train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.metrics import explained_variance_score, mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor, StackingRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./solarpowergeneration.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance-to-solar-noon</th>\n",
       "      <th>temperature</th>\n",
       "      <th>wind-direction</th>\n",
       "      <th>wind-speed</th>\n",
       "      <th>sky-cover</th>\n",
       "      <th>visibility</th>\n",
       "      <th>humidity</th>\n",
       "      <th>average-wind-speed-(period)</th>\n",
       "      <th>average-pressure-(period)</th>\n",
       "      <th>power-generated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.859897</td>\n",
       "      <td>69</td>\n",
       "      <td>28</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>75</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.82</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.628535</td>\n",
       "      <td>69</td>\n",
       "      <td>28</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.397172</td>\n",
       "      <td>69</td>\n",
       "      <td>28</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.89</td>\n",
       "      <td>5418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.165810</td>\n",
       "      <td>69</td>\n",
       "      <td>28</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.91</td>\n",
       "      <td>25477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.065553</td>\n",
       "      <td>69</td>\n",
       "      <td>28</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.89</td>\n",
       "      <td>30069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance-to-solar-noon  temperature  wind-direction  wind-speed  sky-cover  \\\n",
       "0                0.859897           69              28         7.5          0   \n",
       "1                0.628535           69              28         7.5          0   \n",
       "2                0.397172           69              28         7.5          0   \n",
       "3                0.165810           69              28         7.5          0   \n",
       "4                0.065553           69              28         7.5          0   \n",
       "\n",
       "   visibility  humidity  average-wind-speed-(period)  \\\n",
       "0        10.0        75                          8.0   \n",
       "1        10.0        77                          5.0   \n",
       "2        10.0        70                          0.0   \n",
       "3        10.0        33                          0.0   \n",
       "4        10.0        21                          3.0   \n",
       "\n",
       "   average-pressure-(period)  power-generated  \n",
       "0                      29.82                0  \n",
       "1                      29.85                0  \n",
       "2                      29.89             5418  \n",
       "3                      29.91            25477  \n",
       "4                      29.89            30069  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2920 entries, 0 to 2919\n",
      "Data columns (total 10 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   distance-to-solar-noon       2920 non-null   float64\n",
      " 1   temperature                  2920 non-null   int64  \n",
      " 2   wind-direction               2920 non-null   int64  \n",
      " 3   wind-speed                   2920 non-null   float64\n",
      " 4   sky-cover                    2920 non-null   int64  \n",
      " 5   visibility                   2920 non-null   float64\n",
      " 6   humidity                     2920 non-null   int64  \n",
      " 7   average-wind-speed-(period)  2919 non-null   float64\n",
      " 8   average-pressure-(period)    2920 non-null   float64\n",
      " 9   power-generated              2920 non-null   int64  \n",
      "dtypes: float64(5), int64(5)\n",
      "memory usage: 228.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance-to-solar-noon</th>\n",
       "      <th>temperature</th>\n",
       "      <th>wind-direction</th>\n",
       "      <th>wind-speed</th>\n",
       "      <th>sky-cover</th>\n",
       "      <th>visibility</th>\n",
       "      <th>humidity</th>\n",
       "      <th>average-wind-speed-(period)</th>\n",
       "      <th>average-pressure-(period)</th>\n",
       "      <th>power-generated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.503294</td>\n",
       "      <td>58.468493</td>\n",
       "      <td>24.953425</td>\n",
       "      <td>10.096986</td>\n",
       "      <td>1.987671</td>\n",
       "      <td>9.557705</td>\n",
       "      <td>73.513699</td>\n",
       "      <td>10.129154</td>\n",
       "      <td>30.017760</td>\n",
       "      <td>6979.846233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.298024</td>\n",
       "      <td>6.841200</td>\n",
       "      <td>6.915178</td>\n",
       "      <td>4.838185</td>\n",
       "      <td>1.411978</td>\n",
       "      <td>1.383884</td>\n",
       "      <td>15.077139</td>\n",
       "      <td>7.261547</td>\n",
       "      <td>0.142006</td>\n",
       "      <td>10312.336413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.050401</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.480000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.243714</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.920000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.478957</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.739528</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>30.110000</td>\n",
       "      <td>12723.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.141361</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>26.600000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>30.530000</td>\n",
       "      <td>36580.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       distance-to-solar-noon  temperature  wind-direction   wind-speed  \\\n",
