4_clasification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Machine Learning Fundamentals\n",
        "### Hesus Garcia\n",
        "## Leccion 4 - Clasificación (Parte 1)\n",
        "### Usando los modelos para predecir si un tumor cancerigeno es benigno o maligno\n",
        "Se importanlas paqueterias"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:33:23.613722Z",
          "start_time": "2020-06-28T21:32:51.533902Z"
        }
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "#import sklearn\n",
        "\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "from sklearn.metrics import roc_curve, roc_auc_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Matriz de confusion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:33:23.663619Z",
          "start_time": "2020-06-28T21:33:23.613722Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Confusion Matrix: \n",
            " [[7 1]\n",
            " [2 5]]\n",
            "Confusion Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.88      0.82         8\n",
            "           1       0.83      0.71      0.77         7\n",
            "\n",
            "    accuracy                           0.80        15\n",
            "   macro avg       0.81      0.79      0.80        15\n",
            "weighted avg       0.80      0.80      0.80        15\n",
            "\n"
          ]
        }
      ],
      "source": [
        "\n",
        "y = np.array([0,1,0,1,0,0,1,0,0,1,0,1,1,0,1])\n",
        "yest = np.array([0,1,0,0,1,0,1,0,0,1,0,1,0,0,1])\n",
        "\n",
        "print('Confusion Matrix: \\n', confusion_matrix(y, yest))\n",
        "print('Confusion Report: \\n', classification_report(y, yest))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Curva ROC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:33:28.352243Z",
          "start_time": "2020-06-28T21:33:27.689300Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "AUC:  0.9\n"
          ]
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "y = np.array([0,0,0,0,0,0,1,1,1,1,1])\n",
        "y_prob = np.array([0.000001,0.015,0.025,0.08,0.23,0.95,0.39,0.79,0.91,0.98,0.9998])\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y, y_prob)\n",
        "print('AUC: ', roc_auc_score(y, y_prob))\n",
        "\n",
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, lw=2)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Clasificación de tumores cancerigenos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se leen los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:33:30.783903Z",
          "start_time": "2020-06-28T21:33:30.393908Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(683, 9) (683, 1)\n"
          ]
        }
      ],
      "source": [
        "url = 'https://raw.githubusercontent.com/Dr-Carlos-Villasenor/ReconocimientoPatrones/master/Datasets/cancer.csv'\n",
        "df = pd.read_csv(url,parse_dates=[0])\n",
        "\n",
        "# Toma las columnas del dataframe y las convierte a numeros\n",
        "df_columns = df.columns\n",
        "for i in df_columns:\n",
        "    df[i] = pd.to_numeric(df[i])\n",
        "\n",
        "x = np.asanyarray(df.drop(columns=['Class']))\n",
        "y = np.asanyarray(df[['Class']])\n",
        "print(x.shape, y.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T03:45:17.504620Z",
