hand_data_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 정확도\n",
    "def get_clf_eval(y_test, y_pred):\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print('\\n정확도: {:.4f}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.x</th>\n",
       "      <th>0.y</th>\n",
       "      <th>1.x</th>\n",
       "      <th>1.y</th>\n",
       "      <th>2.x</th>\n",
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       "      <th>4.y</th>\n",
       "      <th>...</th>\n",
       "      <th>16.y</th>\n",
       "      <th>17.x</th>\n",
       "      <th>17.y</th>\n",
       "      <th>18.x</th>\n",
       "      <th>18.y</th>\n",
       "      <th>19.x</th>\n",
       "      <th>19.y</th>\n",
       "      <th>20.x</th>\n",
       "      <th>20.y</th>\n",
       "      <th>order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>156.763935</td>\n",
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       "      <td>374.050598</td>\n",
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       "      <td>373.679523</td>\n",
       "      <td>239.323101</td>\n",
       "      <td>371.736917</td>\n",
       "      <td>217.546730</td>\n",
       "      <td>367.677984</td>\n",
       "      <td>incr</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>143.475761</td>\n",
       "      <td>344.199715</td>\n",
       "      <td>171.049080</td>\n",
       "      <td>306.954203</td>\n",
       "      <td>212.921429</td>\n",
       "      <td>261.331959</td>\n",
       "      <td>236.131897</td>\n",
       "      <td>224.286804</td>\n",
       "      <td>245.477505</td>\n",
       "      <td>188.725119</td>\n",
       "      <td>...</td>\n",
       "      <td>341.208458</td>\n",
       "      <td>183.233604</td>\n",
       "      <td>376.094484</td>\n",
       "      <td>246.375809</td>\n",
       "      <td>377.355795</td>\n",
       "      <td>230.304756</td>\n",
       "      <td>373.775711</td>\n",
       "      <td>207.152386</td>\n",
       "      <td>369.440460</td>\n",
       "      <td>incr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150.850964</td>\n",
       "      <td>344.401903</td>\n",
       "      <td>173.355370</td>\n",
       "      <td>305.092764</td>\n",
       "      <td>211.292782</td>\n",
       "      <td>259.847116</td>\n",
       "      <td>231.831303</td>\n",
       "      <td>223.427854</td>\n",
       "      <td>236.018009</td>\n",
       "      <td>188.927751</td>\n",
       "      <td>...</td>\n",
       "      <td>340.057840</td>\n",
       "      <td>182.300110</td>\n",
       "      <td>377.768726</td>\n",
       "      <td>243.829746</td>\n",
       "      <td>380.075541</td>\n",
       "      <td>226.517715</td>\n",
       "      <td>375.745382</td>\n",
       "      <td>202.731972</td>\n",
       "      <td>370.644321</td>\n",
       "      <td>incr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142.125597</td>\n",
       "      <td>348.627262</td>\n",
       "      <td>169.593620</td>\n",
       "      <td>310.114374</td>\n",
       "      <td>211.022224</td>\n",
       "      <td>263.854265</td>\n",
       "      <td>231.385136</td>\n",
       "      <td>227.210426</td>\n",
       "      <td>235.420151</td>\n",
       "      <td>192.465291</td>\n",
       "      <td>...</td>\n",
       "      <td>346.207466</td>\n",
       "      <td>179.731350</td>\n",
       "      <td>380.584631</td>\n",
       "      <td>242.883186</td>\n",
       "      <td>380.245771</td>\n",
       "      <td>225.718746</td>\n",
       "      <td>377.690277</td>\n",
       "      <td>202.591286</td>\n",
       "      <td>375.531893</td>\n",
       "      <td>incr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148.053875</td>\n",
       "      <td>347.714310</td>\n",
