reuse_rate_ml.ipynb

{
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  "metadata": {
    "colab": {
      "name": "CMPUT_663_ReuseRate_ML",
      "provenance": [],
      "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/main/CMPUT_663_ReuseRate_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4AwX7NeJJMGR"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.linear_model import  LogisticRegression\n",
        "from sklearn.linear_model import BayesianRidge\n",
        "from sklearn.linear_model import SGDRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.linear_model import ElasticNet\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import KFold \n",
        "from sklearn.metrics import mean_absolute_error \n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import statistics\n",
        "\n",
        "import warnings"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYhNw2ytM6Qa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40d60250-068a-403f-b86f-775e13ee73da"
      },
      "source": [
        "!git clone https://github.com/NeuralClassifier/ReusabilityRate_ML"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ReusabilityRate_ML'...\n",
            "remote: Enumerating objects: 147, done.\u001b[K\n",
            "remote: Counting objects: 100% (147/147), done.\u001b[K\n",
            "remote: Compressing objects: 100% (139/139), done.\u001b[K\n",
            "remote: Total 147 (delta 61), reused 15 (delta 6), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (147/147), 9.04 MiB | 11.78 MiB/s, done.\n",
            "Resolving deltas: 100% (61/61), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jg0SWfgoJUJB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3016f341-8137-4118-afc1-d09ec0959ada"
      },
      "source": [
        "data = pd.read_excel('/content/ReusabilityRate_ML/DataSets/NoClass_to_rate.xlsx')\n",
        "data.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
        "data"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Project</th>\n",
              "      <th>NumberOfClasses</th>\n",
              "      <th>AvgReuseRateClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ch.qos.logback/logback-classic</td>\n",
              "      <td>145</td>\n",
              "      <td>4.855172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>com.android.support/appcompat-v7</td>\n",
              "      <td>299</td>\n",
              "      <td>3.003344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>com.fasterxml.jackson.core/jackson-annotations</td>\n",
              "      <td>18</td>\n",
              "      <td>2.055556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>com.fasterxml.jackson.core/jackson-databind</td>\n",
              "      <td>535</td>\n",
              "      <td>6.596262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>com.google.code.gson/gson</td>\n",
              "      <td>158</td>\n",
              "      <td>14.506329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>com.google.guava/guava</td>\n",
              "      <td>1701</td>\n",
              "      <td>6.313933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>com.google.gwt/gwt-user</td>\n",
              "      <td>4177</td>\n",
              "      <td>3.402921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>commons-beanutils/commons-beanutils</td>\n",
              "      <td>127</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>commons-codec/commons-codec</td>\n",
              "      <td>83</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>commons-collections/commons-collections</td>\n",
              "      <td>467</td>\n",
              "      <td>0.055675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>commons-logging/commons-logging</td>\n",
              "      <td>27</td>\n",
              "      <td>53.148148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>dom4j/dom4j</td>\n",
              "      <td>160</td>\n",
              "      <td>2.393750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>javax.mail/mail</td>\n",
              "      <td>120</td>\n",
              "      <td>13.716667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>javax.servlet/servlet-api</td>\n",
              "      <td>28</td>\n",
              "      <td>105.357143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>joda-time/joda-time</td>\n",
              "      <td>220</td>\n",
              "      <td>12.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>junit/junit</td>\n",
              "      <td>272</td>\n",
              "      <td>34.801471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>log4j/log4j</td>\n",
              "      <td>181</td>\n",
              "      <td>6.502762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>org.apache.commons/commons-lang3</td>\n",
              "      <td>235</td>\n",
              "      <td>5.502128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>org.apache.httpcomponents/httpclient</td>\n",
              "      <td>390</td>\n",
              "      <td>1.905128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>org.apache.maven/maven-core</td>\n",
              "      <td>281</td>\n",
              "      <td>4.704626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>org.apache.maven/maven-plugin-api</td>\n",
