cost-model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "engaged-phrase",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA as sklearnPCA\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from warnings import simplefilter\n",
    "simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "spatial-royalty",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 217 ms, sys: 34 ms, total: 251 ms\n",
      "Wall time: 251 ms\n"
     ]
    },
    {
     "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>Kernel</th>\n",
       "      <th>Architechture</th>\n",
       "      <th>Vendor</th>\n",
       "      <th>CPUs</th>\n",
       "      <th>Cycle</th>\n",
       "      <th>L1</th>\n",
       "      <th>L2</th>\n",
       "      <th>L3</th>\n",
       "      <th>RAM)</th>\n",
       "      <th>Threads</th>\n",
       "      <th>...</th>\n",
       "      <th>addInt</th>\n",
       "      <th>addFloat</th>\n",
       "      <th>mulInt</th>\n",
       "      <th>mulFloat</th>\n",
       "      <th>assignInt</th>\n",
       "      <th>assignFloat</th>\n",
       "      <th>addAssignInt</th>\n",
       "      <th>addAssignFloat</th>\n",
       "      <th>postIncInt</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kernel_100_100_1_5_9_0</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kernel_100_100_1_5_9_1</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kernel_100_100_1_5_9_2</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>603</td>\n",
       "      <td>0</td>\n",
       "      <td>703</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kernel_100_100_1_5_9_3</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>603</td>\n",
       "      <td>0</td>\n",
       "      <td>1003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>201</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kernel_100_100_1_5_9_4</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1007</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0.001</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138363</th>\n",
       "      <td>kernel_600_600_1_5_9_123</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>3603</td>\n",
       "      <td>3603</td>\n",
       "      <td>8403</td>\n",
       "      <td>4803</td>\n",
       "      <td>0</td>\n",
       "      <td>6007</td>\n",
       "      <td>4801</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138364</th>\n",
       "      <td>kernel_600_600_1_5_9_124</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>3603</td>\n",
       "      <td>3000</td>\n",
       "      <td>4803</td>\n",
       "      <td>6007</td>\n",
       "      <td>6007</td>\n",
       "      <td>4201</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138365</th>\n",
       "      <td>kernel_600_600_1_5_9_125</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>3603</td>\n",
       "      <td>4800</td>\n",
       "      <td>4803</td>\n",
       "      <td>6007</td>\n",
       "      <td>6007</td>\n",
       "      <td>4801</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138366</th>\n",
       "      <td>kernel_600_600_1_5_9_126</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>3603</td>\n",
       "      <td>3603</td>\n",
       "      <td>7203</td>\n",
       "      <td>4803</td>\n",
       "      <td>6007</td>\n",
       "      <td>6007</td>\n",
       "      <td>4801</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138367</th>\n",
       "      <td>kernel_600_600_1_5_9_127</td>\n",
       "      <td>x86_64</td>\n",
       "      <td>Intel</td>\n",
       "      <td>40</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>20480</td>\n",
       "      <td>27648</td>\n",
       "      <td>187.57</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>3603</td>\n",
       "      <td>3603</td>\n",
       "      <td>9003</td>\n",
       "      <td>4803</td>\n",
       "      <td>6007</td>\n",
       "      <td>6007</td>\n",
       "      <td>5401</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138368 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Kernel Architechture Vendor  CPUs  Cycle   L1  \\\n",
       "0         kernel_100_100_1_5_9_0        x86_64  Intel    40   1000  640   \n",
       "1         kernel_100_100_1_5_9_1        x86_64  Intel    40   1000  640   \n",
       "2         kernel_100_100_1_5_9_2        x86_64  Intel    40   1000  640   \n",
       "3         kernel_100_100_1_5_9_3        x86_64  Intel    40   1000  640   \n",
       "4         kernel_100_100_1_5_9_4        x86_64  Intel    40   1000  640   \n",
       "...                          ...           ...    ...   ...    ...  ...   \n",
       "138363  kernel_600_600_1_5_9_123        x86_64  Intel    40   1000  640   \n",
       "138364  kernel_600_600_1_5_9_124        x86_64  Intel    40   1000  640   \n",
       "138365  kernel_600_600_1_5_9_125        x86_64  Intel    40   1000  640   \n",
       "138366  kernel_600_600_1_5_9_126        x86_64  Intel    40   1000  640   \n",
       "138367  kernel_600_600_1_5_9_127        x86_64  Intel    40   1000  640   \n",
       "\n",
       "           L2     L3    RAM)  Threads  ...  addInt  addFloat  mulInt  \\\n",
