classifier_pred_reusibility.ipynb

{
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  "metadata": {
    "colab": {
      "name": "ClassifierPred_Reusability",
      "provenance": [],
      "authorship_tag": "ABX9TyMXmZ3aKFIkXZrNTusOw0BV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NeuralClassifier/ReusabilityRate_ML/blob/ClassificationModelsPred/ClassifierPred_Reusability.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nu0m6sm400QQ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "from sklearn.model_selection import KFold \n",
        "import statistics\n",
        "\n",
        "import warnings"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvzU9oZ91KuN",
        "outputId": "3f32ae8d-7095-49eb-daab-59da798575a5"
      },
      "source": [
        "!git clone https://github.com/NeuralClassifier/ReusabilityRate_ML.git"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ReusabilityRate_ML'...\n",
            "remote: Enumerating objects: 122, done.\u001b[K\n",
            "remote: Counting objects: 100% (122/122), done.\u001b[K\n",
            "remote: Compressing objects: 100% (114/114), done.\u001b[K\n",
            "remote: Total 122 (delta 46), reused 16 (delta 6), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (122/122), 9.02 MiB | 14.48 MiB/s, done.\n",
            "Resolving deltas: 100% (46/46), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "E0sFV31_1Olg",
        "outputId": "3203bc61-030e-4873-a569-3b9be3494ef3"
      },
      "source": [
        "class_df = pd.read_csv('https://github.com/raw/NeuralClassifier/ReusabilityRate_ML/ClassificationModelsPred/DataSets/cls_normalized.csv')\n",
        "class_df.drop(['Unnamed: 0'],axis = 1, inplace=True)\n",
        "class_df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Name</th>\n",
              "      <th>LCOM5</th>\n",
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              "      <th>NLE</th>\n",
              "      <th>WMC</th>\n",
              "      <th>CBO</th>\n",
              "      <th>CBOI</th>\n",
              "      <th>NII</th>\n",
              "      <th>NOI</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ch.qos.logback/logback-classic</td>\n",
              "      <td>ch.qos.logback.classic.AsyncAppender</td>\n",
              "      <td>AsyncAppender</td>\n",
              "      <td>-0.976331</td>\n",
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              "      <td>-0.110417</td>\n",
              "      <td>-0.110417</td>\n",
              "      <td>-0.995522</td>\n",
              "      <td>-0.995522</td>\n",
              "      <td>-0.995389</td>\n",
              "      <td>-0.993651</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.991793</td>\n",
              "      <td>-0.993978</td>\n",
              "      <td>-0.993978</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.995344</td>\n",
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              "      <td>-1.0000</td>\n",
              "      <td>-0.995408</td>\n",
              "      <td>-0.996554</td>\n",
              "      <td>-0.997253</td>\n",
              "      <td>5.307617</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ch.qos.logback/logback-classic</td>\n",
              "      <td>ch.qos.logback.classic.BasicConfigurator</td>\n",
              "      <td>BasicConfigurator</td>\n",
              "      <td>-0.988166</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.996216</td>\n",
              "      <td>-0.957447</td>\n",
              "      <td>-0.996370</td>\n",
              "      <td>-0.998538</td>\n",
              "      <td>-0.988095</td>\n",
              "      <td>-0.987484</td>\n",
              "      <td>-0.333334</td>\n",
              "      <td>-0.322223</td>\n",
              "      <td>-0.322223</td>\n",
              "      <td>-0.996802</td>\n",
              "      <td>-0.996802</td>\n",
              "      <td>-0.997695</td>\n",
              "      <td>-0.993651</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-0.990828</td>\n",
              "      <td>-0.993548</td>\n",
              "      <td>-0.993548</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.994796</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0000</td>\n",
              "      <td>-0.997704</td>\n",
              "      <td>-0.993109</td>\n",
              "      <td>-0.997253</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ch.qos.logback/logback-classic</td>\n",
              "      <td>ch.qos.logback.classic.ClassicConstants</td>\n",
              "      <td>ClassicConstants</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.974592</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.346428</td>\n",
