modelo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criação do Modelo de Regressão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 import linear_model\n",
    "from sklearn import tree\n",
    "from sklearn import svm\n",
    "from sklearn import neighbors\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.experimental import enable_hist_gradient_boosting\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import lightgbm as lgb\n",
    "from skopt import dummy_minimize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importando datasets\n",
    "\n",
    "Serão testados 2 modelos com duas abordagens diferentes:\n",
    "- Dataset sem valores nulos\n",
    "- Dataset dropando as linhas que possuem valores nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('casas_ponta_grossa.csv')       # Não existem valores nulos\n",
    "df_na = pd.read_csv('casas_ponta_grossa_NA.csv') # Vários valores nulos que serão dropados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset dropando valores nulos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_na.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_na.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1681, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformação de variáveis categóricas usando LabelEncoder\n",
    "\n",
    "Variável Bairro, será transformada em valor numério usando Label encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bairro'] = label_encoder.fit_transform(df['Bairro'])\n",
    "df_na['Bairro'] = label_encoder.fit_transform(df_na['Bairro'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizando dados do LabelEncoder\n",
    "\n",
    "mean = df['Bairro'].mean()\n",
    "std = df['Bairro'].std()\n",
    "df['Bairro'] = (df['Bairro'] - mean) / std\n",
    "\n",
    "mean = df_na['Bairro'].mean()\n",
    "std = df_na['Bairro'].std()\n",
    "df_na['Bairro'] = (df_na['Bairro'] - mean) / std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separando variáveis alvos \n",
    "\n",
    "A variável alvo do modelo será o Valor de Venda do imóvel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Valor Venda']\n",
    "y_na = df_na['Valor Venda']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removendo colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns='Valor Venda', inplace=True)\n",
    "df.drop(columns='Referência', inplace=True)\n",
    "df_na.drop(columns='Valor Venda', inplace=True)\n",
    "df_na.drop(columns='Referência', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separando dados em treinamento e teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_treinamento, X_teste, y_treinamento, y_teste = train_test_split(df, y, test_size=0.3)\n",
    "X_treinamento_na, X_teste_na, y_treinamento_na, y_teste_na = train_test_split(df_na, y_na, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criação do Modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Modelo Base (Random Forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(n_estimators=1000, random_state=0, min_samples_leaf=1,  n_jobs=6)\n",
    "rf_na = RandomForestRegressor(n_estimators=1000, random_state=0, min_samples_leaf=1,  n_jobs=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=1000, n_jobs=6, random_state=0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_treinamento, y_treinamento)\n",
    "rf_na.fit(X_treinamento_na, y_treinamento_na)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Métricas de Avaliação\n",
    "\n",
    "### Métrica Primária: MedAE - Median Absolute Error\n",
    "\n",
    "Preço de casas na Zillow - https://www.zillow.com/research/putting-accuracy-in-context-3255/\n",
    " \n",
    "### Métrica Secundária: (R)MSLE - (Root) Mean Squared Logarithmic Error - Raiz Quadrada do Erro Médio Logarítmico Quadrado\n",
    "\n",
    "Se importa com a diferença \"relativa\" dos valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import median_absolute_error\n",
    "from sklearn.metrics import mean_squared_log_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = rf.predict(X_teste)\n",
    "p_na =  rf_na.predict(X_teste_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MedAE: 99591.71\n",
      "MedAE drop NA: 85913.0\n",
      "MSLE: 0.22\n",
      "MSLE drop NA: 0.15\n"
     ]
    }
   ],
   "source": [
    "medae = median_absolute_error(y_teste, p)\n",
    "medae_na = median_absolute_error(y_teste_na, p_na)\n",
    "\n",
    "msle = mean_squared_log_error(y_teste, p)\n",
    "msle_na = mean_squared_log_error(y_teste_na, p_na)\n",
    "\n",
    "print('MedAE:',round(medae,2))\n",
    "print('MedAE drop NA:',round(medae_na,2))\n",
    "\n",
    "print('MSLE:',round(msle,2))\n",
    "print('MSLE drop NA:',round(msle_na,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Modelo LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm = lgb.LGBMRegressor(random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LightGBM não aceita caracteres non-ASCII no nome das colunas, então temos que mudar o nome das colunas dos datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "X_teste = X_teste.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))\n",
    "X_teste_na = X_teste_na.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))\n",
    "X_treinamento = X_treinamento.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))\n",
    "X_treinamento_na = X_treinamento_na.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm = lgbm.fit(X_treinamento, y_treinamento)\n",
