Multiple_Linear_Regression_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5be0cb8c",
   "metadata": {},
   "source": [
    "# Importar librerias y configuraciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a001f68b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import shapiro\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.stats.diagnostic import het_breuschpagan\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "from typing import Union"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b708a7a9",
   "metadata": {},
   "source": [
    "# RLM - 1 -  Índices espectrales Climáticas Topográficas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f9449c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'D:\\Documents\\Documents\\academico\\tesis_maie\\data_sets\\dataframe\\dataframe_tesis_final_correcciones.csv')\n",
    "df = df.drop(columns = ['Lote', 'REND_range', 'REND_stde', 'Año'])\n",
    "X = df.select_dtypes(include=['float64', 'int64'])\n",
    "\n",
    "X = df.drop(columns = ['REND_mean',\n",
    "                       'NDRE_stde', 'NDRE_range', \n",
    "                       'SLOPE_stde','SLOPE_range', \n",
    "                       'GNDVI_stde', 'GNDVI_range',\n",
    "                        'NDVI_stde', 'NDVI_range', \n",
    "                       'OSAVI_stde', 'OSAVI_range',\n",
    "                       'RECI_stde', 'RECI_range',\n",
    "                       'TCARIOSAVI_stde', 'TCARIOSAVI_range', \n",
    "                       'TCARI_stde', 'TCARI_range'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22afa1bd",
   "metadata": {},
   "source": [
    "#### Modelado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75f3441a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              REND_mean   R-squared:                       0.680\n",
      "Model:                            OLS   Adj. R-squared:                  0.384\n",
      "Method:                 Least Squares   F-statistic:                     2.301\n",
      "Date:                Thu, 28 Sep 2023   Prob (F-statistic):             0.0752\n",
      "Time:                        13:16:39   Log-Likelihood:                -45.867\n",
      "No. Observations:                  26   AIC:                             117.7\n",
      "Df Residuals:                      13   BIC:                             134.1\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const              39.7584     29.792      1.335      0.205     -24.604     104.120\n",
      "DSM                 0.0153      0.023      0.680      0.509      -0.033       0.064\n",
      "EVAPO               6.1256     13.064      0.469      0.647     -22.097      34.348\n",
      "PP                 -0.0016      0.007     -0.232      0.820      -0.016       0.013\n",
      "TEMP               -1.1787      0.933     -1.264      0.228      -3.193       0.836\n",
      "GNDVI_mean        -67.0999     92.700     -0.724      0.482    -267.366     133.166\n",
      "NDRE_mean         -27.2944     61.324     -0.445      0.664    -159.776     105.187\n",
      "NDVI_mean          43.0146     85.594      0.503      0.624    -141.900     227.929\n",
      "OSAVI_mean         76.9884    103.380      0.745      0.470    -146.351     300.328\n",
      "RECI_mean          -0.6998      4.049     -0.173      0.865      -9.448       8.048\n",
      "SLOPE_mean          1.0406      1.941      0.536      0.601      -3.152       5.233\n",
      "TCARIOSAVI_mean    45.0343     84.360      0.534      0.602    -137.215     227.284\n",
      "TCARI_mean       -321.6798    200.344     -1.606      0.132    -754.497     111.138\n",
      "==============================================================================\n",
      "Omnibus:                        1.373   Durbin-Watson:                   1.990\n",
      "Prob(Omnibus):                  0.503   Jarque-Bera (JB):                0.834\n",
      "Skew:                           0.438   Prob(JB):                        0.659\n",
      "Kurtosis:                       2.962   Cond. No.                     5.38e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.38e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "El error (rmse) de test es: 1.83\n",
      "El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\n",
      "Estadística de prueba: 0.9734340310096741\n",
      "Valor p: 0.7134025692939758\n"
     ]
    }
   ],
   "source": [
    "y = df['REND_mean']\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.8,random_state=1234,shuffle=True) #.values.reshape(-1,1),\n",
    "X_train = sm.add_constant(X_train, prepend=True)\n",
    "modelo = sm.OLS(endog=y_train, exog=X_train,)\n",
    "modelo = modelo.fit()\n",
    "print(modelo.summary())\n",
    "\n",
    "#El error (rmse) del test\n",
    "X_test = sm.add_constant(X_test, prepend=True)\n",
    "predicciones = modelo.predict(exog = X_test)\n",
    "rmse = mean_squared_error(y_true  = y_test,y_pred  = predicciones,squared = False)\n",
    "print(f\"El error (rmse) de test es: {rmse:.2f}\")\n",
    "\n",
    "#Test de normalidad de los reiduos -  Shapiro Wilk \n",
    "prediccion_train= modelo.predict(exog = X_train)\n",
    "residuos_train= prediccion_train-y_train\n",
    "\n",
    "shapiro_test = stats.shapiro(residuos_train)\n",
    "\n",
    "if shapiro_test.pvalue > 0.05:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\")\n",
    "else:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos no son normalmente distribuidos (p <= 0.05)\")\n",
    "\n",
    "print(\"Estadística de prueba:\", shapiro_test.statistic)\n",
    "print(\"Valor p:\", shapiro_test.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84a03eb",
   "metadata": {},
   "source": [
    "#### Grafico de los residuos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28de851c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dncarcedo\\AppData\\Local\\Temp\\ipykernel_1640\\1078019318.py:14: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k--\" (-> color='k'). The keyword argument will take precedence.\n",
      "  axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\miner-tool\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:1045: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b\" (-> color=(0.0, 0.0, 1.0, 1)). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importar la librería Seaborn y establecer un esquema de color azul (celeste)\n",
    "\n",
    "sns.set_palette(\"ocean\")\n",
    "# Diagnóstico errores (residuos) de las predicciones de entrenamiento\n",
    "# ==============================================================================\n",
    "\n",
    "prediccion_train = modelo.predict(exog = X_train)\n",
    "residuos_train   = prediccion_train - y_train\n",
    "# Gráficos\n",
    "# ==============================================================================\n",
