PCT'],100.-param['TRAIN_PCT']))\n",
    "np.random.seed(param['SEED'])\n",
    "data_pd['train'] = np.random.random(len(data_pd))<.01*param['TRAIN_PCT']\n",
    "pd.pivot_table(data_pd.groupby(['train','label']).size().reset_index(name='counts'), \n",
    "        values='counts', index=['label'], columns=['train'], aggfunc=np.sum, margins=True).loc[\n",
    "            [1,0,'All']].applymap(THOU)\n",
    "\n",
    "print('\\nSplit Train into folds (test data is in fold 0)')\n",
    "data_pd['fold'] = 0\n",
    "data_pd.loc[data_pd['train'], 'fold'] = np.random.randint(low=1, high=1+param['XVAL_FOLDS'], size=sum(data_pd['train']))\n",
    "pd.pivot_table(data_pd.groupby(['fold','label']).size().reset_index(name='counts'), \n",
    "        values='counts', index=['label'], columns=['fold'], margins=True, aggfunc=np.sum).loc[\n",
    "            [1,0,'All']].applymap(THOU)\n",
    "\n",
    "print('\\nSample from dataset')\n",
    "data_pd.sample(4, random_state=123456).style.set_properties(subset=['tweet'], **{'width': '500px'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Determine Vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing size of vocabulary at different thresholds of document frequency\n",
      "Current: 0 0.01 0.1 0.5 1 2 5 "
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_df_pct</th>\n",
       "      <th>vocab_sz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00%</td>\n",
       "      <td>42,077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01%</td>\n",
       "      <td>5,772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.10%</td>\n",
       "      <td>1,048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50%</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00%</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.00%</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.00%</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  min_df_pct vocab_sz\n",
       "0      0.00%   42,077\n",
       "1      0.01%    5,772\n",
       "2      0.10%    1,048\n",
       "3      0.50%      298\n",
       "4      1.00%      151\n",
       "5      2.00%       84\n",
       "6      5.00%       30"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Computing size of vocabulary at different thresholds of document frequency')\n",
    "mindf,sz = [0,.01,.1,.5,1,2,5],[]\n",
    "print('Current: ', end='')\n",
    "for pct in mindf:\n",
    "    print(pct, end=' ')\n",
    "    count_vect = CountVectorizer(ngram_range=param['NGRAM_RANGE'], min_df=.01*pct)\n",
    "    counts_train = count_vect.fit_transform(data_pd[data_pd['train']]['tweet'].tolist())\n",
    "    sz.append(len(count_vect.vocabulary_))\n",
    "\n",
    "mindf_pd = pd.DataFrame(dict(min_df_pct=list(map(lambda x:'%.02f%%'%x,mindf)), vocab_sz=sz))\n",
    "mindf_pd['vocab_sz'] = mindf_pd['vocab_sz'].apply(THOU)\n",
    "mindf_pd\n",
    "\n",
    "del counts_train, count_vect, mindf, pct, sz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Model Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: 58,820 x 1,048\n",
      "\n",
      "vocabulary: 991 (dropping words in ignore list)\n",
      "\n",
      "X_train: 58,820 x 991\n",
      "y_train: 58,820\n",
      "folds: 58,820\n",
      "\n",
      "X_test:  15,124 x 991\n",
      "y_train: 15,124\n",
      "\n",
      "Using binary model matrices for generalization. Replaced word frequency with 1s.\n"
     ]
    }
   ],
   "source": [
    "#create vocabulary\n",
    "count_vect = CountVectorizer(ngram_range=param['NGRAM_RANGE'], min_df=.01*param['MIN_DF_PCT'])\n",
    "X_train = count_vect.fit_transform(data_pd[data_pd['train']]['tweet'].tolist())\n",
    "print('X_train: {:,} x {:,}'.format(*X_train.shape))\n",
    "\n",
    "vocab = list(set(count_vect.vocabulary_.keys())-ignore_words)\n",
    "print('\\nvocabulary: {:s} (dropping words in ignore list)'.format(THOU(len(vocab))))\n",
    "\n",
    "#training matrix using updated vocabulary\n",
    "count_vect = CountVectorizer(ngram_range=param['NGRAM_RANGE'], vocabulary=vocab)\n",
    "X_train = count_vect.transform(data_pd[data_pd['train']]['tweet'].tolist())\n",
    "X_train.data = np.ones(X_train.data.shape[0])\n",
    "print('\\nX_train: {:,} x {:,}'.format(*X_train.shape))\n",
    "\n",
    "y_train = data_pd.loc[data_pd['train'], 'label'].values\n",
    "print('y_train: {:,}'.format(*y_train.shape))\n",
    "\n",
    "folds = data_pd.loc[data_pd['train'], 'fold'].values\n",
    "print('folds: {:,}'.format(*folds.shape))\n",
    "\n",
    "#test matrix with same vocabulary\n",
    "X_test = count_vect.transform(data_pd[~data_pd['train']]['tweet'].tolist())\n",
    "X_test.data = np.ones(X_test.data.shape[0])\n",
    "print('\\nX_test:  {:,} x {:,}'.format(*X_test.shape))\n",
    "\n",
    "y_test = data_pd.loc[~data_pd['train'], 'label'].values\n",
    "print('y_train: {:,}'.format(*y_test.shape))\n",
    "\n",
    "print('\\nUsing binary model matrices for generalization. Replaced word frequency with 1s.')\n",
    "del count_vect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_npz('saved/X_train', matrix=X_train)\n",
    "np.save('saved/y_train', y_train)\n",
    "save_npz('saved/X_test', matrix=X_test)\n",
    "np.save('saved/y_test', y_test)\n",
    "\n",
    "with open('saved/vocab.pkl', 'wb') as f:\n",
    "    pickle.dump(vocab, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GLM Model Using h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "; Java HotSpot(TM) 64-Bit Server VM (build 25.181-b13, mixed mode)\n",
      "  Starting server from c:\\python37\\lib\\site-packages\\h2o\\backend\\bin\\h2o.jar\n",
      "  Ice root: C:\\DOWNLO~1\\Temp\\tmp33b34j8f\n",
      "  JVM stdout: C:\\DOWNLO~1\\Temp\\tmp33b34j8f\\h2o_Divya_Nori_started_from_python.out\n",
      "  JVM stderr: C:\\DOWNLO~1\\Temp\\tmp33b34j8f\\h2o_Divya_Nori_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>05 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/New_York</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.24.0.3</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>4 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_Divya_Nori_hcf8lj</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>11.50 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>12</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>Amazon S3, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.7.3 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------------------\n",
       "H2O cluster uptime:         05 secs\n",
       "H2O cluster timezone:       America/New_York\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.24.0.3\n",
       "H2O cluster version age:    4 days\n",
       "H2O cluster name:           H2O_from_python_Divya_Nori_hcf8lj\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    11.50 Gb\n",
       "H2O cluster total cores:    12\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.7.3 final\n",
       "--------------------------  ------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init(ip='localhost', port=54321, nthreads=param['NUM_THREAD'], min_mem_size='12g')\n",
    "#h2o.init(ip='localhost', port=60111)\n",
    "h2o.no_progress()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data into h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_h2o: 58,820 x 993 (including label, fold)\n",
      "test_h2o: 15,124 x 992 (no label, fold=0)\n",
      "\n",
      "  time: 0.2min\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "train_h2o = h2o.H2OFrame(X_train)\n",
    "train_h2o = train_h2o.cbind(h2o.H2OFrame(np.c_[y_train,folds]))\n",
    "train_h2o.names = vocab + ['label','fold']\n",
    "print('train_h2o: {:,} x {:,} (including label, fold)'.format(*train_h2o.shape))\n",
    "\n",
    "test_h2o = h2o.H2OFrame(X_test)\n",
    "test_h2o = test_h2o.cbind(0)\n",
    "test_h2o.names = vocab + ['fold']\n",
    "print('test_h2o: {:,} x {:,} (no label, fold=0)'.format(*test_h2o.shape))\n",
    "\n",
    "print('\\n  time: {0:.1f}min'.format((time()-start)/60.))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Regularization Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key lambdas: {'lambda.max': 0.05865, 'lambda.min': 0.0003204, 'lambda.1se': 0.0009784}\n",
      "\n",
      "  time: 1.0min\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "\n",
    "glm_h2o = H2OGeneralizedLinearEstimator(family='binomial', alpha=1., lambda_search=True, nlambdas=100, seed=param['SEED'])\n",
    "glm_h2o.train(y='label', training_frame=train_h2o, fold_column='fold')\n",
    "\n",
    "try:\n",
    "    key_lambdas = dict(map(lambda x:tuple(x.split(' = ')), glm_h2o.summary()['lambda_search'][0].split(', ')))\n",
    "    key_lambdas = dict((k,float(v)) for k,v in key_lambdas.items() if k not in {'nlambda'})\n",
    "except:\n",
    "    key_lambdas = dict()\n",
    "    print('Unable to parse lambda summary')\n",
    "print('Key lambdas:', key_lambdas)\n",
    "\n",
    "regpath_h2o = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(glm_h2o)\n",
    "regpath_pd = pd.DataFrame(index=np.arange(len(regpath_h2o['lambdas'])), columns=['lambda','ncoef','auc'])\n",
    "for n,(lamb,coefs) in enumerate(zip(regpath_h2o['lambdas'],regpath_h2o['coefficients'])):\n",
    "    mod = H2OGeneralizedLinearEstimator.makeGLMModel(model=glm_h2o, coefs=coefs)\n",
    "    regpath_pd.loc[n] = [lamb, sum(1 for x in coefs.values() if abs(x)>1E-3), mod.model_performance(train_h2o).auc()]\n",
    "    h2o.remove(mod)\n",
    "\n",
    "print('\\n  time: {0:.1f}min'.format((time()-start)/60.))\n",
    "del glm_h2o, mod, regpath_h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx1 = np.abs(regpath_pd['lambda'].values-key_lambdas['lambda.1se']).argmin()\n",
    "idx2 = np.argmax(regpath_pd['ncoef'].values>=param['GLM_MAX_PRED'])\n",
    "\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = [fig.add_subplot(121),fig.add_subplot(122)]\n",
    "for n in range(len(ax)):\n",
    "    s = 'auc' if n else 'lambda'\n",
    "    _ = ax[n].plot(regpath_pd['ncoef'].values, regpath_pd[s].values)\n",
    "    _ = ax[n].plot(regpath_pd.loc[idx1,'ncoef'], regpath_pd.loc[idx1,s], 'x', color='k', ms=8,\n",
    "                   zorder=200, label='lambda.1se')\n",
    "    _ = ax[n].plot(regpath_pd.loc[idx2,'ncoef'], regpath_pd.loc[idx2,s], 'o', color='k', ms=8, \n",
    "                   markerfacecolor='none', zorder=200, label='ncoef = {}'.format(regpath_pd.loc[idx2,'ncoef']))\n",
    "    _ = ax[n].set_xlabel('Number of Coefficients')\n",
    "    _ = ax[n].set_ylabel('Mean Cross-validated AUC' if n else 'Lambda Penalty')\n",
    "    _ = ax[n].legend()\n",
    "    _ = ax[n].grid()\n",
    "\n",
    "del idx1, idx2, fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Final Model and Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lambda</th>\n",
       "      <th>ncoef</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0109902</td>\n",
       "      <td>217</td>\n",
