{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "iraqi-wound",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "legal-router",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imbalanced-learn in /opt/conda/lib/python3.8/site-packages (0.8.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /opt/conda/lib/python3.8/site-packages (from imbalanced-learn) (1.6.0)\n",
      "Requirement already satisfied: scikit-learn>=0.24 in /opt/conda/lib/python3.8/site-packages (from imbalanced-learn) (0.24.1)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /opt/conda/lib/python3.8/site-packages (from imbalanced-learn) (1.20.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.8/site-packages (from imbalanced-learn) (1.0.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn>=0.24->imbalanced-learn) (2.1.0)\n",
      "Requirement already satisfied: python-slugify in /opt/conda/lib/python3.8/site-packages (4.0.1)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in /opt/conda/lib/python3.8/site-packages (from python-slugify) (1.3)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -U imbalanced-learn\n",
    "!{sys.executable} -m pip install -U python-slugify"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "superior-clinton",
   "metadata": {},
   "source": [
    "## Dataset overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "assumed-progress",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Bankrupt?</th>\n",
       "      <th>ROA(C) before interest and depreciation before interest</th>\n",
       "      <th>ROA(A) before interest and % after tax</th>\n",
       "      <th>ROA(B) before interest and depreciation after tax</th>\n",
       "      <th>Operating Gross Margin</th>\n",
       "      <th>Realized Sales Gross Margin</th>\n",
       "      <th>Operating Profit Rate</th>\n",
       "      <th>Pre-tax net Interest Rate</th>\n",
       "      <th>After-tax net Interest Rate</th>\n",
       "      <th>Non-industry income and expenditure/revenue</th>\n",
       "      <th>...</th>\n",
       "      <th>Net Income to Total Assets</th>\n",
       "      <th>Total assets to GNP price</th>\n",
       "      <th>No-credit Interval</th>\n",
       "      <th>Gross Profit to Sales</th>\n",
       "      <th>Net Income to Stockholder's Equity</th>\n",
       "      <th>Liability to Equity</th>\n",
       "      <th>Degree of Financial Leverage (DFL)</th>\n",
       "      <th>Interest Coverage Ratio (Interest expense to EBIT)</th>\n",
       "      <th>Net Income Flag</th>\n",
       "      <th>Equity to Liability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.370594</td>\n",
       "      <td>0.424389</td>\n",
       "      <td>0.405750</td>\n",
       "      <td>0.601457</td>\n",
       "      <td>0.601457</td>\n",
       "      <td>0.998969</td>\n",
       "      <td>0.796887</td>\n",
       "      <td>0.808809</td>\n",
       "      <td>0.302646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.716845</td>\n",
       "      <td>0.009219</td>\n",
       "      <td>0.622879</td>\n",
       "      <td>0.601453</td>\n",
       "      <td>0.827890</td>\n",
       "      <td>0.290202</td>\n",
       "      <td>0.026601</td>\n",
       "      <td>0.564050</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.464291</td>\n",
       "      <td>0.538214</td>\n",
       "      <td>0.516730</td>\n",
       "      <td>0.610235</td>\n",
       "      <td>0.610235</td>\n",
       "      <td>0.998946</td>\n",
       "      <td>0.797380</td>\n",
       "      <td>0.809301</td>\n",
       "      <td>0.303556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.795297</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.623652</td>\n",
       "      <td>0.610237</td>\n",
       "      <td>0.839969</td>\n",
       "      <td>0.283846</td>\n",
       "      <td>0.264577</td>\n",
       "      <td>0.570175</td>\n",
       "      <td>1</td>\n",
       "      <td>0.020794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.426071</td>\n",
       "      <td>0.499019</td>\n",
       "      <td>0.472295</td>\n",
       "      <td>0.601450</td>\n",
       "      <td>0.601364</td>\n",
       "      <td>0.998857</td>\n",
       "      <td>0.796403</td>\n",
       "      <td>0.808388</td>\n",
       "      <td>0.302035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774670</td>\n",
       "      <td>0.040003</td>\n",
       "      <td>0.623841</td>\n",
       "      <td>0.601449</td>\n",
       "      <td>0.836774</td>\n",
       "      <td>0.290189</td>\n",
       "      <td>0.026555</td>\n",
       "      <td>0.563706</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.399844</td>\n",
       "      <td>0.451265</td>\n",
       "      <td>0.457733</td>\n",
       "      <td>0.583541</td>\n",
       "      <td>0.583541</td>\n",
       "      <td>0.998700</td>\n",
       "      <td>0.796967</td>\n",
       "      <td>0.808966</td>\n",
       "      <td>0.303350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.739555</td>\n",
       "      <td>0.003252</td>\n",
       "      <td>0.622929</td>\n",
       "      <td>0.583538</td>\n",
       "      <td>0.834697</td>\n",
       "      <td>0.281721</td>\n",
       "      <td>0.026697</td>\n",
       "      <td>0.564663</td>\n",
       "      <td>1</td>\n",
       "      <td>0.023982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.465022</td>\n",
       "      <td>0.538432</td>\n",
       "      <td>0.522298</td>\n",
       "      <td>0.598783</td>\n",
       "      <td>0.598783</td>\n",
       "      <td>0.998973</td>\n",
       "      <td>0.797366</td>\n",
       "      <td>0.809304</td>\n",
       "      <td>0.303475</td>\n",
       "      <td>...</td>\n",
       "      <td>0.795016</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.623521</td>\n",
       "      <td>0.598782</td>\n",
       "      <td>0.839973</td>\n",
       "      <td>0.278514</td>\n",
       "      <td>0.024752</td>\n",
       "      <td>0.575617</td>\n",
       "      <td>1</td>\n",
       "      <td>0.035490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bankrupt?   ROA(C) before interest and depreciation before interest  \\\n",
       "0          1                                           0.370594          \n",
       "1          1                                           0.464291          \n",
       "2          1                                           0.426071          \n",
       "3          1                                           0.399844          \n",
       "4          1                                           0.465022          \n",
       "\n",
       "    ROA(A) before interest and % after tax  \\\n",
       "0                                 0.424389   \n",
       "1                                 0.538214   \n",