          "start_time": "2020-06-28T03:45:17.500607Z"
        }
      },
      "source": [
        "Se hace la separación de los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:33:32.272804Z",
          "start_time": "2020-06-28T21:33:32.263526Z"
        }
      },
      "outputs": [],
      "source": [
        "xtrain, xtest, ytrain, ytest = train_test_split(x,y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se crea el modelo y se entrena"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:33:36.323665Z",
          "start_time": "2020-06-28T21:33:35.503597Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train:  0.97265625\n",
            "Test:  0.9649122807017544\n",
            "Classification Train-Set Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.97      0.96       190\n",
            "           1       0.98      0.98      0.98       322\n",
            "\n",
            "    accuracy                           0.97       512\n",
            "   macro avg       0.97      0.97      0.97       512\n",
            "weighted avg       0.97      0.97      0.97       512\n",
            "\n",
            "Classification Test-Set Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.94      0.94        49\n",
            "           1       0.98      0.98      0.98       122\n",
            "\n",
            "    accuracy                           0.96       171\n",
            "   macro avg       0.96      0.96      0.96       171\n",
            "weighted avg       0.96      0.96      0.96       171\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = Pipeline([('scaler', StandardScaler()),\n",
        "                  ('classifier', Perceptron())])\n",
        "\n",
        "model.fit(xtrain, ytrain.ravel())\n",
        "\n",
        "print('Train: ', model.score(xtrain, ytrain))\n",
        "print('Test: ', model.score(xtest, ytest))\n",
        "\n",
        "plot_confusion_matrix(model, xtrain, ytrain)\n",
        "plt.title('Confusion matrix (train-set)')\n",
        "\n",
        "plot_confusion_matrix(model, xtest, ytest)\n",
        "plt.title('Confusion matrix (test-set)')\n",
        "\n",
        "print('Classification Train-Set Report: \\n', \n",
        "      classification_report(ytrain, model.predict(xtrain)))\n",
        "\n",
        "print('Classification Test-Set Report: \\n', \n",
        "      classification_report(ytest, model.predict(xtest)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:33:39.243570Z",
          "start_time": "2020-06-28T21:33:38.343728Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train:  0.97265625\n",
            "Test:  0.9707602339181286\n",
            "Classification Train-Set Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.96      0.96       190\n",
            "           1       0.98      0.98      0.98       322\n",
            "\n",
            "    accuracy                           0.97       512\n",
            "   macro avg       0.97      0.97      0.97       512\n",
            "weighted avg       0.97      0.97      0.97       512\n",
            "\n",
            "Classification Test-Set Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.94      0.95        49\n",
            "           1       0.98      0.98      0.98       122\n",
            "\n",