       "      <td>172.695675</td>\n",
       "      <td>308.838501</td>\n",
       "      <td>211.616974</td>\n",
       "      <td>262.712288</td>\n",
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       "      <td>226.218281</td>\n",
       "      <td>237.092056</td>\n",
       "      <td>192.339993</td>\n",
       "      <td>...</td>\n",
       "      <td>344.090796</td>\n",
       "      <td>183.466053</td>\n",
       "      <td>380.830336</td>\n",
       "      <td>244.465427</td>\n",
       "      <td>381.354532</td>\n",
       "      <td>226.849499</td>\n",
       "      <td>377.753248</td>\n",
       "      <td>203.172493</td>\n",
       "      <td>373.360634</td>\n",
       "      <td>incr</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>136738</th>\n",
       "      <td>248.432503</td>\n",
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       "      <td>265.310726</td>\n",
       "      <td>306.027775</td>\n",
       "      <td>275.181456</td>\n",
       "      <td>319.545994</td>\n",
       "      <td>290.986977</td>\n",
       "      <td>317.130318</td>\n",
       "      <td>300.376024</td>\n",
       "      <td>...</td>\n",
       "      <td>283.291740</td>\n",
       "      <td>255.531254</td>\n",
       "      <td>269.706402</td>\n",
       "      <td>267.899227</td>\n",
       "      <td>301.770487</td>\n",
       "      <td>262.710514</td>\n",
       "      <td>297.024794</td>\n",
       "      <td>258.991432</td>\n",
       "      <td>286.459322</td>\n",
       "      <td>stab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136739</th>\n",
       "      <td>248.390541</td>\n",
       "      <td>264.477196</td>\n",
       "      <td>276.602688</td>\n",
       "      <td>268.609056</td>\n",
       "      <td>305.068531</td>\n",
       "      <td>279.112158</td>\n",
       "      <td>316.315346</td>\n",
       "      <td>294.590263</td>\n",
       "      <td>310.545712</td>\n",
       "      <td>305.292435</td>\n",
       "      <td>...</td>\n",
       "      <td>283.541594</td>\n",
       "      <td>252.688084</td>\n",
       "      <td>268.396797</td>\n",
       "      <td>263.601418</td>\n",
       "      <td>301.859322</td>\n",
       "      <td>259.492493</td>\n",
       "      <td>296.834450</td>\n",
       "      <td>256.921978</td>\n",
       "      <td>286.232986</td>\n",
       "      <td>stab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136740</th>\n",
       "      <td>250.757103</td>\n",
       "      <td>265.041561</td>\n",
       "      <td>276.498489</td>\n",
       "      <td>268.494730</td>\n",
       "      <td>303.859234</td>\n",
       "      <td>279.009418</td>\n",
       "      <td>313.550014</td>\n",
       "      <td>294.703789</td>\n",
       "      <td>305.350323</td>\n",
       "      <td>304.742517</td>\n",
       "      <td>...</td>\n",
       "      <td>281.854134</td>\n",
       "      <td>250.359993</td>\n",
       "      <td>268.238468</td>\n",
       "      <td>261.352577</td>\n",
       "      <td>301.316700</td>\n",
       "      <td>257.921677</td>\n",
       "      <td>294.966888</td>\n",
       "      <td>255.545692</td>\n",
       "      <td>283.302097</td>\n",
       "      <td>stab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136741</th>\n",
       "      <td>255.372181</td>\n",
       "      <td>265.463705</td>\n",
       "      <td>279.066601</td>\n",
       "      <td>268.013163</td>\n",
       "      <td>303.448582</td>\n",
       "      <td>277.165375</td>\n",
       "      <td>311.417427</td>\n",
       "      <td>293.135862</td>\n",
       "      <td>304.155235</td>\n",
       "      <td>306.247330</td>\n",
       "      <td>...</td>\n",
       "      <td>284.711838</td>\n",
       "      <td>249.222660</td>\n",