    "lgbm_na = lgbm.fit(X_treinamento_na, y_treinamento_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = lgbm.predict(X_teste)\n",
    "p_na =  lgbm_na.predict(X_teste_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MedAE: 83958.24\n",
      "MedAE drop NA: 83443.92\n",
      "MSLE: 0.19\n",
      "MSLE drop NA: 0.16\n"
     ]
    }
   ],
   "source": [
    "medae = median_absolute_error(y_teste, p)\n",
    "medae_na = median_absolute_error(y_teste_na, p_na)\n",
    "\n",
    "msle = mean_squared_log_error(y_teste, p)\n",
    "msle_na = mean_squared_log_error(y_teste_na, p_na)\n",
    "\n",
    "print('MedAE:',round(medae,2))\n",
    "print('MedAE drop NA:',round(medae_na,2))\n",
    "\n",
    "print('MSLE:',round(msle,2))\n",
    "print('MSLE drop NA:',round(msle_na,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resultados Preliminares:\n",
    "\n",
    "+ Random Forest:\n",
    "\n",
    " + MedAE: 99591.71    \n",
    " + MedAE drop NA: 85913.0    \n",
    " + MSLE: 0.22  \n",
    " + MSLE drop NA: 0.15   \n",
    "  \n",
    "  \n",
    "+ LightGBM:\n",
    "\n",
    " + MedAE: 83958.24 \n",
    " + MedAE drop NA: 83443.92 \n",
    " + MSLE: 0.19  \n",
    " + MSLE drop NA: 0.16 \n",
    " \n",
    "Podemos perceber que os modelos que foram treinados usando o dataset com os NA dropados obtiveram melhor score.  \n",
    "Na parte seguinte utilizaremos somente o dataset com os NA dropados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tunando HiperParâmetros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def treinar_modelo(parametros):\n",
    "    \n",
    "    learning_rate = parametros[0]\n",
    "    num_leaves = parametros[1]\n",
    "    min_child_samples = parametros[2]\n",
    "    subsample = parametros[3]\n",
    "    colsample_bytree = parametros[4]\n",
    "    \n",
    "    print(parametros)\n",
    "    \n",
    "    mdl = lgb.LGBMRegressor(learning_rate=learning_rate, num_leaves=num_leaves, \n",
    "                            min_child_samples=min_child_samples, subsample=subsample, \n",
    "                            colsample_bytree=colsample_bytree, random_state=0, \n",
    "                            subsample_freq=1, n_estimatos=100)\n",
    "    mdl.fit(X_treinamento_na, y_treinamento_na)\n",
    "    \n",
    "    p = mdl.predict(X_teste_na)\n",
    "    \n",
    "    return median_absolute_error(y_teste_na, p)   \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "espaco_de_treinamento = [(1e-9, 1e-1, 'log-uniform'), \n",
    "                         (2, 256),\n",
    "                         (1, 100),\n",
    "                         (0.05, 1.0),\n",
    "                         (0.1, 1.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 1 started. Evaluating function at random point.\n",
      "[0.09494639669696242, 74, 10, 0.3372159440002478, 0.23208030173540176]\n",
      "Iteration No: 1 ended. Evaluation done at random point.\n",
      "Time taken: 0.1197\n",
      "Function value obtained: 90544.0699\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 2 started. Evaluating function at random point.\n",
      "[5.479094086332446e-09, 206, 72, 0.3782826906908954, 0.457090726807603]\n",
      "Iteration No: 2 ended. Evaluation done at random point.\n",
      "Time taken: 0.0459\n",
      "Function value obtained: 356224.5744\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 3 started. Evaluating function at random point.\n",
      "[2.044249367384062e-05, 256, 19, 0.5483207515942279, 0.49910760440160107]\n",
      "Iteration No: 3 ended. Evaluation done at random point.\n",
      "Time taken: 0.1267\n",
      "Function value obtained: 355800.5785\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 4 started. Evaluating function at random point.\n",
      "[6.864639102632861e-08, 144, 51, 0.9182639233502714, 0.5114843271882895]\n",
      "Iteration No: 4 ended. Evaluation done at random point.\n",
      "Time taken: 0.0778\n",
      "Function value obtained: 356223.2583\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 5 started. Evaluating function at random point.\n",
      "[2.789899361414508e-06, 224, 97, 0.7894697745197069, 0.7443734643715278]\n",
      "Iteration No: 5 ended. Evaluation done at random point.\n",
      "Time taken: 0.0677\n",
      "Function value obtained: 356201.4103\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 6 started. Evaluating function at random point.\n",
      "[0.0026427740364791757, 63, 23, 0.5422449214947946, 0.8785182267810852]\n",
      "Iteration No: 6 ended. Evaluation done at random point.\n",
      "Time taken: 0.0977\n",
      "Function value obtained: 294941.2399\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 7 started. Evaluating function at random point.\n",
      "[0.004297098005754833, 218, 14, 0.30939747550591, 0.15331888117140713]\n",
      "Iteration No: 7 ended. Evaluation done at random point.\n",
      "Time taken: 0.0728\n",
      "Function value obtained: 280105.7254\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 8 started. Evaluating function at random point.\n",
      "[0.00023132593197812873, 73, 4, 0.6880713925510277, 0.47060909107214777]\n",
      "Iteration No: 8 ended. Evaluation done at random point.\n",
      "Time taken: 0.2478\n",
      "Function value obtained: 350553.7542\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 9 started. Evaluating function at random point.\n",
      "[3.8054601974919564e-08, 26, 44, 0.7021758812975045, 0.8511631047076357]\n",
