Modelo1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9ef495b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import sklearn\n",
    "from sklearn import metrics, model_selection, tree\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import joblib\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from statistics import mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e32207e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train data\n",
    "X_train = pd.read_csv('x_train.csv').to_numpy()\n",
    "y_train = pd.read_csv('y_train.csv')\n",
    "y_train = y_train['LABEL']\n",
    "\n",
    "# Test data\n",
    "X_test = pd.read_csv('x_test.csv').to_numpy()\n",
    "y_test = pd.read_csv('y_test.csv')\n",
    "y_test = y_test['LABEL']\n",
    "\n",
    "# Validation data\n",
    "X_validation = pd.read_csv('x_validation.csv').to_numpy()\n",
    "y_validation = pd.read_csv('y_validation.csv')\n",
    "y_validation = y_validation['LABEL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4af5f08f",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7923b4f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=1.0, max_depth=1, random_state=7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3358f65b",
   "metadata": {},
   "source": [
    "#### Validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9bedb0af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999227745273617"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_validation, y_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3bd85ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_validation = clf.predict(X_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "17dec065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999227745273617\n",
      "Matriz de confusion [[68451    10     3     1]\n",
      " [    7 64954     0     0]\n",
      " [    0     0 92344     0]\n",
      " [    0     0     0 46161]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     68465\n",
      "           1       1.00      1.00      1.00     64961\n",
      "           2       1.00      1.00      1.00     92344\n",
      "           3       1.00      1.00      1.00     46161\n",
      "\n",
      "    accuracy                           1.00    271931\n",
      "   macro avg       1.00      1.00      1.00    271931\n",
      "weighted avg       1.00      1.00      1.00    271931\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(y_validation, pred_validation))\n",
    "print('Matriz de confusion', metrics.confusion_matrix(y_validation, pred_validation))\n",
    "print(metrics.classification_report(y_validation, pred_validation, target_names=['0', '1', '2', '3']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a9f3fa",
   "metadata": {},
   "source": [
    "#### Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "979e49e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999021916319507"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49eb404b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "78ff4c03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999021916319507\n",
      "Matriz de confusion [[138522     21      5      1]\n",
      " [    26 133267      0      0]\n",
      " [     0      0 187447      0]\n",
      " [     1      0      0  92810]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    138549\n",
      "           1       1.00      1.00      1.00    133293\n",
      "           2       1.00      1.00      1.00    187447\n",
      "           3       1.00      1.00      1.00     92811\n",
      "\n",
      "    accuracy                           1.00    552100\n",
      "   macro avg       1.00      1.00      1.00    552100\n",
      "weighted avg       1.00      1.00      1.00    552100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(y_test, pred))\n",
    "print('Matriz de confusion', metrics.confusion_matrix(y_test, pred))\n",
    "print(metrics.classification_report(y_test, pred, target_names=['0', '1', '2', '3']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63f24c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# metrics.plot_roc_curve(clf, X_test, y_test)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1a47199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['modelo1_v2.h5']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(clf, 'modelo1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "86030530",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraido de: https://stackoverflow.com/questions/45332410/roc-for-multiclass-classification\n",
    "def plot_multiclass_roc(clf, X_test, y_test, n_classes, figsize=(17, 6)):\n",
    "    y_score = clf.decision_function(X_test)\n",
    "\n",
    "    # structures\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "\n",
    "    # calculate dummies once\n",
    "    y_test_dummies = pd.get_dummies(y_test, drop_first=False).values\n",
    "    for i in range(n_classes):\n",
    "        fpr[i], tpr[i], _ = metrics.roc_curve(y_test_dummies[:, i], y_score[:, i])\n",
