teste_regressao.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import models\n",
    "from models import get_raw_data\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pasta = f'{models.path_dados_tratados}/harmonicos_8'\n",
    "arquivo = 'Dados_Normalizados.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_raw_data.GetData(pasta,arquivo).GetDataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>rotacao_hz</th>\n",
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       "      <th>desalinhamento_vertical</th>\n",
       "      <th>desbalanceamento</th>\n",
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      "text/plain": [
       "       rotacao_hz    maximo       rms  assimetria   curtose  fator_crista  \\\n",
       "0            0.00  0.282094  0.275661    0.466259  0.146040      0.341173   \n",
       "1            0.00  0.332706  0.276122    0.467238  0.148418      0.466092   \n",
       "2            0.00  0.332706  0.276035    0.463734  0.148229      0.466341   \n",
       "3            0.00  0.359523  0.276068    0.479110  0.160277      0.533047   \n",
       "4            0.00  0.359523  0.274593    0.476260  0.156181      0.537617   \n",
       "...           ...       ...       ...         ...       ...           ...   \n",
       "49603        0.95  1.420854  2.157980    0.081319  0.035974      0.094412   \n",
       "49604        0.95  1.582628  2.152515    0.086307  0.037944      0.151765   \n",
       "49605        0.95  1.582628  2.155161    0.086497  0.040828      0.151093   \n",
       "49606        0.95  1.560321  2.158060    0.064818  0.044697      0.142641   \n",
       "49607        0.95  1.600151  2.164308    0.085052  0.046138      0.154826   \n",
       "\n",
       "       pot_ball_fault  pot_outer_race  pot_rotacao_hz  sensor  normal  \\\n",
       "0            0.020548        0.007628        0.012659     0.0       1   \n",
       "1            0.021268        0.007418        0.012973     0.0       1   \n",
       "2            0.021562        0.007572        0.013581     0.0       1   \n",
       "3            0.020470        0.008216        0.013286     0.0       1   \n",
       "4            0.020818        0.007933        0.011916     0.0       1   \n",
       "...               ...             ...             ...     ...     ...   \n",
       "49603        1.001262        0.912434       39.572743     1.0       0   \n",
       "49604        0.815213        0.834230       39.038905     1.0       0   \n",
       "49605        0.930660        0.925486       38.970751     1.0       0   \n",
       "49606        0.850407        0.935185       38.747965     1.0       0   \n",
       "49607        0.864680        0.958156       38.920267     1.0       0   \n",
       "\n",
       "       desalinhamento_horizontal  desalinhamento_vertical  desbalanceamento  \\\n",
       "0                            0.0                      0.0               0.0   \n",
       "1                            0.0                      0.0               0.0   \n",
       "2                            0.0                      0.0               0.0   \n",
       "3                            0.0                      0.0               0.0   \n",
       "4                            0.0                      0.0               0.0   \n",
       "...                          ...                      ...               ...   \n",
       "49603                        0.0                      0.0               0.0   \n",
       "49604                        0.0                      0.0               0.0   \n",
       "49605                        0.0                      0.0               0.0   \n",
       "49606                        0.0                      0.0               0.0   \n",
       "49607                        0.0                      0.0               0.0   \n",
       "\n",
       "       ball_fault  outer_race  \n",
       "0             0.0         0.0  \n",
       "1             0.0         0.0  \n",
       "2             0.0         0.0  \n",
       "3             0.0         0.0  \n",
       "4             0.0         0.0  \n",
       "...           ...         ...  \n",
       "49603         0.0        35.0  \n",
       "49604         0.0        35.0  \n",
       "49605         0.0        35.0  \n",
       "49606         0.0        35.0  \n",
       "49607         0.0        35.0  \n",
       "\n",
       "[49608 rows x 16 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[models.colunas_x]\n",
    "y = df[models.defeitos_gerais]\n",
    "scaler = MinMaxScaler()\n",
    "y = scaler.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "# Separando os dados em conjunto de treinamento e conjunto de teste\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Criando o modelo de regressão com o algoritmo Random Forest\n",
    "regressor = RandomForestRegressor(n_estimators=100)\n",
    "\n",
    "# Treinando o modelo com os dados de treinamento\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erro médio quadrático: 0.00629911819992502\n"
     ]
    }
   ],
   "source": [
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# Fazendo previsões com os dados de teste\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Calculando o erro médio quadrático das previsões\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "# Imprimindo o erro médio quadrático\n",