      "Iteration No: 9 ended. Evaluation done at random point.\n",
      "Time taken: 0.0838\n",
      "Function value obtained: 356223.7633\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 10 started. Evaluating function at random point.\n",
      "[1.4005745122180686e-09, 43, 83, 0.98941803446117, 0.7733490889418555]\n",
      "Iteration No: 10 ended. Evaluation done at random point.\n",
      "Time taken: 0.0598\n",
      "Function value obtained: 356224.5587\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 11 started. Evaluating function at random point.\n",
      "[1.7520719488914147e-07, 217, 8, 0.14806470624875995, 0.5031041735583147]\n",
      "Iteration No: 11 ended. Evaluation done at random point.\n",
      "Time taken: 0.0858\n",
      "Function value obtained: 356220.7307\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 12 started. Evaluating function at random point.\n",
      "[0.018567952854650637, 233, 24, 0.163515944581681, 0.572319439183401]\n",
      "Iteration No: 12 ended. Evaluation done at random point.\n",
      "Time taken: 0.0549\n",
      "Function value obtained: 135135.9757\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 13 started. Evaluating function at random point.\n",
      "[4.66642227349692e-09, 168, 9, 0.9149259627034465, 0.36903710098874454]\n",
      "Iteration No: 13 ended. Evaluation done at random point.\n",
      "Time taken: 0.1955\n",
      "Function value obtained: 356224.4953\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 14 started. Evaluating function at random point.\n",
      "[4.7327434804916324e-05, 89, 26, 0.633241401906253, 0.9608820910034701]\n",
      "Iteration No: 14 ended. Evaluation done at random point.\n",
      "Time taken: 0.1107\n",
      "Function value obtained: 354933.3370\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 15 started. Evaluating function at random point.\n",
      "[1.2241421076749223e-07, 162, 89, 0.5567760660749166, 0.9549443246793757]\n",
      "Iteration No: 15 ended. Evaluation done at random point.\n",
      "Time taken: 0.0519\n",
      "Function value obtained: 356224.0748\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 16 started. Evaluating function at random point.\n",
      "[8.799879968505213e-06, 130, 78, 0.09307844214592059, 0.225969507272543]\n",
      "Iteration No: 16 ended. Evaluation done at random point.\n",
      "Time taken: 0.0249\n",
      "Function value obtained: 356224.5876\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 17 started. Evaluating function at random point.\n",
      "[0.002183879574206963, 221, 22, 0.8889692020284521, 0.5867093706408683]\n",
      "Iteration No: 17 ended. Evaluation done at random point.\n",
      "Time taken: 0.1307\n",
      "Function value obtained: 310721.8805\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 18 started. Evaluating function at random point.\n",
      "[3.835671903920338e-06, 45, 21, 0.40870512409353993, 0.5845822248519866]\n",
      "Iteration No: 18 ended. Evaluation done at random point.\n",
      "Time taken: 0.0708\n",
      "Function value obtained: 356150.2219\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 19 started. Evaluating function at random point.\n",
      "[0.00016534499620765082, 59, 83, 0.6559446598566927, 0.2136833965403995]\n",
      "Iteration No: 19 ended. Evaluation done at random point.\n",
      "Time taken: 0.0578\n",
      "Function value obtained: 354346.9151\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 20 started. Evaluating function at random point.\n",
      "[0.0003323813918000474, 137, 25, 0.3814534248789504, 0.3429351025885235]\n",
      "Iteration No: 20 ended. Evaluation done at random point.\n",
      "Time taken: 0.0623\n",
      "Function value obtained: 349766.6843\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 21 started. Evaluating function at random point.\n",
      "[0.014692298579366013, 96, 61, 0.9665980447909663, 0.6970973480366033]\n",
      "Iteration No: 21 ended. Evaluation done at random point.\n",
      "Time taken: 0.0798\n",
      "Function value obtained: 144412.2167\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 22 started. Evaluating function at random point.\n",
      "[9.409482993800857e-05, 238, 93, 0.9520147957717178, 0.5049209201319466]\n",
      "Iteration No: 22 ended. Evaluation done at random point.\n",
      "Time taken: 0.0640\n",
      "Function value obtained: 354920.4034\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 23 started. Evaluating function at random point.\n",
      "[4.2375702903698395e-05, 84, 87, 0.27517563123087635, 0.9130415685060285]\n",
      "Iteration No: 23 ended. Evaluation done at random point.\n",
      "Time taken: 0.0394\n",
      "Function value obtained: 357541.8474\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 24 started. Evaluating function at random point.\n",
      "[3.885401409413291e-05, 17, 6, 0.6362876679396877, 0.3939804115948866]\n",
      "Iteration No: 24 ended. Evaluation done at random point.\n",
      "Time taken: 0.0758\n",
      "Function value obtained: 355524.8596\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 25 started. Evaluating function at random point.\n",
      "[1.6461326086976984e-05, 231, 14, 0.3894062720023748, 0.9176816358278194]\n",
      "Iteration No: 25 ended. Evaluation done at random point.\n",
      "Time taken: 0.1068\n",