       "      <td>268.496876</td>\n",
       "      <td>258.868675</td>\n",
       "      <td>301.370859</td>\n",
       "      <td>255.776291</td>\n",
       "      <td>296.767216</td>\n",
       "      <td>253.617897</td>\n",
       "      <td>285.626621</td>\n",
       "      <td>stab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136742</th>\n",
       "      <td>250.886135</td>\n",
       "      <td>268.522081</td>\n",
       "      <td>277.164955</td>\n",
       "      <td>273.359985</td>\n",
       "      <td>301.985779</td>\n",
       "      <td>281.426039</td>\n",
       "      <td>309.765339</td>\n",
       "      <td>293.839474</td>\n",
       "      <td>302.346439</td>\n",
       "      <td>303.898373</td>\n",
       "      <td>...</td>\n",
       "      <td>283.475132</td>\n",
       "      <td>245.803986</td>\n",
       "      <td>268.398342</td>\n",
       "      <td>254.763374</td>\n",
       "      <td>300.532408</td>\n",
       "      <td>252.043762</td>\n",
       "      <td>295.439987</td>\n",
       "      <td>250.359421</td>\n",
       "      <td>283.771305</td>\n",
       "      <td>stab</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136743 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0.x         0.y         1.x         1.y         2.x  \\\n",
       "0       156.763935  341.113615  181.715050  305.809135  220.757751   \n",
       "1       143.475761  344.199715  171.049080  306.954203  212.921429   \n",
       "2       150.850964  344.401903  173.355370  305.092764  211.292782   \n",
       "3       142.125597  348.627262  169.593620  310.114374  211.022224   \n",
       "4       148.053875  347.714310  172.695675  308.838501  211.616974   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "136738  248.432503  263.389206  277.443581  265.310726  306.027775   \n",
       "136739  248.390541  264.477196  276.602688  268.609056  305.068531   \n",
       "136740  250.757103  265.041561  276.498489  268.494730  303.859234   \n",
       "136741  255.372181  265.463705  279.066601  268.013163  303.448582   \n",
       "136742  250.886135  268.522081  277.164955  273.359985  301.985779   \n",
       "\n",
       "               2.y         3.x         3.y         4.x         4.y  ...  \\\n",
       "0       259.460821  241.802940  223.171864  251.327152  189.498496  ...   \n",
       "1       261.331959  236.131897  224.286804  245.477505  188.725119  ...   \n",
       "2       259.847116  231.831303  223.427854  236.018009  188.927751  ...   \n",
       "3       263.854265  231.385136  227.210426  235.420151  192.465291  ...   \n",
       "4       262.712288  231.736317  226.218281  237.092056  192.339993  ...   \n",
       "...            ...         ...         ...         ...         ...  ...   \n",
       "136738  275.181456  319.545994  290.986977  317.130318  300.376024  ...   \n",
       "136739  279.112158  316.315346  294.590263  310.545712  305.292435  ...   \n",
       "136740  279.009418  313.550014  294.703789  305.350323  304.742517  ...   \n",
       "136741  277.165375  311.417427  293.135862  304.155235  306.247330  ...   \n",
       "136742  281.426039  309.765339  293.839474  302.346439  303.898373  ...   \n",
       "\n",
       "              16.y        17.x        17.y        18.x        18.y  \\\n",
       "0       338.358421  192.194786  374.050598  255.231705  373.679523   \n",
       "1       341.208458  183.233604  376.094484  246.375809  377.355795   \n",
       "2       340.057840  182.300110  377.768726  243.829746  380.075541   \n",
       "3       346.207466  179.731350  380.584631  242.883186  380.245771   \n",
       "4       344.090796  183.466053  380.830336  244.465427  381.354532   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "136738  283.291740  255.531254  269.706402  267.899227  301.770487   \n",