       "      <td>0.78907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       lambda ncoef      auc\n",
       "18  0.0109902   217  0.78907"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.argmax(regpath_pd['ncoef'].values>=param['GLM_MAX_PRED'])\n",
    "regpath_pd.iloc[[idx]]\n",
    "use_lambda = regpath_pd.loc[idx,'lambda']\n",
    "\n",
    "glm_h2o = H2OGeneralizedLinearEstimator(family='binomial', alpha=1., lambda_search=False, Lambda=use_lambda, seed=param['SEED'])\n",
    "glm_h2o.train(y='label', training_frame=train_h2o, fold_column='fold')\n",
    "\n",
    "predict_pd = data_pd.copy(deep=True)[['label']]\n",
    "predict_pd['score'] = 0\n",
    "predict_pd.loc[data_pd['train'],'score'] = glm_h2o.predict(train_h2o).as_data_frame()['p1'].values\n",
    "predict_pd.loc[~data_pd['train'],'score'] = glm_h2o.predict(test_h2o).as_data_frame()['p1'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4GFOLJ1I3yAR3f4PXoyukEBClWrZsGVFRUSQlJaGUcrpCwB5kmkAI4dTOZOQxdek2mhz8hI/c16K9fLF0nYhXh8fB3cvoeMIJhIWFUa1aNXx8XHcdiRQDQginlFdo5ov1B8nd/BkvqUUEuOdhbv0gHj0ngF+Y0fGEEzh16hS1a9emffv2rFmzBuXCO0ukGBBCOBWtNT/vSmLz8m8YVTib+qZk8mvfiqnfe5iqOPeZ8qLsrF69mn79+rFkyRJuu+02ly4EQIoBIYQT2Z2YwZxFS/hH2mdMcttPXkgDuOMHvBv1kn4B4pp06dKFcePG0aVLF6OjlAtSDAghyr3kzHw+XfoHkfFT+cB9A0XegVh6vI9P20fkFEFxTZYtW0bPnj3x9fXl3XffNTpOuSHFgBCi3MovMjNzQzx5m6bxL7UYH49CiqOfwKv7C+DjOnvAhX0cOHCA/v378/bbb/Piiy8aHadckWJACFHuaK1ZtucM25bOZGTBHGqZUsmrdxtud7yNW1jFPTlOOFbTpk1ZunQpPXv2NDpKuSPFgBCiXNl7OpO5Py1icOqnTDQd4mJoE+j/FT71bzU6mnBSU6ZMoUePHkRFRXH77bcbHadckmJACFEunMvO54slm2gW/yHvuv1Onncolt5T8bvpfjBV/INihGNcuHCB//znP5w4cYKpU6caHafckmJACGGo/CIzczYeoHjThzzHL3i4Q0GHZ/Dp+jx4BRgdTzgprTVKKUJCQti2bZtLHTp0PaQYEEIYQmvNyr1niF36OY8WfENVdYGLDfvjfsdbuIfUMTqecGJaa5577jlCQ0MZP3481atXNzpSuSfFgBCizO0/k8l3P37P4NRP6Ws6RnZYS7hzAX61OxgdTVQAWmvS0tIwm81/jRCI0kkxIIQoM6nZBcxatp7m+z/gDbftXPSpgrnP5wRE3QMmOTdN3BiLxUJOTg6BgYHMmjULk8kkhYCNpBgQQjhcQbGZuRv3YfntA55hGXi4k9/pBfxueQY8fY2OJyqIUaNGERsby++//463t7fRcZyKFANCCIfRWrNm/xn2/PIxDxbMpbLKIrvxPwi4YyIEyjyusK/+/ftTu3ZtKQSug9JaG53BrqKjo3VMTIzRMYRweQeTs1i08FsGnfuUSFMimZWjCRo4CWrcZHQ0UYEUFhayZ88eoqOjjY5iKKVUrNb6uv8nyMiAEMKu0nMK+HrJWloemMTLbrvI9qtB8e2zCWo+UA4TEnY3YcIEPv74Yw4fPkzNmjWNjuO0pBgQQthFYbGF736Lg9/eZ4xehcXDi7yb/01AlyfBQ4ZthWP861//olWrVlII3CApBoQQN0RrzYb4JPYt/g/3FywgSOWS3ew+gm5/DfyrGB1PVEC5ublMnz6dsWPHEhYWxtChQ42O5PSkGBBCXLfDyVn8vHA2g859SjfTWc5X7YQaNImgqs2NjiYqsO+//57nn3+e9u3b06lTJ6PjVAhSDAghrllGbiFzf1lJywPv87xpLxn+dSjuP59KTfrKugDhcA8++CCtW7cmKirK6CgVhnT5EELYrMhsYcH6WNa8P5Qn4ofTxuMEF7u/SfBzsbhH3i6FgHCYCxcuMGTIEE6ePIlSSgoBO5ORASGETTYeSCTh50ncm/89vqqQrBYPE3L7K+BbyehowgWcPn2aTZs2ER8fT506cnaFvUkxIIQo1dFz2az4/gv6n/ucW03nSK3ejYC73iekcoTR0YQLKCwsxNPTkxYtWnD06FH8/PyMjlQhyTSBEOKKMnOLmLFgEenTejIm7Q0CAgIpHPojlR9fjJJCQJSBc+fOcdNNNzFr1iwAKQQcSEYGhBD/pdhsYfGmWDw3TGSE/o1cjyByuk+iUodHwE3+yRBlJzAwkCZNmlC/fn2jo1R48jdbCPGXP+ITObL4bf6R/yMeysL5Vk8Q1vcl8A4yOppwIadPnyY0NBQfHx8WLlxodByXIMWAEILjqdms/f4T7jg3nU7qPMk1byN88HuEVapndDThYvLy8rjlllto164dCxYsMDqOy5BiQAgXlpVfxE8/L6LV/vd4zHSUcwGRFAz6mqoNbjY6mnBRPj4+vP766zRr1szoKC5FigEhXJDZolm6aRveG17nQf0HmZ5hZPWcSpV2D4BJ1hWLsnfgwAEuXrxI27ZteeCBB4yO43KkGBDCxWw7eJLjiycyKG8xKBMprccS3vcF8JSV2sIYWmtGjBhBZmYme/fuxc3NzehILkeKASFcxKnUbDZ8/yF9z82gvcrkdO3+1Bj8DuHBtYyOJlycUor58+dTWFgohYBBpBgQooLLKShmyeIFRB2YxHB1grNBLSm46wNq1m1ndDTh4rZv387q1asZP348devWNTqOS5PJQSEqKItFs3zD78S824f74p+kqmc+GXdMp9qzv+ElhYAoBxYsWMCsWbPIzMw0OorLU1prozPYVXR0tI6JiTE6hhCGik04wamfXuWOvCWYTZ5caPMU1W8bBx4+RkcTAq01SiksFgvp6elUrlzZ6EhOTykVq7WOvt7Xy8iAEBXI6fQsvvv0FerN68yd+T9zpu4gvMftpnq/8VIIiHLh119/pVOnTqSnp2MymaQQKCdkzYAQFcDFgmJWLv6GqAOTuEclkRgcje8/PqBurVZGRxPiv2itsVgsmM1mo6OIS5T5yIBSqpJS6iel1EWl1Eml1NCrXOellPpcKZWilDqvlFqilKpR1nmFKM8sFs3qDRvY824PBsc/Q5AnnO8/m1rPrMVbCgFRjqSlpQHQs2dPtmzZQpUqVQxOJC5lxDTBJ0AhEA4MAz5TSl2p1dRYoCPQEqgOZAAfl1VIIcq73QlHWfn+MLqvH0QLDpPYbgKVX9hNpTaDQCmj4wnxl7Vr11K3bl02bNgAgEkaW5U7ZTpNoJTyAwYDzbXWOcDvSqlfgAeAFy+7vB6wSmudUvLaBcCUsswrRHl0Jj2T7Qveofu5OTRX+Zyody/1/zERf/8wo6MJcUXR0dHcf//9tG7d2ugo4irKujyLAMxa60OXPBYHXGlk4Cugs1KqulLKF+sowooyyChEuZRXUMwvC76gaGpbBqZ+RlpIKwof20TDhz7DJIWAKIc2btyI2WwmODiYzz//nKAgOf2yvCrrYsAfuHxDaSYQcIVrDwGngCQgC4gE3rjSN1VKjVRKxSilYlJTU+0YVwjjaa1Zv2EtB969lQEH/4mnlzepA+dR/5kV+NZobnQ8Ia5o7969dOvWjQ8//NDoKMIGZb2bIAcIvOyxQCD7Ctd+BngDocBF4F9YRwbaX36h1voL4Auw9hmwY14hDLUvIYHknybQPW8N2aYATrSfSN3eo8FNNgKJ8q1FixbMnTuXgQMHGh1F2KCsRwYOAe5KqUaXPBYF7L/CtVHAbK31ea11AdbFg+2UUjIeKiq85PQLLP/kOerNu5lb89dxqMGDBPxzD3X7Pi2FgCjXvvjiC44cOQLAfffdh4+P9LdwBmVaDGitLwKLgDeUUn5Kqc7AncA3V7h8BzBcKRWklPIARgNntNZpZZdYiLKVX1jMyvnTsEyN5vbULzldqSOFj2+hyfCPMPmGGB1PiFKlp6czfvx4mRpwQkZ8xBgNzATOAenAKK31fqXUzcAKrbV/yXXPA1OBw4AnsA8YZEBeIRxOa83mjSsJ2vgqfXQCp7waknLHdBpH9TQ6mhA2Cw0NZevWrdSuXdvoKOIalXkxoLU+D/zPJJLWehPWBYZ/3k/HuoNAiArt4MF4Uhe/yM35GzivQjjS8T0a9noMTHKUqyj/tNa8+uqrVKtWjVGjRtGgQQOjI4nrIJOPQhgkNT2duPmv0SV1PnWVYl+Dx4gc8iqVvK+0uUaI8slsNrN7926Sk5P/OoBIOB8pBoQoY/mFRWz+8WNaHJxKT3WB/aG9qX3P+zQPr2d0NCFsprWmoKAAb29vFi5ciLu7uxQCTkyKASHKiNaa7RuWEPzbq/TQxzjmHUlx/69p1vwWo6MJcc3Gjh3L3r17WblyJV5eXkbHETdIigEhysChg3FcWPwS7fM3c06FkdD5PzTu+bCcISCcVocOHfDz88PT09PoKMIOpBgQwoHS01PZP38C7VMXUlO5sbvRGJoPfpkq3n5GRxPimpnNZg4dOkRkZCRDh17xwFnhpOToKCEcQWvOrvsc9fFNdEn9jviw2ygeFUOrYW/hLoWAcFIvv/wy7du358yZM0ZHEXYmIwNC2JvFzLkFY6h2aB47VTNC7ppEq5adjU4lxA17+umnqV+/PtWrVzc6irAzGRkQwp6K8jk3816qHJrHXI9/UOWpNdSTQkA4sYKCAmbOnInWmho1avD4448bHUk4gBQDQthLfiapn/ejyunVzPAbSZ+xn1KzkkwJCOc2Z84cRowYwbZt24yOIhxIpgmEsIfsZNKn9yco+yjTQl/kocf/ib+X/PUSzu+xxx6jadOmdOjQwegowoFkZECIG6TTjpAxrSve2SeZXuNtHhv9LykEhFPLzs5mxIgRpKSkoJSiS5cuRkcSDmbTv1hKKT+gE9AOqAp4A+exHkm8WWt90GEJhSjHihNjyZ99F+ZiM/MipjH6viG4maR3gHBuCQkJ/PjjjwwaNIh+/foZHUeUgVKLAaVUO+AprKcF+gIpQBpQAAQDNQBPpVQ88BUwXWud69DEQpQThQlrsSy4nwyLH6vbfMmo/j2lHatwamazGTc3N6Kjozl27BiVKlUyOpIoI1edJlBKLQXWlty9HwjXWlfTWrfQWkdrrRsCAUAb4FvgAeC4UqqPo0MLYbTcnd9hmn8Px82V+ePW+TwyoJcUAsKppaen07FjRxYtWgQghYCLKW1kYCtwv9Y642oXaK2Lgd0lX+8qpW7FWiAIUWFlb/yYgPUT2G5pQlr/OdzdtonRkYS4YZ6engQEBODnJztgXJHSWhudwa6io6N1TEyM0TFERaQ1mcv+TVDMVNbotnjfO5ubI2sanUqIG3Lu3DlCQkLw8PCQI4idmFIqVmsdfb2vt2k3gVLqTaVUg+v9IUI4PXMxFxY8QVDMVBbSk7CHF0ghIJxebm4uXbp0+auRkBQCrsvW/U8PAy8ppTYDM4EftNYXHRdLiHKkKI/zc+6n0um1zHQbwi0jp9AwPNDoVELcMF9fX5566imio6/7A6WoIGyaJlBKmYDbgIeAAUARsBCYpbXe5MiA10qmCYRd5V3gwleDCUrdyTTvxxg86g1qBPsYnUqIG3L06FEKCwuJjIw0OoqwkxudJrBpZEBrbQFWACuUUsHAMOBBYINS6igwC/haa510vUGEKG8sGUlkzBiAX84JJgW9yGOPj6OSn5zdLpyb1pp7772X/Px84uLiMJmk95y4jnbEJbsLPlFK7QTeBW4G3gTeUEotAp7VWsv5lsKpZSdswPz9w3gU5/Fp9XcZ89DD+ElXQVEBKKWYM2cOZrNZCgHxl2v6TVBKVVNKvVDSZGgTYMbaXyAYuAdoDnxn95RClBWtOb3sPXzmD+J8sRe/dvqGZ0Y+KoWAcHp79uzhs88+A6Bp06a0aNHC4ESiPLG1HfEQrIsIe2HtQjgH+EprfeySyxYppS4Aq+yeUogyoPMyODHzYeqlrmO9qSNhw2YwsEEto2MJYRfTpk1jxYoVDBs2jMBAWQAr/putH3fmAkuBgcCKkjUEV5IAfGCPYEKUpewTu8n9dig1i5L5Pmw0t414nSBfWR8gKo5PPvmElJQUKQTEFdk6TVBTa32X1npZKYUAWuszWuuX7JRNiDJxct2XeMzuhS7KZVXbLxky5m0pBESFsHnzZvr27Ut2djYeHh7UrCm9McSV2VoMbFBKXXGCSSnVVCl1wI6ZhCgTuiiPgzMeoc5vz7HfFEHasDX063eXNF4RFUZKSgqnTp0iOzvb6CiinLN1mqAJcLXN1f5AI/vEEaJsZJ05woXZ99Kk8DDLgu6j82NTCPb3NTqWEHaRnZ1NQEAAd911F/3798fDw8PoSKKcK+3UQl+lVBWlVJWSh0L+vH/JV21gMCD9BYTTOPbHIvjiVkIKkljd8kNuf+YzKQREhbFu3Trq1avHtm3bAKQQEDYpbWTgn8CrgC75Wn6V6xQg6wREuafNxez59kWijs8gQdWn6B+z6d08yuhYQthUFBK6AAAgAElEQVRV06ZN6dWrFw0bNjQ6inAipRUD3wP7sL7Zfw+8DBy+7JpC4KDW+vLHhShXss+f5fSMYUTlxfKbf19aPjad4KAgo2MJYTexsbHcdNNNVK1alfnz5xsdRziZqxYDWut4IB5AKdUX2KK1ziqrYELYS+LBGDy/u4d6lkw2RP6bW+4eh8kkiwRFxbF7927at2/PlClTePrpp42OI5yQrWcTSCMh4ZT2//4LtdeOJA8fjg5YRNc2txgdSQi7i4qK4uOPP+aBBx4wOopwUqUtIDyllIoquZ1Ycv+qX2UXWQjbbF/8CRFrHiLNVIXih1fTTAoBUcHMmzePpKQklFKMGjUKf39/oyMJJ1XayMBcIO2S239/1rEQ5YC2WPhj1gt0TvyCvd6tqT3qR4KCQ42OJYRdpaWlMWrUKO6//34++eQTo+MIJ1famoGXLrn9YtnEEeLG5Ofns/vTB+mctZKY4D60HDUHTy9vo2MJYXdhYWFs2rSJxo0bGx1FVAA2dSBUSr2klKrn6DBC3Ijz6WkkTOlDh6yVbK/7OG2eni+FgKhw3n//febNmwdAy5Yt8fLyMjiRqAhsbUf8PHBEKbVdKTVOKSUNrkW5cuL4Ic5/0oOmBXuIa/M27R56HyVntYsKpri4mBUrVrBy5Uqjo4gKxtZ2xOFAb+Bu4BXgfaXUVmA+sFBrneKgfEL8rbgdv1N12QP4kceJPnOI6tjf6EhC2JXWGrPZjLu7O8uWLZPRAGF3Nn100loXa62Xa60fwloYDAZOAe8Ap5VSax0XUYir27TiOxos/QcmpcgZupRGUgiICujFF19k8ODBFBUV4evri5ubm9GRRAVj68jAX7TWhcDPSqllwCDgQ6CbvYMJURqtNau//YDuR97mjGdtQh79mcDwOkbHEsIh6tSpQ25urhQBwmGuqRhQSpmAHsA9WAuBIGAL8Lb9owlxZfmFxWyYPo4+6XM4FNCWeqMX4uEbbHQsIezKYrGQmJhInTp1GD16tNFxRAVn626Crkqpz4BkYBXQAngLqKO1vllrLZtcRZk4n5XD1il30yd9DvFV76TRM8ulEBAV0ssvv0x0dDTJyclGRxEuwNaRgXXAHmAK8J3W+rjjIglxZSdOJ5E+8x66WvaS0HQskUNeByVnDIiK6dFHHyUsLIzw8HCjowgXoLT++8aCSqnGWuuEMshzw6Kjo3VMTIzRMYSdxR86jPu8u6jLGZJueZ+63UcYHUkIuysqKmLJkiXcddddRkcRTkYpFau1jr7e19u6m8ApCgFRMcXu2YPP3P7U4Bzpd34rhYCosKZPn87gwYPZsWOH0VGEi7nqNIFS6jdgpNb6YMntUmmt5RQYYXebt22j3vL7CFT55N2zkKqRNxsdSQiHGTVqFA0aNKBt27ZGRxEuprSRgSSgsOT2mZL7pX0JYVdr1v9KxPIh+JqKsQxfQqgUAqICys3NZdy4cWRkZODm5kbfvn2NjiRcUGkHFd13ye17yyaOEFZLlv3CzdufwOLug+eIJfhWb2p0JCEcYufOnXz22Wfceuut3HnnnUbHES7K1q2FnyqlrtjRRSlVSyn1qX1jCVelteb7H+bRbftjFHkE4jdqrRQCokL6c/F2ly5dOHr0qBQCwlC2nuTyBNY2xFdSBXjcPnGEK7NYNN9+M4MB+54mx7sqIWN+xStMDssUFU9GRgY9evRg7VprJ/fq1asbnEi4umvpQHi1PYiNgTQ7ZBEurMhs4duvPuL+pImk+TWk6pPLUX5hRscSwiHMZjPZ2dnk5OQYHUUIoPTdBE8CT5bc1cB3Sqn8yy7zBmoB8xwTT7iCCxcLWfjVuzySPoWUoCiqjfoZ5SNdBUXFk5GRQWBgIKGhoWzdulXOGhDlRmkjA8eAX0tuNwFigMuPKi4EDgJz7R9NuILYk+f54+tXecr8NWcrd6L6yIXg6Wd0LCHsLicnhy5dutC9e3emTp0qhYAoV0rbTbACWAGglMoBPtFany6rYKJis1g0M347ivr1VZ5yW0pGvX5UGzYT3OWcdlEx+fn5MWTIEG6+WbbIivLHpnbEzkTaEZd/Fy4W8q/vd9Lr6Nvc7b6RwtYP49n/AzDJJyVR8SQmJmKxWKhTR47YFo5zo+2IS1sz8DXwqtb6eMnt0mit9YPXG0K4jp2nLjDu2y1MyJ9MT/dY9K0v4Nn1JTlwSFRIWmsGDhyI1prY2FiU/J6Lcqq0NQONsC4QBIjg6rsJ+JvnhEBrzZebjvPpylhmeU8hyhQPt09GtXvM6GhCOIxSis8++wyTySSFgCjXSlsz0PGS2x3KJo6oiDJyC3n+hzji4hP4JfADahYnov7xFTQfbHQ0IRwiISGB2NhYhg4dSrt27YyOI8TfupY+A/9DKeWjtc6zVxhR8ew6dYEx83bhnX2SX0MmE1B8ATX0O2jYw+hoQjjMW2+9xZo1a+jfvz8BAQFGxxHib9najniEUmrcJfebK6WOATlKqc1KqWoOSyicknVa4BhDPt9CI32clYFvEUgu6sElUgiICm/69On8/vvvUggIp2FrO+JngUsbDn0MZAAjgEDgHTvnEk4sM7eIkd/E8uayeJ6oc5ZZ+lU8PLzgkZVQs43R8YRwiD+nBfLz8/Hx8aFBgwZGRxLCZrZOE9QB4gGUUmHAzcBtWutfS3oQfOSgfMLJZOQWMuTzLZxIv8iX7VLosf8lVEhdeGARBNU0Op4QDrN//362bt1KWloaNWvK77pwLraODBQBniW3u2IdJdhYcj8VCLFvLOGM8grNjJgTw8nzuSy/JZGee59DVW1hHRGQQkBUUAUFBQAMHz6c/fv3SyEgnJKtxUAMMFIp1QDreQWrtdbFJc/VA846IpxwHsVmC2Pm7WTnqQt82y2PRltfgnq3wvCfwbeS0fGEcIhNmzbRsGFD4uLiAPDx8TE4kRDXx9ZpgueBZcBhrG/8vS557m5gi51zCSeitebln/by68Fz/Kd3MO12DIOwCLjnG/DyNzqeEA5Tq1YtWrRoQXj41U54F8I52FQMaK33KKVqA9WBlEtGBQBeBZIcEU44hw9WH+L7mNM817UGgw6OAq3hvnngJSupRcWUkJBAREQEdevWZfny5UbHEeKG2TpNgLZKuqwQQGu9Q2t9xv7RhDOYvfk409YfYWjbmozJnAypB2HILKhU3+hoQjjEzp07adGiBV9++aXRUYSwG5ubDimlWgCDgJr8f5viP8nZBC5o6Z4zvL70AL2bhjOx0krUxiXQ+y1o0N3oaEI4TKtWrXj99dcZMmSI0VGEsBubigGl1AjgCyALOAYUXnaJnE3gYv44ksa47+JoW6cS0246i9vCt6HlvdDxSaOjCeEQv/zyC507dyY0NJSXXnrJ6DhC2JWtIwMvAXOBx7TWBQ7MI5zAvqRMRn4TS70wP7663R/Pb/8B1VtD/w/l9EFRIaWkpHDffffx8MMPM23aNKPjCGF3thYDVYE5UgiIU+m5PDRrB0E+Hnw9tBEBC/qChy/cMxc8ZFuVqJjCw8NZu3YtUVFRRkcRwiFsXUC4GpA+si4uPaeA4TO3UWyxMOehmwhfPRoyT8M930JQDaPjCWF3n3zyCcuWLQOgY8eO+Pr6GpxICMewdWRgMvCVsh7IvQbruQT/RWt9zJ7BRPlisWie/T6OM5n5LBjZgYZ7JsPRddB/KtRub3Q8IeyusLCQWbNm0ahRI+644w6j4wjhULYWA7+X/Pcd4O3LnlNYFxC62SuUKH++2HSM3w6l8ubA5tyUsQb++BjaPgZtZBOJqHgsFguenp6sXbsWPz8/o+MI4XC2FgN9HZpClGuxJy8waVUCt7eoyrA6GfDVU1CnC/SRwypFxfPaa69x7NgxZs2aRXBwsNFxhCgTtnYgXOXoIKJ8yswt4un5u6gW5M07t9dBfd0DfEKsjYXcPIyOJ4Tdubu74+YmA53CtdjcdAhAKdUNiAZqAe9rrU8rpToAx7XWKY4IKIyjteZfP8aRkpXPwic6ELSiZMHgQ8vBv4rR8YSwG601qampVKlShQkTJqC1Rsk2WeFCbNpNoJQKU0r9BqwFxmI9ufDPd4PRwCu2/kClVCWl1E9KqYtKqZNKqaGlXHuTUuo3pVSOUipFKTXW1p8jbtw3W0+yan8KL/RpQquTs+DQCmuHQVkwKCqYV155hTZt2pCamgoghYBwObaODEwFwoEWwCH+uwPhaq6hGAA+KXl9ONAKWKaUitNa77/0IqVUGLASeBZYCHhibYUsysD+M5m8uTSebo0rM6L6KZj7JjQfDO0fNzqaEHY3ZMgQ3N3dCQsLMzqKEIawtRi4HRihtT6glLp8Mi0RG9+klVJ+wGCgudY6B/hdKfUL8ADw4mWXjwNWaa3nltwvAOJtzCtuwMWCYp6at4sQPw+m9K2C6ese1iOJ+0+VDoOiwjCbzWzYsIEePXoQFRUlDYWES7O16ZAJ65vxlVQC8m38PhGAWWt96JLH4oBmV7i2A3BeKfWHUuqcUmpJyTHKwsFeWbyPE+kXmTqkGSFLH4XifLj7G/DyNzqaEHYzbdo0evbsya5du4yOIoThbB0Z2AyMUkotu+SxPw8negjYYOP38QcyL3ssE7jSwfc1gZuAXsBe4H1gPtD58guVUiOBkQC1a0u9cCO+2XqSRbuSeKZnI9of/g+c3gFD5kDlCKOjCWFXTzzxBFWrVqV169ZGRxHCcLaODLwE3AzsBiZgLQSGK6VWA11LHrNFDhB42WOBQPYVrs0DftJa79Ba5wOvA52UUkGXX6i1/kJrHa21jq5cubKNUcTl1iec49Wf99G9SRWerhIH26dDxzHQbKDR0YSwi4KCAl5//XVyc3Px8vLinnvuMTqSEOWCTcWA1no30B7r4sGxWLsOPoz1Tbyj1trWufxDgLtSqtElj0UB+69w7R7++2jkP2/LpLUDHDiTxZi5O4msFsjHd9bGtPw5qNUeer5mdDQh7Gbz5s1MnDiR1atXGx1FiHLF5j4DJW/4QwCUUiatteVaf5jW+qJSahHwhlLqUay7Ce4EOl3h8lnAj0qpqViLhVeA37XW/3MugrgxZzPzeGT2DgJ9PJj5UFv8fnsJCnKg/0fSWEhUKN27d+fgwYM0bNjQ6ChClCu2ThNczkcp1UIpFX4drx0N+ADnsK4BGKW13q+UulkplfPnRVrrdcDLwLKSaxsCV+1JIK5PTkExj8yOIaegmJkPtSU89zDEzoa2j0KVSKPjCXHDcnJyuPPOO9m2bRuAFAJCXMFViwGl1O1Kqfev8PgrQDrW9QNnlFILlFKetv5ArfV5rfVArbWf1rq21npeyeObtNb+l137mda6htY6RGvdX2udaPOfTPytYrOFp+bt5FBKNp8Mu4nIqgGw4kXwDoZuLxkdTwi7yM7OJiEhgePHjxsdRYhyq7RpgjFY3/T/opS6E+tCvnXA51i3Ck4ouXaKgzIKB9Ba8/qSA6xPSOWtQc25NaIy7F8MJ3+HO6ZYzx8Qwonl5ubi4+NDtWrV2LNnD56eNn9mEcLllDZNEIW1A+ClHgMygDu11gu11m9j3fI33EH5hIN89ftxvtl6ksdvqc+w9nWgKA9WvwLhzaHNQ0bHE+KG5OTkcMstt/DKK9bmqFIICFG60oqBSli7CwJQ0nmwG7BWa33xkus2AXUdkk44xC9xZ3hzWTy3t6jKC32aWB/cPBUyT0Hf98AkJ7YJ5+bn50eXLl3o2LGj0VGEcAqlTROcxfom/1vJ/XZYF/5tvMK1+gqPiXJo0+FUnvt+N+3qVWLK3a0wmZT1JMLf/wNNB0LdLkZHFOK6JScno5QiPDycDz/80Og4QjiN0oqB1cBLSqkYIAXr2oAiYPFl10UDJxySTthVXGIGj38TS4PK/swYHo23R8kIwJp/Axp6TzQ0nxA3wmKxcMcdd+Dh4cGWLVvk5EEhrkFpxcC/sU4B7L3ksZe11kl/3lFKmbA2H1rqmHjCXo6l5vDw7B1U8vPk60faEeRT0j9g80ew70e49QUIllbOwnmZTCYmTZqEp6enFAJCXKOrFgNa63NKqRZAbyAY2HX5McMlj08EfndcRHGjUrLyeeCr7SjgmxHtqRLobX1i0wfw6xvQ7C645Z+GZhTieh0/fpz9+/fTr18/unfvbnQcIZxSqR0ItdaFlPKpX2t9Hph7teeF8TLzinhw5nYycgtZMLIj9cL8rE9sfB/WvwUthsDAz8HN5maUQpQrL7zwAr/99htHjx7Fz8/P6DhCOKWrvgMopdpqrXdcyzdTSvkBta/hrALhQPlFZh6bE8PR1BxmPdSOFjWDQGvY8A5sfA9a3gsDP5XdA8KpzZgxg9OnT0shIMQNKG1r4XKl1K9KqaFKqVIPsldKtVFKTQJOYd1+KAxWbLbw1Pxd7Dh5nil3t6JLozBrIbDuTWsh0Op+KQSE09q3bx+jR4+muLiYoKAgmjVrZnQkIZxaacVAPazbCj8A0pRSsUqpOUqpD5RSbyulPlVKrVBKnQO2As2BAVrrT8sgtyiF1poJi/ex5kAKr/ZrSv+o6tZCYO1rsGky3DQcBnwshYBwWhs3buTnn3/m7NmzRkcRokJQWpfeIqCk2VBfoDvQBqgKeAPngQRgM/CT1vq0Y6PaJjo6WsfExBgdw1CTVyUwbf0RxnRryPO3NbYWAqsnwJZpED0Cbp8Mpus9o0oI45jNZtzcrEVsRkYGwcHBBicSonxQSsVqraOv9/V/u2pMa23GuohQtg86gdmbjzNt/RHua1eL53pHWAuBlS/Bts+g3ePWDoOy7Uo4oa1bt/Lwww/z888/ExERIYWAEHYkS8grkF/izvD60gP0bhrOxDubowCW/xN2zIAOo+G2t6UQEE4rMDCQ0NBQfHx8jI4iRIUjxUAF8Web4bZ1KjH1vta4K2DZOIiZCZ2egl4TpRAQTun06dPUrFmTpk2bsmnTJmkoJIQDyMRxBXAwOYsn/mwz/GA03m4Klo61FgJdnpVCQDit2NhYGjVqxLfffgsghYAQDiIjA04ut7CYMfN24evlzpxH2hHkZYJfxsDuudaugt3GSyEgnFbLli15+umn6dOnj9FRhKjQZGTAyb3+ywGOpubw0T2tCA/wgiVjrYVA15eh+wQpBIRT+vXXX8nOzsbDw4P33nuPsLAwoyMJUaFdUzGglPJXSrVVSt2llAoqeUzebQzy8+4kvotJ5MmuDenUMAx2zoFd38DNz0PXF4yOJ8R1OXv2LP369eOVV14xOooQLsOmaYKSN/zXgWcAf0ADbYGdwAql1B9a6zccllL8j5PpFxn/0z6i64TwTM9GkLwXlv8LGnS3Tg0I4aSqVavGzz//TMeOHY2OIoTLsHVkYCLWQuAFoClw6WjAYmCAnXOJUhQWW1sNmxR8dF9r3Ity4PsHwbcSDPpCGgoJpzRr1iw2bdoEQO/evQkICDA4kRCuw9YFhA8DL2mtPyvpSHipI0BD+8YSpZm06iB7Tmfy+f1tqBHkDQtHw4Xj8OBS8K9sdDwhrllBQQGTJk2iWbNm3HzzzUbHEcLl2FoMVMLaevhq30N2JZSR9QfPMWPTcR7oUIc+zavCji9h/yLo8W+o29noeEJcM601Xl5erF+/nqCgIKPjCOGSbB1PPgDcfpXnegO77RNHlCYlK5/nfoijSdUAxt8RCWd2W1sNN+wFnZ81Op4Q12zy5Mk888wzaK0JDw/H29vb6EhCuCRbP9G/AyxQSnkCC7EuIIxUSvUFngTuclA+UUJrzbjvd5NXaGba0NZ4m3PghwfBrzIMmi7rBIRTOnv2LMnJyZjNZtzdZYBRCKPY9LdPa71QKfUI8C4wuuThb4BU4DGt9TIH5RMllu09y+Yj6Uwc2JyGlf3h++GQkQgPLwe/UKPjCWEzrTVZWVkEBQUxefJkLBbLXycRCiGMYfPHSa3110BNoBXQE7gJqF7yuHCg/CIz7yw/SGS1QIa2qw3bv4D4X6Dnq1C7g9HxhLgmb7zxBu3ateP8+fMopaQQEKIcsLXPwL+Ar7XWycCey54LBx7UWr/vgHwC+Or34yRl5DFpSEvczu6EVeMhog90fMroaEJcs549e5KZmSlHEAtRjtg6MvAOUPsqz9UseV44wLnsfD5df4TeTcPpVMsHFo6AgKow8DNZJyCchsViYfv27QB07tyZKVOmYJLfXyHKDVv/NiqsiwavpDqQYZ844nIfrDpEodnCy7dHwpp/w4UTMOhza4MhIZzE1KlT6dixI3FxcUZHEUJcwVWnCZRSw4BhJXc18KFSKvOyy7yxrh3Y4JB0Lm5fUibfxybyaJd61M3cZu0p0OFJqNvF6GhCXJNHH30Uf39/WrZsaXQUIcQVlDYyYAHMJV/qsvt/fl0APgFGOjam69FaM3HpAUJ8PRnTqQosfhLCGkMPObxFOIfi4mI+/PBDCgsL8ff359FHH0XONROifLrqyIDWej4wH0ApNR8Yr7U+VlbBXN2q/SlsO36eiQObE7RhAuSkwL3fgoeP0dGEsMnatWt59tlnqVOnDoMGDTI6jhCiFLb2GbjP0UHE/ysoNvPOingiwv0ZGrAHVs6HW/4FNdoYHU0Im/Xp04edO3fSunVro6MIIf6GzS2/lFI1gPuACKxrBf6L1nq4HXO5tDl/nOBkei7zhzbAbdkjULUl3PJPo2MJ8bfy8vIYOXIkL774Is2aNZNCQAgnYWufgShgE5AG1AEOAiFAVeAscNJRAV3N+YuFfLzuCF0jwuh4YCIUZMGgJeDuaXQ0If5WSkoK69evp1evXjRr1szoOEIIG9k6MjAZWAoMBwqBB7TWO5VS3YHZgKxqs5OP1h4it9DMu40S4Nel0OsNCG9qdCwhSlVUVISHhwd169bl4MGD+Pv7Gx1JCHENbO0z0Br4GuuOAiiZJtBarwMmApPsH831HE3NYe62UwxrU5WqMZOgemvoOMboWEKUKicnh27dujF58mQAKQSEcEK2FgMmIF9rbcF6OFGtS547DjS2dzBX9M7yg3h7uPHP6rsh8xR0Gw8m6dsuyjcfHx/q169P3bp1jY4ihLhOtk4TxAP1sTYX2gaMVUr9gXXK4FnghCPCuZItR9NZG5/CC73qE7Dtn9ZRgYY9jY4lxFWlp6fj5uZGcHAwX38t55UJ4cxsLQa+4v/PJhgPrOL/C4B84G77xnItFovmreUHqBHsw6NBMZBxEvq+B9KgRZRTFouFPn364O/vz7p166SZkBBOztY+AzMvub1XKdUUuBnwATZrrZMclM8l/LQriX1JWXw0pDkem5+zbiWM6GN0LCGuymQyMX78ePz9/aUQEKICsLnPwKW01hnAkj/vK6WqaK3P2S2VC8krNDNpVQItawbR3+0POH8M7pkrowKiXEpKSuLIkSPceuutDBw40Og4Qgg7uaEzRJVSEUqp6ciages2c/NxkrPymdC3MaZNkyG8BTS5w+hYQlzRmDFjuPfee8nLyzM6ihDCjkodGVBK3YW1t0AtrLsG3tNa71BKNQbeBu4EcoD/ODpoRZSZW8TnG4/SM7IK7XI3QvoRuPtrGRUQ5db06dNJTEzEx0fOyBCiIrnqyIBSajiwEGgOJFKym0Ap9SiwG+gOvAbU0VqPd3zUimfGpmNk5xczrkdD2Pg+VGkKTfobHUuI/3Lo0CHGjx+P1poqVarQpo2ckSFERVPaNMEzWE8tjNBaD9Ra3wS8AUwH4oBGWus3tdaZZZCzwknPKWDm5uPc0bIaTS9ug7QEuOV5MN3QzI0Qdrdo0SJmzJjB6dOnjY4ihHCQ0t55GgKzShoN/ekLQAFvaK3THJqsgvt841Hyi8w82zMC4uaDbyhEDjA6lhB/0VoD8MILL7Bnzx5q1ar1N68QQjir0ooBfyDrssf+vJ/smDiuISUrn6+3nGRQ65o0DCiChBXQYgi4eRgdTQgAdu3aRfv27UlMTEQpRdWqVY2OJIRwoL/bWhitlLq00bgJ0EBbpVTwpReWnFMgbDBt3RHMFs3YHo1g/3dgLoCoe42OJf6PvfsOj6pM/z/+fia9ACEkIYAQqtIRiIgoYkW6i2BBRQRdVxSXFbtfC6KCZXFXXNQFCyu6wiJYQAFhxUVBpJfQOwECCUkIpCeT+/fHJPklkIRJyMzJTO7XdZ2LzDlnzvnMIZl55jlPUcXy8/PJyckhLy/P6ihKKTe4UGHgH+Ws/+CcxwLoIPpOiE/JZM66I9xxRVOaNQiGb+ZAZFtodLnV0ZQiOTmZBg0acMUVV7Bp0yZs2oZFqVqhosJAO7elqEXe+2kvxhgeu6G1Y4Ch+DVw00TtTqgst2nTJq677jo++eQThg0bpgUBpWqRcgsDIrLbnUFqgwNJ6czfeIxRVzWnUb0gWDEXMNBJp3ZQ1mvbti0jRoygV69eVkdRSrmZFv3d6O/L9+LvY2Psda1AxNGLoGUfqNfE6miqFluzZg3Z2dkEBQXx4Ycf0qhRI6sjKaXcTAsDbrL7xFkWbj3OqF7NiawTAEfWOGYn7DLC6miqFjt27BjXX389L7zwgtVRlFIWqtJERary/rZsDyH+vvzp2paOFVu+BL8QaDvI2mCqVmvSpAmzZ8/mxhtvtDqKUspCWjPgBnHH0liy/QQPXNOC+iH+kJcF27+B9kMgIPTCB1Cqms2bN4+NGzcCMHz4cOrXr29xIqWUlbQw4AbvLNtDvSA/HujdwrFi9w+Qk6ZjCyhLZGdn88wzz/Daa69ZHUUpVUM4fZvAGBMOjAdiccxieKeI7DTGjAXWich6F2X0aBuPpPLTrkSeuuUy6gYWjjD4+z8hrBk0721tOFUrBQYGsmLFCiIiIqyOopSqIZyqGTDGdAP2AaOB00AHoGgO05bAUy5J5wXe+XEPDUL8ub9Xc8eKI79D/O9w1Tiw6ThNyn3ef/99Jk2aBEBMTAwhISEWJ1JK1RTO3ib4O/AbjsmLRuGYrKjIb0DPas7lFTYdSeXXfad4uE8rQrmDcEQAACAASURBVAIKK2FWT4Og+tD1XmvDqVpFRFi/fj3r16/HbrdbHUcpVcM4e5sgFhgqIrnGmHO/zp4CGlZvLO/wyapD1AnwZcSVzRwrTu2DXd87pir2129lyj2ys7MJDAxk5syZ2O12fHy0RkopVZqzNQNngfBytrUAkqonjvc4fjqLH7YlcFePpoQW1Qr89h74+EOPh6wNp2qNKVOm0KtXL9LS0vDx8cHf39/qSEqpGsjZwsAiYKIxpuSE5lI4c+EE4JtqT+bh/vXbIUSE+65q7liRngibv4TLR0BolJXRVC3StWtXunfvTmiodmFVSpXP2cLAM0AesAtYVrjuXaBo/oIXqzmXR8vMzefL34/Qr2M0TcODHSvXzgB7Llz1mLXhlNcTEXbu3AlAv379mDlzpt4aUEpVyKnCgIicwtFu4GkcvQl+BVKA14CeInLaZQk90PwNRzmTnc8D1xSOK5CTDmtnQtuBENHa2nDK67377rt07dqV7du3Wx1FKeUhnB5nQESygemFiypHQYHwyapDdLmkHt2aFY7q9uvfIPs0XPO4teFUrXDfffdht9tp37691VGUUh7C2XEGlhpjRhe2EVAV+HlPIgdPZTDmmhYYYyB5v6M7Yec74ZJYq+MpL2W325k1axYFBQWEh4fzxBNPOH7/lFLKCc62GcgDPgBOGGMWGmPuNsZoi6QyfPzrQaLrBjKgUyPHNMVLngWfALh5ktXRlBf7/vvvGT16NAsXLrQ6ilLKAznbZmAQjrEExuK4tTALOGmM+coYc7sxJtB1ET3HnpNnWbUvmZFXxeDnY4M9S2Dvj3Dds1An2up4yosNGTKEFStWcOutt1odRSnlgZyeqEhE0kTkUxHpDzQCHgfCgC+ARBfl8yif/XYIf18bI3o0g7xsWPwMRLaFK/9kdTTlhXJzc3n00Uc5ePAgANddd521gZRSHqtKsxaKSDKwAdiEo1dBrR9O70x2Hgs2HmNw58aEh/jDqnfh9GHo/xb4+FkdT3mhQ4cOMWfOHFasWGF1FKWUh3O6NwGAMaYzcGfh0gLYD8wE5lR/NM+yYMNRMnPtjOoVA6mH4dd3oMNQaNnH6mjKyxQUFGCz2bj00kvZs2cPDRo0sDqSUsrDOdubYKIxZieOmoC7gfnAFSJyqYi8KCK1ukNzQYHw2W+HubxpGJ0vCYOlz4OxQV+dL15Vr4yMDG655RY++ugjAC0IKKWqhbM1A38E5gGjRWSNC/N4pFX7T3HgVAZ/u7MLnNgGuxbBdc9DvUusjqa8jK+vL4GBgQQGaptdpVT1cbYwcImIiEuTeLB/rT5MgxB/R3fCb18B/1C4UicjUtUnLS0NPz8/goOD+e6773QMAaVUtSr3NoExxlb6obFVtLgha40Un5LJf3edZESPZgSkH4O4+dD9fgiqb3U05SXsdjv9+vVj+PDhiIgWBJRS1a6imoE8Y8xVIrIWyAcuVDPg1Ewoxphw4GOgL3AKeE5E/l3B/v7AViBURGpcvfvnvx/GZgx3X9kMfnsFjIGeY62OpbyIj48PY8eOpX79+loQUEq5REWFgUeAAyV+rq7bBNOBXByDGF0OfG+M2VJBI8SncIxjUONGPMzNL2De+qPc3K4hjf2zYOO/oNPt2lZAVYvExETi4+Pp3r079913n9VxlFJerNzCgIj8s8TPH1bHyYwxIcAwoKOIpAO/GmO+A0YCz5axfwvgXmACji6MNcrKPUmkZORyxxWXwPqPIS8TeukUxap6PPDAA2zcuJH9+/drg0GllEs527VwhzGmUznb2htjdjh5vksBu4jsKbFuC9ChnP3fA54Hspw8vlt9s/kY4SH+9G4eCr//E9r0hYblvRSlKmf69OnMnTtXCwJKKZdztuFfWyConG2hQBsnjxMKpJ2zLg2oc+6OxpihgK+IfH2hgxpjHjLGrDfGrE9KSnIyysU5m53Hsh0nGdipEX5xcyEjCa4e75ZzK+916NAh3n77bUSEZs2acc0111gdSSlVC1TUmyDYGBNljIkqXFW/6HGJpRmOav9jTp4vHah7zrq6wNlzzh0CvAU4VecuIjNEJFZEYiMjI52McnGWbj9JTn4Bf+gSDavfgybdIeZqt5xbea9PPvmEyZMnc+yYs39SSil18SpqQPgU8DKOhoMC/FDOfgZ4zsnz7QF8jTFtRGRv4bouwLmNB9sAzYFfCltP+wP1jDEngJ4icsjJ87nMt5uP0Sw8mG656yDlANw+y9GTQKmLMHHiREaPHs0ll2gjVKWU+1RUGPgPEIfjw/4/OO7d7z1nn1xgV4kP9gqJSIYxZgEwyRjzII7eBLcCvc7ZNQ5oWuJxL+AfQDfAPfcBKpB4JptV+07x6PWtMetfgNBoaDvI6ljKQ+3YsYPx48fz73//m8jISFq0aGF1JKVULVNRb4KdwE4AY0x/4DcROVMN53wE+ARHd8FkYKyIbDfG9AYWi0ioiOQDJ4qeYIxJAQpE5ESZR3SzxXEnKBAY1tIOq5fBtU/pzISqyhITEzlw4AApKSm46zaXUkqV5NRwxCKytLpOKCIpwB/KWP8L5YwlICI/AzWm3nRJ3AnaRIXS/PBXjlsD3bQPuKq89PR0QkNDue6669i1axd+flqgVEpZo6IGhEeMMV0Kf44vfFzu4r7I1krJyOX3g8kMaN8ANs6GNrdAWNMLP1GpEjZv3kzLli1ZvHgxgBYElFKWqqhm4AscwwUX/awTFQHLd5x03CII2QIZiRA7xupIygO1aNGCG264gfbt21sdRSmlKmwz8FyJn88bHbC2WrL9BJfUD6Lp/g+gXjNofaPVkZQH2bp1K+3bt6devXrMmTPH6jhKKQU4P+jQeYwxLY0x/YwxtabF09nsPH7de4q7W+VgDv0CsfeDzan5mZQiPj6enj178vLLL1sdRSmlSnGqAaEx5j3AiMi4wsdDgbmFz08zxtxSOLuhV1uxO4lcewFDzS9gfODye62OpDxI06ZNee+99xgyZIjVUZRSqhRnawYGA7+VeDwZmA+0BP4HvF7NuWqkpXEniAr1I/rwd47bA3UaWh1JeYBFixaxZ49jOo4HHnhAuw8qpWocZwsDDYEjAMaYVsBlwJTCkQDfxzEYkFfLybfz8+5EHmp2HHPmGHS+0+pIygNkZWXx8MMP8+yz2uxGKVVzOXWbAEgFir7O3AQkisjWwscCeH2/qN/2J5ORa2eg/A8C6kLbgVZHUh4gKCiI5cuX07hxY6ujKKVUuZwtDPwITDTG1AeeBr4qsa0DcKiac9U4y3eeJNw/n+hjP0KHP4BfeZM4KgX/+te/OHv2LOPGjaNt27ZWx1FKqQo5e5tgAo75Ap4FNgIvlth2F7C8mnPVKCLC8h2JPBq9G5ObDl1GWB1J1WAiwsKFC/nuu++w2+1Wx1FKqQtydjjiFODucrb1rNZENdD242c4cSabgfV+hnpNodm58yop5ZCfn4+vry///ve/KSgowMdHu54qpWq+So0zYIyJMMYMNMaMLPw3wlXBapIfd5wkyqTR8NQa6HQ72Ko8PIPyYn//+9+56aabyMzMxN/fn8DAQKsjKaWUU5wdZ8AG/BV4lNKNBfOMMf8AnhQRrx2uePmOk/wpYivmrB0632F1HFVDNWrUiOjoaJ1nQCnlcZz9ivsiMA54DWgL1C/897XC9S+4JF0NEJ+SyY6EMww0qyCqA0S1szqSqmGOHHHM03XnnXfy5ZdfamFAKeVxnC0MjAFeEpFXRWSPiKQV/vsq8DLwoOsiWuuHbQlcYhKJPrMVOg23Oo6qYaZNm0aHDh3YvXs3AMYYixMppVTlOdu1sCGwoZxtGwq3e6XvtyXwUP1NkAl0HGZ1HFXDDB8+nOTkZNq0aWN1FKWUqjJnawb2AeV9LR5euN3rHE7OYOvRNAaZVdC0J9SPsTqSqgFEhAULFiAiNG7cmFdeeQWbNipVSnkwZ9/BpgB/NMYsMsbcb4zpb4wZZYxZhOMWwWTXRbTO99sSuMwcITxjn94iUMUWLFjAsGHDWLRokdVRlFKqWjg7zsAXxpgzwCTgY8DgGIZ4C3CriHjlu+L3WxMYU38rZNmg/a1Wx1E1xG233ca3337LoEGDrI6ilFLVwum6TRFZKCJdgSCgORAkIt28tSBw6FQG24+f4SbbOsctgtAoqyMpC+Xn5/Pcc8+RkJCAMYYhQ4ZoY0GllNeosDBgjPEvHFzoUWPMMGNMAxHJFZEjIpLrrpBWWLr9BE3NSRqk79VJiRR79uzhvffe47vvvrM6ilJKVbtybxMYY2JwTFBUspl0qjFmuIiscHkyiy3feZL76m939CJoO8DqOMoiIoIxhvbt27Nr1y4uueQSqyMppVS1q6hm4C0gALgZCAe6A7uAGW7IZank9Bw2HE6lv+8Gx0BD4S2tjqQskJ2dzdChQ5k3bx6AFgSUUl6rosLA1cD/ich/ReS0iGwCHgBaGmOi3RPPGj/tSiRMztDk7Ba9RVCL2e12UlJSSElJsTqKUkq5VEW9CRpx/vgBe3H0JGgEnHBVKKst23GSoaHbMfkFeougFsrIyMDPz4+QkBBWrFihMw8qpbxeRTUDBihwV5CaIjvPzi97T3Fr6E4IiYLoLlZHUm6Un5/PgAEDGDlyJCKiBQGlVK1woXEGFhpjyuo18IMxJq/kChFpVn2xrLN6/yly8vJol7ke2vXX6YprGV9fX2677TYaNWqkXQeVUrVGRYWBN92WogZZtiORKwMO45d7GlrfZHUc5SYpKSkkJibStm1bxo8fb3UcpZRyq3ILAyLynDuD1AQiwv92J/Jc+B5INdDyeqsjKTcZOXIkcXFx7Nmzh4CAAKvjKKWUWzk7a2GtsD8pneNp2VwVtAmadIOQBlZHUm4ydepU4uPjtSCglKqV9IZ4CSv3nCKMszRIi4PWN1sdR7nY8ePH+eijjwBo27YtN9+s/+dKqdpJCwMlrNybxNB6+zBSAK1vtDqOcrG//e1vTJgwgYSEBKujKKWUpbQwUCgn386aA8kMCt0NAXWhcTerIykXmzJlCmvWrKFRo0ZWR1FKKUtpYaDQ+kOpZOcV0C57EzS/Bny0OYU32rdvH7fffjtnzpzB19eX9u3bWx1JKaUsV6nCgDGmlTHmdmPMBGNMVOG6psaYYNfEc5+Ve5Jo4ZNEcEY8tLzO6jjKRXbv3s2qVas4evSo1VGUUqrGcOrrrzEmCPgnMALHyIQG+BlIBP4O7Aeedk1E91hzIJk7GxyAM0CLPlbHUdUsJyeHgIAABg4cyL59+wgO9vjyq1JKVRtnawam4pi9cAhQD0dhoMj3QP9qzuVWmbn5xB0/Qx+/HRAaDZGXWR1JVaNt27Zx6aWXsnLlSgAtCCil1DmcLQzcDjwjIouB7HO2HQRiqjWVm22OP429oIBW6Rsctwh0GFqv0rBhQ9q3b0/jxo2tjqKUUjWSs63kQoCTFWzz6AmN1h9KpZXtOP45KdD8aqvjqGqyb98+WrZsSVRUFIsXL7Y6jlJK1VjO1gxsAO4uZ9ttwO/VE8ca6w6lMLjeYceDZr2sDaOqxaFDh+jatSuvv/661VGUUqrGc7Zm4CVgqTGmATAPEOAmY8xYHIUEjx3EP99ewMbDqUxosA9skdCgldWRVDWIiYlh4sSJjBgxwuooSilV4zlVMyAiK4B+QBTwCY4GhG8A3YABIvKbyxK62O6TZ8nItXNpznZo1lPbC3i4n376ifj4eIwxPPHEE9pOQCmlnOD0OAMi8pOI9ADCgdZAAxHpLiI/uSydG2yJTyOKVEIy46HZVVbHURchMzOTESNG8Je//MXqKEop5VEqPcyeiKQBaS7IYomtR09zbdB+x42Ppj2tjqMuQnBwMD/88AMtWrSwOopSSnkUZwcd+uxC+4jIfRcfx/02x5/moTqJcNYGDXVoWk80b948cnJyuPfee+nevbvVcZRSyuM4WzPQpox14UBL4BSOsQY8Tlaunb2J6XRolAD1m4NfkNWRVCWJCB999BE5OTncfffd2Gw63YZSSlWWU4UBESnzZroxphWO3gWTqjOUu2w/noa9QGiSdwQatbU6jqokEcEYw4IFCxARLQgopVQVXdS7p4jsB6YAf62eOO615WgavuQTkn5IhyD2MDNmzODWW28lNzeXkJAQQkNDrY6klFIeqzq+SuXgocMRb44/Tfc6pzEFeRCpNQOeREQoKCigoMCjB79USqkawdkGhC3LWO0PtMNRM7CxOkO5y8bDqYyKSIUEtGbAQyQlJREZGcmf/vQn/vjHP+qtAaWUqgbOvpPuA/aes2wH5uOoGXjIJelc6OSZbI6dzqJ7YAJgIOJSqyOpC3j//fdp164dBw4cANCCgFJKVRNnexOUNUVxNnC0sN2Ax9l4OBWA1vl7HLUC/iEWJ1IXcsstt7B3716aNWtmdRSllPIqFywMGGMCgI7AjyKyzfWR3GPD4VT8fQ11k7fApWWVdVRNICKsWLGCG264gVatWvG3v/3N6khKKeV1LljPKiI5OLoOhrs+jvtsOJLKzdFZmMxkuEQHqqmp5s2bx4033qhTECullAtVZgrjLq4M4k5ZuXbijqVxc9gxx4omsdYGUuUaNmwYn332Gf369bM6ilJKeS1nCwPjgUeNMQ8aYxobY3yMMbaSiytDVrdN8ank2YVutv3gGwRROgxxTVJQUMCUKVNITU3Fx8eHkSNHYnQ2SaWUcpnK1Ay0Af4JxAO5QN45i8f4/UAKNgONM3dBo87gU+n5mpQLbdu2jYkTJzJnzhyroyilVK3g7KfgIzjm9fMKaw+m0KFRKL6J26HLXVbHUefo0qULW7du5dJLtbunUkq5Q7mFAWPMtcBGEUkXkQ/dmMmlcvLtbDySyrguNth+1lEzoCyXl5fHmDFjGDlyJH379uWyy3QQKKWUcpeKbhOsALzuZvruE2fJyS/gmtDjjhXRnawNpADIyMggLi6OnTt3Wh1FKaVqnYpuE3hli61jqVkAXJKzD2y+ENnO4kS1W05ODr6+voSFhbFmzRoCAgKsjqSUUrWOR/UCqA7HTjsKA2Gn4xyTE/kFWpyo9srLy2Pw4MGMHTsWQAsCSillkQs1IBxgjHFqOj8R+awa8rjc8dPZ1PEH3+PrtfGgxfz8/OjVqxfNmze3OopSStVqFyoMvOTkcQTwkMJAFtfWOY7JSIeYq62OUyudOXOG1NRUYmJimDhxotVxlFKq1rtQYeB6YL07grhLQloWI/x2Ox5oYcASI0aMYO/evcTFxeHv7291HKWUqvUuVBjIEpEMtyRxk2Ons7k8KA4atIY6Da2OUytNmjSJY8eOaUFAKaVqiFrVgDA7z05KehYtMrdqrYCbJSUlMW/ePAC6d+/OkCFDLE6klFKqSK0qDBxNzaSdOUyAPQOaX2N1nFrl1VdfZfTo0Zw8edLqKEoppc5RbmFARGwistadYVztcHImXWwHHA+a9rA2TC3z1ltv8fPPP9Owod6aUUqpmqZW1QwcSnbUDBQE1IWwGKvjeL0jR47w4IMPkpWVRWBgILGxOlW0UkrVRLWqMHAkOYNOPkcwDTuATonrcmvWrGHBggXs27fP6ihKKaUqUMsKA+lcZo5gdD4Cl7Lb7QDccccd7N+/n06d9HorpVRNVqsKA3mnDhJENjTsaHUUr7Vr1y46derE+vWO4Snq169vcSKllFIXUmsKA/YCIezMLscDrRlwmeDgYMLCwggKCrI6ilJKKSddaNAhr5GQlkUbDlOADVuUzlRY3Y4fP06jRo1o1qwZq1atwmibDKWU8hi1pmbgSHIml5mjZNdtDn76rbU6HTx4kI4dO/LOO+8AaEFAKaU8TK0pDBxOyeRSEw+RWitQ3WJiYnjkkUcYNmyY1VGUUkpVgdsLA8aYcGPM18aYDGPMYWPM3eXs95QxJs4Yc9YYc9AY89TFnPdYUgox5iSBTTpczGFUCWvWrCEpKQmbzcZrr72mUxErpZSHsqJmYDqQCzQE7gE+MMaU9QltgPuA+kA/YJwx5q6qnjT3xC58jGBr2L6qh1AlpKenM3jwYMaNG2d1FKWUUhfJrQ0IjTEhwDCgo4ikA78aY74DRgLPltxXRN4q8XC3MeZb4GpgTlXO7Z9cOG2x3iaoFqGhoXz11Ve0bdvW6ihKKaUukrtrBi4F7CKyp8S6LUCFdffG0SKtN7C9KicVERplbCfXFgQRbapyCFVo0aJFLFy4EIA+ffroXANKKeUF3N21MBRIO2ddGlDnAs+biKPg8mlZG40xDwEPATRr1uy87Ulnc+gke0iu15FGNp9KRlZFRIQ333wTEWHQoEHaa0AppbyEuwsD6UDdc9bVBc6W9wRjzDgcbQd6i0hOWfuIyAxgBkBsbKycu/3wiVNcbo5wvHG/quZWOLoMfvfddxhjtCCglFJexN23CfYAvsaYknX1XSin+t8YMwZHW4IbReRoVU96+tBm/Iyd4JgrqnqIWu3zzz9n1KhR2O126tevT1hYmNWRlFJKVSO3FgZEJANYAEwyxoQYY64GbgVmn7uvMeYeYDJws4gcuJjzZiY4Gg/Wj9FuhVVx9OhRjh49Sk5OmRUzSimlPJwVXQsfAYKAROBLYKyIbDfG9DbGpJfY7zWgAbDOGJNeuHxYlRMWJO/Hjg3fBi0vOnxtcvas4+7Ns88+y9KlSwkODrY4kVJKKVdwe2FARFJE5A8iEiIizUTk34XrfxGR0BL7tRARPxEJLbE8XJVzhpw9RKpfNPj6V9fL8HozZsygffv2xMfHA+DrW2umsVBKqVrH64cjTs3IpZH9GFl1YqyO4lGuuuoq+vbtq10HlVKqFvD6wsDuk2dpapKwNWhhdRSPsH79egA6derExx9/jL+/1qYopZS38/rCwOETyYSZDEIjtWbgQubMmcMVV1zBf//7X6ujKKWUciOvLwyknDgEQJ3IptYG8QC33XYb7733Htddd53VUZRSSrmR1xcGziQ6hiew1W1kcZKaSUSYPn06GRkZ+Pv7M27cOHx8dJRGpZSqTby+MJCfVjhWUZ3G1gapoTZs2MCf//xnPvvsM6ujKKWUsohX9xcrKBB800+AD6A1A2WKjY1l7dq1dOvWzeooSimlLOLVNQOJZ3OIklPk+oRAYD2r49QYdrudRx99lNWrVwPQvXt3nWtAKaVqMa8uDBxNzaSRSSEvRGsFSkpNTWXZsmX8+uuvVkdRSilVA3j1bYKjqVm0MMmYsPOnNa6N8vPz8fHxISIigg0bNlCnzoVmjlZKKVUb1IqaAf9w7VaYl5fHsGHDeOqppwC0IKCUUqqYV9cMHE85S4RJw1avidVRLOfr60tMTAwtWuhIjEoppUrz6sJAVmoCNgTqRFsdxTKZmZmcPXuWhg0bMm3aNKvjKKWUqoG8+jaBnElw/FC39o4xcOedd3LTTTeRl5dndRSllFI1lFfXDPhmnHD8UItrBp588kkSEhLw8/OzOopSSqkaymsLA7n5BQTnJIIfUKd2dS1MTU1l7dq13HLLLfTp08fqOEoppWo4r71NkHg2m0YmBbvxheAIq+O41fPPP8+wYcNISkqyOopSSikP4LWFgZNncmhkkskNjgab177MMr311lv88MMPREZGWh1FKaWUB/DaT8mTZ7JpbJIpqFs7uhUmJCTw5JNPkpeXR506dbj22mutjqSUUspDeG1h4ESaozDgG1Y7BhxaunQpH374Ibt27bI6ilJKKQ/jtYWBxDMZNCTV60cfFBEA7r//fvbu3UunTp0sTqSUUsrTeG1hIPPUUfyMHVM/xuooLnPgwAGuuuqq4tqARo1qV68JpZRS1cNrCwMFKYccP3hxYSAvL4+zZ8+SmZlpdRSllFIezGvHGfA7e8TxQ5j3FQZSUlIIDw/nsssuY+vWrfj4+FgdSSmllAfzypqB7Dw7IdmJjgde1pvg4MGDtGvXjn/+858AWhBQSil10byyMHAqPYc6JpN8WwD4BVodp1pdcsklDB8+nOuvv97qKEoppbyEV94mOJ2ZRx0ysfvV8ZoXuGXLFlq0aEHdunWZPn261XGUUkp5Ea+sGUjJyKWOyaIgoI7VUarFmTNnuPHGG3nkkUesjqKUUsoLecsX51JSM3MJIxMTWNfqKNWibt26zJo1i8svv9zqKKoWKigo4NSpU5w+fRq73W51HKVqHR8fH8LCwoiIiMDmouH1vbIwkJKRSzuTjK2eZ394/ve//8XX15c+ffowaNAgq+OoWuro0aMYY2jevDl+fn4YY6yOpFStISLk5eVx8uRJjh49SrNmzVxyHq8sDJw6m80l5hR+DZpbHaXKCgoKeOaZZ/D392fVqlX6Bqwsk5GRwWWXXeaybyRKqfIZY/D396dJkybs3r3bZefxysJAdloiwSYHwlxTgnIHm83GokWL8PX11YKAspwWBJSylqv/Br3yL9x+5oTjh7qeNzzv/PnzefzxxxERoqOjiYiIsDqSUkopL+eVhQFbVorjh+AG1gapgnXr1rF27VqysrKsjqKUctKhQ4cwxrB+/XqXnmf//v1ERUWRmprq0vNY6cknn+Sxxx6zOkat45WFAb+cwj8UDyoM5OTkADBlyhSWL19OcHCwxYmU8nzu+pBu2rQpCQkJLu/x8/zzz/OnP/2J+vXrl1pvjDlviYuLK95++vRpHnjgAaKjowkNDaVXr178+uuvlTr39u3bueWWW4pbtQ8bNoz4+Pji7TNnzuSKK66gbt26REZGcvvtt3Po0KFSx9ixYwe9e/cmKCiIli1b8q9//eu88zzzzDPMmjWLffv2VSpfkWPHjjFw4ECCg4Np3Lgxb775ZqWef//995d5PQcOHFi8z4WuxZkzZxg7dixNmjQhKCiIPn36sG3btuLtu3fv5vrrrycqKorg4GA6d+7M559/XqXXW128sjAQkFtYGAgKtzaIk/71r3/RuXNnTpw4gTGGoKAgqyMppSrBx8eH6OhofH1d1wzr6NGjkyAMyQAAIABJREFULFiwgDFjxpS5febMmSQkJBQvbdu2Ld42YcIEVq1axddff83mzZvp2LEjAwcOJC0tzenzDxkyhODgYNasWcPy5ctJSEhg5MiRxdt//fVXHnzwQVatWsXy5ctJSUmhX79+5OfnA44vPAMGDCAqKop169bxl7/8hTFjxpxXKImMjKRv3768//77lbk8xYYPH86ZM2dYvXo1U6dO5aWXXuKLL75w+vnvvvtuqet4+PBh6tWrx7Bhw5y+FhMmTGD58uXMnTuXzZs307hxY/r37188qZyvry/33nsvy5cvZ8eOHYwfP57Ro0ezbNmyKr3maiEiXrV0795d3p/4R5GX64rk5Ygn+O233+T222+XzMxMq6ModZ4dO3ZYHaHSDh48KECZS1n7LVy4UPr37y9BQUHSsGFDWb58uYiITJ06VTp16iTBwcESHh4uI0eOlOTk5OLnJyQklDr2unXryjz+xx9/LF27dpXg4GAZPHiwpKWlVfo1/fWvf5VOnTqVuQ2QefPmlfvcDh06yMSJE8/LtX79eqfOnZSUJID8/PPPxes+/fRTCQ0NLfc5GzZsEEA2b94sIiJff/21+Pj4lLp+N910k9xzzz3nPXfWrFkSHR3tVLaSNm3aJIBs2bKleN2DDz4oV199daWPVWTu3LkSGhoqZ8+eFRHnrkVUVJRMnz69+HFaWpoYYyr8P+rWrZs899xzFWap6G8RWC8X8dnplTUDwfYz5NiCwdff6igV2rVrFwA9e/bkP//5j9YIKFVNiqrt165dC8CSJUuKv+mV5cknn+QPf/gDW7duZdasWYSEhACQmprKq6++ypYtW1i8eDFxcXGMHTu2+HlRUVGlzlOe6dOn889//pPFixezcuXKKn3rXblyJT169Ch3+1/+8hciIyO56qqr+Oabb0ptu/baa1m6dCmnTp3Cbrfz5Zdf0rx5c9q3b+/UuRs0aECHDh346quvyM3NJT09nW+++aZU1fm5Tp48CUBYWBjgaA/VoUMHwsP/f43ttddeW+YtnB49enDixIlK3ypYt24d9erVo3PnzqXOsXHjRhyfl5U3c+ZMbr/9dkJDQwHnrkVubi4BAQHFj/39/THGsGnTpvOOLyIsW7aMHTt20L179yplrA5e2bUwVNLJ9qtHwIV3tczcuXO5++67WbFiBddee63VcZSqlFcWbmfH8TNuPWf7xnV5eXAHp/YtqrbPzs4GHG/g0dHR5e5/xx138NBDDwHQunXr4vWvvvpqqf0ee+wxxo8fX/zYZrOVOk95Hn/8ca644goABgwYwLp165x6HSUdPny43DYJkydP5vrrr8fX15e5c+cydOhQli5dSt++fQH4+9//ztixY4mMjMTHx4eYmBiWLl3q9BcQYww//vgjQ4cOJSgoCBHh5ptv5ssvvyxzf7vdzuTJkxk+fDgxMY5p5JOSkoiIiCA+Pp5u3brx8ssvExERQVJS0nnPb9KkSfFrLvn/cSFJSUk0aNCAzMxMOnTowO233871119PVlYW6enp1KlTuSHqDx48yH//+19++eWX4nXOXIs+ffowc+ZMBg0aRP369XnllVew2WznvdZevXqxfv16jDFMmzat1K0Id/O6moECEULJIt83xOooFRo0aBCTJk3iqquusjqKUrVe7969y1z/008/cdNNN9GkSRNCQ0N55JFHSE9Pr/TxS36ghYeHk5KSUuljZGVlERhY9iyszz33HD179iQ2Npa3336bIUOG8O677xZvnz59OmvXruXHH39k3bp13HjjjQwcOJAzZ5wr0IkI48aNo0mTJqxZs4aVK1eSnp7O/fffX+b+jz32GMnJycycObPUMYoG0ImJiSE8PLzcb+tFr7PoHruzis7h4+ND06ZNiYqKqnKNAMBHH33EpZdeytVXX13qHBe6FtOmTcPPz4/o6GiCg4M5dOgQ3bp1O2+sgLlz57JhwwamTJnC888/z+rVq6uc9aJdzD2Gmrh0ubybrHyhl5z8W+8K771YoaCgQGbPni05OZ7RlkEpEc9sM1Ck6N74uffyndl+6NAhCQwMlEceeUTWrVsne/fulalTp57X7qCi45S1/tFHH5U+ffpU+rX06tVLnnzySaf2feWVV6R9+/YiIpKdnS0BAQGycOHC4u12u10iIiJkxowZTh1vxYoVYowp1dZh69atAsiePXtK7fv0009LixYt5OjRo6XWP//889K5c+dS6yZNmiSXXXbZeecraovx22+/OZWvyIwZM6RevXql1n322WcSFBQkBQUFlTpWXl6eNGrUSN54441S6ytzLc6cOSNJSUkiItKkSRN55ZVXyj3fmDFjpH///hVm0jYDlWAXIdRkI36hVkc5z9q1axk5ciSffPKJ1VGUqhX8/R3thopatFfG+vXryc3NZdq0acTGxtK6dWuOHz9e3RGd1rVr11LdBSuya9cumjdvDji+Xefk5JT6Vlr07dnZ8UxOnz4NlB4Fr6jnRMljTJw4kS+//JKffvqpuKq/SGxsLNu3by9VK7Jy5UpiY2PPO19cXBy+vr507NjRqXwlz5GWlsbWrVtLnaNbt27njeR66tQpDh06VO7vxvfff09iYiL33XdfqfXOXguAOnXqEBERwcqVKzl27BjXXXddudl9fHzIyMi48It0lYspSdTEpX2ny2X3i+3kxIzhFZawrLJixQqx2+1Wx1DKaZ5cM5Cfny+hoaHy9NNPS1pammRlZZXaXlHNwJYtWwSQadOmyYEDB2TWrFkSFRVVqmbg9OnTkpCQIGvXrhVAlixZIgkJCXL69Olyj1/VmoFFixZJWFiY5Obmllr/7bffyvvvvy9bt26V3bt3y1tvvSU2m01++OGH4n2uvPJK6dq1q6xevVr27t0rTz31lAQEBMjOnTtLHWvFihUCyMsvv1xq/YkTJ6RevXpy9913y44dO2Tz5s3St29fad26dXGeKVOmSFhYmPzyyy+SkJBQvBT1ksrOzpaYmBi57bbbJC4uTqZNmyY2m01Wrlx53mt94YUXqnSNRER69uwp11xzjWzevFm+/PJL8ff3l88///y8/UaNGiWAHDx4sMzjDBw4UAYNGnTeemeuxY4dO2T+/Ply8OBBWb58ubRu3VpuuOGG4mN88skn8vHHH8vWrVtl3759MmPGDAkICJC///3vFb42V9YMWP7hXd3LZR27SPJLjSXhi7EVXlR3sdvt8vTTT5fq6qKUJ/HkwoCIyOzZs6V58+Zis9nK7VpY3m2Ed999V5o0aSJBQUEyYMAAee+990odo+gD5dxl1KhR5R6/qoWB/Px8adasmXz99del1v/www/Svn17CQkJkZCQEImNjZX58+eX2ic+Pl7uuusuiYqKkpCQELnyyitl6dKl551j0aJFAsgHH3xw3rbVq1dLnz59pG7duhIeHi5DhgwpVS0eExNT5rX49NNPi/eJi4uTq6++WgICAqR58+altpXUunXrMj/AnREfHy/9+/eXwMBAiY6OlilTppS5X0WFgfj4ePHx8ZEFCxaU+dwLXYtt27ZJ+/btxd/fXyIjI+Whhx4qLiCKOLordu/eXerWrSuBgYHSrl07eeeddy54K8OVhQHjOIb3uLR9Z9lzx2ESY58gatBLVsfhxIkTxMbG8vDDD/PCCy9YHUepStu5cyft2rWzOobC0aBt9uzZ/O9//3PJ8Z955hm++OIL9u3bV25jRVdbtGgRTz31FNu2bXPpIE6eqKK/RWPMBhE5/56Lk7zuShfY8wDwC2tsbY6CguJuR5s3b6ZBA88ZGlkpVTONGTOGpKQkUlNTzxuSuDr89NNPvPjii5YVBMDRxuHTTz/VgoCbed3VlgI7AIGh1g1FnJeXx8iRI+ncuTPPP/+8zjyolKoWNpuN5557zmXHr8r4B9XtjjvusDpCreR1vQkoLAz4h9SzLILNZsPPzw8/Pz/LMiillFLO8rqaAcRRGPAJrOv2U+fk5JCVlUVYWBifffbZeV1ZlFJKqZrI62oGTIGjzQB1G7n1vCLCnXfeyS233EJ+fr4WBJRSSnkMr6sZMAV52LHhE1r+OOQuOa8xPPjggyQmJmrDF6WUUh7F6z61bAX5pNnCCPdxz0s7e/Ys