    "        roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
    "\n",
    "    # roc for each class\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    ax.set_xlim([0.0, 1.0])\n",
    "    ax.set_ylim([0.0, 1.05])\n",
    "    ax.set_xlabel('False Positive Rate')\n",
    "    ax.set_ylabel('True Positive Rate')\n",
    "    ax.set_title('Receiver operating characteristic curves')\n",
    "    for i in range(n_classes):\n",
    "        ax.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f) for label %i' % (roc_auc[i], i))\n",
    "    ax.legend(loc=\"best\")\n",
    "    ax.grid(alpha=.4)\n",
    "    sns.despine()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0c12aec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multiclass_roc(clf, X_test, y_test, n_classes=4, figsize=(16, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac17e66",
   "metadata": {},
   "source": [
    "### Cross Validation\n",
    "Fuente de consulta: https://machinelearningmastery.com/repeated-k-fold-cross-validation-with-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d9e0bb0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.99989078 0.99992057 0.99996028 0.99995035 0.9997617  0.99988085\n",
      " 0.99990071 0.99991064 0.99974185 0.99997021]\n"
     ]
    }
   ],
   "source": [
    "rf_cv_score=cross_val_score(estimator=clf, X=X_train, y=y_train, cv=10, n_jobs=-1)\n",
    "print(rf_cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d4dc407",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare the cross-validation procedure\n",
    "cv = KFold(n_splits=10, random_state=1, shuffle=True)\n",
    "# evaluate model\n",
    "scores = cross_val_score(clf, X_train, y_train, scoring='accuracy', cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4043b622",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.000 (0.000)\n"
     ]
    }
   ],
   "source": [
    "# report performance\n",
    "print('Accuracy: %.3f (%.3f)' % (np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7762b4e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: [      0       2       3 ... 1007146 1007147 1007148] Validation: [      1       6      15 ... 1007125 1007126 1007134]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      8      46      55 ... 1007132 1007133 1007141]\n",
      "Train: [      0       1       3 ... 1007146 1007147 1007148] Validation: [      2       4      39 ... 1007102 1007105 1007109]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      3      13      16 ... 1007119 1007122 1007138]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     21      31      44 ... 1007097 1007110 1007142]\n",
      "Train: [      0       1       2 ... 1007143 1007146 1007148] Validation: [     11      26      43 ... 1007144 1007145 1007147]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     10      12      22 ... 1007130 1007139 1007143]\n",
      "Train: [      1       2       3 ... 1007146 1007147 1007148] Validation: [      0       5      17 ... 1007107 1007113 1007135]\n",
      "Train: [      0       1       2 ... 1007144 1007145 1007147] Validation: [      9      14      19 ... 1007137 1007146 1007148]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      7      32      38 ... 1007104 1007124 1007140]\n",
      "Train: [      0       2       3 ... 1007146 1007147 1007148] Validation: [      1      10      26 ... 1007128 1007134 1007140]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      7      30      46 ... 1007124 1007126 1007129]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      5      17      24 ... 1007115 1007135 1007138]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     19      36      39 ... 1007101 1007111 1007125]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     16      31      41 ... 1007123 1007131 1007142]\n",
      "Train: [      0       1       5 ... 1007145 1007146 1007148] Validation: [      2       3       4 ... 1007122 1007139 1007147]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     22      25      38 ... 1007127 1007137 1007143]\n",
      "Train: [      0       1       2 ... 1007143 1007147 1007148] Validation: [     13      18      21 ... 1007144 1007145 1007146]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007147] Validation: [     14      20      32 ... 1007133 1007136 1007148]\n",
      "Train: [      1       2       3 ... 1007146 1007147 1007148] Validation: [      0       6       8 ... 1007095 1007105 1007121]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007147] Validation: [     22      37      42 ... 1007124 1007137 1007148]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      4       6       8 ... 1007136 1007138 1007145]\n",
      "Train: [      0       1       2 ... 1007145 1007147 1007148] Validation: [     10      12      14 ... 1007126 1007129 1007146]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007148] Validation: [     34      49      56 ... 1007139 1007141 1007147]\n",