    "print('Erro médio quadrático:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_p = pd.DataFrame(y_pred)\n",
    "y_p.columns = pd.DataFrame(y_test).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_t = scaler.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_p = scaler.inverse_transform(y_pred)\n",
    "y_t = pd.DataFrame(y_t)\n",
    "y_p = pd.DataFrame(y_p)\n",
    "y_p.columns = models.defeitos_gerais\n",
    "y_t.columns = models.defeitos_gerais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "      <th>1242</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>23.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1.4780</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.2700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.060</td>\n",
       "      <td>1.4168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.9000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normal  desalinhamento_horizontal  desalinhamento_vertical  \\\n",
       "1238    0.00                      0.000                   0.0140   \n",
       "1238    0.00                      0.000                   0.0000   \n",
       "1239    0.00                      0.000                   0.0000   \n",
       "1239    0.00                      0.000                   0.0000   \n",
       "1240    0.12                      0.845                   0.0000   \n",
       "1240    0.00                      1.000                   0.0000   \n",
       "1241    0.00                      0.000                   1.8245   \n",
       "1241    0.00                      0.000                   1.9000   \n",
       "1242    0.00                      0.000                   0.0000   \n",
       "1242    0.00                      0.000                   0.0000   \n",
       "1243    0.00                      0.000                   0.0000   \n",
       "1243    0.00                      0.000                   0.0000   \n",
       "1244    0.00                      0.000                   0.0000   \n",
       "1244    0.00                      0.000                   0.0000   \n",
       "1245    0.00                      0.000                   0.0000   \n",
       "1245    0.00                      0.000                   0.0000   \n",
       "1246    0.00                      0.020                   1.4780   \n",
       "1246    0.00                      0.000                   1.2700   \n",
       "1247    0.01                      0.060                   1.4168   \n",
       "1247    0.00                      0.000                   1.9000   \n",
       "\n",
       "      desbalanceamento  ball_fault  outer_race  \n",
       "1238               0.0         0.0        6.50  \n",
       "1238               0.0         0.0        6.00  \n",
       "1239              24.8         0.0        0.70  \n",
       "1239              25.0         0.0        0.00  \n",
       "1240               0.0         0.0        0.18  \n",
       "1240               0.0         0.0        0.00  \n",
       "1241               0.0         0.0        0.00  \n",
       "1241               0.0         0.0        0.00  \n",
       "1242               0.0         6.0        0.00  \n",
       "1242               0.0         6.0        0.00  \n",
       "1243              23.9         0.0        0.95  \n",
       "1243              25.0         0.0        0.00  \n",
       "1244              30.1         0.0        0.00  \n",
       "1244              30.0         0.0        0.00  \n",
       "1245               0.0         0.0       35.00  \n",
       "1245               0.0         0.0       35.00  \n",
       "1246               0.0         0.0        0.48  \n",
       "1246               0.0         0.0        0.00  \n",
       "1247               0.0         0.0        0.32  \n",
       "1247               0.0         0.0        0.00  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linha = 1238\n",
    "\n",
    "lista = list()\n",
    "num_amostras = 10\n",
    "\n",
    "for i in range(num_amostras):\n",
    "    lista.append(y_p[linha+i:linha+i+1])\n",
    "    lista.append(y_t[linha+i:linha+i+1])\n",
    "\n",
    "pd.concat(lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.698812597340561"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(y_t, y_p)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normal</th>\n",
       "      <th>desalinhamento_horizontal</th>\n",
       "      <th>desalinhamento_vertical</th>\n",
       "      <th>desbalanceamento</th>\n",
       "      <th>ball_fault</th>\n",
       "      <th>outer_race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.0405</td>\n",
       "      <td>18.94</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.1602</td>\n",
       "      <td>-8.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>-0.0252</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.0380</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>-0.6148</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9917</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.3281</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9918</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.0251</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9919</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.0140</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9920</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.310</td>\n",
