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v1.0

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.circleci/config.yml 0 → 100644
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version: 2.1
jobs:
nbdev-tests:
resource_class: xlarge
docker:
- image: mambaorg/micromamba:1.5-focal
steps:
- checkout
- run:
name: Install dependencies
command: micromamba install -n base -c conda-forge -y python=3.10 git -f environment-cpu.yml
- run:
name: Run nbdev tests
command: |
eval "$(micromamba shell hook --shell bash)"
micromamba activate base
pip install ".[dev]"
nbdev_test --do_print --timing --n_workers 1
test-model-performance:
resource_class: xlarge
docker:
- image: mambaorg/micromamba:1.5-focal
steps:
- checkout
- run:
name: Install dependencies
command: micromamba install -n base -c conda-forge -y python=3.10 -f environment-cpu.yml
- run:
name: Run model performance tests
command: |
eval "$(micromamba shell hook --shell bash)"
micromamba activate base
pip install -e ".[dev]"
export LD_LIBRARY_PATH=/opt/conda/lib:$LD_LIBRARY_PATH
cd ./action_files/test_models/
pip install -r requirements.txt
python -m src.models
python -m src.evaluation
cd ../../
- store_artifacts:
path: ./action_files/test_models/data/evaluation.csv
destination: evaluation.csv
test-model-performance2:
resource_class: xlarge
docker:
- image: mambaorg/micromamba:1.5-focal
steps:
- checkout
- run:
name: Install dependencies
command: micromamba install -n base -c conda-forge -y python=3.10 -f environment-cpu.yml
- run:
name: Run model performance tests
command: |
eval "$(micromamba shell hook --shell bash)"
micromamba activate base
pip install -e ".[dev]"
export LD_LIBRARY_PATH=/opt/conda/lib:$LD_LIBRARY_PATH
cd ./action_files/test_models/
pip install -r requirements.txt
python -m src.models2
python -m src.evaluation2
cd ../../
- store_artifacts:
path: ./action_files/test_models/data/evaluation.csv
destination: evaluation.csv
workflows:
sample:
jobs:
- nbdev-tests
- test-model-performance
- test-model-performance2
.gitignore 0 → 100644
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.ipynb_checkpoints
__pycache__
*.egg-info
Gemfile*
Gemfile.lock
docs/_site
build
dist
.vscode
*.gif
*.csv
*/data/*
*.parquet
tmp
_docs/
sidebar.yml
.DS_Store
.gitattributes
.gitconfig
nbs/.last_checked
_proc
lightning_logs/
/**/data/
debug_run
longhorizon/
_old/
.pre-commit-config.yaml 0 → 100644
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fail_fast: true
repos:
- repo: local
hooks:
- id: imports_with_code
name: Cells with imports and code
entry: python action_files/imports_with_code.py
language: system
- repo: https://github.com/fastai/nbdev
rev: 2.2.10
hooks:
- id: nbdev_clean
- id: nbdev_export
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.2.1
hooks:
- id: ruff
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v1.8.0
hooks:
- id: mypy
args: [--ignore-missing-imports]
exclude: 'setup.py'
CODE_OF_CONDUCT.md 0 → 100644
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# Contributor Covenant Code of Conduct
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CONTRIBUTING.md 0 → 100644
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# How to contribute
## Did you find a bug?
* Ensure the bug was not already reported by searching on GitHub under Issues.
* If you're unable to find an open issue addressing the problem, open a new one. Be sure to include a title and clear description, as much relevant information as possible, and a code sample or an executable test case demonstrating the expected behavior that is not occurring.
* Be sure to add the complete error messages.
## Do you have a feature request?
* Ensure that it hasn't been yet implemented in the `main` branch of the repository and that there's not an Issue requesting it yet.
* Open a new issue and make sure to describe it clearly, mention how it improves the project and why its useful.
## Do you want to fix a bug or implement a feature?
Bug fixes and features are added through pull requests (PRs).
## PR submission guidelines
* Keep each PR focused. While it's more convenient, do not combine several unrelated fixes together. Create as many branches as needing to keep each PR focused.
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* Do not turn an already submitted PR into your development playground. If after you submitted PR, you discovered that more work is needed - close the PR, do the required work and then submit a new PR. Otherwise each of your commits requires attention from maintainers of the project.
* If, however, you submitted a PR and received a request for changes, you should proceed with commits inside that PR, so that the maintainer can see the incremental fixes and won't need to review the whole PR again. In the exception case where you realize it'll take many many commits to complete the requests, then it's probably best to close the PR, do the work and then submit it again. Use common sense where you'd choose one way over another.
### Local setup for working on a PR
#### Clone the repository
* HTTPS: `git clone https://github.com/Nixtla/neuralforecast.git`
* SSH: `git clone git@github.com:Nixtla/neuralforecast.git`
* GitHub CLI: `gh repo clone Nixtla/neuralforecast`
#### Set up a conda environment
The repo comes with an `environment.yml` file which contains the libraries needed to run all the tests. In order to set up the environment you must have `conda` installed, we recommend [miniconda](https://docs.conda.io/en/latest/miniconda.html).