    "fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(9, 8))\n",
    "\n",
    "axes[0, 0].scatter(y_train, prediccion_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
    "axes[0, 0].set_title('Valor predicho vs valor real', fontsize=10)\n",
    "axes[0, 0].set_xlabel('Real')\n",
    "axes[0, 0].set_ylabel('Predicción')\n",
    "axes[0, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 2 - Gráfico Residuos del Modelo\n",
    "axes[0, 1].scatter(list(range(len(y_train))), residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 1].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[0, 1].set_title('Residuos del modelo', fontsize=10)\n",
    "axes[0, 1].set_xlabel('id')\n",
    "axes[0, 1].set_ylabel('Residuos')\n",
    "axes[0, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 3 - Gráfico Distribución Residuos del Modelo\n",
    "sns.histplot(data=residuos_train, stat=\"count\", kde=True, line_kws={'linewidth': 1},alpha=0.3, ax=axes[1, 0])\n",
    "axes[1, 0].set_title('Distribución residuos del modelo', fontsize=10)\n",
    "axes[1, 0].set_xlabel(\"Residuos\")\n",
    "axes[1, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 4 - Gráfico Q-Q Residuos del Modelo\n",
    "sm.qqplot(residuos_train, fit=True, line='45', ax=axes[1, 1], color='black', alpha=0.4, lw=2)\n",
    "axes[1, 1].set_title('Q-Q residuos del modelo', fontsize=10)\n",
    "axes[1, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 5 - Gráfico Residuos del Modelo vs Predicción\n",
    "axes[2, 0].scatter(prediccion_train, residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[2, 0].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[2, 0].set_title('Residuos del modelo vs predicción', fontsize=10)\n",
    "axes[2, 0].set_xlabel('Predicción')\n",
    "axes[2, 0].set_ylabel('Residuos')\n",
    "axes[2, 0].tick_params(labelsize=7)\n",
    "\n",
    "# Eliminar los axes vacíos\n",
    "fig.delaxes(axes[2, 1])\n",
    "\n",
    "# Ajustar diseño de la figura y título\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(top=0.9)\n",
    "#fig.suptitle('Diagnóstico residuos RLM - MEDIAS + clima ', fontsize=12)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d42fec",
   "metadata": {},
   "source": [
    "#  RLM - 2- PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11a97eb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Varianza explicada por cada componente principal: [0.47614894 0.21919704 0.1113091 ]\n",
      "Varianza total explicada: 0.8066550785957869\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(r'D:\\Documents\\Documents\\academico\\tesis_maie\\data_sets\\dataframe\\dataframe_tesis_final_correcciones.csv')\n",
    "df = df.drop(columns = ['Lote', 'REND_range', 'REND_stde', 'Año'])\n",
    "df = df.drop(columns = [\n",
    "                       'NDRE_stde', 'NDRE_range', \n",
    "                       'SLOPE_stde','SLOPE_range', \n",
    "                       'GNDVI_stde', 'GNDVI_range',\n",
    "                        'NDVI_stde', 'NDVI_range', \n",
    "                       'OSAVI_stde', 'OSAVI_range',\n",
    "                       'RECI_stde', 'RECI_range',\n",
    "                       'TCARIOSAVI_stde', 'TCARIOSAVI_range', \n",
    "                       'TCARI_stde', 'TCARI_range'])\n",
    "df = df.astype(float)\n",
    "#df.info()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_std = scaler.fit_transform(df)\n",
    "n_components = 3  # Número de componentes principales a conservar\n",
    "pca = PCA(n_components=n_components)\n",
    "principal_components = pca.fit_transform(X_std)\n",
    "principal_df = pd.DataFrame(data=principal_components, columns=[f'PC{i}' for i in range(1, n_components + 1)])\n",
    "#principal_df.info()\n",
    "\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "print(\"Varianza explicada por cada componente principal:\", explained_variance_ratio)\n",
    "print(\"Varianza total explicada:\", sum(explained_variance_ratio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8fdd2565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 33 entries, 0 to 32\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   PC1     33 non-null     float64\n",
      " 1   PC2     33 non-null     float64\n",
      " 2   PC3     33 non-null     float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 920.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# Concatenar las componentes principales con tu DataFrame original\n",
    "df_with_pca = pd.concat([df['REND_mean'], principal_df], axis=1) #['REND_mean']\n",
    "X = df_with_pca.drop(columns = ['REND_mean'])\n",
    "y = df_with_pca['REND_mean']\n",
    "\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4516dd85",
   "metadata": {},
   "source": [
    "#### Modelado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee8cada0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              REND_mean   R-squared:                       0.639\n",
      "Model:                            OLS   Adj. R-squared:                  0.590\n",
      "Method:                 Least Squares   F-statistic:                     13.00\n",
      "Date:                Thu, 28 Sep 2023   Prob (F-statistic):           4.24e-05\n",
      "Time:                        13:16:41   Log-Likelihood:                -47.416\n",
      "No. Observations:                  26   AIC:                             102.8\n",
      "Df Residuals:                      22   BIC:                             107.9\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          9.3159      0.323     28.833      0.000       8.646       9.986\n",
      "PC1           -0.2965      0.133     -2.222      0.037      -0.573      -0.020\n",
      "PC2           -1.1620      0.211     -5.510      0.000      -1.599      -0.725\n",
      "PC3           -0.1535      0.260     -0.591      0.561      -0.692       0.385\n",
      "==============================================================================\n",
      "Omnibus:                        7.755   Durbin-Watson:                   1.707\n",
      "Prob(Omnibus):                  0.021   Jarque-Bera (JB):                5.782\n",
      "Skew:                           0.930   Prob(JB):                       0.0555\n",
      "Kurtosis:                       4.370   Cond. No.                         2.50\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "El error (rmse) de test es: 1.80\n",
      "El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\n",
      "Estadística de prueba: 0.9362507462501526\n",
      "Valor p: 0.10926259309053421\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=1234, shuffle=True)\n",
    "X_train = sm.add_constant(X_train, prepend=True)\n",
    "modelo = sm.OLS(endog=y_train, exog=X_train,)\n",
    "modelo = modelo.fit()\n",
