Modelo3Adaboost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b49c8766",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn import datasets\n",
    "# Import train_test_split function\n",
    "from sklearn.model_selection import train_test_split\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\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\n",
    "import numpy as np\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5bd538f",
   "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": "5abd745d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(learning_rate=1, n_estimators=100, random_state=0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create adaboost classifer object\n",
    "abc = AdaBoostClassifier(n_estimators=100,\n",
    "                         learning_rate=1, random_state=0)\n",
    "# Train Adaboost Classifer\n",
    "abc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a2898c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['modelo3.h5']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(abc, 'modelo3.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0994de4f",
   "metadata": {},
   "source": [
    "#### Validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f720bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7060063030695287"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abc.score(X_validation, y_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1294dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_validation = abc.predict(X_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "695c2fc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7060063030695287\n",
      "Matriz de confusion [[48353  6443 13669     0]\n",
      " [13672 51289     0     0]\n",
      " [    1     0 92343     0]\n",
      " [    0     0 46161     0]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\maria\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.71      0.74     68465\n",
      "           1       0.89      0.79      0.84     64961\n",
      "           2       0.61      1.00      0.76     92344\n",
      "           3       0.00      0.00      0.00     46161\n",
      "\n",
      "    accuracy                           0.71    271931\n",
      "   macro avg       0.57      0.62      0.58    271931\n",
      "weighted avg       0.61      0.71      0.64    271931\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\maria\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\users\\maria\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\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": "4bde0549",
   "metadata": {},
   "source": [
    "#### Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8dfd3fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict the response for test dataset\n",
    "y_pred = abc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03a713b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7067342872667995\n"
     ]
    }
   ],
   "source": [
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ae11c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusion [[ 97584  13225  27740      0]\n",
      " [ 28130 105161      2      0]\n",
      " [     4      0 187443      0]\n",
      " [     0      0  92811      0]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\maria\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\users\\maria\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.70      0.74    138549\n",
      "           1       0.89      0.79      0.84    133293\n",
      "           2       0.61      1.00      0.76    187447\n",
      "           3       0.00      0.00      0.00     92811\n",
      "\n",
      "    accuracy                           0.71    552100\n",
      "   macro avg       0.57      0.62      0.58    552100\n",
      "weighted avg       0.62      0.71      0.64    552100\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\maria\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print('Matriz de confusion', metrics.confusion_matrix(y_test, y_pred))\n",
    "print(metrics.classification_report(y_test, y_pred, target_names=['0', '1', '2', '3']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc83b7c7",
   "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",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05d48eb0",
   "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(abc, X_test, y_test, n_classes=4, figsize=(16, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412805cc",
   "metadata": {},
   "source": [
    "### Cross Validation\n",
    "Fuente de consulta: https://machinelearningmastery.com/repeated-k-fold-cross-validation-with-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c57e3ba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7063496  0.70525741 0.70383756 0.70785881 0.70697513 0.70661768\n",
      " 0.70757087 0.70540634 0.70711413 0.70469845]\n"
     ]
    }
   ],
   "source": [
    "rf_cv_score=cross_val_score(estimator=abc, X=X_train, y=y_train, cv=10, n_jobs=-1)\n",
    "print(rf_cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b28054e6",
   "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(abc, X_train, y_train, scoring='accuracy', cv=cv, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03f8cd5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.706 (0.001)\n"
     ]
    }
   ],
   "source": [
    "# report performance\n",
    "print('Accuracy: %.3f (%.3f)' % (np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b1682c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: [      0       1       2 ... 1007143 1007146 1007148] Validation: [     26      30      40 ... 1007144 1007145 1007147]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     22      23      25 ... 1007101 1007125 1007137]\n",
