RedesRecorrentes_DoencasPulmonares_GRU.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RedesRecorrentes_DoencasPulmonares_GRU.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rrfsantos/Projeto-Rede-Neurais--Series-Temporais/blob/main/RedesRecorrentes_DoencasPulmonares_GRU.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bgc8yb4c2W0g"
      },
      "source": [
        "# Recurrent Neural Network - LSTM\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aew2N81e35Uc"
      },
      "source": [
        "## Parte 1 - Preprocessamento de Dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0vulSxH2hHu"
      },
      "source": [
        "### Importar as bibliotecas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2SjiqlE2O08"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import os\n",
        "import math\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM, GRU\n",
        "from keras.layers import Dropout\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "np.random.seed(0)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36Q16Udt2l3W"
      },
      "source": [
        "### Importar a base de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "or7UuAX_PgCb",
        "outputId": "224eb402-e1f8-42e3-c5a7-1686d37a9212"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import os\n",
        "workdir_path = '/content/drive/My Drive/Trabalho Redes Neurais/'  # Inserir o local da pasta onde estão os arquivos de entrada (treino e teste)\n",
        "os.chdir(workdir_path)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ivhnWxD2eEG"
      },
      "source": [
        "dataset_train = pd.read_csv('train.csv')\n",
        "training_set = dataset_train.iloc[:, 1:2].values #só coluna da série em si"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kd39T1zK2ql1"
      },
      "source": [
        "### gráfico da série temporal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMsAFuya2rhr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "e2805297-6c16-4828-e197-6bf754587738"
      },
      "source": [
        "plt.plot(training_set)\n",
        "plt.xlabel(\"Tempo\")\n",
        "plt.ylabel(\"Numero de Mortes\")\n",
        "plt.title(\"Doenças Pulmonares\")\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7p1F7HdB2uJU"
      },
      "source": [
        "### Normalização"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "foeHRZr-2u-L"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "training_set_scaled = sc.fit_transform(training_set)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQP8Q7MI21Y9"
      },
      "source": [
        "### Criar a estrutura de dados com janela 15 e output 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6Ge9ql922cb"
      },
      "source": [
        "window = 15\n",
        "trainSize = len(training_set_scaled)\n",
        "X_train = []\n",
        "y_train = []\n",
        "for i in range(window, trainSize):\n",
        "    X_train.append(training_set_scaled[i-window:i, 0])\n",
        "    y_train.append(training_set_scaled[i, 0])\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPBC2LoG24dU"
      },
      "source": [
        "### Reshaping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGwgQUDbdmnS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5249953-04f7-419f-adea-4be01229fdd7"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9ZfLlGH25fr"
      },
      "source": [
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxU0axM9diHO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04f0afca-31ca-4248-f579-c06fcf8fd7aa"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45, 15, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZhjp7Tf282b"
      },
      "source": [
        "## Parte 2 - Construção da RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kordpD0_3H7b"
      },
      "source": [
        "### Construção da Rede completa"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EskB17uo3Iso",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43c37980-f051-4a8b-f91d-9a769081d79f"
      },
      "source": [
        "# Inicializar a RNN\n",
        "regressor = Sequential()\n",
        "\n",
        "# Adicionar a primeira camada LSTM e Dropout \n",
        "regressor.add(GRU(units = 48, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
        "regressor.add(Dropout(0.2))\n",
        " \n",
        "# Adicionar a segunda camada LSTM e Dropout\n",
        "regressor.add(GRU(units = 80, return_sequences = False))\n",
        "regressor.add(Dropout(0.2))\n",
        " \n",
        "# Adicionar a terceira camada LSTM e Dropout\n",
        "#regressor.add(LSTM(units = 50))\n",
        "#regressor.add(Dropout(0.2))\n",
        " \n",
        "# camada de saída\n",
        "regressor.add(Dense(units = 1))\n",
        " \n",
        "# Compilar a rede\n",
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "\n",
        "# Visualizar a rede\n",
        "regressor.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "gru (GRU)                    (None, 15, 48)            7344      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 15, 48)            0         \n",
            "_________________________________________________________________\n",
            "gru_1 (GRU)                  (None, 80)                31200     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 80)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 81        \n",
            "=================================================================\n",
            "Total params: 38,625\n",
            "Trainable params: 38,625\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5j1w28g83iSR"
      },
      "source": [
        "### Treinamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWKAuKyp3jXv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6cd6cad-139c-4f1b-e5be-a384462e2388"
      },
      "source": [
        "regressor.fit(X_train, y_train, epochs = 500, batch_size = 32)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 4s 24ms/step - loss: 0.1093\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0776\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0596\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0643\n",
            "Epoch 5/500\n",
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            "2/2 [==============================] - 0s 21ms/step - loss: 0.0088\n",
            "Epoch 423/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0083\n",
            "Epoch 424/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0074\n",
            "Epoch 425/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 0.0077\n",
            "Epoch 426/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0091\n",
            "Epoch 427/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0089\n",
            "Epoch 428/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0081\n",
            "Epoch 429/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0093\n",
            "Epoch 430/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0070\n",
            "Epoch 431/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0095\n",
            "Epoch 432/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0065\n",
            "Epoch 433/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0075\n",
            "Epoch 434/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0084\n",
            "Epoch 435/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0074\n",
            "Epoch 436/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0077\n",
            "Epoch 437/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 0.0063\n",
            "Epoch 438/500\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 0.0067\n",
            "Epoch 439/500\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 0.0067\n",
            "Epoch 440/500\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0072\n",
            "Epoch 441/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0084\n",
            "Epoch 442/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0098\n",
            "Epoch 443/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0077\n",
            "Epoch 444/500\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0067\n",
            "Epoch 445/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 0.0071\n",
            "Epoch 446/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0067\n",
            "Epoch 447/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0078\n",
            "Epoch 448/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0075\n",