              "      <td>-0.346428</td>\n",
              "      <td>-0.997121</td>\n",
              "      <td>-0.997121</td>\n",
              "      <td>-0.997036</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.991311</td>\n",
              "      <td>-0.992687</td>\n",
              "      <td>-0.992687</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.995070</td>\n",
              "      <td>-0.952381</td>\n",
              "      <td>-1.0000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>5.307617</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ch.qos.logback/logback-classic</td>\n",
              "      <td>ch.qos.logback.classic.Level</td>\n",
              "      <td>Level</td>\n",
              "      <td>-0.988166</td>\n",
              "      <td>-0.916667</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>-0.912961</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.883848</td>\n",
              "      <td>-0.970760</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.967459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.242484</td>\n",
              "      <td>-0.242484</td>\n",
              "      <td>-0.969615</td>\n",
              "      <td>-0.969615</td>\n",
              "      <td>-0.969043</td>\n",
              "      <td>-0.923810</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.923244</td>\n",
              "      <td>-0.941284</td>\n",
              "      <td>-0.941284</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.956450</td>\n",
              "      <td>-0.932773</td>\n",
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              "      <td>-0.985075</td>\n",
              "      <td>-0.966527</td>\n",
              "      <td>-0.984890</td>\n",
              "      <td>5.307617</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ch.qos.logback/logback-classic</td>\n",
              "      <td>ch.qos.logback.classic.Logger</td>\n",
              "      <td>Logger</td>\n",
              "      <td>-0.988166</td>\n",
              "      <td>-0.750000</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>-0.708609</td>\n",
              "      <td>-0.957447</td>\n",
              "      <td>-0.916515</td>\n",
              "      <td>-0.969298</td>\n",
              "      <td>-0.956349</td>\n",
              "      <td>-0.732165</td>\n",
              "      <td>-0.851852</td>\n",
              "      <td>-0.581752</td>\n",
              "      <td>-0.581752</td>\n",
              "      <td>-0.958420</td>\n",
              "      <td>-0.958420</td>\n",
              "      <td>-0.960481</td>\n",
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              "      <td>-1.000000</td>\n",
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              "      <th>24793</th>\n",
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              "</div>"
            ],
            "text/plain": [
              "                              Project  ... ReuseRate_lbl\n",
              "0      ch.qos.logback/logback-classic  ...             4\n",
              "1      ch.qos.logback/logback-classic  ...             3\n",
              "2      ch.qos.logback/logback-classic  ...             4\n",
              "3      ch.qos.logback/logback-classic  ...             4\n",
              "4      ch.qos.logback/logback-classic  ...             4\n",
              "...                               ...  ...           ...\n",
              "24789               xerces/xercesImpl  ...             0\n",
              "24790               xerces/xercesImpl  ...             0\n",
              "24791               xerces/xercesImpl  ...             0\n",
              "24792               xerces/xercesImpl  ...             0\n",
              "24793               xerces/xercesImpl  ...             0\n",
              "\n",
              "[24794 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
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        "id": "9zy6vEfT16XD",
        "outputId": "3d608b03-41fe-469c-e189-2f731288e7c6"
      },
      "source": [
        "package_df = pd.read_csv('https://github.com/raw/NeuralClassifier/ReusabilityRate_ML/ClassificationModelsPred/DataSets/pkg_normalized.csv')\n",
        "package_df.drop(['Unnamed: 0'],axis = 1, inplace=True)\n",
        "package_df"
      ],
      "execution_count": 4,
      "outputs": [
        {
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              "                             Project  ... ReuseRate_lbl\n",
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              "\n",
              "[2193 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