Once you have `conda` go to the top level directory of the repository and run the following lines:
```
conda create -n neuralforecast python=3.10
conda activate neuralforecast
```
Then, run one of the following commands:
```
conda env update -f environment-cpu.yml # choose this if you want to install the CPU-only version of neuralforecast
conda env update -f environment-cuda.yml # choose this if you want to install the CUDA-enabled version of neuralforecast
```
#### Install the library
Once you have your environment setup, activate it using `conda activate neuralforecast` and then install the library in editable mode using `pip install -e ".[dev]"`
#### Install git hooks
Before doing any changes to the code, please install the git hooks that run automatic scripts during each commit and merge to strip the notebooks of superfluous metadata (and avoid merge conflicts).
```
nbdev_install_hooks
pre-commit install
```
### Preview Changes
You can preview changes in your local browser before pushing by using the `nbdev_preview`.
### Building the library
The library is built using the notebooks contained in the `nbs` folder. If you want to make any changes to the library you have to find the relevant notebook, make your changes and then call:
```
nbdev_export
```
### Running tests
If you're working on the local interface you can just use `nbdev_test --n_workers 1 --do_print --timing`.
### Cleaning notebooks
Since the notebooks output cells can vary from run to run (even if they produce the same outputs) the notebooks are cleaned before committing them. Please make sure to run `nbdev_clean --clear_all` before committing your changes. If you clean the library's notebooks with this command please backtrack the changes you make to the example notebooks `git checkout nbs/examples`, unless you intend to change the examples.
## Do you want to contribute to the documentation?
* Docs are automatically created from the notebooks in the `nbs` folder.
* In order to modify the documentation:
1. Find the relevant notebook.
2. Make your changes.
3. Run all cells.
4. If you are modifying library notebooks (not in `nbs/examples`), clean all outputs using `Edit > Clear All Outputs`.
5. Run `nbdev_preview`.
6. Clean the notebook metadata using `nbdev_clean`.
LICENSE 0 → 100644
View file @ e3f7f7b3
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MANIFEST.in 0 → 100644
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## Include List
include README.md
include LICENSE
include settings.ini
recursive-include neuralforecast *
## Exclude List
exclude CONTRIBUTING.md
exclude Makefile
exclude environment.yml
exclude .gitconfig
exclude .gitignore
exclude .gitmodules
recursive-exclude .github *
recursive-exclude docs *
recursive-exclude examples *
recursive-exclude experiments *
recursive-exclude nbs *
README.md 0 → 100644
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# iTransformer
iTransformer可以跨多个时间步查看一个特征，能高效利用长程时序特征，本步骤适于时序预测库neuralforecast中的iTransformer算法，其它算法的使用依此类推。
## 论文
`iTransformer: Inverted Transformers Are Effective for Time Series Forecasting`
- https://arxiv.org/pdf/2310.06625
## 模型结构
采用标准的transformer decoder结构，对于backbone，无需修改transformer标准结构的代码即可实现本算法。
<div align=center>
<img src="./doc/transformer.png"/>
</div>
## 算法原理
iTransformer通过简单地转置输入的形状来实现跨多个时间步查看一个特征，模型不是对输入的子序列进行令牌化，而是对整个输入序列进行令牌化，通过这种方式，注意力层可以专注于学习多元相关性，而前馈网络则负责对整个输入序列进行编码。
<div align=center>
<img src="./doc/iTransformer.png"/>
</div>
## 环境配置
```
mv neuralforecast-itransformer_pytorch neuralforecast # 去框架名后缀
```
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk23.10-py38
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：ffa1f63239fc
docker run -it --shm-size=32G -v $PWD/neuralforecast:/home/neuralforecast -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name neuralforecast <your IMAGE ID> bash
cd /home/neuralforecast
pip install -r requirements.txt # requirements.txt
```
### Dockerfile（方法二）
```
cd neuralforecast/docker
docker build --no-cache -t neuralforecast:latest .
docker run --shm-size=32G --name neuralforecast -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../neuralforecast:/home/neuralforecast -it neuralforecast bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.hpccube.com/tool/
```
DTK驱动:dtk23.10
python:python3.8
torch:2.1.0
torchvision:0.16.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
pip install -r requirements.txt # requirements.txt
```
## 数据集
本步骤说明采用LongHorizon中的`ETTm2`
- https://nhits-experiments.s3.amazonaws.com/datasets.zip
数据目录结构如下：
```
ETT-small/longhorizon/datasets/ETTm2
├── df_x.csv
├── df_y.csv
├── M
├── ├── df_x.csv
├── └── df_y.csv
└── S
├── ├── df_x.csv
├── └── df_y.csv
```
## 训练
### 单机单卡
```
export HIP_VISIBLE_DEVICES=0
cd neuralforecast
python trainval.py # 该库中的iTransformer目前仅支持单指标预测的训练与推理，多指标预测敬请等待作者开源。
```
更多资料可参考源项目的[`README_origin`](./README_origin.md)
## 推理
```
export HIP_VISIBLE_DEVICES=0
python infer.py
# 默认按天预测, 故pred_len为96。
```
## result
# 输入
```
ds,unique_id,y
2016-07-01 00:00:00,OT,1.0180321560148238
2016-07-01 00:15:00,OT,0.9801244795019736
2016-07-01 00:30:00,OT,0.9042228236873991
2016-07-01 00:45:00,OT,0.8852256693365586
...