    "print(modelo.summary())\n",
    "\n",
    "#El error (rmse) del test\n",
    "X_test = sm.add_constant(X_test, prepend=True)\n",
    "predicciones = modelo.predict(exog = X_test)\n",
    "rmse = mean_squared_error(y_true  = y_test,y_pred  = predicciones,squared = False)\n",
    "print(f\"El error (rmse) de test es: {rmse:.2f}\")\n",
    "\n",
    "#Test de normalidad de los reiduos -  Shapiro Wilk \n",
    "prediccion_train = modelo.predict(exog = X_train)\n",
    "residuos_train   = prediccion_train - y_train\n",
    "\n",
    "shapiro_test = stats.shapiro(residuos_train)\n",
    "\n",
    "if shapiro_test.pvalue > 0.05:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\")\n",
    "else:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos no son normalmente distribuidos (p <= 0.05)\")\n",
    "\n",
    "print(\"Estadística de prueba:\", shapiro_test.statistic)\n",
    "print(\"Valor p:\", shapiro_test.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00688254",
   "metadata": {},
   "source": [
    "#### Grafico de los residuos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c769616",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dncarcedo\\AppData\\Local\\Temp\\ipykernel_1640\\2563696618.py:10: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k--\" (-> color='k'). The keyword argument will take precedence.\n",
      "  axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\miner-tool\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:1045: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b\" (-> color=(0.0, 0.0, 1.0, 1)). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importar la librería Seaborn y establecer un esquema de color azul (celeste)\n",
    "\n",
    "sns.set_palette(\"ocean\")\n",
    "\n",
    "# Gráficos\n",
    "# ==============================================================================\n",
    "fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(9, 8))\n",
    "\n",
    "axes[0, 0].scatter(y_train, prediccion_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
    "axes[0, 0].set_title('Valor predicho vs valor real', fontsize=10)\n",
    "axes[0, 0].set_xlabel('Real')\n",
    "axes[0, 0].set_ylabel('Predicción')\n",
    "axes[0, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 2 - Gráfico Residuos del Modelo\n",
    "axes[0, 1].scatter(list(range(len(y_train))), residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 1].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[0, 1].set_title('Residuos del modelo', fontsize=10)\n",
    "axes[0, 1].set_xlabel('id')\n",
    "axes[0, 1].set_ylabel('Residuos')\n",
    "axes[0, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 3 - Gráfico Distribución Residuos del Modelo\n",
    "sns.histplot(data=residuos_train, stat=\"count\", kde=True, line_kws={'linewidth': 1},alpha=0.3, ax=axes[1, 0])\n",
    "axes[1, 0].set_title('Distribución residuos del modelo', fontsize=10)\n",
    "axes[1, 0].set_xlabel(\"Residuos\")\n",
    "axes[1, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 4 - Gráfico Q-Q Residuos del Modelo\n",
    "sm.qqplot(residuos_train, fit=True, line='45', ax=axes[1, 1], color='black', alpha=0.4, lw=2)\n",
    "axes[1, 1].set_title('Q-Q residuos del modelo', fontsize=10)\n",
    "axes[1, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 5 - Gráfico Residuos del Modelo vs Predicción\n",
    "axes[2, 0].scatter(prediccion_train, residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[2, 0].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[2, 0].set_title('Residuos del modelo vs predicción', fontsize=10)\n",
    "axes[2, 0].set_xlabel('Predicción')\n",
    "axes[2, 0].set_ylabel('Residuos')\n",
    "axes[2, 0].tick_params(labelsize=7)\n",
    "\n",
    "# Eliminar los axes vacíos\n",
    "fig.delaxes(axes[2, 1])\n",
    "\n",
    "# Ajustar diseño de la figura y título\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(top=0.9)\n",
    "#fig.suptitle('Diagnóstico residuos PCA(medias + clima + topo)', fontsize=12)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71f9069",
   "metadata": {},
   "source": [
    "# RLM - 3 - variables seleccionadas por stepwise (backward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e9ca09c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'D:\\Documents\\Documents\\academico\\tesis_maie\\data_sets\\dataframe\\dataframe_tesis_final_correcciones.csv')\n",
    "df = df.drop(columns = ['Lote', 'REND_range', 'REND_stde', 'Año'])\n",
    "df = df.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "03499caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 33 entries, 0 to 32\n",
      "Data columns (total 12 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   DSM              33 non-null     float64\n",
      " 1   EVAPO            33 non-null     float64\n",
      " 2   PP               33 non-null     float64\n",
      " 3   TEMP             33 non-null     float64\n",
      " 4   GNDVI_mean       33 non-null     float64\n",
      " 5   NDRE_mean        33 non-null     float64\n",
      " 6   NDVI_mean        33 non-null     float64\n",
      " 7   OSAVI_mean       33 non-null     float64\n",
      " 8   RECI_mean        33 non-null     float64\n",
      " 9   SLOPE_mean       33 non-null     float64\n",
      " 10  TCARIOSAVI_mean  33 non-null     float64\n",
      " 11  TCARI_mean       33 non-null     float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 3.2 KB\n"
     ]
    }
   ],
   "source": [
    "# Concatenar las componentes principales con tu DataFrame original\n",
    "X = df.drop(columns = ['REND_mean',\n",
    "                       'NDRE_stde', 'NDRE_range', \n",
    "                       'SLOPE_stde','SLOPE_range', \n",
    "                       'GNDVI_stde', 'GNDVI_range',\n",
    "                        'NDVI_stde', 'NDVI_range', \n",
    "                       'OSAVI_stde', 'OSAVI_range',\n",
    "                       'RECI_stde', 'RECI_range',\n",
    "                       'TCARIOSAVI_stde', 'TCARIOSAVI_range', \n",
    "                       'TCARI_stde', 'TCARI_range'])\n",
    "\n",
    "y = df['REND_mean']\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "06d58a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['REND_mean']\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.8,random_state=1234,shuffle=True) #.values.reshape(-1,1),\n",
    "X_train = sm.add_constant(X_train, prepend=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f1306a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funcion de selecciçon backward para modelos lineales de statsmodels\n",
    "# ==============================================================================\n",
    "def backward_selection(\n",
    "    X: pd.DataFrame,\n",
    "    y: pd.Series,\n",
    "    criterio: str='aic',\n",