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      ]
    },
    {
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        "id": "d0gQr6HV5-O9",
        "outputId": "7963b8d6-ed39-4d05-cc86-698a0621c688"
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      "source": [
        "package_df.iloc[:,3:-2]"
      ],
      "execution_count": 5,
      "outputs": [
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              "      <td>-0.999712</td>\n",
              "      <td>-0.999885</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.999838</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.253473</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.959769</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.961194</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.956864</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.959792</td>\n",
              "      <td>-0.908012</td>\n",
              "      <td>-0.952302</td>\n",
              "      <td>-0.956669</td>\n",
              "      <td>-0.941933</td>\n",
              "      <td>-0.962812</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.253473</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.959769</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.961194</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.956864</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.959792</td>\n",
              "      <td>-0.908012</td>\n",
              "      <td>-0.952302</td>\n",
              "      <td>-0.956669</td>\n",
              "      <td>-0.941933</td>\n",
              "      <td>-0.962812</td>\n",
              "      <td>-1.000000</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",
              "      <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>2188</th>\n",
              "      <td>0.968254</td>\n",
              "      <td>0.033413</td>\n",
              "      <td>0.083579</td>\n",
              "      <td>-0.977705</td>\n",
              "      <td>-0.993081</td>\n",
              "      <td>-0.931073</td>\n",
              "      <td>-0.989014</td>\n",
              "      <td>-0.992390</td>\n",
              "      <td>-0.987862</td>\n",
              "      <td>-0.995148</td>\n",
              "      <td>-0.940460</td>\n",
              "      <td>-0.996360</td>\n",
              "      <td>-0.991098</td>\n",
              "      <td>-0.995164</td>\n",
              "      <td>-0.996647</td>\n",
              "      <td>-0.993460</td>\n",
              "      <td>-0.996841</td>\n",
              "      <td>-0.977604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2189</th>\n",
              "      <td>0.402986</td>\n",
              "      <td>-0.011642</td>\n",
              "      <td>0.036337</td>\n",
              "      <td>-0.964447</td>\n",
              "      <td>-0.988967</td>\n",
              "      <td>-0.947749</td>\n",
              "      <td>-0.991672</td>\n",
              "      <td>-0.986761</td>\n",
              "      <td>-0.979412</td>\n",
              "      <td>-0.991769</td>\n",
              "      <td>-0.953992</td>\n",
              "      <td>-0.993669</td>\n",
              "      <td>-0.988131</td>\n",
              "      <td>-0.996263</td>\n",
              "      <td>-0.995377</td>\n",
              "      <td>-0.987849</td>\n",
              "      <td>-0.996484</td>\n",
              "      <td>-0.977604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2190</th>\n",
              "      <td>0.081272</td>\n",
              "      <td>-0.000149</td>\n",
              "      <td>0.048387</td>\n",
              "      <td>-0.940306</td>\n",
              "      <td>-0.981476</td>\n",
              "      <td>-0.829906</td>\n",
              "      <td>-0.972889</td>\n",
              "      <td>-0.978261</td>\n",
              "      <td>-0.965010</td>\n",
              "      <td>-0.986012</td>\n",
              "      <td>-0.745602</td>\n",
              "      <td>-0.989603</td>\n",
              "      <td>-0.978087</td>\n",
              "      <td>-0.979338</td>\n",
              "      <td>-0.984151</td>\n",
              "      <td>-0.985873</td>\n",
              "      <td>-0.983313</td>\n",
              "      <td>-0.966405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2191</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>-0.532498</td>\n",
              "      <td>-0.509803</td>\n",
              "      <td>-0.920482</td>\n",
              "      <td>-0.975324</td>\n",
              "      <td>-0.984436</td>\n",
              "      <td>-0.997519</td>\n",
              "      <td>-0.907371</td>\n",
