Step_3_Results_Analysis_Classification.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Step 3 - Results Analysis - Classification",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "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"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MateusGheorghe/LAMP/blob/master/Step_3_Results_Analysis_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UkXy5yk1KorI"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNdTxcu-NMwB"
      },
      "source": [
        "AIRIS=1 # Using G-drive cloud AIRIS= 0 / Using AIRIS , AIRIS =1\n",
        "GEN_SHEET=1 # Generate spreadsheet GEN_SHEET=1 / or not GEN_SHEET=0"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fyAGSC7ANO7J"
      },
      "source": [
        "if AIRIS==0:\n",
        "  from google.colab import drive\n",
        "  drive.mount(\"/gdrive\")\n",
        "  import os\n",
        "  os.chdir(\"/gdrive/My Drive/MESTRADO/ProjetoRepsol/PZFLEXData/AIRIS\")\n",
        "  # os.chdir(\"/gdrive/My Drive/MESTRADO/ProjetoRepsol/COMSOLData\")"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NqrRNF_5GrHW"
      },
      "source": [
        "# TensorFlow e tf.keras\n",
        "import tensorflow as tf"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3ZY_4RZGrLl"
      },
      "source": [
        "# Librariesauxiliares\n",
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "import pandas as pd\n",
        "import time\n",
        "sns.set()\n",
        "import plotly.express as px\n",
        "from sklearn import preprocessing\n",
        "#Model Selection\n",
        "from sklearn.model_selection import StratifiedKFold, RepeatedStratifiedKFold, cross_validate, cross_val_score, RandomizedSearchCV, GridSearchCV\n",
        "from sklearn import metrics\n",
        "import pickle"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQx7Da05qsBc"
      },
      "source": [
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 10, 10\n",
        "import matplotlib.pyplot as plt\n",
        "SMALL_SIZE = 22\n",
        "MEDIUM_SIZE = 42\n",
        "BIGGER_SIZE = 62\n",
        "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=SMALL_SIZE)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
        "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Afdn1kRmGrO3",
        "outputId": "1aebad25-066b-4ef9-a41f-d69a6a5c2026",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print('The tensorflow version is {}.'.format(tf.__version__))\n",
        "print('The scikit-learn version is {}.'.format(sklearn.__version__))"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The tensorflow version is 2.1.0.\n",
            "The scikit-learn version is 0.22.1.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zd8SGmR7vSal"
      },
      "source": [
        "def tic():\n",
        "    #Homemade version of matlab tic and toc functions\n",
        "    import time\n",
        "    global startTime_for_tictoc\n",
        "    startTime_for_tictoc = time.time()\n",
        "\n",
        "def toc():\n",
        "    import time\n",
        "    if 'startTime_for_tictoc' in globals():\n",
        "        print ('\\nElapsed time is ')\n",
        "        print (str(time.time() - startTime_for_tictoc))\n",
        "        print('seconds.\\n')\n",
        "    else:\n",
        "        print ('\\nToc: start time not set\\n')"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqPCCX8XvSao"
      },
      "source": [
        "def TIC():\n",
        "    #Homemade version of matlab tic and toc functions\n",
        "    import time\n",
        "    global startTime_for_tictoc1\n",
        "    startTime_for_tictoc1 = time.time()\n",
        "\n",
        "def TOC():\n",
        "    import time\n",
        "    if 'startTime_for_tictoc' in globals():\n",
        "        print ('\\nElapsed time is ')\n",
        "        print (str(time.time() - startTime_for_tictoc1))\n",
        "        print('seconds.\\n')\n",
        "    else:\n",
        "        print ('\\nToc: start time not set\\n')"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQYrEhpq1xpF"
      },
      "source": [
        "from numpy.random import seed\n",
        "seed(1)\n",
        "tf.random.set_seed(2)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glZ0sdLw2kia",
        "outputId": "a294f088-b10f-421e-9737-60ca73df695f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Batches_plus_multi-defect\t       PCs_446samples.mat\r\n",
            "Batches_PZTs\t\t\t       __pycache__\r\n",
            "Datasets\t\t\t       PZFlexData\r\n",
            "DimensionalityReduction-Copy1.py       Relatorio_V1_Results\r\n",