      "Function value obtained: 355769.6414\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 26 started. Evaluating function at random point.\n",
      "[9.702439119689858e-05, 98, 56, 0.9329653720565734, 0.7218072257652316]\n",
      "Iteration No: 26 ended. Evaluation done at random point.\n",
      "Time taken: 0.0670\n",
      "Function value obtained: 354366.8168\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 27 started. Evaluating function at random point.\n",
      "[0.09518813187534293, 251, 84, 0.18027896214743389, 0.9393359167334473]\n",
      "Iteration No: 27 ended. Evaluation done at random point.\n",
      "Time taken: 0.0365\n",
      "Function value obtained: 159216.1636\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 28 started. Evaluating function at random point.\n",
      "[0.0003754441069324634, 45, 7, 0.7676898999723432, 0.7784885696151218]\n",
      "Iteration No: 28 ended. Evaluation done at random point.\n",
      "Time taken: 0.1696\n",
      "Function value obtained: 345620.8247\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 29 started. Evaluating function at random point.\n",
      "[0.024221235058878427, 139, 67, 0.16805741387355647, 0.11789212045581604]\n",
      "Iteration No: 29 ended. Evaluation done at random point.\n",
      "Time taken: 0.0410\n",
      "Function value obtained: 168250.9052\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 30 started. Evaluating function at random point.\n",
      "[1.6206450050335527e-09, 23, 41, 0.28390051422289364, 0.8740251538145993]\n",
      "Iteration No: 30 ended. Evaluation done at random point.\n",
      "Time taken: 0.0610\n",
      "Function value obtained: 356224.5819\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 31 started. Evaluating function at random point.\n",
      "[2.044789068496487e-05, 170, 79, 0.8499293477416255, 0.21175598360792006]\n",
      "Iteration No: 31 ended. Evaluation done at random point.\n",
      "Time taken: 0.0654\n",
      "Function value obtained: 355934.5019\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 32 started. Evaluating function at random point.\n",
      "[1.7118646824970908e-07, 197, 67, 0.44703021223082173, 0.4310589165745866]\n",
      "Iteration No: 32 ended. Evaluation done at random point.\n",
      "Time taken: 0.0489\n",
      "Function value obtained: 356223.4719\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 33 started. Evaluating function at random point.\n",
      "[0.003191242176281103, 37, 54, 0.7315220211949195, 0.6516532834550335]\n",
      "Iteration No: 33 ended. Evaluation done at random point.\n",
      "Time taken: 0.1107\n",
      "Function value obtained: 289682.7841\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 34 started. Evaluating function at random point.\n",
      "[2.601828133743158e-06, 56, 33, 0.4564220009627061, 0.4855441646487244]\n",
      "Iteration No: 34 ended. Evaluation done at random point.\n",
      "Time taken: 0.0850\n",
      "Function value obtained: 356181.1244\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 35 started. Evaluating function at random point.\n",
      "[7.856616270806769e-07, 224, 75, 0.9403308081800065, 0.9247688859458619]\n",
      "Iteration No: 35 ended. Evaluation done at random point.\n",
      "Time taken: 0.0970\n",
      "Function value obtained: 356205.9902\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 36 started. Evaluating function at random point.\n",
      "[0.001801638690897487, 207, 51, 0.08419946455755176, 0.6888913945205538]\n",
      "Iteration No: 36 ended. Evaluation done at random point.\n",
      "Time taken: 0.0260\n",
      "Function value obtained: 356224.5876\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 37 started. Evaluating function at random point.\n",
      "[1.0918346463388088e-08, 115, 21, 0.5896954388239452, 0.28296391119489145]\n",
      "Iteration No: 37 ended. Evaluation done at random point.\n",
      "Time taken: 0.1035\n",
      "Function value obtained: 356224.3903\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 38 started. Evaluating function at random point.\n",
      "[1.043772755082479e-07, 21, 27, 0.941776114719294, 0.43402051085992155]\n",
      "Iteration No: 38 ended. Evaluation done at random point.\n",
      "Time taken: 0.0930\n",
      "Function value obtained: 356222.2822\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 39 started. Evaluating function at random point.\n",
      "[2.5127234168743673e-09, 112, 61, 0.33374622999937253, 0.28392231507569654]\n",
      "Iteration No: 39 ended. Evaluation done at random point.\n",
      "Time taken: 0.0510\n",
      "Function value obtained: 356224.5851\n",
      "Current minimum: 90544.0699\n",
      "Iteration No: 40 started. Evaluating function at random point.\n",
      "[0.07145837394580029, 197, 25, 0.7713813552085116, 0.6860085030125076]\n",
      "Iteration No: 40 ended. Evaluation done at random point.\n",
      "Time taken: 0.1510\n",
      "Function value obtained: 85327.6743\n",
      "Current minimum: 85327.6743\n",
      "Iteration No: 41 started. Evaluating function at random point.\n",
      "[2.077150748856388e-09, 135, 80, 0.7744444779337113, 0.5097542202891869]\n",
      "Iteration No: 41 ended. Evaluation done at random point.\n",
      "Time taken: 0.0809\n",
      "Function value obtained: 356224.5599\n",
      "Current minimum: 85327.6743\n",
      "Iteration No: 42 started. Evaluating function at random point.\n",
      "[1.797262905003977e-05, 196, 89, 0.18873110868881726, 0.5579098649014042]\n",