              "      <td>-0.904436</td>\n",
              "      <td>-0.961795</td>\n",
              "      <td>-0.907984</td>\n",
              "      <td>-0.955701</td>\n",
              "      <td>-0.924218</td>\n",
              "      <td>-0.992527</td>\n",
              "      <td>-0.985268</td>\n",
              "      <td>-0.919689</td>\n",
              "      <td>-0.996603</td>\n",
              "      <td>-0.935050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2192</th>\n",
              "      <td>-0.082568</td>\n",
              "      <td>-0.165001</td>\n",
              "      <td>-0.124467</td>\n",
              "      <td>-0.981520</td>\n",
              "      <td>-0.994265</td>\n",
              "      <td>-0.944414</td>\n",
              "      <td>-0.991140</td>\n",
              "      <td>-0.990704</td>\n",
              "      <td>-0.987393</td>\n",
              "      <td>-0.994960</td>\n",
              "      <td>-0.940460</td>\n",
              "      <td>-0.995554</td>\n",
              "      <td>-0.990870</td>\n",
              "      <td>-0.995164</td>\n",
              "      <td>-0.992990</td>\n",
              "      <td>-0.993128</td>\n",
              "      <td>-0.992431</td>\n",
              "      <td>-0.959686</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2193 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            AD        CD       TCD  ...      TNOS      TNPM       NCL\n",
              "0    -1.000000 -1.000000 -0.255446  ... -0.941933 -0.962812 -1.000000\n",
              "1    -1.000000 -1.000000 -0.253473  ... -0.941933 -0.962812 -1.000000\n",
              "2    -1.000000 -1.000000 -1.000000  ... -1.000000 -1.000000 -1.000000\n",
              "3    -1.000000 -1.000000 -0.253473  ... -0.941933 -0.962812 -1.000000\n",
              "4    -1.000000 -1.000000 -0.253473  ... -0.941933 -0.962812 -1.000000\n",
              "...        ...       ...       ...  ...       ...       ...       ...\n",
              "2188  0.968254  0.033413  0.083579  ... -0.993460 -0.996841 -0.977604\n",
              "2189  0.402986 -0.011642  0.036337  ... -0.987849 -0.996484 -0.977604\n",
              "2190  0.081272 -0.000149  0.048387  ... -0.985873 -0.983313 -0.966405\n",
              "2191  0.400000 -0.532498 -0.509803  ... -0.919689 -0.996603 -0.935050\n",
              "2192 -0.082568 -0.165001 -0.124467  ... -0.993128 -0.992431 -0.959686\n",
              "\n",
              "[2193 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHOjv_h_4VLi"
      },
      "source": [
        "warnings.filterwarnings('ignore')\n",
        "class ReuseTradML:\n",
        "  def __init__(self,level):\n",
        "    self.ml_algo_list = ['GNB',\n",
        "                         'LRC',\n",
        "                         'KNN',\n",
        "                         'DTC',\n",
        "                         'RFC',\n",
        "                         'BNB',\n",
        "                         'SVC',\n",
        "                         'MLPC']\n",
        "\n",
        "    self.metrics = ['Accuracy',\n",
        "                    'Precision',\n",
        "                    'Recall']\n",
        "    self.data_columns = None\n",
        "    self.level=level\n",
        "\n",
        "    if self.level == 'package':\n",
        "      #target_scaler = MinMaxScaler()\n",
        "      data = pd.read_csv('https://github.com/raw/NeuralClassifier/ReusabilityRate_ML/ClassificationModelsPred/DataSets/pkg_normalized.csv')\n",
        "      data.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
        "      df = data.iloc[:,3:-2]\n",
        "      self.data_columns = df.columns\n",
        "      self.X_np = np.array(df)\n",
        "      #target_scaler = MinMaxScaler()\n",
        "      #target_scaler.fit(self.X_np)\n",
        "      #self.X = target_scaler.transform(self.X_np)\n",
        "\n",
        "      self.Y = data.iloc[:,-1]\n",
        "      #self.Y = self.Y.values.reshape(-1, 1)\n",
        "      #target_scaler = MinMaxScaler()\n",
        "      #target_scaler.fit(self.Y)\n",
        "      #self.Y = target_scaler.transform(self.Y)\n",
        "\n",
        "      self.other = data.iloc[:,:3]\n",
        "\n",
        "    elif self.level == 'class':\n",
        "      #target_scaler = MinMaxScaler()\n",
        "      data = pd.read_csv('https://github.com/raw/NeuralClassifier/ReusabilityRate_ML/ClassificationModelsPred/DataSets/cls_normalized.csv')\n",
        "      data.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
        "      df = data.iloc[:,3:-2]\n",
        "      self.data_columns = df.columns\n",
        "      self.X_np = np.array(df)\n",