              "      <td>16</td>\n",
              "      <td>74.812500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>org.clojure/clojure</td>\n",
              "      <td>368</td>\n",
              "      <td>0.828804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>org.clojure/tools.nrepl</td>\n",
              "      <td>4</td>\n",
              "      <td>1.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>org.codehaus.jackson/jackson-core-asl</td>\n",
              "      <td>89</td>\n",
              "      <td>19.359551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>org.codehaus.jackson/jackson-mapper-asl</td>\n",
              "      <td>446</td>\n",
              "      <td>2.233184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>org.easymock/easymock</td>\n",
              "      <td>88</td>\n",
              "      <td>2.704545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>org.eclipse.jetty/jetty-server</td>\n",
              "      <td>215</td>\n",
              "      <td>5.465116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>org.hamcrest/hamcrest-all</td>\n",
              "      <td>41</td>\n",
              "      <td>12.487805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>org.hamcrest/hamcrest-core</td>\n",
              "      <td>41</td>\n",
              "      <td>12.487805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>org.hamcrest/hamcrest-library</td>\n",
              "      <td>57</td>\n",
              "      <td>0.526316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>org.hibernate/hibernate-core</td>\n",
              "      <td>3212</td>\n",
              "      <td>2.020548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>org.hibernate/hibernate-entitymanager</td>\n",
              "      <td>3195</td>\n",
              "      <td>2.031299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>org.hibernate/hibernate-validator</td>\n",
              "      <td>657</td>\n",
              "      <td>0.025875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>org.javassist/javassist</td>\n",
              "      <td>389</td>\n",
              "      <td>3.138817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>org.mockito/mockito-all</td>\n",
              "      <td>400</td>\n",
              "      <td>4.772500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>org.mockito/mockito-core</td>\n",
              "      <td>400</td>\n",
              "      <td>4.772500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>org.powermock/powermock-api-mockito</td>\n",
              "      <td>52</td>\n",
              "      <td>1.115385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>org.powermock/powermock-module-junit4</td>\n",
              "      <td>21</td>\n",
              "      <td>25.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>org.projectlombok/lombok</td>\n",
              "      <td>357</td>\n",
              "      <td>1.114846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>org.reflections/reflections</td>\n",
              "      <td>83</td>\n",
              "      <td>2.963855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>org.slf4j/jcl-over-slf4j</td>\n",
              "      <td>8</td>\n",
              "      <td>177.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>org.slf4j/jul-to-slf4j</td>\n",
              "      <td>1</td>\n",
              "      <td>17.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>org.slf4j/slf4j-api</td>\n",
              "      <td>25</td>\n",
              "      <td>167.520000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>org.slf4j/slf4j-simple</td>\n",
              "      <td>6</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>org.spockframework/spock-core</td>\n",
              "      <td>398</td>\n",
              "      <td>1.005025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>org.springframework.boot/spring-boot-starter-test</td>\n",
              "      <td>95</td>\n",
              "      <td>1.631579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>org.springframework.boot/spring-boot-starter-web</td>\n",
              "      <td>470</td>\n",
              "      <td>1.321277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>org.springframework/spring-aop</td>\n",
              "      <td>205</td>\n",
              "      <td>0.926829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>org.springframework/spring-beans</td>\n",
              "      <td>278</td>\n",
              "      <td>10.525180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>org.springframework/spring-context</td>\n",
              "      <td>558</td>\n",
              "      <td>4.677419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>org.springframework/spring-context-support</td>\n",
              "      <td>99</td>\n",
              "      <td>1.707071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>org.springframework/spring-core</td>\n",
              "      <td>463</td>\n",
              "      <td>6.818575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>org.springframework/spring-jdbc</td>\n",
              "      <td>208</td>\n",