    "    add_constant: bool=True,\n",
    "    verbose: bool=True\n",
    ")-> list:\n",
    "    \n",
    "    \"\"\"\n",
    "    Realiza un procedimiento de selección de variables hacia atrás (backward) \n",
    "    utilizando como criterio de bondad la métrica especificada. El procedimiento\n",
    "    se detiene cuando no es posible mejorar más el modelo eliminando variables.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    X: pd.DataFrame\n",
    "        Matriz de predictores\n",
    "    y: pd.Series\n",
    "        Variable respuesta\n",
    "    metrica: str, default='aic'\n",
    "        Métrica utilizada para seleccionar las variables. Debe ser una de las\n",
    "        siguientes opciones: 'aic', 'bic', 'rsquared_adj'.\n",
    "    add_constant: bool, default=True\n",
    "        Si `True` añade una columna de 1s a la matriz de predictores con el\n",
    "        con el nombre de intercept.\n",
    "    verbose: bool, default=True\n",
    "        Si `True` muestra por pantalla los resultados de cada iteración.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    seleccion: list\n",
    "        Lista con las variables seleccionadas.\n",
    "    \"\"\"\n",
    "\n",
    "    if add_constant:\n",
    "        X = sm.add_constant(X, prepend=True).rename(columns={'const':'intercept'})\n",
    "\n",
    "    # Se inicia con todas las variables como predictores\n",
    "    seleccion = X.columns.to_list()\n",
    "    modelo  = sm.OLS(endog=y, exog=X[seleccion])\n",
    "    modelo_res = modelo.fit()\n",
    "    ultima_metrica = getattr(modelo_res, criterio)\n",
    "    mejor_metrica = ultima_metrica\n",
    "    if verbose:\n",
    "            print(f'variables: {seleccion} | {criterio}: {mejor_metrica:.3f}')\n",
    "\n",
    "    while seleccion:\n",
    "        metricas = []\n",
    "        for candidata in seleccion:\n",
    "            seleccion_temp = seleccion.copy()\n",
    "            seleccion_temp.remove(candidata)\n",
    "            modelo  = sm.OLS(endog=y, exog=X[seleccion_temp])\n",
    "            modelo_res = modelo.fit()\n",
    "            metrica = getattr(modelo_res, criterio)\n",
    "            metricas.append(metrica)\n",
    "        if criterio == 'rsquared_adj':\n",
    "            mejor_metrica = max(metricas)\n",
    "            if mejor_metrica > ultima_metrica:\n",
    "                peor_variable = seleccion[np.argmax(metricas)]\n",
    "            else:\n",
    "                break\n",
    "        else:\n",
    "            mejor_metrica = min(metricas)\n",
    "            if mejor_metrica < ultima_metrica:\n",
    "                peor_variable = seleccion[np.argmin(metricas)]\n",
    "            else:\n",
    "                break\n",
    "                \n",
    "        seleccion.remove(peor_variable)\n",
    "        ultima_metrica = mejor_metrica\n",
    "\n",
    "        if verbose:\n",
    "            print(f'variables: {seleccion} | {criterio}: {mejor_metrica:.3f}')\n",
    "\n",
    "    return sorted(seleccion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c400ed63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variables: ['const', 'DSM', 'EVAPO', 'PP', 'TEMP', 'GNDVI_mean', 'NDRE_mean', 'NDVI_mean', 'OSAVI_mean', 'RECI_mean', 'SLOPE_mean', 'TCARIOSAVI_mean', 'TCARI_mean'] | aic: 117.734\n",
      "variables: ['const', 'DSM', 'EVAPO', 'PP', 'TEMP', 'GNDVI_mean', 'NDRE_mean', 'NDVI_mean', 'OSAVI_mean', 'SLOPE_mean', 'TCARIOSAVI_mean', 'TCARI_mean'] | aic: 115.794\n",
      "variables: ['const', 'DSM', 'EVAPO', 'TEMP', 'GNDVI_mean', 'NDRE_mean', 'NDVI_mean', 'OSAVI_mean', 'SLOPE_mean', 'TCARIOSAVI_mean', 'TCARI_mean'] | aic: 113.931\n",
      "variables: ['const', 'DSM', 'EVAPO', 'TEMP', 'GNDVI_mean', 'NDRE_mean', 'NDVI_mean', 'OSAVI_mean', 'SLOPE_mean', 'TCARI_mean'] | aic: 112.460\n",
      "variables: ['const', 'DSM', 'EVAPO', 'TEMP', 'GNDVI_mean', 'NDRE_mean', 'NDVI_mean', 'OSAVI_mean', 'TCARI_mean'] | aic: 110.835\n",
      "variables: ['const', 'DSM', 'EVAPO', 'TEMP', 'GNDVI_mean', 'NDRE_mean', 'NDVI_mean', 'TCARI_mean'] | aic: 109.207\n",
      "variables: ['const', 'DSM', 'EVAPO', 'TEMP', 'NDRE_mean', 'NDVI_mean', 'TCARI_mean'] | aic: 107.331\n",
      "variables: ['const', 'DSM', 'EVAPO', 'TEMP', 'NDVI_mean', 'TCARI_mean'] | aic: 106.016\n",
      "variables: ['const', 'EVAPO', 'TEMP', 'NDVI_mean', 'TCARI_mean'] | aic: 104.743\n",
      "variables: ['const', 'TEMP', 'NDVI_mean', 'TCARI_mean'] | aic: 103.163\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['NDVI_mean', 'TCARI_mean', 'TEMP', 'const']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selección de variables hacia backward\n",
    "# ==============================================================================\n",
    "predictores= backward_selection(\n",
    "    X            = X_train,\n",
    "    y            = y_train,\n",
    "    criterio     = 'aic',\n",
    "    add_constant = False, # Ya se le añadió anteriormente\n",
    "    verbose      = True\n",
    ")\n",
    "predictores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8dcfff1b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              REND_mean   R-squared:                       0.635\n",
      "Model:                            OLS   Adj. R-squared:                  0.585\n",
      "Method:                 Least Squares   F-statistic:                     12.75\n",
      "Date:                Tue, 10 Oct 2023   Prob (F-statistic):           4.87e-05\n",
      "Time:                        17:28:57   Log-Likelihood:                -47.581\n",
      "No. Observations:                  26   AIC:                             103.2\n",
      "Df Residuals:                      22   BIC:                             108.2\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "NDVI_mean     17.8847      3.599      4.970      0.000      10.422      25.348\n",
      "TCARI_mean  -120.7217     28.371     -4.255      0.000    -179.559     -61.884\n",
      "TEMP          -0.8089      0.326     -2.484      0.021      -1.484      -0.134\n",
      "const         26.2496      7.942      3.305      0.003       9.779      42.720\n",
      "==============================================================================\n",
      "Omnibus:                        2.932   Durbin-Watson:                   1.972\n",
      "Prob(Omnibus):                  0.231   Jarque-Bera (JB):                1.818\n",
      "Skew:                           0.639   Prob(JB):                        0.403\n",
      "Kurtosis:                       3.209   Cond. No.                     2.05e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.05e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "El error (rmse) de test es: 1.79\n",
      "El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\n",
