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doe2vec-0.8.0
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مشاهده بیشترتوضیحات
Feature extraction for exploratory landscape analysis
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | doe2vec-0.8.0 |
| نام | doe2vec |
| نسخه کتابخانه | 0.8.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Bas van Stein <b.van.stein@liacs.leidenuniv.nl> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/doe2vec/ |
| مجوز | MIT License Copyright (c) 2022 Bas van Stein Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
<h1><img src="docs/logo.png" width="128" style="float:left;">DoE2Vec</h1>
DoE2Vec is a self-supervised approach to learn exploratory landscape analysis features from design of experiments.
The model can be used for downstream meta-learning tasks such as learninig which optimizer works best on a given optimization landscape.
Or to classify optimization landscapes in function groups.
The approach uses randomly generated functions and can also be used to find a "cheap" reference function given a DOE.
The model uses Sobol sequences as the default sampling method. A custom sampling method can also be used.
Both the samples and the landscape should be scaled between 0 and 1.
## Install package via pip
```zsh
`pip install doe2vec`
```
Afterwards you can use the package via:
```python
from doe2vec import doe_model
```
## Load a model from the HuggingFace Hub
Available models can be viewed here: https://huggingface.co/BasStein
A model name is build up like BasStein/doe2vec-d2-m8-ls16-VAE-kl0.001
Where d is the number of dimensions, 8 the number (2^8) of samples, 16 the latent size, VAE the model type (variational autoencoder) and 0.001 the KL loss weight.
Example code of loading a huggingface model
```python
obj = doe_model(
2,
8,
n= 50000,
latent_dim=16,
kl_weight=0.001,
use_mlflow=False,
model_type="VAE"
)
obj.load_from_huggingface()
#test the model
obj.plot_label_clusters_bbob()
```
## How to Setup your Environment for Development
- `python3.8 -m venv env`
- `source ./env/bin/activate`
- `pip install -r requirements.txt`
## Generate the Data Set
To generate the artificial function dataset for a given dimensionality and sample size
run the following code
```python
from doe2vec inport doe_model
obj = doe_model(d, m, n=50000, latent_dim=latent_dim)
if not obj.load():
obj.generateData()
obj.compile()
obj.fit(100)
obj.save()
```
Where `d` is the number of dimensions, `m` the number of samples (2^`m`) per DOE, `n` the number of functions generated and `latent_dim` the size of the output encoding vector.
Once a data set and encoder has been trained it can be loaded with the `load()` function.
نیازمندی
| مقدار | نام |
|---|---|
| - | scikit-learn |
| - | tensorflow |
| - | matplotlib |
| - | mlflow |
| - | pandas |
| - | huggingface-hub |
| - | datasets |
| - | black |
| - | isort |
| - | pip-tools |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl doe2vec-0.8.0:
pip install doe2vec-0.8.0.whl
نصب پکیج tar.gz doe2vec-0.8.0:
pip install doe2vec-0.8.0.tar.gz