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ML-For-Beginners
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ML-For-Beginners/translations/pt/7-TimeSeries/3-SVR/working/notebook.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "fv9OoQsMFk5A"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Neste caderno, demonstramos como:\n",
"\n",
"- preparar dados de séries temporais 2D para treinar um modelo de regressão SVM \n",
"- implementar SVR utilizando o kernel RBF \n",
"- avaliar o modelo utilizando gráficos e MAPE \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importar módulos\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('../../')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "M687KNlQFp0-"
},
"outputs": [],
"source": [
"import os\n",
"import warnings\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import datetime as dt\n",
"import math\n",
"\n",
"from sklearn.svm import SVR\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from common.utils import load_data, mape"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Cj-kfVdMGjWP"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8fywSjC6GsRz"
},
"source": [
"### Carregar dados\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 363
},
"id": "aBDkEB11Fumg",
"outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>load</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-01-01 00:00:00</th>\n",
" <td>2698.0</td>\n",
" </tr>\n",
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" <th>2012-01-01 01:00:00</th>\n",
" <td>2558.0</td>\n",
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" <th>2012-01-01 02:00:00</th>\n",
" <td>2444.0</td>\n",
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" <tr>\n",
" <th>2012-01-01 03:00:00</th>\n",
" <td>2402.0</td>\n",
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" <th>2012-01-01 04:00:00</th>\n",
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"text/plain": [
" load\n",
"2012-01-01 00:00:00 2698.0\n",
"2012-01-01 01:00:00 2558.0\n",
"2012-01-01 02:00:00 2444.0\n",
"2012-01-01 03:00:00 2402.0\n",
"2012-01-01 04:00:00 2403.0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"energy = load_data('../../data')[['load']]\n",
"energy.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "O0BWP13rGnh4"
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 486
},
"id": "hGaNPKu_Gidk",
"outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
},
"outputs": [],
"source": [
"energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
"plt.xlabel('timestamp', fontsize=12)\n",
"plt.ylabel('load', fontsize=12)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IPuNor4eGwYY"
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ysvsNyONGt0Q"
},
"outputs": [],
"source": [
"train_start_dt = '2014-11-01 00:00:00'\n",
"test_start_dt = '2014-12-30 00:00:00'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 548
},
"id": "SsfdLoPyGy9w",
"outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
},
"outputs": [],
"source": [
"energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
" .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
" .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
"plt.xlabel('timestamp', fontsize=12)\n",
"plt.ylabel('load', fontsize=12)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XbFTqBw6G1Ch"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Agora, precisa preparar os dados para treino, realizando a filtragem e a escalagem dos seus dados.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cYivRdQpHDj3",
"outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
},
"outputs": [],
"source": [
"train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
"test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
"\n",
"print('Training data shape: ', train.shape)\n",
"print('Test data shape: ', test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Escalone os dados para estarem no intervalo (0, 1).\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 363
},
"id": "3DNntGQnZX8G",
"outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
},
"outputs": [],
"source": [
"scaler = MinMaxScaler()\n",
"train['load'] = scaler.fit_transform(train)\n",
"train.head(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "26Yht-rzZexe",
"outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
},
"outputs": [],
"source": [
"test['load'] = scaler.transform(test)\n",
"test.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x0n6jqxOQ41Z"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fdmxTZtOQ8xs"
},
"source": [
"Para o nosso SVR, transformamos os dados de entrada para terem a forma `[batch, timesteps]`. Assim, remodelamos os dados existentes `train_data` e `test_data` de modo a que haja uma nova dimensão que se refere aos timesteps. No nosso exemplo, consideramos `timesteps = 5`. Assim, as entradas para o modelo são os dados dos primeiros 4 timesteps, e a saída será os dados do 5º timestep.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Rpju-Sc2HFm0"
},
"outputs": [],
"source": [
"# Converting to numpy arrays\n",
"\n",
"train_data = train.values\n",
"test_data = test.values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Selecting the timesteps\n",
"\n",
"timesteps=None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "O-JrsrsVJhUQ",
"outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
},
"outputs": [],
"source": [
"# Converting data to 2D tensor\n",
"\n",
"train_data_timesteps=None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "exJD8AI7KE4g",
"outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
},
"outputs": [],
"source": [
"# Converting test data to 2D tensor\n",
"\n",
"test_data_timesteps=None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2u0R2sIsLuq5"
},
"outputs": [],
"source": [
"x_train, y_train = None\n",
"x_test, y_test = None\n",
"\n",
"print(x_train.shape, y_train.shape)\n",
"print(x_test.shape, y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8wIPOtAGLZlh"
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EhA403BEPEiD"
},
"outputs": [],
"source": [
"# Create model using RBF kernel\n",
"\n",
"model = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GS0UA3csMbqp",
"outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
},
"outputs": [],
"source": [
"# Fit model on training data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Rz_x8S3UrlcF"
},
"source": [
"### Fazer previsão do modelo\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XR0gnt3MnuYS",
"outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
},
"outputs": [],
"source": [
"# Making predictions\n",
"\n",
"y_train_pred = None\n",
"y_test_pred = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_2epncg-SGzr"
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Scaling the predictions\n",
"\n",
"y_train_pred = scaler.inverse_transform(y_train_pred)\n",
"y_test_pred = scaler.inverse_transform(y_test_pred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xmm_YLXhq7gV",
"outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
},
"outputs": [],
"source": [
"# Scaling the original values\n",
"\n",
"y_train = scaler.inverse_transform(y_train)\n",
"y_test = scaler.inverse_transform(y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "u3LBj93coHEi",
"outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
},
"outputs": [],
"source": [
"# Extract the timesteps for x-axis\n",
"\n",
"train_timestamps = None\n",
"test_timestamps = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(25,6))\n",
"# plot original output\n",
"# plot predicted output\n",
"plt.legend(['Actual','Predicted'])\n",
"plt.xlabel('Timestamp')\n",
"plt.title(\"Training data prediction\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LnhzcnYtXHCm",
"outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
},
"outputs": [],
"source": [
"print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 225
},
"id": "53Q02FoqQH4V",
"outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
},
"outputs": [],
"source": [
"plt.figure(figsize=(10,3))\n",
"# plot original output\n",
"# plot predicted output\n",
"plt.legend(['Actual','Predicted'])\n",
"plt.xlabel('Timestamp')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "clOAUH-SXCJG",
"outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
},
"outputs": [],
"source": [
"print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DHlKvVCId5ue"
},
"source": [
"## Previsão do conjunto de dados completo\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cOFJ45vreO0N",
"outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
},
"outputs": [],
"source": [
"# Extracting load values as numpy array\n",
"data = None\n",
"\n",
"# Scaling\n",
"data = None\n",
"\n",
"# Transforming to 2D tensor as per model input requirement\n",
"data_timesteps=None\n",
"\n",
"# Selecting inputs and outputs from data\n",
"X, Y = None, None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ESSAdQgwexIi"
},
"outputs": [],
"source": [
"# Make model predictions\n",
"\n",
"# Inverse scale and reshape\n",
"Y_pred = None\n",
"Y = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328
},
"id": "M_qhihN0RVVX",
"outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
},
"outputs": [],
"source": [
"plt.figure(figsize=(30,8))\n",
"# plot original output\n",
"# plot predicted output\n",
"plt.legend(['Actual','Predicted'])\n",
"plt.xlabel('Timestamp')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AcN7pMYXVGTK",
"outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
},
"outputs": [],
"source": [
"print('MAPE: ', mape(Y_pred, Y)*100, '%')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n---\n\n**Aviso**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, é importante notar que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autoritária. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes da utilização desta tradução.\n"
]
}
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