2018-02-20 23:00:00,OT,-1.5813253301696957
2018-02-20 23:15:00,OT,-1.5813253301696957
2018-02-20 23:30:00,OT,-1.5623282581688442
2018-02-20 23:45:00,OT,-1.5623282581688442
```
# 输出
```
unique_id ds iTransformer
0 OT 2018-02-21 00:00:00 -1.567348
1 OT 2018-02-21 00:15:00 -1.577499
2 OT 2018-02-21 00:30:00 -1.579781
3 OT 2018-02-21 00:45:00 -1.587281
4 OT 2018-02-21 01:00:00 -1.578477
...
92 OT 2018-02-21 23:00:00 -1.569653
93 OT 2018-02-21 23:15:00 -1.546048
94 OT 2018-02-21 23:30:00 -1.561576
95 OT 2018-02-21 23:45:00 -1.542416
```
### 精度
测试数据：[`ETTm2`](./ETT-small/longhorizon/datasets/ETTm2/df_y.csv)中划出一部分作验证集，max_steps=1000，推理框架：pytorch。
| device | valid_loss |
|:---------:|:----------:|
| DCU Z100L | 0.226 |
| GPU V100S | 0.224 |
## 应用场景
### 算法类别
`时序预测`
### 热点应用行业
`金融,运维,电商,制造,能源,医疗`
## 源码仓库及问题反馈
- http://developer.hpccube.com/codes/modelzoo/neuralforecast-itransformer_pytorch.git
## 参考资料
- https://github.com/thuml/iTransformer.git
README_origin.md 0 → 100644
View file @ e3f7f7b3
# Nixtla &nbsp; [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Statistical%20Forecasting%20Algorithms%20by%20Nixtla%20&url=https://github.com/Nixtla/neuralforecast&via=nixtlainc&hashtags=StatisticalModels,TimeSeries,Forecasting) &nbsp;[![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ)
<div align="center">
<img src="https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_new.png">
<h1 align="center">Neural 🧠 Forecast</h1>
<h3 align="center">User friendly state-of-the-art neural forecasting models</h3>
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<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[![All Contributors](https://img.shields.io/badge/all_contributors-11-orange.svg?style=flat-square)](#contributors-)
<!-- ALL-CONTRIBUTORS-BADGE:END -->
**NeuralForecast** offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like `RNN` to the latest transformers: `MLP`, `LSTM`, `GRU`,`RNN`,`TCN`, `DeepAR`, `NBEATS`, `NBEATSx`, `NHITS`, `DLinear`, `NLinear`,`TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`,`StemGNN`, and `TimesNet`.
</div>
## Installation
You can install `NeuralForecast` with:
```python
pip install neuralforecast
```
or
```python
conda install -c conda-forge neuralforecast
```
Vist our [Installation Guide](https://nixtla.github.io/neuralforecast/examples/installation.html) for further details.
## Quick Start
**Minimal Example**
```python
from neuralforecast import NeuralForecast
from neuralforecast.models import NBEATS
from neuralforecast.utils import AirPassengersDF
nf = NeuralForecast(
models = [NBEATS(input_size=24, h=12, max_steps=100)],
freq = 'M'
)
nf.fit(df=AirPassengersDF)
nf.predict()
```
**Get Started with this [quick guide](https://nixtla.github.io/neuralforecast/examples/getting_started.html).**
## Why?
There is a shared belief in Neural forecasting methods' capacity to improve forecasting pipeline's accuracy and efficiency.
Unfortunately, available implementations and published research are yet to realize neural networks' potential. They are hard to use and continuously fail to improve over statistical methods while being computationally prohibitive. For this reason, we created `NeuralForecast`, a library favoring proven accurate and efficient models focusing on their usability.
## Features
* Fast and accurate implementations of `MLP`, `LSTM`, `GRU`,`RNN`,`TCN`, `DeepAR`, `NBEATS`, `NBEATSx`, `NHITS`, `DLinear`,`TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`,`StemGNN`, and `TimesNet`. See the entire [collection here](https://nixtla.github.io/neuralforecast/models.html).
* Support for exogenous variables and static covariates.
* Interpretability methods for trend, seasonality and exogenous components.
* Probabilistic Forecasting with adapters for quantile losses and parametric distributions.
* Train and Evaluation Losses with scale-dependent, percentage and scale independent errors, and parametric likelihoods.
* Automatic Model Selection with distributed automatic hyperparameter tuning.