              "      <td>4.456731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>org.springframework/spring-test</td>\n",
              "      <td>274</td>\n",
              "      <td>10.043796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>org.springframework/spring-tx</td>\n",
              "      <td>159</td>\n",
              "      <td>5.886792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>org.springframework/spring-web</td>\n",
              "      <td>607</td>\n",
              "      <td>4.052718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>org.testng/testng</td>\n",
              "      <td>384</td>\n",
              "      <td>2.895833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>xerces/xercesImpl</td>\n",
              "      <td>801</td>\n",
              "      <td>0.217228</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                                              Project  ...  AvgReuseRateClass\n",
              "0                      ch.qos.logback/logback-classic  ...           4.855172\n",
              "1                    com.android.support/appcompat-v7  ...           3.003344\n",
              "2      com.fasterxml.jackson.core/jackson-annotations  ...           2.055556\n",
              "3         com.fasterxml.jackson.core/jackson-databind  ...           6.596262\n",
              "4                           com.google.code.gson/gson  ...          14.506329\n",
              "5                              com.google.guava/guava  ...           6.313933\n",
              "6                             com.google.gwt/gwt-user  ...           3.402921\n",
              "7                 commons-beanutils/commons-beanutils  ...           0.000000\n",
              "8                         commons-codec/commons-codec  ...           6.000000\n",
              "9             commons-collections/commons-collections  ...           0.055675\n",
              "10                    commons-logging/commons-logging  ...          53.148148\n",
              "11                                        dom4j/dom4j  ...           2.393750\n",
              "12                                    javax.mail/mail  ...          13.716667\n",
              "13                          javax.servlet/servlet-api  ...         105.357143\n",
              "14                                joda-time/joda-time  ...          12.090909\n",
              "15                                        junit/junit  ...          34.801471\n",
              "16                                        log4j/log4j  ...           6.502762\n",
              "17                   org.apache.commons/commons-lang3  ...           5.502128\n",
              "18               org.apache.httpcomponents/httpclient  ...           1.905128\n",
              "19                        org.apache.maven/maven-core  ...           4.704626\n",
              "20                  org.apache.maven/maven-plugin-api  ...          74.812500\n",
              "21                                org.clojure/clojure  ...           0.828804\n",
              "22                            org.clojure/tools.nrepl  ...           1.250000\n",
              "23              org.codehaus.jackson/jackson-core-asl  ...          19.359551\n",
              "24            org.codehaus.jackson/jackson-mapper-asl  ...           2.233184\n",
              "25                              org.easymock/easymock  ...           2.704545\n",
              "26                     org.eclipse.jetty/jetty-server  ...           5.465116\n",
              "27                          org.hamcrest/hamcrest-all  ...          12.487805\n",
              "28                         org.hamcrest/hamcrest-core  ...          12.487805\n",
              "29                      org.hamcrest/hamcrest-library  ...           0.526316\n",
              "30                       org.hibernate/hibernate-core  ...           2.020548\n",
              "31              org.hibernate/hibernate-entitymanager  ...           2.031299\n",
              "32                  org.hibernate/hibernate-validator  ...           0.025875\n",
              "33                            org.javassist/javassist  ...           3.138817\n",
              "34                            org.mockito/mockito-all  ...           4.772500\n",
              "35                           org.mockito/mockito-core  ...           4.772500\n",
              "36                org.powermock/powermock-api-mockito  ...           1.115385\n",
              "37              org.powermock/powermock-module-junit4  ...          25.571429\n",
              "38                           org.projectlombok/lombok  ...           1.114846\n",
              "39                        org.reflections/reflections  ...           2.963855\n",
              "40                           org.slf4j/jcl-over-slf4j  ...         177.875000\n",
              "41                             org.slf4j/jul-to-slf4j  ...          17.000000\n",
              "42                                org.slf4j/slf4j-api  ...         167.520000\n",
              "43                             org.slf4j/slf4j-simple  ...           0.000000\n",
              "44                      org.spockframework/spock-core  ...           1.005025\n",
