dataset_prep_banknote.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f50d086f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORT Generals \n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6d298f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD the Banknote Dataset \n",
    "np_data = np.genfromtxt('source_data/data_banknote_authentication.csv', delimiter=',')\n",
    "data_cols = ['variance', 'skewness', 'curtosis', 'entropy', 'class']\n",
    "\n",
    "df_banknote = pd.DataFrame(data=np_data, columns=data_cols)\n",
    "\n",
    "# 1. variance of Wavelet Transformed image (continuous)\n",
    "# 2. skewness of Wavelet Transformed image (continuous)\n",
    "# 3. curtosis of Wavelet Transformed image (continuous)\n",
    "# 4. entropy of image (continuous)\n",
    "# 5. class of banknote - real/fake (integer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2462d243",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPLIT DATASET - Original & Normalized Original\n",
    "data_train_orig, data_test_orig = train_test_split(df_banknote, test_size=0.25, random_state=42)\n",
    "\n",
    "# SAVE original train & test datasets\n",
    "data_train_orig.to_csv('source_data/banknote_orig_train.csv', index=False)\n",
    "data_test_orig.to_csv('source_data/banknote_orig_test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b695efe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fundamental_data_analytics(df_source):\n",
    "    \n",
    "    list_min = df_source.min().to_list()\n",
    "    list_max = df_source.max().to_list()\n",
    "    list_mean = df_source.mean().to_list()\n",
    "    list_var = df_source.var().to_list()\n",
    "    \n",
    "    list_mean = [round(num, 4) for num in list_mean]\n",
    "\n",
    "    # aggregate information within one single dataframe \n",
    "    joint_df = pd.DataFrame(list(zip(data_cols, list_mean, list_var, list_min, list_max)),\n",
    "                            columns =['attribute name', 'mean', 'var', 'min', 'max'])\n",
    "\n",
    "    print(df_source.info())\n",
    "    print('\\n', df_source.hist(bins=100, figsize=(15, 10), color='#86BC25'))\n",
    "    display(joint_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0a3e9213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1372 entries, 0 to 1371\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   variance  1372 non-null   float64\n",
      " 1   skewness  1372 non-null   float64\n",
      " 2   curtosis  1372 non-null   float64\n",
      " 3   entropy   1372 non-null   float64\n",
      " 4   class     1372 non-null   float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 53.7 KB\n",
      "None\n",
      "\n",
      " [[<AxesSubplot:title={'center':'variance'}>\n",
      "  <AxesSubplot:title={'center':'skewness'}>]\n",
      " [<AxesSubplot:title={'center':'curtosis'}>\n",
      "  <AxesSubplot:title={'center':'entropy'}>]\n",
      " [<AxesSubplot:title={'center':'class'}> <AxesSubplot:>]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute name</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>variance</td>\n",
       "      <td>0.4337</td>\n",
       "      <td>8.081299</td>\n",
       "      <td>-7.0421</td>\n",
       "      <td>6.8248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>skewness</td>\n",
       "      <td>1.9224</td>\n",
       "      <td>34.445710</td>\n",
       "      <td>-13.7731</td>\n",
       "      <td>12.9516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>curtosis</td>\n",
       "      <td>1.3976</td>\n",
       "      <td>18.576359</td>\n",
       "      <td>-5.2861</td>\n",
       "      <td>17.9274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>entropy</td>\n",
       "      <td>-1.1917</td>\n",
       "      <td>4.414256</td>\n",
       "      <td>-8.5482</td>\n",
       "      <td>2.4495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>class</td>\n",
       "      <td>0.4446</td>\n",
       "      <td>0.247112</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  attribute name    mean        var      min      max\n",
       "0       variance  0.4337   8.081299  -7.0421   6.8248\n",
       "1       skewness  1.9224  34.445710 -13.7731  12.9516\n",
       "2       curtosis  1.3976  18.576359  -5.2861  17.9274\n",
       "3        entropy -1.1917   4.414256  -8.5482   2.4495\n",
       "4          class  0.4446   0.247112   0.0000   1.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fundamental_data_analytics(df_banknote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2b67fc48",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1029 entries, 1282 to 1126\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   variance  1029 non-null   float64\n",
