17_KMEANS - Detección de transacciones bancarias fraudulentas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div><div style=\"float:left; clear:both;\"><img src=\"img/colab.png\" align=\"left\" width=\"200\" height=\"200\" /><br></div><div style=\"float:left; clear:both;\"><a href=\"https://colab.research.google.com/drive/1Ss7QNdkJ1OnQAtso-pXXp4scU7ED3rn4\">Abre este Jupyter en Google Colab</a></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Caso Práctico: KMEANS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conjunto de datos: Detección de transacciones bancarias fraudulentas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descripción\n",
    "The datasets contains transactions made by credit cards in September 2013 by european cardholders. \n",
    "This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n",
    "\n",
    "It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n",
    "\n",
    "### Descarga de los ficheros de datos\n",
    "https://www.kaggle.com/mlg-ulb/creditcardfraud#creditcard.csv\n",
    "\n",
    "### Referencias adicionales sobre el conjunto de datos\n",
    "_The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection.\n",
    "More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.gridspec as gridspec\n",
    "from collections import Counter\n",
    "from sklearn import metrics\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original code: https://bit.ly/2TNHBZ5\n",
    "def plot_data(X, y):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], 'k.', markersize=2)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], 'r.', markersize=2)\n",
    "\n",
    "def plot_centroids(centroids, weights=None, circle_color='w', cross_color='k'):\n",
    "    if weights is not None:\n",
    "        centroids = centroids[weights > weights.max() / 10]\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "                marker='o', s=30, linewidths=8,\n",
    "                color=circle_color, zorder=10, alpha=0.9)\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "                marker='x', s=50, linewidths=50,\n",
    "                color=cross_color, zorder=11, alpha=1)\n",
    "\n",
    "def plot_decision_boundaries(clusterer, X, y, resolution=1000, show_centroids=True):\n",
    "    mins = X.min(axis=0) - 0.1\n",
    "    maxs = X.max(axis=0) + 0.1\n",
    "    xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\n",
    "                         np.linspace(mins[1], maxs[1], resolution))\n",
    "    Z = clusterer.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    plt.contourf(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\n",
    "                cmap=\"Pastel2\")\n",
    "    plt.contour(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\n",
    "                linewidths=1, colors='k')\n",
    "    plot_data(X, y)\n",
    "    if show_centroids:\n",
    "        plot_centroids(clusterer.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def purity_score(y_true, y_pred):\n",
    "    # compute contingency matrix (also called confusion matrix)\n",
    "    contingency_matrix = metrics.cluster.contingency_matrix(y_true, y_pred)\n",
    "    # return purity\n",
    "    return np.sum(np.amax(contingency_matrix, axis=0)) / np.sum(contingency_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Lectura del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"datasets/creditcard.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Visualización del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
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       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
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       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
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       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
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     "execution_count": 5,
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   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de características: 31\n",
      "Longitud del conjunto de datos: 284807\n"
     ]
    }
   ],
   "source": [
    "print(\"Número de características:\", len(df.columns))\n",
    "print(\"Longitud del conjunto de datos:\", len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 492 transacciones fraudulentas, 284315 transacciones legitimas\n",
    "# El conjunto de datos se encuntra desequilabrado\n",
    "df[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "# Visualizamos los tipos de cada uno de los atributos\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      False\n",
       "V1        False\n",
       "V2        False\n",
       "V3        False\n",
       "V4        False\n",
       "V5        False\n",
       "V6        False\n",