            "draw_neural_net.py\t\t       repo\r\n",
            "ExcelFileResults\t\t       Results_Step2\r\n",
            "Features_Manifold_Learning\t       Severity_446samples.mat\r\n",
            "Features_TSFEL\t\t\t       Step2-12-may-2020\r\n",
            "GridResult_500p_Classification.pickle  Step2-22-may-2020\r\n",
            "GridResult_500p_Regression.pickle      Step2_image\r\n",
            "Image_Approach\t\t\t       teste-16-apr-2020\r\n",
            "Old\t\t\t\t       teste-26-apr-2020\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FbMRH7317ZF"
      },
      "source": [
        "# Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKZojliXvSaq"
      },
      "source": [
        "TIC()"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBGMKsz1y79X"
      },
      "source": [
        "# with open('Step2-22-may-2020/CLASSIFICATION-Dataset_r0_Batch1-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_05_27-14_35_20.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename)\n",
        "# with open('Step2-22-may-2020/CLASSIFICATION-Dataset_r1_Batch1-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_05_30-09_29_01.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename)\n",
        "# with open('Step2-22-may-2020/CLASSIFICATION-Dataset_r0_r1_Batch1-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_05_27-14_04_42.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename)\n",
        "# with open('Step2-22-may-2020/CLASSIFICATION-Dataset_r1_Batch2and4-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_06_14-11_20_41.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename) # R1 - Batches 2 e 4\n",
        "with open('Step2-22-may-2020/CLASSIFICATION-Dataset_r0_Batch2and4-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_06_07-16_40_37.pickle', 'rb') as filename:\n",
        "   TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename) # R0 - Batches 2 e 4\n",
        "# with open('Step2-22-may-2020/CLASSIFICATION-J10-Dataset_r0_r1_Batch2and4-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_06_16-13_21_36.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename)\n",
        "# with open('Step2-22-may-2020/CLASSIFICATION-J7-Dataset_r0_r1_Batch1-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_06_12-10_12_20.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename)\n",
        "#Temp R0\n",
        "# with open('Features_TSFEL/CLASSIFICATION-Dataset_Temp_r0_Batch_1-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_10_05-14_26_02.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename)\n",
        "# #Temp R1\n",
        "# with open('Features_TSFEL/CLASSIFICATION-Dataset_Temp_r0_Batch_1-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_10_05-14_26_02.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename)   \n",
        "# #Stat R0\n",
        "# with open('Features_TSFEL/CLASSIFICATION-Dataset_Temp_r0_Batch_1-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_10_05-14_26_02.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename)   \n",
        "# #Stat R1\n",
        "# with open('Features_TSFEL/CLASSIFICATION-Dataset_Temp_r0_Batch_1-Models_6_Kfold_10_Combinations_100_Repetitions_10_date_2020_10_05-14_26_02.pickle', 'rb') as filename:\n",
        "#    TIME,Kf,Exec,Rep,DF,BestCases,BestScores,AllResults = pickle.load(filename)   \n"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICgPo2ND2u1S",
        "outputId": "42d81e31-9eb9-4d0e-a929-24d14103c285",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        }
      },
      "source": [
        "DF"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    estimator  mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "0         SVM       0.193155      0.033080         0.003872        0.000611   \n",
              "1         SVM       0.202741      0.035010         0.003843        0.000253   \n",
              "2         SVM       0.852418      0.130363         0.003497        0.000327   \n",
              "3         SVM       0.202607      0.028929         0.003157        0.000053   \n",
              "4         SVM       0.096149      0.006363         0.004340        0.000572   \n",
              "..        ...            ...           ...              ...             ...   \n",
              "595       SVM       0.326057      0.005564         0.010488        0.000365   \n",
              "596       SVM       0.322349      0.002719         0.010366        0.000202   \n",
              "597       SVM       0.320553      0.002669         0.010593        0.000183   \n",
              "598       SVM       0.156886      0.003533         0.006814        0.000185   \n",