       "count             2920.000000  2920.000000     2920.000000  2920.000000   \n",
       "mean                 0.503294    58.468493       24.953425    10.096986   \n",
       "std                  0.298024     6.841200        6.915178     4.838185   \n",
       "min                  0.050401    42.000000        1.000000     1.100000   \n",
       "25%                  0.243714    53.000000       25.000000     6.600000   \n",
       "50%                  0.478957    59.000000       27.000000    10.000000   \n",
       "75%                  0.739528    63.000000       29.000000    13.100000   \n",
       "max                  1.141361    78.000000       36.000000    26.600000   \n",
       "\n",
       "         sky-cover   visibility     humidity  average-wind-speed-(period)  \\\n",
       "count  2920.000000  2920.000000  2920.000000                  2919.000000   \n",
       "mean      1.987671     9.557705    73.513699                    10.129154   \n",
       "std       1.411978     1.383884    15.077139                     7.261547   \n",
       "min       0.000000     0.000000    14.000000                     0.000000   \n",
       "25%       1.000000    10.000000    65.000000                     5.000000   \n",
       "50%       2.000000    10.000000    77.000000                     9.000000   \n",
       "75%       3.000000    10.000000    84.000000                    15.000000   \n",
       "max       4.000000    10.000000   100.000000                    40.000000   \n",
       "\n",
       "       average-pressure-(period)  power-generated  \n",
       "count                2920.000000      2920.000000  \n",
       "mean                   30.017760      6979.846233  \n",
       "std                     0.142006     10312.336413  \n",
       "min                    29.480000         0.000000  \n",
       "25%                    29.920000         0.000000  \n",
       "50%                    30.000000       404.000000  \n",
       "75%                    30.110000     12723.500000  \n",
       "max                    30.530000     36580.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask2 = np.triu(np.ones_like(df.corr(), dtype=bool))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['power-generated'], axis=1)\n",
    "y = df['power-generated']\n",
    "corr_features = [\"distance-to-solar-noon\",\n",
    "                 \"wind-direction\", 'wind-speed',\n",
    "                 'humidity',\n",
    "                 'average-pressure-(period)']\n",
    "X.drop(X.keys().difference(corr_features), axis=1,\n",
    "       inplace=True)\n",
    "\n",
    "X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y,\n",
    "                                                                train_size=0.8,\n",
    "                                                                test_size=0.2,\n",
    "                                                                random_state=0)\n",
    "\n",
    "# \"Cardinality\" means the number of unique values in a column\n",
    "# Select categorical columns with relatively low cardinality (convenient but arbitrary)\n",
    "categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and\n",
    "                    X_train_full[cname].dtype == \"object\"]\n",
    "\n",
    "# Select numerical columns\n",
    "numerical_cols = [\n",
    "    cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]\n",
    "\n",
    "# Keep selected columns only\n",
    "my_cols = categorical_cols + numerical_cols\n",
    "X_train = X_train_full[my_cols].copy()\n",
    "X_valid = X_valid_full[my_cols].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(30, 20))\n",
    "sns.heatmap(df.corr(method='spearman'), mask=mask2, cmap='jet', vmax=.3, center=0, annot=True, fmt='.2f',\n",
    "            square=True, linewidths=.9, cbar_kws={\"shrink\": .5})\n",
    "plt.title('Spearman Correlation analysis')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(30, 20))\n",
    "sns.heatmap(df.corr(method='kendall'), mask=mask2, cmap='jet', vmax=.3, center=0, annot=True, fmt='.2f',\n",
    "            square=True, linewidths=.9, cbar_kws={\"shrink\": .5})\n",
    "plt.title('Kendall Correlation analysis')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 15))\n",
    "cor = df.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.CMRmap_r)\n",
    "plt.title('Pearnson Correlation analysis')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()\n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i, j]) > threshold:\n",
    "                colname = corr_matrix.columns[i]\n",
    "                col_corr.add(colname)\n",
    "    return col_corr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features = correlation(X_train, 0.3)\n",
    "len(set(corr_features))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['humidity', 'average-pressure-(period)', 'wind-speed']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features = list(corr_features)\n",
    "corr_features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Preprocessing for numerical data\n",
    "numerical_transformer = SimpleImputer(strategy='constant')\n",
    "\n",
    "# Preprocessing for categorical data\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])\n",
    "\n",
    "# Bundle preprocessing for numerical and categorical data\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numerical_transformer, numerical_cols),\n",
    "        ('cat', categorical_transformer, categorical_cols)\n",
    "    ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor(n_estimators=1000, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1544.3556626712327\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# Bundle preprocessing and modeling code in a pipeline\n",
    "my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                              ('model', model)\n",
    "                              ])\n",
    "\n",
