PyLDL

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Label distribution learning (LDL) and label enhancement (LE) toolkit implemented in python.

PyLDL 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# PyLDL

Label distribution learning (LDL) and label enhancement (LE) toolkit implemented in python, including:

+ LDL algorithms:
  + ([Geng, Yin, and Zhou 2013](https://doi.org/10.1109/tpami.2013.51)) [*TPAMI*]: `CPNN`$^1$.
  + ([Geng and Hou 2015](https://www.ijcai.org/Abstract/15/494)) [*IJCAI*]: `LDSVR`.
  + ⭐([Geng 2016](https://doi.org/10.1109/TKDE.2016.2545658)) [*TKDE*]: `SA_BFGS`, `SA_IIS`, `AA_KNN`, `AA_BP`, `PT_Bayes`, and `PT_SVM`.
  + ([Yang, Sun, and Sun 2017](https://doi.org/10.1609/aaai.v31i1.10485)) [*AAAI*]: `BCPNN` and `ACPNN`.
  + ([Xu and Zhou 2017](https://doi.org/10.24963/ijcai.2017/443)) [*IJCAI*]: `IncomLDL`$^2$.
  + ([Shen et al. 2017](https://papers.nips.cc/paper_files/paper/2017/hash/6e2713a6efee97bacb63e52c54f0ada0-Abstract.html)) [*NeurIPS*]: `LDLF`.
  + ([Zhao and Zhou 2018](https://doi.org/10.1609/aaai.v32i1.11609)) [*AAAI*]: `LALOT`.
  + ([Jia et al. 2018](https://doi.org/10.1609/aaai.v32i1.11664)) [*AAAI*]: `LDLLC`$^\dagger$.
  + ([Chen et al. 2018](https://doi.org/10.1016/j.neucom.2018.09.002)) [*Neurocomputing*]: `StructTree` and `StructRF`.
  + ([Ren et al. 2019a](https://doi.org/10.24963/ijcai.2019/460)) [*IJCAI*]: `LDLSF`.
  + ([Ren et al. 2019b](https://doi.org/10.24963/ijcai.2019/461)) [*IJCAI*]: `LDL_LCLR`$^\dagger$.
  + ([Wang and Geng 2019](https://doi.org/10.24963/ijcai.2019/515)) [*IJCAI*]: `LDL4C`$^{3\dagger}$.
  + ([González et al. 2021a](https://doi.org/10.1016/j.ins.2020.07.071)) [*Inf. Sci.*]: `SSG_LDL`$^{4}$.
  + ([González et al. 2021b](https://doi.org/10.1016/j.inffus.2020.08.024)) [*Inf. Fusion*]: `DF_LDL`.
  + ([Wang and Geng 2021a](https://doi.org/10.24963/ijcai.2021/426)) [*IJCAI*]: `LDL_HR`$^{3^\dagger}$.
  + ([Wang and Geng 2021b](https://proceedings.mlr.press/v139/wang21h.html)) [*ICML*]: `LDLM`$^{3^\dagger}$.
  + ([Jia et al. 2021](https://doi.org/10.1109/TKDE.2019.2943337)) [*TKDE*]: `LDL_SCL`.
  + ([Liu  et al. 2021](https://doi.org/10.1016/j.knosys.2020.106690)) [*KBS*]: `BD_LDL`.
  + ([Żychowski and Mańdziuk 2021](https://doi.org/10.1016/j.asoc.2021.107585)) [*Appl. Soft Comput.*]: `Duo_LDL`.
  + ([Li et al. 2022](https://doi.org/10.1109/CVPR52688.2022.01986)) [*CVPR*]: `unimodal_loss`$^1$ and `concentrated_loss`$^1$.
  + ([Jia et al. 2023a](https://doi.org/10.1109/TKDE.2021.3099294)) [*TKDE*]: `LDL_LRR`$^\dagger$.
  + ([Jia et al. 2023b](https://doi.org/10.1109/TNNLS.2023.3258976)) [*TNNLS*]: `LDL_DPA`$^\dagger$.
  + ([Wen et al. 2023](https://doi.org/10.1109/ICCV51070.2023.02146)) [*ICCV*]: `cad`$^1$, `qfd2`$^1$, and `cjs`$^1$.
  + ([Li and Chen 2024](https://doi.org/10.24963/ijcai.2024/494)) [*IJCAI*]: `WInLDL`$^2$.
  + ([Kou et al. 2024](https://doi.org/10.24963/ijcai.2024/478)) [*IJCAI*]: `TLRLDL`$^\dagger$ and `TKLRLDL`$^\dagger$.
  + ([Lin et al. 2024](https://doi.org/10.1109/TBDATA.2023.3338023)) [*TBD*]: `LDL_HVLC`$^\dagger$.
  + ([Wu, Li, and Jia 2025](https://doi.org/10.1109/TBDATA.2024.3442562)) [*TBD*]: `LDL_DA`$^5$.
  + ([Wang et al. 2025](https://doi.org/10.1016/j.patcog.2024.111006)) [*Pattern Recognit.*]: `RKNN_LDL`$^\dagger$.
  + ([Tan et al. 2025](https://doi.org/10.1016/j.knosys.2025.113666)) [*KBS*]: `RG4LDL`.
  + ([Wu, Li, and Jia 2025](https://icml.cc/virtual/2025/poster/44379)) [*ICML*]: `S_LRR`, `S_SCL`, `S_KLD`, `S_CJS` and `S_QFD2`.
  + ([Li et al. 2025](https://icml.cc/virtual/2025/poster/46395)) [*ICML*]: `Delta_LDL`.
+ LE algorithms:
  + ([Xu, Liu, and Geng 2019](https://doi.org/10.1109/TKDE.2019.2947040)) [*TKDE*]: `FCM`, `KM`, `LP`, `ML`, and `GLLE`.
  + ([Xu et al. 2020](https://proceedings.mlr.press/v119/xu20g.html)) [*ICML*]: `LEVI`.
  + ([Zheng, Zhu, and Tang 2023](https://doi.org/10.1109/CVPR52729.2023.00724)) [*CVPR*]: `LIBLE`.
  + ([Wang et al. 2023](https://doi.org/10.24963/ijcai.2023/484)) [*IJCAI*]: `ConLE`.
+ LDL metrics: `chebyshev`, `clark`, `canberra`, `kl_divergence`, `cosine`, `intersection`, etc.
+ Structured LDL datasets: *Human_Gene*, *Movie*, *Natural_Scene*, *s-BU_3DFE*, *s-JAFFE*, *Yeast*, etc.
+ LDL applications:
  + Facial emotion recognition (supported datasets: [*JAFFE*](https://zenodo.org/records/3451524) and [*BU-3DFE*](https://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html)).
  + ([Shirani et al. 2019](https://doi.org/10.18653/v1/P19-1112)) [*ACL*]: Emphasis selection (supported datasets: [*SemEval2020*](https://github.com/RiTUAL-UH/SemEval2020_Task10_Emphasis_Selection); pre-trained GloVe embeddings can be downloaded [here](https://nlp.stanford.edu/projects/glove/)).
  + ([Wu et al. 2019](https://doi.org/10.1109/ICCV.2019.01074)) [*ICCV*]: Lesion counting (supported datasets: [*ACNE04*](https://drive.google.com/drive/folders/18yJcHXhzOv7H89t-Lda6phheAicLqMuZ)).
  + ([Chen et al. 2020](https://doi.org/10.1109/CVPR42600.2020.01400)) [*CVPR*]: Facial emotion recognition with auxiliary label space graphs (supported datasets: [*CK+*](https://www.jeffcohn.net/Resources/); OpenFace can be downloaded [here](https://github.com/TadasBaltrusaitis/OpenFace/releases), and the required models can be downloaded [here](https://github.com/TadasBaltrusaitis/OpenFace/wiki/Model-download)).