       "136739  283.541594  252.688084  268.396797  263.601418  301.859322   \n",
       "136740  281.854134  250.359993  268.238468  261.352577  301.316700   \n",
       "136741  284.711838  249.222660  268.496876  258.868675  301.370859   \n",
       "136742  283.475132  245.803986  268.398342  254.763374  300.532408   \n",
       "\n",
       "              19.x        19.y        20.x        20.y  order  \n",
       "0       239.323101  371.736917  217.546730  367.677984   incr  \n",
       "1       230.304756  373.775711  207.152386  369.440460   incr  \n",
       "2       226.517715  375.745382  202.731972  370.644321   incr  \n",
       "3       225.718746  377.690277  202.591286  375.531893   incr  \n",
       "4       226.849499  377.753248  203.172493  373.360634   incr  \n",
       "...            ...         ...         ...         ...    ...  \n",
       "136738  262.710514  297.024794  258.991432  286.459322   stab  \n",
       "136739  259.492493  296.834450  256.921978  286.232986   stab  \n",
       "136740  257.921677  294.966888  255.545692  283.302097   stab  \n",
       "136741  255.776291  296.767216  253.617897  285.626621   stab  \n",
       "136742  252.043762  295.439987  250.359421  283.771305   stab  \n",
       "\n",
       "[136743 rows x 43 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 훈련 데이터\n",
    "incr1 = pd.read_csv('./hand_incr1.csv')\n",
    "decr1 = pd.read_csv('./hand_decr1.csv')\n",
    "back1 = pd.read_csv('./hand_back1.csv')\n",
    "stab1 = pd.read_csv('./hand_stab1.csv')\n",
    "incr2 = pd.read_csv('./hand_incr2.csv')\n",
    "decr2 = pd.read_csv('./hand_decr2.csv')\n",
    "back2 = pd.read_csv('./hand_back2.csv')\n",
    "stab2 = pd.read_csv('./hand_stab2.csv')\n",
    "dataset = pd.concat([incr1,decr1,back1,stab1,incr2,decr2,back2,stab2], axis=0, ignore_index = True)\n",
    "dataset = dataset.dropna()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:58:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "자체테스트\n",
      "\n",
      "정확도: 0.9699\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "X_features = dataset.drop(['order'],axis=1)\n",
    "y_label = dataset['order']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_features, y_label, test_size=0.2, random_state=156)\n",
    "xgb_wrapper = XGBClassifier(n_estimators = 400, learning_rate = 0.1, max_depth = 4)\n",
    "xgb_wrapper.fit(X_train, y_train)\n",
    "yhat = xgb_wrapper.predict(X_test)\n",
    "print(get_clf_eval(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.9783\n"
     ]
    }
   ],
   "source": [
    "# 테스트셋\n",
    "incr1 = pd.read_csv('./hand0048.csv')\n",
    "decr1 = pd.read_csv('./hand0049.csv')\n",
    "back1 = pd.read_csv('./hand0051.csv')\n",
    "stab1 = pd.read_csv('./hand0053.csv')\n",
    "incr3 = pd.read_csv('./handbacktest.csv')\n",
    "decr3 = pd.read_csv('./handbacktest2.csv')\n",
    "back3 = pd.read_csv('./handincrtest.csv')\n",
    "stab3 = pd.read_csv('./handincrtest2.csv')\n",
    "incr4 = pd.read_csv('./handsidetest.csv')\n",
    "decr4 = pd.read_csv('./handsidetest2.csv')\n",
    "back4 = pd.read_csv('./handstabtest.csv')\n",
    "stab4 = pd.read_csv('./handstabtest2.csv')\n",
    "\n",
    "dataset2 = pd.concat([incr1,decr1,back1,stab1,incr3,decr3,back3,stab3,incr4,decr4,back4,stab4], axis=0, ignore_index = True)\n",
    "dataset2 = dataset.dropna()\n",
    "\n",
    "X_test = dataset2.drop(['order'],axis=1)\n",
    "y_test = dataset2['order']\n",
    "yhat = xgb_wrapper.predict(X_test)\n",
    "print(get_clf_eval(y_test, yhat))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#모델저장\n",
    "xgb_wrapper.save_model('./xgb_class.model')"
   ]
  }
 ],
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