      "Train: [      0       2       3 ... 1007146 1007147 1007148] Validation: [      1      11      40 ... 1007070 1007088 1007096]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      3      20      50 ... 1007105 1007134 1007140]\n",
      "Train: [      1       3       4 ... 1007146 1007147 1007148] Validation: [      0       2       7 ... 1007125 1007128 1007133]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     24      60      62 ... 1007130 1007142 1007144]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      5      26      39 ... 1007101 1007119 1007143]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     18      30      33 ... 1007113 1007117 1007132]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     12      67      70 ... 1007128 1007130 1007135]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     11      30      40 ... 1007092 1007116 1007121]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     10      22      24 ... 1007136 1007137 1007143]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      3      18      33 ... 1007126 1007139 1007145]\n",
      "Train: [      1       2       3 ... 1007145 1007146 1007148] Validation: [      0       4       6 ... 1007119 1007144 1007147]\n",
      "Train: [      0       1       2 ... 1007145 1007147 1007148] Validation: [     21      23      37 ... 1007129 1007138 1007146]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007147] Validation: [      9      13      28 ... 1007118 1007122 1007148]\n",
      "Train: [      0       1       3 ... 1007146 1007147 1007148] Validation: [      2       7       8 ... 1007090 1007112 1007131]\n",
      "Train: [      0       2       3 ... 1007146 1007147 1007148] Validation: [      1       5      14 ... 1007134 1007140 1007142]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     17      20      27 ... 1007132 1007133 1007141]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     14      19      26 ... 1007115 1007123 1007135]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     13      28      37 ... 1007095 1007113 1007117]\n",
      "Train: [      0       2       3 ... 1007146 1007147 1007148] Validation: [      1       8      17 ... 1007104 1007114 1007120]\n",
      "Train: [      1       2       4 ... 1007146 1007147 1007148] Validation: [      0       3      29 ... 1007132 1007133 1007139]\n",
      "Train: [      0       1       3 ... 1007145 1007146 1007147] Validation: [      2       5       7 ... 1007137 1007138 1007148]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     12      18      35 ... 1007134 1007141 1007143]\n",
      "Train: [      0       1       2 ... 1007144 1007146 1007148] Validation: [      6      11      41 ... 1007127 1007145 1007147]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     16      25      32 ... 1007101 1007116 1007140]\n",
      "Train: [      0       1       2 ... 1007145 1007147 1007148] Validation: [     21      22      31 ... 1007119 1007124 1007146]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      4      23      30 ... 1007136 1007142 1007144]\n"
     ]
    }
   ],
   "source": [
    "kf = RepeatedKFold(n_splits=10, n_repeats=5, random_state=None) \n",
    "\n",
    "for train_index, test_index in kf.split(X_train):\n",
    "      print(\"Train:\", train_index, \"Validation:\",test_index)\n",
    "      x_train, x_test = X_train[train_index], X_train[test_index] \n",
    "      Y_train, Y_test = y_train[train_index], y_train[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51500b13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 100715  100716  100717 ... 1007146 1007147 1007148] [     0      1      2 ... 100712 100713 100714]\n",
      "[      0       1       2 ... 1007146 1007147 1007148] [100715 100716 100717 ... 201427 201428 201429]\n",
      "[      0       1       2 ... 1007146 1007147 1007148] [201430 201431 201432 ... 302142 302143 302144]\n",
      "[      0       1       2 ... 1007146 1007147 1007148] [302145 302146 302147 ... 402857 402858 402859]\n",
      "[      0       1       2 ... 1007146 1007147 1007148] [402860 402861 402862 ... 503572 503573 503574]\n",
      "[      0       1       2 ... 1007146 1007147 1007148] [503575 503576 503577 ... 604287 604288 604289]\n",
      "[      0       1       2 ... 1007146 1007147 1007148] [604290 604291 604292 ... 705002 705003 705004]\n",
      "[      0       1       2 ... 1007146 1007147 1007148] [705005 705006 705007 ... 805717 805718 805719]\n",
      "[      0       1       2 ... 1007146 1007147 1007148] [805720 805721 805722 ... 906432 906433 906434]\n",
      "[     0      1      2 ... 906432 906433 906434] [ 906435  906436  906437 ... 1007146 1007147 1007148]\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=10)\n",
    "for train, test in kf.split(X_train):\n",
    "    print(\"%s %s\" % (train, test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1815c375",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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