SMOTE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "001d7c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "935ae006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6819 entries, 0 to 6818\n",
      "Data columns (total 96 columns):\n",
      " #   Column                                                    Non-Null Count  Dtype  \n",
      "---  ------                                                    --------------  -----  \n",
      " 0   Bankrupt?                                                 6819 non-null   int64  \n",
      " 1    ROA(C) before interest and depreciation before interest  6819 non-null   float64\n",
      " 2    ROA(A) before interest and % after tax                   6819 non-null   float64\n",
      " 3    ROA(B) before interest and depreciation after tax        6819 non-null   float64\n",
      " 4    Operating Gross Margin                                   6819 non-null   float64\n",
      " 5    Realized Sales Gross Margin                              6819 non-null   float64\n",
      " 6    Operating Profit Rate                                    6819 non-null   float64\n",
      " 7    Pre-tax net Interest Rate                                6819 non-null   float64\n",
      " 8    After-tax net Interest Rate                              6819 non-null   float64\n",
      " 9    Non-industry income and expenditure/revenue              6819 non-null   float64\n",
      " 10   Continuous interest rate (after tax)                     6819 non-null   float64\n",
      " 11   Operating Expense Rate                                   6819 non-null   float64\n",
      " 12   Research and development expense rate                    6819 non-null   float64\n",
      " 13   Cash flow rate                                           6819 non-null   float64\n",
      " 14   Interest-bearing debt interest rate                      6819 non-null   float64\n",
      " 15   Tax rate (A)                                             6819 non-null   float64\n",
      " 16   Net Value Per Share (B)                                  6819 non-null   float64\n",
      " 17   Net Value Per Share (A)                                  6819 non-null   float64\n",
      " 18   Net Value Per Share (C)                                  6819 non-null   float64\n",
      " 19   Persistent EPS in the Last Four Seasons                  6819 non-null   float64\n",
      " 20   Cash Flow Per Share                                      6819 non-null   float64\n",
      " 21   Revenue Per Share (Yuan ¥)                               6819 non-null   float64\n",
      " 22   Operating Profit Per Share (Yuan ¥)                      6819 non-null   float64\n",
      " 23   Per Share Net profit before tax (Yuan ¥)                 6819 non-null   float64\n",
      " 24   Realized Sales Gross Profit Growth Rate                  6819 non-null   float64\n",
      " 25   Operating Profit Growth Rate                             6819 non-null   float64\n",
      " 26   After-tax Net Profit Growth Rate                         6819 non-null   float64\n",