      "Train: [      1       4       5 ... 1007145 1007146 1007147] Validation: [      0       2       3 ... 1007129 1007131 1007148]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     12      18      28 ... 1007116 1007120 1007139]\n",
      "Train: [      0       2       3 ... 1007146 1007147 1007148] Validation: [      1       9      17 ... 1007113 1007135 1007136]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      5       7      10 ... 1007130 1007140 1007141]\n",
      "Train: [      0       1       2 ... 1007145 1007147 1007148] Validation: [      4      15      21 ... 1007133 1007143 1007146]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     44      85      86 ... 1007099 1007112 1007122]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     16      19      42 ... 1007119 1007124 1007138]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      8      11      13 ... 1007117 1007123 1007132]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      7      16      18 ... 1007134 1007141 1007143]\n",
      "Train: [      2       3       4 ... 1007146 1007147 1007148] Validation: [      0       1       6 ... 1007109 1007120 1007127]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     14      29      77 ... 1007102 1007107 1007136]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007147] Validation: [     10      13      26 ... 1007129 1007140 1007148]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      4       9      21 ... 1007122 1007123 1007135]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     12      17      34 ... 1007125 1007128 1007139]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007148] Validation: [      3      19      38 ... 1007133 1007144 1007147]\n",
      "Train: [      0       1       3 ... 1007146 1007147 1007148] Validation: [      2      20      27 ... 1007117 1007118 1007137]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      8      23      28 ... 1007126 1007131 1007142]\n",
      "Train: [      0       1       2 ... 1007144 1007147 1007148] Validation: [      5      11      30 ... 1007138 1007145 1007146]\n",
      "Train: [      0       3       4 ... 1007145 1007147 1007148] Validation: [      1       2       9 ... 1007086 1007141 1007146]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     63      70      76 ... 1007130 1007134 1007142]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007147] Validation: [      3       8      11 ... 1007140 1007144 1007148]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      6      19      46 ... 1007106 1007139 1007145]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     17      22      28 ... 1007101 1007108 1007132]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      7      20      21 ... 1007129 1007131 1007138]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     16      23      34 ... 1007122 1007124 1007136]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007148] Validation: [      5      13      24 ... 1007120 1007135 1007147]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      4      18      38 ... 1007118 1007123 1007125]\n",
      "Train: [      1       2       3 ... 1007146 1007147 1007148] Validation: [      0      15      25 ... 1007115 1007127 1007143]\n",
      "Train: [      0       1       3 ... 1007146 1007147 1007148] Validation: [      2      10      13 ... 1007115 1007127 1007128]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007147] Validation: [     14      31      59 ... 1007111 1007137 1007148]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      3      19      20 ... 1007116 1007132 1007142]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      8      22      42 ... 1007120 1007125 1007126]\n",
      "Train: [      1       2       3 ... 1007146 1007147 1007148] Validation: [      0      15      17 ... 1007093 1007114 1007124]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     18      33      39 ... 1007123 1007133 1007143]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     23      29      44 ... 1007131 1007144 1007145]\n",
      "Train: [      0       2       3 ... 1007145 1007147 1007148] Validation: [      1       5       6 ... 1007122 1007134 1007146]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007148] Validation: [      4       9      11 ... 1007140 1007141 1007147]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      7      16      21 ... 1007130 1007136 1007139]\n",
      "Train: [      0       1       2 ... 1007145 1007147 1007148] Validation: [     12      16      30 ... 1007137 1007139 1007146]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      3      21      27 ... 1007107 1007119 1007125]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      6       7      20 ... 1007130 1007131 1007133]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [     17      34      37 ... 1007120 1007134 1007141]\n",
      "Train: [      0       1       3 ... 1007146 1007147 1007148] Validation: [      2       9      11 ... 1007091 1007100 1007122]\n",
      "Train: [      0       1       2 ... 1007144 1007146 1007147] Validation: [      4      24      32 ... 1007143 1007145 1007148]\n",
      "Train: [      1       2       3 ... 1007146 1007147 1007148] Validation: [      0      14      60 ... 1007115 1007129 1007136]\n",
      "Train: [      0       1       2 ... 1007146 1007147 1007148] Validation: [      5      10      19 ... 1007118 1007126 1007144]\n",
      "Train: [      0       2       3 ... 1007146 1007147 1007148] Validation: [      1       8      18 ... 1007116 1007138 1007140]\n",
      "Train: [      0       1       2 ... 1007145 1007146 1007148] Validation: [     13      15      25 ... 1007128 1007142 1007147]\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": 17,
   "id": "eec9dd30",
   "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": "2b88d02d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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