            "    accuracy                           0.97       171\n",
            "   macro avg       0.97      0.96      0.96       171\n",
            "weighted avg       0.97      0.97      0.97       171\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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yH5yZ5Vo4wZlZXnmQwcxyKaJx+uAaYyjEzOqIaG3rVdTSbUnSVyVNk/SkpEmS+knaRtIUSTMkXSepT7mROsGZWckiVNTSFUmbA18GxkbELmSTchwJfB84PyJGA4uB48uN0wnOzErSfi9qhWb0bQb6S2oGBgDzgH2B36b9VwGHlRurE5yZlSayfrhiFmBY+yMJ0nLi28VEvAj8L/ACWWJ7HXgIeC0iVqXDOn3UQbE8yGBmJSthFHVhRIztbIekoWQPotoGeA34DdlT+Doq+1EGTnBmVpJIgwwVsD/wXES8AiDpBuB9wBBJzakWt06POnAT1cxKVkITtSsvAHtJGiBJwH5kjxW9Ezg8HXMscFO5cTrBmVnJKjGKGhFTyAYTHgaeIMtHE4FvAadKmglsDFxebpxuoppZSbLaWWUu9I2IM4EzO2yeRfbg+HXmBGdmJWuUOxmc4MysZEX0r9UFJzgzK0kg2jzhpZnlVYNU4JzgzKxEFRxkqDYnODMrXYNU4daa4CQN7uqNEbGk8uGYWSPIQw1uGlmeLvwk7esBbFXFuMysTgXQ1tbgCS4itlyfgZhZgwigQWpwRY31SjpS0hnp9RaS9qhuWGZWzyp0L2rVdZvgJF0EfAj4VNq0HLikmkGZWZ2LIpcaK2YU9X0RsbukRwAiYtG6zJFuZo2u+xvp60UxCa5FUi9SPpa0MdBW1ajMrL7VQe2sGMUkuIuB64FNJJ0FfBw4q6pRmVn9CohGH0VtFxG/lPQQ2eybAEdExJPVDcvM6ltOElzSBLSQVUwb4y5bM6ueBmmiFjOK+m1gEjCSbH70X0s6vdqBmVkdy9Eo6ieBPSJiOYCkc8ge7fW9agZmZnWqgS70LSbBze5wXDPZlMJm1kPVw0W8xejqZvvzyXL1cmCapFvT+gHAvesnPDOrSzkYRW0fKZ0G/LFg+/3VC8fMGoEavQYXEWU/qsvMcqxOBhCK0W0fnKTtgHOAMUC/9u0RsUMV4zKzuqWGGWQo5pq2K4FfkF3Z92FgMnBtFWMys3rXIJeJFJPgBkTErQAR8WxEfIdsdhEz66nailxqrJjLRFZIEvCspJOAF4FNqxuWmdWtnF0H91VgIPBlsr64DYHPVjMoM6tvDT+K2i4ipqSXb7B60ksz68kaPcFJupEuPkZEfKwqEZlZjyFpCHAZsAtZvvksMB24DhgFPA98PCIWl1N+VzW4i8opcF30XrCMzX78j/V9WlsHt770aK1DsBKMO3B5RcqpYBP1AuDPEXF4mil8AHAGcEdEnCfpNOA04FvlFN7Vhb53lFOgmeVcUJFbtdKzl/cBjgOIiJXASkmHAuPTYVcBd1FmgvPcbmZWuuKvgxsmaWrBcmJBKdsCrwC/kPSIpMskbQAMj4h5AOnfsq/aKHbCSzOzt5XQRF0YEWPXsq8Z2B34UkRMkXQBWXO0YoquwUnqW8kTm1kDq8ydDHOBuQVXavyWLOHNlzQCIP27oNwwi5nRd5ykJ4AZaf09kn5S7gnNLAcqkOAi4mVgjqQd06b9gH8CNwPHpm3HAjeVG2YxTdQLgUOA36WgHpPkW7XMeihFRUdRvwRck0ZQZwGfIat4TZZ0PPACcES5hReT4HpFxOzsbq23tZZ7QjPLgQpNeBkRjwKd9dHtV4nyi0lwcySNA0JSE1nGfaYSJzezxpSbW7WAk8maqVsB84Hb0zYz66nykuAiYgFw5HqIxcwaQWX74KqqmBl9L6WTfB0RJ3ZyuJn1BHlJcGRN0nb9gH8D5lQnHDNrBKqDySyLUUwT9brCdUlXA7dVLSIzswop51atbYCtKx2ImTWQvDRRJS1m9cfpBSyiwveLmVkDycsgQ3oWw3vInsMA0BYRDfLRzKxqGiQLdHkvakpmN0ZEa1oa5GOZWVXl6LGBD0javeqRmFlDENkoajFLrXX1TIbmiFgF7A2cIOlZYBnZ54uIcNIz64ly0gf3ANncTIetp1jMrFHkIMEJsqfZr6dYzKxR5CDBbSLp1LXtjIgfVSEeM2sAeWiiNpE90b4yEz+ZWX7kIMHNi4iz11skZtYYoj5GSIvRbR+cmdk75KAGV5Epg80sfxq+Dy4iFq3PQMysgTR6gjMz61Sd3IZVDCc4MyuJyEET1cxsbZzgzCy/nODMLLec4Mwsl3Iym4iZWeec4MwsrxrlVq1iZvQ1M1uDorilqLKkJkmPSPpDWt9G0hRJMyRdJ6lPuXE6wZlZaYp9HkPxzdhTgKcK1r8PnB8Ro4HFwPHlhuoEZ2alq1CCk7QFcDBwWVoXsC/w23TIVazDrOLugzOzkpR4J8MwSVML1idGxMSC9R8D3wQGpfWNgdfS82AA5gKblxurE5yZlUxtRWe4hRExttMypEOABRHxkKTx7Zs7ObTsMVsnODMrTeVutn8/8FFJBwH9gMFkNbohBU/12wJ4qdwTuA/OzEpWiVHUiDg9IraIiFHAkcBfI+IY4E7g8HTYscBN5cbpBGdmpavuk+2/BZwqaSZZn9zl5RbkJqqZlazSt2pFxF3AXen1LGBcJcp1gjOz0vlWLTPLpZw8VcvM7B08o6+Z5Vs0RoZzgjOzkrkG18NtMnIl37jgBYZuuopog1t+tTG/u3yTWodlwA+/uiVTbh/MkGGrmHjndAAuPXsk9982mN59ghFbr+Br589h4IatAFz7k03586SNaeoVnPzfLzJ2/Bu1DL/2GuipWlW7Dk7SFZIWSHqyWueoZ62rxMSzR3LCB3filENG85HjFrLV6LdqHZYBB3xiEedcM2uNbbvv8wYT73yaS+6YzubbruDan2wKwOxn+nLXTUOZeOfTnPPrWVx0+ha0ttYi6vqituKWWqvmhb5XAhOqWH5dW7SgNzOfGADAm8uamDOzH8NGtNQ4KgP4l72WMWjomllqj/Fv0JTaMzvvsZyF83oDcN+tGzL+0MX06RtsttVKRo5awfRHBqzvkOtOj09wEXE3sKha5TeS4VusZLtd3uTph/2L0QhunbQRe+6bNUMXzuvNJiNX/2EaNqKFV1/uXavQ6kOQDTIUs9RYzfvgJJ0InAjQj/wlgH4DWvmPy57nkv8cyfKlTbUOx7rx6wuG09Qc7PuxxdmGzn5HO5vvoofxIEOR0txQEwEGa6MG+dqK09Qc/Mdlz/PXG4by9z8NqXU41o3bJg/lgdsHc951M1FKYsNGtvDKS6trbAvn9Wbj4e5q6PGDDBac+sM5zJnRjxsmevS03j145yAmXzyc7145i34DVv/27nXAEu66aSgrV4iXX+jDi8/1Zcfdltcw0tprv9C3Us9kqKaa1+Dy6l3jlrH/EYuZ9c9+/PS27FKEX3xvBA/+dXCNI7Pvnbw1j983kNcXNXPMHmP41Nde5tqLhtOyQpz+ie0B2GmPZZzy/bmM2vEt9vnIa5w4fieamoIvnjuXpp7e0xBRyoSXNVW1BCdpEjCebMriucCZEVH2tCeNZtoDAzlw5HtqHYZ14vSfzX7HtglHr3087OhT5nP0KfOrGVLjaYz8Vr0EFxFHVatsM6utemh+FsNNVDMrTQA9vYlqZjnWGPnNCc7MSucmqpnlVo8fRTWznGqg2USc4MysJNmFvo2R4ZzgzKx0dTBTSDGc4MysZK7BmVk+uQ/OzPLL96KaWZ65iWpmudRAD372fHBmVroKTFkuaUtJd0p6StI0Saek7RtJuk3SjPTv0HLDdIIzs9JFkUvXVgFfi4idgb2AL0gaA5wG3BERo4E70npZnODMrGRqaytq6UpEzIuIh9PrN4CngM2BQ4Gr0mFXAYeVG6f74MysNEHFL/SVNArYDZgCDI+IeZAlQUmblluuE5yZlUREKRf6DpM0tWB9YnrQ1OrypIHA9cBXImKJVLnHljnBmVnpik9wCyNi7Np2SupNltyuiYgb0ub5kkak2tsIYEG5YboPzsxKV5lRVAGXA09FxI8Kdt0MHJteHwvcVG6YrsGZWWkq1wf3fuBTwBOSHk3bzgDOAyZLOh54ATii3BM4wZlZybobIS1GRNxLNvtSZ/Zb5xPgBGdmJeu++VkvnODMrDSBE5yZ5ViD3IvqBGdmJfOEl2aWX05wZpZLEdDaGG1UJzgzK51rcGaWW05wZpZLAfiZDGaWTwHhPjgzy6PAgwxmlmPugzOz3HKCM7N88s32ZpZXAVRguqT1wQnOzErnGpyZ5ZNv1TKzvAoIXwdnZrnlOxnMLLfcB2dmuRThUVQzyzHX4Mwsn4Joba11EEVxgjOz0ni6JDPLNV8mYmZ5FEC4BmdmuRSe8NLMcqxRBhkUdTTcK+kVYHat46iCYcDCWgdhJcnrz2zriNhkXQqQ9Gey76cYCyNiwrqcb13UVYLLK0lTI2JsreOw4vlnlg+9ah2AmVm1OMGZWW45wa0fE2sdgJXMP7MccB+cmeWWa3BmlltOcGaWW05wVSRpgqTpkmZKOq3W8Vj3JF0haYGkJ2sdi607J7gqkdQEXAx8GBgDHCVpTG2jsiJcCdTswlSrLCe46hkHzIyIWRGxErgWOLTGMVk3IuJuYFGt47DKcIKrns2BOQXrc9M2M1tPnOCqR51s8zU5ZuuRE1z1zAW2LFjfAnipRrGY9UhOcNXzIDBa0jaS+gBHAjfXOCazHsUJrkoiYhXwReBW4ClgckRMq21U1h1Jk4D7gB0lzZV0fK1jsvL5Vi0zyy3X4Mwst5zgzCy3nODMLLec4Mwst5zgzCy3nOAaiKRWSY9KelLSbyQNWIeyxkv6Q3r90a5mO5E0RNK/l3GO70r6erHbOxxzpaTDSzjXKM8AYh05wTWWNyNi14jYBVgJnFS4U5mSf6YRcXNEnNfFIUOAkhOcWa05wTWue4DtU83lKUk/BR4GtpR0gKT7JD2canoD4e356Z6WdC/wsfaCJB0n6aL0erikGyU9lpb3AecB26Xa4w/Scd+Q9KCkxyWdVVDWt9MceLcDO3b3ISSdkMp5TNL1HWql+0u6R9Izkg5JxzdJ+kHBuT+/rl+k5ZcTXAOS1Ew2z9wTadOOwC8jYjdgGfAdYP+I2B2YCpwqqR9wKfAR4APAZmsp/kLgbxHxHmB3YBpwGvBsqj1+Q9IBwGiyKaF2BfaQtI+kPchuSduNLIHuWcTHuSEi9kznewoovHNgFPBB4GDgkvQZjgdej4g9U/knSNqmiPNYD9Rc6wCsJP0lPZpe3wNcDowEZkfE/Wn7XmQTbP5dEkAfsluPdgKei4gZAJJ+BZzYyTn2BT4NEBGtwOuShnY45oC0PJLWB5IlvEHAjRGxPJ2jmHtvd5H032TN4IFkt7a1mxwRbcAMSbPSZzgAeHdB/9yG6dzPFHEu62Gc4BrLmxGxa+GGlMSWFW4CbouIozoctyuVm65JwPci4ucdzvGVMs5xJXBYRDwm6ThgfMG+jmVFOveXIqIwESJpVInntR7ATdT8uR94v6TtASQNkLQD8DSwjaTt0nFHreX9dwAnp/c2SRoMvEFWO2t3K/DZgr69zSVtCtwN/Juk/pIGkTWHuzMImCepN3BMh31HSOqVYt4WmJ7OfXI6Hkk7SNqgiPNYD+QaXM5ExCupJjRJUt+0+TsR8YykE4E/SloI3Avs0kkRpwAT0ywarcDJEXGfpL+nyzD+lPrhdgbuSzXIpcAnI+JhSdcBjwKzyZrR3fkPYEo6/gnWTKTTgb8Bw4GTIuItSZeR9c09rOzkrwCHFfftWE/j2UTMLLfcRDWz3HKCM7PccoIzs9xygjOz3HKCM7PccoIzs9xygjOz3Po/3Pp739erHYQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = Pipeline([('scaler', StandardScaler()),\n",
        "                  ('classifier', LogisticRegression())])\n",
        "\n",
        "model.fit(xtrain, ytrain.ravel())\n",
        "\n",
        "print('Train: ', model.score(xtrain, ytrain))\n",
        "print('Test: ', model.score(xtest, ytest))\n",
        "\n",
        "plot_confusion_matrix(model, xtrain, ytrain)\n",
        "plt.title('Confusion matrix (train-set)')\n",
        "\n",
        "plot_confusion_matrix(model, xtest, ytest)\n",
        "plt.title('Confusion matrix (test-set)')\n",
        "\n",