      "Iteration No: 42 ended. Evaluation done at random point.\n",
      "Time taken: 0.0480\n",
      "Function value obtained: 356402.4559\n",
      "Current minimum: 85327.6743\n",
      "Iteration No: 43 started. Evaluating function at random point.\n",
      "[5.3113173014688755e-08, 246, 79, 0.489044887212467, 0.21904054701105766]\n",
      "Iteration No: 43 ended. Evaluation done at random point.\n",
      "Time taken: 0.0711\n",
      "Function value obtained: 356224.3410\n",
      "Current minimum: 85327.6743\n",
      "Iteration No: 44 started. Evaluating function at random point.\n",
      "[0.001299968623160196, 40, 42, 0.12054551169026874, 0.18587732372790575]\n",
      "Iteration No: 44 ended. Evaluation done at random point.\n",
      "Time taken: 0.0429\n",
      "Function value obtained: 341195.8372\n",
      "Current minimum: 85327.6743\n",
      "Iteration No: 45 started. Evaluating function at random point.\n",
      "[1.9844679200150206e-08, 50, 51, 0.14054242010799667, 0.2289367055452678]\n",
      "Iteration No: 45 ended. Evaluation done at random point.\n",
      "Time taken: 0.0440\n",
      "Function value obtained: 356224.6698\n",
      "Current minimum: 85327.6743\n",
      "Iteration No: 46 started. Evaluating function at random point.\n",
      "[0.028061020820448004, 249, 17, 0.8476629531276791, 0.6609629015398555]\n",
      "Iteration No: 46 ended. Evaluation done at random point.\n",
      "Time taken: 0.2320\n",
      "Function value obtained: 91493.1111\n",
      "Current minimum: 85327.6743\n",
      "Iteration No: 47 started. Evaluating function at random point.\n",
      "[3.945844545740944e-07, 140, 69, 0.7835691280660845, 0.9170342672531717]\n",
      "Iteration No: 47 ended. Evaluation done at random point.\n",
      "Time taken: 0.0811\n",
      "Function value obtained: 356215.9766\n",
      "Current minimum: 85327.6743\n",
      "Iteration No: 48 started. Evaluating function at random point.\n",
      "[0.028561206786025597, 133, 57, 0.2726439818153495, 0.6551005213014919]\n",
      "Iteration No: 48 ended. Evaluation done at random point.\n",
      "Time taken: 0.0560\n",
      "Function value obtained: 123016.7827\n",
      "Current minimum: 85327.6743\n",
      "Iteration No: 49 started. Evaluating function at random point.\n",
      "[0.03909584151509792, 254, 15, 0.5788205287853987, 0.9240457147896471]\n",
      "Iteration No: 49 ended. Evaluation done at random point.\n",
      "Time taken: 0.1895\n",
      "Function value obtained: 77290.0112\n",
      "Current minimum: 77290.0112\n",
      "Iteration No: 50 started. Evaluating function at random point.\n",
      "[0.00013568432284486877, 188, 86, 0.5116911337420643, 0.643879434627976]\n",
      "Iteration No: 50 ended. Evaluation done at random point.\n",
      "Time taken: 0.0500\n",
      "Function value obtained: 354936.6394\n",
      "Current minimum: 77290.0112\n"
     ]
    }
   ],
   "source": [
    "resultado = dummy_minimize(treinar_modelo, espaco_de_treinamento, random_state=1, verbose=1, n_calls=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.03909584151509792, 254, 15, 0.5788205287853987, 0.9240457147896471]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado.x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise do Resultado:\n",
    "Com os parâmetros:\n",
    "\n",
    "learning_rate = 0.03909584151509792  \n",
    "num_leaves = 254  \n",
    "min_child_samples = 15  \n",
    "subsample = 0.5788205287853987  \n",
    "colsample_bytree = 0.9240457147896471\n",
    "\n",
    "Temos que nossa métrica principal(MedAE) foi de 77290.0112\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Optimization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skopt import gp_minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 1 started. Evaluating function at random point.\n",
      "[0.09494639669696242, 239, 14, 0.9990884895579377, 0.3124800792567785]\n",
      "Iteration No: 1 ended. Evaluation done at random point.\n",
      "Time taken: 0.1476\n",
      "Function value obtained: 87977.9001\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 2 started. Evaluating function at random point.\n",
      "[1.488146949236865e-06, 101, 67, 0.9387621172657304, 0.8616798250174156]\n",
      "Iteration No: 2 ended. Evaluation done at random point.\n",
      "Time taken: 0.0678\n",
      "Function value obtained: 356193.2319\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 3 started. Evaluating function at random point.\n",
      "[3.2076585745369137e-07, 135, 45, 0.2680983530433343, 0.5809725180523154]\n",
      "Iteration No: 3 ended. Evaluation done at random point.\n",
      "Time taken: 0.0469\n",
      "Function value obtained: 356223.8836\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 4 started. Evaluating function at random point.\n",
      "[0.02049727824731352, 118, 44, 0.9421713999524447, 0.8005503127028804]\n",
      "Iteration No: 4 ended. Evaluation done at random point.\n",
      "Time taken: 0.0928\n",
      "Function value obtained: 113421.5964\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 5 started. Evaluating function at random point.\n",
      "[0.0005342741078030066, 206, 10, 0.5422449214947946, 0.8785182267810853]\n",
      "Iteration No: 5 ended. Evaluation done at random point.\n",
      "Time taken: 0.1745\n",
      "Function value obtained: 341608.5411\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 6 started. Evaluating function at random point.\n",