      "Estadística de prueba: 0.952816903591156\n",
      "Valor p: 0.2696804404258728\n"
     ]
    }
   ],
   "source": [
    "# Entrenamiento del modelo con las variables seleccionadas\n",
    "# ==============================================================================\n",
    "modelo_final  = sm.OLS(endog=y_train, exog=X_train[predictores])\n",
    "modelo_final_res = modelo_final.fit()\n",
    "print(modelo_final_res.summary())\n",
    "\n",
    "\n",
    "#El error (rmse) del test\n",
    "X_test = sm.add_constant(X_test, prepend=True)\n",
    "predicciones = modelo_final_res.predict(exog = X_test[predictores])\n",
    "rmse = mean_squared_error(y_true  = y_test,y_pred  = predicciones,squared = False)\n",
    "print(f\"El error (rmse) de test es: {rmse:.2f}\")\n",
    "\n",
    "#Test de normalidad de los reiduos -  Shapiro Wilk \n",
    "prediccion_train = modelo_final_res.predict(X_train[predictores])\n",
    "\n",
    "residuos_train= prediccion_train-y_train\n",
    "\n",
    "shapiro_test = stats.shapiro(residuos_train)\n",
    "\n",
    "if shapiro_test.pvalue > 0.05:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\")\n",
    "else:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos no son normalmente distribuidos (p <= 0.05)\")\n",
    "\n",
    "print(\"Estadística de prueba:\", shapiro_test.statistic)\n",
    "print(\"Valor p:\", shapiro_test.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c1dcc5",
   "metadata": {},
   "source": [
    "##### Inspección visual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b29c29a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dncarcedo\\AppData\\Local\\Temp\\ipykernel_15272\\465318298.py:10: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k--\" (-> color='k'). The keyword argument will take precedence.\n",
      "  axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\miner-tool\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:1045: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b\" (-> color=(0.0, 0.0, 1.0, 1)). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importar la librería Seaborn y establecer un esquema de color azul (celeste)\n",
    "\n",
    "sns.set_palette(\"ocean\")\n",
    "\n",
    "# Gráficos\n",
    "# ==============================================================================\n",
    "fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(9, 8))\n",
    "\n",
    "axes[0, 0].scatter(y_train, prediccion_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
    "axes[0, 0].set_title('Valor predicho vs valor real', fontsize=10)\n",
    "axes[0, 0].set_xlabel('Real')\n",
    "axes[0, 0].set_ylabel('Predicción')\n",
    "axes[0, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 2 - Gráfico Residuos del Modelo\n",
    "axes[0, 1].scatter(list(range(len(y_train))), residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 1].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[0, 1].set_title('Residuos del modelo', fontsize=10)\n",
    "axes[0, 1].set_xlabel('id')\n",
    "axes[0, 1].set_ylabel('Residuos')\n",
    "axes[0, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 3 - Gráfico Distribución Residuos del Modelo\n",
    "sns.histplot(data=residuos_train, stat=\"count\", kde=True, line_kws={'linewidth': 1},alpha=0.3, ax=axes[1, 0])\n",
    "axes[1, 0].set_title('Distribución residuos del modelo', fontsize=10)\n",
    "axes[1, 0].set_xlabel(\"Residuos\")\n",
    "axes[1, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 4 - Gráfico Q-Q Residuos del Modelo\n",
    "sm.qqplot(residuos_train, fit=True, line='45', ax=axes[1, 1], color='black', alpha=0.4, lw=2)\n",
    "axes[1, 1].set_title('Q-Q residuos del modelo', fontsize=10)\n",
    "axes[1, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 5 - Gráfico Residuos del Modelo vs Predicción\n",
    "axes[2, 0].scatter(prediccion_train, residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[2, 0].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[2, 0].set_title('Residuos del modelo vs predicción', fontsize=10)\n",
    "axes[2, 0].set_xlabel('Predicción')\n",
    "axes[2, 0].set_ylabel('Residuos')\n",
    "axes[2, 0].tick_params(labelsize=7)\n",
    "\n",
    "# Eliminar los axes vacíos\n",
    "fig.delaxes(axes[2, 1])\n",
    "\n",
    "# Ajustar diseño de la figura y título\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(top=0.9)\n",
    "#fig.suptitle('Diagnóstico residuos RLM MEDIAS + CLIMA + INTERACCIONES', fontsize=12)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c490b11f",
   "metadata": {},
   "source": [
    "# RLM - 4 - variables seleccionadas por stepwise (forward)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "535d11ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'D:\\Documents\\Documents\\academico\\tesis_maie\\data_sets\\dataframe\\dataframe_tesis_final_correcciones.csv')\n",
    "df = df.drop(columns = ['Lote', 'REND_range', 'REND_stde', 'Año'])\n",
    "df = df.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3342b91e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 33 entries, 0 to 32\n",
      "Data columns (total 12 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   DSM              33 non-null     float64\n",
      " 1   EVAPO            33 non-null     float64\n",
      " 2   PP               33 non-null     float64\n",
      " 3   TEMP             33 non-null     float64\n",
      " 4   GNDVI_mean       33 non-null     float64\n",
      " 5   NDRE_mean        33 non-null     float64\n",
      " 6   NDVI_mean        33 non-null     float64\n",
      " 7   OSAVI_mean       33 non-null     float64\n",
      " 8   RECI_mean        33 non-null     float64\n",
      " 9   SLOPE_mean       33 non-null     float64\n",
      " 10  TCARIOSAVI_mean  33 non-null     float64\n",
      " 11  TCARI_mean       33 non-null     float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 3.2 KB\n"
     ]
    }
   ],
   "source": [
    "# Concatenar las componentes principales con tu DataFrame original\n",
    "X = df.drop(columns = ['REND_mean',\n",
    "                       'NDRE_stde', 'NDRE_range', \n",
    "                       'SLOPE_stde','SLOPE_range', \n",
    "                       'GNDVI_stde', 'GNDVI_range',\n",
    "                        'NDVI_stde', 'NDVI_range', \n",
    "                       'OSAVI_stde', 'OSAVI_range',\n",
    "                       'RECI_stde', 'RECI_range',\n",
    "                       'TCARIOSAVI_stde', 'TCARIOSAVI_range', \n",
    "                       'TCARI_stde', 'TCARI_range'])\n",
    "\n",
    "y = df['REND_mean']\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3b3a8a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['REND_mean']\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.8,random_state=1234,shuffle=True) #.values.reshape(-1,1),\n",
