Time 8 - Determinação de Coeficientes para cálculo da Temperatura Equivalente - Varios Modelos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATATHONS 3ª EDIÇÃO - EQUIPE 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.0 IMPORTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from sklearn.ensemble      import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgbm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y, yhat):\n",
    "    return np.mean(np.abs((y - yhat) / y))\n",
    "\n",
    "def mean_percentage_error(y, yhat):\n",
    "    return np.mean((y - yhat) / y)\n",
    "\n",
    "def ml_error(model_name, y, yhat):\n",
    "    mae = mean_absolute_error(y, yhat)\n",
    "    mape = mean_absolute_percentage_error(y, yhat)\n",
    "    rmse = np.sqrt(mean_squared_error(y, yhat))\n",
    "    \n",
    "    return pd.DataFrame({   'Model Name': model_name,'MAE': mae,'MAPE': mape,'RMSE': rmse}, index=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.0 DATA SELECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primeiramente, é feita a leitura de dados da ANEEL para determinar os coeficientes CP (COEF_SP) e Alfa (COEF_RE, COEF_IN, COEF_CO, COEF_RU, COEF_OU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Leitura dos Indicadores\n",
    "filenameATRB = \"Atributos/Atributos.txt\"\n",
    "\n",
    "#Energia consumida residencial nos últimos 12 meses (ano móvel) MT e BT\n",
    "CMMTRE = 156\n",
    "\n",
    "#Energia consumida industrial nos últimos 12 meses (ano móvel) MT e BT\n",
    "CMMTIN = 157 \n",
    "\n",
    "#Energia consumida comercial nos últimos 12 meses (ano móvel) MT e BT\n",
    "CMMTCO = 158\n",
    "\n",
    "#Energia consumida rural nos últimos 12 meses (ano móvel) MT e BT Trimestral Atributos\n",
    "CMMTRU = 159\n",
    "\n",
    "#Energia consumida outras classes nos últimos 12 meses (ano móvel) MT e BT\n",
    "CMMTOU = 160\n",
    "\n",
    "data = pd.read_csv(filenameATRB, sep='\\t',encoding = \"ISO-8859-1\")\n",
    "\n",
    "#ENEL, ELEKTRO, EDP, SSE, CPFLpl, CPFLpr, CPFLsc\n",
    "IdAgente = [390,385,391,5216,63,2937,69]\n",
    "\n",
    "dados_cons = []\n",
    "for k in IdAgente:\n",
    "    E_RE = data[(data['IdAgente'] == k) & (data['PrdCptAnoInd'] == 2013) & (data['PrdCptInd'] == 4) & (data['IdeIdc'] == CMMTRE)].VlrInd.sum()\n",
    "    E_IN = data[(data['IdAgente'] == k) & (data['PrdCptAnoInd'] == 2013) & (data['PrdCptInd'] == 4) & (data['IdeIdc'] == CMMTIN)].VlrInd.sum()\n",
    "    E_CO = data[(data['IdAgente'] == k) & (data['PrdCptAnoInd'] == 2013) & (data['PrdCptInd'] == 4) & (data['IdeIdc'] == CMMTCO)].VlrInd.sum()\n",
    "    E_RU = data[(data['IdAgente'] == k) & (data['PrdCptAnoInd'] == 2013) & (data['PrdCptInd'] == 4) & (data['IdeIdc'] == CMMTRU)].VlrInd.sum()\n",
    "    E_OU = data[(data['IdAgente'] == k) & (data['PrdCptAnoInd'] == 2013) & (data['PrdCptInd'] == 4) & (data['IdeIdc'] == CMMTOU)].VlrInd.sum()\n",
    "    E_TOT = E_RE + E_IN + E_CO + E_RU + E_OU\n",
    "    COEF_RE = E_RE/E_TOT\n",
    "    COEF_IN = E_IN/E_TOT\n",
    "    COEF_CO = E_CO/E_TOT\n",
    "    COEF_RU = E_RU/E_TOT\n",
    "    COEF_OU = E_OU/E_TOT\n",
    "    dados_cons.append([k,E_RE,E_IN,E_CO,E_RU,E_OU,E_TOT,COEF_RE,COEF_IN,COEF_CO,COEF_RU,COEF_OU])    \n",
    "    \n",
    "DATAC = pd.DataFrame(dados_cons,columns=['Dist','E_RE','E_IN','E_CO','E_RU','E_OU','E_TOT','COEF_RE','COEF_IN','COEF_CO','COEF_RU','COEF_OU'])\n",