            "Epoch 449/500\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 0.0101\n",
            "Epoch 450/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0059\n",
            "Epoch 451/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0094\n",
            "Epoch 452/500\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 0.0068\n",
            "Epoch 453/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0074\n",
            "Epoch 454/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0068\n",
            "Epoch 455/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0084\n",
            "Epoch 456/500\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0065\n",
            "Epoch 457/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0078\n",
            "Epoch 458/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0078\n",
            "Epoch 459/500\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 0.0091\n",
            "Epoch 460/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0081\n",
            "Epoch 461/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0079\n",
            "Epoch 462/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0084\n",
            "Epoch 463/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0080\n",
            "Epoch 464/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0073\n",
            "Epoch 465/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0073\n",
            "Epoch 466/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0084\n",
            "Epoch 467/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0092\n",
            "Epoch 468/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0078\n",
            "Epoch 469/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0098\n",
            "Epoch 470/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0066\n",
            "Epoch 471/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0084\n",
            "Epoch 472/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0084\n",
            "Epoch 473/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0083\n",
            "Epoch 474/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0084\n",
            "Epoch 475/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0095\n",
            "Epoch 476/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0075\n",
            "Epoch 477/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0099\n",
            "Epoch 478/500\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 0.0060\n",
            "Epoch 479/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0101\n",
            "Epoch 480/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0096\n",
            "Epoch 481/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0081\n",
            "Epoch 482/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0054\n",
            "Epoch 483/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0080\n",
            "Epoch 484/500\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 0.0103\n",
            "Epoch 485/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0082\n",
            "Epoch 486/500\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 0.0074\n",
            "Epoch 487/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0075\n",
            "Epoch 488/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0096\n",
            "Epoch 489/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0061\n",
            "Epoch 490/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0056\n",
            "Epoch 491/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0099\n",
            "Epoch 492/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0062\n",
            "Epoch 493/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0088\n",
            "Epoch 494/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0091\n",
            "Epoch 495/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0080\n",
            "Epoch 496/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0063\n",
            "Epoch 497/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.0062\n",
            "Epoch 498/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0053\n",
            "Epoch 499/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0070\n",
            "Epoch 500/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 0.0088\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f76757248d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwpyImwA3yon"
      },
      "source": [
        "## Parte 3 - Fazer as previsões e analisar os resultados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkXRvDjT4V-B"
      },
      "source": [
        "### Base de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bn_n72dh30K-"
      },
      "source": [
        "dataset_test = pd.read_csv('test.csv')\n",
        "test_pul = dataset_test.iloc[:, 1:2].values"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOo84exH4bfs"
      },
      "source": [
        "### Construir a estrutura para teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ob_-mGA_B5px"
      },
      "source": [
        "dataset_total = pd.concat((dataset_train.iloc[:,1], dataset_test.iloc[:,1]), axis = 0)\n",
        "testLength = len(dataset_test)\n",
        "inputs = dataset_total[len(dataset_total) - testLength - window:].values\n",
        "inputs = inputs.reshape(inputs.shape[0],1)\n",
        "\n",
        "inputs = sc.transform(inputs)\n",
        "X_test = []\n",
        "for i in range(window, inputs.shape[0]): \n",
        "    X_test.append(inputs[i-window:i, 0])\n",
        "X_test = np.array(X_test)\n",
        "\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtkB4NA74e1B"
      },
      "source": [
        "### Fazer as previsões"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKIq1h-E4g9Z"
      },
      "source": [
        "predicted = regressor.predict(X_test)\n",
        "predicted = sc.inverse_transform(predicted)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guvk7yXr4o0g"
      },
      "source": [
        "### Visualizar os resultados de treino e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tstXl2oS4rIo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "387de7b0-c7f5-4c84-c655-5447e7be62db"
      },
      "source": [
        "allTargetData = np.vstack((training_set, test_pul))\n",
        "training_predicted = regressor.predict(X_train)\n",
        "training_predicted = sc.inverse_transform(training_predicted)\n",
        "allForecastedData = np.vstack((training_set[0:window], training_predicted, predicted))\n",
        "plt.plot(allTargetData, color = 'red', label = 'Real')\n",
        "plt.plot(allForecastedData, color = 'blue', label = 'Previsto')\n",
        "plt.xlabel(\"Tempo\")\n",
        "plt.ylabel(\"Numero de Mortes\")\n",
        "plt.title(\"Doenças Pulmonares\")\n",
        "plt.legend()\n",
        "plt.savefig('predictions_training_test.svg')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytEVhQht4we3"
      },
      "source": [
        "## Parte 4 - Métricas de avaliação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqOonVj64ydu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e244922-2dd9-43b8-87ba-31ea2fc15bf9"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(test_pul, predicted))\n",
        "print('RMSE: ', rmse)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE:  194.65751948392472\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODeley1K42WW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "913535d8-6017-404b-b05e-448ffe89b1f7"
      },
      "source": [
        "mse = mean_squared_error(test_pul, predicted)\n",
        "print('MSE: ',mse)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE:  37891.54989163453\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7_uU_8T44QA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6d8e395-6da9-4e9e-be41-9ec0a90fa2a9"
      },
      "source": [
        "mape = np.mean(np.abs((test_pul - predicted) / test_pul)) * 100\n",
        "print('MAPE: ',mape, '%')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAPE:  7.848280972351739 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgmtdSOq7rG0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f8d5ed3-3f3c-4b12-f648-cd0052baea71"
      },
      "source": [
        "mae = mean_absolute_error(test_pul, predicted)\n",
        "print('MAE: ',mae)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE:  156.87832641601562\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}