{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "monthly-annotation",
   "metadata": {},
   "source": [
    "# **Ph 및 Brix 모델 및 데이터 예측**\n",
    "## **Sklearn 으로 예측한 모델을 활용한 예측**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fatty-range",
   "metadata": {},
   "outputs": [
    {
     "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>b</th>\n",
       "      <th>greenbean name</th>\n",
       "      <th>fin time</th>\n",
       "      <th>fin bean tem</th>\n",
       "      <th>brix</th>\n",
       "      <th>tds</th>\n",
       "      <th>ph</th>\n",
       "      <th>time/temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>에디오피아 예가체프 G2 81</td>\n",
       "      <td>174</td>\n",
       "      <td>197</td>\n",
       "      <td>1.601</td>\n",
       "      <td>1.2808</td>\n",
       "      <td>4.940</td>\n",
       "      <td>0.883249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71</td>\n",
       "      <td>에디오피아 예가체프 G2 71</td>\n",
       "      <td>196</td>\n",
       "      <td>207</td>\n",
       "      <td>1.601</td>\n",
       "      <td>1.2808</td>\n",
       "      <td>4.934</td>\n",
       "      <td>0.946860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>82</td>\n",
       "      <td>에디오피아 예가체프 G2 82</td>\n",
       "      <td>200</td>\n",
       "      <td>187</td>\n",
       "      <td>1.607</td>\n",
       "      <td>1.2856</td>\n",
       "      <td>4.934</td>\n",
       "      <td>1.069519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    b    greenbean name  fin time  fin bean tem   brix     tds     ph  \\\n",
       "0  81  에디오피아 예가체프 G2 81       174           197  1.601  1.2808  4.940   \n",
       "1  71  에디오피아 예가체프 G2 71       196           207  1.601  1.2808  4.934   \n",
       "2  82  에디오피아 예가체프 G2 82       200           187  1.607  1.2856  4.934   \n",
       "\n",
       "   time/temperature  \n",
       "0          0.883249  \n",
       "1          0.946860  \n",
       "2          1.069519  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from model.snoopers import loading_dataset\n",
    "file_name = './data/BrixTDSphDataSet_over.csv'\n",
    "table = loading_dataset(file_name)\n",
    "table.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "maritime-welcome",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feacture : -1.0892247354198625\n",
      "bias : 6.989671895125806\n",
      "LinearRegression() Pipeline(steps=[('polynomial', PolynomialFeatures(degree=5)),\n",
      "                ('modal', LinearRegression())])\n"
     ]
    }
   ],
   "source": [
    "file1 = \"./model/predict_ph.model\"\n",
    "file2 = \"./model/predict_brix.model\"\n",
    "from model.snoopers import loading_model\n",
    "model_brix, model_ph = loading_model(file1, file2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "indonesian-insured",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 0.8667100746914103\n",
      "MSE Score : 0.001352366822621339\n",
      "RMSE Score : 0.03677454041346185\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 0.8998287880365746\n",
      "MSE Score : 0.0013400321517410725\n",
      "RMSE Score : 0.03660644959212888\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>b</th>\n",
       "      <th>greenbean name</th>\n",
       "      <th>fin time</th>\n",
       "      <th>fin bean tem</th>\n",
       "      <th>brix</th>\n",
       "      <th>tds</th>\n",
       "      <th>ph</th>\n",
       "      <th>time/temperature</th>\n",
       "      <th>ph_predict</th>\n",
       "      <th>brix_predict</th>\n",
       "      <th>tds_predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>에디오피아 예가체프 G2 81</td>\n",
       "      <td>174</td>\n",
       "      <td>197</td>\n",
       "      <td>1.601</td>\n",
       "      <td>1.2808</td>\n",
       "      <td>4.940</td>\n",
       "      <td>0.883249</td>\n",
       "      <td>4.913951</td>\n",
       "      <td>1.608902</td>\n",
       "      <td>1.287121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71</td>\n",
       "      <td>에디오피아 예가체프 G2 71</td>\n",
       "      <td>196</td>\n",
       "      <td>207</td>\n",
       "      <td>1.601</td>\n",
       "      <td>1.2808</td>\n",
       "      <td>4.934</td>\n",
       "      <td>0.946860</td>\n",
       "      <td>4.935368</td>\n",
       "      <td>1.615437</td>\n",
       "      <td>1.292350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>82</td>\n",
       "      <td>에디오피아 예가체프 G2 82</td>\n",
       "      <td>200</td>\n",
       "      <td>187</td>\n",
       "      <td>1.607</td>\n",
       "      <td>1.2856</td>\n",
       "      <td>4.934</td>\n",
       "      <td>1.069519</td>\n",
       "      <td>4.961284</td>\n",
       "      <td>1.615437</td>\n",
       "      <td>1.292350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    b    greenbean name  fin time  fin bean tem   brix     tds     ph  \\\n",
       "0  81  에디오피아 예가체프 G2 81       174           197  1.601  1.2808  4.940   \n",
       "1  71  에디오피아 예가체프 G2 71       196           207  1.601  1.2808  4.934   \n",
       "2  82  에디오피아 예가체프 G2 82       200           187  1.607  1.2856  4.934   \n",
       "\n",
       "   time/temperature  ph_predict  brix_predict  tds_predict  \n",
       "0          0.883249    4.913951      1.608902     1.287121  \n",
       "1          0.946860    4.935368      1.615437     1.292350  \n",
       "2          1.069519    4.961284      1.615437     1.292350  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from model.snoopers import predict_model\n",
    "table_predict = predict_model(table, model_ph, model_brix)\n",
    "table_predict.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hundred-commitment",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CSV 파일로 저장하기\n",
    "file_csv = 'ph_brix.csv'\n",
    "table_predict.to_csv(file_csv, index=None)\n",
    "print(f\"결과값을 {file_csv} 파일로 저장하였습니다.\")"
   ]
  }
 ],
 "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
}