    "DATAC['COEF_SP'] = DATAC.E_TOT/DATAC.E_TOT.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Leitura da base de dados do INMET e de CARGAS disponibilizado pelo ONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "INMET = pd.read_csv('Datathons_pec_carga/inmet.csv',sep=';')\n",
    "CARGA = pd.read_csv('Datathons_pec_carga/seriesCargaSP.csv',sep=';',decimal=',')\n",
    "MUN = pd.read_csv('Datathons_pec_carga/Municipios_SP.csv',sep=',')\n",
    "\n",
    "INMET['datetime'] = pd.to_datetime(INMET['din_medicao'], infer_datetime_format = True, format=\"%Y-%M-%D %H:%M:%S\")\n",
    "CARGA['datetime1'] = pd.to_datetime(CARGA['din_ocorrencia'], infer_datetime_format = True, format=\"%Y-%M-%D %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Foi selecionada uma Estação de Medição de Temperatura para ser associada com cada uma das 7 distribuidoras de SP. Esta seleção foi feita de maneira visual considerando a proximidade da estação com o centro de carga"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ENEL\n",
    "TEMP_ENEL = INMET[(INMET['id_varmeteo'] == 'TEM_INS') & (INMET['id_estacaometeo'] == 'A701')].sort_values(by=['datetime']).reset_index().drop(columns=['index','val_latitude','val_longitude','din_medicao','val_altitude','id_varmeteo','din_inclusaodl','nom_longo','id_estacaometeo','nom_varmeteo'])\n",
    "CARGA_ENEL = CARGA[CARGA['nom_seriehistorica'] == 'SP-ELETROPAULO'].sort_values(by=['datetime1']).reset_index().drop(columns=['index','din_ocorrencia'])\n",
    "ENEL = pd.concat([TEMP_ENEL,CARGA_ENEL],axis=1).drop(columns=['datetime1'])\n",
    "\n",
    "#ELEKTRO\n",
    "TEMP_ELEKTRO = INMET[(INMET['id_varmeteo'] == 'TEM_INS') & (INMET['id_estacaometeo'] == 'A729')].sort_values(by=['datetime']).reset_index().drop(columns=['index','val_latitude','val_longitude','din_medicao','val_altitude','id_varmeteo','din_inclusaodl','nom_longo','id_estacaometeo','nom_varmeteo'])\n",
    "CARGA_ELEKTRO = CARGA[CARGA['nom_seriehistorica'] == 'SP-ELEKTRO'].sort_values(by=['datetime1']).reset_index().drop(columns=['index','din_ocorrencia'])\n",
    "ELEKTRO = pd.concat([TEMP_ELEKTRO,CARGA_ELEKTRO],axis=1).drop(columns=['datetime1'])\n",
    "\n",
    "#EDP\n",
    "TEMP_EDP = INMET[(INMET['id_varmeteo'] == 'TEM_INS') & (INMET['id_estacaometeo'] == 'A728')].sort_values(by=['datetime']).reset_index().drop(columns=['index','val_latitude','val_longitude','din_medicao','val_altitude','id_varmeteo','din_inclusaodl','nom_longo','id_estacaometeo','nom_varmeteo'])\n",
    "CARGA_EDP = CARGA[CARGA['nom_seriehistorica'] == 'SP-EDP SP'].sort_values(by=['datetime1']).reset_index().drop(columns=['index','din_ocorrencia'])\n",
    "EDP = pd.concat([TEMP_EDP,CARGA_EDP],axis=1).drop(columns=['datetime1'])\n",
    "\n",
    "#SSE\n",
    "TEMP_SSE = INMET[(INMET['id_varmeteo'] == 'TEM_INS') & (INMET['id_estacaometeo'] == 'A707')].sort_values(by=['datetime']).reset_index().drop(columns=['index','val_latitude','val_longitude','din_medicao','val_altitude','id_varmeteo','din_inclusaodl','nom_longo','id_estacaometeo','nom_varmeteo'])\n",
    "CARGA_SSE = CARGA[CARGA['nom_seriehistorica'] == 'SP-ENERGISA SSE-SP'].sort_values(by=['datetime1']).reset_index().drop(columns=['index','din_ocorrencia'])\n",
    "SSE = pd.concat([TEMP_SSE,CARGA_SSE],axis=1).drop(columns=['datetime1'])\n",
    "\n",
    "#CPFL paulista\n",
    "TEMP_CPFLpl = INMET[(INMET['id_varmeteo'] == 'TEM_INS') & (INMET['id_estacaometeo'] == 'A737')].sort_values(by=['datetime']).reset_index().drop(columns=['index','val_latitude','val_longitude','din_medicao','val_altitude','id_varmeteo','din_inclusaodl','nom_longo','id_estacaometeo','nom_varmeteo'])\n",