       "V7        False\n",
       "V8        False\n",
       "V9        False\n",
       "V10       False\n",
       "V11       False\n",
       "V12       False\n",
       "V13       False\n",
       "V14       False\n",
       "V15       False\n",
       "V16       False\n",
       "V17       False\n",
       "V18       False\n",
       "V19       False\n",
       "V20       False\n",
       "V21       False\n",
       "V22       False\n",
       "V23       False\n",
       "V24       False\n",
       "V25       False\n",
       "V26       False\n",
       "V27       False\n",
       "V28       False\n",
       "Amount    False\n",
       "Class     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comprobamos si alguna columna tiene valores nulos\n",
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.165980e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.373150e-15</td>\n",
       "      <td>2.086869e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.490107e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.177556e-16</td>\n",
       "      <td>-2.406455e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.656562e-16</td>\n",
       "      <td>-3.444850e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.471968e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.687098e-15</td>\n",
       "      <td>-3.666453e-16</td>\n",
       "      <td>-1.220404e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.490107e-15 -5.556467e-16  1.177556e-16 -2.406455e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.656562e-16 -3.444850e-16  2.578648e-16  4.471968e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.687098e-15 -3.666453e-16 -1.220404e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2304 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representamos gráficamente las características\n",
    "features = df.drop(\"Class\", axis=1)\n",
    "\n",
    "plt.figure(figsize=(12,32))\n",
    "gs = gridspec.GridSpec(8, 4)\n",
    "gs.update(hspace=0.8)\n",
    "\n",
    "for i, f in enumerate(features):\n",
    "    ax = plt.subplot(gs[i])\n",
    "    sns.distplot(df[f][df[\"Class\"] == 1])\n",
    "    sns.distplot(df[f][df[\"Class\"] == 0])\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_title('feature: ' + str(f))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representación gráfica de dos características\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(df[\"V10\"][df['Class'] == 0], df[\"V14\"][df['Class'] == 0], c=\"g\", marker=\".\")\n",
    "plt.scatter(df[\"V10\"][df['Class'] == 1], df[\"V14\"][df['Class'] == 1], c=\"r\", marker=\".\")\n",
    "plt.xlabel(\"V10\", fontsize=14)\n",
    "plt.ylabel(\"V14\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Preparación del conjunto de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para este tipo de algoritmos es importante que todos los datos se encuentren en un rango similar, por lo tanto, podemos aplicar una función de escalado o normalización. Otra opción, es eliminar las características que no se encuentran en un rango similar siempre y cuando no sean muy influyentes para la predicción."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"Time\", \"Amount\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Kmeans con un conjunto de datos de dos dimensiones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de comenzar con el entrenamiento de KMEANS para todos los atributos del conjunto de datos, se realiza una prueba para dos atributos con el objetivo de comprender como construye el límite de decisión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V10</th>\n",
       "      <th>V14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.311169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.166974</td>\n",
       "      <td>-0.143772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.207643</td>\n",
       "      <td>-0.165946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.054952</td>\n",
       "      <td>-0.287924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.753074</td>\n",
       "      <td>-1.119670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>4.356170</td>\n",
       "      <td>4.626942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>-0.975926</td>\n",
       "      <td>-0.675143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>-0.484782</td>\n",
       "      <td>-0.510602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>-0.399126</td>\n",
       "      <td>0.449624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>-0.915427</td>\n",
       "      <td>-0.084316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             V10       V14\n",
       "0       0.090794 -0.311169\n",
       "1      -0.166974 -0.143772\n",
       "2       0.207643 -0.165946\n",