              "599       SVM       0.363132      0.011483         0.012561        0.000886   \n",
              "\n",
              "                                                params  mean_test_Accuracy  \\\n",
              "0    {'kernel': 'rbf', 'gamma': 0.1, 'degree': 3, '...            0.990341   \n",
              "1    {'kernel': 'rbf', 'gamma': 0.1, 'degree': 3, '...            0.990341   \n",
              "2    {'kernel': 'poly', 'gamma': 10.0, 'degree': 2,...            0.989003   \n",
              "3    {'kernel': 'poly', 'gamma': 1.0, 'degree': 4, ...            0.988852   \n",
              "4    {'kernel': 'rbf', 'gamma': 0.1, 'degree': 3, '...            0.988336   \n",
              "..                                                 ...                 ...   \n",
              "595  {'kernel': 'sigmoid', 'gamma': 1e-11, 'degree'...            0.199345   \n",
              "596  {'kernel': 'sigmoid', 'gamma': 1e-05, 'degree'...            0.199345   \n",
              "597  {'kernel': 'sigmoid', 'gamma': 1e-14, 'degree'...            0.199345   \n",
              "598  {'kernel': 'poly', 'gamma': 1e-07, 'degree': 3...            0.197255   \n",
              "599  {'kernel': 'rbf', 'gamma': 1e-15, 'degree': 4,...            0.193076   \n",
              "\n",
              "     std_test_Accuracy  mean_test_AUC  std_test_AUC  \n",
              "0             0.008305       0.999045      0.001279  \n",
              "1             0.008305       0.999045      0.001279  \n",
              "2             0.009251       0.999382      0.000908  \n",
              "3             0.007758       0.998437      0.002178  \n",
              "4             0.008993       0.999216      0.001233  \n",
              "..                 ...            ...           ...  \n",
              "595           0.065298       0.100697      0.010501  \n",
              "596           0.065298       0.115433      0.014323  \n",
              "597           0.065298       0.100696      0.010503  \n",
              "598           0.057931       0.500000      0.000000  \n",
              "599           0.049934       0.168447      0.035219  \n",
              "\n",
              "[600 rows x 10 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>estimator</th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>params</th>\n",
              "      <th>mean_test_Accuracy</th>\n",
              "      <th>std_test_Accuracy</th>\n",
              "      <th>mean_test_AUC</th>\n",
              "      <th>std_test_AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.193155</td>\n",
              "      <td>0.033080</td>\n",
              "      <td>0.003872</td>\n",
              "      <td>0.000611</td>\n",
              "      <td>{'kernel': 'rbf', 'gamma': 0.1, 'degree': 3, '...</td>\n",
              "      <td>0.990341</td>\n",
              "      <td>0.008305</td>\n",
              "      <td>0.999045</td>\n",
              "      <td>0.001279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.202741</td>\n",
              "      <td>0.035010</td>\n",
              "      <td>0.003843</td>\n",
              "      <td>0.000253</td>\n",
              "      <td>{'kernel': 'rbf', 'gamma': 0.1, 'degree': 3, '...</td>\n",
              "      <td>0.990341</td>\n",
              "      <td>0.008305</td>\n",
              "      <td>0.999045</td>\n",
              "      <td>0.001279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.852418</td>\n",
              "      <td>0.130363</td>\n",
              "      <td>0.003497</td>\n",
              "      <td>0.000327</td>\n",
              "      <td>{'kernel': 'poly', 'gamma': 10.0, 'degree': 2,...</td>\n",
              "      <td>0.989003</td>\n",
              "      <td>0.009251</td>\n",
              "      <td>0.999382</td>\n",
              "      <td>0.000908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.202607</td>\n",
              "      <td>0.028929</td>\n",
              "      <td>0.003157</td>\n",
              "      <td>0.000053</td>\n",
              "      <td>{'kernel': 'poly', 'gamma': 1.0, 'degree': 4, ...</td>\n",
              "      <td>0.988852</td>\n",
              "      <td>0.007758</td>\n",
              "      <td>0.998437</td>\n",
              "      <td>0.002178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.096149</td>\n",
              "      <td>0.006363</td>\n",
              "      <td>0.004340</td>\n",
              "      <td>0.000572</td>\n",
              "      <td>{'kernel': 'rbf', 'gamma': 0.1, 'degree': 3, '...</td>\n",
              "      <td>0.988336</td>\n",
              "      <td>0.008993</td>\n",