* Familiar sklearn syntax: `.fit` and `.predict`.
## Highlights
* Official `NHITS` implementation, published at AAAI 2023. See [paper](https://ojs.aaai.org/index.php/AAAI/article/view/25854) and [experiments](./experiments/).
* Official `NBEATSx` implementation, published at the International Journal of Forecasting. See [paper](https://www.sciencedirect.com/science/article/pii/S0169207022000413).
* Unified with`StatsForecast`, `MLForecast`, and `HierarchicalForecast` interface `NeuralForecast().fit(Y_df).predict()`, inputs and outputs.
* Built-in integrations with `utilsforecast` and `coreforecast` for visualization and data-wrangling efficient methods.
* Integrations with `Ray` and `Optuna` for automatic hyperparameter optimization.
* Predict with little to no history using Transfer learning. Check the experiments [here](https://github.com/Nixtla/transfer-learning-time-series).
Missing something? Please open an issue or write us in [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A)
## Examples and Guides
The [documentation page](https://nixtla.github.io/neuralforecast/) contains all the examples and tutorials.
📈 [Automatic Hyperparameter Optimization](https://nixtla.github.io/neuralforecast/examples/automatic_hyperparameter_tuning.html): Easy and Scalable Automatic Hyperparameter Optimization with `Auto` models on `Ray` or `Optuna`.
🌡️ [Exogenous Regressors](https://nixtla.github.io/neuralforecast/examples/exogenous_variables.html): How to incorporate static or temporal exogenous covariates like weather or prices.
🔌 [Transformer Models](https://nixtla.github.io/neuralforecast/examples/longhorizon_with_transformers.html): Learn how to forecast with many state-of-the-art Transformers models.
👑 [Hierarchical Forecasting](https://nixtla.github.io/neuralforecast/examples/hierarchicalnetworks.html): forecast series with very few non-zero observations.
👩‍🔬 [Add Your Own Model](https://nixtla.github.io/neuralforecast/examples/how_to_add_models.html): Learn how to add a new model to the library.
## Models
|Model | Structure | Sampling | Point Forecast | Probabilistic Forecast | Exogenous features | `Auto` Model
|:------|:-------------:|:-----------------:|:---------------------:|:----------------------------:|:---------------------:|:---------------------:|
|[LSTM](https://nixtlaverse.nixtla.io/neuralforecast/models.lstm.html)| RNN | recurrent |✅|✅|✅|✅|
|[GRU](https://nixtlaverse.nixtla.io/neuralforecast/models.gru.html)| RNN | recurrent |✅|✅|✅|✅|
|[RNN](https://nixtlaverse.nixtla.io/neuralforecast/models.rnn.html)| RNN | recurrent |✅|✅|✅|✅|
|[DilatedRNN](https://nixtlaverse.nixtla.io/neuralforecast/models.dilated_rnn.html)| RNN | recurrent |✅|✅|✅|✅|
|[DeepAR](https://nixtlaverse.nixtla.io/neuralforecast/models.deepar.html)| RNN | recurrent ||✅|✅|✅|
|[TCN](https://nixtlaverse.nixtla.io/neuralforecast/models.tcn.html)| CNN | recurrent |✅|✅|✅|✅|
|[TimesNet](https://nixtlaverse.nixtla.io/neuralforecast/models.timesnet.html)| CNN | windows |✅|✅||✅|
|[DLinear](https://nixtlaverse.nixtla.io/neuralforecast/models.dlinear.html)| Linear | windows |✅|✅||✅|
|[MLP](https://nixtlaverse.nixtla.io/neuralforecast/models.mlp.html)| MLP | windows |✅|✅|✅|✅|
|[NBEATS](https://nixtlaverse.nixtla.io/neuralforecast/models.nbeats.html)| MLP | windows |✅|✅||✅|
|[NBEATSx](https://nixtlaverse.nixtla.io/neuralforecast/models.nbeatsx.html)| MLP | windows |✅|✅|✅|✅|
|[NHITS](https://nixtlaverse.nixtla.io/neuralforecast/models.nhits.html)| MLP | windows |✅|✅|✅|✅|
|[TFT](https://nixtlaverse.nixtla.io/neuralforecast/models.tft.html)| Transformer | windows |✅|✅|✅|✅|
|[Transformer](https://nixtlaverse.nixtla.io/neuralforecast/models.vanillatransformer.html)| Transformer | windows |✅|✅|✅|✅|
|[Informer](https://nixtlaverse.nixtla.io/neuralforecast/models.informer.html)| Transformer | windows |✅|✅|✅|✅|
|[Autoformer](https://nixtlaverse.nixtla.io/neuralforecast/models.autoformer.html)| Transformer | windows |✅|✅|✅|✅|
|[FEDFormer](https://nixtlaverse.nixtla.io/neuralforecast/models.fedformer.html)| Transformer | windows |✅|✅|✅|✅|
|[PatchTST](https://nixtlaverse.nixtla.io/neuralforecast/models.patchtst.html)| Transformer | windows |✅|✅||✅|
|[StemGNN](https://nixtlaverse.nixtla.io/neuralforecast/models.stemgnn.html)| GNN | multivariate |✅|||✅|
Missing a model? Please open an issue or write us in [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A)
## How to contribute
If you wish to contribute to the project, please refer to our [contribution guidelines](https://github.com/Nixtla/neuralforecast/blob/main/CONTRIBUTING.md).
