msb-public
/
ML-For-Beginners
mirror of https://github.com/microsoft/ML-For-Beginners.git
1
0
Code Issues Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'main'
${ noResults }
ML-For-Beginners/translations/br/7-TimeSeries/3-SVR/working/notebook.ipynb

695 lines
16 KiB
Raw Permalink Blame History

{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "fv9OoQsMFk5A"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Neste notebook, demonstramos como:\n",
"\n",
"- preparar dados de séries temporais 2D para treinar um modelo de regressão SVM\n",
"- implementar SVR usando o kernel RBF\n",
"- avaliar o modelo utilizando gráficos e MAPE\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importando 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",
" }\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>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",
" <tr>\n",
" <th>2012-01-01 01:00:00</th>\n",
" <td>2558.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 02:00:00</th>\n",
" <td>2444.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 03:00:00</th>\n",
" <td>2402.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 04:00:00</th>\n",
" <td>2403.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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, você precisa preparar os dados para treinamento realizando a filtragem e a escalonamento 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 estar na faixa (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 nosso SVR, transformamos os dados de entrada para o formato `[batch, timesteps]`. Assim, remodelamos os dados existentes `train_data` e `test_data` de forma que haja uma nova dimensão que se refere aos timesteps. Para 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 Legal**: \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, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "Recurrent_Neural_Networks.ipynb",
"provenance": []
},
"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": "3.7.1"
},
"coopTranslator": {
"original_hash": "e86ce102239a14c44585623b9b924a74",
"translation_date": "2025-08-29T23:24:53+00:00",
"source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb",
"language_code": "br"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Reference in new issue View Git Blame Copy Permalink