              "      <td>0.999216</td>\n",
              "      <td>0.001233</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.326057</td>\n",
              "      <td>0.005564</td>\n",
              "      <td>0.010488</td>\n",
              "      <td>0.000365</td>\n",
              "      <td>{'kernel': 'sigmoid', 'gamma': 1e-11, 'degree'...</td>\n",
              "      <td>0.199345</td>\n",
              "      <td>0.065298</td>\n",
              "      <td>0.100697</td>\n",
              "      <td>0.010501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.322349</td>\n",
              "      <td>0.002719</td>\n",
              "      <td>0.010366</td>\n",
              "      <td>0.000202</td>\n",
              "      <td>{'kernel': 'sigmoid', 'gamma': 1e-05, 'degree'...</td>\n",
              "      <td>0.199345</td>\n",
              "      <td>0.065298</td>\n",
              "      <td>0.115433</td>\n",
              "      <td>0.014323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.320553</td>\n",
              "      <td>0.002669</td>\n",
              "      <td>0.010593</td>\n",
              "      <td>0.000183</td>\n",
              "      <td>{'kernel': 'sigmoid', 'gamma': 1e-14, 'degree'...</td>\n",
              "      <td>0.199345</td>\n",
              "      <td>0.065298</td>\n",
              "      <td>0.100696</td>\n",
              "      <td>0.010503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.156886</td>\n",
              "      <td>0.003533</td>\n",
              "      <td>0.006814</td>\n",
              "      <td>0.000185</td>\n",
              "      <td>{'kernel': 'poly', 'gamma': 1e-07, 'degree': 3...</td>\n",
              "      <td>0.197255</td>\n",
              "      <td>0.057931</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.363132</td>\n",
              "      <td>0.011483</td>\n",
              "      <td>0.012561</td>\n",
              "      <td>0.000886</td>\n",
              "      <td>{'kernel': 'rbf', 'gamma': 1e-15, 'degree': 4,...</td>\n",
              "      <td>0.193076</td>\n",
              "      <td>0.049934</td>\n",
              "      <td>0.168447</td>\n",
              "      <td>0.035219</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 10 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jPJbG5GWd6T",
        "outputId": "d7729acc-faff-43b5-e8aa-6de87bc1fbdc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "DF.groupby('estimator').size()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "estimator\n",
              "ABST    100\n",
              "BAG     100\n",
              "GBST    100\n",
              "MLP     100\n",
              "RF      100\n",
              "SVM     100\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gPpz6oqa_-zd"
      },
      "source": [
        "# Randomized CV Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFhG0Zvc_8Zx"
      },
      "source": [
        "DF_sorted_by_esitmator=DF.sort_values(by=['estimator'])\n",
        "Best_of_each_case = DF.groupby('estimator').first() "
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFGGaz88b3dO",
        "outputId": "d6b50123-8908-4b9d-bf5b-1d173219fb7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "Best_of_each_case"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "estimator                                                                 \n",
              "ABST            0.007261      0.000320         0.003561        0.000161   \n",
              "BAG             0.086859      0.000923         0.007842        0.000080   \n",
              "GBST            0.965862      0.006917         0.004481        0.000132   \n",
              "MLP            58.762246      4.443611         0.086055        0.007155   \n",
              "RF              0.266636      0.003487         0.018085        0.000404   \n",
              "SVM             0.193155      0.033080         0.003872        0.000611   \n",
              "\n",
              "                                                      params  \\\n",
              "estimator                                                      \n",
              "ABST               {'n_estimators': 4, 'learning_rate': 0.1}   \n",
              "BAG        {'n_estimators': 32, 'max_samples': 0.8, 'max_...   \n",
              "GBST       {'subsample': 0.8, 'n_estimators': 256, 'min_s...   \n",
              "MLP        {'t_activation': 'relu', 'n_neurons': 100, 'n_...   \n",
              "RF         {'n_estimators': 128, 'min_samples_split': 6, ...   \n",
              "SVM        {'kernel': 'rbf', 'gamma': 0.1, 'degree': 3, '...   \n",
              "\n",