      "[0.004297098005754833, 213, 28, 0.1062810412364853, 0.7034752360620511]\n",
      "Iteration No: 6 ended. Evaluation done at random point.\n",
      "Time taken: 0.0369\n",
      "Function value obtained: 291742.5391\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 7 started. Evaluating function at random point.\n",
      "[5.552954901935064e-05, 173, 42, 0.23767335308554222, 0.3606666764912312]\n",
      "Iteration No: 7 ended. Evaluation done at random point.\n",
      "Time taken: 0.0419\n",
      "Function value obtained: 356136.4928\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 8 started. Evaluating function at random point.\n",
      "[1.3707589298832816e-08, 201, 42, 0.0824627456265471, 0.661626987829618]\n",
      "Iteration No: 8 ended. Evaluation done at random point.\n",
      "Time taken: 0.0339\n",
      "Function value obtained: 356224.8531\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 9 started. Evaluating function at random point.\n",
      "[0.00019279059421219578, 78, 45, 0.2610183201586061, 0.16602775456833962]\n",
      "Iteration No: 9 ended. Evaluation done at random point.\n",
      "Time taken: 0.0419\n",
      "Function value obtained: 354271.7470\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 10 started. Evaluating function at random point.\n",
      "[5.674246670658105e-06, 26, 90, 0.163515944581681, 0.5723194391834011]\n",
      "Iteration No: 10 ended. Evaluation done at random point.\n",
      "Time taken: 0.4717\n",
      "Function value obtained: 356313.5613\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 11 started. Searching for the next optimal point.\n",
      "[0.1, 220, 99, 0.8840948657786365, 0.4438713265104992]\n",
      "Iteration No: 11 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.4718\n",
      "Function value obtained: 98128.6475\n",
      "Current minimum: 87977.9001\n",
      "Iteration No: 12 started. Searching for the next optimal point.\n",
      "[0.058961368132889914, 8, 31, 1.0, 0.595277639471008]\n",
      "Iteration No: 12 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.4843\n",
      "Function value obtained: 84864.4193\n",
      "Current minimum: 84864.4193\n",
      "Iteration No: 13 started. Searching for the next optimal point.\n",
      "[0.056967363635587374, 243, 14, 1.0, 0.23279794161117645]\n",
      "Iteration No: 13 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.5529\n",
      "Function value obtained: 93005.6454\n",
      "Current minimum: 84864.4193\n",
      "Iteration No: 14 started. Searching for the next optimal point.\n",
      "[0.1, 2, 100, 1.0, 0.1]\n",
      "Iteration No: 14 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.4508\n",
      "Function value obtained: 115305.9958\n",
      "Current minimum: 84864.4193\n",
      "Iteration No: 15 started. Searching for the next optimal point.\n",
      "[0.1, 57, 1, 0.7268542296882123, 1.0]\n",
      "Iteration No: 15 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.5954\n",
      "Function value obtained: 75376.3659\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 16 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 1.0, 1.0]\n",
      "Iteration No: 16 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3254\n",
      "Function value obtained: 81763.4696\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 17 started. Searching for the next optimal point.\n",
      "[0.1, 57, 1, 0.5596552059329392, 0.3949478876200797]\n",
      "Iteration No: 17 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.5542\n",
      "Function value obtained: 88229.8425\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 18 started. Searching for the next optimal point.\n",
      "[0.1, 10, 1, 0.8092217048977793, 0.7083229899004123]\n",
      "Iteration No: 18 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.5027\n",
      "Function value obtained: 96512.2229\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 19 started. Searching for the next optimal point.\n",
      "[0.05149524250754597, 253, 1, 0.7721734221924847, 1.0]\n",
      "Iteration No: 19 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3624\n",
      "Function value obtained: 83248.5316\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 20 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 0.05, 1.0]\n",
      "Iteration No: 20 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.5645\n",
      "Function value obtained: 101082.1556\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 21 started. Searching for the next optimal point.\n",
      "[0.061058863085385155, 122, 1, 1.0, 1.0]\n",
      "Iteration No: 21 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.8442\n",
      "Function value obtained: 85734.1491\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 22 started. Searching for the next optimal point.\n",
      "[0.06197802444480476, 100, 3, 1.0, 1.0]\n",
      "Iteration No: 22 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.7063\n",
      "Function value obtained: 79687.9773\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 23 started. Searching for the next optimal point.\n",
      "[0.0677804571934665, 230, 1, 0.7278222891656693, 1.0]\n",
      "Iteration No: 23 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.1997\n",