    "X_train = sm.add_constant(X_train, prepend=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7f885dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funcion de selecciçon backward para modelos lineales de statsmodels\n",
    "# ==============================================================================\n",
    "\n",
    "def forward_selection(\n",
    "    X: pd.DataFrame,\n",
    "    y: pd.Series,\n",
    "    criterio: str='aic',\n",
    "    add_constant: bool=True,\n",
    "    verbose: bool=True\n",
    ")-> list:\n",
    "    \n",
    "    \"\"\"\n",
    "    Realiza un procedimiento de selección de variables hacia adelante (forward)\n",
    "    utilizando como criterio de bondad la métrica especificada. El procedimiento\n",
    "    se detiene cuando no es posible mejorar más el modelo añadiendo variables.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    X: pd.DataFrame\n",
    "        Matriz de predictores\n",
    "    y: pd.Series\n",
    "        Variable respuesta\n",
    "    metrica: str, default='aic'\n",
    "        Métrica utilizada para seleccionar las variables. Debe ser una de las\n",
    "        siguientes opciones: 'aic', 'bic', 'rsquared_adj'.\n",
    "    add_constant: bool, default=True\n",
    "        Si `True` añade una columna de 1s a la matriz de predictores con el\n",
    "        con el nombre de intercept.\n",
    "    verbose: bool, default=True\n",
    "        Si `True` muestra por pantalla los resultados de cada iteración.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    seleccion: list\n",
    "        Lista con las variables seleccionadas.\n",
    "    \"\"\"\n",
    "\n",
    "    if add_constant:\n",
    "        X = sm.add_constant(X, prepend=True).rename(columns={'const':'intercept'})\n",
    "\n",
    "    restantes = X.columns.to_list()\n",
    "    seleccion = []\n",
    "    if criterio == 'rsquared_adj':\n",
    "        mejor_metrica = -np.inf\n",
    "        ultima_metrica = -np.inf\n",
    "    else:\n",
    "        mejor_metrica = np.inf\n",
    "        ultima_metrica = np.inf\n",
    "\n",
    "    while restantes:\n",
    "        metricas = []\n",
    "        for candidata in restantes:\n",
    "            seleccion_temp = seleccion + [candidata]\n",
    "            modelo  = sm.OLS(endog=y, exog=X[seleccion_temp])\n",
    "            modelo_res = modelo.fit()\n",
    "            metrica = getattr(modelo_res, criterio)\n",
    "            metricas.append(metrica)\n",
    "        if criterio == 'rsquared_adj':\n",
    "            mejor_metrica = max(metricas)\n",
    "            if mejor_metrica > ultima_metrica:\n",
    "                mejor_variable = restantes[np.argmax(metricas)]\n",
    "            else:\n",
    "                break\n",
    "        else:\n",
    "            mejor_metrica = min(metricas)\n",
    "            if mejor_metrica < ultima_metrica:\n",
    "                mejor_variable = restantes[np.argmin(metricas)]\n",
    "            else:\n",
    "                break\n",
    "                \n",
    "        seleccion.append(mejor_variable)\n",
    "        restantes.remove(mejor_variable)\n",
    "        ultima_metrica = mejor_metrica\n",
    "\n",
    "        if verbose:\n",
    "            print(f'variables: {seleccion} | {criterio}: {mejor_metrica:.3f}')\n",
    "\n",
    "    return sorted(seleccion)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8bce1a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variables: ['GNDVI_mean'] | aic: 119.308\n",
      "variables: ['GNDVI_mean', 'TCARI_mean'] | aic: 110.848\n",
      "variables: ['GNDVI_mean', 'TCARI_mean', 'EVAPO'] | aic: 109.814\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['EVAPO', 'GNDVI_mean', 'TCARI_mean']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selección de variables hacia forward\n",
    "# ==============================================================================\n",
    "predictores = forward_selection(\n",
    "    X            = X_train,\n",
    "    y            = y_train,\n",
    "    criterio     = 'aic',\n",
    "    add_constant = False, # Ya se le añadió anteriormente\n",
    "    verbose      = True\n",
    ")\n",
    "predictores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e9ce2b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:              REND_mean   R-squared (uncentered):                   0.967\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.963\n",
      "Method:                 Least Squares   F-statistic:                              225.8\n",
      "Date:                Tue, 10 Oct 2023   Prob (F-statistic):                    3.38e-17\n",
      "Time:                        17:29:35   Log-Likelihood:                         -51.907\n",
      "No. Observations:                  26   AIC:                                      109.8\n",
      "Df Residuals:                      23   BIC:                                      113.6\n",
      "Df Model:                           3                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "EVAPO         -5.3180      3.152     -1.687      0.105     -11.838       1.202\n",
      "GNDVI_mean    28.0408      5.407      5.186      0.000      16.855      39.226\n",
      "TCARI_mean   -92.8194     32.447     -2.861      0.009    -159.940     -25.698\n",
      "==============================================================================\n",
      "Omnibus:                        4.133   Durbin-Watson:                   2.027\n",
      "Prob(Omnibus):                  0.127   Jarque-Bera (JB):                2.472\n",
      "Skew:                           0.477   Prob(JB):                        0.291\n",
      "Kurtosis:                       4.172   Cond. No.                         53.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "El error (rmse) de test es: 2.24\n",
      "El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\n",
      "Estadística de prueba: 0.9462195038795471\n",
      "Valor p: 0.1888933777809143\n"
     ]
    }
   ],
   "source": [
    "# Entrenamiento del modelo con las variables seleccionadas\n",
    "# ==============================================================================\n",
    "modelo_final  = sm.OLS(endog=y_train, exog=X_train[predictores])\n",
    "modelo_final_res = modelo_final.fit()\n",
    "print(modelo_final_res.summary())\n",
    "\n",
    "\n",
    "#El error (rmse) del test\n",
    "X_test = sm.add_constant(X_test, prepend=True)\n",
    "predicciones = modelo_final_res.predict(exog = X_test[predictores])\n",
    "rmse = mean_squared_error(y_true  = y_test,y_pred  = predicciones,squared = False)\n",
    "print(f\"El error (rmse) de test es: {rmse:.2f}\")\n",
    "\n",
    "#Test de normalidad de los reiduos -  Shapiro Wilk \n",