        "      #target_scaler = MinMaxScaler()\n",
        "      #target_scaler.fit(self.X_np)\n",
        "      #self.X = target_scaler.transform(self.X_np)\n",
        "\n",
        "      self.Y = data.iloc[:,-1]\n",
        "      #self.Y = self.Y.values.reshape(-1, 1)\n",
        "      #target_scaler = MinMaxScaler()\n",
        "      #target_scaler.fit(self.Y)\n",
        "      #self.Y = target_scaler.transform(self.Y)\n",
        "\n",
        "      self.other = data.iloc[:,:3]\n",
        "\n",
        "    elif self.level == 'project':\n",
        "      self.X_np = np.array(data.iloc[:,4:])\n",
        "      self.other = data.iloc[:,:4]\n",
        "\n",
        "    else:\n",
        "      print('Error, Please try: class, package, or project. Thanks!')\n",
        "      self.X=None\n",
        "      self.Y=None\n",
        "      self.other=None\n",
        "\n",
        "  def apply_ml(self):\n",
        "    kf = KFold(n_splits=10, random_state=None, shuffle=True)\n",
        "  \n",
        "    metric = []\n",
        "    metric_count = 0\n",
        "\n",
        "    level = []\n",
        "    model = []\n",
        "\n",
        "    mean_acc = []\n",
        "    stdv_acc = []\n",
        "    mean_pre = []\n",
        "    stdv_pre = []\n",
        "    mean_rec = []\n",
        "    stdv_rec = []\n",
        "    \n",
        "\n",
        "    for i in range(self.X_np.shape[1]):\n",
        "      #print(self.X[i])\n",
        "      for j in self.ml_algo_list:\n",
        "          \n",
        "        warnings.filterwarnings('ignore')\n",
        "        #['Polynomial Regression','MLPRegressor','BayesianRidge','SGD Regressor','XGB Regressor','Decision Tree Regressor','ElasticNet','SVM Regressor',]\n",
        "        if j == 'GNB':\n",
        "          clss_model = GaussianNB()\n",
        "          print('Metric: ',self.data_columns[i])\n",
        "          print('Level: ',self.level)\n",
        "          print('Model: ',j)\n",
        "          print()\n",
        "          print(\"**************************************************************\")\n",
        "          print()\n",
        "          model.append(j)\n",
        "\n",
        "        #if j == 'Logistic Regression':\n",
        "          #reg = LogisticRegression()\n",
        "          #print('Metric: ',self.data_columns[i])\n",
        "          #print('Level: ',self.level)\n",
        "          #print('Model: ',j)\n",
        "\n",
        "        if j == 'LRC':\n",
        "          clss_model = LogisticRegression()\n",
        "          print('Metric: ',self.data_columns[i])\n",
        "          print('Level: ',self.level)\n",
        "          print('Model: ',j)\n",
        "          print()\n",
        "          print(\"**************************************************************\")\n",
        "          print()\n",
        "          model.append(j)\n",
        "\n",
        "        if j == 'KNN':\n",
        "          clss_model = KNeighborsClassifier()\n",
        "          print('Metric: ',self.data_columns[i])\n",
        "          print('Level: ',self.level)\n",
        "          print('Model: ',j)\n",
        "          print()\n",
        "          print(\"**************************************************************\")\n",
        "          print()\n",
        "          model.append(j)\n",
        "\n",
        "        if j == 'DTC':\n",
        "          clss_model = DecisionTreeClassifier()\n",
        "          print('Metric: ',self.data_columns[i])\n",
        "          print('Level: ',self.level)\n",
        "          print('Model: ',j)\n",
        "          print()\n",
        "          print(\"**************************************************************\")\n",
        "          print()\n",
        "          model.append(j)\n",
        "\n",
        "        if j == 'RFC':\n",
        "          clss_model = RandomForestClassifier()\n",
        "          print('Metric: ',self.data_columns[i])\n",
        "          print('Level: ',self.level)\n",
        "          print('Model: ',j)\n",
        "          print()\n",
        "          print(\"**************************************************************\")\n",
        "          print()\n",
        "          model.append(j)\n",
        "\n",
        "        if j == 'BNB':\n",
        "          clss_model = BernoulliNB()\n",
        "          print('Metric: ',self.data_columns[i])\n",
        "          print('Level: ',self.level)\n",
        "          print('Model: ',j)\n",
        "          print()\n",
        "          print(\"**************************************************************\")\n",