       "      <td>-0.2691</td>\n",
       "      <td>-6.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9921</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.110</td>\n",
       "      <td>-0.0063</td>\n",
       "      <td>-3.15</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9922 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      normal  desalinhamento_horizontal  desalinhamento_vertical  \\\n",
       "0       0.00                     -0.500                  -0.0405   \n",
       "1      -0.02                      0.000                   1.1602   \n",
       "2       0.00                     -0.045                  -0.0252   \n",
       "3       0.00                      0.000                  -0.0380   \n",
       "4       0.00                     -0.040                  -0.6148   \n",
       "...      ...                        ...                      ...   \n",
       "9917    0.00                      0.000                  -0.3281   \n",
       "9918    0.00                      0.000                  -0.0251   \n",
       "9919    0.00                      0.000                  -0.0140   \n",
       "9920    0.00                     -0.310                  -0.2691   \n",
       "9921    0.00                      0.110                  -0.0063   \n",
       "\n",
       "      desbalanceamento  ball_fault  outer_race  \n",
       "0                18.94        -0.4       -0.46  \n",
       "1                -8.01         0.0        0.00  \n",
       "2                 0.00         0.0        0.00  \n",
       "3                 1.45         0.0       -0.75  \n",
       "4                 0.72         0.0        0.00  \n",
       "...                ...         ...         ...  \n",
       "9917             -0.75         0.0        0.00  \n",
       "9918              0.00         0.0        0.00  \n",
       "9919             -2.00         0.0        0.42  \n",
       "9920             -6.69         0.0        5.08  \n",
       "9921             -3.15        -0.2        0.00  \n",
       "\n",
       "[9922 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erro = y_t - y_p\n",
    "erro = pd.DataFrame(erro)\n",
    "erro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\leona\\Documents\\ProjetoFinal_LeonardoPacheco_UFRJ_LAVI\\.venv\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3803\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m   3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[1;32mc:\\Users\\leona\\Documents\\ProjetoFinal_LeonardoPacheco_UFRJ_LAVI\\.venv\\lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\leona\\Documents\\ProjetoFinal_LeonardoPacheco_UFRJ_LAVI\\.venv\\lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[14], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m medias \u001b[39m=\u001b[39m {}\n\u001b[0;32m      2\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m6\u001b[39m):\n\u001b[1;32m----> 3\u001b[0m     medias[i] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mmean(erro[i])\n\u001b[0;32m      5\u001b[0m medias \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mjson_normalize(medias)\n\u001b[0;32m      6\u001b[0m medias\n",
      "File \u001b[1;32mc:\\Users\\leona\\Documents\\ProjetoFinal_LeonardoPacheco_UFRJ_LAVI\\.venv\\lib\\site-packages\\pandas\\core\\frame.py:3805\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3803\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m   3804\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3805\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[0;32m   3806\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3807\u001b[0m     indexer \u001b[39m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mc:\\Users\\leona\\Documents\\ProjetoFinal_LeonardoPacheco_UFRJ_LAVI\\.venv\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3803\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m   3806\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m   3807\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3810\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "medias = {}\n",
    "for i in range(6):\n",
    "    medias[i] = np.mean(erro[i])\n",
    "\n",
    "medias = pd.json_normalize(medias)\n",
    "medias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "desvio_padrao = {}\n",
    "for i in range(6):\n",
    "    desvio_padrao[i] = np.std(erro[i])\n",
    "\n",
    "desvio_padrao = pd.json_normalize(desvio_padrao)\n",
    "desvio_padrao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "maximo = {}\n",
    "for i in range(6):\n",
    "    maximo[i] = np.max(np.abs(erro[i]))\n",
    "\n",
    "maximo = pd.json_normalize(maximo)\n",
    "maximo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "erro_10 = erro[5][erro[5] <= 0.1*np.max(y_t[5])]\n",
    "\n",
    "acerto = len(erro_10)/len(erro)\n",
    "acerto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "erro_10 = pd.DataFrame()\n",
    "\n",
    "# Loop para aplicar o filtro em cada coluna\n",
    "for col in erro.columns:\n",
    "    erro_10[col] = erro[col][erro[col] <= 0.1*np.max(y_t[col])]\n",
    "\n",
    "erro_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "erro_10 = erro_10.dropna()\n",
    "erro_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acerto = len(erro_10)/len(erro)\n",
    "acerto"
   ]
  }
 ],
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