27Zto1evXgwaNMgt51RKKaWqk9fdJvCRfNJ83NeTYMKECfTr14/k5GS3nVMppZSqTt5XM4CddN/q739bnilTpjBs2DAdR0AppZTH8sKaATs5fq7tVnjq1Clef/11CgoKiIiIoF+/fi49n1Kqdnj11VfPmxjHm+Tk5NCsWTPWr19vdRR1Dq8rDNgoINfPtd0K586dy2uvvcaOHTtceh6l1MU5dOgQxhiXf/gYY/jqq68u6hhJSUm8/fbbvPRS6WHUs7OzGTNmDB06dMDHx4f777+/zPOfuzg7w2GRxx9/nG7duuHn53fe7Hqpqak8/PDDtGjRgqCgIFq3bl38hags77zzDsYY/vrXv5ZaHxAQwJNPPsmTTz5ZqWwlff3111x22WUEBgYSGxtb6YGSyrpWxhjmzZtXvM/HH39Mu3btCAoKokWLFkyePLnUMUSEN954g5YtWxIQEEDLli3P+///8MMPiYmJISgoiOuvv559+/ZV+TW7g1cWBuy+rp2++JFHHmHbtm2Vnl5TKaXKM3PmTLp160br1q1Lrbfb7fj6+vL444/To0ePCp+fkJBQvLRt27ZS58/NzeXBBx9k4MCB5207efIkycnJTJ8+nbi4OKZOncrbb7/N66+/ft6+cXFxzJo1i+josnt03XPPPaxevbrUNMPOiouL44477uDee+9l48aNdO7cmQEDBpCWlub0MUpeo4SEBGbMmEFISEhxDe///vc/HnroISZMmMDOnTv561//yquvvsqsWbOKj/Hyyy/z9ttvM2nSJHbu3MmcOXOKp4wGWLJkCePGjeP//u//WLt2LcHBwQwePBi73V7p1+w2FzPLUU1cujeyycqPnq5w5qeqiI+Pl5tvvlkOHz5c7cdWqibzxFkLi2YLLGspafPmzXLDDTdIUFCQNGvWTF588UXJy8sr3p6amiojRoyQBg0aSHBwsHTt2lVWrFhRvL28c5Q3LW5FOnbsKFOnTq1wn4EDBxbPiFgSIPPmzav0Ocvi7KyKjz32mFx++eWl1uXk5EjXrl1l5cqVEhMTI2+//XaZz73uuuvk2WefrXS28ePHS9euXYsfZ2dnS2hoqMycObPSxyrSv3//Utf07bfflubNm5fap0+fPjJu3DgREUlPT5fg4GD57LPPyj3mrbfeKkOHDi1+fPToUQFk2bJlVc4p4tpZC72uZgDAN6j6bxMkJiaye/duEhISqv3YSqnq1bRpUxISEli7di3g+KZW9E2wSHJyMjfccAOxsbFs2bKF2bNn88UXX/DOO+8U7/PSSy+xadMmlixZwtatW3nuuedKVY2XPGbJb+ZNmzatVN6UlBS2b99e4Tf/C/nLX/5CZGQkV111Fd98802Vj+OskydPnjdZ0gsvvECvXr3o3bt3hc/t0aMHv/zyS6XPuW7dulLHDggIoEePHlW+DRQfH8/SpUsZM2ZM8bprrrmG48ePF88MuW3bNrZt20b//v2LM2RmZuLj40OHDh1o2rQpI0eO5NSpU+XmbNKkCS1btqzRbSW8rjcBgC00qtqOlZGRQUhICN26dWPv3r34+/tX27GV8liLn4UT29x7zuhO0P8Np3b18fEhOjqa7OxsABo0aHBetfU//vEPWrduzZtvvglAmzZteOKJJ5g2bRpPP/00AAcPHqRDhw7Exjpmhm3VqlXpSCWOGRYWVm7V+IUcOXIEEaFJkyZVev7kyZO5/vrr8fX1Ze7cuQwdOpSlS5fSt2/fKh3vQrZt28Y333zDggULitf973//Y/78+WzZsuWCz2/SpAmHDx+u9HmTkpKIiIjgm2++YfTo0SxbtoyIiAiSkpIqfSxwtA1o1aoV1157bfG6nj17MmfOHAYPHkxWVhYA7733HgMGDAAcBUBjDJMnT2bq1KmEhoby6KOPcv/997No0aJSOadNm8Zrr73Ghg0bLiqnO3hlYYCQ6unmd/DgQfr06cPkyZO59957tSCglBfZunUrGzduJDT0/7cxstvtpe7rjhkzhhEjRnDFFVfQu3dv+vfvz80331ztWYo+dKo6dXDJmQxjY2PZs2cP7777rksKAydOnOAPf/gDTzzxRHH7grNnzzJq1ChmzpxZ6nqWJzAwkMzMzEqfW0QwxlCnTh2aNWtGcHAwjhryyisoKOCTTz5h7Nixpdbv2rWLP//5z0yZMoVrr72WzZs3M378eBo3bsyQIUMoKChARHjmmWeK2xm88cYbDBgwgNOnTxMWFgY4GiqGh4cTExNDQEBAlXO6i1cWBox/9TQgbNSoEb1796ZLly7VcjylvIaT39BrukGDBvH222+Xu33o0KEcPnyYZcuWsWTJEvr378/rr7/OM888U605iqY5T0lJoVGji59XpXv37sydO/eij3OuU6dOcdNNN3HTTTeVamG/f/9+Dh8+zODBg4vX5eTk8Nxzz/Hll1+yYcOGUsdJSUkhMjKy0uePiooiKSmJG2+8sbgGIjk5mTZt2lT6WEuWLOH48eOMGjWq1Po33niDK6+8kkcffRSATp06sXnzZt58802GDBlS/H916aWXFj+nZcuWABw7doywsDAiIyNJSkri8ccf59577y3OWZXX7C5e2WbA+FWtdF1k9+7dZGVlERgYyBdffEGnTp2qKZlSyp2KavPy8/PP29apUyd27dpFy5Ytad26damlpIYNG3Lvvffy+eef89BDD5V5P97Pz6/MczirVatW1KtXr9LdAcuza9euUq3bq0Nqaip9+/ale/fufPjhh6W2tWvXjp07d7J58+bipXHjxkyYMKHMLpdxcXF07dq10hliY2NLtTXIyclh7dq1xbdxSjp06BAnTpwo91gzZ87klltuoXHjxqXWnz59Gput9Eejr69vce3N5ZdfjjGGAwcOFG8/cuQIQPFtnnNzHjt2jAMHDpSZs8a4mNaHNXHp3sgmv69b60S7zLKdPn1aIiIiZPTo0VU+hlLexBN7ExTJz8+X0NBQefrppyUtLU2ysrKKtyUlJUl4eLiMGjVKNm/eLNu3b5eZM2fKiy++WLzPyy+/LF9//bXs27dP1qxZI23btpU//vGP552nXbt2cvfdd0tKSopkZWVJQUFBpbMOHz5cHnnkkTK3bd++XTZt2iS9e/eWwYMHy6ZNm2Tv3r0iIvLtt9/K+++/L1u3bpXdu3fLW2+9JTabTX744YfzjjNq1Khyezvs3btXNm3aJHfeead0795dNm3aJNu3bxcRkbS0NLniiiukb9++cuzYMUlISCheylNeb4KCggJp1qyZfPrpp05cldK2bdsmvr6+8uqrr8r27dtl9OjR0qBBAzl9+vR5+wLl9opISEgQX19f+eqrr87b9sEHH4ifn5/MmjVLDhw4IN9++62Eh4fLSy+9VLzPkCFDpE2bNrJq1SrZvHmzXHnllTJgwIDi7YsXLxYfHx+ZMWOGbN26VQYMGCBt27aV/Pz8Sr/mklzZm8DyD+/qXro3ssn6LVucvbZl+uKLL2T//v0XdQylvIUnFwZERGbPni3NmzcXm812XtfCTZs2Sd++fSUkJETq1q0rvXr1kn//+9/F2ydPnizt2rWTwMBAiYqKklGjRpX5wbN06VJp27at+Pr6Vrlr4bJlyyQyMlJyc3PP2xYTE3Ne98WiD7offvhB2rdvLyEhIRISEiKxsbEyf/78Ms8xfPhwCQwMlNTU1PO29enT57xzxMTEiIjIihUrnOqqeW7msgoDv/76q9SrV08yMjKcuCrnmz9/vrRp00b8/f2le/fu8vvvv5e5X0WFgcmTJ0tERESZ17qgoEDeeustadOmjQQGBkrz5s3P63KanJwsd911l9StW1caNGggd911lyQmJpY6zvvvvy9NmzaVgIAA6dOnj+zZs6dKr7ckVxYGjOMY3iO2sY/MXrmHdq1bXXjnElatWkVgYCDdu3d3UTKlPNPOnTtp166d1TFqhd69ezN69OhSXd2qU3R0NPfccw9Tp051yfGdMXz4cC6//HJeeOEFyzJ4qor+Fo0xG0SkyvchvLLNQEBAQKX2LygoYOzYsYwbNw5vKxwppTzHBx98gDHGJcfevn07mZmZpXoeuFtOTg5dunThiSeesCyDKptX9ibw8a1cYcBms/Hdd98REBDgsj9EpZS6kI4dO7psmPMOHTpw5swZlxzbWQEBAbz44ouWZlBl88qaAZufc4WBxYsXM2nSJACaN29eLV16lFJKKU/jhYUBg4+PcxUeCxcu5Ntvvy3uMqKUUkrVRl53m0AAX5+Kyzh2ux0fHx/+8Y9/kJGRQVBQkHvCKaWUUjWQ19UMCAabrfz7/nPmzKFHjx6kpKRgs9moU6eOG9Mp5Zm0Ya1S1nL136DXFQYA/Gzlv6zw8HAiIyN1ngGlnOTn56e30pSyWFZWFn5+fi47vlcWBsoqC8THxwPQt29fFi9e7NRkGkopx3jwx44dIzMzU2sIlHIzESEzM5Njx44RFVV9M/Key+vaDAD4ndNm4D//+Q/33XcfP//8Mz179tTug0pVQt26dQE4fvw4eXl5FqdRqvbx8/OjYcOGxX+LrlArCgM333wz48ePr9LEGEopR4HAlW9ESilreedtgsIv/t999x12u5369evz5ptvVnpkQqWUUqo2cHthwBgTboz52hiTYYw5bIy5u5z9jDHmTWNMcuHylqlE/f6qVau49dZb+eSTT6ovvFJKKeWFrLhNMB3IBRoClwPfG2O2iMj2c/Z7CPgD0AXH8AHLgAPAh1yAMYarr76ab775hkGDBlVreKWUUsrbuHXWQmNMCJAKdBSRPYXrZgPHROTZc/ZdDcwSkRmFjx8A/igiPSs6R+O6PvLLpj20alW5WQuVUkopT+VpsxZeCtiLCgKFtgAdyti3Q+G2C+1XSmJ6AbNnz76okEoppVRt4u6agd7APBGJLrHuj8A9InLdOfvagQ4isqvwcRtgD2CTc0IbYx7CcVsBoCMQ57IXoQAigFNWh6gF9Dq7nl5j19Nr7B6XiUiVh9R1d5uBdODc/kl1gbNO7FsXSD+3IABQeCuh6HbC+oupKlEXptfYPfQ6u55eY9fTa+wexpj1F/N8d98m2AP4Fn7LL9IFOLfxIIXrujixn1JKKaUuglsLAyKSASwAJhljQowxVwO3AmXd5P8MmGCMaWKMaQw8AcxyW1illFKqlrBi0KFHgCAgEfgSGCsi240xvY0x6SX2+yewENiGow3A94XrLmRGNedV59Nr7B56nV1Pr7Hr6TV2j4u6zm5tQKiUUkqpmscrhyNWSimllPO0MKCUUkrVch5ZGHDX/Aa1WSWu8VPGmDhjzFljzEFjzFPuzuqpnL3GJfb3N8bsMsYcdVdGT1eZa2yM6WaMWWmMSTfGnDTGjHdnVk9WifeLAGPMh4XXN8UYs9AY08TdeT2NMWacMWa9MSbHGDPrAvs+bow5YYxJM8Z8YoxxaoY+jywMUHp+g3uAD4wxZY1OWHJ+g87AIOBP7grp4Zy9xga4D6gP9APGGWPucltKz+bsNS7yFI6Gt8p5Tl1jY0wEsARHI+UGQGvgRzfm9HTO/i6PB67C8X7cGDgNvOeukB7sOPAaUOHMe8aYW4BngRuB5kBL4BVnTuBxDQjdMb9BbVeZa1zGc6fh+L16zPVJPVdlr7ExpgXwAzABmCkil7gzryeq5HvFZKCpiIx0f1LPVsnr/AFwVkSeLnw8EHhHRC5zc2yPZIx5DbhERO4vZ/u/gUMi8nzh4xuBL0qO+lseT6wZcPn8BqpS17hY4S2Y3ujgUM6o7DV+D3geyHJ1MC9SmWvcE0gxxqw2xiQWVl83c0tKz1eZ6/wxcLUxprExJhhHLcJiN2SsLcr6zGtojGlwoSd6YmEgFEg7Z10aUNaYzOfumwaEaruBC6rMNS5pIo7fqU9dkMnbOH2NjTFDAV8R+dodwbxIZX6PLwFG4ajGbgYcxDEOirqwylznPcAR4BhwBmgHTHJputqlrM88uPB7t0cWBlwyv4EqpTLXGHA0cMHRdmCgiOS4MJu3cOoaF1bBvgXobZfKq8zvcRbwtYisE5FsHPdZexlj6rk4ozeozHX+AAjE0S4jBMeItFozUH3K+syDCt67i3hiYUDnN3C9ylxjjDFjKGy0IiLa0t05zl7jNjgaAv1ijDmB482zUWFr4eZuyOnJKvN7vBUo+SWh6GetRbywylznLjjacaUUfml4D+hR2IBTXbyyPvNOikjyBZ8pIh63AHNwVOGFAFfjqArpUMZ+DwM7gSY4Wq5uBx62Or8nLJW4xvcAJ4B2Vmf2tMWZa4xjZtHoEsttOFoWRwM+Vr+Gmr5U4vf4BhyN4C4H/IC/Ab9Ynd9Tlkpc50+B+UC9wuv8PI6Ghpa/hpq8FL4PBAJTcMzlE4jj1uG5+/UrfD9uj6OH10/AG06dw+oXWcULEw58A2TguP90d+H63jhuAxTtZ3BUsaYULm9R2INCl2q7xgeBPBzVU0XLh1bn94TF2Wt8znOuA45and1TlspcY2AsjnvZqTjmRWlqdX5PWSrxftEA+AJHF9nTwK9AD6vz1/QFR3ssOWeZiKN9SzrQrMS+E4CTONpkfAoEOHMOj+taqJRSSqnq5YltBpRSSilVjbQwoJRSStVyWhhQSimlajktDCillFK1nBYGlFJKqVpOCwNKKaVULaeFAeUyxpiJxhgpY1leyeP8aoyZ46qcJc7z2jk5jxlj5hljWrrgPCdKPG5beK3qnrPfg4U5Aqvz/OVkan3Oaz9rjNlcOLpkVY53lzHmvmrOGFo4idDVJdaNMMZ8XTgioxhj7q3E8SKNMe8bYw4YY7KNMceNMUuMMUOqM3cl8pz3/22M6VD4+59RuO2Syv49GGNuKnyD1OBBAAAL9ElEQVRu28LHgYW/b52rkLGnMSbJGHPBse6VZ/G1OoDyemk4RsU6d11NlQIMLPy5FY45xJcbYzqKSGY1neNDHMMKF2kLvAx8hGOgkCLfAnGAO+d6eBxYg2NM81HAx8aYTBGpbGHsLhyTpnxWjdnGA7tFZFWJdXfgGHjle8Dpgosxxp//1965B3tVVXH88xUzdBAvipDKy8cojqmNgoo6GooPqNRSfE0ilgPRVE75IJWRAtQyzdGszBc42higDVGMijx8QBCghCkCkmNejEDeykuQ1R9r/bjnHn6Pe+HiBe7+zJz5cfbr7Mfh7rXXWvtseAX/Ct4w4D2gPXA+0B0Y20B1rg/Fxvs+vB+/AazDP9bTD/i0HuXOALoB78d9c/x9W4h/hrnOmNl0SXPxsRhWn7yJXZskDCR2NpvNbHpjV6IebMrUd7qkD4HJwAVAg5waaH5+Q8UzHMzsI+CjhnhmPZhXaH9ocLrgB1DtdM1MOSQ1A74PDMpFXWpmWyRVUQ9hADgXPzHvJDObnQl/qrFONS0x3p2BUWY2KRM2t57lrsEFvIZiOHCnpLvN7LMGLDfRiCQzQaJRkXSzpFmS1khaIukvko6skKeDpGdDXble0kJJP8ulOVvSq5LWSVou6Q+SWmxHFV+P306Zsq+U9JakjZI+kDQkJqtCfCtJT0haHOrn/0h6OBO/1UwgqQc1QkZ1qHMXRtxWtbGcakl3FemPMZImZ+4PkvRoqNQ3hFq5a30bbmZb8JVq+9zzrpM0VdKKuCZKOikT/zRwMXBuxuwwKBP/LUmvR90WS/qFpEoLk/OAtuQEsqjj9lAVv//LR1jms6yFsZJ0lqTZUefZkk7P55PUX9LceC/el3RjkTRflfRyqP1XSZos6cSIy473UZIM6AjcrIx5rZiZQNKJksZJWi038UyXdE7EbTUTRD+vjGxPZcannaQ3JD1apM5/lDQjEzQGH4se5To4sXuRhIHETkfS3rkru/JqBzwIXISrP78ITFF5m+TTwCHA9UAv/PCOrJ31LOAl/Dvzl+Lf6r4IV8PXl07xW5i8e+EHsszAJ7zf4Sc2PpDJ8wBwGq5KvQC4vUz5M4CB8e+LcHXuZflEMUGNAq7Ihsv9DC4ERsZ9c/xwku7AjcAl+B//CZLaVG7uNnTAz5/I0hEYAfSm5qCq1yR1jPjBwKvAzGhPN3w1iaSrgdHAtGjvMHzFX0nlfC7wjpmt2o42FGM2/n33EZLOqCCMtACexMe6N/4t+BckHVxIIOlW/AS+53Az0yPA3ZK+l0nTA5iAH5fcB7gK+Dt+iFqearzfPsIPpulGiWOsJR0HTAUOBvrj7/xYfOxqYWabccEK/Nv2hfFZCjwOXCFpv0zZLYFvEuMXZazCD4BLwsCeRGMfwJCuPfei+OEaBvQokb4ZsB9+2MnVmfApwJ8y9xuAnmWeOw14KRd2PrAF6Fwm3zB8Yts7rmPwSW010DbSzCpS9m3AZuCQuJ8HDKj0nMz9JdEv7XLpro/w5nHfNe67ZNJcgx8U1Tru+0f/HJFJsw9uL767TJ2OirJ7RdsPxIWJDcAZZfLtFekXArdlwscAE4qkXQQ8mgvvh9vDW5V5ziTgmTLxVVH/b9fj/bwl+s7i+c/jZof8WBlweSasZbwTwzLPXgfcnst7Fy6QFs6AmYmr64selpYf7whbRO7UuSL/H0bjhwM1L1Fujyi3c7m+ivD1wDW5sVkPVOXSPg28Ute+TteufyXNQGJnsxqfxLLXPwqRkk6XNEHScnxCXYsLBEeXKfOfwC8lXSspr8JuAZwKjMpqI/BJfQtwcoX6tsUniE34pN4e6G1mSyR9AT/idnQuz0hckDktU7+Bkgao9hnvO4SZzcQd3bLagSuASWa2LO574JPOB5m2b8Hb36UOjxmHt305cC/wE6vtsFfwcB8jaQnwWaQ/kvJjBm6jP4xtx2YSsC9+7GopvgQsKxNfkpxWaqs5x8zuAQ7HV9zj8BXys5KG5oowXLgp5FuDr/BPiaAzov6jc+2aiK/6D40V9snAkxazaQNyDi4obdiRQsxX/H8G+maC+wJjbFuNzDJ8TBJ7CEkYSOxsNpvZrNz1MYCkw4EX8QmlH/5HtSvu0V9uO91l+IT7AD7pvSGpe8QdhB9d/Qg1k/omfHXTjJz9uwjLow5dgMPM7HAzGx9xbaKMJbk8hfsD43cA8DdcM7JA0gJJvSs8t66MBC6X0wrXeGTtx62BM6nd9k24BqFS2wF+hLf/67jQdr+kLxciJR0AjMcnuR/jR9R2xX0LKm2BbB2/43N1ezfCy9WvOduxq0LSUblnzc/Gm9kiM3vIzHrjJquXgJ/KHRILrDazvPf+UtxUBTXtmp97VmELbXv83RCwuL5tqAOtGrDcx4HukjpKOhoXkJ4okm4jlcc7sRuRdhMkGpOeuI/AJWa2HrZu+aoql8ncG79PrPJOAYYAY0NLUHCOGoQLGnk+rFCnzWY2q0TcUlxwydve28bviqjfSuAHkn4InID7BDwj6U0zm8+OMRK4FddCHIuvWrNOdStwVXQx+3JdVo7vFtovaRqu/r8b39oGLrAdCpxtZgsLmXKTZylWxO93gH8ViX+vQt66PCNPNS6sFCjZB2b2iaTf4zb1I6lxHj1A0j45gaANNRNwoV09Ka69mIcvvIwaAaIhWdmA5U7GfUSuxbUd1biGI08VNe1O7AEkYSDRmOyLT66bM2FXUkeNlfm2pmmShuBq8A5m9qakmcDRZnZnQ1bWzDZJmo07kWW9ri/H2zE9l96AOZIG4s5ix5BbmQaFSabiSsvM5kiah5sHjgVezKlwJwJDgfczpoPtwsxWSPoVvo3sODN7Gx8zyKzSw2GzXS77p2zbnrm4T0YnMxtO/ZiPq/TrhZltxP08aiHpIGCVbbs1rmDWWZpNjvt1jIq8++PmmN9E/FRcyDjEzF4oVRdJs3Ah9uEGNhVMBK6UdEe0txIl3zczM0lPAN/FfU2GW/EdG52ABdtZ38QuSBIGEo3JROAeYLik4cDxuOp5TakM8Uf8r7iH9QJ8croJ+C81E+0twPjYtPAc7v3dEffyHmhm/96BOg8Gxkl6DPcdOBE3BzxsZoujjtPwieNtfCLpB3yM2/KLMS9+B0gaBaw1s7fK1GEk7oHfitr2XXCv7/7Ay5Luw1fbrXFNQrWZPVjnljq/xfvzJuA63Pt9HfCYpHtxj/XBeP/n29RL0sW4NuZDM1ss6SZ8vKtwzc0m4AjcY/3iMpPZVFwoUXYiDU/6Y3E/E4CukjYAS8zstTLtOg8YEu/dLHzVfjq+M2SMmVVn0q7FfVRa4iahW3CB9UHYKjQNBR4K09eUiD8GOMvMLo1yBuImknGxhW8drmmZZmbPl6lrJQbju1JekXQ/buo6KfrgyXxiM1snqRrfOfAOLtjNMbNNkWQE8PNow4gSzzwZ18gl9hQa24MxXXvuhU+Syyqk6YtPWOvxiaYLOQ9qMt7T+OT/GD7xr8O3Xo0FjsuV2w2fbNbgf8zn4l9za1mmLrW8/Mukuwq3kX8adR0KNMvE/xpXg3+Cq3AnkfHIL/YcfIL5ANeSLIywbbzLI7wzNR7wLYrUrwpftS7K1PFZ4LQybSrsJriwSNwQfMI4LO57RX9uAObgWxvzHu5tcKe7lVHuoEzc1yL92hif2fGMvcrU79Dom25FxqzYjpUJpcqKfB3ifZiDO7l+HGM2ENg3P1bA2fjX+jbi/ipnFimzD/BG9MtKXFN0Qy5N92j7emBVvBsnlBpv6rCbIMK+ArwQ79wafEdN94irtZsgwnpGezdQfCfLdODlEn3XFdeEtf+8/paka+dfhS0viUQisUsjaRwuKN3wOT5zGHC9mTUZz/n4fkI10N+KaBbCdHS8meU/M57YjUm7CRKJxO7CUKBv7GhINDCS9pd0KvAQrtkYVSRNC9yfIJ1LsIeRhIFEIrFbYH5mwq0U+bJeokE4FTcPnAL0sdjhk6MjcIeZTflca5bY6SQzQSKRSCQSTZykGUgkEolEoomThIFEIpFIJJo4SRhIJBKJRKKJk4SBRCKRSCSaOEkYSCQSiUSiiZOEgUQikUgkmjj/B7zyTpNLkjUIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bcm_glm = BinaryClassificationMetrics()\n",