      "Function value obtained: 83457.7145\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 24 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 1.0, 1.0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Victor\\anaconda3\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 24 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3640\n",
      "Function value obtained: 81763.4696\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 25 started. Searching for the next optimal point.\n",
      "[0.06153633059295451, 182, 1, 1.0, 1.0]\n",
      "Iteration No: 25 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.0507\n",
      "Function value obtained: 79632.4002\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 26 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 0.7315188737268884, 1.0]\n",
      "Iteration No: 26 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3406\n",
      "Function value obtained: 81722.8570\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 27 started. Searching for the next optimal point.\n",
      "[0.06185759587591597, 218, 1, 1.0, 1.0]\n",
      "Iteration No: 27 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.2367\n",
      "Function value obtained: 77440.9265\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 28 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 0.6916125588992938, 1.0]\n",
      "Iteration No: 28 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3993\n",
      "Function value obtained: 83608.3993\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 29 started. Searching for the next optimal point.\n",
      "[0.06058540315394872, 253, 1, 1.0, 1.0]\n",
      "Iteration No: 29 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3759\n",
      "Function value obtained: 79580.1396\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 30 started. Searching for the next optimal point.\n",
      "[0.06310350822918322, 39, 1, 1.0, 1.0]\n",
      "Iteration No: 30 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.6125\n",
      "Function value obtained: 75476.8215\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 31 started. Searching for the next optimal point.\n",
      "[0.06077054884880476, 256, 1, 1.0, 1.0]\n",
      "Iteration No: 31 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.4314\n",
      "Function value obtained: 83540.6856\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 32 started. Searching for the next optimal point.\n",
      "[0.06552774998908875, 9, 1, 1.0, 1.0]\n",
      "Iteration No: 32 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.5630\n",
      "Function value obtained: 88753.9138\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 33 started. Searching for the next optimal point.\n",
      "[0.07126062724893133, 178, 1, 0.7752187300191238, 1.0]\n",
      "Iteration No: 33 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.1994\n",
      "Function value obtained: 84956.5507\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 34 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 1.0, 1.0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Victor\\anaconda3\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 34 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.4726\n",
      "Function value obtained: 81763.4696\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 35 started. Searching for the next optimal point.\n",
      "[0.06506008044798631, 2, 1, 1.0, 1.0]\n",
      "Iteration No: 35 ended. Search finished for the next optimal point.\n",
      "Time taken: 0.5984\n",
      "Function value obtained: 117483.1898\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 36 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 0.6637905628074865, 1.0]\n",
      "Iteration No: 36 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.5268\n",
      "Function value obtained: 87636.3902\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 37 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 1.0, 1.0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Victor\\anaconda3\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 37 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.6517\n",
      "Function value obtained: 81763.4696\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 38 started. Searching for the next optimal point.\n",
      "[0.0583556619892153, 256, 1, 1.0, 1.0]\n",
      "Iteration No: 38 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.6173\n",
      "Function value obtained: 86939.6912\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 39 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 0.6935594744248024, 1.0]\n",
      "Iteration No: 39 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.6736\n",
      "Function value obtained: 75645.6987\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 40 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 0.6663941225085838, 1.0]\n",
      "Iteration No: 40 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.5649\n",
      "Function value obtained: 84024.2133\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 41 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 0.6918011562107678, 1.0]\n",
      "Iteration No: 41 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.6332\n",
      "Function value obtained: 83608.3993\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 42 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 0.7332620957015819, 1.0]\n",
      "Iteration No: 42 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.7433\n",