## References
This work is highly influenced by the fantastic work of previous contributors and other scholars on the neural forecasting methods presented here. We want to highlight the work of [Boris Oreshkin](https://arxiv.org/abs/1905.10437), [Slawek Smyl](https://www.sciencedirect.com/science/article/pii/S0169207019301153), [Bryan Lim](https://www.sciencedirect.com/science/article/pii/S0169207021000637), and [David Salinas](https://arxiv.org/abs/1704.04110). We refer to [Benidis et al.](https://arxiv.org/abs/2004.10240) for a comprehensive survey of neural forecasting methods.
## Contributors ✨
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
<table>
<tbody>
<tr>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/FedericoGarza"><img src="https://avatars.githubusercontent.com/u/10517170?v=4?s=100" width="100px;" alt="fede"/><br /><sub><b>fede</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=FedericoGarza" title="Code">💻</a> <a href="#maintenance-FedericoGarza" title="Maintenance">🚧</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/cchallu"><img src="https://avatars.githubusercontent.com/u/31133398?v=4?s=100" width="100px;" alt="Cristian Challu"/><br /><sub><b>Cristian Challu</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=cchallu" title="Code">💻</a> <a href="#maintenance-cchallu" title="Maintenance">🚧</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/jmoralez"><img src="https://avatars.githubusercontent.com/u/8473587?v=4?s=100" width="100px;" alt="José Morales"/><br /><sub><b>José Morales</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=jmoralez" title="Code">💻</a> <a href="#maintenance-jmoralez" title="Maintenance">🚧</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/mergenthaler"><img src="https://avatars.githubusercontent.com/u/4086186?v=4?s=100" width="100px;" alt="mergenthaler"/><br /><sub><b>mergenthaler</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=mergenthaler" title="Documentation">📖</a> <a href="https://github.com/Nixtla/neuralforecast/commits?author=mergenthaler" title="Code">💻</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/kdgutier"><img src="https://avatars.githubusercontent.com/u/19935241?v=4?s=100" width="100px;" alt="Kin"/><br /><sub><b>Kin</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=kdgutier" title="Code">💻</a> <a href="https://github.com/Nixtla/neuralforecast/issues?q=author%3Akdgutier" title="Bug reports">🐛</a> <a href="#data-kdgutier" title="Data">🔣</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/gdevos010"><img src="https://avatars.githubusercontent.com/u/15316026?v=4?s=100" width="100px;" alt="Greg DeVos"/><br /><sub><b>Greg DeVos</b></sub></a><br /><a href="#ideas-gdevos010" title="Ideas, Planning, & Feedback">🤔</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/alejandroxag"><img src="https://avatars.githubusercontent.com/u/64334543?v=4?s=100" width="100px;" alt="Alejandro"/><br /><sub><b>Alejandro</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=alejandroxag" title="Code">💻</a></td>
</tr>
<tr>
<td align="center" valign="top" width="14.28%"><a href="http://lavattiata.com"><img src="https://avatars.githubusercontent.com/u/48966177?v=4?s=100" width="100px;" alt="stefanialvs"/><br /><sub><b>stefanialvs</b></sub></a><br /><a href="#design-stefanialvs" title="Design">🎨</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://bandism.net/"><img src="https://avatars.githubusercontent.com/u/22633385?v=4?s=100" width="100px;" alt="Ikko Ashimine"/><br /><sub><b>Ikko Ashimine</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/issues?q=author%3Aeltociear" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/vglaucus"><img src="https://avatars.githubusercontent.com/u/75549033?v=4?s=100" width="100px;" alt="vglaucus"/><br /><sub><b>vglaucus</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/issues?q=author%3Avglaucus" title="Bug reports">🐛</a></td>
<td align="center" valign="top" width="14.28%"><a href="https://github.com/pitmonticone"><img src="https://avatars.githubusercontent.com/u/38562595?v=4?s=100" width="100px;" alt="Pietro Monticone"/><br /><sub><b>Pietro Monticone</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/issues?q=author%3Apitmonticone" title="Bug reports">🐛</a></td>
</tr>
</tbody>
</table>
<!-- markdownlint-restore -->
<!-- prettier-ignore-end -->
<!-- ALL-CONTRIBUTORS-LIST:END -->
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
action_files/imports_with_code.py 0 → 100644
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import sys
import warnings
from pathlib import Path
from nbdev.processors import NBProcessor, _do_eval
def check_nb(nb_path: str) -> None:
with warnings.catch_warnings(record=True) as issued_warnings:
NBProcessor(nb_path, _do_eval, process=True)
if any(
"Found cells containing imports and other code" in str(w)
for w in issued_warnings
):
print(f"{nb_path} has cells containing imports and code.")