        "plot_roc_curve(model,xtrain, ytrain)\n",
        "plt.title('ROC curve (train-set)')\n",
        "\n",
        "plot_roc_curve(model,xtest, ytest)\n",
        "plt.title('ROC curve (test-set)')\n",
        "\n",
        "\n",
        "print('Classification Train-Set Report: \\n', \n",
        "      classification_report(ytrain, model.predict(xtrain)))\n",
        "\n",
        "print('Classification Test-Set Report: \\n', \n",
        "      classification_report(ytest, model.predict(xtest)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Clasificador de diabetes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se leen los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:35:44.652906Z",
          "start_time": "2020-06-28T21:35:43.524626Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(768, 8) (768, 1)\n"
          ]
        }
      ],
      "source": [
        "url = 'https://raw.githubusercontent.com/Dr-Carlos-Villasenor/ReconocimientoPatrones/master/Datasets/diabetes.csv'\n",
        "df = pd.read_csv(url,parse_dates=[0])\n",
        "\n",
        "# Toma las columnas del dataframe y las convierte a numeros\n",
        "df_columns = df.columns\n",
        "for i in df_columns:\n",
        "    df[i] = pd.to_numeric(df[i])\n",
        "\n",
        "x = np.asanyarray(df.drop(columns=['Outcome']))\n",
        "y = np.asanyarray(df[['Outcome']])\n",
        "print(x.shape, y.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se hace la separación de datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:36:06.692146Z",
          "start_time": "2020-06-28T21:36:06.683138Z"
        }
      },
      "outputs": [],
      "source": [
        "xtrain, xtest, ytrain, ytest = train_test_split(x,y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Se crea el modelo y se entrena"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:36:35.369985Z",
          "start_time": "2020-06-28T21:36:35.012566Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train:  0.640625\n",
            "Test:  0.6510416666666666\n",
            "Classification Train-Set Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.65      0.70       379\n",
            "           1       0.48      0.62      0.54       197\n",
            "\n",
            "    accuracy                           0.64       576\n",
            "   macro avg       0.62      0.64      0.62       576\n",
            "weighted avg       0.67      0.64      0.65       576\n",
            "\n",
            "Classification Test-Set Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.62      0.69       121\n",
            "           1       0.52      0.70      0.60        71\n",
            "\n",
            "    accuracy                           0.65       192\n",
            "   macro avg       0.65      0.66      0.65       192\n",