       "3      -0.054952 -0.287924\n",
       "4       0.753074 -1.119670\n",
       "...          ...       ...\n",
       "284802  4.356170  4.626942\n",
       "284803 -0.975926 -0.675143\n",
       "284804 -0.484782 -0.510602\n",
       "284805 -0.399126  0.449624\n",
       "284806 -0.915427 -0.084316\n",
       "\n",
       "[284807 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[[\"V10\", \"V14\"]].copy()\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generamos los clusters para nuestro conjunto de datos sin etiquetar\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=42)\n",
    "clusters = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plot_decision_boundaries(kmeans, X.values, df[\"Class\"].values)\n",
    "plt.xlabel(\"V10\", fontsize=14)\n",
    "plt.ylabel(\"V14\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 0 has 134343 samples - 15 are malicious samples\n",
      "Label 1 has 71527 samples - 16 are malicious samples\n",
      "Label 2 has 2484 samples - 1 are malicious samples\n",
      "Label 3 has 389 samples - 317 are malicious samples\n",
      "Label 4 has 76064 samples - 143 are malicious samples\n"
     ]
    }
   ],
   "source": [
    "counter = Counter(clusters.tolist())\n",
    "bad_counter = Counter(clusters[df['Class'] == 1].tolist())\n",
    "\n",
    "for key in sorted(counter.keys()):\n",
    "    print(\"Label {0} has {1} samples - {2} are malicious samples\".format(\n",
    "        key, counter[key], bad_counter[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Kmeans con un conjunto de datos multidimensional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al tratarse de un algoritmo basado en aprendizaje no supervisado, no se requieren las etiquetas para entrenar o predir. Hay que tener en cuenta que en este tipo de problemas se presupone que no se dispone de las etiquetas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Class\", axis=1)\n",
    "y = df[\"Class\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=42)\n",
    "clusters = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 0 has 124575 samples - 145 are malicious samples\n",
      "Label 1 has 107374 samples - 129 are malicious samples\n",
      "Label 2 has 3562 samples - 12 are malicious samples\n",
      "Label 3 has 43256 samples - 4 are malicious samples\n",
      "Label 4 has 6040 samples - 202 are malicious samples\n"
     ]
    }
   ],
   "source": [
    "# Evaluamos los clusters y el contenido que se han formado\n",
    "counter = Counter(clusters.tolist())\n",
    "bad_counter = Counter(clusters[y == 1].tolist())\n",
    "\n",
    "for key in sorted(counter.keys()):\n",
    "    print(\"Label {0} has {1} samples - {2} are malicious samples\".format(\n",
    "        key, counter[key], bad_counter[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Reducción del número de características"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En muchas ocasiones Kmeans funciona mejor con un número de características bajo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplicamos selección de características con _Random Forest_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=-1,\n",
       "                       oob_score=False, random_state=42, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Utilizamos Random Forest para realizar selección de características\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf_rnd = RandomForestClassifier(n_estimators=50, random_state=42, n_jobs=-1)\n",
    "clf_rnd.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seleccionamos las características más importantes\n",
    "feature_importances = {name: score for name, score in zip(list(df), clf_rnd.feature_importances_)}\n",
    "feature_importances_sorted = pd.Series(feature_importances).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reducimos el conjunto de datos a las 7 características más importantes\n",
    "X_reduced = X[list(feature_importances_sorted.head(7).index)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>V17</th>\n",
       "      <th>V14</th>\n",
       "      <th>V16</th>\n",
       "      <th>V12</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.207971</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.551600</td>\n",
       "      <td>0.025791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>-0.183361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>-0.121359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.684093</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>1.965775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>-0.038195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>1.991691</td>\n",
       "      <td>4.626942</td>\n",
       "      <td>1.107641</td>\n",
       "      <td>2.711941</td>\n",
       "      <td>4.356170</td>\n",