       "2                                 0.499019   \n",
       "3                                 0.451265   \n",
       "4                                 0.538432   \n",
       "\n",
       "    ROA(B) before interest and depreciation after tax  \\\n",
       "0                                           0.405750    \n",
       "1                                           0.516730    \n",
       "2                                           0.472295    \n",
       "3                                           0.457733    \n",
       "4                                           0.522298    \n",
       "\n",
       "    Operating Gross Margin   Realized Sales Gross Margin  \\\n",
       "0                 0.601457                      0.601457   \n",
       "1                 0.610235                      0.610235   \n",
       "2                 0.601450                      0.601364   \n",
       "3                 0.583541                      0.583541   \n",
       "4                 0.598783                      0.598783   \n",
       "\n",
       "    Operating Profit Rate   Pre-tax net Interest Rate  \\\n",
       "0                0.998969                    0.796887   \n",
       "1                0.998946                    0.797380   \n",
       "2                0.998857                    0.796403   \n",
       "3                0.998700                    0.796967   \n",
       "4                0.998973                    0.797366   \n",
       "\n",
       "    After-tax net Interest Rate   Non-industry income and expenditure/revenue  \\\n",
       "0                      0.808809                                      0.302646   \n",
       "1                      0.809301                                      0.303556   \n",
       "2                      0.808388                                      0.302035   \n",
       "3                      0.808966                                      0.303350   \n",
       "4                      0.809304                                      0.303475   \n",
       "\n",
       "   ...   Net Income to Total Assets   Total assets to GNP price  \\\n",
       "0  ...                     0.716845                    0.009219   \n",
       "1  ...                     0.795297                    0.008323   \n",
       "2  ...                     0.774670                    0.040003   \n",
       "3  ...                     0.739555                    0.003252   \n",
       "4  ...                     0.795016                    0.003878   \n",
       "\n",
       "    No-credit Interval   Gross Profit to Sales  \\\n",
       "0             0.622879                0.601453   \n",
       "1             0.623652                0.610237   \n",
       "2             0.623841                0.601449   \n",
       "3             0.622929                0.583538   \n",
       "4             0.623521                0.598782   \n",
       "\n",
       "    Net Income to Stockholder's Equity   Liability to Equity  \\\n",
       "0                             0.827890              0.290202   \n",
       "1                             0.839969              0.283846   \n",
       "2                             0.836774              0.290189   \n",
       "3                             0.834697              0.281721   \n",
       "4                             0.839973              0.278514   \n",
       "\n",
       "    Degree of Financial Leverage (DFL)  \\\n",
       "0                             0.026601   \n",
       "1                             0.264577   \n",
       "2                             0.026555   \n",
       "3                             0.026697   \n",
       "4                             0.024752   \n",
       "\n",
       "    Interest Coverage Ratio (Interest expense to EBIT)   Net Income Flag  \\\n",
       "0                                           0.564050                   1   \n",
       "1                                           0.570175                   1   \n",
       "2                                           0.563706                   1   \n",
       "3                                           0.564663                   1   \n",
       "4                                           0.575617                   1   \n",
       "\n",
       "    Equity to Liability  \n",
       "0              0.016469  \n",
       "1              0.020794  \n",
       "2              0.016474  \n",
       "3              0.023982  \n",
       "4              0.035490  \n",
       "\n",
       "[5 rows x 96 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('company_bankrupcy.csv').dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "premier-lincoln",
   "metadata": {},
   "source": [
    "## Imbalance Analysis\n",
    "\n",
    "\n",
    "Is always helpful to know how imbalance the dataset is. Are there more positive samples than negatives ? How much ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cooked-april",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OK</th>\n",
       "      <td>6599</td>\n",
       "      <td>0.967737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>220</td>\n",
       "      <td>0.032263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count       pct\n",
       "OK   6599  0.967737\n",
       "KO    220  0.032263"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = len(df)\n",
    "stats = df[['Bankrupt?', ' After-tax net Interest Rate']]\\\n",
    "    .groupby(['Bankrupt?'])\\\n",
    "    .agg(['count'])\\\n",
    "    .reset_index(drop=True)\\\n",
    "    .rename(index={0: \"OK\", 1: \"KO\"})\\\n",
    "    .T.reset_index(drop=True, level=0).T\n",
    "\n",
    "stats['pct'] = stats['count'] / count\n",
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "based-virtue",
   "metadata": {},
   "source": [
    "It seems this dataset is highly imbalanced. Most of the samples are negative target samples, whereas only 3% of the samples are of type positive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imperial-clinton",
   "metadata": {},
   "source": [
    "### Imbalanced: Resampling & Feature selection\n",
    "\n",
    "We need to narrow down the number of features that are really relevant for classifying the samples between bankrupcy and bussiness as usual. The steps I'm going to do are:\n",
    "\n",
    "- Correlation Matrix\n",
    "- Scatter Matrix\n",
    "- Lasso feature selection\n",
    "- SBKBest feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sitting-posting",
   "metadata": {},
   "source": [
    "First I'm normalizing the **dataframe's columns names**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "delayed-velvet",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bankrupt</th>\n",
       "      <th>roa-c-before-interest-and-depreciation-before-interest</th>\n",