        "          print()\n",
        "          model.append(j)\n",
        "\n",
        "        if j == 'SVC':\n",
        "          clss_model = SVC()\n",
        "          print('Metric: ',self.data_columns[i])\n",
        "          print('Level: ',self.level)\n",
        "          print('Model: ',j)\n",
        "          print()\n",
        "          print(\"**************************************************************\")\n",
        "          print()\n",
        "          model.append(j)\n",
        "        \n",
        "        if j == 'MLPC':\n",
        "          clss_model = MLPClassifier()\n",
        "          print('Metric: ',self.data_columns[i])\n",
        "          print('Level: ',self.level)\n",
        "          print('Model: ',j)\n",
        "          print()\n",
        "          print(\"**************************************************************\")\n",
        "          print()\n",
        "          model.append(j)\n",
        "        \n",
        "        print('At i: ',i,' x = ',self.X_np[:,i])\n",
        "        print()\n",
        "        #temp_data = self.X_np[:,i].reshape(-1, 1)\n",
        "        #target_scaler = MinMaxScaler()\n",
        "        #target_scaler.fit(temp_data)\n",
        "        #temp_data = target_scaler.transform(temp_data)\n",
        "        level.append(self.level)\n",
        "        metric.append(self.data_columns[i])\n",
        "\n",
        "        cnt=0\n",
        "        acc=[]\n",
        "        pre=[]\n",
        "        rec=[]\n",
        "        for train_index, test_index in kf.split(self.X_np):\n",
        "          cnt = cnt+1\n",
        "          #print('Fold: ',cnt)\n",
        "          X_train, X_test = self.X_np[train_index], self.X_np[test_index]\n",
        "          y_train, y_test = self.Y[train_index], self.Y[test_index]\n",
        "\n",
        "          clss_model.fit(X_train,y_train)\n",
        "          y_pred = clss_model.predict(X_test)\n",
        "          acc.append(accuracy_score(y_test, y_pred))\n",
        "          pre.append(precision_score(y_test,y_pred,average='weighted'))\n",
        "          rec.append(recall_score(y_test,y_pred,average='weighted'))\n",
        "\n",
        "        mean_acc.append(statistics.mean(acc))\n",
        "        stdv_acc.append(statistics.pstdev(acc))\n",
        "        mean_pre.append(statistics.mean(pre))\n",
        "        stdv_pre.append(statistics.pstdev(pre))\n",
        "        mean_rec.append(statistics.mean(rec))\n",
        "        stdv_rec.append(statistics.pstdev(rec))\n",
        "    \n",
        "\n",
        "      \n",
        "\n",
        "    #break\n",
        "    metric = pd.DataFrame(metric,columns=['Metric'])\n",
        "    level = pd.DataFrame(level,columns=['Level'])\n",
        "    model = pd.DataFrame(model,columns=['Model'])\n",
        "    print()\n",
        "\n",
        "    mean_acc = pd.DataFrame(mean_acc,columns=['Acc'])\n",
        "    stdv_acc = pd.DataFrame(stdv_acc,columns=['STD Acc'])\n",
        "    mean_pre = pd.DataFrame(mean_pre,columns=['Pre'])\n",
        "    stdv_pre = pd.DataFrame(stdv_pre,columns=['STD Pre'])\n",
        "    mean_rec = pd.DataFrame(mean_rec,columns=['Rec'])\n",
        "    stdv_rec = pd.DataFrame(stdv_rec,columns=['STD Rec'])\n",
        "\n",
        "\n",
        "    res_df = pd.concat([metric,level,model,mean_acc,stdv_acc,mean_pre,stdv_pre,mean_rec,stdv_rec],axis=1)\n",
        "\n",
        "    return res_df"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wo0mG1WrBQGc",
        "outputId": "b3f3c04d-9893-48ba-c30e-867e603e8c72"
      },
      "source": [
        "rtml=ReuseTradML('class')\n",
        "redf=rtml.apply_ml()\n",
        "redf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metric:  LCOM5\n",
            "Level:  class\n",
            "Model:  GNB\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "At i:  0  x =  [-0.97633136 -0.98816568 -1.         ... -0.98816568 -0.98816568\n",
            " -0.98816568]\n",
            "\n",
            "Metric:  LCOM5\n",
            "Level:  class\n",
            "Model:  LRC\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "At i:  0  x =  [-0.97633136 -0.98816568 -1.         ... -0.98816568 -0.98816568\n",
            " -0.98816568]\n",
            "\n",
            "Metric:  LCOM5\n",
            "Level:  class\n",
            "Model:  KNN\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "At i:  0  x =  [-0.97633136 -0.98816568 -1.         ... -0.98816568 -0.98816568\n",
            " -0.98816568]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDmPCggMBojF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}