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cblearn-0.1.0
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Comparison-based Machine Learning in Python.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cblearn-0.1.0 |
| نام | cblearn |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | David-Elias Künstle |
| ایمیل نویسنده | david-elias.kuenstle@uni-tuebingen.de |
| آدرس صفحه اصلی | https://github.com/dekuenstle/cblearn |
| آدرس اینترنتی | https://pypi.org/project/cblearn/ |
| مجوز | - |
# cblearn
## Comparison-based Machine Learning in Python
:warning: **cblearn** is **work in progress**. The API can change and bugs appear. Please help us by posting an [issue]( https://github.com/dekuenstle/cblearn/issues/new) :construction:
[](https://pypi.python.org/pypi/cblearn)
[](https://cblearn.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/dekuenstle/cblearn/actions)
[](https://codecov.io/gh/dekuenstle/cblearn)
Comparison-based Learning algorithms are the Machine Learning algorithms to use when training data contains similarity comparisons ("A and B are more similar than C and D") instead of data points.
**:eyes: VSS 2022: Please find an example of psychophysical scaling with triplets and ordinal embedding [here](https://cblearn.readthedocs.io/en/latest/generated_examples/plot_psychophysical_scales.html) :eyes:**
Triplet comparisons from human observers help model the perceived similarity of objects.
These human triplets are collected in studies, asking questions like
"Which of the following bands is most similar to Queen?" or
"Which colour appears most similar to the reference?".
This library provides an easy to use interface to comparison-based learning algorithms.
It plays hand-in-hand with scikit-learn:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from cblearn.datasets import make_random_triplets
from cblearn.embedding import SOE
from cblearn.metrics import QueryScorer
X = load_iris().data
triplets = make_random_triplets(X, result_format="list-order", size=1000)
estimator = SOE(n_components=2)
# Measure the fit with scikit-learn's cross-validation
scores = cross_val_score(estimator, triplets, cv=5)
print(f"The 5-fold CV triplet error is {sum(scores) / len(scores)}.")
# Estimate the scale on all triplets
embedding = estimator.fit_transform(triplets)
print(f"The embedding has shape {embedding.shape}.")
```
Please try the [Examples](https://cblearn.readthedocs.io/en/latest/generated_examples/index.html).
## Getting Started
Install cblearn as described [here](https://cblearn.readthedocs.io/en/latest/install.html) and try the [examples](https://cblearn.readthedocs.io/en/latest/generated_examples/index.html).
Find a theoretical introduction to comparison-based learning, the datatypes,
algorithms, and datasets in the [User Guide](https://cblearn.readthedocs.io/en/latest/user_guide/index.html).
## Features
### Datasets
*cblearn* provides utility methods to simplify the loading and conversion
of your comparison datasets. In addition, some functions download and load multiple real-world comparisons.
| Dataset | Query | #Object | #Response | #Triplet |
| --- | --- | ---:| ---:| ---:|
| Vogue Cover | Odd-out Triplet | 60 | 1,107 | 2,214 |
| Nature Scene | Odd-out Triplet | 120 | 3,355 | 6,710 |
| Car | Most-Central Triplet | 60 | 7,097 | 14,194 |
| Material | Standard Triplet | 100 | 104,692 |104,692 |
| Food | Standard Triplet | 100 | 190,376 |190,376 |
| Musician | Standard Triplet | 413 | 224,792 |224,792 |
| Things Image Testset | Odd-out Triplet | 1,854 | 146,012 | 292,024 |
| ImageNet Images v0.1 | Rank 2 from 8 | 1,000 | 25,273 | 328,549 |
| ImageNet Images v0.2 | Rank 2 from 8 | 50,000 | 384,277 | 5M |
### Embedding Algorithms
| Algorithm | Default | Pytorch (GPU) | Reference Wrapper |
| --------------------------- | :---: | :-----------: | :---------------: |
| Crowd Kernel Learning (CKL) | X | X | |
| FORTE | | X | |
| GNMDS | X | X | |
| Maximum-Likelihood Difference Scaling (MLDS) | X | | [MLDS (R)](https://cran.r-project.org/web/packages/MLDS/index.html)|
| Soft Ordinal Embedding (SOE) | X | X | [loe (R)](https://cran.r-project.org/web/packages/loe/index.html) |
| Stochastic Triplet Embedding (STE/t-STE) | X | X | |
## Contribute
We are happy about your bug reports, questions or suggestions as Github Issues and code or documentation contributions as Github Pull Requests.
Please see our [Contributor Guide](https://cblearn.readthedocs.io/en/latest/contributor_guide/index.html).
## Authors and Acknowledgement
*cblearn* was initiated by current and former members of the [Theory of Machine Learning group](http://www.tml.cs.uni-tuebingen.de/index.php) of Prof. Dr. Ulrike von Luxburg at the University of Tübingen.
The leading developer is [David-Elias Künstle](http://www.tml.cs.uni-tuebingen.de/team/kuenstle/index.php).
We would like to thank all the contributors here on Github.
This work has been supported by the Machine Learning Cluster of Excellence, funded by EXC number 2064/1 – Project number 390727645. The authors would like to thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting David-Elias Künstle.
## License
This library is free to use under the [MIT License](https://github.com/dekuenstle/cblearn/blob/master/LICENSE) conditions.
Please reference this library appropriately if it contributes to your scientific publication. We would also appreciate a short email (optionally) to see how our library is being used.
# Changelog
## 0.1.0
- Support python 3.9 and 3.10.
- Introduce semantic versioning
- Publish to PyPI
MIT License
Copyright (c) 2020-2021 The cblearn developers.
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.
نیازمندی
| مقدار | نام |
|---|---|
| ~=1.23 | numpy |
| ~=1.10 | scipy |
| ~=1.2 | scikit-learn |
| ~=0.14 | sparse |
| ~=6.1 | sphinx |
| ~=1.2 | sphinx-rtd-theme |
| ~=0.12 | sphinx-gallery |
| ~=3.7 | matplotlib |
| ~=5.6 | oct2py |
| ~=3.5 | rpy2 |
| ~=7.2 | pytest |
| ~=4.0 | pytest-cov |
| ~=0.12 | pytest-doctestplus |
| ~=0.4 | pytest-remotedata |
| ~=6.0 | flake8 |
| ~=1.1 | mypy |
| ~=1.5 | pandas |
| ~=2.0.0 | torch |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| <3.11,>=3.9 | Python |
نحوه نصب
نصب پکیج whl cblearn-0.1.0:
pip install cblearn-0.1.0.whl
نصب پکیج tar.gz cblearn-0.1.0:
pip install cblearn-0.1.0.tar.gz