14.3.7.회귀성능 평가지표.ipynb

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   "source": [
    "# 필요한 패키지 설치\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error # MSE\n",
    "from sklearn.metrics import mean_absolute_error # MAE\n",
    "from sklearn.metrics import mean_absolute_percentage_error # MAPE\n",
    "from sklearn.metrics import mean_squared_log_error # MSLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d01a91f2",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
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       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
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       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/harlfoxem/housesalesprediction\n",
    "df = pd.read_csv(\"datasets/kc_house_data.csv\")\n",
    "\n",
    "# 데이터 샘플 확인\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e6c73dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 독립변수와 종속변수 분리하여 생성\n",
    "x = df[[ 'bedrooms', 'bathrooms', 'sqft_living',\n",
    "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
    "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', \n",
    "       'sqft_living15', 'sqft_lot15']]\n",
    "# 'id', 'date'는 키값에 해당하므로 변수에서 제외 해준다.\n",
    "y = df[['price']]\n",
    "\n",
    "# 학습셋과 테스트셋 분리하여 생성(7:3)\n",
    "# df_train, df_test = train_test_split(df, test_size = 0.4) \n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.6, test_size=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffb7e63a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 다중회귀모델 생성\n",
    "mreg = LinearRegression(fit_intercept=True)\n",
    "mreg.fit(x_train, y_train)\n",
    "\n",
    "# 테스트셋에 모델 적용\n",
    "y_predict = mreg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5548daf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 214125.86\n",
      "MAE : 139072.03\n",
      "MAPE : 0.29\n",
      "RMSLE : 0.69\n"
     ]
    }
   ],
   "source": [
    "# RMSE 산출 (MSE에 루트 적용)\n",
    "MSE = mean_squared_error(y_test, y_predict)\n",
    "RMSE = np.sqrt(MSE)\n",
    "print((\"RMSE : {:.2f}\".format(RMSE)))\n",
    "\n",
    "# MAE 산출\n",
    "MAE = mean_absolute_error(y_test, y_predict)\n",
    "print((\"MAE : {:.2f}\".format(MAE)))\n",
    "\n",
    "# MAPE 산출\n",
    "MAPE = mean_absolute_percentage_error(y_test, y_predict)\n",
    "print((\"MAPE : {:.2f}\".format(MAPE)))\n",
    "\n",
    "# RMSLE 산출 (MSLE에 루트 적용)\n",
    "\n",
    "# 음수값 전처리\n",
    "y_predict_df = pd.DataFrame(y_predict,columns=['price2'])\n",
    "y_predict_df2 = y_predict_df.copy()\n",
    "y_predict_df2.loc[y_predict_df2['price2'] < 0, 'price2'] = 0\n",
    "y_predict_rmsle = y_predict_df2.to_numpy()\n",
    "\n",
    "MSLE = mean_squared_log_error(y_test, (y_predict_rmsle))\n",
    "RMSLE = np.sqrt(MSLE)\n",
    "print((\"RMSLE : {:.2f}\".format(RMSLE)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ad066b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\angry\\AppData\\Local\\Temp\\ipykernel_28972\\1962255028.py:8: RuntimeWarning: invalid value encountered in log\n",
      "  log_y_predict = np.log(y_predict + 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "price    0.3703\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSLE ver. 2\n",
    "  \n",
    "def rmsle(predicted_values, actual_values):\n",
    "    \n",
    "    # 테스트셋 y 값과 예측값에 +1 및 로그 \n",
    "    log_y_test = np.log(y_test + 1)\n",
    "    log_y_predict = np.log(y_predict + 1)\n",
    "\n",
    "    # 테스트셋 y 값 - 예측값 및 제곱\n",
    "    diff = log_y_predict - log_y_test\n",
    "    diff_square = np.square(diff)\n",
    "\n",
    "    # 차이값 평균 및 루트\n",
    "    mean_diff = diff_square.mean()\n",
    "    final_rmsle = np.sqrt(mean_diff)  \n",
    "\n",
    "    return final_rmsle\n",
    "\n",
    "rmsle(y_test, y_predict)"
   ]
  }
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