    "prediccion_train = modelo_final_res.predict(X_train[predictores])\n",
    "\n",
    "residuos_train= prediccion_train-y_train\n",
    "\n",
    "shapiro_test = stats.shapiro(residuos_train)\n",
    "\n",
    "if shapiro_test.pvalue > 0.05:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\")\n",
    "else:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos no son normalmente distribuidos (p <= 0.05)\")\n",
    "\n",
    "print(\"Estadística de prueba:\", shapiro_test.statistic)\n",
    "print(\"Valor p:\", shapiro_test.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca21d8a",
   "metadata": {},
   "source": [
    "##### Inspección visual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2089da22",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dncarcedo\\AppData\\Local\\Temp\\ipykernel_15272\\465318298.py:10: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k--\" (-> color='k'). The keyword argument will take precedence.\n",
      "  axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\miner-tool\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:1045: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b\" (-> color=(0.0, 0.0, 1.0, 1)). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importar la librería Seaborn y establecer un esquema de color azul (celeste)\n",
    "\n",
    "sns.set_palette(\"ocean\")\n",
    "\n",
    "# Gráficos\n",
    "# ==============================================================================\n",
    "fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(9, 8))\n",
    "\n",
    "axes[0, 0].scatter(y_train, prediccion_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
    "axes[0, 0].set_title('Valor predicho vs valor real', fontsize=10)\n",
    "axes[0, 0].set_xlabel('Real')\n",
    "axes[0, 0].set_ylabel('Predicción')\n",
    "axes[0, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 2 - Gráfico Residuos del Modelo\n",
    "axes[0, 1].scatter(list(range(len(y_train))), residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 1].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[0, 1].set_title('Residuos del modelo', fontsize=10)\n",
    "axes[0, 1].set_xlabel('id')\n",
    "axes[0, 1].set_ylabel('Residuos')\n",
    "axes[0, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 3 - Gráfico Distribución Residuos del Modelo\n",
    "sns.histplot(data=residuos_train, stat=\"count\", kde=True, line_kws={'linewidth': 1},alpha=0.3, ax=axes[1, 0])\n",
    "axes[1, 0].set_title('Distribución residuos del modelo', fontsize=10)\n",
    "axes[1, 0].set_xlabel(\"Residuos\")\n",
    "axes[1, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 4 - Gráfico Q-Q Residuos del Modelo\n",
    "sm.qqplot(residuos_train, fit=True, line='45', ax=axes[1, 1], color='black', alpha=0.4, lw=2)\n",
    "axes[1, 1].set_title('Q-Q residuos del modelo', fontsize=10)\n",
    "axes[1, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 5 - Gráfico Residuos del Modelo vs Predicción\n",
    "axes[2, 0].scatter(prediccion_train, residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[2, 0].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[2, 0].set_title('Residuos del modelo vs predicción', fontsize=10)\n",
    "axes[2, 0].set_xlabel('Predicción')\n",
    "axes[2, 0].set_ylabel('Residuos')\n",
    "axes[2, 0].tick_params(labelsize=7)\n",
    "\n",
    "# Eliminar los axes vacíos\n",
    "fig.delaxes(axes[2, 1])\n",
    "\n",
    "# Ajustar diseño de la figura y título\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(top=0.9)\n",
    "#fig.suptitle('Diagnóstico residuos RLM MEDIAS + CLIMA + INTERACCIONES', fontsize=12)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca186d3",
   "metadata": {},
   "source": [
    "## RLM  - 5 - INTERACCIONES STEPWISE (forward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2ed8c6cb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'D:\\Documents\\Documents\\academico\\tesis_maie\\data_sets\\dataframe\\dataframe_tesis_final_correcciones.csv')\n",
    "df = df.drop(columns = ['Lote', 'REND_range', 'REND_stde', 'Año'])\n",
    "\n",
    "X = df.drop(columns = ['REND_mean',\n",
    "                         \n",
    "                       'NDRE_stde', 'NDRE_range', \n",
    "                       'SLOPE_stde','SLOPE_range', \n",
    "                       'GNDVI_stde', 'GNDVI_range',\n",
    "                        'NDVI_stde', 'NDVI_range', \n",
    "                       'OSAVI_stde', 'OSAVI_range',\n",
    "                       'RECI_stde', 'RECI_range',\n",
    "                       'TCARIOSAVI_stde', 'TCARIOSAVI_range', \n",
    "                       'TCARI_stde', 'TCARI_range'])\n",
    "\n",
    "\n",
    "\n",
    "y = df['REND_mean']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc60dde",
   "metadata": {},
   "source": [
    "#### Modelado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "da3e3332",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              REND_mean   R-squared:                       0.729\n",
      "Model:                            OLS   Adj. R-squared:                  0.323\n",
      "Method:                 Least Squares   F-statistic:                     1.796\n",
      "Date:                Tue, 10 Oct 2023   Prob (F-statistic):              0.176\n",
      "Time:                        17:36:58   Log-Likelihood:                -43.687\n",
      "No. Observations:                  26   AIC:                             119.4\n",
      "Df Residuals:                      10   BIC:                             139.5\n",
      "Df Model:                          15                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "const               36.0407     38.983      0.925      0.377     -50.819     122.901\n",
      "DSM                  0.0224      0.026      0.876      0.402      -0.035       0.080\n",
      "EVAPO              -57.3398     79.823     -0.718      0.489    -235.197     120.518\n",
      "PP                   0.0011      0.007      0.147      0.886      -0.016       0.018\n",
      "TEMP                -1.7854      1.284     -1.391      0.194      -4.645       1.074\n",
      "GNDVI_mean         -87.3950    128.331     -0.681      0.511    -373.334     198.544\n",
      "NDRE_mean          -20.0867     78.710     -0.255      0.804    -195.463     155.289\n",
      "NDVI_mean           48.6019     90.002      0.540      0.601    -151.934     249.138\n",
      "OSAVI_mean         129.7035    123.475      1.050      0.318    -145.417     404.824\n",
      "RECI_mean           -2.5376      4.573     -0.555      0.591     -12.727       7.652\n",
      "SLOPE_mean           0.7014      2.096      0.335      0.745      -3.969       5.372\n",
      "TCARIOSAVI_mean     81.3906     95.933      0.848      0.416    -132.361     295.142\n",