       "      <th>roa-a-before-interest-and-after-tax</th>\n",
       "      <th>roa-b-before-interest-and-depreciation-after-tax</th>\n",
       "      <th>operating-gross-margin</th>\n",
       "      <th>realized-sales-gross-margin</th>\n",
       "      <th>operating-profit-rate</th>\n",
       "      <th>pre-tax-net-interest-rate</th>\n",
       "      <th>after-tax-net-interest-rate</th>\n",
       "      <th>non-industry-income-and-expenditure-revenue</th>\n",
       "      <th>...</th>\n",
       "      <th>net-income-to-total-assets</th>\n",
       "      <th>total-assets-to-gnp-price</th>\n",
       "      <th>no-credit-interval</th>\n",
       "      <th>gross-profit-to-sales</th>\n",
       "      <th>net-income-to-stockholder-s-equity</th>\n",
       "      <th>liability-to-equity</th>\n",
       "      <th>degree-of-financial-leverage-dfl</th>\n",
       "      <th>interest-coverage-ratio-interest-expense-to-ebit</th>\n",
       "      <th>net-income-flag</th>\n",
       "      <th>equity-to-liability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.370594</td>\n",
       "      <td>0.424389</td>\n",
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       "      <td>0.601457</td>\n",
       "      <td>0.601457</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.716845</td>\n",
       "      <td>0.009219</td>\n",
       "      <td>0.622879</td>\n",
       "      <td>0.601453</td>\n",
       "      <td>0.827890</td>\n",
       "      <td>0.290202</td>\n",
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       "      <td>0.564050</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.464291</td>\n",
       "      <td>0.538214</td>\n",
       "      <td>0.516730</td>\n",
       "      <td>0.610235</td>\n",
       "      <td>0.610235</td>\n",
       "      <td>0.998946</td>\n",
       "      <td>0.797380</td>\n",
       "      <td>0.809301</td>\n",
       "      <td>0.303556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.795297</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.623652</td>\n",
       "      <td>0.610237</td>\n",
       "      <td>0.839969</td>\n",
       "      <td>0.283846</td>\n",
       "      <td>0.264577</td>\n",
       "      <td>0.570175</td>\n",
       "      <td>1</td>\n",
       "      <td>0.020794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.426071</td>\n",
       "      <td>0.499019</td>\n",
       "      <td>0.472295</td>\n",
       "      <td>0.601450</td>\n",
       "      <td>0.601364</td>\n",
       "      <td>0.998857</td>\n",
       "      <td>0.796403</td>\n",
       "      <td>0.808388</td>\n",
       "      <td>0.302035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774670</td>\n",
       "      <td>0.040003</td>\n",
       "      <td>0.623841</td>\n",
       "      <td>0.601449</td>\n",
       "      <td>0.836774</td>\n",
       "      <td>0.290189</td>\n",
       "      <td>0.026555</td>\n",
       "      <td>0.563706</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.399844</td>\n",
       "      <td>0.451265</td>\n",
       "      <td>0.457733</td>\n",
       "      <td>0.583541</td>\n",
       "      <td>0.583541</td>\n",
       "      <td>0.998700</td>\n",
       "      <td>0.796967</td>\n",
       "      <td>0.808966</td>\n",
       "      <td>0.303350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.739555</td>\n",
       "      <td>0.003252</td>\n",
       "      <td>0.622929</td>\n",
       "      <td>0.583538</td>\n",
       "      <td>0.834697</td>\n",
       "      <td>0.281721</td>\n",
       "      <td>0.026697</td>\n",
       "      <td>0.564663</td>\n",
       "      <td>1</td>\n",
       "      <td>0.023982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.465022</td>\n",
       "      <td>0.538432</td>\n",
       "      <td>0.522298</td>\n",
       "      <td>0.598783</td>\n",
       "      <td>0.598783</td>\n",
       "      <td>0.998973</td>\n",
       "      <td>0.797366</td>\n",
       "      <td>0.809304</td>\n",
       "      <td>0.303475</td>\n",
       "      <td>...</td>\n",
       "      <td>0.795016</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.623521</td>\n",
       "      <td>0.598782</td>\n",
       "      <td>0.839973</td>\n",
       "      <td>0.278514</td>\n",
       "      <td>0.024752</td>\n",
       "      <td>0.575617</td>\n",
       "      <td>1</td>\n",
       "      <td>0.035490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bankrupt  roa-c-before-interest-and-depreciation-before-interest  \\\n",
       "0         1                                           0.370594        \n",
       "1         1                                           0.464291        \n",
       "2         1                                           0.426071        \n",
       "3         1                                           0.399844        \n",
       "4         1                                           0.465022        \n",
       "\n",
       "   roa-a-before-interest-and-after-tax  \\\n",
       "0                             0.424389   \n",
       "1                             0.538214   \n",
       "2                             0.499019   \n",
       "3                             0.451265   \n",
       "4                             0.538432   \n",
       "\n",
       "   roa-b-before-interest-and-depreciation-after-tax  operating-gross-margin  \\\n",
       "0                                          0.405750                0.601457   \n",
       "1                                          0.516730                0.610235   \n",
       "2                                          0.472295                0.601450   \n",
       "3                                          0.457733                0.583541   \n",
       "4                                          0.522298                0.598783   \n",
       "\n",
       "   realized-sales-gross-margin  operating-profit-rate  \\\n",
       "0                     0.601457               0.998969   \n",
       "1                     0.610235               0.998946   \n",
       "2                     0.601364               0.998857   \n",
       "3                     0.583541               0.998700   \n",
       "4                     0.598783               0.998973   \n",
       "\n",
       "   pre-tax-net-interest-rate  after-tax-net-interest-rate  \\\n",
       "0                   0.796887                     0.808809   \n",
       "1                   0.797380                     0.809301   \n",
       "2                   0.796403                     0.808388   \n",
       "3                   0.796967                     0.808966   \n",
       "4                   0.797366                     0.809304   \n",
       "\n",
       "   non-industry-income-and-expenditure-revenue  ...  \\\n",