    "# Preprocessing of training data, fit model\n",
    "my_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Preprocessing of validation data, get predictions\n",
    "preds = my_pipeline.predict(X_valid)\n",
    "\n",
    "# Evaluate the model\n",
    "score = mean_absolute_error(y_valid, preds)\n",
    "print('MAE:', score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Multiply by -1 since sklearn calculates *negative* MAE\n",
    "scores = -1 * cross_val_score(my_pipeline, X_valid, y_valid,\n",
    "                              cv=5,\n",
    "                              scoring='neg_mean_absolute_error')\n",
    "\n",
    "print(\"MAE scores:\\n\", scores)\n",
    "print(\"Average MAE score (across experiments):\")\n",
    "print(scores.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Y')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = my_pipeline.predict(X_valid)\n",
    "plt.scatter(y_valid, predictions)\n",
    "plt.xlabel('Y Test')\n",
    "plt.ylabel('Predicted Y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1544.3556626712327\n",
      "MSE: 8475567.947579047\n",
      "RMSE: 2911.282869729262\n",
      "R2 Score:  0.9203998517550523\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_valid, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_valid, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_valid, predictions)))\n",
    "print(\"R2 Score: \", my_pipeline.score(X_valid, y_valid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "xg_boost = XGBRegressor(n_estimators=10000, learning_rate=0.005, n_jobs=100)\n",
    "xg_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                              ('model', xg_boost)\n",
    "                              ])\n",
    "\n",
    "# Preprocessing of training data, fit model\n",
    "xg_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Preprocessing of validation data, get predictions\n",
    "xg_preds = xg_pipeline.predict(X_valid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Multiply by -1 since sklearn calculates *negative* MAE\n",
    "xg_scores = -1 * cross_val_score(xg_pipeline, X_valid, y_valid,\n",
    "                                 cv=5,\n",
    "                                 scoring='neg_mean_absolute_error')\n",
    "\n",
    "print(\"MAE scores:\\n\", xg_scores)\n",
    "print(\"Average MAE score (across experiments):\")\n",
    "print(xg_scores.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Y')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = xg_pipeline.predict(X_valid)\n",
    "plt.scatter(y_valid, predictions)\n",
    "plt.xlabel('Y Test')\n",
    "plt.ylabel('Predicted Y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 240.5830164670638\n",
      "MSE: 181832.9705986999\n",
      "RMSE: 426.41877374090825\n",
      "R2 Score:  0.9982922759271123\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_valid, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_valid, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_valid, predictions)))\n",
    "print(\"R2 Score: \", xg_pipeline.score(X_valid, y_valid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True output: 455\n",
      "Predicted output: 460.55908203125\n",
      "Prediction Error: 1.2217762706043955%\n"
     ]
    }
   ],
   "source": [
    "predictions = xg_pipeline.predict(X_valid)\n",
    "print(f\"True output: {y_valid.values[4]}\")\n",
    "print(f\"Predicted output: {predictions[4]}\")\n",
    "print(\n",
    "    f\"Prediction Error: {(predictions[4] - y_valid.values[4])/ y_valid.values[4] * 100}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # export the model\n",
    "# import pickle\n",
    "# with open(\"model_pickle\", \"wb\") as f:\n",
    "#     pickle.dump(xg_pipeline, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # test import\n",
    "import pickle\n",
    "with open('model_pickle', \"rb\") as f:\n",
    "    imported_model = pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Y')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im_predictions = imported_model.predict(X_valid)\n",
    "plt.scatter(y_valid, im_predictions)\n",
    "plt.xlabel('Y Test')\n",
    "plt.ylabel('Predicted Y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 240.5830164670638\n",
      "MSE: 181832.9705986999\n",
      "RMSE: 426.41877374090825\n",
      "R2 Score:  0.9982922759271123\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_valid, im_predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_valid, im_predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_valid, im_predictions)))\n",
    "print(\"R2 Score: \", imported_model.score(X_valid, y_valid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['distance-to-solar-noon',\n",
       " 'wind-direction',\n",
       " 'wind-speed',\n",
       " 'humidity',\n",
       " 'average-pressure-(period)']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\"distance-to-solar-noon\",\n",
    " \"wind-direction\",\n",
    " 'wind-speed',\n",
    " 'humidity',\n",
    " 'average-pressure-(period)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "zipped = list(zip([0], [50], [8], [40], [10]))\n",
    "df_ = pd.DataFrame(zipped, columns=list(X.keys()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>distance-to-solar-noon</th>\n",
       "      <th>wind-direction</th>\n",
       "      <th>wind-speed</th>\n",
       "      <th>humidity</th>\n",
       "      <th>average-pressure-(period)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance-to-solar-noon  wind-direction  wind-speed  humidity  \\\n",
       "0                       0              50           8        40   \n",
       "\n",
       "   average-pressure-(period)  \n",
       "0                         10  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([34640.523], dtype=float32)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imported_model.predict(df_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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