              "45  org.springframework.boot/spring-boot-starter-test  ...           1.631579\n",
              "46   org.springframework.boot/spring-boot-starter-web  ...           1.321277\n",
              "47                     org.springframework/spring-aop  ...           0.926829\n",
              "48                   org.springframework/spring-beans  ...          10.525180\n",
              "49                 org.springframework/spring-context  ...           4.677419\n",
              "50         org.springframework/spring-context-support  ...           1.707071\n",
              "51                    org.springframework/spring-core  ...           6.818575\n",
              "52                    org.springframework/spring-jdbc  ...           4.456731\n",
              "53                    org.springframework/spring-test  ...          10.043796\n",
              "54                      org.springframework/spring-tx  ...           5.886792\n",
              "55                     org.springframework/spring-web  ...           4.052718\n",
              "56                                  org.testng/testng  ...           2.895833\n",
              "57                                  xerces/xercesImpl  ...           0.217228\n",
              "\n",
              "[58 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ss5FfUVpLxbN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "5c044b3a-874f-4a53-ed9a-2da22117cca1"
      },
      "source": [
        "df.iloc[:,3:-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>AD</th>\n",
              "      <th>CD</th>\n",
              "      <th>TCD</th>\n",
              "      <th>CLOC</th>\n",
              "      <th>TCLOC</th>\n",
              "      <th>PDA</th>\n",
              "      <th>TPDA</th>\n",
              "      <th>LLOC</th>\n",
              "      <th>LOC</th>\n",
              "      <th>TLOC</th>\n",
              "      <th>NG</th>\n",
              "      <th>TLLOC</th>\n",
              "      <th>TNA</th>\n",
              "      <th>TNG</th>\n",
              "      <th>TNM</th>\n",
              "      <th>TNOS</th>\n",
              "      <th>TNPM</th>\n",
              "      <th>NCL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.342362</td>\n",
              "      <td>0</td>\n",
              "      <td>4146</td>\n",
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              "      <td>217</td>\n",
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              "      <td>3498</td>\n",
              "      <td>624</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.343269</td>\n",
              "      <td>0</td>\n",
              "      <td>4146</td>\n",
              "      <td>0</td>\n",
              "      <td>219</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14250</td>\n",
              "      <td>0</td>\n",
              "      <td>7932</td>\n",
              "      <td>403</td>\n",
              "      <td>217</td>\n",
              "      <td>853</td>\n",
              "      <td>3498</td>\n",
              "      <td>624</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.343269</td>\n",
              "      <td>0</td>\n",
              "      <td>4146</td>\n",
              "      <td>0</td>\n",
              "      <td>219</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14250</td>\n",
              "      <td>0</td>\n",
              "      <td>7932</td>\n",
              "      <td>403</td>\n",
              "      <td>217</td>\n",
              "      <td>853</td>\n",
              "      <td>3498</td>\n",
              "      <td>624</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.343269</td>\n",
              "      <td>0</td>\n",
              "      <td>4146</td>\n",
              "      <td>0</td>\n",
              "      <td>219</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14250</td>\n",
              "      <td>0</td>\n",
              "      <td>7932</td>\n",
              "      <td>403</td>\n",
              "      <td>217</td>\n",
              "      <td>853</td>\n",
              "      <td>3498</td>\n",
              "      <td>624</td>\n",
              "      <td>0</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>0.984127</td>\n",
              "      <td>0.498253</td>\n",
              "      <td>0.498253</td>\n",
              "      <td>713</td>\n",
              "      <td>713</td>\n",
              "      <td>62</td>\n",
              "      <td>62</td>\n",
              "      <td>718</td>\n",
              "      <td>1603</td>\n",
              "      <td>1603</td>\n",
              "      <td>22</td>\n",
              "      <td>718</td>\n",
              "      <td>39</td>\n",
              "      <td>22</td>\n",
              "      <td>66</td>\n",
              "      <td>394</td>\n",
              "      <td>53</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2189</th>\n",
              "      <td>0.701493</td>\n",
              "      <td>0.476530</td>\n",
              "      <td>0.476530</td>\n",
              "      <td>1137</td>\n",
              "      <td>1137</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>1249</td>\n",
              "      <td>2719</td>\n",
              "      <td>2719</td>\n",
              "      <td>17</td>\n",