    "CARGA_CPFLpl = CARGA[CARGA['nom_seriehistorica'] == 'SP-CPFL PAULISTA'].sort_values(by=['datetime1']).reset_index().drop(columns=['index','din_ocorrencia'])\n",
    "CPFLpl = pd.concat([TEMP_CPFLpl,CARGA_CPFLpl],axis=1).drop(columns=['datetime1'])\n",
    "\n",
    "#CPFL piratininga\n",
    "TEMP_CPFLpr = INMET[(INMET['id_varmeteo'] == 'TEM_INS') & (INMET['id_estacaometeo'] == 'A713')].sort_values(by=['datetime']).reset_index().drop(columns=['index','val_latitude','val_longitude','din_medicao','val_altitude','id_varmeteo','din_inclusaodl','nom_longo','id_estacaometeo','nom_varmeteo'])\n",
    "CARGA_CPFLpr = CARGA[CARGA['nom_seriehistorica'] == 'SP-CPFL PIRATININGA'].sort_values(by=['datetime1']).reset_index().drop(columns=['index','din_ocorrencia'])\n",
    "CPFLpr = pd.concat([TEMP_CPFLpr,CARGA_CPFLpr],axis=1).drop(columns=['datetime1'])\n",
    "\n",
    "#CPFL santa cruz\n",
    "TEMP_CPFLsc = INMET[(INMET['id_varmeteo'] == 'TEM_INS') & (INMET['id_estacaometeo'] == 'A725')].sort_values(by=['datetime']).reset_index().drop(columns=['index','val_latitude','val_longitude','din_medicao','val_altitude','id_varmeteo','din_inclusaodl','nom_longo','id_estacaometeo','nom_varmeteo'])\n",
    "CARGA_CPFLsc = CARGA[CARGA['nom_seriehistorica'] == 'SP-CPFL SANTA CRUZ'].sort_values(by=['datetime1']).reset_index().drop(columns=['index','din_ocorrencia'])\n",
    "CPFLsc = pd.concat([TEMP_CPFLsc,CARGA_CPFLsc],axis=1).drop(columns=['datetime1'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.0 Análise Exploratória"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotar os dados de carga e temperatura para cada distribuidora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#PLOTAR OS DADOS\n",
    "d1 = '2018-01-01 17:00:00'\n",
    "d2 = '2018-01-02 17:00:00'\n",
    "filtroEDP = (EDP['datetime'] >= d1) & (EDP['datetime'] <= d2)\n",
    "filtroENEL = (ENEL['datetime'] >= d1) & (ENEL['datetime'] <= d2)\n",
    "filtroELEKTRO = (ELEKTRO['datetime'] >= d1) & (ELEKTRO['datetime'] <= d2)\n",
    "filtroSSE = (SSE['datetime'] >= d1) & (SSE['datetime'] <= d2)\n",
    "filtroCPFLpl = (CPFLpl['datetime'] >= d1) & (CPFLpl['datetime'] <= d2)\n",
    "filtroCPFLpr = (CPFLpr['datetime'] >= d1) & (CPFLpr['datetime'] <= d2)\n",
    "filtroCPFLsc = (CPFLsc['datetime'] >= d1) & (CPFLsc['datetime'] <= d2)\n",
    "\n",
    "# Plot a simple line chart\n",
    "plt.plot(CPFLpl[filtroCPFLpl].datetime, CPFLpl[filtroCPFLpl].val_itemserieoriginal, color='g', label='CPFLpl')\n",
    "plt.plot(CPFLpr[filtroCPFLpr].datetime, CPFLpr[filtroCPFLpr].val_itemserieoriginal, color='r', label='CPFLpr')\n",
    "plt.plot(CPFLsc[filtroCPFLsc].datetime, CPFLsc[filtroCPFLsc].val_itemserieoriginal, color='b', label='CPFLsc')\n",
    "plt.plot(ENEL[filtroENEL].datetime, ENEL[filtroENEL].val_itemserieoriginal, color='y', label='ENEL')\n",
    "plt.plot(ELEKTRO[filtroELEKTRO].datetime, ELEKTRO[filtroELEKTRO].val_itemserieoriginal, color='c', label='ELEKTRO')\n",
    "plt.plot(SSE[filtroSSE].datetime, SSE[filtroSSE].val_itemserieoriginal, color='k', label='SSE')\n",
    "plt.plot(EDP[filtroEDP].datetime, EDP[filtroEDP].val_itemserieoriginal, color='navy', label='EDP')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plot a simple line chart\n",
    "plt.plot(CPFLpl[filtroCPFLpl].datetime, CPFLpl[filtroCPFLpl].val_medicao, color='g', label='CPFLpl')\n",
    "plt.plot(CPFLpr[filtroCPFLpr].datetime, CPFLpr[filtroCPFLpr].val_medicao, color='r', label='CPFLpr')\n",
    "plt.plot(CPFLsc[filtroCPFLsc].datetime, CPFLsc[filtroCPFLsc].val_medicao, color='b', label='CPFLsc')\n",