      " 27   Regular Net Profit Growth Rate                           6819 non-null   float64\n",
      " 28   Continuous Net Profit Growth Rate                        6819 non-null   float64\n",
      " 29   Total Asset Growth Rate                                  6819 non-null   float64\n",
      " 30   Net Value Growth Rate                                    6819 non-null   float64\n",
      " 31   Total Asset Return Growth Rate Ratio                     6819 non-null   float64\n",
      " 32   Cash Reinvestment %                                      6819 non-null   float64\n",
      " 33   Current Ratio                                            6819 non-null   float64\n",
      " 34   Quick Ratio                                              6819 non-null   float64\n",
      " 35   Interest Expense Ratio                                   6819 non-null   float64\n",
      " 36   Total debt/Total net worth                               6819 non-null   float64\n",
      " 37   Debt ratio %                                             6819 non-null   float64\n",
      " 38   Net worth/Assets                                         6819 non-null   float64\n",
      " 39   Long-term fund suitability ratio (A)                     6819 non-null   float64\n",
      " 40   Borrowing dependency                                     6819 non-null   float64\n",
      " 41   Contingent liabilities/Net worth                         6819 non-null   float64\n",
      " 42   Operating profit/Paid-in capital                         6819 non-null   float64\n",
      " 43   Net profit before tax/Paid-in capital                    6819 non-null   float64\n",
      " 44   Inventory and accounts receivable/Net value              6819 non-null   float64\n",
      " 45   Total Asset Turnover                                     6819 non-null   float64\n",
      " 46   Accounts Receivable Turnover                             6819 non-null   float64\n",
      " 47   Average Collection Days                                  6819 non-null   float64\n",
      " 48   Inventory Turnover Rate (times)                          6819 non-null   float64\n",
      " 49   Fixed Assets Turnover Frequency                          6819 non-null   float64\n",
      " 50   Net Worth Turnover Rate (times)                          6819 non-null   float64\n",
      " 51   Revenue per person                                       6819 non-null   float64\n",
      " 52   Operating profit per person                              6819 non-null   float64\n",
      " 53   Allocation rate per person                               6819 non-null   float64\n",
      " 54   Working Capital to Total Assets                          6819 non-null   float64\n",
      " 55   Quick Assets/Total Assets                                6819 non-null   float64\n",
      " 56   Current Assets/Total Assets                              6819 non-null   float64\n",
      " 57   Cash/Total Assets                                        6819 non-null   float64\n",
      " 58   Quick Assets/Current Liability                           6819 non-null   float64\n",