       "      <td>-1.593105</td>\n",
       "      <td>0.510632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>-0.025693</td>\n",
       "      <td>-0.675143</td>\n",
       "      <td>-0.711757</td>\n",
       "      <td>0.915802</td>\n",
       "      <td>-0.975926</td>\n",
       "      <td>-0.150189</td>\n",
       "      <td>-1.221179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>0.313502</td>\n",
       "      <td>-0.510602</td>\n",
       "      <td>0.140716</td>\n",
       "      <td>0.063119</td>\n",
       "      <td>-0.484782</td>\n",
       "      <td>0.411614</td>\n",
       "      <td>0.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>0.509928</td>\n",
       "      <td>0.449624</td>\n",
       "      <td>-0.608577</td>\n",
       "      <td>-0.962886</td>\n",
       "      <td>-0.399126</td>\n",
       "      <td>-1.933849</td>\n",
       "      <td>1.113981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>-0.660377</td>\n",
       "      <td>-0.084316</td>\n",
       "      <td>-0.302620</td>\n",
       "      <td>-0.031513</td>\n",
       "      <td>-0.915427</td>\n",
       "      <td>-1.040458</td>\n",
       "      <td>0.167430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             V17       V14       V16       V12       V10       V11       V18\n",
       "0       0.207971 -0.311169 -0.470401 -0.617801  0.090794 -0.551600  0.025791\n",
       "1      -0.114805 -0.143772  0.463917  1.065235 -0.166974  1.612727 -0.183361\n",
       "2       1.109969 -0.165946 -2.890083  0.066084  0.207643  0.624501 -0.121359\n",
       "3      -0.684093 -0.287924 -1.059647  0.178228 -0.054952 -0.226487  1.965775\n",
       "4      -0.237033 -1.119670 -0.451449  0.538196  0.753074 -0.822843 -0.038195\n",
       "...          ...       ...       ...       ...       ...       ...       ...\n",
       "284802  1.991691  4.626942  1.107641  2.711941  4.356170 -1.593105  0.510632\n",
       "284803 -0.025693 -0.675143 -0.711757  0.915802 -0.975926 -0.150189 -1.221179\n",
       "284804  0.313502 -0.510602  0.140716  0.063119 -0.484782  0.411614  0.395652\n",
       "284805  0.509928  0.449624 -0.608577 -0.962886 -0.399126 -1.933849  1.113981\n",
       "284806 -0.660377 -0.084316 -0.302620 -0.031513 -0.915427 -1.040458  0.167430\n",
       "\n",
       "[284807 rows x 7 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_reduced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrenamiento de KMEANS con el conjunto de datos reducido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=42)\n",
    "clusters = kmeans.fit_predict(X_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 0 has 20162 samples - 45 are malicious samples\n",
      "Label 1 has 100980 samples - 90 are malicious samples\n",
      "Label 2 has 311 samples - 268 are malicious samples\n",
      "Label 3 has 116590 samples - 69 are malicious samples\n",
      "Label 4 has 46764 samples - 20 are malicious samples\n"
     ]
    }
   ],
   "source": [
    "# Evaluamos los clusters y el contenido que se han formado\n",
    "counter = Counter(clusters.tolist())\n",
    "bad_counter = Counter(clusters[y == 1].tolist())\n",
    "\n",
    "for key in sorted(counter.keys()):\n",
    "    print(\"Label {0} has {1} samples - {2} are malicious samples\".format(\n",
    "        key, counter[key], bad_counter[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Evaluación de los resultados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay que tener cuidado con los conjuntos de datos desequilibrados cuando se utilizan métricas para medir la pureza de los clusters. Una posible solución es utilizar técnicas de equilibrado del conjunto de datos, como la generación de más ejemplos de transacciones fraudulentas o la disminución de ejemplos de transacciones legítimas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purity Score: 0.9990625230419197\n"
     ]
    }
   ],
   "source": [
    "# Calculamos el purity score, es importante darse cuenta de que recibe las etiquetas\n",
    "print(\"Purity Score:\", purity_score(y, clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shiloutte:  0.175661363674322\n"
     ]
    }
   ],
   "source": [
    "# Calculamos el coeficiente de Shiloutte, es importante darse cuenta de que no le pasamos las etiquetas\n",
    "print(\"Shiloutte: \", metrics.silhouette_score(X_reduced, clusters, sample_size=10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calinski harabasz:  40537.024698689966\n"
     ]
    }
   ],
   "source": [
    "# Calculamos el Calinski harabasz score, es importante darse cuenta de que no le pasamos las etiquetas\n",
    "print(\"Calinski harabasz: \", metrics.calinski_harabasz_score(X_reduced, clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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