       "0       20480  27648  187.57       28  ...       0         0       0   \n",
       "1       20480  27648  187.57       28  ...     200         0       0   \n",
       "2       20480  27648  187.57       28  ...       0         0     603   \n",
       "3       20480  27648  187.57       28  ...     200         0     603   \n",
       "4       20480  27648  187.57       28  ...       0         0       0   \n",
       "...       ...    ...     ...      ...  ...     ...       ...     ...   \n",
       "138363  20480  27648  187.57       28  ...    1200      1200    3603   \n",
       "138364  20480  27648  187.57       28  ...       0      1200       0   \n",
       "138365  20480  27648  187.57       28  ...    1200      1200       0   \n",
       "138366  20480  27648  187.57       28  ...       0      1200    3603   \n",
       "138367  20480  27648  187.57       28  ...    1200      1200    3603   \n",
       "\n",
       "        mulFloat  assignInt  assignFloat  addAssignInt  addAssignFloat  \\\n",
       "0              0          0            0             0               0   \n",
       "1              0        300            0             0               0   \n",
       "2              0        703            0             0               0   \n",
       "3              0       1003            0             0               0   \n",
       "4              0        100            0          1007               0   \n",
       "...          ...        ...          ...           ...             ...   \n",
       "138363      3603       8403         4803             0            6007   \n",
       "138364      3603       3000         4803          6007            6007   \n",
       "138365      3603       4800         4803          6007            6007   \n",
       "138366      3603       7203         4803          6007            6007   \n",
       "138367      3603       9003         4803          6007            6007   \n",
       "\n",
       "        postIncInt   Time  \n",
       "0                1  0.001  \n",
       "1              101  0.001  \n",
       "2              101  0.001  \n",
       "3              201  0.001  \n",
       "4              101  0.001  \n",
       "...            ...    ...  \n",
       "138363        4801  0.057  \n",
       "138364        4201  0.061  \n",
       "138365        4801  0.061  \n",
       "138366        4801  0.065  \n",
       "138367        5401  0.065  \n",
       "\n",
       "[138368 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "dataset = pd.read_csv(\"data_seawulf.csv\")\n",
    "dataset.columns = ((dataset.columns.str).replace(\"^ \",\"\")).str.replace(\" $\",\"\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "wicked-westminster",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 319 ms, sys: 77.5 ms, total: 397 ms\n",
      "Wall time: 419 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "scaled_features = dataset.copy()\n",
    "# col_names = ['CPUs','Cycle','L1','L2','L3','RAM)','Threads','Iterations','varDecl','refExpr','intLiteral','floatLiteral','addInt','addFloat','mulInt','mulFloat','assignInt','assignFloat','addAssignInt','addAssignFloat','postIncInt','Time']\n",
    "col_names = ['CPUs','Cycle','L1','L2','L3','RAM)','Threads','Iterations','varDecl','refExpr','intLiteral','floatLiteral','addInt','addFloat','mulInt','mulFloat','assignInt','assignFloat','addAssignInt','addAssignFloat','postIncInt']\n",
    "features = scaled_features[col_names]\n",
    "scaler = StandardScaler().fit(features.values)\n",
    "features = scaler.transform(features.values)\n",
    "scaled_features[col_names] = features\n",
    "#scaled_features\n",
    "x=scaled_features.iloc[:,3:24].values\n",
    "y=scaled_features.iloc[:,24:].values\n",
    "# st_x = StandardScaler()\n",
    "# st_y = StandardScaler()\n",
    "# X = st_x.fit_transform(x)\n",
    "# Y = st_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "selective-occasions",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "regressor_rbf = SVR(kernel='rbf')\n",
    "regressor_rbf.fit(x,y.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "exclusive-secret",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12min 23s, sys: 2.74 s, total: 12min 26s\n",
      "Wall time: 12min 27s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR(kernel='linear')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "regressor_linear = SVR(kernel='linear')\n",
    "regressor_linear.fit(x,y.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "julian-treasurer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2h 1min 15s, sys: 22.8 s, total: 2h 1min 37s\n",
      "Wall time: 2h 6min 15s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR(kernel='poly')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "regressor_poly = SVR(kernel='poly')\n",
    "regressor_poly.fit(x,y.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "republican-application",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 42s, sys: 299 ms, total: 1min 42s\n",
      "Wall time: 1min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "prediction_rbf = regressor_rbf.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "constant-china",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 4s, sys: 243 ms, total: 3min 5s\n",
