Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importamos las librerías\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from pylab import rcParams\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#set up graphic style in this case I am using the color scheme from xkcd.com\n",
    "rcParams['figure.figsize'] = 14, 8.7 # Golden Mean\n",
    "LABELS = [\"Normal\",\"Fraud\"]\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</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>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "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",
       "\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",
       "\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",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cargamos los datos\n",
    "df = pd.read_csv(\"creditcard.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creamos el dataset\n",
    "y = df['Class']\n",
    "X = df.drop('Class', axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mostrar_resultados(y_test, pred_y):\n",
    "    conf_matrix = confusion_matrix(y_test, pred_y)\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "    plt.title(\"Confusion matrix\")\n",
    "    plt.ylabel('True class')\n",
    "    plt.xlabel('Predicted class')\n",
    "    plt.show()\n",
    "    print (classification_report(y_test, pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 20building tree 2 of 20\n",
      "\n",
      "building tree 3 of 20\n",
      "building tree 4 of 20\n",
      "building tree 5 of 20\n",
      "building tree 6 of 20\n",
      "building tree 7 of 20\n",
      "building tree 8 of 20\n",
      "building tree 9 of 20\n",
      "building tree 10 of 20\n",
      "building tree 11 of 20\n",
      "building tree 12 of 20\n",
      "building tree 13 of 20\n",
      "building tree 14 of 20\n",
      "building tree 15 of 20\n",
      "building tree 16 of 20\n",
      "building tree 17 of 20\n",
      "building tree 18 of 20\n",
      "building tree 19 of 20\n",
      "building tree 20 of 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done  20 out of  20 | elapsed:   17.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=20, n_jobs=2, verbose=2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear el modelo con 20 arboles\n",
    "model = RandomForestClassifier(n_estimators=20, \n",
    "                               bootstrap = True,verbose=2,\n",
    "                               max_features = 'auto', n_jobs= 2 )\n",
    "# entrenar!\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  20 out of  20 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85292\n",
      "           1       0.92      0.83      0.87       151\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.96      0.91      0.94     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred_y = model.predict(X_test)\n",
    "mostrar_resultados(y_test, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9138428004086002\n"
     ]
    }
   ],
   "source": [
    "#Comprobar los resultados\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Calculate roc auc\n",
    "roc_value = roc_auc_score(y_test, pred_y)\n",
    "print(roc_value)\n",
    "#El valor de roc cuanto más cerca de 1, mejor. si fuera 0.5 daría igual que fuesen valores aleatorios y sería un mal modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}