Comparison_LinerReg_RandomFrst.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arff, numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = arff.load(open('cocomo811.arff'))\n",
    "data=np.array(dataset['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[:,0:16]\n",
    "Y=data[:,16:17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=Y.reshape(63)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, Y, test_size=0.30, random_state=42)\n",
    "from sklearn import preprocessing\n",
    "lab_enc = preprocessing.LabelEncoder()\n",
    "training_scores_encoded = lab_enc.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "MMRE_list=[]\n",
    "MdMRE_list=[]\n",
    "Pred_list= []\n",
    "for i in np.arange(1,11):   \n",
    "    clf= RandomForestClassifier(n_estimators=100,max_features=i)\n",
    "    clf.fit(X_train, training_scores_encoded)\n",
    "    Y_pred=clf.predict(X_test)\n",
    "    diff=np.absolute(y_test-Y_pred)\n",
    "    MRE=diff/y_test\n",
    "    MMRE=np.mean(MRE)\n",
    "    MdMRE=np.median(MRE)\n",
    "    P=MRE[MRE<.25]\n",
    "    Pred=(P.size/MRE.size) * 100\n",
    "    MMRE_list.append(MMRE)\n",
    "    MdMRE_list.append(MdMRE)\n",
    "    Pred_list.append(Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8191628522560052,\n",
       " 0.8167810664161552,\n",
       " 0.8266952733946559,\n",
       " 0.8224046398769348,\n",
       " 0.7995433864474922,\n",
       " 0.8098648409339096,\n",
       " 0.8273091632211803,\n",
       " 0.8532167684259131,\n",
       " 0.8502297799004133,\n",
       " 0.845698095057094]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MMRE_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 10.526315789473683,\n",
       " 10.526315789473683,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pred_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1,11)\n",
    "\n",
    "plt.plot(x, MMRE_list)\n",
    "plt.plot(x, Pred_list)\n",
    "\n",
    "plt.legend(['MMRE', 'PRED(.25)'], loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "MMRE_list=[]\n",
    "MdMRE_list=[]\n",
    "Pred_list= []\n",
    "for i in np.arange(100,2000,100):   \n",
    "    clf= RandomForestClassifier(n_estimators=i,max_features=7)\n",
    "    clf.fit(X_train, training_scores_encoded)\n",
    "    Y_pred=clf.predict(X_test)\n",
    "    diff=np.absolute(y_test-Y_pred)\n",
    "    MRE=diff/y_test\n",
    "    MMRE=np.mean(MRE)\n",
    "    MdMRE=np.median(MRE)\n",
    "    P=MRE[MRE<.25]\n",
    "    Pred=(P.size/MRE.size) * 100\n",
    "    MMRE_list.append(MMRE)\n",
    "    MdMRE_list.append(MdMRE)\n",
    "    Pred_list.append(Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(100,2000,100)\n",
    "\n",
    "plt.plot(x, MMRE_list)\n",
    "plt.plot(x, Pred_list)\n",
    "\n",
    "plt.legend(['MMRE', 'PRED(.25)'], loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8385882830375305,\n",
       " 0.8286124783212316,\n",
       " 0.857380862101287,\n",
       " 0.8430833852055422,\n",
       " 0.8573900957116285,\n",
       " 0.8194859276694129,\n",
       " 0.8112446504287296,\n",
       " 0.8601608890918403,\n",
       " 0.857380862101287,\n",
       " 0.857380862101287,\n",
       " 0.8556357097467163,\n",
       " 0.8448377711704543,\n",
       " 0.842057744179901,\n",
       " 0.8577409729046109,\n",
       " 0.8580602427391163,\n",
       " 0.842057744179901,\n",
       " 0.8580602427391163,\n",
       " 0.8556264761363745,\n",
       " 0.8403033582149887]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MMRE_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842,\n",
       " 5.263157894736842]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pred_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# data to plot\n",
    "n_groups = 3\n",
    "RT = (.840, .877, 0.756)\n",
    "RF = (.493, .821, 0.751)\n",
    " \n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, RT, bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Regression Tree')\n",
    " \n",
    "rects2 = plt.bar(index + bar_width+.05, RF, bar_width,\n",
    "alpha=opacity,\n",
    "color='g',\n",
    "label='Random Forest')\n",
    " \n",
    "plt.xlabel('Datasets')\n",
    "plt.ylabel('MMRE')\n",
    "#plt.title('Scores by person')\n",
    "plt.xticks(index + bar_width, ('Albrecht', 'Desharnais', 'COCOMO81'))\n",
    "plt.legend()\n",
    " \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# data to plot\n",
    "n_groups = 3\n",
    "RT = (.472, .504, 0.83940)\n",
    "RF = (.465, .3291, 0.7836)\n",
    " \n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, RT, bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Regression Tree')\n",
    " \n",
    "rects2 = plt.bar(index + bar_width+.05, RF, bar_width,\n",
    "alpha=opacity,\n",
    "color='g',\n",
    "label='Random Forest')\n",
    " \n",
    "plt.xlabel('Datasets')\n",
    "plt.ylabel('MdMRE')\n",
    "#plt.title('Scores by person')\n",
    "plt.xticks(index + bar_width, ('Albrecht', 'Desharnais', 'COCOMO81'))\n",
    "plt.legend()\n",
    " \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# data to plot\n",
    "n_groups = 3\n",
    "RT = (25, 36, 5.26)\n",
    "RF = (25, 40, 5.26)\n",
    " \n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "opacity = 0.8\n",
    " \n",
    "rects1 = plt.bar(index, RT, bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Regression Tree')\n",
    " \n",
    "rects2 = plt.bar(index + bar_width+.05, RF, bar_width,\n",
    "alpha=opacity,\n",
    "color='g',\n",
    "label='Random Forest')\n",
    " \n",
    "plt.xlabel('Datasets')\n",
    "plt.ylabel('PRED(25)')\n",
    "#plt.title('Scores by person')\n",
    "plt.xticks(index + bar_width, ('Albrecht', 'Desharnais', 'COCOMO81'))\n",
    "plt.legend()\n",
    " \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}