            "weighted avg       0.68      0.65      0.66       192\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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VqWy/jqR7JU1Lf66d2iXpklTZ/ilJIzuKtZbn4B4FhkvaDJhJVpH6yzXcX11MmbA69/1+HTb7+GJO3mdLAI4/83VG7T2/3c88dPdAdthjPqv1b15VYVqJ8w79CzsMe52B/Zdw52nXceX9O7Lb8FfZdN13aQ4xa96aXPDn3QE4cc+JDOi/hDM++yAATc09OObKL9Yz/C6hSrOoLZXtJ0laE5go6V7gOOC+iLhQ0neB75IVoTmArNDMcGBn4PL0Z7v0YUWu6pN0IPBTsstEfp2ep96uHT+xWkwYN7TcJtbF7Hj2yfUOwXJ47taLWfTWays1dlx7qyGx168PrWjbW3e7fGJHdVFbSPoTcGlaRkfELEkbAPdHxJaSfple35C2n9qyXXt91vQ6uIi4izIFJMysMeWYQOhMZfv1WpJWSnJD0mbtTVzWJ8GZWfHkvJMhd2V7tXfhqCvbm9mqUOPK9m9K2qBkiDo7teeeuGyM+y3MrMuo1nVw7VW2J6tgf2x6fSzwp5L2Y9Js6i7AvHLn38BHcGbWCVW6xq29yvYXAjdLOgF4FTgsrbsLOBB4AVgEHN/RDpzgzCyXCPigCg+8LFPZHmDvNrYP4NQ8+3CCM7PcusJtWJVwgjOzXFx0xswKLZzgzKyousKN9JVwgjOzXCJ8Ds7MCks0uWygmRWVz8GZWSG5qpaZFVdk5+EagROcmeXmWVQzK6TwJIOZFZmHqGZWWJ5FNbNCinCCM7MC82UiZlZYjXIOrjGmQsysywhEc3OPipaOSPq1pNmSni5p217SeElPSHpM0qjUnrvwsxOcmeUWFS4VuAbYv1XbfwHnRsT2wA/Se1ix8PNJZIWfy3KCM7N80iRDJUuHXUU8AMz96B5YK70ewIeVs8YA10ZmPDAwVd1ql8/BmVl+lZ+Dq6jwcyvfAsZJ+m+yg7BdU3v1Cj9LWqu9dQAR8V4HQZpZQeW4TKTDws9tOBn4t4j4g6TDyUoL7kOVCz9PSR8u7bTlfQCb5InYzIohgObmml4mcizwzfT6FuBX6XXuws/tJriIGNreOjPrxgKo7XVwrwN7AvcDewHTUvvtwDck3QjsTLUKP0s6AvhYRJwvaWNgvYiY2MngzazBVes6OEk3AKPJztXNAM4GTgR+JqkXsIRsxhRqUfhZ0qVAb2AP4PzU8RXATjm/i5kVRZUSXEQc2c6qHdrYtiaFn3eNiJGSHk87mSupT56dmFmRVHYJSFdQSYJbJqkHKWdLWhdormlUZta1NcitWpUkuMuAPwCDJZ0LHA6cW9OozKzrCojazqJWTYcJLiKulTSR7DoUgMMi4ulynzGzoitIgkt6AsvIDkx9e5dZd9cgQ9QOk5Wks4AbgA3JLqz7H0ln1jowM+vCqni3fS1VcgT3FWCHiFgEIOk8YCJwQS0DM7MuqvYX+lZNJQlueqvtegEv1SYcM2sEjfLAy3I3219MlqsXAVMkjUvv9wX+sWrCM7MuqQCzqC0zpVOAO0vax9cuHDNrBGr0I7iIuHpVBmJmDaKLTCBUopJ7UTcHzgO2BlZraY+IETWMy8y6LDXMJEMl17RdA/yG7Mq+A4CbgRtrGJOZdXUNcplIJQmuf0SMA4iIFyPi+8CnaxuWmXVpzRUudVbJZSJLJQl4UdJYYCYwpLZhmVmXVbDr4P4NWAP4V7JzcQOAr9YyKDPr2hplFrXDIWpEPBIR8yPi1Yg4OiIOjoiHVkVwZtZFVekcXFuFn1P7v0iaKmmKpP8qaT8zFX6eKmm/jvovd6HvbeVCjIgvdBy+mVlZ1wCXAte2NEj6NFkN1O0iYqmkIal9a+AIYBuye+P/ImlERDS113m5IeqlKx97Ps8/1Z/9Ntx+Ve/WVkLfwxtkrGIA9Gg3FeRTrSFqRDwgaVir5pOBCyNiadpmdmofA9yY2l+W9AIwCni4vf7LXeh730rEbWZFFeS5VaszhZ9HALunB3ssAU6PiEfJijyX3knVUvi5Xa5sb2b5VX4E15nCz72AtYFdyIpb3SzpY1S58LOZWZtqPIs6A7g1VdGaIKkZGEQnCj9X/HReSX07EaiZFVFt72T4I1nBZySNAPoAc8gKPx8hqa+kzYDhwIRyHVXyRN9RkiaTqktL+oSkn3c6dDNrfNW7TOQGskmCLSXNkHQC8GvgY+nSkRuBYyMzhexW0WeA/wVOLTeDCpUNUS8BDiLLqkTEk2ka18y6IUVVZ1HbK/z8lXa2P4/shoOKVJLgekTE9OxureWqNNlsZg2pAA+8bPGapFFASOoJ/AvwfG3DMrOurFFu1aokwZ1MNkzdBHgT+EtqM7PuqigJLl1FfMQqiMXMGkEVz8HVWiVP9L2KNvJ1RJxUk4jMrOsrSoIjG5K2WA34PPBabcIxs0agLvAwy0pUMkS9qfS9pOuAe2sWkZlZlXTmVq3NgE2rHYiZNZCiDFElvcOHX6cHMBf4bi2DMrMurCiTDKkWwyfI6jAANKcbYM2sO2uQLFD2XtSUzG6LiKa0NMjXMrOaKlDZwAmSRtY8EjNrCCKbRa1kqbdyNRl6RcQHwKeAEyW9CCwk+34REU56Zt1RQc7BTQBGAoesoljMrFEUIMEJsmr2qygWM2sUBUhwgyWd1t7KiPhJDeIxswbQKEPUcpMMPckq2q/ZzmJm3VWNCz+ndadLCkmD0ntJuiQVfn6qksnPckdwsyLihx2HaGbdSlR1hvQaWhV+BpA0FPgM8GpJ8wFkdRiGAzsDl6c/21XuCK4xHtlpZqtelY7gIuIBsrujWrsY+E6rXsYA16b6DOOBgZI2KNd/uQS3d8fhmVl31FKXoaOlU31LBwMzI+LJVqs2YsUnGXW+8HNEtJVVzczyzKLmqmwvqT9wFrBvW6vzRuLCz2aWT77bsPJWtt+c7IlFT6ZCVxsDk1JdmNoVfjYzg3SrVo2GqBExOSKGRMSwiBhGltRGRsQbZIWfj0mzqbsA8yJiVrn+nODMLLdqJbh2Cj+35y7gJeAF4CrglI769xDVzPKrfeHnlvXDSl4HcGqe/p3gzCy/BrmTwQnOzPIpyNNEzMza5gRnZkXVFR5mWQknODPLzUNUMyumLlJvoRJOcGaWnxOcmRVRy50MjcAJzsxyU3NjZDgnODPLx+fgzKzIPEQ1s+JygjOzovIRnJkVlxOcmRVSdatq1ZQTnJnl4uvgzKzYojEynB9Zbma5VfGR5R+pbC/pIknPper1t0kaWLLuzFTZfqqk/Trq30dwVbTx5kv43hXTl79ff5P3ue6i9Vl9QBMHfPlt5s3Nfty/uWADHv3rWvUKs9s788j72W3r6byzoB9H//hwAE49+GF22+ZVljX1YOactTj/htEsWNyXnUbMYOznHqF3z2aWNfXgstt3YdK0sqU4i6+6F/pew0cr298LnBkRH0j6MXAmcIakrYEjgG2ADYG/SBoREU3tdV6zBCfp18BBwOyI2LZW++lKZry4Gqd8ZksAevQIrp/0DA/dPYB9j5jLbVcN5vdXDKlzhAZw1yMj+MOD2/AfR/1tedujUzfmijt2pqm5Byd/bjxH7/M4l/95F95duBpnXLU/c95bnc3Wn8vFY+/kkHOOrmP0XUO1Jhki4gFJw1q13VPydjxwaHo9BrgxIpYCL0t6ARhFVrSmTbUcol4D7F/D/ru07XdfwKzpfZg9s0+9Q7