support_vector.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>6</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>103.13</td>\n",
       "      <td>103.13</td>\n",
       "      <td>4.166667</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.08</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>66.25</td>\n",
       "      <td>103.13</td>\n",
       "      <td>3.703704</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.84</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>125.99</td>\n",
       "      <td>103.13</td>\n",
       "      <td>1.063830</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.74</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>119.12</td>\n",
       "      <td>103.13</td>\n",
       "      <td>5.555556</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.22</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>138.06</td>\n",
       "      <td>103.13</td>\n",
       "      <td>1.149425</td>\n",
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       "      <td>1.20</td>\n",
       "      <td>6.93</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>660.68</td>\n",
       "      <td>191.95</td>\n",
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       "      <th>4501</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1.51</td>\n",
       "      <td>7.24</td>\n",
       "      <td>3.703704</td>\n",
       "      <td>560.99</td>\n",
       "      <td>92.26</td>\n",
       "      <td>0.793651</td>\n",
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       "      <th>4502</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1.51</td>\n",
       "      <td>6.93</td>\n",
       "      <td>3.703704</td>\n",
       "      <td>660.68</td>\n",
       "      <td>92.26</td>\n",
       "      <td>0.854701</td>\n",
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       "    <tr>\n",
       "      <th>4503</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1.20</td>\n",
       "      <td>6.31</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>860.06</td>\n",
       "      <td>191.95</td>\n",
       "      <td>1.010101</td>\n",
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       "    <tr>\n",
       "      <th>4504</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1.20</td>\n",
       "      <td>6.62</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>760.37</td>\n",
       "      <td>191.95</td>\n",
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      "text/plain": [
       "         0     1     2         3       4       5         6\n",
       "0     0.32  0.47  0.47  4.166667  103.13  103.13  4.166667\n",
       "1     0.32  0.47  1.08  4.166667   66.25  103.13  3.703704\n",
       "2     0.32  0.47  0.84  4.166667  125.99  103.13  1.063830\n",
       "3     0.32  0.47  0.74  4.166667  119.12  103.13  5.555556\n",
       "4     0.32  0.47  0.22  4.166667  138.06  103.13  1.149425\n",
       "...    ...   ...   ...       ...     ...     ...       ...\n",
       "4500  0.46  1.20  6.93  5.555556  660.68  191.95  0.854701\n",
       "4501  0.46  1.51  7.24  3.703704  560.99   92.26  0.793651\n",
       "4502  0.46  1.51  6.93  3.703704  660.68   92.26  0.854701\n",
       "4503  0.46  1.20  6.31  5.555556  860.06  191.95  1.010101\n",
       "4504  0.46  1.20  6.62  5.555556  760.37  191.95  0.925926\n",
       "\n",
       "[4505 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('quad_1.csv')\n",
    "\n",
    "#X_train = dataset.iloc[:, [ 2,3,6,7,13,15,16,17,18,19 ]].values\n",
    "#y_train = dataset.iloc[:, 24].values\n",
    "\n",
    "#X_train = dataset.iloc[:, [3,4,8,9,15,18,19,20,21,22  ]].values\n",
    "#y_train = dataset.iloc[:, 28].values\n",
    "\n",
    "X_train = dataset.iloc[:, [\n",
    "                    #16,18,22,21,27,28,31,34\n",
    "                    17,24,33,23,35,26,32\n",
    "                        #6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26\n",
    "                          ]].values\n",
    "y_train = dataset.iloc[:, 4].values\n",
    "\n",
    "dataset = pd.read_csv('quad_2_3_4.csv')\n",
    "X_test2 = dataset.iloc[:, [\n",
    "                     #6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26\n",
    "                    #16,18,22,21,27,28,31,34\n",
    "                    17,24,33,23,35,26,32\n",
    "                        ]].values\n",
    "y_test2 = dataset.iloc[:, 4].values\n",
    "\n",
    "#df_X = pd.DataFrame(X_train, columns=[\"DPU size MAC\", \"DPU size wts\", \"#dpus\", \"mops\", \"time1\", \"wts1\", \"layers1\", \"MBpl1\",\n",
    "                                     # \"BW1\", \"MACpl1\", \"MACwt1\", \"mops_others\", \"time_others\", \"wts_others\", \"layers_others\", \"MBpl_others\",\n",
    "                                     # \"BW_others\", \"MACpl_other\", \"MACpt_others\", \"MACwt_others\"])\n",
    "#X_train.head()\n",
    "pd.DataFrame(X_train)\n",
    "X_train[:,3]=1/X_train[:,3]\n",
    "X_test2[:,3]=1/X_test2[:,3]\n",
    "X_train[:,6]=1/X_train[:,6]\n",
    "X_test2[:,6]=1/X_test2[:,6]\n",
    "pd.DataFrame(X_train)\n",
    "#X_train, X_test2, y_train, y_test2 = train_test_split(X, y, test_size=0.20, random_state=5, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.838074954227193"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test2 = sc_X.transform(X_test2)\n",
    "sc_y = StandardScaler()\n",
    "\n",
    "regressor = SVR(kernel = 'rbf')\n",
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_train)\n",
    "y_pred1 = regressor.predict(X_test2)\n",
    "#y_pred_total = regressor.predict(X)\n",
    "np.savetxt(\"test.txt\", y_pred1,fmt='%1.1f')\n",
    "\n",
    "regressor.score(X_test2,y_test2)\n",
    "\n",
    "#from sklearn.ensemble import RandomForestRegressor\n",
    "#regressor = RandomForestRegressor(n_estimators = 100,random_state = 0)\n",
    "#regressor.fit(X_train, y_train)\n",
    "\n",
    "#regressor.score(X_train,y_train)\n",
    "#importance = regressor.feature_importances_\n",
    "\n",
    "#regressor.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#klearn.metrics.classification_report()\n",
    "#egressor.summary()\n",
    "#regressor.score(X_train,y_train)\n",
    "#importance = regressor.feature_importances_\n",
    "#print(importance)\n",
    "#for i,v in enumerate(importance):\n",
    "#\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "#plot feature importance\n",
    "#plt.bar([x for x in range(len(importance))], importance)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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