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Chronos: how to create a forecaster (intel-analytics#5172)
* how to guide from_tsdataset * improve md * remove redundant message * fix known issues * add rst * move create-forecaster to new line * fix known issue * fix syntax error * fix syntax error again * add open-colab icon * fix known issues * fix some words * fix typo * fix syntax error * add some update Co-authored-by: theaperdeng <[email protected]>
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docs/readthedocs/source/doc/Chronos/Howto/how-to-create-forecaster.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/docs/readthedocs/source/doc/Chronos/Howto/how-to-create-forecaster.ipynb)\n", | ||
| "\n", | ||
| "<style>\n", | ||
| " .rst-content blockquote {\n", | ||
| " margin-left: 0px;\n", | ||
| " }\n", | ||
| " \n", | ||
| " blockquote > div {\n", | ||
| " margin: 1.5625em auto;\n", | ||
| " padding: 20px 15px 1px;\n", | ||
| " border-left: 0.2rem solid rgb(59, 136, 219); \n", | ||
| " border-radius: 0.2rem;\n", | ||
| " box-shadow: 0 0.2rem 0.5rem rgb(0 0 0 / 5%), 0 0 0.0625rem rgb(0 0 0 / 10%);\n", | ||
| " }\n", | ||
| "</style>\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# How to create a Forecaster" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Introduction\n", | ||
| "\n", | ||
| "In Chronos, Forecaster (`bigdl.chronos.forecaster.Forecaster`) is the forecasting abstraction. It hides the complex logic of model's creation, training, scaling to cluster, tuning, optimization and inferencing while expose some APIs for users to control.\n", | ||
| "\n", | ||
| "In this guide, we will use the `TCNForecaster` and nyc_taxi datasets as examples to describe **how to create a Forecaster**." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Prepare Environments" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Before creating a forecaster, we need to install Chronos. Chronos supports deep learning backend implemented by pytorch and tensorflow and machine learning methods based on arima and prophet." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# uncomment following 1 lines for pytorch backend\n", | ||
| "!pip install --pre --upgrade bigdl-chronos[pytorch]\n", | ||
| "\n", | ||
| "# uncomment following 2 lines for tensorflow backend\n", | ||
| "# !pip install --pre --upgrade bigdl-chronos\n", | ||
| "# !pip install --pre --upgrade bigdl-nano[tensorflow]\n", | ||
| "\n", | ||
| "# installation trick on colab (no need to do these on your own environment)\n", | ||
| "!pip uninstall torchtext -y\n", | ||
| "exit()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Create a forecaster\n", | ||
| "We provide two ways to create a Forecaster.\n", | ||
| "\n", | ||
| "* Create by `Forecaster.from_tsdataset`(**Recommended if valid**)\n", | ||
| "* Create by `Forecaster(...)` directly" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Before creating a Forecaster, We need to know the four parameters `past_seq_len`, `future_seq_len`, `input_feature_num`, `output_feature_num`, which represent the time step and feature column, As shown below.\n", | ||
| "\n", | ||
| "<img src=\"../Image/forecast-RR.png\" title=\"time series\" style=\"width:600px\">\n", | ||
| "\n", | ||
| "* **past_seq_len**: Sampled input length, represents the history time step length. (i.e. lookback)\n", | ||
| "* **future_seq_len**: Sampled output length, represents the output time step length.(i.e. horizon)\n", | ||
| "* **input_feature_num**: All feature column(s), including extra feature column(s) and target column(s).\n", | ||
| "* **output_feature_num**: Only target column(s).\n", | ||
| "\n", | ||
| "More Forecaster info, please refer to [Time Series Forecasting OverView](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/forecasting.html#time-series-forecasting-overview)\n", | ||
| "\n", | ||
| "If you want to create a traditional statistic forecaster(e.g. [ProphetForecaster](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/forecasters.html#prophetforecaster) or [ARIMAForecaster](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/forecasters.html#arimaforecaster)), you may refer to their API doc directly since they are relatively easy and do not have required parameters to create them." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# create a TSDataset\n", | ||
| "from bigdl.chronos.data.repo_dataset import get_public_dataset\n", | ||
| "\n", | ||
| "tsdataset = get_public_dataset('nyc_taxi', with_split=False)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Forecaster.from_tsdataset\n", | ||
| "\n", | ||
| "`from_tsdataset` is a classmethod, so you can call `Forecsater.from_tsdataset`, then input a `TSDataset` instance, where `TSDataset` is a built-in time series preprocessing class.\n", | ||
| "\n", | ||
| "If the `roll` or `to_torch_data_loader` method has been called by tsdataset, `past_seq_len` and `future_seq_len` do not need to be specified for from_tsdataset, otherwise both must be specified." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# uncomment following 1 lines for pytorch backend\n", | ||
| "from bigdl.chronos.forecaster import TCNForecaster\n", | ||
| "\n", | ||
| "# uncomment following 1 lines for tensorflow backend\n", | ||
| "# from bigdl.chronos.forecaster.tf import TCNForecaster\n", | ||
| "\n", | ||
| "tcn = TCNForecaster.from_tsdataset(tsdataset,\n", | ||
| " past_seq_len=48,\n", | ||
| " future_seq_len=5)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "> 📝 **Note**\n", | ||
| ">\n", | ||
| "> We recommend to use `Forecsater.from_tsdataset` if possible. While for some reasons, some forecasters (e.g. `ProphetForecaster` and `ARIMAForecaster`) does not support this API. Or maybe you want to process your data customizedly without using `TSDataset`, you may create a forecaster directly by calling `Forecaster(...)`." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Create a forecaster directly\n", | ||
| "You can also create forecaster directly, the parameters mentioned above still need to be specified." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "tcn = TCNForecaster(past_seq_len=48,\n", | ||
| " future_seq_len=5,\n", | ||
| " input_feature_num=2,\n", | ||
| " output_feature_num=2)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3.7.13 ('chronos-deps')", | ||
| "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": "3.7.13" | ||
| }, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "1a7f5d41b94c9ba67b8a1438ec071a63464312bab4ac0991ee31faebf1c9c228" | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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