    "bcm_glm.addModel('train', predict_pd[data_pd['train']])\n",
    "bcm_glm.addModel('test', predict_pd[~data_pd['train']])\n",
    "\n",
    "bcm_glm.plotROC(params={'legloc':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bcm_glm.plot(model_names=['test'], chart_types=[1,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>thresh</th>\n",
       "      <th>tp</th>\n",
       "      <th>tn</th>\n",
       "      <th>fp</th>\n",
       "      <th>fn</th>\n",
       "      <th>sens</th>\n",
       "      <th>spec</th>\n",
       "      <th>ppv</th>\n",
       "      <th>npv</th>\n",
       "      <th>accu</th>\n",
       "      <th>prev</th>\n",
       "      <th>lift</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.36</td>\n",
       "      <td>19132</td>\n",
       "      <td>21498</td>\n",
       "      <td>12576</td>\n",
       "      <td>5614</td>\n",
       "      <td>0.773135</td>\n",
       "      <td>0.630921</td>\n",
       "      <td>0.603381</td>\n",
       "      <td>0.792933</td>\n",
       "      <td>0.690751</td>\n",
       "      <td>0.420707</td>\n",
       "      <td>1.837703</td>\n",
       "      <td>0.677791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.36</td>\n",
       "      <td>4847</td>\n",
       "      <td>5603</td>\n",
       "      <td>3237</td>\n",
       "      <td>1437</td>\n",
       "      <td>0.771324</td>\n",
       "      <td>0.633824</td>\n",
       "      <td>0.599579</td>\n",
       "      <td>0.795881</td>\n",
       "      <td>0.690955</td>\n",
       "      <td>0.415499</td>\n",
       "      <td>1.856382</td>\n",
       "      <td>0.674694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model  thresh     tp     tn     fp    fn      sens      spec       ppv       npv      accu      prev      lift        f1\n",
       "0  train    0.36  19132  21498  12576  5614  0.773135  0.630921  0.603381  0.792933  0.690751  0.420707  1.837703  0.677791\n",
       "1   test    0.36   4847   5603   3237  1437  0.771324  0.633824  0.599579  0.795881  0.690955  0.415499  1.856382  0.674694"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcm_glm.confusionMatrixKeyValue(key='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>pain</td>\n",
       "      <td>1.398537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>depression</td>\n",
       "      <td>1.346171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>fat</td>\n",
       "      <td>1.338551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>death</td>\n",
       "      <td>1.139708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>die</td>\n",
       "      <td>1.079382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>alone</td>\n",
       "      <td>1.015601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>disappointment</td>\n",
       "      <td>0.988867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>depressed</td>\n",
       "      <td>0.971719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>shit</td>\n",
       "      <td>0.947672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>happiness</td>\n",
       "      <td>0.926700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>apartment</td>\n",
       "      <td>-0.500547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>birthday</td>\n",
       "      <td>-0.543040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>summer</td>\n",
       "      <td>-0.551339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>guys</td>\n",
       "      <td>-0.551375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-0.579403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>omg</td>\n",
       "      <td>-0.598836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>college</td>\n",
       "      <td>-0.653152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>lol</td>\n",
       "      <td>-0.792300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>videos</td>\n",
       "      <td>-0.848667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>new</td>\n",
       "      <td>-1.029133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>218 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              names  coefficients\n",
       "46             pain      1.398537\n",
       "40       depression      1.346171\n",
       "80              fat      1.338551\n",
       "77            death      1.139708\n",
       "110             die      1.079382\n",
       "126           alone      1.015601\n",
       "33   disappointment      0.988867\n",
       "64        depressed      0.971719\n",
       "18             shit      0.947672\n",
       "92        happiness      0.926700\n",
       "..              ...           ...\n",
       "193       apartment     -0.500547\n",
       "214        birthday     -0.543040\n",
       "37           summer     -0.551339\n",
       "144            guys     -0.551375\n",
       "0         Intercept     -0.579403\n",
       "166             omg     -0.598836\n",
       "88          college     -0.653152\n",
       "128             lol     -0.792300\n",
       "179          videos     -0.848667\n",
       "91              new     -1.029133\n",
       "\n",
       "[218 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_dt = glm_h2o._model_json['output']['coefficients_table'].as_data_frame()[['names','coefficients']]\n",
    "coef_dt = coef_dt[abs(coef_dt['coefficients'])>1E-3].reset_index(drop=True)\n",
    "coef_dt.to_csv('coefficients.txt', index=False)\n",
    "\n",
    "coef_dt['sorted'] = np.abs(coef_dt['coefficients'])\n",
    "coef_dt = coef_dt.sort_values('coefficients', ascending=False).drop(['sorted'], axis=1)\n",
    "\n",
    "with pd.option_context('display.max_rows', 20, 'display.max_columns', None):\n",
    "    coef_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "del bcm_glm, coef_dt, glm_h2o, predict_pd, regpath_pd\n",
    "\n",
    "#h2o.remove_all()\n",
    "#h2o.cluster().shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GBM Model Using LightGBM\n",
    "\n",
    "#### Parameter Description\n",
    "<pre>\n",
    "boosting_type      method used in boosting (gbdt); gbdt=gradient boosting decision tree, rf=random forest\n",
    "feature_fraction   randomly select part of features on each iteration\n",
    "learning_rate      shrinkage rate (.1)\n",
    "min_data_in_leaf   minimal number of rows of data in one leaf. Can be used to deal with over-fitting (20)\n",
    "num_boost_round    number of boosting iterations (100)\n",
    "num_leaves         max number of leaves in one tree (31)\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#row in train_gbm: 58,820\n"
     ]
    }
   ],
   "source": [
    "train_lgb = lgb.Dataset(X_train, y_train, feature_name=vocab).construct()\n",
    "print('#row in train_gbm: {:s}'.format(THOU(train_lgb.num_data())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fold_gen():\n",
    "    for n in range(1,param['XVAL_FOLDS']+1):\n",
    "        yield np.where(folds!=n)[0], np.where(folds==n)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current best after N searches (total 24) \n",
      "\n",
      "1 2 3 4 5 6 7 8 9 10  (3.7min)\n",
      "   learning_rate  num_leaves  feature_fraction  min_data_in_leaf  binary_logloss-mean  nboost\n",
      "4           0.01          63              0.25               100             0.564167     300\n",
      "3           0.01          63              0.25               125             0.569475     300\n",
      "1           0.01          63              0.20               100             0.570743     300 \n",
      "\n",
      "11 12 13 14 15 16 17 18 19 20  (3.7min)\n",
      "    learning_rate  num_leaves  feature_fraction  min_data_in_leaf  binary_logloss-mean  nboost\n",
      "16          0.015          63              0.25               100             0.543520     300\n",
      "15          0.015          63              0.25               125             0.548858     300\n",
      "13          0.015          63              0.20               100             0.549853     300 \n",
      "\n",
      "21 22 23 24  (1.2min)\n",
      "    learning_rate  num_leaves  feature_fraction  min_data_in_leaf  binary_logloss-mean  nboost\n",
      "16          0.015          63              0.25               100             0.543520     300\n",
      "15          0.015          63              0.25               125             0.548858     300\n",
      "13          0.015          63              0.20               100             0.549853     300 \n",
      "\n",
      "  time: 8.6min\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "\n",
    "metric = 'binary_logloss'\n",
    "param_fixed = dict(objective='binary', boosting_type='gbdt', metric=metric, num_thread=param['NUM_THREAD'],\n",
    "                   num_boost_round=300, early_stopping_rounds=5, seed=param['SEED'])\n",
    "\n",
    "param_grid = OrderedDict([\n",
    "    ('learning_rate',    [.01, .015]),\n",
    "    ('num_leaves',       [63, 31]),\n",
    "    ('feature_fraction', [.2, .25]),\n",
    "    ('min_data_in_leaf', [125, 100, 150])\n",
    "])\n",
    "param_cart = list(itertools.product(*param_grid.values()))\n",
    "\n",
    "def lgb_cv(vals):\n",
    "    prm = {**param_fixed, **dict(zip(param_grid.keys(),vals))}\n",
    "    out = lgb.cv(prm, train_lgb, folds=fold_gen(), verbose_eval=0, show_stdv=False)\n",
    "    return(list(vals)+[out[metric+'-mean'][-1],len(out[metric+'-mean'])])\n",
    "\n",
    "print('Current best after N searches (total {})'.format(len(param_cart)),'\\n')\n",
    "result,st = list(),time()       \n",
    "for n,x in enumerate(param_cart,1):\n",
    "    print(n, end=' '); sys.stdout.flush()\n",
    "    result.append(lgb_cv(x))\n",
    "    if n%10==0 or n==len(param_cart):\n",
    "        print(' ({0:.1f}min)'.format((time()-st)/60.)); st = time()\n",
    "        tmp = pd.DataFrame(result, columns=list(param_grid.keys())+[metric+'-mean','nboost'])\n",
    "        print(tmp.sort_values([metric+'-mean'])[:3], '\\n')\n",
    "\n",
    "print('  time: {0:.1f}min'.format((time()-start)/60.))\n",
    "del param_cart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results sorted on binary_logloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>num_leaves</th>\n",
       "      <th>feature_fraction</th>\n",
       "      <th>min_data_in_leaf</th>\n",
       "      <th>binary_logloss-mean</th>\n",
       "      <th>nboost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.015</td>\n",
       "      <td>63</td>\n",
       "      <td>0.25</td>\n",
       "      <td>100</td>\n",
       "      <td>0.543520</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.015</td>\n",
       "      <td>63</td>\n",
       "      <td>0.25</td>\n",
       "      <td>125</td>\n",
       "      <td>0.548858</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.015</td>\n",
       "      <td>63</td>\n",
       "      <td>0.20</td>\n",