FWnnxpQ95btNoKbROmDqWpOfvvMOWV9RgyYCEA02YOYs57qwPw8htr06d3E717tnvA0G2oubKFVPi5ZDkp566+CtydXlevsv3Kaiszdyejx7zD/X9ce/n7zx0/h70PfYdpT/XjynM3ZME8nx3oqj6783Pc9/jmH2kf/YmXeX7GIJY19axDVF1IkGeSIW/h5+UknQV8AFzf0tRONO2q+ySDpJNasvsyltY7nKro1buZXfZ9jwf+PACAO367Lsf/88c55TMjmPtmb046u2wxbqujYz4ziabmHtwzcfgK7ZutP5dTPvcIF928e50i61pqVfh5ef/SsWSnuI5K5QKhESvbR8SVEbFjROzYm771DqcqdtprPi9M7se7c3oD8O6c3jQ3iwhx9/XrsuX2i+scobXlgJ2msts20zn3ur0oPVgYPGAB53/1Hn50/aeZ+faA+gXYlUSFSydI2h84Azg4IhaVrLodOEJSX0mbAcOBCeX68jipBkYf8u4Kw9N1hixj7uws2e16wDxembpaex+1Otl5q1c5au8n+MbPD2bpst7L29fot5SLTrqbX94xiskvr1/HCLuOal7omyrbjyY7VzcDOJts1rQvcK8kgPERMTYipki6GXiGbOh6arkZVHCCq7q+/ZoZuft8fvadjZe3nfD9WWy+zWIi4M0ZfbikZJ2teucc8xc+ufksBq6xhNvO+R1X370jR+/zOL17NfHTU+4EYMorQ7jolj344qemsPGg9zhuv0kct98kAL51+Wd5d0G/en6F+oqo2gMv26lsf3WZ7c8Dzqu0f0WNrkguzczAm8DZEdFu4ABraZ3YWXvXJB6rjQWH71LvECyHyff8lAVzX2vrZH3F1hy4cXxyj29WtO2Df/7OxM5OMlRDLWdR28rMZlYAvhfVzIopANdkMLPCaoz85gRnZvl5iGpmheWygWZWTC4baGZFlV3o2xgZzgnOzPJzTQYzKyofwZlZMfkcnJkVV/XuRa01Jzgzy89DVDMrJBd+NrNC8xGcmRVWY+S3+j+y3Mwaj5qbK1o67Kftws/rSLpX0rT059qpXZIuSYWfn5I0sqP+neDMLJ8gu9C3kqVj1/DR8qLfBe6LiOHAfek9wAFkdRiGAycBl3fUuROcmeUiAkVlS0ci4gFgbqvmMcBv0+vfAoeUtF8bmfHAQEkblOvfCc7M8ouobOlc4ef1ImJWtpuYBQxJ7V2n8LOZFdgqKPzchsYr/GxmDaa65+Da8mbL0DP9OTu1N17hZzNrPNWaRW3H7cCx6fWxwJ9K2o9Js6m7APNahrLt8RDVzHKKql3o207h5wuBmyWdALwKHJY2vws4EHgBWAQc31H/TnBmlk9QtQRXprzoRwokR1bE+dQ8/TvBmVl+vhfVzIrKD7w0s+JygjOzQoqApsYYozrBmVl+PoIzs8JygjOzQgrANRnMrJgCwufgzKyIAk8ymFmB+RycmRWWE5yZFVP1bravNSc4M8sngM4/CmmVcoIzs/x8BGdmxeRbtcysqAKiQa6D8yPLzSy/5qhs6YCkf5M0RdLTkm6QtJqkzSQ9kgo/3ySpT2fDdIIzs/wqLxvYLkkbAf8K7BgR2wI9gSOAHwMXp8LP7wAndDZMJzgzyycim0WtZOlYL6CfpF5Af2AWsBfw+7S+tPBzbk5wZpZfFQo/R8RM4L/JCsvMAuYBE4F3I+KDtFmHxZ3L8SSDmeUURFNTpRu3W/hZ0trAGGAz4F3gFuCANnfYSU5wZpZP9R6XtA/wckS8BSDpVmBXYKCkXukorsPizuV4iGpm+UVzZUt5rwK7SOovSWSlAp8B/gYcmrYpLfycmxOcmeUSQDRHRUvZfiIeIZtMmARMJstHVwJnAKdJegFYF7i6s7F6iGpm+UT1HngZEWeTVbMv9RIwqhr9O8GZWW45JhnqStGFbpqV9BYwvd5x1MAgYE69g7Bcivp3tmlEDF6ZDiT9L9nPpxJzImL/ldnfyuhSCa6oJD3W3lS5dU3+OysGTzKYWWE5wZlZYTnBrRpX1jsAy81/ZwXgc3BmVlg+gjOzwnKCM7PCcoKrIUn7S5oq6QVJ3613PNYxSb+WNFvS0/WOxVaeE1yNSOoJXEb2+JetgSMlbV3fqKwC1wB1uzDVqssJrnZGAS9ExEsR8T5wI9mzr6wLi4gHgLn1jsOqwwmudjYCXit5v1JPJjWz/JzgakdttPmaHLNVyAmudmYAQ0ver9STSc0sPye42nkUGJ5qPPYhK4d2e51jMutWnOBqJD1P/hvAOOBZ4OaImFLfqKwjkm4AHga2lDRDUqdrclr9+VYtMyssH8GZWWE5wZlZYTnBmVlhOcGZWWE5wZlZYTnBNRBJTZKekPS0pFsk9V+JvkZLuiO9Prjc004kDZR0Sif2cY6k0yttb7XNNZIOLbdNq+2H+Qkg1poTXGNZHBHbR8S2wPvA2NKVyuT+O42I2yPiwqul1CcAAAK/SURBVDKbDARyJzizenOCa1wPAlukI5dnJf0CmAQMlbSvpIclTUpHemvA8ufTPSfpH8AXWjqSdJykS9Pr9STdJunJtOwKXAhsno4eL0rb/bukRyU9Jenckr7OSs/A+wuwZUdfQtKJqZ8nJf2h1VHpPpIelPS8pIPS9j0lXVSy76+v7A/SissJrgFJ6kX2nLnJqWlL4NqI+CSwEPg+sE9EjAQeA06TtBpwFfA5YHdg/Xa6vwT4e0R8AhgJTAG+C7yYjh7/XdK+wHCyR0JtD+wgaQ9JO5DdkvZJsgS6UwVf59aI2Cnt71mg9M6BYcCewGeBK9J3OAGYFxE7pf5PlLRZBfuxbqhXvQOwXPpJeiK9fhC4GtgQmB4R41P7LmQP2HxIEkAfsluPtgJejohpAJJ+B5zUxj72Ao4BiIgmYJ6ktVtts29aHk/v1yBLeGsCt0XEorSPSu693VbSf5INg9cgu7Wtxc0R0QxMk/RS+g77AtuVnJ8bkPb9fAX7sm7GCa6xLI6I7UsbUhJbWNoE3BsRR7babnuq97gmARdExC9b7eNbndjHNcAhEfGkpOOA0SXrWvcVad//EhGliRBJw3Lu17oBD1GLZzywm6QtACT1lzQCeA7YTNLmabsj2/n8fcDJ6bM9Ja0FzCc7OmsxDvhqybm9jSQNAR4APi+pn6Q1yYbDHVkTmCWpN3BUq3WHSeqRYv4YMDXt++S0PZJGSFq9gv1YN+QjuIKJiLfSkdANkvqm5u9HxPOSTgLulDQH+AewbRtdfBO4Mj1Fowk4OSIelvRQugzj7nQe7uPAw+kIcgHwlYiYJOkm4AlgOtkwuiP/ATyStp/Miol0KvB3YD1gbEQskfQrsnNzk5Tt/C3gkMp+Otbd+GkiZlZYHqKaWWE5wZlZYTnBmVlhOcGZWWE5wZlZYTnBmVlhOcGZWWH9PzEntaD5x1BfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = Pipeline([('scaler', StandardScaler()),\n",
