{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "def plot_predict_vs_actual(test_set, prediction):\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(go.Scatter(x=test_set, y=test_set, mode='lines', name='Ideal Line', line=dict(color='red', dash='dash')))\n",
    "    fig.add_trace(go.Scatter(x=test_set, y=prediction, mode='markers', name='Predictions', marker=dict(color='blue', opacity=0.5)))\n",
    "    fig.update_layout(xaxis_title='Actual Values', yaxis_title='Predicted Values', title='Actual vs. Predicted Values', showlegend=True, legend=dict(x=0, y=1))\n",
    "    fig.update_layout(xaxis=dict(showgrid=True), yaxis=dict(showgrid=True))\n",
    "    fig.show()\n",
    "\n",
    "\n",
    "def evaluate_regression(test_set, prediction):\n",
    "    mse = mean_squared_error(test_set, prediction)\n",
    "    rmse = mean_squared_error(test_set, prediction, squared=False)\n",
    "    mae = mean_absolute_error(test_set, prediction)\n",
    "    min_actual = min(test_set)\n",
    "    max_actual = max(test_set)\n",
    "    min_pred = min(prediction)\n",
    "    max_pred = max(prediction)\n",
    "\n",
    "    print('Mean Squared Error (MSE):', mse)\n",
    "    print('Root Mean Squared Error (RMSE):', rmse)\n",
    "    print('Mean Absolute Error (MAE):', mae)\n",
    "    print('Range of Actual Values:', min_actual, '-', max_actual)\n",
    "    print('Range of Predicted Values:', min_pred, '-', max_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('out/data_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 0.15151159842304576\n",
      "Root Mean Squared Error (RMSE): 0.3892449080245569\n",
      "Mean Absolute Error (MAE): 0.25791334929289644\n",
      "Range of Actual Values: 27.0 - 33.48197937011719\n",
      "Range of Predicted Values: 26.980206 - 33.49997\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = data.drop(columns=['indoor_temp', 'indoor_light', 'outdoor_weather', 'timestamp', 'outdoor_pm25', 'outdoor_pm10'])\n",
    "y = data['indoor_temp']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "xgboost = XGBRegressor(n_estimators=1000, max_depth=5, subsample=0.5, colsample_bytree=0.5, reg_alpha=0.1, reg_lambda=0.1, random_state=42)\n",
    "xgboost.fit(X_train, y_train)\n",
    "\n",
    "y_pred = xgboost.predict(X_test)\n",
    "evaluate_regression(y_test, y_pred)"
   ]
  },
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    "plot_predict_vs_actual(y_test, y_pred)"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['out/xgboost_model.pkl']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(xgboost, 'out/xgboost_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load model\n",
    "\n",
    "# xgboost = joblib.load('out/xgboost_model.pkl')"
   ]
  }
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