      "Function value obtained: 84855.4703\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 43 started. Searching for the next optimal point.\n",
      "[0.06673818395793009, 256, 1, 0.8851164257300453, 1.0]\n",
      "Iteration No: 43 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.7220\n",
      "Function value obtained: 78766.1094\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 44 started. Searching for the next optimal point.\n",
      "[0.07031056922676693, 251, 1, 0.7663387518926763, 1.0]\n",
      "Iteration No: 44 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.6794\n",
      "Function value obtained: 89944.0674\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 45 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 1.0, 1.0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Victor\\anaconda3\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 45 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.7543\n",
      "Function value obtained: 81763.4696\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 46 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 1.0, 1.0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Victor\\anaconda3\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 46 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.7539\n",
      "Function value obtained: 81763.4696\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 47 started. Searching for the next optimal point.\n",
      "[0.06174362024647415, 256, 1, 1.0, 1.0]\n",
      "Iteration No: 47 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.7220\n",
      "Function value obtained: 79498.7532\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 48 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 1.0, 1.0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Victor\\anaconda3\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 48 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.7902\n",
      "Function value obtained: 81763.4696\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 49 started. Searching for the next optimal point.\n",
      "[0.06508842228016615, 256, 1, 0.9920497868264317, 0.1402589168927174]\n",
      "Iteration No: 49 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.3459\n",
      "Function value obtained: 91787.4542\n",
      "Current minimum: 75376.3659\n",
      "Iteration No: 50 started. Searching for the next optimal point.\n",
      "[0.1, 256, 1, 1.0, 1.0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Victor\\anaconda3\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration No: 50 ended. Search finished for the next optimal point.\n",
      "Time taken: 1.8650\n",
      "Function value obtained: 81763.4696\n",
      "Current minimum: 75376.3659\n"
     ]
    }
   ],
   "source": [
    "resultado_bayesian = gp_minimize(treinar_modelo, espaco_de_treinamento, random_state=1, verbose=1, n_calls=50, n_random_starts=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1, 57, 1, 0.7268542296882123, 1.0]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado_bayesian.x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise do Resultado:\n",
    "Com os parâmetros:\n",
    "\n",
    "learning_rate = 0.1\n",
    "num_leaves = 57 \n",
    "min_child_samples = 1  \n",
    "subsample = 0.7268542296882123 \n",
    "colsample_bytree = 1.0  \n",
    "\n",
    "Temos que nossa métrica principal(MedAE) foi de 75376.3659 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analisando as duas otimizações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skopt.plots import plot_convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c17eae7c8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_convergence(resultado, resultado_bayesian)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treinando Modelo Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def treinar_modelo_final(parametros):\n",
    "    \n",
    "    learning_rate = parametros[0]\n",
    "    num_leaves = parametros[1]\n",
    "    min_child_samples = parametros[2]\n",
    "    subsample = parametros[3]\n",
    "    colsample_bytree = parametros[4]\n",
    "    \n",
    "    \n",
    "    mdl = lgb.LGBMRegressor(learning_rate=learning_rate, num_leaves=num_leaves, \n",
    "                            min_child_samples=min_child_samples, subsample=subsample, \n",
    "                            colsample_bytree=colsample_bytree, random_state=0, \n",
    "                            subsample_freq=1, n_estimatos=100)\n",
    "    mdl.fit(X_treinamento_na, y_treinamento_na)\n",
    "    \n",
    "    return mdl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo_final = treinar_modelo_final(resultado_bayesian.x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_na_final = modelo_final.predict(X_teste_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MedAE drop NA: 75376.37\n",
      "MSLE drop NA: 0.15\n"
     ]
    }
   ],
   "source": [
    "medae_na = median_absolute_error(y_teste_na, p_na_final)\n",
    "msle_na = mean_squared_log_error(y_teste_na, p_na_final)\n",
    "\n",
    "print('MedAE drop NA:',round(medae_na,2))\n",
    "print('MSLE drop NA:',round(msle_na,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Salvando Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib as jb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lightGBM.pkl.z']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jb.dump(modelo_final, 'lightGBM.pkl.z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}