sys.exit(1)
if __name__ == "__main__":
repo_root = Path(__file__).parents[1]
for nb_path in (repo_root / "nbs").glob("*.ipynb"):
check_nb(str(nb_path))
action_files/test_models/requirements.txt 0 → 100644
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fire
datasetsforecast
\ No newline at end of file
action_files/test_models/src/data.py 0 → 100644
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import fire
import pandas as pd
from datasetsforecast.m3 import M3, M3Info
dict_datasets = {
'M3': (M3, M3Info),
}
def get_data(directory: str, dataset: str, group: str, train: bool = True):
if dataset not in dict_datasets.keys():
raise Exception(f'dataset {dataset} not found')
dataclass, datainfo = dict_datasets[dataset]
if group not in datainfo.groups:
raise Exception(f'group {group} not found for {dataset}')
Y_df, *_ = dataclass.load(directory, group)
horizon = datainfo[group].horizon
freq = datainfo[group].freq
seasonality = datainfo[group].seasonality
Y_df_test = Y_df.groupby('unique_id').tail(horizon)
Y_df = Y_df.drop(Y_df_test.index)
if train:
return Y_df, horizon, freq, seasonality
return Y_df_test, horizon, freq, seasonality
def save_data(dataset: str, group: str, train: bool = True):
df, *_ = get_data('data', dataset, group, train)
if train:
df.to_csv(f'data/{dataset}-{group}.csv', index=False)
else:
df.to_csv(f'data/{dataset}-{group}-test.csv', index=False)
if __name__=="__main__":
fire.Fire(save_data)
\ No newline at end of file
action_files/test_models/src/evaluation.py 0 → 100644
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from itertools import product
import numpy as np
import pandas as pd
from src.data import get_data
def mae(y, y_hat, axis):
delta_y = np.abs(y - y_hat)
mae = np.average(delta_y, axis=axis)
return mae
def smape(y, y_hat, axis):
delta_y = np.abs(y - y_hat)
scale = np.abs(y) + np.abs(y_hat)
smape = delta_y / scale
smape = 200 * np.average(smape, axis=axis)
return smape
def evaluate(model: str, dataset: str, group: str):
try:
forecast = pd.read_csv(f'data/{model}-forecasts-{dataset}-{group}.csv')
except:
return None
y_test, horizon, freq, seasonality = get_data('data/', dataset, group, False)
y_hat = forecast[model].values.reshape(-1, horizon)
y_test = y_test['y'].values.reshape(-1, horizon)
evals = {}
for metric in (mae, smape):
metric_name = metric.__name__
loss = metric(y_test, y_hat, axis=1).mean()
evals[metric_name] = loss
evals = pd.DataFrame(evals, index=[f'{dataset}_{group}']).rename_axis('dataset').reset_index()
times = pd.read_csv(f'data/{model}-time-{dataset}-{group}.csv')
evals = pd.concat([evals, times], axis=1)
return evals
if __name__ == '__main__':
groups = ['Monthly']
models = ['AutoDilatedRNN', 'RNN', 'TCN', 'DeepAR',
'NHITS', 'TFT', 'AutoMLP', 'DLinear', 'VanillaTransformer']
datasets = ['M3']
evaluation = [evaluate(model, dataset, group) for model, group in product(models, groups) for dataset in datasets]
evaluation = [eval_ for eval_ in evaluation if eval_ is not None]
evaluation = pd.concat(evaluation)
evaluation = evaluation[['dataset', 'model', 'time', 'mae', 'smape']]
evaluation['time'] /= 60 #minutes
evaluation = evaluation.set_index(['dataset', 'model']).stack().reset_index()
evaluation.columns = ['dataset', 'model', 'metric', 'val']
evaluation = evaluation.set_index(['dataset', 'metric', 'model']).unstack().round(3)
evaluation = evaluation.droplevel(0, 1).reset_index()
evaluation['AutoARIMA'] = [666.82, 15.35, 3.000]
evaluation.to_csv('data/evaluation.csv')
print(evaluation.T)
action_files/test_models/src/evaluation2.py 0 → 100644
View file @ e3f7f7b3
from itertools import product
import numpy as np
import pandas as pd
from src.data import get_data
def mae(y, y_hat, axis):
delta_y = np.abs(y - y_hat)
mae = np.average(delta_y, axis=axis)
return mae
def smape(y, y_hat, axis):
delta_y = np.abs(y - y_hat)
scale = np.abs(y) + np.abs(y_hat)
smape = delta_y / scale
smape = 200 * np.average(smape, axis=axis)
return smape
def evaluate(model: str, dataset: str, group: str):
try:
forecast = pd.read_csv(f'data/{model}-forecasts-{dataset}-{group}.csv')
except:
return None
y_test, horizon, freq, seasonality = get_data('data/', dataset, group, False)
y_hat = forecast[model].values.reshape(-1, horizon)
y_test = y_test['y'].values.reshape(-1, horizon)
evals = {}
for metric in (mae, smape):
metric_name = metric.__name__