       "      <td>100</td>\n",
       "      <td>0.549853</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015</td>\n",
       "      <td>63</td>\n",
       "      <td>0.25</td>\n",
       "      <td>150</td>\n",
       "      <td>0.555256</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.015</td>\n",
       "      <td>63</td>\n",
       "      <td>0.20</td>\n",
       "      <td>125</td>\n",
       "      <td>0.555823</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.015</td>\n",
       "      <td>31</td>\n",
       "      <td>0.25</td>\n",
       "      <td>100</td>\n",
       "      <td>0.555881</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.015</td>\n",
       "      <td>31</td>\n",
       "      <td>0.25</td>\n",
       "      <td>125</td>\n",
       "      <td>0.558369</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.015</td>\n",
       "      <td>31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>100</td>\n",
       "      <td>0.559095</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.015</td>\n",
       "      <td>31</td>\n",
       "      <td>0.25</td>\n",
       "      <td>150</td>\n",
       "      <td>0.562003</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.015</td>\n",
       "      <td>63</td>\n",
       "      <td>0.20</td>\n",
       "      <td>150</td>\n",
       "      <td>0.562118</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.015</td>\n",
       "      <td>31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>125</td>\n",
       "      <td>0.562240</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.010</td>\n",
       "      <td>63</td>\n",
       "      <td>0.25</td>\n",
       "      <td>100</td>\n",
       "      <td>0.564167</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.015</td>\n",
       "      <td>31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>150</td>\n",
       "      <td>0.566317</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.010</td>\n",
       "      <td>63</td>\n",
       "      <td>0.25</td>\n",
       "      <td>125</td>\n",
       "      <td>0.569475</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.010</td>\n",
       "      <td>63</td>\n",
       "      <td>0.20</td>\n",
       "      <td>100</td>\n",
       "      <td>0.570743</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.010</td>\n",
       "      <td>63</td>\n",
       "      <td>0.25</td>\n",
       "      <td>150</td>\n",
       "      <td>0.575246</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.010</td>\n",
       "      <td>31</td>\n",
       "      <td>0.25</td>\n",
       "      <td>100</td>\n",
       "      <td>0.575481</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.010</td>\n",
       "      <td>63</td>\n",
       "      <td>0.20</td>\n",
       "      <td>125</td>\n",
       "      <td>0.576400</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.010</td>\n",
       "      <td>31</td>\n",
       "      <td>0.25</td>\n",
       "      <td>125</td>\n",
       "      <td>0.577958</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.010</td>\n",
       "      <td>31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>100</td>\n",
       "      <td>0.579099</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.010</td>\n",
       "      <td>31</td>\n",
       "      <td>0.25</td>\n",
       "      <td>150</td>\n",
       "      <td>0.581198</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.010</td>\n",
       "      <td>31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>125</td>\n",
       "      <td>0.582158</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.010</td>\n",
       "      <td>63</td>\n",
       "      <td>0.20</td>\n",
       "      <td>150</td>\n",
       "      <td>0.582185</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.010</td>\n",
       "      <td>31</td>\n",
       "      <td>0.20</td>\n",
       "      <td>150</td>\n",
       "      <td>0.585824</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    learning_rate  num_leaves  feature_fraction  min_data_in_leaf  binary_logloss-mean  nboost\n",
       "0           0.015          63              0.25               100             0.543520     300\n",
       "1           0.015          63              0.25               125             0.548858     300\n",
       "2           0.015          63              0.20               100             0.549853     300\n",
       "3           0.015          63              0.25               150             0.555256     300\n",
       "4           0.015          63              0.20               125             0.555823     300\n",
       "5           0.015          31              0.25               100             0.555881     300\n",
       "6           0.015          31              0.25               125             0.558369     300\n",
       "7           0.015          31              0.20               100             0.559095     300\n",
       "8           0.015          31              0.25               150             0.562003     300\n",
       "9           0.015          63              0.20               150             0.562118     300\n",
       "10          0.015          31              0.20               125             0.562240     300\n",
       "11          0.010          63              0.25               100             0.564167     300\n",
       "12          0.015          31              0.20               150             0.566317     300\n",
       "13          0.010          63              0.25               125             0.569475     300\n",
       "14          0.010          63              0.20               100             0.570743     300\n",
       "15          0.010          63              0.25               150             0.575246     300\n",
       "16          0.010          31              0.25               100             0.575481     300\n",
       "17          0.010          63              0.20               125             0.576400     300\n",
       "18          0.010          31              0.25               125             0.577958     300\n",
       "19          0.010          31              0.20               100             0.579099     300\n",
       "20          0.010          31              0.25               150             0.581198     300\n",
       "21          0.010          31              0.20               125             0.582158     300\n",
       "22          0.010          63              0.20               150             0.582185     300\n",
       "23          0.010          31              0.20               150             0.585824     300"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridresult_pd = pd.DataFrame(result, columns=list(param_grid.keys())+[metric+'-mean','nboost'])\n",
    "gridresult_pd = gridresult_pd.sort_values([metric+'-mean']).reset_index(drop=True)\n",
    "with pd.option_context(\"display.max_rows\",40):\n",
    "    gridresult_pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Final Model and Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "param_final: {'objective': 'binary', 'boosting_type': 'gbdt', 'metric': 'binary_logloss', 'num_thread': 8, 'num_boost_round': 300, 'seed': 123456, 'learning_rate': 0.015, 'num_leaves': 63, 'feature_fraction': 0.25, 'min_data_in_leaf': 100} \n",
      "\n",
      "  time: 0.1min\n"
     ]
    }
   ],
   "source": [
    "model_index_selected = 0\n",
    "\n",
    "start = time()\n",
    "tmp = gridresult_pd.iloc[model_index_selected].to_dict()\n",
    "param_final = {**param_fixed, **tmp}\n",
    "param_final['num_boost_round'] = param_final['nboost']\n",
    "\n",
    "for x in ['num_leaves','min_data_in_leaf','num_boost_round']:\n",
    "    if x in param_final:\n",
    "        param_final[x] = int(param_final[x])\n",
    "for x in ['early_stopping_rounds','nboost','binary_logloss-mean']:\n",
    "    if x in param_final:\n",
    "        del param_final[x]\n",
    "print('param_final:', param_final, '\\n')\n",
    "\n",
    "model_gbm = lgb.train(param_final, train_lgb, verbose_eval=False)\n",
    "_ = model_gbm.save_model(filename='saved/lgbm.txt')\n",
    "\n",
    "print('  time: {0:.1f}min'.format((time()-start)/60.))\n",
    "del model_index_selected, param_final, tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp1 = data_pd[data_pd['train']==True][['train','label']].reset_index(drop=True)\n",
    "tmp1['score'] = model_gbm.predict(X_train)\n",
    "tmp2 = data_pd[data_pd['train']==False][['train','label']].reset_index(drop=True)\n",
    "tmp2['score'] = model_gbm.predict(X_test)\n",
    "\n",
    "predict_gbm_pd = pd.concat([tmp1,tmp2]).reset_index(drop=True)\n",
    "predict_gbm_pd.rename({'is_case':'label'}, axis='columns', inplace=True)\n",
    "predict_gbm_pd['label'] = predict_gbm_pd['label'].astype(np.int8)\n",
    "\n",
    "del tmp1, tmp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bcm_gbm = BinaryClassificationMetrics()\n",
    "bcm_gbm.addModel('train', predict_gbm_pd.loc[predict_gbm_pd['train'], ['label','score']])\n",
    "bcm_gbm.addModel('test', predict_gbm_pd.loc[~predict_gbm_pd['train'], ['label','score']])\n",
    "\n",
    "bcm_gbm.plotROC(params={'legloc':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bcm_gbm.plot(model_names=['test'], chart_types=[1,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>thresh</th>\n",
       "      <th>tp</th>\n",
       "      <th>tn</th>\n",
       "      <th>fp</th>\n",
       "      <th>fn</th>\n",
       "      <th>sens</th>\n",
       "      <th>spec</th>\n",
       "      <th>ppv</th>\n",
       "      <th>npv</th>\n",
       "      <th>accu</th>\n",
       "      <th>prev</th>\n",
       "      <th>lift</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.38</td>\n",
       "      <td>18982</td>\n",
       "      <td>24099</td>\n",
       "      <td>9975</td>\n",
       "      <td>5764</td>\n",
       "      <td>0.767073</td>\n",
       "      <td>0.707255</td>\n",
       "      <td>0.655524</td>\n",
       "      <td>0.806985</td>\n",
       "      <td>0.732421</td>\n",
       "      <td>0.420707</td>\n",
       "      <td>1.823295</td>\n",
       "      <td>0.706925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.39</td>\n",
       "      <td>4664</td>\n",
       "      <td>6333</td>\n",
       "      <td>2507</td>\n",
       "      <td>1620</td>\n",
       "      <td>0.742202</td>\n",
       "      <td>0.716403</td>\n",
       "      <td>0.650397</td>\n",
       "      <td>0.796303</td>\n",
       "      <td>0.727122</td>\n",
       "      <td>0.415499</td>\n",
       "      <td>1.786294</td>\n",
       "      <td>0.693274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model  thresh     tp     tn    fp    fn      sens      spec       ppv       npv      accu      prev      lift        f1\n",
       "0  train    0.38  18982  24099  9975  5764  0.767073  0.707255  0.655524  0.806985  0.732421  0.420707  1.823295  0.706925\n",