              "           mean_test_Accuracy  std_test_Accuracy  mean_test_AUC  std_test_AUC  \n",
              "estimator                                                                      \n",
              "ABST                 0.615760           0.047176       0.866892      0.012890  \n",
              "BAG                  0.983654           0.009967       0.998052      0.002551  \n",
              "GBST                 0.982911           0.010890       0.998826      0.001466  \n",
              "MLP                  0.986551           0.009732       0.998850      0.001490  \n",
              "RF                   0.981348           0.011828       0.998738      0.001747  \n",
              "SVM                  0.990341           0.008305       0.999045      0.001279  "
            ],
            "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>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>params</th>\n",
              "      <th>mean_test_Accuracy</th>\n",
              "      <th>std_test_Accuracy</th>\n",
              "      <th>mean_test_AUC</th>\n",
              "      <th>std_test_AUC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>estimator</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ABST</th>\n",
              "      <td>0.007261</td>\n",
              "      <td>0.000320</td>\n",
              "      <td>0.003561</td>\n",
              "      <td>0.000161</td>\n",
              "      <td>{'n_estimators': 4, 'learning_rate': 0.1}</td>\n",
              "      <td>0.615760</td>\n",
              "      <td>0.047176</td>\n",
              "      <td>0.866892</td>\n",
              "      <td>0.012890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BAG</th>\n",
              "      <td>0.086859</td>\n",
              "      <td>0.000923</td>\n",
              "      <td>0.007842</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>{'n_estimators': 32, 'max_samples': 0.8, 'max_...</td>\n",
              "      <td>0.983654</td>\n",
              "      <td>0.009967</td>\n",
              "      <td>0.998052</td>\n",
              "      <td>0.002551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GBST</th>\n",
              "      <td>0.965862</td>\n",
              "      <td>0.006917</td>\n",
              "      <td>0.004481</td>\n",
              "      <td>0.000132</td>\n",
              "      <td>{'subsample': 0.8, 'n_estimators': 256, 'min_s...</td>\n",
              "      <td>0.982911</td>\n",
              "      <td>0.010890</td>\n",
              "      <td>0.998826</td>\n",
              "      <td>0.001466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLP</th>\n",
              "      <td>58.762246</td>\n",
              "      <td>4.443611</td>\n",
              "      <td>0.086055</td>\n",
              "      <td>0.007155</td>\n",
              "      <td>{'t_activation': 'relu', 'n_neurons': 100, 'n_...</td>\n",
              "      <td>0.986551</td>\n",
              "      <td>0.009732</td>\n",
              "      <td>0.998850</td>\n",
              "      <td>0.001490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RF</th>\n",
              "      <td>0.266636</td>\n",
              "      <td>0.003487</td>\n",
              "      <td>0.018085</td>\n",
              "      <td>0.000404</td>\n",
              "      <td>{'n_estimators': 128, 'min_samples_split': 6, ...</td>\n",
              "      <td>0.981348</td>\n",
              "      <td>0.011828</td>\n",
              "      <td>0.998738</td>\n",
              "      <td>0.001747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVM</th>\n",
              "      <td>0.193155</td>\n",
              "      <td>0.033080</td>\n",
              "      <td>0.003872</td>\n",
              "      <td>0.000611</td>\n",
              "      <td>{'kernel': 'rbf', 'gamma': 0.1, 'degree': 3, '...</td>\n",
              "      <td>0.990341</td>\n",
              "      <td>0.008305</td>\n",
              "      <td>0.999045</td>\n",
              "      <td>0.001279</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LjnEAGjn4uQ"
      },
      "source": [
        "N_models=np.size(Best_of_each_case,axis=0)\n",
        "N_splits=Kf*Rep\n",
        "name=[]\n",
        "ACC=[]\n",
        "AUC=[]\n",
        "TimeForTrain=[]\n",
        "Params=[]\n",
        "for i in range(0,N_models):\n",
        "  name.append(AllResults[i][0])\n",
        "  Aux=pd.DataFrame(AllResults[i][1])\n",
        "  I=np.where(np.array(Aux.loc[:,'mean_test_Accuracy'])==Aux.loc[:,'mean_test_Accuracy'].max())[0][0]\n",
        "  ACC.append(Aux.loc[I,\"split0_test_Accuracy\":\"split\"+str(N_splits-1)+\"_test_Accuracy\"])\n",