      " 1   skewness  1029 non-null   float64\n",
      " 2   curtosis  1029 non-null   float64\n",
      " 3   entropy   1029 non-null   float64\n",
      " 4   class     1029 non-null   float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 48.2 KB\n",
      "None\n",
      "\n",
      " [[<AxesSubplot:title={'center':'variance'}>\n",
      "  <AxesSubplot:title={'center':'skewness'}>]\n",
      " [<AxesSubplot:title={'center':'curtosis'}>\n",
      "  <AxesSubplot:title={'center':'entropy'}>]\n",
      " [<AxesSubplot:title={'center':'class'}> <AxesSubplot:>]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute name</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>variance</td>\n",
       "      <td>0.4134</td>\n",
       "      <td>8.074647</td>\n",
       "      <td>-7.0421</td>\n",
       "      <td>6.8248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>skewness</td>\n",
       "      <td>1.7954</td>\n",
       "      <td>35.121728</td>\n",
       "      <td>-13.7731</td>\n",
       "      <td>12.9516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>curtosis</td>\n",
       "      <td>1.4847</td>\n",
       "      <td>18.824753</td>\n",
       "      <td>-5.2613</td>\n",
       "      <td>17.9274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>entropy</td>\n",
       "      <td>-1.2003</td>\n",
       "      <td>4.380593</td>\n",
       "      <td>-8.5482</td>\n",
       "      <td>2.1625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>class</td>\n",
       "      <td>0.4451</td>\n",
       "      <td>0.247225</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  attribute name    mean        var      min      max\n",
       "0       variance  0.4134   8.074647  -7.0421   6.8248\n",
       "1       skewness  1.7954  35.121728 -13.7731  12.9516\n",
       "2       curtosis  1.4847  18.824753  -5.2613  17.9274\n",
       "3        entropy -1.2003   4.380593  -8.5482   2.1625\n",
       "4          class  0.4451   0.247225   0.0000   1.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fundamental_data_analytics(data_train_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4c02cd1",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 343 entries, 430 to 1022\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   variance  343 non-null    float64\n",
      " 1   skewness  343 non-null    float64\n",
      " 2   curtosis  343 non-null    float64\n",
      " 3   entropy   343 non-null    float64\n",
      " 4   class     343 non-null    float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 16.1 KB\n",
      "None\n",
      "\n",
      " [[<AxesSubplot:title={'center':'variance'}>\n",
      "  <AxesSubplot:title={'center':'skewness'}>]\n",
      " [<AxesSubplot:title={'center':'curtosis'}>\n",
      "  <AxesSubplot:title={'center':'entropy'}>]\n",
      " [<AxesSubplot:title={'center':'class'}> <AxesSubplot:>]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute name</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>variance</td>\n",
       "      <td>0.4948</td>\n",
       "      <td>8.119933</td>\n",
       "      <td>-6.7526</td>\n",
       "      <td>5.9374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>skewness</td>\n",
       "      <td>2.3031</td>\n",
       "      <td>32.320596</td>\n",
       "      <td>-13.2869</td>\n",
       "      <td>12.5398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>curtosis</td>\n",
       "      <td>1.1363</td>\n",
       "      <td>17.792747</td>\n",
       "      <td>-5.2861</td>\n",
       "      <td>17.1116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>entropy</td>\n",
       "      <td>-1.1657</td>\n",
       "      <td>4.527449</td>\n",
       "      <td>-7.7853</td>\n",
       "      <td>2.4495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>class</td>\n",
       "      <td>0.4431</td>\n",
       "      <td>0.247489</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  attribute name    mean        var      min      max\n",
       "0       variance  0.4948   8.119933  -6.7526   5.9374\n",
       "1       skewness  2.3031  32.320596 -13.2869  12.5398\n",
       "2       curtosis  1.1363  17.792747  -5.2861  17.1116\n",
       "3        entropy -1.1657   4.527449  -7.7853   2.4495\n",
       "4          class  0.4431   0.247489   0.0000   1.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fundamental_data_analytics(data_test_orig)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}