       "0                                     0.302646  ...   \n",
       "1                                     0.303556  ...   \n",
       "2                                     0.302035  ...   \n",
       "3                                     0.303350  ...   \n",
       "4                                     0.303475  ...   \n",
       "\n",
       "   net-income-to-total-assets  total-assets-to-gnp-price  no-credit-interval  \\\n",
       "0                    0.716845                   0.009219            0.622879   \n",
       "1                    0.795297                   0.008323            0.623652   \n",
       "2                    0.774670                   0.040003            0.623841   \n",
       "3                    0.739555                   0.003252            0.622929   \n",
       "4                    0.795016                   0.003878            0.623521   \n",
       "\n",
       "   gross-profit-to-sales  net-income-to-stockholder-s-equity  \\\n",
       "0               0.601453                            0.827890   \n",
       "1               0.610237                            0.839969   \n",
       "2               0.601449                            0.836774   \n",
       "3               0.583538                            0.834697   \n",
       "4               0.598782                            0.839973   \n",
       "\n",
       "   liability-to-equity  degree-of-financial-leverage-dfl  \\\n",
       "0             0.290202                          0.026601   \n",
       "1             0.283846                          0.264577   \n",
       "2             0.290189                          0.026555   \n",
       "3             0.281721                          0.026697   \n",
       "4             0.278514                          0.024752   \n",
       "\n",
       "   interest-coverage-ratio-interest-expense-to-ebit  net-income-flag  \\\n",
       "0                                          0.564050                1   \n",
       "1                                          0.570175                1   \n",
       "2                                          0.563706                1   \n",
       "3                                          0.564663                1   \n",
       "4                                          0.575617                1   \n",
       "\n",
       "   equity-to-liability  \n",
       "0             0.016469  \n",
       "1             0.020794  \n",
       "2             0.016474  \n",
       "3             0.023982  \n",
       "4             0.035490  \n",
       "\n",
       "[5 rows x 96 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from slugify import slugify\n",
    "\n",
    "columns = [slugify(name) for name in df.columns.values]\n",
    "df.columns = columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interim-danger",
   "metadata": {},
   "source": [
    "Now I'm getting **target column** and the **rest of the columns** as features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "structural-probability",
   "metadata": {},
   "outputs": [],
   "source": [
    "target   = 'bankrupt'\n",
    "features = [name for name in df.columns.values if name != 'bankrupt']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "informational-popularity",
   "metadata": {},
   "source": [
    "## RESAMPLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "joint-evidence",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "X = df[features]\n",
    "y = df[target]\n",
    "\n",
    "# RESAMPLING\n",
    "rnd = SMOTE()\n",
    "X, y = rnd.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "raising-administrator",
   "metadata": {},
   "source": [
    "## SCALE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sized-translator",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X_scaled = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elegant-halloween",
   "metadata": {},
   "source": [
    "## FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "becoming-omaha",
   "metadata": {},
   "source": [
    "Creating initial target/features selection. I tried ALL but even a trivial matrix plot was taking forever. That's why I decided to do a top 10 feature selection directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adult-excellence",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [93] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx,\n",
      "/opt/conda/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:116: RuntimeWarning: invalid value encountered in true_divide\n",
      "  f = msb / msw\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['roa-c-before-interest-and-depreciation-before-interest',\n",
       " 'roa-a-before-interest-and-after-tax',\n",
       " 'tax-rate-a',\n",
       " 'net-value-per-share-c',\n",
       " 'operating-profit-per-share-yuan-y',\n",
       " 'total-debt-total-net-worth',\n",
       " 'debt-ratio',\n",
       " 'operating-profit-paid-in-capital',\n",
       " 'liability-assets-flag']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif\n",
    "\n",
    "fs            = SelectKBest(f_classif, k=10)\n",
    "X_scaled      = fs.fit_transform(X_scaled, y)\n",
    "cols          = fs.get_support(indices=True)\n",
    "cols          = [name for name in df.columns[cols] if name != 'bankrupt']\n",
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broad-rings",
   "metadata": {},
   "source": [
    "Now creating the scatter matrix to see feature distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electoral-transparency",
   "metadata": {},
   "source": [
    "## CROSS VALIDATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "consistent-chinese",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "\n",
    "X = X_scaled\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "emerging-mailman",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n",
      "==================\n",
      "{'C': 1} 0.8926910223286709\n",
      "---\n",
      "KNeighborsClassifier\n",
      "====================\n",
      "{'n_neighbors': 5} 0.9531649031498819\n",
      "---\n",
      "DecisionTreeClassifier\n",
      "======================\n",
      "{'max_depth': 6} 0.916512484124205\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def print_search_result(name, search_result):\n",
    "    print(name)\n",
    "    print(\"=\" * len(name))\n",
    "    print(search_result.best_params_, search_result.best_score_)\n",
    "    print('---')\n",
    "    \n",
    "classifiers = [\n",
    "    {\n",
    "        'classifier': LogisticRegression(),\n",
    "        'params': { 'C': [1, 5, 10, 20, 40] }\n",
    "    },\n",
    "    {\n",
    "        'classifier': KNeighborsClassifier(),\n",
    "        'params': { 'n_neighbors': [5, 10, 15] }\n",
    "    },\n",
    "    {\n",
    "        'classifier': DecisionTreeClassifier(),\n",