              "      <td>1249</td>\n",
              "      <td>52</td>\n",
              "      <td>17</td>\n",
              "      <td>91</td>\n",
              "      <td>732</td>\n",
              "      <td>59</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2190</th>\n",
              "      <td>0.540636</td>\n",
              "      <td>0.482071</td>\n",
              "      <td>0.482071</td>\n",
              "      <td>1909</td>\n",
              "      <td>1909</td>\n",
              "      <td>153</td>\n",
              "      <td>153</td>\n",
              "      <td>2051</td>\n",
              "      <td>4621</td>\n",
              "      <td>4621</td>\n",
              "      <td>94</td>\n",
              "      <td>2051</td>\n",
              "      <td>96</td>\n",
              "      <td>94</td>\n",
              "      <td>312</td>\n",
              "      <td>851</td>\n",
              "      <td>280</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2191</th>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.225403</td>\n",
              "      <td>0.225403</td>\n",
              "      <td>2543</td>\n",
              "      <td>2543</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>8739</td>\n",
              "      <td>12621</td>\n",
              "      <td>12621</td>\n",
              "      <td>34</td>\n",
              "      <td>8739</td>\n",
              "      <td>332</td>\n",
              "      <td>34</td>\n",
              "      <td>290</td>\n",
              "      <td>4838</td>\n",
              "      <td>57</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2192</th>\n",
              "      <td>0.458716</td>\n",
              "      <td>0.402589</td>\n",
              "      <td>0.402589</td>\n",
              "      <td>591</td>\n",
              "      <td>591</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>877</td>\n",
              "      <td>1665</td>\n",
              "      <td>1665</td>\n",
              "      <td>22</td>\n",
              "      <td>877</td>\n",
              "      <td>40</td>\n",
              "      <td>22</td>\n",
              "      <td>138</td>\n",
              "      <td>414</td>\n",
              "      <td>127</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2193 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            AD        CD       TCD  CLOC  TCLOC  ...  TNG  TNM  TNOS  TNPM  NCL\n",
              "0     0.000000  0.000000  0.342362     0   4146  ...  217  853  3498   624    0\n",
              "1     0.000000  0.000000  0.343269     0   4146  ...  217  853  3498   624    0\n",
              "2     0.000000  0.000000  0.000000     0      0  ...    0    0     0     0    0\n",
              "3     0.000000  0.000000  0.343269     0   4146  ...  217  853  3498   624    0\n",
              "4     0.000000  0.000000  0.343269     0   4146  ...  217  853  3498   624    0\n",
              "...        ...       ...       ...   ...    ...  ...  ...  ...   ...   ...  ...\n",
              "2188  0.984127  0.498253  0.498253   713    713  ...   22   66   394    53   10\n",
              "2189  0.701493  0.476530  0.476530  1137   1137  ...   17   91   732    59   10\n",
              "2190  0.540636  0.482071  0.482071  1909   1909  ...   94  312   851   280   15\n",
              "2191  0.700000  0.225403  0.225403  2543   2543  ...   34  290  4838    57   29\n",
              "2192  0.458716  0.402589  0.402589   591    591  ...   22  138   414   127   18\n",
              "\n",
              "[2193 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpKGhecOif-m",
        "outputId": "234811b8-4671-424f-d093-ed4be057ea66"
      },
      "source": [
        "len(rtm.X_np[:,0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24794"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mp8YZVRIqKPr"
      },
      "source": [
        "warnings.filterwarnings('ignore')\n",
        "class ReuseTradML:\n",
        "  def __init__(self,level):\n",
        "    self.ml_algo_list = ['PolR',\n",
        "                         'MLPR',\n",
        "                         'BR',\n",
        "                         'SGDR',\n",
        "                         'XGBR',\n",
        "                         'DTR',\n",
        "                         'EN',\n",
        "                         'SVR']\n",
        "\n",
        "    self.metrics = ['RMSE',\n",
        "                    'MAE']\n",
        "    self.data_columns = None\n",
        "    self.level=level\n",
        "\n",
        "    if self.level.lower() == 'package':\n",
        "      target_scaler = MinMaxScaler()\n",
        "      data = pd.read_excel('/content/ReusabilityRate_ML/DataSets/packages.xlsx')\n",
        "      data.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
        "      df = data.iloc[:,3:-1]\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.lower() == 'class':\n",
        "      target_scaler = MinMaxScaler()\n",
        "      data = pd.read_excel('/content/ReusabilityRate_ML/DataSets/classes.xlsx')\n",
        "      data.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
        "      df = data.iloc[:,3:-1]\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.lower() == 'class_frequency':\n",
        "      target_scaler = MinMaxScaler()\n",