    "plt.plot(ENEL[filtroENEL].datetime, ENEL[filtroENEL].val_medicao, color='y', label='ENEL')\n",
    "plt.plot(ELEKTRO[filtroELEKTRO].datetime, ELEKTRO[filtroELEKTRO].val_medicao, color='c', label='ELEKTRO')\n",
    "plt.plot(SSE[filtroSSE].datetime, SSE[filtroSSE].val_medicao, color='k', label='SSE')\n",
    "plt.plot(EDP[filtroEDP].datetime, EDP[filtroEDP].val_medicao, color='navy', label='EDP')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determinar as Matrizes M1 e M2 com base nos coeficientes previamente calculados e nas medições de temperatura de cada estação associada. Plotar o resultado para verificar intervalos válidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "M1 = np.array([list(TEMP_ENEL.val_medicao*DATAC.COEF_SP.iloc[0]),list(TEMP_ELEKTRO.val_medicao*DATAC.COEF_SP.iloc[1]),list(TEMP_EDP.val_medicao*DATAC.COEF_SP.iloc[2]),\n",
    "     list(TEMP_SSE.val_medicao*DATAC.COEF_SP.iloc[3]),list(TEMP_CPFLpl.val_medicao*DATAC.COEF_SP.iloc[4]),list(TEMP_CPFLpr.val_medicao*DATAC.COEF_SP.iloc[5]),\n",
    "     list(TEMP_CPFLsc.val_medicao*DATAC.COEF_SP.iloc[6])]).T\n",
    "\n",
    "M2 = np.array([list(DATAC.COEF_RE),list(DATAC.COEF_IN),list(DATAC.COEF_CO),list(DATAC.COEF_RU),list(DATAC.COEF_OU)]).T\n",
    "\n",
    "M0 = np.matmul(M1, M2)\n",
    "\n",
    "Mref = pd.DataFrame(M0)\n",
    "\n",
    "plt.plot(list(Mref.index), Mref[0], color='g', label='c0')\n",
    "plt.plot(list(Mref.index), Mref[1], color='r', label='c0')\n",
    "plt.plot(list(Mref.index), Mref[2], color='b', label='c0')\n",
    "plt.plot(list(Mref.index), Mref[3], color='y', label='c0')\n",
    "plt.plot(list(Mref.index), Mref[4], color='c', label='c0')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.0 FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre selecionar os dados que serão utilizados para treino e teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Limitar\n",
    "a = Mref[0].values  # Extract out relevant column from dataframe as array\n",
    "m = np.concatenate(( [True], np.isnan(a), [True] ))  # Mask\n",
    "ss = np.flatnonzero(m[1:] != m[:-1]).reshape(-1,2)   # Start-stop limits\n",
    "start,stop = ss[(ss[:,1] - ss[:,0]).argmax()]  # Get max interval, interval limits\n",
    "\n",
    "d0 = start\n",
    "d1 = stop\n",
    "\n",
    "#d0 = 20000\n",
    "#d1 = 21000\n",
    "\n",
    "Mref_cut = Mref[d0:d1]\n",
    "\n",
    "Ctot = ELEKTRO.val_itemserieoriginal+ENEL.val_itemserieoriginal+SSE.val_itemserieoriginal+EDP.val_itemserieoriginal+CPFLpl.val_itemserieoriginal+CPFLpr.val_itemserieoriginal+CPFLsc.val_itemserieoriginal\n",
    "Ctot_cut = Ctot[d0:d1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.0 MODEL EVALUATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizar regressao linear para obter os coeficientes Tau. Avaliar o coeficiente de determinação."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coeficiente de Determinação (R²): 0.4106335287285241\n"
     ]
    }
   ],
   "source": [
    "#Separando os dados de Treino e Teste\n",
    "impx = SimpleImputer(missing_values=np.nan, strategy='median') \n",
    "impx = impx.fit(Mref_cut)\n",
    "Mref_cut_imp = impx.transform(Mref_cut)\n",
    "\n",
    "impy = SimpleImputer(missing_values=np.nan, strategy='most_frequent') \n",
    "impy = impy.fit(np.array(Ctot_cut).reshape(-1,1))\n",
    "Ctot_cut_imp = impy.transform(np.array(Ctot_cut).reshape(-1,1))\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(Mref_cut_imp, Ctot_cut_imp, test_size=0.2, random_state=0)\n",