      " 59   Cash/Current Liability                                   6819 non-null   float64\n",
      " 60   Current Liability to Assets                              6819 non-null   float64\n",
      " 61   Operating Funds to Liability                             6819 non-null   float64\n",
      " 62   Inventory/Working Capital                                6819 non-null   float64\n",
      " 63   Inventory/Current Liability                              6819 non-null   float64\n",
      " 64   Current Liabilities/Liability                            6819 non-null   float64\n",
      " 65   Working Capital/Equity                                   6819 non-null   float64\n",
      " 66   Current Liabilities/Equity                               6819 non-null   float64\n",
      " 67   Long-term Liability to Current Assets                    6819 non-null   float64\n",
      " 68   Retained Earnings to Total Assets                        6819 non-null   float64\n",
      " 69   Total income/Total expense                               6819 non-null   float64\n",
      " 70   Total expense/Assets                                     6819 non-null   float64\n",
      " 71   Current Asset Turnover Rate                              6819 non-null   float64\n",
      " 72   Quick Asset Turnover Rate                                6819 non-null   float64\n",
      " 73   Working capitcal Turnover Rate                           6819 non-null   float64\n",
      " 74   Cash Turnover Rate                                       6819 non-null   float64\n",
      " 75   Cash Flow to Sales                                       6819 non-null   float64\n",
      " 76   Fixed Assets to Assets                                   6819 non-null   float64\n",
      " 77   Current Liability to Liability                           6819 non-null   float64\n",
      " 78   Current Liability to Equity                              6819 non-null   float64\n",
      " 79   Equity to Long-term Liability                            6819 non-null   float64\n",
      " 80   Cash Flow to Total Assets                                6819 non-null   float64\n",
      " 81   Cash Flow to Liability                                   6819 non-null   float64\n",
      " 82   CFO to Assets                                            6819 non-null   float64\n",
      " 83   Cash Flow to Equity                                      6819 non-null   float64\n",
      " 84   Current Liability to Current Assets                      6819 non-null   float64\n",
      " 85   Liability-Assets Flag                                    6819 non-null   int64  \n",
      " 86   Net Income to Total Assets                               6819 non-null   float64\n",
      " 87   Total assets to GNP price                                6819 non-null   float64\n",
      " 88   No-credit Interval                                       6819 non-null   float64\n",
      " 89   Gross Profit to Sales                                    6819 non-null   float64\n",
      " 90   Net Income to Stockholder's Equity                       6819 non-null   float64\n",