        "      data = pd.read_excel('/content/ReusabilityRate_ML/DataSets/NoClass_to_rate.xlsx')\n",
        "      data.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
        "      df = data.iloc[:,1]\n",
        "      #self.data_columns = df.columns\n",
        "      self.X_np = np.array(df)\n",
        "      target_scaler.fit(self.X_np.reshape(-1, 1))\n",
        "      self.X = target_scaler.transform(self.X_np.reshape(-1, 1))\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.X_np = np.array(data.iloc[:,4:])\n",
        "      #self.other = data.iloc[:,:4]\n",
        "    \n",
        "    elif self.level.lower() == 'package_frequency':\n",
        "      target_scaler = MinMaxScaler()\n",
        "      data = pd.read_excel('/content/ReusabilityRate_ML/DataSets/NoPackage_to_rate.xlsx')\n",
        "      data.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
        "      df = data.iloc[:,1]\n",
        "      #self.data_columns = df.columns\n",
        "      self.X_np = np.array(df)\n",
        "      target_scaler.fit(self.X_np.reshape(-1, 1))\n",
        "      self.X = target_scaler.transform(self.X_np.reshape(-1, 1))\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.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_RMSE = []\n",
        "    stdv_RMSE = []\n",
        "    mean_MAE = []\n",
        "    stdv_MAE = []\n",
        "\n",
        "    if self.level.lower() == 'class' or self.level.lower() == 'package':\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",
        "          if j == 'PolR':\n",
        "            \n",
        "            #temp_data = self.X[i]\n",
        "            level.append(self.level)\n",
        "            metric.append(self.data_columns[i])\n",
        "\n",
        "            cnt=0\n",
        "            RMSE=[]\n",
        "            MAE=[]\n",
        "            poly_reg = PolynomialFeatures(degree=7)\n",
        "            temp_data = poly_reg.fit_transform(self.X_np[:,i].reshape(-1, 1))\n",
        "            pol_reg = LinearRegression()\n",
        "            model.append('PolR')\n",
        "            warnings.filterwarnings('ignore')\n",
        "            print('Metric: ',self.data_columns[i])\n",
        "            print('Level: ',self.level)\n",
        "            print('Model: ',j)\n",
        "            print()\n",
        "            print(\"**************************************************************\")\n",
        "            print()\n",
        "            print('At i: ',i,' x = ',self.X_np[:,i])\n",
        "            print()\n",
        "\n",
        "            for train_index, test_index in kf.split(temp_data):\n",
        "              cnt = cnt+1\n",
        "              #print('Fold: ',cnt)\n",
        "              X_train, X_test = temp_data[train_index], temp_data[test_index]\n",
        "              y_train, y_test = self.Y[train_index], self.Y[test_index]\n",
        "\n",
        "              pol_reg.fit(X_train,y_train)\n",
        "              y_pred = pol_reg.predict(X_test)\n",
        "              RMSE.append(np.sqrt(mean_squared_error(y_test,y_pred)))\n",
        "              MAE.append(mean_absolute_error(y_test,y_pred))\n",
        "            \n",
        "            mean_RMSE.append(statistics.mean(RMSE))\n",
        "            stdv_RMSE.append(statistics.pstdev(RMSE))\n",
        "            mean_MAE.append(statistics.mean(MAE))\n",
        "            stdv_MAE.append(statistics.pstdev(MAE))\n",
        "\n",
        "          else:\n",
        "            \n",
        "            warnings.filterwarnings('ignore')\n",
        "            #['Polynomial Regression','MLPRegressor','BayesianRidge','SGD Regressor','XGB Regressor','Decision Tree Regressor','ElasticNet','SVM Regressor',]\n",
        "            if j == 'MLPR':\n",
        "              reg = MLPRegressor()\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 == 'BR':\n",
        "              reg = BayesianRidge()\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 == 'SGDR':\n",
        "              reg = SGDRegressor()\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 == 'XGBR':\n",
        "              reg = XGBRegressor()\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 == 'DTR':\n",
        "              reg = DecisionTreeRegressor()\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 == 'EN':\n",
        "              reg = ElasticNet()\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 == 'SVR':\n",
        "              reg = SVR()\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",
        "            RMSE=[]\n",
        "            MAE=[]\n",
        "            for train_index, test_index in kf.split(temp_data):\n",
        "              cnt = cnt+1\n",
        "              #print('Fold: ',cnt)\n",
        "              X_train, X_test = temp_data[train_index], temp_data[test_index]\n",
        "              y_train, y_test = self.Y[train_index], self.Y[test_index]\n",
        "\n",
        "              reg.fit(X_train,y_train)\n",
        "              y_pred = reg.predict(X_test)\n",
        "              RMSE.append(np.sqrt(mean_squared_error(y_test,y_pred)))\n",
        "              MAE.append(mean_absolute_error(y_test,y_pred))\n",