    "\n",
    "#Criando um Objeto de Regressão Linear\n",
    "lr = LinearRegression()\n",
    "\n",
    "#Treinando o Modelo\n",
    "lr.fit(x_train,y_train)\n",
    "\n",
    "#Verificando o resultado\n",
    "r_sq = lr.score(Mref_cut_imp, Ctot_cut_imp)\n",
    "print('Coeficiente de Determinação (R²):', r_sq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coeficientes 'Tau' encontrados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 22088.84762443,  -1310.13342155, -31358.45943791,\n",
       "         -2665.3563016 ,     62.01019589]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coeficiente de Determinação (R²) de Random Forest: 0.810748235564894\n"
     ]
    }
   ],
   "source": [
    "# model\n",
    "rf = RandomForestRegressor(n_estimators=1000, n_jobs=-1, random_state=42).fit(x_train, y_train)\n",
    "# prediction\n",
    "# yhat_rf = rf.predict(x_test)\n",
    "\n",
    "score=rf.score(Mref_cut_imp, Ctot_cut_imp)\n",
    "print('Coeficiente de Determinação (R²) de Random Forest:', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3 LGBM Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score:  0.4233737999988276\n"
     ]
    }
   ],
   "source": [
    "lgbm_model = lgbm.LGBMRegressor(n_estimators=100, learning_rate=0.01, metric='r2', boosting_type='goss')\n",
    "\n",
    "lgbm_model.fit(x_train, y_train)\n",
    "# y_pred = lgbm_model.predict(X_test)\n",
    "\n",
    "\n",
    "score=lgbm_model.score(Mref_cut_imp, Ctot_cut_imp)\n",
    "print('R2 score: ', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.4 XGB Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score:  0.8514433015744849\n"
     ]
    }
   ],
   "source": [
    "# model\n",
    "model_xgb = xgb.XGBRegressor(n_estimators=1000, booster='dart').fit(x_train, y_train)\n",
    "# prediction\n",
    "# yhat_xgb = model_xgb.predict(X_test)\n",
    "\n",
    "score=model_xgb.score(Mref_cut_imp, Ctot_cut_imp)\n",
    "print('R2 score: ', score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Coefficients are not defined for Booster type dart",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_15852/3236023656.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel_xgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32me:\\python3\\Python310\\lib\\site-packages\\xgboost\\sklearn.py\u001b[0m in \u001b[0;36mcoef_\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1062\u001b[0m         \"\"\"\n\u001b[0;32m   1063\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'booster'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'gblinear'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1064\u001b[1;33m             raise AttributeError(\n\u001b[0m\u001b[0;32m   1065\u001b[0m                 \u001b[1;34mf\"Coefficients are not defined for Booster type {self.booster}\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1066\u001b[0m             )\n",
      "\u001b[1;31mAttributeError\u001b[0m: Coefficients are not defined for Booster type dart"
     ]
    }
   ],
   "source": [
    "model_xgb.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Perceba que, apesar de mais eficientes, os algoritmos mais robustos baseados em árvores de decisão NÃO POSSUEM COEFICIENTES, e assim, perde-se a representação física e a relação direta obtida para os coeficientes 'Tau'. \n",
    "Ainda assim, são os mais indicados a serem aplicados em produção.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align='center'> Obrigado! </h1>"
   ]
  }
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