    "        'params': { 'max_depth': [3, 4, 5, 6] }\n",
    "    }\n",
    "]\n",
    "\n",
    "for entry in classifiers:\n",
    "    classifier = entry['classifier']\n",
    "    classifier_name = type(classifier).__name__\n",
    "    \n",
    "    search_params   = entry['params']\n",
    "    search_result   = GridSearchCV(classifier, param_grid=search_params, scoring='recall')\\\n",
    "        .fit(X_train, y_train)\n",
    "    \n",
    "    print_search_result(classifier_name, search_result)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "boxed-worry",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "log = LogisticRegression(C=1).fit(X_train, y_train)\n",
    "knn = KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)\n",
    "dtc = DecisionTreeClassifier(max_depth=6).fit(X_train, y_train)\n",
    "\n",
    "lst = [log, knn, dtc]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "champion-control",
   "metadata": {},
   "source": [
    "## Decision functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "moral-papua",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_y_predict(clsf, samples):\n",
    "    dec_fun = getattr(clsf, 'decision_function', None)\n",
    "    if dec_fun:\n",
    "        return clsf.decision_function(samples)\n",
    "    else:\n",
    "        # only interested in positive score\n",
    "        return clsf.predict_proba(samples)[0:, 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oriental-sharing",
   "metadata": {},
   "source": [
    "## Precision/Recall curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "native-restoration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# threshold I'm interested in\n",
    "THRESHOLD = 0.75\n",
    "\n",
    "plt.figure()\n",
    "_, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "cols  = 0\n",
    "\n",
    "# plotting precision-recall charts\n",
    "for classifier in lst:\n",
    "    y_predict = get_y_predict(classifier, X_test)\n",
    "    precision, recall, thresholds = precision_recall_curve(y_test, y_predict)\n",
    "    ax[cols].title.set_text(str(type(classifier).__name__))\n",
    "    ax[cols].set(xlabel='Precision', ylabel='Recall')\n",
    "    ax[cols].step(precision, recall)\n",
    "    criteria = np.argmin(np.abs(thresholds - THRESHOLD))\n",
    "    ax[cols].plot(precision[criteria], recall[criteria], 'o', c='r')\n",
    "    ax[cols].grid(axis='both', linestyle='--', c='#cccccc')\n",
    "    cols+=1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "printable-eight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "plt.figure()\n",
    "_, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "cols  = 0\n",
    "\n",
    "for classifier in lst:\n",
    "    classifier_name      = type(classifier).__name__\n",
    "    # getting decision function prediction\n",
    "    y_predict            = get_y_predict(classifier, X_test)\n",
    "\n",
    "    # calculating FPR and TPR\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_predict)\n",
    "\n",
    "    # calculating the area under the curve\n",
    "    roc_auc              = auc(fpr, tpr)\n",
    "    \n",
    "    ax[cols].title.set_text(\"{0} (AUC={1:.2f})\".format(classifier_name, roc_auc))\n",
    "    ax[cols].set(xlabel='False Positive Rate', ylabel='True Positive Rate')\n",
    "    ax[cols].plot(fpr, tpr, c='k')\n",
    "    ax[cols].plot([0, 1], [0, 1], c='k', linestyle='--')\n",
    "    ax[cols].fill_between(fpr, tpr, hatch='\\\\', color='none', edgecolor='#cccccc')\n",
    "    cols+=1\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "acceptable-adaptation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x2ptUXstqrIycN5Kv//qa0S1G80rLV9wdUo7THmVmYi/BoeW2YXeKaR72Ru1l+YnlhIX2YPOROO1NKq+VZE1ixJwRfP3X17zW6jXeb/8+ksmUKPmV9igzs3c+WBNvGnZ/vv1zgnwK8ttaWy1K7U0qb3U98To7I3fyVpu3GHvvWHeH4zKaKDOzezYUrQLl6gO2/z1n7p/JyhMriT/fAaxBWohXeaXEpESSTBKFAwuzdsRagvyC3B2SS+nQOyPXo+DwKqjd2zHs/uHAD4zfOJ4ga0USLt6jSVJ5pXhLPH1/7EufH/pgNVaPT5KgiTJje+eCSUo17F58dDF3FrmTqgmv0bRyOU2SyuvEJsbSe1Zv5u6fS7fq3fLlc9u3wjuO8lbsng3F77RN+QBciL3A1nNbKePXlI1HLrk5OKVyX0xiDD1m9mDRwUVMvG8iTzR+wt0h5RpNlOm5FglH/7D1Ju3D7qXHlmIwnD6lF3CUdxr6y1CWH1nOdz2/45EGmU/R7Gn0Yk569swBY7Wdn8R2EWf63uncVfQufM+UpWlV0WG38jqvtX6NfrX60b9Of3eHkuu0R5me3b9AyRpQuhYAC48s5OjVo4wKG4XgefeIKZWRS7GXmLhlIgDhZcK9MkmCJsqbXT0Dx9Y5LuIYY/hm5zfcVfQuIs9U588jF90coFK5IyomiogpETz121MciDrg7nDcShNlWnt+BYxj2H3w8kEOXTlE/7v7M3f7GUDPTyrPF3k9kraT27Ln/B7mDJjDXcXvcndIbuXSRCkinUVkv4gcFJEx6SwPFZF5IrJdRHaLyHBXxuOUXbOhdB0oafvF+OPUHwC0qdgGQB9XVB7vTPQZ2kxqw8GLB1kwaAGd7+zs7pDczmWJUkR8gc+ALkAtYKCI1ErT7ElgjzEmDGgDfCgi7iuDfPkEnNzo6E2CbS6cO0LvYOnOOB12K6+w4eQGTkWf4rfBvxFRLcLd4eQJrrzq3QQ4aIw5DCAiM4GewJ4UbQwQIran6AsBFwGLC2PK3J5fbd/tlcwTkxLZGrmVXnf2Ys422yxyOuxWnioxKRF/X3961+xN68qtKV6guLtDyjNcOfQuD5xI8f6k/bOU/gvUBE4DO4FnjTFWF8aUuV2zoWw4FLPNe7Pjwg5iLbE0LWOrXq7DbuWpDl86TJ0v6rDo4CIATZJpuDJRpncfjUnzvhOwDSgHhAP/FZHCN21I5DER2Swim8+fP5/TcdpcPAKnt6aaF2fjmY0IQqMyjTJZUan87UDUAVp/15oLMRcoVbCUu8PJk1yZKE8CFVO8r4Ct55jScGC2sTkIHAFqpN2QMeYrY0wjY0yjkiVLuiba5GF3rV6Oj/48+yc1i9ckNDDUNftUys32nt/LvZPuJSEpgRUPraBB2QbuDilPcmWi3ARUF5Gq9gs0A4C5adocByIARKQ0cDdw2IUxZezAYls5taKVAdtzrdvPb6dp2abM+PO4XshRHufk1ZPcO+leAFYOW0m90vXcHFHe5bJEaYyxAE8Bi4G9wA/GmN0iMkpERtmbvQ20EJGdwDLgZWPMBVfFlKnr56FoVcfbbZHbsFgtNC3TVC/kKI9ULqQcjzZ4lFXDVlGrZNobUlRKLn3W2xizEFiY5rMvU7w+DXR0ZQxOi78GgYUcb9edXoefjx/1S9UHtuuFHOUxNp3aRLHgYtxR7A7GRYxzdzj5gj6Zkyw+GgJCHG//ivyL8JLhFPAv4MaglMpZ606sI2JKBI/Oe9TdoeQrmigBrFZIvA6BtkRpjOHI1SPcUeQONwemVM5ZfWw1Had2pEyhMkzpPcXd4eQrmigBEq7ZvtuH3lFxUUQnRFOlcBX3xaRUDlp2eBmdp3WmUmglVg1bRYXCFdwdUr6i9SjhRqIMsCXKbZHbAKgSWsU98SiVg4wxjFszjjuL3cnSoUv1XslboIkSbOcnwTH0XnJsCQB1itdx3BrUtGoxd0Wn1C0zxiAi/NL/FxKtiZQoUMLdIeVLOvQG2xVvcCTKc9fP0bhMY4oEFdFbg1S+9fOen+kyvQsxiTGEBoVqkrwNmigBEuw9SvvQ+3zseUoE3fil0luDVH4zc9dM+v/Un+iEaCxW99WZ8RSaKCFFj7IQxhgiYyIpVUDP46j8acr2KQyePZh7Kt3DosGLKBx4U/kElU2aKCHVxZwr8VeIT4qndMHS+uiiynem7ZjGsF+H0bZKWxYOWkhIYEjWK6ks6cUcSHExpzDnYs4BcOCUD1OX7QT0/KTKP+qXqc+guoOYeN9Egv2D3R2Ox9AeJaRIlIWIjIkEYNsRW0W4d3vX1fOTKs9bc2wNxhhql6rNtPunaZLMYZoowTb0Fl/wC3L0KP1NEb2Io/KFf639F60nteb7Xd+7OxSPpYkSbhTEECEyJhJB8ENrUKq87+1Vb/Py0pcZUGcA/Wr1c3c4HksTJaQqiHEu5hzFg4sjevpW5WHGGMYuH8vrK19nSL0hTOs9DX9ff3eH5bE0UYLtPsrAG4lSbw1Sed2uyF2898d7PFz/Yb7r+R2+Pr7uDsmjabcJUtWijIyJpEKhCpxzc0hKZaZu6bqse3gdjco1wke0v+Nq+hMG28Uc+1M5565rj1LlTVZj5ZnfnmHOvjkANCnfRJNkLtGfMtjOUQYWItYSy9WEq5yNCtQbzVWekmRN4rF5j/Gfjf9h46mN7g7H6+jQG+xD78KOeyj/Pm0736M3mqu8wGK1MGLOCKbumMrY1mP5vzb/5+6QvI4mSrBdzAm4cbO5H3oPpcobLFYLD85+kFm7Z/F227d5rfVr7g7JK2miNMZxMefs9bOA7WZzpfICX/GlTKEy/Kv9vxh9z2h3h+O1NFFa4sAkpepRbjsKTSu7Nyzl3eIt8ZyOPk3VolX5uNPHiIi7Q/JqejEnRXXzyJhIfEwQWAP1/KRym9jEWHrO7Emr71pxLeGaJsk8QHuUKRLl5oObSEworOcnldtcT7hOj5k9WHFkBV/3+JpCAYWyXkm5nCbKFLUoj189g0kMpWcj7U2q3BcdH023Gd1Ye2ItU3pP4cF6D7o7JGXn9NBbRAq6MhC3SVHd3CKXKR5UUnuTyi3GrhjLuhPrmHH/DE2SeUyWiVJEWojIHmCv/X2YiHzu8shyi33obfErgIUr+FPUzQEpb/V227dZ/OBi+tfp7+5QVBrO9Cg/BjoBUQDGmO1Aa1cGlavsQ+8oSQIx+OmtQSoXXYi5wKj5o7iWcI2QwBAiqkW4OySVDqeG3saYE2k+SnJBLO5h71GeS4oFwN9oj1LljsjrkbSd3JbJ2yez49wOd4ejMuHMxZwTItICMCISADyDfRjuEew9ykiL7bv2KFVuOBN9hogpERy9fJT5A+fTomILd4ekMuFMj3IU8CRQHjgJhANPuDCm3GXvUS74+yigT+Uo1zt59ST3TrqX41eOs+jBRTrczgec6VHebYwZnPIDEbkHWOuakHJZvK3E2l+nj2N8fLk/7G53R6Q8XGxiLD7iw5IhS7QnmU8406P8j5Of5U/2ghgWuUwARRjcrIq7I1IeKvJ6JMYYqhevzu4ndmuSzEcy7FGKSHOgBVBSRF5Isagw4Dl15+0FMRLlEn56a5Bykf0X9hMxJYLh4cN5u93bOnVDPpPZ0DsAKGRvE5Li86tAX1cGlavs1c0tXCbI6I3mKuftOb+HdpPbYTA8UPsBd4ejbkGGidIYswpYJSKTjDHHcjGm3BUfzdl4P+IDLlLIhLk7GuVhdpzbQfsp7fH18WXF0BXULFnT3SGpW+DMxZwYEfkAqA0EJX9ojGnnsqhyU/w19sYEI6EW6pfR2moq58QkxtBpWicCfANY/tBy7ip+l7tDUrfImUQ5HZgFdMd2q9BDwHlXBpWrEqI562c7s9C5pl7xVjmngH8BJt43kVola1GtaDV3h6NugzNXvYsbY74BEo0xq4wxI4BmLo4r98RfI9LP9v9F6QKl3RyM8gRrj6/lh90/AND9ru6aJD2AMz3KRPv3MyLSDTgNVHBdSLksPpoLwbaXOk2tul0rj66k+4zuVC5Smd41euPv6+/ukFQOcKZH+Y6IhAL/AF4Evgaec2bjItJZRPaLyEERGZNBmzYisk1EdovIKmcDzxFJiZAUzyX7nRolg0vm6u6VZ1l6eCldp3elcpHKLB2yVJOkB8myR2mMmW9/eQVoC44nczIlIr7AZ0AHbI8+bhKRucaYPSnaFAE+BzobY46LSO526eyPL55MTMDHFCDANyBXd688x8K/F3L/rPu5q/hdLB26lFIFdXTiSTLsUYqIr4gMFJEXRaSO/bPuIrIO+K8T224CHDTGHDbGJAAzgZ5p2gwCZhtjjgMYYyJv6Shulb0gxjXfJIoEFsvVXSvPsv7EemqXqs2Kh1ZokvRAmQ29vwEeAYoDn4rId8AE4F/GmPpObLs8kLI820n7ZyndBRQVkZUiskVEhjofeg6w9yglCKoU0V9ulX3XE64D8Fbbt1gzfA3FCxR3c0TKFTIbejcC6hljrCISBFwA7jTGnHVy2+lNHWfS2X9DIAIIBtaLyAZjzIFUGxJ5DHgMoFKlHHx6xj4NRIIkUcC/QM5tV3mFGTtn8OKSF1k5bCV3Fb9Lf4c8WGY9ygRjjBXAGBMHHMhGkgRbD7JiivcVsF0xT9tmkTHmujHmArAauOnxGGPMV8aYRsaYRiVL5uAFlwRbjzJBkijgp7/kynmTt03mwdkPclfxuygXUs7d4SgXyyxR1hCRHfavnSne7xQRZ8oxbwKqi0hVe8HfAcDcNG3mAK1ExE9ECgBNyc2iwPYepUUs2htQTpu4ZSLD5wwnoloECwcv1CllvUBmQ+/beijVGGMRkaeAxdiqDX1rjNktIqPsy780xuwVkUXADsAKfG2M2XU7+82W5InFJFF7lMopc/bN4bH5j9Hlzi7M7j+bIL+grFdS+V5mRTFuuxCGMWYhsDDNZ1+mef8B8MHt7uuWJFzDAEkkaI9SOaXDHR148943GdNyDIF+ge4OR+USp+f19kjx10gEjFgJ9gt2dzQqD5u8bTJX4q5QwL8Ab7R5Q5Okl/HuRJkQzWUf203mOvRW6THG8Naqtxg2Zxj/2eg5hf1V9jiVKEUkWEQ8rrTOgRNnuWCvHKdDb5WWMYaxK8byxso3GBY+jFdavuLukJSbZJkoReQ+YBuwyP4+XETSXr3Ol85FRnJR7IlSe5QqBWMML/3+EuPWjOPRBo/yTY9vdPoGL+ZMj/JNbI8jXgYwxmwDqrgqoNwUZGKJCSwIoOcoVSpRsVHM2j2LJxs/yZfdv8RHvPsslbdzpsyaxRhzRSS9B23yt2ATy1GfACBWh94KAKvtGQtKFCjB5sc2U7JASTzxd19ljzP/Te4SkUGAr4hUF5H/AOtcHFeuCDYxRPvYrl7q0FslWZN4ZO4jPLngSYwxlCpYSpOkApxLlE9jmy8nHpiBrdzacy6MKdcEW2O46mPrVAf769Dbm1msFh769SG+2/YdpQtppXuVmjND77uNMa8Cr7o6mNwWZGK5Zk+U2qP0XolJiTz4y4P8sPsHxrUbxz9b/dPdIak8xpke5Ucisk9E3haR2i6PKBcFp0iUejHHez3060P8sPsHJnSYoElSpcuZCudtRaQM8ADwlYgUBmYZY95xeXSuZLUSbGK5br/lQ3uU3mtIvSG0qNiCp5o85e5QVB7l1D0PxpizxphPsU1Xuw143ZVB5Qp7dfNYEfx9/HV+Ey8TmxjLooOLAOhSvYsmSZUpZ244rykib4rILmxTQKzDE2ZhTE6UPqK3BnmZ6wnX6TajG/d9fx9HLh1xdzgqH3DmYs53wPdAR2NM2sK7+Vd8co9Sz096k+j4aLrN6MbaE2uZ3GsyVYtWdXdIKh9w5hxls9wIJNfZq5vHi9Hzk17iStwVOk/vzKZTm5hx/wz61+nv7pBUPpFhohSRH4wxD9irm6ec60YAY4yp5/LoXMneo4wXK4X9tEK1N/hxz49sOb2FH/v9SO+avd0djspHMutRPmv/3j03Asltq3cdpjU6sZg3MMYgIjxc/2FaVmpJjRI13B2SymcyvJhjjDljf/mEMeZYyi/gidwJz3V2HDoJQFCw6NDbg527do42k9uw/ex2RESTpLolztwe1CGdz7rkdCC5LdjEAuAfYPRijoc6HX2aNpPbsPn0Zi7GXnR3OCofy+wc5ePYeo7V0sy6GAKsdXVgrhZkT5QxSXE69PZAJ66coN2Udpy9dpZFgxfRqnIrd4ek8rHMzlHOAH4D3gPGpPg82hiT7/97DrbGYMWH2KQ47VF6mFNXT9F6Umsuxl5kyYNLaF6xubtDUvlcZkNvY4w5CjwJRKf4QkSKuT4015nx53GuXLlEjAQTkxijPUoPU6JACZpXaM6yocs0SaockVWPsjuwBdvtQSkL8xmgmgvjcqk5207Rl1jiA0Mw6DlKT3Eg6gAlCpSgWHAxZvSZ4e5wlAfJbF7v7vbvHvnoQsVCVigcCsTqVW8PsCtyFxFTImhUrhELBi1wdzjKwzjzrPc9IlLQ/vpBEflIRCq5PjTXCrbGEGsfcuvQO3/bfnY7bSe3xVd8+bDjh+4OR3kgZ24P+gKIEZEw4CXgGDDVpVHlgmATQ0yAbcitPcr8a/PpzbSd3JYgvyBWDVul90kql3AmUVqMMQboCXxijPkE2y1C+VqwiSUmQOf0zs+MMYycP5LQoFBWD1tN9eLV3R2S8lDOVA+KFpFXgCFAKxHxBfJ98cYgE0uMXyAkaPWg/EpEmP3AbESESqH5/myQysOc6VH2xzax2AhjzFmgPPCBS6PKBcHWGGL9AgAdeuc3K46s4MkFT2I1VioXqaxJUrlclonSnhynA6Ei0h2IM8ZMcXlkrmSM7Rylr31iMR165xtLDi2h64yurDy2kitxV9wdjvISzlz1fgDYCPTDNm/OnyLS19WBuZI/CfhiJdZXZ2DMTxb+vZAe3/fg7uJ3s/KhlRQNLurukJSXcOYc5atAY2NMJICIlASWAj+5MjBXmfHncfYePQ1BEJM8sZj2KPO8Ofvm0O/HftQrXY8lQ5ZQLDhfPxym8hlnzlH6JCdJuygn18uT5mw7RUGJAyDG/qxRkG+QGyNSzigUUIiWlVqydOhSTZIq1znTo1wkIouxzZsDtos7C10Xkus1L+8PF2wzMAb5BuFr71mqvOfQxUPcUewOIqpF0K5qO0Qk65WUymHOXMwZDfwPqAeEAV8ZY152dWCuFGy1l1jDqsPuPOy7v77j7v/ezfwD8wE0SSq3yaweZXVgAnAHsBN40RhzKrcCc6UgEwPYEqXeQ5k3fbXlK0bOH0mHah1oV7Wdu8NRXi6zHuW3wHygD7YKQv/JlYhyQXJ18xiTpIkyD/rvxv8ycv5IulXvxtyBc7XXr9wus3OUIcaYifbX+0Vka24ElBuCk3uU1kT9I8xjtpzewtO/PU2vGr2Y1XcWAb4B7g5JqUwTZZCI1OdGHcrglO+NMfk2cSafo4y1JlIgQKeqzUsalmvI7Adm0/2u7vj75vsnZZWHyCxRngE+SvH+bIr3Bsi3J44c5yiT4inuV8rN0ShjDO+vfZ+IqhE0Lt9Y59xWeU5mhXvb5mYguSnYxIJ/QWIsOg2EuxljeHX5q7z3x3s80+QZGpdv7O6QlLqJS28cF5HOIrJfRA6KyJhM2jUWkaTcejQy2BoDgYWItcTqxRw3Msbw4pIXee+P9xjZcCQfd/7Y3SEplS6XJUp7ObbPsM0BXgsYKCK1Mmj3PrDYVbGkFWxiIaCQbWIxfc7bLazGyjO/PcNHGz7i6SZP80W3L/CRfPvAl/JwrvzNbAIcNMYcNsYkADOxFf9N62ngZyAynWUuEWRiSAosRJzO6e02SdYkTlw9wYvNX+STzp/ozeQqT8vyEUax/QYPBqoZY96yz5dTxhizMYtVywMnUrw/CTRNs+3yQG9sF4Zy7eRUsIklLjAUuKQ9ylyWZE3iavxVigYX5acHfsJXfDVJqjzPmR7l50BzYKD9fTS2IXVW0vvtN2ne/xt42RiTlOmGRB4Tkc0isvn8+fNO7DpzQdZYYvxt5yb1HGXusVgtDP11KPdOupfYxFj8fPw0Sap8wZlE2dQY8yQQB2CMuQQ4cxfwSaBiivcVgNNp2jQCZorIUaAv8LmI9Eq7IWPMV8aYRsaYRiVLlnRi1+mb8edx/jxyMfXEYjr0zhWJSYkM+nkQM3bOYFDdQQT7639QKv9wpnpQov2CiwFHPUqrE+ttAqqLSFXgFDAAGJSyQco5w0VkEjDfGPOrU5HfgjnbbI+qF/GN56hfIKBFe3NDvCWe/j/1Z87+OXzY8UNeaP6Cu0NSKluc6VF+CvwClBKRccAfwLtZrWSMsQBPYbuavRf4wRizW0RGicio24j5tjStWowgawwx/rZEqT0b13txyYvM2T+H/3T5jyZJlS9l2aM0xkwXkS1ABLbzjr2MMXud2bgxZiFpalcaY77MoO0wZ7Z5u3yNBSxxxNgfj9MepeuNaTmGZhWaMbjeYHeHotQtcWbOnEpADDAPmAtct3+WLwUlVw6yz5ejF3Nc41rCNd5d8y4Wq4XyhctrklT5mjPnKBdgOz8pQBBQFdgP1HZhXC4TbLU/5+2r8+W4ytX4q3Sd3pUNJzdwb+V7uafSPe4OSanb4szQu27K9yLSABjpsohcLLkWZayPrTOtQ++cdTnuMp2ndWbLmS3M7DtTk6TyCM70KFMxxmwVkXxbueBGdXPb/Xvao8w5UTFRdJzWkZ3ndvJTv5/oWSO9B7GUyn+ceTIn5WVKH6ABcPt3fbtJgeREKQZBdAbGHHTw4kGOXT7GrwN+pWv1ru4OR6kc40yPMiTFawu2c5Y/uyYc1wtKMbFYsF+wPhmSA+IscQT5BdG0QlOOPHuEkMCQrFdSKh/JNFHabzQvZJ+J0SM4zlHqDIw54tTVU0RMieD5Zs8zstFITZLKI2U2C6OfMcZiv3jjMRznKK0WvZBzm45fOU67ye2IvB5J7VL58iYIpZySWY9yI7bzkdtEZC7wI3A9eaExZraLY3OJVPPlaI/ylh25dIR2U9pxKfYSvw/5naYVmma9klL5lDPnKIsBUdhKoSXfT2mA/JkoTQz4BhCTFKc3m9+i6Pho7p10L9cSrrFs6DIalmvo7pCUcqnMEmUp+xXvXdxIkMnSlkvLN1JWNw8NDHV3OPlSSGAIr7R8hRYVWxBWJszd4Sjlcpk9wugLFLJ/haR4nfyVrySXWAsyMRAYQqwlVofe2bQrchdrj68F4PHGj2uSVF4j0+lqjTFv5VokLpZcYq1qiAEJIcYSo0PvbNh2dhvtp7SnZMGS7Hp8F74+vu4OSalck1mP0uNuMGxatRgVC1hsQ29NlE7bfHoz7Sa3o4B/AeYNnKdJUnmdzBJlRK5FkZsSrkGgfQZGHXpnaf2J9URMiSA0KJTVw1dzZ7E73R2SUrkuw0RpjLmYm4HkmvhrJAYUItGaqPdROuGbv76hVMFSrB62mipFqrg7HKXcIttFMfK9hGvE+gdDrFYOyozVWPERH77o9gWX4i5RqmApd4eklNt434zz8dHEBNgKYejQO32LDy6m0VeNOHftHP6+/pokldfzqkQpxgoJ14ixTyymF3NuNv/AfHrM7IHB6EUbpey8KlEGmjgAYv10vpz0/LL3F+6fdT/1Stdj2dBllChQwt0hKZUneFWiTK4cFONnm5Zch943LDiwgH4/9qNRuUYsHbKUYsHF3B2SUnmGVyXK5MpBsfYhpfYob2hcvjEPhT3E4gcXExqkj3YqlZJXJcoCyROLJc+Xoz1Kfj/0O4lJiZQqWIpven6j9SSVSodXJUrHVLX2qubefjHny81f0nFaRz5a/5G7Q1EqT/OqROk4R+ljn1jMi4fen/75KY8veJzud3Xn2WbPujscpfI0r0qUQfahd6y9Spy3Dr0/WPsBzy56lt41evPzAz8T5KcTrCmVGa9KlI4ZGI0VP/HD38ffzRHlvjPRZ3hnzTv0r92fWX1nEeAb4O6QlMrzvOoRRsc5SixeOwNj2ZCybHh4A9WLV8fPx6v++ZW6ZV7Ro0wu2htsYkB8iElKJNjfey7kGGMYs3QMH677EICaJWtqklQqG7wiUSYX7a1d3BcCQohNivWaCznGGF5Y/ALvr32fQ5cOYUy+ncVDKbfxikQJtqK9dxfFq2pRWo2VpxY+xb///DfPNn2Wz7p+5pWnG5S6Xd41/oqPdlQ39/QepTGGx+c/zldbv2J0i9G83/59TZJK3SKv6VECqaqbe/rN5iJCeJlwXmv1miZJpW6Tl/Uor3n8DIwWq4XdkbsJKxPG440fd3c4SnkE7+tRevDQOyEpgQE/DaDFty04dfWUu8NRymN4V6KMj7b1KBM9r0cZb4mn34/9+Hnvz4xrN47yhcu7OySlPIaXDb2jMf4FiYn2rHOUsYmx9PmhD78d/I3Pun7GE42fcHdISnkU70mUxkDCNRICC5Jkkjxq6P3F5i9YdHARE++byCMNHnF3OEp5HK9JlP4kgtVCrL9tvhxPGno/2/RZGpRtQJsqbdwdilIeyWvOUQYnF+21TyyW33uUV+Ov8uDsBzl59SS+Pr6aJJVyIZcmShHpLCL7ReSgiIxJZ/lgEdlh/1onImGuiiU4uXKQfWKx/Pys96XYS3SY2oFZu2ex7ew2d4ejlMdz2dBbRHyBz4AOwElgk4jMNcbsSdHsCHCvMeaSiHQBvgKauiIeR+UgezGI/NqjjIqJosPUDuyK3MVP/X6i+13d3R2SUh7PlecomwAHjTGHAURkJtATcCRKY8y6FO03ABVcFYyjurmvbWKx/HjVO/J6JO2ntOdA1AHmDJhDl+pd3B2SUl7BlUPv8sCJFO9P2j/LyMPAb64KJth6HUgxA2M+vJjjK74UCijE/EHzNUkqlYtc2aNM7+HidGt8iUhbbImyZQbLHwMeA6hUqdItBePoUdqjyk9D77PXzlI0qCjFCxRn7Yi1+ty2UrnMlT3Kk0DFFO8rAKfTNhKResDXQE9jTFR6GzLGfGWMaWSMaVSyZMlbCubGDIy2XJ1fht7HLh/jnm/v4eG5DwNoklTKDVyZKDcB1UWkqogEAAOAuSkbiEglYDYwxBhzwIWx3Lg9KB9NLHb40mFaT2pNVEwUTzd52t3hKOW1XDb0NsZYROQpYDHgC3xrjNktIqPsy78EXgeKA5/be0oWY0wjV8STfHtQrLHa3ufxHuWBqAO0m9yOWEssyx9aToOyDdwdklJey6VP5hhjFgIL03z2ZYrXjwC58sxdsIkF/wLEJMUS4BOQp2dgtBorvWf1JiEpgRUPraBe6XruDkkpr+Y1jzAGWWNtJdbywTQQPuLD5F6TKeBfgFola7k7HKW8nvc8wmhiHEV78+qw+68zfzlmSmxUrpEmSaXyCO/pUZrYGxOL5cFbgzae2kinaZ0oHFiYRxo8QmhQqLtDcpnExEROnjxJXFycu0NRXigoKIgKFSrg7+/86TevSZQFTAwEFMmT00CsO7GOztM6U7JgSZYPXe7RSRLg5MmThISEUKVKFb3dSeUqYwxRUVGcPHmSqlWrOr2e1wy9g6z2HmUemwZi1dFVdJzakTKFyrBq2CoqF6ns7pBcLi4ujuLFi2uSVLlORChevHi2RzNekyiDTYzjYk5eOkd59PJRqhSpwqphq6hQ2GWPuuc5miSVu9zK757XJErbOcoQYiwxeaLE2uW4ywA8FP4QW0dupWxIWfcG5MWqVKnCrl27bns7c+fOZfTo0Zm2WblyJUuWLHG8P336NG3bts1y223atKFatWqEh4dz9913884779x2vK7i7DHlJ16TKAtYYyCwkO0cpZuH3vP2z6Pyvyvzx/E/AAjwDXBrPCpn9OjRgw8++CDTNmkTZbly5VixYoVT2//000/Ztm0bK1euZMKECfz555+3FW9KFoslx7aVnWPKL7wiUfqYJAJIgIAQt99H+fOen7n/h/u5u/jd1C5Z221xqMxNmTKFunXrUq9ePXr37k1kZCQACQkJPPbYY9x11120bNmSp556ir59+wIwadIkx+v9+/fTvHlzwsLCqFOnDhMmTGDnzp18+eWXTJkyhfDwcMaPH8/Ro0cpUaKEY7/r16+nZcuWhIWFERYWliqpJitbtix33303x48fB+DMmTP07duXJk2aULduXd59911H2zVr1jiO49lnn6Vy5cqO3nOVKlV4++23adu2LSNHjiQhIYHRo0fTpEkTwsPDGTJkCNeuXQPgq6++ombNmoSHh1OvXj327duH1WrliSeeoEaNGoSFhXHPPfcA3HRMixYton79+tSrV4+IiAgOHjwI2P7TCA8PZ+TIkdSrV4+wsDD27t2bM/+AOcwrrnonP75oDSjo1h7lzF0zeXD2gzSt0JSFgxZ6/NVtZ/zfvN3sOX3VJduuVa4wb9yX/f+Mdu3axZgxY9iyZQtly5Zl7NixPP3008yaNYv//e9/HD9+nD179mCxWGjTpg0VKtx8bvnzzz+na9eujB07FoBLly5RtGhRRo0axbVr15gwYQJgSyrJLl68SO/evZk9ezYtWrQgKSmJq1dv/tkcOHCAqKgo2rRpA8DQoUMZO3YsrVu3JiEhgYiICBo3bkzr1q0ZOHAg33//Pa1ateKXX37h008/TbWtM2fOOHp/77zzDqGhoWzcuBGAl19+mffee49x48YxevRodu3aRcWKFYmPjycpKYnt27ezdOlS9u3bh4+PD5cuXbop1sjISIYMGcKqVauoVasW33zzDYMHD3b0hnfv3s13333H//73P8aNG8c777zD9OnTs/kv5npekSiTKwfFBQRhMG65mLP+xHoGzx5My0otmT9wPiGBIbkeg3LOihUr6Nq1K2XL2s4bjxw5krCwMMeyIUOG4Ofnh5+fHwMHDmTNmjU3baN169a8+OKLJCQk0LZtW6fO2a1fv55atWrRokULAHx9fSlatKhj+TPPPMPLL7/Mvn37+PTTTylZsiTXr19n5cqVnD9/3tEuOjqavXv3Urp0aYKDg2nVqhUAvXv3pkiRIqn2OXToUMfruXPncvXqVX766ScA4uPjHcfdrl07hg8fTs+ePenWrRvVqlWjWrVqJCUl8fDDD9OuXTu6d7+52v6ff/5JWFgYtWrZHp4YPnw4TzzxBNHR0QDcfffd1K9fH4BmzZoxb968LH9O7uAVibJAcuUg+7lAdwy9m1ZoyoQOE3is4WMUDCiY6/vPq26lx+dqxpibrowmv09vWXr69OlD8+bNWbJkCePHj+fbb79l2rRpWe43M59++indu3dn7dq1dOjQgdatWzvuRd20adNNN1Bv3749y1gLFSqUav+ff/457dq1u6nd7Nmz2bRpE8uXL6dt27Z8+eWXdOnShd27d7Ny5UqWLVvGyy+/zNatW286psxiCAoKcrz29fXN0XOlOckrzlEGJVcO8rMnylwcek/aNomjl4/iIz483/x5TZL5QEREBAsXLuTs2bMATJw4kfbt2wPQtm1bpk2bhsViIS4ujlmzZqW7jYMHD1KmTBmGDRvGG2+84RjOFi5cmCtXrqS7TosWLdizZw/r168HICkpKd3h7D333MOTTz7J66+/TkhICK1atWL8+PGO5SdOnODs2bPUqFGD69evs3btWgDmzJnD5cuXMzzuHj168NFHHxEbaxuBJfdMLRYLhw8fpkmTJowZM4aOHTvy119/cf78eWJjY+ncuTPjx48nNDSUw4cPp9pm8+bN2bZtG/v27QNg8uTJ1K9fn5CQ/DWi8ooepaO6eS5PA/HvDf/m+cXP80SjJ/is22e5sk91a9q3b4+f340/h3fffZcOHTogIlSrVo3//e9/AIwaNYrt27dTu3ZtKlasSMOGDYmJiblpez/88APTp08nICAAEeGTTz4BbMPfqVOnEh4ezoABAxgwYIBjnWLFijF79mxeeOEFrl+/jo+PDxMmTHAk6ZReffVV7rzzTrZt28b06dN5/vnnqVu3LgAhISF8++23lClThhkzZjBq1CgKFChAu3btKF26NKGh6Z8bHzNmDG+++SaNGzfGx8cHEeGNN96gWrVqDBs2jMuXL+Pj40PFihUZP348x44d49FHH8VisWCxWOjSpQvNmjVzXGQCKFmyJFOnTmXQoEFYLBZKliyZZc86TzLG5Kuvhg0bmuya8PG/jHmjsPlrz8+mzqQ65o+Tf2R7G9n1/h/vG97E9JnVx8Rb4l2+v/xkz5497g7htly9etUYY0xcXJzp2LGjmThxopsjylhyrMYYs3z5clOxYkWTlJTkxojyhvR+B4HNJoO84yU9Svs5SvuJBldfzHln9TuMXTGWAXUGMLX3VPx8vOLH7DXat29PfHw8cXFxtG/fnmHDhrk7pAz9/PPPfPzxx1itVoKCgvj+++/x8fGKM245yiv+gh2J0n5S2ZVD7zhLHHP2z2FIvSF81/M7fO3DfeU5cvJGb1cbNmxYnk7k+YXHJ8oZfx7nQtRF8IfY5PlyXHAxxxhDojWRIL8glg1dRkH/gpoklfIQHt8Hn7PtFIUkliTxI8aaCOR8j9IYw/OLn+e+7+8jISmBwoGFNUkq5UE8PlECVCts8A0qTIzFNgTPyXOUVmPlyYVP8smfn1CrRK08PRePUurWePzQG5KngSjkSJRBvkFZrOGcJGsSI+eP5Ju/vuGlFi8xvv14LR+mlAfyih5lsDUWAkKITbTNl5NTw+J/LPkH3/z1DWNbj9UkqZQH84pE6Zgvx5KzRXtH1B/BBx0+4K22b2mSzMdS1qOMiYmhU6dODB8+nCFDhhAUFJTqBuphw4bx3//+N8ttvv766xk+tZPSm2++yYsvvpjtZTnlwIED9O7dm2rVqlGnTh2aNWvGr7/+CthqYM6fPz9H95e2Zudrr71GjRo1aNWqFZs3b2bw4ME5ur+c4hWJMnkGxpyYBiIhKYEZO2dgjKFe6Xq82MK1v8gq91y5coWOHTtSo0YNvv32W3x9fSlTpgxvvPFGtrf11ltv0b9/fxdEmT2ZPTt95swZWrduzf3338/hw4fZtWsXv/zyS7oVi3JK2pqdH374IWvWrGHNmjU0atTolioHJSUl5WSI6fKORGlNMQ3EbVQ3j7fE0/eHvgyePZg/T+Wfe+lU1iIjI2nTpg3t2rXjk08+cYwQHn/8cX7//Xf27Nlz0zqZ1W9M2fO8cuUKffr0oUaNGkRERDB06NBUPcVTp07RtWtXatSoQbdu3VI9Enn8+HG6du1K7dq16dOnj+M58WvXrjF8+HDq1KlDnTp1eP/99x3rtGnThn/+859ERETQs2dPIiMjad++PXXr1qVu3bo8//zzAHz22We0bduWIUOGONYtW7ZsqopCyWbMmEHTpk2pX78+9evXZ9myZQAZ1qTMaJ8pa3a2atWKuLg4IiIiGD16NCtXrqRRo0aOfS5cuJB77rmHhg0b0rx5czZs2ADcqGP59NNP06xZM3777Tcn/oVvj5dczLn9icViE2PpPas3iw8t5vOun9OsQrMcjtJL/TYGzu50zbbL1IUu47NuBzzwwAM88cQTvPXWW6k+L1iwIK+88gqvvvoqv/zyS6pl//rXvzKs35jSW2+9RdGiRdm3bx8XL16kYcOG9OnTx7F88+bNbNq0idDQUDp16sT06dN59NFHAVvh3W3btlG6dGlGjBjB22+/zYQJE3j77bexWq3s3LmT6OhomjdvTr169ejSpQtgq6m5ePFi/Pz8+Pjjj6lcuTJLly4FcBTa2Lp1Kx07dnTq59OpUycGDhyIiLB//34iIiI4efJkhjUpp0+fnu4+U1qzZg0iwrp16yhUqBArV650LDt06BBvv/02ixcvpnDhwuzevZsuXbo4ToPs3LmTL774gv/85z9OxX+7vKJHGWTsF3NusWjv9YTrdP++O0sOLeHr+77m8caPuyBK5U5du3Zl1qxZnDlz5qZljz32GDt27LjpiZy5c+cybdo0wsPDCQ8PZ+7cuRw6dOim9VesWMHw4cMBW+GLXr16pVreqVMnihQpgojQtGnTVNvo3r07pUuXBuDhhx9m+fLlACxdupRHH30UEaFw4cIMHDjQkZQABg0a5Cjy0axZM5YsWcLo0aOZP3++o7SayaKsW0qHDh2iU6dO1K5dm/79+3P27FnOnj2bqibl1KlTHe0z2qezFi9ezKFDh2jdujXh4eEMHjwYi8XCuXPnAKhevTrNmzfP1jZvh+f3KI25MbFYdAwlg0tmexNrT6xlzbE1TO41mSFhQ7JeQTnPyR6fq7300kvMmzePNm3asHLlSkfRXgB/f3/+7//+j1deeYVKlSo5PjeZ1G9MyWSzJmNymbPMtpPeNlO+T5mYkkud/f7770ydOpXx48fzxx9/0LBhQ0dvOCsDBw7kww8/pFevXlitVgoUKEBcXBxlypRJtyZlRvt0ljGGzp07M2XKlJuW7d27N9uJ93Z5fI8y0MThg7ENvbM5VW3y/7gd7+jIwWcOapL0cK+88goPPfQQbdq0ualnOWjQIKKioli1apXjs4zqN6bVtm1bJk+eDNiGoHPmzHE6pgULFjiql0+aNMlRKb1Dhw58/fXXGGOIjo5m5syZ6ZZjAzhy5AiFCxdmwIABfPTRR2zZssVxbnHZsmXMmDHD0fb06dNMnDjxpm1cvnyZqlWrAvDNN98QHx8PkGFNyoz26ayOHTuyaNEidu/e7fhs06ZNTq+f0zw+URawF8QgoBCX4i9RJLCIU+tdir1Eq+9aseDAAgAqhVbKYg3lCf75z38ydOhQ2rRpw+nTpx2f+/j4MG7cuFRz3IwZM4