        "\n",
        "            mean_RMSE.append(statistics.mean(RMSE))\n",
        "            stdv_RMSE.append(statistics.pstdev(RMSE))\n",
        "            mean_MAE.append(statistics.mean(MAE))\n",
        "            stdv_MAE.append(statistics.pstdev(MAE))\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_RMSE = pd.DataFrame(mean_RMSE,columns=['RMSE'])\n",
        "      stdv_RMSE = pd.DataFrame(stdv_RMSE,columns=['STD RMSE'])\n",
        "      mean_MAE = pd.DataFrame(mean_MAE,columns=['MAE'])\n",
        "      stdv_MAE = pd.DataFrame(stdv_MAE,columns=['STD MAE'])\n",
        "\n",
        "      res_df = pd.concat([metric,level,model,mean_RMSE,stdv_RMSE,mean_MAE,stdv_MAE],axis=1)\n",
        "\n",
        "      return res_df\n",
        "    \n",
        "    else:\n",
        "\n",
        "      for j in self.ml_algo_list:\n",
        "        if j == 'PolR':\n",
        "          #temp_data = self.X[i]\n",
        "          level.append(self.level)\n",
        "          #metric.append(self.data_columns[i])\n",
        "\n",
        "          cnt=0\n",
        "          RMSE=[]\n",
        "          MAE=[]\n",
        "          poly_reg = PolynomialFeatures()\n",
        "          temp_data = poly_reg.fit_transform(self.X.reshape(-1, 1))\n",
        "          pol_reg = LinearRegression()\n",
        "          model.append('PolR')\n",
        "          warnings.filterwarnings('ignore')\n",
        "          #print('Metric: ',self.data_columns[i])\n",
        "          print('Level: ',self.level)\n",
        "          print('Model: ',j)\n",
        "          print()\n",
        "          print(\"**************************************************************\")\n",
        "          print()\n",
        "          #print('At i: ',i,' x = ',self.X)\n",
        "          print()\n",
        "\n",
        "          for train_index, test_index in kf.split(temp_data):\n",
        "            cnt = cnt+1\n",
        "            #print('Fold: ',cnt)\n",
        "            X_train, X_test = temp_data[train_index], temp_data[test_index]\n",
        "            y_train, y_test = self.Y[train_index], self.Y[test_index]\n",
        "\n",
        "            pol_reg.fit(X_train,y_train)\n",
        "            y_pred = pol_reg.predict(X_test)\n",
        "            RMSE.append(np.sqrt(mean_squared_error(y_test,y_pred)))\n",
        "            MAE.append(mean_absolute_error(y_test,y_pred))\n",
        "          \n",
        "          mean_RMSE.append(statistics.mean(RMSE))\n",
        "          stdv_RMSE.append(statistics.pstdev(RMSE))\n",
        "          mean_MAE.append(statistics.mean(MAE))\n",
        "          stdv_MAE.append(statistics.pstdev(MAE))\n",
        "\n",
        "        else:\n",
        "          \n",
        "          warnings.filterwarnings('ignore')\n",
        "          #['Polynomial Regression','MLPRegressor','BayesianRidge','SGD Regressor','XGB Regressor','Decision Tree Regressor','ElasticNet','SVM Regressor',]\n",
        "          if j == 'MLPR':\n",
        "            reg = MLPRegressor()\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 == 'BR':\n",
        "            reg = BayesianRidge()\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 == 'SGDR':\n",
        "            reg = SGDRegressor()\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 == 'XGBR':\n",
        "            reg = XGBRegressor()\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 == 'DTR':\n",
        "            reg = DecisionTreeRegressor()\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 == 'EN':\n",
        "            reg = ElasticNet()\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 == 'SVR':\n",
        "            reg = SVR()\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[:,0])\n",
        "          #print()\n",
        "          #temp_data = self.X.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",
        "          temp_data = self.X\n",
        "\n",
        "          cnt=0\n",
        "          RMSE=[]\n",
        "          MAE=[]\n",
        "          for train_index, test_index in kf.split(temp_data):\n",
        "            cnt = cnt+1\n",
        "            #print('Fold: ',cnt)\n",
        "            X_train, X_test = temp_data[train_index], temp_data[test_index]\n",
        "            y_train, y_test = self.Y[train_index], self.Y[test_index]\n",
        "\n",
        "            reg.fit(X_train,y_train)\n",
        "            y_pred = reg.predict(X_test)\n",
        "            RMSE.append(np.sqrt(mean_squared_error(y_test,y_pred)))\n",
        "            MAE.append(mean_absolute_error(y_test,y_pred))\n",
        "\n",
        "          mean_RMSE.append(statistics.mean(RMSE))\n",
        "          stdv_RMSE.append(statistics.pstdev(RMSE))\n",
        "          mean_MAE.append(statistics.mean(MAE))\n",
        "          stdv_MAE.append(statistics.pstdev(MAE))\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_RMSE = pd.DataFrame(mean_RMSE,columns=['RMSE'])\n",
        "    stdv_RMSE = pd.DataFrame(stdv_RMSE,columns=['STD RMSE'])\n",
        "    mean_MAE = pd.DataFrame(mean_MAE,columns=['MAE'])\n",
        "    stdv_MAE = pd.DataFrame(stdv_MAE,columns=['STD MAE'])\n",
        "\n",