       "1   test    0.39   4664   6333  2507  1620  0.742202  0.716403  0.650397  0.796303  0.727122  0.415499  1.786294  0.693274"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcm_gbm.confusionMatrixKeyValue(key='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variable Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var</th>\n",
       "      <th>varimp_split</th>\n",
       "      <th>varimp_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>new</td>\n",
       "      <td>73</td>\n",
       "      <td>20021.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>you</td>\n",
       "      <td>106</td>\n",
       "      <td>15563.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>pain</td>\n",
       "      <td>93</td>\n",
       "      <td>13943.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>feel</td>\n",
       "      <td>83</td>\n",
       "      <td>12071.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>myself</td>\n",
       "      <td>80</td>\n",
       "      <td>10453.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>alone</td>\n",
       "      <td>80</td>\n",
       "      <td>9075.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>lol</td>\n",
       "      <td>86</td>\n",
       "      <td>8362.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>never</td>\n",
       "      <td>106</td>\n",
       "      <td>7943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>die</td>\n",
       "      <td>65</td>\n",
       "      <td>7911.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>people</td>\n",
       "      <td>85</td>\n",
       "      <td>7844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>someone</td>\n",
       "      <td>99</td>\n",
       "      <td>7518.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>depression</td>\n",
       "      <td>77</td>\n",
       "      <td>7327.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>fat</td>\n",
       "      <td>71</td>\n",
       "      <td>7321.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>anymore</td>\n",
       "      <td>87</td>\n",
       "      <td>7302.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>hate</td>\n",
       "      <td>93</td>\n",
       "      <td>6721.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>shit</td>\n",
       "      <td>73</td>\n",
       "      <td>6614.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>on</td>\n",
       "      <td>107</td>\n",
       "      <td>6289.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>care</td>\n",
       "      <td>89</td>\n",
       "      <td>6170.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>hurt</td>\n",
       "      <td>98</td>\n",
       "      <td>6159.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>nothing</td>\n",
       "      <td>97</td>\n",
       "      <td>6106.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            var  varimp_split  varimp_gain\n",
       "394         new            73      20021.5\n",
       "68          you           106      15563.3\n",
       "177        pain            93      13943.8\n",
       "8          feel            83      12071.1\n",
       "538      myself            80      10453.6\n",
       "550       alone            80       9075.8\n",
       "569         lol            86       8362.9\n",
       "81        never           106       7943.0\n",
       "479         die            65       7911.8\n",
       "40       people            85       7844.0\n",
       "340     someone            99       7518.4\n",
       "158  depression            77       7327.0\n",
       "338         fat            71       7321.4\n",
       "730     anymore            87       7302.9\n",
       "757        hate            93       6721.7\n",
       "58         shit            73       6614.3\n",
       "621          on           107       6289.1\n",
       "882        care            89       6170.2\n",
       "818        hurt            98       6159.4\n",
       "283     nothing            97       6106.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varimp_pd = pd.DataFrame(OrderedDict([('var', model_gbm.feature_name()), \n",
    "                ('varimp_split', model_gbm.feature_importance()), \n",
    "                ('varimp_gain', np.round(model_gbm.feature_importance(importance_type='gain'),1))]))\n",
    "varimp_pd = varimp_pd[varimp_pd['varimp_gain']>0]\n",
    "\n",
    "with pd.option_context(\"display.max_rows\",20):\n",
    "    varimp_pd.sort_values(['varimp_gain'], ascending=False)[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "del bcm_gbm, gridresult_pd, model_gbm, predict_gbm_pd, train_lgb, varimp_pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Learning Model (Multilayer Perceptron) Using Keras\n",
    "#### Neural Network Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\python37\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From c:\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_inp (Dense)            (None, 50)                49600     \n",
      "_________________________________________________________________\n",
      "drop1 (Dropout)              (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_hid (Dense)            (None, 50)                2550      \n",
      "_________________________________________________________________\n",
      "drop2 (Dropout)              (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_out (Dense)            (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 52,201\n",
      "Trainable params: 52,201\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_mlp = models.Sequential()\n",
    "model_mlp.add(layers.Dense(50, activation='relu', input_shape=(X_train.shape[1],), name='dense_inp'))\n",
    "model_mlp.add(layers.Dropout(rate=.8, name='drop1'))\n",
    "model_mlp.add(layers.Dense(50, activation='relu', name='dense_hid'))\n",
    "model_mlp.add(layers.Dropout(rate=.8, name='drop2'))\n",
    "model_mlp.add(layers.Dense(1, activation='sigmoid', name='dense_out'))\n",
    "model_mlp.summary()\n",
    "\n",
    "model_mlp.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "utils.plot_model(model_mlp, to_file='model.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset_weights(model):\n",
    "    session = backend.get_session()\n",
    "    for layer in model.layers:\n",
    "        if hasattr(layer, 'kernel_initializer'):\n",
    "            layer.kernel.initializer.run(session=session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Training and Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\python37\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      " - 3s - loss: 0.6867 - acc: 0.5663\n",
      "Epoch 2/6\n",
      " - 3s - loss: 0.6528 - acc: 0.6154\n",
      "Epoch 3/6\n",
      " - 2s - loss: 0.6029 - acc: 0.6800\n",
      "Epoch 4/6\n",
      " - 2s - loss: 0.5700 - acc: 0.7116\n",
      "Epoch 5/6\n",
      " - 3s - loss: 0.5498 - acc: 0.7300\n",
      "Epoch 6/6\n",
      " - 3s - loss: 0.5370 - acc: 0.7379\n"
     ]
    }
   ],
   "source": [
    "reset_weights(model_mlp)\n",
    "hist = model_mlp.fit(X_train.todense(), y_train, epochs=6, batch_size=500, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp1 = data_pd[data_pd['train']==True][['train','label']].reset_index(drop=True)\n",
    "tmp1['score'] = model_mlp.predict(X_train.todense()).ravel()\n",
    "tmp2 = data_pd[data_pd['train']==False][['train','label']].reset_index(drop=True)\n",
    "tmp2['score'] = model_mlp.predict(X_test.todense()).ravel()\n",
    "\n",
    "predict_mlp_pd = pd.concat([tmp1,tmp2]).reset_index(drop=True)\n",
    "predict_mlp_pd.rename({'is_case':'label'}, axis='columns', inplace=True)\n",
    "predict_mlp_pd['label'] = predict_mlp_pd['label'].astype(np.int8)\n",
    "\n",
    "del tmp1, tmp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bcm_mlp = BinaryClassificationMetrics()\n",
    "bcm_mlp.addModel('train', predict_mlp_pd.loc[predict_mlp_pd['train'], ['label','score']])\n",
    "bcm_mlp.addModel('test', predict_mlp_pd.loc[~predict_mlp_pd['train'], ['label','score']])\n",
    "\n",
    "bcm_mlp.plotROC(params={'legloc':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bcm_mlp.plot(model_names=['test'], chart_types=[1,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>thresh</th>\n",
       "      <th>tp</th>\n",
       "      <th>tn</th>\n",
       "      <th>fp</th>\n",
       "      <th>fn</th>\n",
       "      <th>sens</th>\n",
       "      <th>spec</th>\n",
       "      <th>ppv</th>\n",
       "      <th>npv</th>\n",
       "      <th>accu</th>\n",
       "      <th>prev</th>\n",
       "      <th>lift</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>0.38</td>\n",
       "      <td>19278</td>\n",
       "      <td>25957</td>\n",
       "      <td>8117</td>\n",
       "      <td>5468</td>\n",
       "      <td>0.779035</td>\n",
       "      <td>0.761783</td>\n",
       "      <td>0.703705</td>\n",
       "      <td>0.825998</td>\n",
       "      <td>0.769041</td>\n",
       "      <td>0.420707</td>\n",
       "      <td>1.851727</td>\n",
       "      <td>0.739456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.38</td>\n",
       "      <td>4776</td>\n",
       "      <td>6644</td>\n",
       "      <td>2196</td>\n",
       "      <td>1508</td>\n",
       "      <td>0.760025</td>\n",
       "      <td>0.751584</td>\n",
       "      <td>0.685026</td>\n",
       "      <td>0.815015</td>\n",
       "      <td>0.755091</td>\n",
       "      <td>0.415499</td>\n",
       "      <td>1.829189</td>\n",
       "      <td>0.720579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model  thresh     tp     tn    fp    fn      sens      spec       ppv       npv      accu      prev      lift        f1\n",
       "0  train    0.38  19278  25957  8117  5468  0.779035  0.761783  0.703705  0.825998  0.769041  0.420707  1.851727  0.739456\n",
       "1   test    0.38   4776   6644  2196  1508  0.760025  0.751584  0.685026  0.815015  0.755091  0.415499  1.829189  0.720579"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcm_mlp.confusionMatrixKeyValue(key='f1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "del bcm_mlp, model_mlp, predict_mlp_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.save_checkpoint();"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook Depression Analysis.ipynb to html\n",
      "[NbConvertApp] Writing 878419 bytes to ./Depression Analysis 20190511-1822.html\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "from pytz import timezone\n",
    "outname = param['NB_NAME'] + datetime.now(timezone('US/Eastern')).strftime(' %Y%m%d-%H%M')\n",
    "\n",
    "display(Javascript('IPython.notebook.save_checkpoint();'))\n",
    "\n",
    "!jupyter nbconvert \"{param['NB_NAME']}\" --to=html --TemplateExporter.exclude_input=True --output-dir={param['DIR']} --output \"{outname}\""
   ]
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}