      "TCARI_mean        -532.3520    403.084     -1.321      0.216   -1430.480     365.776\n",
      "EVAPO_GNDVI_mean   118.9189    139.097      0.855      0.413    -191.009     428.847\n",
      "EVAPO_TCARI_mean    96.4864    457.843      0.211      0.837    -923.651    1116.624\n",
      "TCARI_GNDVI_mean   214.2741    501.213      0.428      0.678    -902.497    1331.046\n",
      "==============================================================================\n",
      "Omnibus:                        3.205   Durbin-Watson:                   1.791\n",
      "Prob(Omnibus):                  0.201   Jarque-Bera (JB):                1.817\n",
      "Skew:                           0.607   Prob(JB):                        0.403\n",
      "Kurtosis:                       3.449   Cond. No.                     1.50e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.5e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "El error (RMSE) en el conjunto de prueba es: 1.48\n",
      "El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\n",
      "Estadística de prueba: 0.9567442536354065\n",
      "Valor p: 0.33153319358825684\n"
     ]
    }
   ],
   "source": [
    "# Divide los datos en conjuntos de entrenamiento y prueba\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=1234, shuffle=True)\n",
    "\n",
    "# Se añade una nueva columna con la interacción\n",
    "#['EVAPO', 'GNDVI_mean', 'TCARI_mean']\n",
    "\n",
    "X_train['EVAPO_GNDVI_mean'] = X_train['EVAPO'] * X_train['GNDVI_mean']\n",
    "X_test['EVAPO_GNDVI_mean'] = X_test['EVAPO'] * X_test['GNDVI_mean']\n",
    "\n",
    "X_train['EVAPO_TCARI_mean']  = X_train['EVAPO'] * X_train['TCARI_mean']\n",
    "X_test['EVAPO_TCARI_mean'] = X_test['EVAPO'] * X_test['TCARI_mean']\n",
    "\n",
    "X_train['TCARI_GNDVI_mean'] = X_train['TCARI_mean'] * X_train['GNDVI_mean']\n",
    "X_test['TCARI_GNDVI_mean'] = X_test['TCARI_mean'] * X_test['GNDVI_mean']\n",
    "\n",
    "#X_train['OSAVI_mean_GNDVI_mean']  = X_train['OSAVI_mean'] * X_train['GNDVI_mean']\n",
    "#X_test['OSAVI_mean_GNDVI_mean'] = X_test['OSAVI_mean'] * X_test['GNDVI_mean']\n",
    "\n",
    "#X_train['TCARI_mean_NDRE_mean'] = X_train['TCARI_mean'] * X_train['NDRE_mean']\n",
    "#X_test['TCARI_mean_NDRE_mean'] = X_test['TCARI_mean'] * X_test['NDRE_mean']\n",
    "\n",
    "\n",
    "# Agrega una columna de 1s para el intercepto del modelo\n",
    "X_train = sm.add_constant(X_train, prepend=True)\n",
    "X_test = sm.add_constant(X_test, prepend=True)\n",
    "\n",
    "# Ajusta el modelo de regresión lineal\n",
    "modelo_interaccion = sm.OLS(endog=y_train, exog=X_train)\n",
    "modelo_interaccion = modelo_interaccion.fit()\n",
    "print(modelo_interaccion.summary())\n",
    "\n",
    "# Calcula el error RMSE en el conjunto de prueba\n",
    "predicciones = modelo_interaccion.predict(exog=X_test)\n",
    "rmse = mean_squared_error(y_true=y_test, y_pred=predicciones, squared=False)\n",
    "print(f\"El error (RMSE) en el conjunto de prueba es: {rmse:.2f}\")\n",
    "\n",
    "# Prueba de normalidad de los residuos - Shapiro-Wilk\n",
    "residuos_train = modelo_interaccion.resid\n",
    "shapiro_test = stats.shapiro(residuos_train)\n",
    "\n",
    "if shapiro_test.pvalue > 0.05:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos son normalmente distribuidos (p > 0.05)\")\n",
    "else:\n",
    "    print(\"El test de Shapiro-Wilk indica que los residuos no son normalmente distribuidos (p <= 0.05)\")\n",
    "\n",
    "print(\"Estadística de prueba:\", shapiro_test.statistic)\n",
    "print(\"Valor p:\", shapiro_test.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a72050",
   "metadata": {},
   "source": [
    "#### Grafico de los residuos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6ee4e56c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dncarcedo\\AppData\\Local\\Temp\\ipykernel_15272\\465318298.py:10: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k--\" (-> color='k'). The keyword argument will take precedence.\n",
      "  axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\miner-tool\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:1045: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b\" (-> color=(0.0, 0.0, 1.0, 1)). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importar la librería Seaborn y establecer un esquema de color azul (celeste)\n",
    "\n",
    "sns.set_palette(\"ocean\")\n",
    "\n",
    "# Gráficos\n",
    "# ==============================================================================\n",
    "fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(9, 8))\n",
    "\n",
    "axes[0, 0].scatter(y_train, prediccion_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 0].plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', color='black', lw=2)\n",
    "axes[0, 0].set_title('Valor predicho vs valor real', fontsize=10)\n",
    "axes[0, 0].set_xlabel('Real')\n",
    "axes[0, 0].set_ylabel('Predicción')\n",
    "axes[0, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 2 - Gráfico Residuos del Modelo\n",
    "axes[0, 1].scatter(list(range(len(y_train))), residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[0, 1].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[0, 1].set_title('Residuos del modelo', fontsize=10)\n",
    "axes[0, 1].set_xlabel('id')\n",
    "axes[0, 1].set_ylabel('Residuos')\n",
    "axes[0, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 3 - Gráfico Distribución Residuos del Modelo\n",
    "sns.histplot(data=residuos_train, stat=\"count\", kde=True, line_kws={'linewidth': 1},alpha=0.3, ax=axes[1, 0])\n",
    "axes[1, 0].set_title('Distribución residuos del modelo', fontsize=10)\n",
    "axes[1, 0].set_xlabel(\"Residuos\")\n",
    "axes[1, 0].tick_params(labelsize=7)\n",
    "\n",
    "# 4 - Gráfico Q-Q Residuos del Modelo\n",
    "sm.qqplot(residuos_train, fit=True, line='45', ax=axes[1, 1], color='black', alpha=0.4, lw=2)\n",
    "axes[1, 1].set_title('Q-Q residuos del modelo', fontsize=10)\n",
    "axes[1, 1].tick_params(labelsize=7)\n",
    "\n",
    "# 5 - Gráfico Residuos del Modelo vs Predicción\n",
    "axes[2, 0].scatter(prediccion_train, residuos_train, edgecolors=(0, 0, 0), alpha=0.4)\n",
    "axes[2, 0].axhline(y=0, linestyle='--', color='black', lw=2)\n",
    "axes[2, 0].set_title('Residuos del modelo vs predicción', fontsize=10)\n",
    "axes[2, 0].set_xlabel('Predicción')\n",
    "axes[2, 0].set_ylabel('Residuos')\n",
    "axes[2, 0].tick_params(labelsize=7)\n",
    "\n",
    "# Eliminar los axes vacíos\n",
    "fig.delaxes(axes[2, 1])\n",
    "\n",
    "# Ajustar diseño de la figura y título\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(top=0.9)\n",
    "#fig.suptitle('Diagnóstico residuos RLM MEDIAS + CLIMA + INTERACCIONES', fontsize=12)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}