        "  AUC.append(Aux.loc[I,\"split0_test_AUC\":\"split\"+str(N_splits-1)+\"_test_AUC\"])\n",
        "  TimeForTrain.append(Aux.loc[I,\"mean_fit_time\"])\n",
        "  Params.append(Aux.loc[I,\"params\"])"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzpsNRpq1-TQ",
        "outputId": "0b5e666e-ff15-4f40-a753-a5115690f98b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        }
      },
      "source": [
        "# Accuracy comparison\n",
        "fig = plt.figure()\n",
        "# fig.suptitle('Accuracy comparison - Best Case')\n",
        "ax = fig.add_subplot(111)\n",
        "ax.set_xticklabels(name)\n",
        "plt.boxplot(ACC)\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Models')\n",
        "plt.show()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d69wY2618VWO",
        "outputId": "a9f2da11-1c6e-4f4b-910e-2c2722f12e92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        }
      },
      "source": [
        "# AUC comparison\n",
        "fig = plt.figure()\n",
        "# fig.suptitle('AUC comparison - Best Case')\n",
        "ax = fig.add_subplot(111)\n",
        "ax.set_xticklabels(name)\n",
        "plt.boxplot(AUC)\n",
        "plt.ylabel('AUC')\n",
        "plt.xlabel('Models')\n",
        "plt.show()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbaXTYP3w3K9",
        "outputId": "f5097870-df40-4461-eb4b-a7ed80ce4e0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        }
      },
      "source": [
        "fig = plt.figure()\n",
        "# fig.suptitle('Mean time trainning each k')\n",
        "plt.bar(name,TimeForTrain, color='#0504aa',alpha=0.7)\n",
        "plt.ylim(min(TimeForTrain), max(TimeForTrain))\n",
        "plt.grid(axis='y', alpha=0.75)\n",
        "plt.ylabel('Time(s)')\n",
        "plt.xlabel('Models')\n",
        "plt.show()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hj0V21fNgo_c",
        "outputId": "fa636e37-e0d3-4115-99e0-9de9171dd132",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "pd.DataFrame(np.array(TimeForTrain),index=name,columns=['Mean Trainning Time'])"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Mean Trainning Time\n",
              "SVM              0.193155\n",
              "RF               0.266636\n",
              "ABST             0.007261\n",
              "GBST             0.965862\n",
              "BAG              0.086859\n",
              "MLP             58.762246"
            ],
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              "      <th></th>\n",
              "      <th>Mean Trainning Time</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SVM</th>\n",
              "      <td>0.193155</td>\n",
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              "    <tr>\n",
              "      <th>RF</th>\n",
              "      <td>0.266636</td>\n",
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              "    <tr>\n",
              "      <th>ABST</th>\n",
              "      <td>0.007261</td>\n",
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              "      <th>GBST</th>\n",
              "      <td>0.965862</td>\n",
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              "      <th>BAG</th>\n",
              "      <td>0.086859</td>\n",
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              "      <th>MLP</th>\n",
              "      <td>58.762246</td>\n",
              "    </tr>\n",
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          "execution_count": 89
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XznWVVT2ni53",
        "outputId": "85473f2f-c177-4ab8-8b7e-747220f75b02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "pd.DataFrame(Params,index=name)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     kernel  gamma  degree             C  n_estimators  min_samples_split  \\\n",
              "SVM     rbf    0.1     3.0  1.000000e+09           NaN                NaN   \n",
              "RF      NaN    NaN     NaN           NaN         128.0                6.0   \n",
              "ABST    NaN    NaN     NaN           NaN           4.0                NaN   \n",
              "GBST    NaN    NaN     NaN           NaN         256.0                9.0   \n",
              "BAG     NaN    NaN     NaN           NaN          32.0                NaN   \n",
              "MLP     NaN    NaN     NaN           NaN           NaN                NaN   \n",
              "\n",
              "      min_samples_leaf  max_features criterion bootstrap  learning_rate  \\\n",
              "SVM                NaN           NaN       NaN       NaN            NaN   \n",