loss = metric(y_test, y_hat, axis=1).mean()
evals[metric_name] = loss
evals = pd.DataFrame(evals, index=[f'{dataset}_{group}']).rename_axis('dataset').reset_index()
times = pd.read_csv(f'data/{model}-time-{dataset}-{group}.csv')
evals = pd.concat([evals, times], axis=1)
return evals
if __name__ == '__main__':
groups = ['Monthly']
models = ['LSTM', 'DilatedRNN', 'GRU', 'NBEATSx',
'PatchTST', 'AutoNHITS', 'AutoNBEATS']
datasets = ['M3']
evaluation = [evaluate(model, dataset, group) for model, group in product(models, groups) for dataset in datasets]
evaluation = [eval_ for eval_ in evaluation if eval_ is not None]
evaluation = pd.concat(evaluation)
evaluation = evaluation[['dataset', 'model', 'time', 'mae', 'smape']]
evaluation['time'] /= 60 #minutes
evaluation = evaluation.set_index(['dataset', 'model']).stack().reset_index()
evaluation.columns = ['dataset', 'model', 'metric', 'val']
evaluation = evaluation.set_index(['dataset', 'metric', 'model']).unstack().round(3)
evaluation = evaluation.droplevel(0, 1).reset_index()
evaluation['AutoARIMA'] = [666.82, 15.35, 3.000]
evaluation.to_csv('data/evaluation.csv')
print(evaluation.T)
action_files/test_models/src/models.py 0 → 100644
View file @ e3f7f7b3
import os
import time
import fire
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import torch
import neuralforecast
from neuralforecast.core import NeuralForecast
from neuralforecast.models.gru import GRU
from neuralforecast.models.rnn import RNN
from neuralforecast.models.tcn import TCN
from neuralforecast.models.lstm import LSTM
from neuralforecast.models.dilated_rnn import DilatedRNN
from neuralforecast.models.deepar import DeepAR
from neuralforecast.models.mlp import MLP
from neuralforecast.models.nhits import NHITS
from neuralforecast.models.nbeats import NBEATS
from neuralforecast.models.nbeatsx import NBEATSx
from neuralforecast.models.tft import TFT
from neuralforecast.models.vanillatransformer import VanillaTransformer
from neuralforecast.models.informer import Informer
from neuralforecast.models.autoformer import Autoformer
from neuralforecast.models.patchtst import PatchTST
from neuralforecast.models.dlinear import DLinear
from neuralforecast.auto import (
AutoMLP, AutoNHITS, AutoNBEATS, AutoDilatedRNN, AutoTFT
)
from neuralforecast.losses.pytorch import SMAPE, MAE
from ray import tune
from src.data import get_data
os.environ['NIXTLA_ID_AS_COL'] = '1'
def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
train, horizon, freq, seasonality = get_data('data/', dataset, group)
train['ds'] = pd.to_datetime(train['ds'])
config_nbeats = {
"input_size": tune.choice([2 * horizon]),
"max_steps": 1000,
"val_check_steps": 300,
"scaler_type": "minmax1",
"random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
}
config = {
"hidden_size": tune.choice([256, 512]),
"num_layers": tune.choice([2, 4]),
"input_size": tune.choice([2 * horizon]),
"max_steps": 1000,
"val_check_steps": 300,
"scaler_type": "minmax1",
"random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
}
config_drnn = {'input_size': tune.choice([2 * horizon]),
'encoder_hidden_size': tune.choice([124]),
"max_steps": 300,
"val_check_steps": 100,
"random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),}
models = [
AutoDilatedRNN(h=horizon, loss=MAE(), config=config_drnn, num_samples=2, cpus=1),
RNN(h=horizon, input_size=2 * horizon, encoder_hidden_size=50, max_steps=300),
TCN(h=horizon, input_size=2 * horizon, encoder_hidden_size=20, max_steps=300),
NHITS(h=horizon, input_size=2 * horizon, dropout_prob_theta=0.5, loss=MAE(), max_steps=1000, val_check_steps=500),
AutoMLP(h=horizon, loss=MAE(), config=config, num_samples=2, cpus=1),
DLinear(h=horizon, input_size=2 * horizon, loss=MAE(), max_steps=2000, val_check_steps=500),
TFT(h=horizon, input_size=2 * horizon, loss=SMAPE(), hidden_size=64, scaler_type='robust', windows_batch_size=512, max_steps=1500, val_check_steps=500),
VanillaTransformer(h=horizon, input_size=2 * horizon, loss=MAE(), hidden_size=64, scaler_type='minmax1', windows_batch_size=512, max_steps=1500, val_check_steps=500),
DeepAR(h=horizon, input_size=2 * horizon, scaler_type='minmax1', max_steps=1000),
]
# Models
for model in models[:-1]:
model_name = type(model).__name__
print(50*'-', model_name, 50*'-')
start = time.time()
fcst = NeuralForecast(models=[model], freq=freq)