      " 91   Liability to Equity                                      6819 non-null   float64\n",
      " 92   Degree of Financial Leverage (DFL)                       6819 non-null   float64\n",
      " 93   Interest Coverage Ratio (Interest expense to EBIT)       6819 non-null   float64\n",
      " 94   Net Income Flag                                          6819 non-null   int64  \n",
      " 95   Equity to Liability                                      6819 non-null   float64\n",
      "dtypes: float64(93), int64(3)\n",
      "memory usage: 5.0 MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data/data.csv\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e80fa84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Bankrupt?\"].value_counts(normalize=True).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "894b1a55",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Bankruptdesc\"] = df[\"Bankrupt?\"].map({\n",
    "    0 : \"Not Bankrupt\",\n",
    "    1 : \"Bankrupt\"\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c50882c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAHsCAYAAAAAfK1NAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAkX0lEQVR4nO3df1DUBf7H8dcuCmgG6JlguIWnVnb+DE9C67o6LvQ8y6u74+wHHqV3dmae27fU8neX2A+JGi3HX5d1mVhpZThc3qY5Jo2JcVmjNqYGp4CayRoW6LLfP5y248Qfq8B7gedjZmfcz34+7JsZkaefX+vw+/1+AQAAGHFaDwAAAJo3YgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJhqYT3Auaiurtb+/ft18cUXy+FwWI8DAADOgd/v19GjR3XppZfK6Tz9/o9GESP79++Xy+WyHgMAAJyH4uJiderU6bSvBx0jGzZs0FNPPaWCggKVlJRo1apVGjZs2Bm3Wb9+vdxutz777DO5XC5NnjxZf/zjH8/5PS+++GJJJ7+ZqKioYEcGAAAGvF6vXC5X4Pf46QQdIxUVFerdu7fuuece3XbbbWddf8+ePRoyZIhGjx6tV155RR6PRyNHjlTHjh2Vmpp6Tu/5/aGZqKgoYgQAgEbmbKdYBB0jgwcP1uDBg895/fnz56tz586aM2eOJKl79+7auHGjnnnmmXOOEQAA0HTV+9U0+fn5SklJqbEsNTVV+fn59f3WAACgEaj3E1hLS0sVGxtbY1lsbKy8Xq++/fZbtWrV6pRtKisrVVlZGXju9Xrre0wAAGAkJO8zkpmZqejo6MCDK2kAAGi66j1G4uLiVFZWVmNZWVmZoqKiat0rIkmTJk1SeXl54FFcXFzfYwIAACP1fpgmOTlZa9asqbFs7dq1Sk5OPu02ERERioiIqO/RAABACAh6z8g333yjwsJCFRYWSjp56W5hYaGKiookndyrkZ6eHlh/9OjR2r17tx5++GHt2LFDzz//vFasWKHx48fXzXcAAAAataBjZMuWLerbt6/69u0rSXK73erbt6+mTp0qSSopKQmEiSR17txZubm5Wrt2rXr37q05c+Zo0aJFXNYLAAAkSQ6/3++3HuJsvF6voqOjVV5ezk3PAABoJM7193dIXk0DAACaD2IEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmKr3O7DiwiRMzLUeAQ1o7+wh1iMAQINjzwgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADB1XjEyb948JSQkKDIyUklJSdq8efMZ18/OztaVV16pVq1ayeVyafz48fruu+/Oa2AAANC0BB0jOTk5crvdmjZtmrZu3arevXsrNTVVBw4cqHX9ZcuWaeLEiZo2bZq2b9+uxYsXKycnR4888sgFDw8AABq/oGMkKytLo0aNUkZGhq6++mrNnz9frVu31pIlS2pdf9OmTRo4cKDuuOMOJSQk6Oabb9bw4cPPujcFAAA0D0HFSFVVlQoKCpSSkvLDF3A6lZKSovz8/Fq3GTBggAoKCgLxsXv3bq1Zs0a/+tWvLmBsAADQVLQIZuVDhw7J5/MpNja2xvLY2Fjt2LGj1m3uuOMOHTp0SNddd538fr9OnDih0aNHn/EwTWVlpSorKwPPvV5vMGMCAIBGpN6vplm/fr1mzZql559/Xlu3btXKlSuVm5urxx577LTbZGZmKjo6OvBwuVz1PSYAADAS1J6R9u3bKywsTGVlZTWWl5WVKS4urtZtpkyZorvvvlsjR46UJPXs2VMVFRX605/+pEcffVRO56k9NGnSJLnd7sBzr9dLkAAA0EQFtWckPDxciYmJ8ng8gWXV1dXyeDxKTk6udZtjx46dEhxhYWGSJL/fX+s2ERERioqKqvEAAABNU1B7RiTJ7XZrxIgR6tevn/r376/s7GxVVFQoIyNDkpSenq74+HhlZmZKkoYOHaqsrCz17dtXSUlJ2rVrl6ZMmaKhQ4cGogQAADRfQcdIWlqaDh48qKlTp6q0tFR9+vRRXl5e4KTWoqKiGntCJk+eLIfDocmTJ2vfvn265JJLNHToUD3++ON1910AAIBGy+E/3bGSEOL1ehUdHa3y8vJmd8gmYWKu9QhoQHtnD7EeAQDqzLn+/uazaQAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmDqvGJk3b54SEhIUGRmppKQkbd68+YzrHzlyRGPGjFHHjh0VERGhK664QmvWrDmvgQEAQNPSItgNcnJy5Ha7NX/+fCUlJSk7O1upqanauXOnOnTocMr6VVVV+uUvf6kOHTro9ddfV3x8vL788kvFxMTUxfwAAKCRCzpGsrKyNGrUKGVkZEiS5s+fr9zcXC1ZskQTJ048Zf0lS5bo8OHD2rRpk1q2bClJSkhIuLCpAQBAkxHUYZqqqioVFBQoJSXlhy/gdColJUX5+fm1bvP2228rOTlZY8aMUWxsrHr06KFZs2bJ5/Od9n0qKyvl9XprPAAAQNMUVIwcOnRIPp9PsbGxNZbHxsaqtLS01m12796t119/XT6fT2vWrNGUKVM0Z84c/e1vfzvt+2RmZio6OjrwcLlcwYwJAAAakXq/mqa6ulodOnTQggULlJiYqLS0ND366KOaP3/+abeZNGmSysvLA4/i4uL6HhMAABgJ6pyR9u3bKywsTGVlZTWWl5WVKS4urtZtOnbsqJYtWyosLCywrHv37iotLVVVVZXCw8NP2SYiIkIRERHBjAYAABqpoPaMhIeHKzExUR6PJ7CsurpaHo9HycnJtW4zcOBA7dq1S9XV1YFln3/+uTp27FhriAAAgOYl6MM0brdbCxcu1NKlS7V9+3bdd999qqioCFxdk56erkmTJgXWv++++3T48GGNGzdOn3/+uXJzczVr1iyNGTOm7r4LAADQaAV9aW9aWpoOHjyoqVOnqrS0VH369FFeXl7gpNaioiI5nT80jsvl0j//+U+NHz9evXr1Unx8vMaNG6cJEybU3XcBAAAaLYff7/dbD3E2Xq9X0dHRKi8vV1RUlPU4DSphYq71CGhAe2cPsR4BAOrMuf7+5rNpAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYOq8YmTdvnhISEhQZGamkpCRt3rz5nLZbvny5HA6Hhg0bdj5vCwAAmqCgYyQnJ0dut1vTpk3T1q1b1bt3b6WmpurAgQNn3G7v3r36v//7P11//fXnPSwAAGh6go6RrKwsjRo1ShkZGbr66qs1f/58tW7dWkuWLDntNj6fT3feeadmzJihH//4xxc0MAAAaFqCipGqqioVFBQoJSXlhy/gdColJUX5+fmn3W7mzJnq0KGD7r333nN6n8rKSnm93hoPAADQNAUVI4cOHZLP51NsbGyN5bGxsSotLa11m40bN2rx4sVauHDhOb9PZmamoqOjAw+XyxXMmAAAoBGp16tpjh49qrvvvlsLFy5U+/btz3m7SZMmqby8PPAoLi6uxykBAIClFsGs3L59e4WFhamsrKzG8rKyMsXFxZ2y/hdffKG9e/dq6NChgWXV1dUn37hFC+3cuVNdunQ5ZbuIiAhFREQEMxoAAGikgtozEh4ersTERHk8nsCy6upqeTweJScnn7L+VVddpW3btqmwsDDwuOWWW3TjjTeqsLCQwy8AACC4PSOS5Ha7NWLECPXr10/9+/dXdna2KioqlJGRIUlKT09XfHy8MjMzFRkZqR49etTYPiYmRpJOWQ4AAJqnoGMkLS1NBw8e1NSpU1VaWqo+ffooLy8vcFJrUVGRnE5u7AoAAM6Nw+/3+62HOBuv16vo6GiVl5crKirKepwGlTAx13oENKC9s4dYjwAAdeZcf3+zCwMAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJg6rxiZN2+eEhISFBkZqaSkJG3evPm06y5cuFDXX3+92rZtq7Zt2yolJeWM6wMAgOYl6BjJycmR2+3WtGnTtHXrVvXu3Vupqak6cOBAreuvX79ew4cP17p165Sfny+Xy6Wbb75Z+/btu+DhAQBA4+fw+/3+YDZISkrST3/6U82dO1eSVF1dLZfLpbFjx2rixIln3d7n86lt27aaO3eu0tPTz+k9vV6voqOjVV5erqioqGDGbfQSJuZaj4AGtHf2EOsRAKDOnOvv76D2jFRVVamgoEApKSk/fAGnUykpKcrPzz+nr3Hs2DEdP35c7dq1C+atAQBAE9UimJUPHTokn8+n2NjYGstjY2O1Y8eOc/oaEyZM0KWXXlojaP5XZWWlKisrA8+9Xm8wYwIAgEakQa+mmT17tpYvX65Vq1YpMjLytOtlZmYqOjo68HC5XA04JQAAaEhBxUj79u0VFhamsrKyGsvLysoUFxd3xm2ffvppzZ49W++++6569ep1xnUnTZqk8vLywKO4uDiYMQEAQCMSVIyEh4crMTFRHo8nsKy6uloej0fJycmn3e7JJ5/UY489pry8PPXr1++s7xMREaGoqKgaDwAA0DQFdc6IJLndbo0YMUL9+vVT//79lZ2drYqKCmVkZEiS0tPTFR8fr8zMTEnSE088oalTp2rZsmVKSEhQaWmpJKlNmzZq06ZNHX4rAACgMQo6RtLS0nTw4EFNnTpVpaWl6tOnj/Ly8gIntRYVFcnp/GGHywsvvKCqqir99re/rfF1pk2bpunTp1/Y9AAAoNEL+j4jFrjPCJoL7jMCoCmpl/uMAAAA1DViBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgKkW1gMAQHOVMDHXegQ0oL2zh1iPELLYMwIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMDUecXIvHnzlJCQoMjISCUlJWnz5s1nXP+1117TVVddpcjISPXs2VNr1qw5r2EBAEDTE3SM5OTkyO12a9q0adq6dat69+6t1NRUHThwoNb1N23apOHDh+vee+/Vxx9/rGHDhmnYsGH69NNPL3h4AADQ+AUdI1lZWRo1apQyMjJ09dVXa/78+WrdurWWLFlS6/rPPvusBg0apIceekjdu3fXY489pmuuuUZz58694OEBAEDjF1SMVFVVqaCgQCkpKT98AadTKSkpys/Pr3Wb/Pz8GutLUmpq6mnXBwAAzUtQt4M/dOiQfD6fYmNjayyPjY3Vjh07at2mtLS01vVLS0tP+z6VlZWqrKwMPC8vL5ckeb3eYMZtEqorj1mPgAbUHP+ON2f8fDcvzfHn+/vv2e/3n3G9kPxsmszMTM2YMeOU5S6Xy2AaoOFEZ1tPAKC+NOef76NHjyo6Ovq0rwcVI+3bt1dYWJjKyspqLC8rK1NcXFyt28TFxQW1viRNmjRJbrc78Ly6ulqHDx/Wj370IzkcjmBGRiPk9XrlcrlUXFysqKgo63EA1CF+vpsXv9+vo0eP6tJLLz3jekHFSHh4uBITE+XxeDRs2DBJJ0PB4/Ho/vvvr3Wb5ORkeTwe/fWvfw0sW7t2rZKTk0/7PhEREYqIiKixLCYmJphR0QRERUXxjxXQRPHz3XycaY/I94I+TON2uzVixAj169dP/fv3V3Z2tioqKpSRkSFJSk9PV3x8vDIzMyVJ48aN0w033KA5c+ZoyJAhWr58ubZs2aIFCxYE+9YAAKAJCjpG0tLSdPDgQU2dOlWlpaXq06eP8vLyAiepFhUVyen84SKdAQMGaNmyZZo8ebIeeeQRdevWTW+++aZ69OhRd98FAABotBz+s53iCjSwyspKZWZmatKkSaccrgPQuPHzjdoQIwAAwBQflAcAAEwRIwAAwBQxAgAATBEjMPfSSy/VuP3/96qqqvTSSy8ZTAQAaEicwApzYWFhKikpUYcOHWos/+qrr9ShQwf5fD6jyQAADYE9IzDn9/trvc3/f/7zn3O6cx+A0HbPPffo6NGjpyyvqKjQPffcYzARQg17RmCmb9++cjgc+ve//62f/OQnatHih3vw+Xw+7dmzR4MGDdKKFSsMpwRwoU639/PQoUOKi4vTiRMnjCZDqAjJT+1F8/D95xsVFhYqNTVVbdq0CbwWHh6uhIQE3X777UbTAbhQXq9Xfr8/8GFpkZGRgdd8Pp/WrFlzSqCgeSJGYGbatGmSpISEBKWlpdX4hwpA4xcTEyOHwyGHw6ErrrjilNcdDodmzJhhMBlCDYdpEDK2bNmi7du3S5KuvvpqJSYmGk8E4EK8//778vv9uummm/TGG2+oXbt2gdfCw8N1+eWXn/Wj5dE8ECMwt2/fPv3hD3/QBx98oJiYGEnSkSNHNGDAAC1fvlydOnWyHRDABfnyyy/lcrlqfIgq8N+IEZgbNGiQjhw5oqVLl+rKK6+UJO3cuVMZGRmKiopSXl6e8YQALtTXX3+txYsX19j7mZGRUWNvCZovYgTmWrVqpU2bNqlv3741lhcUFOj666/XsWPHjCYDUBc2bNigoUOHKjo6Wv369ZN08uf7yJEjWr16tX72s58ZTwhrnMAKcy6XS8ePHz9luc/n43gy0ASMGTNGaWlpeuGFFxQWFibp5M/3X/7yF40ZM0bbtm0znhDWOIAHc0899ZTGjh2rLVu2BJZt2bJF48aN09NPP204GYC6sGvXLj344IOBEJFO3nvE7XZr165dhpMhVHCYBubatm2rY8eO6cSJE4Ebn33/54suuqjGuocPH7YYEcAFGDhwoB566KHAvYW+9+abb2r27Nn68MMPbQZDyOAwDcxlZ2dbjwCgHj3wwAMaN26cdu3apWuvvVaS9OGHH2revHmaPXu2Pvnkk8C6vXr1shoThtgzAgCoV2e7pNfhcAQ+o4oPxmye2DMCc0VFRWd8/bLLLmugSQDUhz179liPgBDHnhGYczqdtX5q7/f4nxIANG3sGYG5jz/+uMbz48eP6+OPP1ZWVpYef/xxo6kA1JWXXnrpjK+np6c30CQIVewZQcjKzc3VU089pfXr11uPAuACtG3btsbz48eP69ixYwoPD1fr1q25Sg7cZwSh68orr9RHH31kPQaAC/T111/XeHzzzTfauXOnrrvuOr366qvW4yEEsGcE5rxeb43nfr9fJSUlmj59unbs2KHCwkKbwQDUqy1btuiuu+7Sjh07rEeBMc4ZgbmYmJhTTmD1+/1yuVxavny50VQA6luLFi20f/9+6zEQAogRmFu3bl2N506nU5dccom6du0auCMrgMbr7bffrvH8+72fc+fO1cCBA42mQijhMA1MHT9+XH/+8581ZcoUde7c2XocAPXgf2965nA4dMkll+imm27SnDlz1LFjR6PJECqIEZiLjo5WYWEhMQIAzRRX08DcsGHD9Oabb1qPAaAeHD9+XF26dNH27dutR0EI44A8zHXr1k0zZ87UBx98oMTExFM+qfeBBx4wmgzAhWrZsqW+++476zEQ4jhMA3NnOjzjcDi0e/fuBpwGQF2bNWuWPv/8cy1atIiT0lErYgQAUK9+85vfyOPxqE2bNurZs+cpez9XrlxpNBlCBYkKc59++ql69OhR62tvvvmmhg0b1rADAahTMTExuv32263HQAhjzwjMxcfHa+PGjaccrnnjjTeUnp6uiooKo8kAAA2Bq2lgbuTIkUpJSVFpaWlgWU5OjtLT0/Xiiy/aDQagTpzp82ceeuihBpwEoYo9IwgJY8eO1bp167Rhwwbl5eVp5MiRevnll9m1CzQBMTExevXVVzV48OAay8ePH6/ly5erpKTEaDKECmIEIePOO+/URx99pH379mnZsmW69dZbrUcCUAdyc3N155136p133tF1110n6eR/QFauXCmPx6OrrrrKeEJYI0Zg4n8/q0I6eXOk8ePH6+abb9Ytt9wSWP7ffwbQOC1btkz333+/1q5dq8WLF+utt97SunXrdMUVV1iPhhBAjMDE/35Wxek4HA75fL56ngZAQ3j++efldrt1ySWXaN26deratav1SAgRxAgAoM653e5al7/22mu65ppr1KVLl8CyrKyshhoLIYoYAQDUuRtvvPGc1nM4HHrvvffqeRqEOmIEIcHj8cjj8ejAgQOqrq6u8dqSJUuMpgIANATuwApzM2bM0MyZM9WvXz917NhRDofDeiQAQANizwjMdezYUU8++aTuvvtu61EA1IOKigrNnj37tHs/+TBMsGcE5qqqqjRgwADrMQDUk5EjR+r999/X3Xffzd5P1Io9IzA3YcIEtWnTRlOmTLEeBUA9iImJUW5urgYOHGg9CkIUe0Zg7rvvvtOCBQv0r3/9S7169VLLli1rvM5lf0Dj1rZtW7Vr1856DIQw9ozA3JkuAeSyP6Dx+8c//qG33npLS5cuVevWra3HQQgiRgAA9apv37764osv5Pf7lZCQcMrez61btxpNhlDBYRoAQL0aNmyY9QgIcewZQUjYsmWLVqxYoaKiIlVVVdV4beXKlUZTAQAawrl9WhlQj5YvX64BAwZo+/btWrVqlY4fP67PPvtM7733nqKjo63HAwDUM2IE5mbNmqVnnnlGq1evVnh4uJ599lnt2LFDv//973XZZZdZjwfgAvl8Pj399NPq37+/4uLi1K5duxoPgBiBuS+++EJDhgyRJIWHh6uiokIOh0Pjx4/XggULjKcDcKFmzJihrKwspaWlqby8XG63W7fddpucTqemT59uPR5CADECc23bttXRo0clSfHx8fr0008lSUeOHNGxY8csRwNQB1555RUtXLhQDz74oFq0aKHhw4dr0aJFmjp1qj788EPr8RACiBGY+9nPfqa1a9dKkn73u99p3LhxGjVqlIYPH65f/OIXxtMBuFClpaXq2bOnJKlNmzYqLy+XJP36179Wbm6u5WgIEVzaC3Nz587Vd999J0l69NFH1bJlS23atEm33367Jk+ebDwdgAvVqVMnlZSU6LLLLlOXLl307rvv6pprrtFHH32kiIgI6/EQAri0FwBQryZOnKioqCg98sgjysnJ0V133aWEhAQVFRVp/Pjxmj17tvWIMEaMIKT4/X6tW7dO3377rQYMGKC2bdtajwSgjn344YfatGmTunXrpqFDh1qPgxDAOSMwc+TIEY0YMUI9e/bUqFGj5PV6df311yslJUVDhw5V9+7d9cknn1iPCeACffXVV4E/FxcXa82aNSopKeE+QghgzwjMjBw5Uhs2bNCIESO0evVqOZ1O+f1+ZWdny+l06uGHH1abNm20evVq61EBnIdt27Zp6NChKi4uVrdu3bR8+XINGjRIFRUVcjqdqqio0Ouvv87t4kGMwE58fLyWLVumG264Qfv27ZPL5dJ7772nn//855KkzZs365ZbblFpaantoADOy+DBg9WiRQtNnDhRL7/8st555x2lpqZq4cKFkqSxY8eqoKCAy3tBjMBOixYtVFxcrI4dO0qSWrdurW3btqlLly6STl4OGB8fL5/PZzkmgPPUvn17vffee+rVq5e++eYbRUVF6aOPPlJiYqIkaceOHbr22mt15MgR20FhjnNGYKa6ulphYWGB52FhYXI4HIHn//1nAI3P4cOHFRcXJ+nk/UUuuuiiGiel//cND9G8cZ8RmFq0aJHatGkjSTpx4oRefPFFtW/fXpL4RwpoAv73PxX8JwO14TANzCQkJJzTP0x79uxpgGkA1DWn06nBgwcHbmy2evVq3XTTTbroooskSZWVlcrLy+NQLIgRAED9yMjIOKf1/v73v9fzJAh1xAgAADDFCawAAMAUMQIAAEwRIwAAwBQxAgAATBEjMBcWFqYDBw6csvyrr76qcVM0AEDTRIzA3Oku6KqsrFR4eHgDTwMAaGjcgRVmnnvuOUkn78j433dilSSfz6cNGzboqquushoPANBAuM8IzHTu3FmS9OWXX6pTp041DsmEh4crISFBM2fOVFJSktWIAIAGQIzA3I033qiVK1fW+AAtAEDzQYwgpHz/15EP0wKA5oMTWBESXnrpJfXs2VOtWrVSq1at1KtXL7388svWYwEAGgAnsMJcVlaWpkyZovvvv18DBw6UJG3cuFGjR4/WoUOHNH78eOMJAQD1icM0MNe5c2fNmDFD6enpNZYvXbpU06dP1549e4wmAwA0BA7TwFxJSYkGDBhwyvIBAwaopKTEYCIAQEMiRmCua9euWrFixSnLc3Jy1K1bN4OJAAANiXNGYG7GjBlKS0vThg0bAueMfPDBB/J4PLVGCgCgaeGcEYSEgoICPfPMM9q+fbskqXv37nrwwQfVt29f48kAAPWNGAEAAKY4ZwQAAJjinBGYcTqdZ73TqsPh0IkTJxpoIgCABWIEZlatWnXa1/Lz8/Xcc8+purq6AScCAFjgnBGElJ07d2rixIlavXq17rzzTs2cOVOXX3659VgAgHrEOSMICfv379eoUaPUs2dPnThxQoWFhVq6dCkhAgDNADECU+Xl5ZowYYK6du2qzz77TB6PR6tXr1aPHj2sRwMANBDOGYGZJ598Uk888YTi4uL06quv6tZbb7UeCQBggHNGYMbpdKpVq1ZKSUlRWFjYaddbuXJlA04FAGho7BmBmfT09LNe2gsAaPrYMwIAAExxAisAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMPX/s7rC1MJKspgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Bankruptdesc\"].value_counts(normalize=True).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7851842b",
   "metadata": {},
   "source": [
    "#### SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5ef3d0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Bankruptdesc\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "918af2ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.pop(\"Bankrupt?\")\n",
    "X = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "639c54ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a57be4e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10572, 95)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "789b5c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Standardise the whole dataset\n",