> $^1$ Technically, these methods are only suitable for totally ordered labels.
>
> $^2$ These are algorithms for incomplete LDL, so you should use `pyldl.utils.random_missing` to generate the missing label distribution matrix and the corresponding mask matrix in the experiments. [Here](https://github.com/SpriteMisaka/PyLDL/blob/main/demo/incomplete_settings.ipynb) is a demo on using the incomplete LDL algorithms.
>
> $^3$ These are LDL classifiers, so you should use `predict_proba` to get label distributions and `predict` to get predicted labels.
>
> $^4$ These are oversampling algorithms for LDL, therefore you should use `fit_transform` to generate synthetic samples.
>
> $^5$ To use domain adaptation methods for LDL, you need to provide the source domain data via parameters `sX` and `sy` of the `fit` method. [Here](https://github.com/SpriteMisaka/PyLDL/blob/main/demo/domain_adaptation.ipynb) is a demo on domain adaptation for LDL.

> $^\dagger$ These methods involve imposing constraints on model parameters, like regularization. Therefore, it is recommended to carefully tune the hyperparameters and apply feature preprocessing techniques like `StandardScaler` or `MinMaxScaler` before conducting experiments to achieve the expected performance.

## Installation

PyLDL is now available on [PyPI](https://pypi.org/project/python-ldl/). Use the following command to install.

```shell
pip install python-ldl
```

To install the newest version, you can clone this repo and run the `setup.py` file.

```shell
python setup.py install
```

## Usage

Here is an example of using PyLDL.

```python
from pyldl.utils import load_dataset
from pyldl.algorithms import SA_BFGS
from pyldl.metrics import score

from sklearn.model_selection import train_test_split

dataset_name = 'SJAFFE'
X, D = load_dataset(dataset_name)
X_train, X_test, D_train, D_test = train_test_split(X, D)

model = SA_BFGS()
model.fit(X_train, D_train)

D_pred = model.predict(X_test)
print(score(D_test, D_pred))
```

For those who would like to use the original implementation:

1. Install MATLAB.
2. Install MATLAB engine for python.
3. Download LDL Package [here](http://palm.seu.edu.cn/xgeng/LDL/download.htm).
3. Get the package directory of PyLDL (...\\Lib\\site-packages\\pyldl).
4. Place the *LDLPackage_v1.2* folder into the *matlab_algorithms* folder.