              "RF                 2.0           3.0      gini      True            NaN   \n",
              "ABST               NaN           NaN       NaN       NaN            0.1   \n",
              "GBST               8.0           2.0       NaN       NaN            0.5   \n",
              "BAG                NaN           3.0       NaN     False            NaN   \n",
              "MLP                NaN           NaN       NaN       NaN            NaN   \n",
              "\n",
              "      subsample  max_samples t_activation  n_neurons  n_hidden_layers  epochs  \\\n",
              "SVM         NaN          NaN          NaN        NaN              NaN     NaN   \n",
              "RF          NaN          NaN          NaN        NaN              NaN     NaN   \n",
              "ABST        NaN          NaN          NaN        NaN              NaN     NaN   \n",
              "GBST        0.8          NaN          NaN        NaN              NaN     NaN   \n",
              "BAG         NaN          0.8          NaN        NaN              NaN     NaN   \n",
              "MLP         NaN          NaN         relu      100.0              1.0   350.0   \n",
              "\n",
              "      batch_size  \n",
              "SVM          NaN  \n",
              "RF           NaN  \n",
              "ABST         NaN  \n",
              "GBST         NaN  \n",
              "BAG          NaN  \n",
              "MLP          6.0  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>kernel</th>\n",
              "      <th>gamma</th>\n",
              "      <th>degree</th>\n",
              "      <th>C</th>\n",
              "      <th>n_estimators</th>\n",
              "      <th>min_samples_split</th>\n",
              "      <th>min_samples_leaf</th>\n",
              "      <th>max_features</th>\n",
              "      <th>criterion</th>\n",
              "      <th>bootstrap</th>\n",
              "      <th>learning_rate</th>\n",
              "      <th>subsample</th>\n",
              "      <th>max_samples</th>\n",
              "      <th>t_activation</th>\n",
              "      <th>n_neurons</th>\n",
              "      <th>n_hidden_layers</th>\n",
              "      <th>epochs</th>\n",
              "      <th>batch_size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SVM</th>\n",
              "      <td>rbf</td>\n",
              "      <td>0.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.000000e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RF</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>128.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>gini</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ABST</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GBST</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>256.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BAG</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLP</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>relu</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWvmp2iqlaOv"
      },
      "source": [
        "# Generate Spreadsheet with results\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQsuZ3enlhXO"
      },
      "source": [
        "if GEN_SHEET==1:\n",
        "  Best_of_each_case.to_excel(r'ExcelFileResults/CLASSIFICATION.xlsx', index = True, header=True)"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOnPQh1e1se-",
        "outputId": "0eb84bf8-23cf-48f7-91dd-79a523ba5882",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "Params"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'kernel': 'rbf', 'gamma': 0.1, 'degree': 3, 'C': 1000000000.0},\n",
              " {'n_estimators': 128,\n",
              "  'min_samples_split': 6,\n",
              "  'min_samples_leaf': 2,\n",
              "  'max_features': 3,\n",
              "  'criterion': 'gini',\n",
              "  'bootstrap': True},\n",
              " {'n_estimators': 4, 'learning_rate': 0.1},\n",
              " {'subsample': 0.8,\n",
              "  'n_estimators': 256,\n",
              "  'min_samples_split': 9,\n",
              "  'min_samples_leaf': 8,\n",
              "  'max_features': 2,\n",
              "  'learning_rate': 0.5},\n",
              " {'n_estimators': 32,\n",
              "  'max_samples': 0.8,\n",
              "  'max_features': 3,\n",
              "  'bootstrap': False},\n",
              " {'t_activation': 'relu',\n",
              "  'n_neurons': 100,\n",
              "  'n_hidden_layers': 1,\n",
              "  'epochs': 350,\n",
              "  'batch_size': 6}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    }
  ]
}