    "std_scaler = StandardScaler().fit(X_train_resampled)\n",
    "\n",
    "def preprocessor(X):\n",
    "    D = np.copy(X)\n",
    "    D = std_scaler.transform(D)\n",
    "    return D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ca28624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FunctionTransformer(func=<function preprocessor at 0x000002122C236EE8>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess_transformer = FunctionTransformer(preprocessor)\n",
    "preprocess_transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69283d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler',\n",
       "                 FunctionTransformer(func=<function preprocessor at 0x000002122C236EE8>)),\n",
       "                ('Logistic Regression', LogisticRegression())])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "p1 = Pipeline([('scaler', preprocess_transformer),\n",
    "              ('Logistic Regression', LogisticRegression())])\n",
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85f849e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, accuracy_score\n",
    "\n",
    "def fit_and_print(p, X_train=X_train_resampled, X_test=X_test, y_train=y_train_resampled, y_test=y_test):\n",
    "    # Fit the transformer\n",
    "    p.fit(X_train, y_train)\n",
    "    # Predict the train and test outputs\n",
    "    test_prediction =p.predict(X_test)\n",
    "    \n",
    "    # Print the errors\n",
    "    print(\"Accuracy Score:   \"+str(accuracy_score(test_prediction, y_test)*100))\n",
    "    print(\"Precision Score:  \"+str(precision_score(test_prediction, y_test)*100))\n",
    "    print(\"Recall Score:     \"+str(recall_score(test_prediction, y_test)*100))\n",
    "    print(\"roc_auc_score:    \"+str(accuracy_score(test_prediction, y_test)*100))\n",
    "    print(\"\\nConfusion Matrix:\\n\", confusion_matrix(test_prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52cf30b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:   88.12316715542522\n",
      "Precision Score:  68.62745098039215\n",
      "Recall Score:     19.337016574585636\n",
      "roc_auc_score:    88.12316715542522\n",
      "\n",
      "Confusion Matrix:\n",
      " [[1167   16]\n",
      " [ 146   35]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "fit_and_print(p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dee5db2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:   94.94134897360705\n",
      "Precision Score:  56.86274509803921\n",
      "Recall Score:     38.15789473684211\n",
      "roc_auc_score:    94.94134897360705\n",
      "\n",
      "Confusion Matrix:\n",
      " [[1266   22]\n",
      " [  47   29]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "p2 = Pipeline([('scaler', preprocess_transformer),\n",
    "              ('RFC', RandomForestClassifier())])\n",
    "fit_and_print(p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23c9e591",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.7.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}