fcst.fit(train)
forecasts = fcst.predict()
end = time.time()
print(end - start)
forecasts.columns = ['unique_id', 'ds', model_name]
forecasts.to_csv(f'data/{model_name}-forecasts-{dataset}-{group}.csv', index=False)
time_df = pd.DataFrame({'time': [end - start], 'model': [model_name]})
time_df.to_csv(f'data/{model_name}-time-{dataset}-{group}.csv', index=False)
# DeepAR
model_name = type(models[-1]).__name__
start = time.time()
fcst = NeuralForecast(models=[models[-1]], freq=freq)
fcst.fit(train)
forecasts = fcst.predict()
end = time.time()
print(end - start)
forecasts = forecasts[['unique_id', 'ds', 'DeepAR-median']]
forecasts.columns = ['unique_id', 'ds', 'DeepAR']
forecasts.to_csv(f'data/{model_name}-forecasts-{dataset}-{group}.csv', index=False)
time_df = pd.DataFrame({'time': [end - start], 'model': [model_name]})
time_df.to_csv(f'data/{model_name}-time-{dataset}-{group}.csv', index=False)
if __name__ == '__main__':
fire.Fire(main)
action_files/test_models/src/models2.py 0 → 100644
View file @ e3f7f7b3
import os
import time
import fire
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import torch
import neuralforecast
from neuralforecast.core import NeuralForecast
from neuralforecast.models.gru import GRU
from neuralforecast.models.rnn import RNN
from neuralforecast.models.tcn import TCN
from neuralforecast.models.lstm import LSTM
from neuralforecast.models.dilated_rnn import DilatedRNN
from neuralforecast.models.deepar import DeepAR
from neuralforecast.models.mlp import MLP
from neuralforecast.models.nhits import NHITS
from neuralforecast.models.nbeats import NBEATS
from neuralforecast.models.nbeatsx import NBEATSx
from neuralforecast.models.tft import TFT
from neuralforecast.models.vanillatransformer import VanillaTransformer
from neuralforecast.models.informer import Informer
from neuralforecast.models.autoformer import Autoformer
from neuralforecast.models.patchtst import PatchTST
from neuralforecast.auto import (
AutoMLP, AutoNHITS, AutoNBEATS, AutoDilatedRNN, AutoTFT
)
from neuralforecast.losses.pytorch import SMAPE, MAE
from ray import tune
from src.data import get_data
os.environ['NIXTLA_ID_AS_COL'] = '1'
def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
train, horizon, freq, seasonality = get_data('data/', dataset, group)
train['ds'] = pd.to_datetime(train['ds'])
config_nbeats = {
"input_size": tune.choice([2 * horizon]),
"max_steps": 1000,
"val_check_steps": 300,
"scaler_type": "minmax1",
"random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
}
config = {
"hidden_size": tune.choice([256, 512]),
"num_layers": tune.choice([2, 4]),
"input_size": tune.choice([2 * horizon]),
"max_steps": 1000,
"val_check_steps": 300,
"scaler_type": "minmax1",
"random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
}
config_drnn = {'input_size': tune.choice([2 * horizon]),
'encoder_hidden_size': tune.choice([124]),
"max_steps": 300,
"val_check_steps": 100,
"random_seed": tune.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),}
models = [
LSTM(h=horizon, input_size=2 * horizon, encoder_hidden_size=50, max_steps=300),
DilatedRNN(h=horizon, input_size=2 * horizon, encoder_hidden_size=50, max_steps=300),
GRU(h=horizon, input_size=2 * horizon, encoder_hidden_size=50, max_steps=300),
AutoNBEATS(h=horizon, loss=MAE(), config=config_nbeats, num_samples=2, cpus=1),
AutoNHITS(h=horizon, loss=MAE(), config=config_nbeats, num_samples=2, cpus=1),
NBEATSx(h=horizon, input_size=2 * horizon, loss=MAE(), max_steps=1000),
PatchTST(h=horizon, input_size=2 * horizon, patch_len=4, stride=4, loss=MAE(), scaler_type='minmax1', windows_batch_size=512, max_steps=1000, val_check_steps=500),
]
# Models
for model in models[:-1]:
model_name = type(model).__name__
print(50*'-', model_name, 50*'-')
start = time.time()
fcst = NeuralForecast(models=[model], freq=freq)
fcst.fit(train)
forecasts = fcst.predict()
end = time.time()
print(end - start)
forecasts.columns = ['unique_id', 'ds', model_name]
forecasts.to_csv(f'data/{model_name}-forecasts-{dataset}-{group}.csv', index=False)
time_df = pd.DataFrame({'time': [end - start], 'model': [model_name]})
time_df.to_csv(f'data/{model_name}-time-{dataset}-{group}.csv', index=False)
if __name__ == '__main__':
fire.Fire(main)
bars.png 0 → 100644
View file @ e3f7f7b3
bars.png

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