      "Wall time: 3min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "prediction_linear = regressor_linear.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "complicated-concord",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 18s, sys: 95.5 ms, total: 1min 18s\n",
      "Wall time: 1min 18s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "prediction_poly = regressor_poly.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "mathematical-whale",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter(transformed,Y.flatten())\n",
    "# plt.scatter(transformed, prediction_rbf, color='cyan')\n",
    "# plt.scatter(transformed, prediction_linear, color='red')\n",
    "# plt.scatter(transformed, prediction_poly, color='yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "excessive-frontier",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mse_rbf=mean_squared_error(Y, prediction_rbf)\n",
    "# rmse_rbf=np.sqrt(mse_rbf)\n",
    "# mse_linear=mean_squared_error(Y, prediction_linear)\n",
    "# rmse_linear=np.sqrt(mse_linear)\n",
    "# mse_poly=mean_squared_error(Y, prediction_poly)\n",
    "# rmse_poly=np.sqrt(mse_poly)\n",
    "# print(f'rbf = {rmse_rbf}, linear = {rmse_linear}, poly = {rmse_poly}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "herbal-impossible",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9ce7f80be0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.scatter(prediction_rbf,y, color='red')\n",
    "ax.scatter(prediction_linear,y, color='yellow')\n",
    "ax.scatter(prediction_poly,y, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "controversial-petite",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbf = 1.4624571406338756, linear = 4.648080339587879, poly = 0.22198870455926456\n"
     ]
    }
   ],
   "source": [
    "mse_rbf=mean_squared_error(y, prediction_rbf)\n",
    "rmse_rbf=np.sqrt(mse_rbf)\n",
    "mse_linear=mean_squared_error(y, prediction_linear)\n",
    "rmse_linear=np.sqrt(mse_linear)\n",
    "mse_poly=mean_squared_error(y, prediction_poly)\n",
    "rmse_poly=np.sqrt(mse_poly)\n",
    "print(f'rbf = {rmse_rbf}, linear = {rmse_linear}, poly = {rmse_poly}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "proud-clone",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 174 ms, sys: 98.7 ms, total: 273 ms\n",
      "Wall time: 239 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4.4674964069567995"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn import linear_model\n",
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(x,y)\n",
    "prediction_linReg = reg.predict(x)\n",
    "mse_linReg=mean_squared_error(y, prediction_linReg)\n",
    "rmse_linReg=np.sqrt(mse_linReg)\n",
    "rmse_linReg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "imposed-coordinate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train = (124531, 21), X_test = (13837, 21), y_train = (124531, 1), y_test = (13837, 1)\n"
     ]
    }
   ],
   "source": [
    "# test = pd.read_csv(\"test_case.csv\")\n",
    "from sklearn.model_selection import train_test_split\n",
    "col_names = ['CPUs','Cycle','L1','L2','L3','RAM)','Threads','Iterations','varDecl','refExpr','intLiteral','floatLiteral','addInt','addFloat','mulInt','mulFloat','assignInt','assignFloat','addAssignInt','addAssignFloat','postIncInt']\n",
    "features = StandardScaler().fit_transform(dataset[col_names])\n",
    "runtimes = dataset[['Time']]\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, runtimes, test_size=0.1, random_state=0)\n",
    "print(f'X_train = {X_train.shape}, X_test = {X_test.shape}, y_train = {y_train.shape}, y_test = {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "distinct-twenty",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 41min 39s, sys: 12.7 s, total: 1h 41min 51s\n",
      "Wall time: 2h 19min 14s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "regressor_poly = SVR(kernel='poly').fit(X_train,y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "binary-major",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.8 s, sys: 56.9 ms, total: 13.8 s\n",
      "Wall time: 13.8 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9996239169793114"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "regressor_poly.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "polish-bennett",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.7 s, sys: 26.7 ms, total: 13.7 s\n",
      "Wall time: 13.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "prediction_poly = regressor_poly.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "stunning-active",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f94e9741400>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.scatter(prediction_poly,y_test, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "victorian-reach",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23770481738140953"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_poly=mean_squared_error(y_test, prediction_poly)\n",
    "rmse_poly=np.sqrt(mse_poly)\n",
    "rmse_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "color-brown",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}