        "    res_df = pd.concat([level,model,mean_RMSE,stdv_RMSE,mean_MAE,stdv_MAE],axis=1)\n",
        "\n",
        "    return res_df\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IxBnOG4tsLq"
      },
      "source": [
        "warnings.filterwarnings('ignore')\n",
        "rtm=ReuseTradML(level='package')\n",
        "res=rtm.apply_ml()\n",
        "res"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNDKHXcZbI-m",
        "outputId": "b57bb98b-3bf4-4e83-e880-08fafe73410a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "warnings.filterwarnings('ignore')\n",
        "rtm=ReuseTradML(level='class_frequency')\n",
        "res=rtm.apply_ml()\n",
        "res"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Level:  class_frequency\n",
            "Model:  PolR\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "\n",
            "Level:  class_frequency\n",
            "Model:  MLPR\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "Level:  class_frequency\n",
            "Model:  BR\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "Level:  class_frequency\n",
            "Model:  SGDR\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "Level:  class_frequency\n",
            "Model:  XGBR\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[00:07:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Level:  class_frequency\n",
            "Model:  DTR\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "Level:  class_frequency\n",
            "Model:  EN\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "Level:  class_frequency\n",
            "Model:  SVR\n",
            "\n",
            "**************************************************************\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "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>Level</th>\n",
              "      <th>Model</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>STD RMSE</th>\n",
              "      <th>MAE</th>\n",
              "      <th>STD MAE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>class_frequency</td>\n",
              "      <td>PolR</td>\n",
              "      <td>0.160590</td>\n",
              "      <td>0.108808</td>\n",
              "      <td>0.112904</td>\n",
              "      <td>0.056627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>class_frequency</td>\n",
              "      <td>MLPR</td>\n",
              "      <td>0.161323</td>\n",
              "      <td>0.122559</td>\n",
              "      <td>0.113221</td>\n",
              "      <td>0.056026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>class_frequency</td>\n",
              "      <td>BR</td>\n",
              "      <td>0.152584</td>\n",
              "      <td>0.117237</td>\n",
              "      <td>0.101454</td>\n",
              "      <td>0.053117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>class_frequency</td>\n",
              "      <td>SGDR</td>\n",
              "      <td>0.151727</td>\n",
              "      <td>0.133897</td>\n",
              "      <td>0.088339</td>\n",
              "      <td>0.059167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>class_frequency</td>\n",
              "      <td>XGBR</td>\n",
              "      <td>0.140964</td>\n",
              "      <td>0.117392</td>\n",
              "      <td>0.075506</td>\n",
              "      <td>0.051277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>class_frequency</td>\n",
              "      <td>DTR</td>\n",
              "      <td>0.190341</td>\n",
              "      <td>0.151364</td>\n",
              "      <td>0.108043</td>\n",
              "      <td>0.075303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>class_frequency</td>\n",
              "      <td>EN</td>\n",
              "      <td>0.156521</td>\n",
              "      <td>0.121989</td>\n",
              "      <td>0.106888</td>\n",
              "      <td>0.058815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>class_frequency</td>\n",
              "      <td>SVR</td>\n",
              "      <td>0.156842</td>\n",
              "      <td>0.112999</td>\n",
              "      <td>0.114055</td>\n",
              "      <td>0.059764</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Level Model      RMSE  STD RMSE       MAE   STD MAE\n",
              "0  class_frequency  PolR  0.160590  0.108808  0.112904  0.056627\n",
              "1  class_frequency  MLPR  0.161323  0.122559  0.113221  0.056026\n",
              "2  class_frequency    BR  0.152584  0.117237  0.101454  0.053117\n",
              "3  class_frequency  SGDR  0.151727  0.133897  0.088339  0.059167\n",
              "4  class_frequency  XGBR  0.140964  0.117392  0.075506  0.051277\n",
              "5  class_frequency   DTR  0.190341  0.151364  0.108043  0.075303\n",
              "6  class_frequency    EN  0.156521  0.121989  0.106888  0.058815\n",
              "7  class_frequency   SVR  0.156842  0.112999  0.114055  0.059764"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlNJ_7Ott0E2"
      },
      "source": [
        "res.to_excel('/content/ReusabilityRate_ML/Results/class_frequencyRes_TraditionalModels.xlsx')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XG3fO6qQ0BpD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}