        "                  ('classifier', Perceptron())])\n",
        "\n",
        "model.fit(xtrain, ytrain.ravel())\n",
        "\n",
        "print('Train: ', model.score(xtrain, ytrain))\n",
        "print('Test: ', model.score(xtest, ytest))\n",
        "\n",
        "plot_confusion_matrix(model, xtrain, ytrain)\n",
        "plt.title('Confusion matrix (train-set)')\n",
        "\n",
        "plot_confusion_matrix(model, xtest, ytest)\n",
        "plt.title('Confusion matrix (test-set)')\n",
        "\n",
        "print('Classification Train-Set Report: \\n', \n",
        "      classification_report(ytrain, model.predict(xtrain)))\n",
        "\n",
        "print('Classification Test-Set Report: \\n', \n",
        "      classification_report(ytest, model.predict(xtest)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-06-28T21:37:12.072473Z",
          "start_time": "2020-06-28T21:37:11.243020Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train:  0.7899305555555556\n",
            "Test:  0.7083333333333334\n",
            "Classification Train-Set Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.89      0.85       379\n",
            "           1       0.74      0.59      0.66       197\n",
            "\n",
            "    accuracy                           0.79       576\n",
            "   macro avg       0.78      0.74      0.75       576\n",
            "weighted avg       0.79      0.79      0.78       576\n",
            "\n",
            "Classification Test-Set Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.80      0.78       121\n",
            "           1       0.62      0.55      0.58        71\n",
            "\n",
            "    accuracy                           0.71       192\n",
            "   macro avg       0.69      0.68      0.68       192\n",
            "weighted avg       0.70      0.71      0.70       192\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = Pipeline([('scaler', StandardScaler()),\n",
        "                  ('classifier', LogisticRegression())])\n",
        "\n",
        "model.fit(xtrain, ytrain.ravel())\n",
        "\n",
        "print('Train: ', model.score(xtrain, ytrain))\n",
        "print('Test: ', model.score(xtest, ytest))\n",
        "\n",
        "plot_confusion_matrix(model, xtrain, ytrain)\n",
        "plt.title('Confusion matrix (train-set)')\n",
        "\n",
        "plot_confusion_matrix(model, xtest, ytest)\n",
        "plt.title('Confusion matrix (test-set)')\n",
        "\n",
        "plot_roc_curve(model,xtrain, ytrain)\n",
        "plt.title('ROC curve (train-set)')\n",
        "\n",
        "plot_roc_curve(model,xtest, ytest)\n",
        "plt.title('ROC curve (test-set)')\n",
        "\n",
        "\n",
        "print('Classification Train-Set Report: \\n', \n",
        "      classification_report(ytrain, model.predict(xtrain)))\n",
        "\n",
        "print('Classification Test-Set Report: \\n', \n",
        "      classification_report(ytest, model.predict(xtest)))"
      ]
    },
    {
      "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": "3.7.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}