awsDAaN25MvXr1aNmyZbqJ8vXXXycyMpLatWvz4IMPcs8992RYEzKtiIgIRowYQe3atbl48aJjDp6xY8dijKFu3bo0b96cIUOG0Llz53S3sXLlSurXr094eDhdunThyy+/xMfHh3LlyrFq1SpmzZpFtWrVqFu3Ln369KFUqVI3bePf//43vXr1omXLlhw9epTixYsDtiLB7du3JywszHGOtFmzZhnu01nVq1dn2rRpPPzww4SFhVGzZk1HTVC3yKj+Wl79ym49ymf/O8uYNwqb61unmjqT6pivd3yd5Trnr5834V+Gm4C3A8zcfXOztT+VtfxejzK7EhISTGxsrDHGmCtXrpi6deua33//3c1ReTetR5lGsNU2LIoS2zC6eHDxTNufu3aO9lPbc/DiQeYMmEPnO9P/X1opZ126dIkuXbqQlJREXFwcgwYNynCYrPImz0+U9mkgorDdlFoiuESGbS/HXabN5DYcu3yM+QPnE1EtIjdCVB6uVKlSbNmyxd1hqNvgBYnSdo7ygkkAoHhQxj3K0MBQulXvRo+7e9C6cutciU8plfd5fKIMsk9VG2W1Jcr0epRHLx/FYrVwZ7E7mdBxQq7Gp5TK+zw+USYPvS9YriMIRYOKplp+6OIh2k1pR5GgIvw18i98xONvBFBKZZP3JMrEaxQNKppqoq/9F/bTbko74i3x/Nr/V02SSql0eXxmCDbXseJDVMKVVFe895zfw72T7sVitbDioRXUL1vfjVEqd6pSpYqjqMOdd95Jz549Wbdu3S1v78svv+Tjjz/OtI2zZdgy8uSTTzoenQwICKBGjRqO9zlVTWfTpk106NDBUYKtbdu2rF69Gkhdmi6npP25DR8+3PHIZNrybLkuo/uG8upXdu+jXPD+EHP9zTJm0PxB5pHFjzg+7zWzlyk7oazZE+ld9/TlBXntPsrKlSubnTt3Ot7//PPPJjQ01GzYsMGNUTkvbfzJEhMTb3mbO3bsMCVLljSLFi1yfPb333+bn376KdN95pSzZ8+akJCQ256D3GKxpPt5du+j9IIeZSyXpQAHLh2gWmg1x+eTe01mzfA11CxZ043Rqbzo/vvvZ9SoUUyYMCHTUmpXrlxhxIgR1K1bl7CwMJ566ikgdcHddevW0aBBA8LDw6lduzbff/89kLoMW1Yl00aPHk3Lli2pVq0aY8aMyTT2YcOG8fTTT9O5c2eaNm0KwOTJk2natCkNGzakXbt27N+/39H+X//6F02aNKFBgwbcd999nD17FoD333+fhx9+mE6dOjna3nnnnamqHiX78MMPady4MfXr13c84w22Isj9+vWjVq1ahIWF8cADDwCwf/9+mjdvTlhYGHXq1GHChAmpfm7R0dG0bduWmJgYGjRowMcff5yqPFtmxzRp0iQ6d+7MkCFDaNiwITt35kxlKi84RxnD5uBg4pLiKOpflN6zejP9/ukUDixM4cDC7g7P672/8X32Xdznkm3XKFaDl5u8fEvrNm3alLlz52ZaSu25556jUKFCbN++HR8fHy5cuHDTdt5//32ef/55hgwZgjHGUU8ypaxKph0/fpzVq1cTHR3NHXfcwcMPP0z16tUzjH39+vWsWrWKggULsmbNGn744QdWr15NYGAgv/32GyNGjGDt2rVMmzaNgwcPsmHDBnx8fPjiiy/4xz/+wfTp09m6dWuqxJSZoUOH8o9//AOwVTUaNWoUGzZsYPHixVy6dMlRyzO51Nrnn39O165dHY9jpi3BFhISwsKFC2nUqJEj6U6aNMmxPLNjAvjjjz/Yvn07d9xxh1PxO8OliVJEOgOfAL7A18aY8WmWi315VyAGGGaM2ZqTMQRbY1hfyI+EhFheWvwSJQuW5FLspRyfslZ5FmMviDJ37lyuXr3KTz/9BEB8fDxhYWEAzJ8/ny1btjieYS5R4uZbz9q2bct7773HsWPH6NChg6OXl9LSpUsdxYJTlkxLTpT9+vXDx8eH0NBQatasyaFDhzJNlH379qVgwYIAzJs3j+3btzv2a4xxJKa5c+eyefNmGjRoANiqoSc/g558/M7YsmUL7777LhcvXsTHx4cDBw4AEBYWxr59+3jyySdp06YN3bp1A6B169a8+OKLJCQk0LZtW0ehD2dldkwALVu2zNEkCS5MlCLiC3wGdABOAptEZK4xJmWp6C5AdftXU+AL+/ccE2Ri+V2iOXz2ONWKVmP50OWUL1w+J3ehbsOt9vhcbdOmTdSpU4cjR444VUotI8899xz33XcfS5cu5emnn6Zjx4688847qdqYLEqmpS3Dltn0DpC6xJoxhhEjRtxUkDh52WuvvcaIESNuWpZcgi1t7cy0EhIS6Nu3L6tXr6ZBgwacPn2a8uVtf1/VqlVj7969LFu2jN9++41//vOf7Ny5kz59+tC8eXOWLFnC+PHj+fbbb5k2bVqm+0kbd0bHlPb4c4orz1E2AQ4aYw4bYxKAmUDPNG16AlPs51I3AEVEpGzaDd2OH3wj2Rp1hNKFSrNq2CpNkipLc+bM4YsvvuCFF17ItJRa9+7d+eCDDxy9r/SG3gcOHOCOO+5g5MiRPPvss+nWf8xOybTsuu+++5gyZQonT54EbPPLJD9O2aNHDz7//HNHbyw+Pp7t27cDMHr0aCZOnJiqGPD+/fuZOXNmqu3HxcVhsVioWLEiYBtWJzt58iS+vr706tWLjz/+mPPnz3Px4kUOHjxImTJlGDZsGG+88YbTNTGdOSZXceXQuzxwIsX7k9zcW0yvTXkgVTFAEXkMeAxIVTg1K/FJ8cwt6kuRy4VY8uASyhQq43z0yqv07duXwMBArl+/Tq1atVi4cCHNmjWjYcOGvPnmmzRu3BgfHx9EhDfeeIOaNWvy8ccf89xzz1GnTh38/Py49957+fTTT1Nt99NPP2XFihUEBAQQGBiY7tQFY8eO5amnnqJu3boAmZZMy67WrVszbtw4evToQVJSEgkJCfTr14+GDRsyZMgQLly4wL333gvcmP8mLCyMsLAw5s2bx6uvvsrIkSMpUKAAJUuWvKkXV7hwYd566y0aN25MpUqVHKcLwDZdQ/LFp6SkJF555RXKlSvHu+++y/Tp0wkICEBE+OSTT3LsmFxFsnMuIlsbFukHdDLGPGJ/PwRoYox5OkWbBcB7xpg/7O+XAS8ZYzL876FRo0Zm8+bNTsex++8FVC5chUKla9/ikaictnfvXmrW1LsNlPuk9zsoIluMMY3Sa+/KHuVJoGKK9xWA07fQ5rbUrt4tJzenlPJCrjxHuQmoLiJVRSQAGADMTdNmLjBUbJoBV4wxN8/upJRSbuSyHqUxxiIiTwGLsd0e9K0xZreIjLIv/xJYiO3WoIPYbg8a7qp4lFLqVrn0PkpjzEJsyTDlZ1+meG2AJ10Zg8qb0rslRqnccCvXZTz+EUaV9wQFBREVFXVLv7BK3Q5jDFFRUanuTXWGxz/CqPKeChUqcPLkScc0rErlpqCgICpUqJCtdTRRqlzn7+/vmCNaqfxAh95KKZUFTZRKKZUFTZRKKZUFTZRKKZUFlz3r7Soich44ls3VSgA3l3bJnzzlWDzlOECPJa/K7rFUNsaUTG9BvkuUt0JENmf0sHt+4ynH4inHAXoseVVOHosOvZVSKguaKJVSKgvekii/cncAOchTjsVTjgP0WPKqHDsWrzhHqZRSt8NbepRKKXXLPCpRikhnEdkvIgdF5KaZ4u0Fgj+1L98hIg3cEWdWnDiOwfb4d4jIOhEJc0eczsjqWFK0aywiSSLi3GTSbuDMsYhIGxHZJiK7RWRVbsfoDCd+v0JFZJ6IbLcfR56tEysi34pIpIjsymB5zvzNG2M84gtbceBDQDUgANgO1ErTpivwGyBAM+BPd8d9i8fRAihqf90lLx6Hs8eSot1ybLVL+7o77tv4dykC7AEq2d+Xcnfct3gc/wTet78uCVwEAtwdewbH0xpoAOzKYHmO/M17Uo8yT0yPmwOyPA5jzDpjTPKM7xuwzTWUFznzbwLwNPAzEJmbwWWTM8cyCJhtjDkOYIzJi8fjzHEYIERslZULYUuUmU8m7ibGmNXY4stIjvzNe1KizGjq2+y2cbfsxvgwtv8x86Isj0VEygO9gS/J25z5d7kLKCoiK0Vki4gMzbXonOfMcfwXqIltor+dwLPGGGvuhJfjcuRv3pPqUaY3r0DaS/rOtHE3p2MUkbbYEmVLl0Z065w5ln8DLxtjkvL41BDOHIsf0BCIAIKB9SKywRhzwNXBZYMzx9EJ2Aa0A+4AfheRNcaYqy6OzRVy5G/ekxJlnpgeNwc4FaOI1AO+BroYY6JyKbbscuZYGgEz7UmyBNBVRCzGmF9zJULnOfv7dcEYcx24LiKrgTAgLyVKZ45jODDe2E7yHRSRI0ANYGPuhJijcuZv3t0nY3PwpK4fcBioyo2T1LXTtOlG6hO7G90d9y0eRyVsM1e2cHe8t3ssadpPIu9ezHHm36UmsMzetgCwC6jj7thv4Ti+AN60vy4NnAJKuDv2TI6pChlfzMmRv3mP6VEaD5ke18njeB0oDnxu74lZTB4sZODkseQLzhyLMWaviCwCdgBW4GtjTLq3rbiLk/8mbwOTRGQntgTzsjEmT1YUEpHvgTZACRE5CbwB+EPO/s3rkzlKKZUFT7rqrZRSLqGJUimlsqCJUimlsqCJUimlsqCJUimlsqCJUjnFXtlnW4qvKpm0vZYD+5skIkfs+9oqIs1vYRtfi0gt++t/plm27nZjtG8n+eeyy15xp0gW7cNFpGtO7FvlHr09SDlFRK4ZYwrldNtMtjEJmG+M+UlEOgITjDH1bmN7tx1TVtsVkcnAAWPMuEzaDwMaGWOeyulYlOtoj1LdEhEpJCLL7L29nSJyU1UgESkrIqtT9Lha2T/vKCLr7ev+KCJZJbDVwJ32dV+wb2uXiDxn/6ygiCyw10/cJSL97Z+vFJFGIjIeCLbHMd2+7Jr9+6yUPTx7T7aPiPiKyAcisslex3CkEz+W9dgLLohIE7HVCv3L/v1uEQkA3gL622Ppb4/9W/t+/krv56jyAHc/fqRf+eMLSMJWKGEb8Au2R+EK25eVwPbkQ/II5Zr9+z+AV+2vfYEQe9vVQEH75y8Dr6ezv0nYH2cE+gF/Yis4sRMoiK38126gPtAHmJhi3VD795XYem+OmFK0SY6xNzDZ/joAW6WZYOAx4DX754HAZqBqOnFeS3F8PwKd7e8LA3721+2Bn+2vhwH/TbH+u8CD9tdFsD0XXtDd/976lfrLYx5hVC4Xa4wJT34jIv7AuyLSGtvjeuWxPRd8NsU6m4Bv7W1/NcZsE5F7gVrAWvvjlwHYemLp+UBEXgPOY6uSFAH8YmxFJxCR2UArYBEwQUTexzZcX5ON4/oN+FREAoHOwGpjTKx9uF9PblRcDwWqA0fSrB8sItuwPW+8Bfg9RfvJIlIdW7Ua/wz23xHoISIv2t8HYXuWf282jkG5mCZKdasGY6t+3dAYkygiR7H9kTsYY1bbE2k3YKqIfABcAn43xgx0Yh+jjTE/Jb8RkfbpNTLGHBCRhtie6X1PRJYYY95y5iCMMXEishJbabH+wPfJuwOeNsYszmITscaYcBEJBeYDTwKfYnteeoUxprf9wtfKDNYXoI8xZr8z8Sr30HOU6laFApH2JNkWqJy2gYhUtreZCHyDrWT/BuAeEUk+51hARO5ycp+rgV72dQpiGzavEZFyQIwxZhowwb6ftBLtPdv0zMRWLKEVtmIR2L8/nryOiNxl32e6jDFXgGeAF+3rhGKrugO24XayaGynIJItBp4We/daROpntA/lPpoo1a2aDjQSkc3Yepf70mnTBtgmIn9hO4/4iTHmPLbE8b2I7MCWOGs4s0NjzFZs5y43Yjtn+bUx5i+gLrDRPgR+FXgnndW/AnYkX8xJYwm2uVeWGtv0CGCr9bkH2Cq2iav+RxYjMHss24EBwL+w9W7XYjt/mWwFUCv5Yg62nqe/PbZd9vcqj9Hbg5RSKgvao1RKqSxoolRKqSxoolRKqSxoolRKqSxoolRKqSxoolRKqSxoolRKqSxoolRKqSz8P83xTDS3DDlwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "for classifier in lst:\n",
    "    classifier_name      = type(classifier).__name__\n",
    "    \n",
    "    # calculating prediction using prediction functions\n",
    "    y_predict            = get_y_predict(classifier, X_test)\n",
    "    \n",
    "    # calculating the roc curves\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_predict)\n",
    "    plt.plot(fpr, tpr, label=classifier_name)\n",
    "    \n",
    "plt.plot([0, 1], [0, 1], c='green', linestyle='--')\n",
    "plt.title('ROC')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\", fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "foreign-revolution",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}