Now, you can load the original implementation of the method, e.g.:

```python
from pyldl.matlab_algorithms import SA_IIS
```

You can visualize the performance of any model on the artificial dataset ([Geng 2016](https://doi.org/10.1109/TKDE.2016.2545658)) with the `pyldl.utils.plot_artificial` function, e.g.:

```python
from pyldl.algorithms import LDSVR, SA_BFGS, SA_IIS, AA_KNN, PT_Bayes, GLLE, LIBLE
from pyldl.utils import plot_artificial

methods = ['LDSVR', 'SA_BFGS', 'SA_IIS', 'AA_KNN', 'PT_Bayes', 'GLLE', 'LIBLE']

plot_artificial(model=None, figname='GT')
for i in methods:
    plot_artificial(model=eval(f'{i}()'), figname=i)
```

The output images are as follows.

| <img src="https://github.com/SpriteMisaka/PyLDL/blob/main/visualization/GT.jpg?raw=true" width=300> | <img src="https://github.com/SpriteMisaka/PyLDL/blob/main/visualization/LDSVR.jpg?raw=true" width=300> |
| :----------------------------------------------------------: | :----------------------------------------------------------: |
|                        (Ground Truth)                        |                           `LDSVR`                            |

| <img src="https://github.com/SpriteMisaka/PyLDL/blob/main/visualization/SA_BFGS.jpg?raw=true" width=300> | <img src="https://github.com/SpriteMisaka/PyLDL/blob/main/visualization/SA_IIS.jpg?raw=true" width=300> |
| :----------------------------------------------------------: | :----------------------------------------------------------: |
|                          `SA_BFGS`                           |                           `SA_IIS`                           |

| <img src="https://github.com/SpriteMisaka/PyLDL/blob/main/visualization/AA_KNN.jpg?raw=true" width=300> | <img src="https://github.com/SpriteMisaka/PyLDL/blob/main/visualization/PT_Bayes.jpg?raw=true" width=300> |
| :----------------------------------------------------------: | :----------------------------------------------------------: |
|                           `AA_KNN`                           |                          `PT_Bayes`                          |

| <img src="https://github.com/SpriteMisaka/PyLDL/blob/main/visualization/GLLE.jpg?raw=true" width=300> | <img src="https://github.com/SpriteMisaka/PyLDL/blob/main/visualization/LIBLE.jpg?raw=true" width=300> |
| :----------------------------------------------------------: | :----------------------------------------------------------: |
|                            `GLLE`                            |                           `LIBLE`                            |

Refer to the [docs](https://spritemisaka.github.io/PyLDL/) for more detailed information. Enjoy! :)

## Experiments

For each algorithm, a ten-fold cross validation is performed, repeated 10 times with *s-JAFFE* dataset and the average metrics are recorded. Therefore, the results do not fully describe the performance of the model.

Results of ours are as follows.

| Algorithm |    Cheby.(↓)    |    Clark(↓)     |     Can.(↓)     |     K-L(↓)      |     Cos.(↑)     |     Int.(↑)     |
| :-------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: |
|  SA-BFGS  | **.092 ± .010** |   .361 ± .029   |   .735 ± .060   | **.051 ± .009** | **.954 ± .009** | **.878 ± .011** |
|  SA-IIS   |   .100 ± .009   |   .361 ± .023   |   .746 ± .050   | **.051 ± .008** |   .952 ± .007   |   .873 ± .009   |
|  AA-kNN   |   .098 ± .011   | **.349 ± .029** | **.716 ± .062** |   .053 ± .010   |   .950 ± .009   |   .877 ± .011   |
|   AA-BP   |   .120 ± .012   |   .426 ± .025   |   .889 ± .057   |   .073 ± .010   |   .931 ± .010   |   .848 ± .011   |
| PT-Bayes  |   .116 ± .011   |   .425 ± .031   |   .874 ± .064   |   .073 ± .012   |   .932 ± .011   |   .850 ± .012   |
|  PT-SVM   |   .117 ± .012   |   .422 ± .027   |   .875 ± .057   |   .072 ± .011   |   .932 ± .011   |   .850 ± .011   |

Results of the original MATLAB implementation ([Geng 2016](https://doi.org/10.1109/TKDE.2016.2545658)) are as follows.

| Algorithm |    Cheby.(↓)    |    Clark(↓)     |     Can.(↓)     |     K-L(↓)      |     Cos.(↑)     |     Int.(↑)     |
| :-------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: |
|  SA-BFGS  | **.107 ± .015** | **.399 ± .044** | **.820 ± .103** | **.064 ± .016** | **.940 ± .015** | **.860 ± .019** |
|  SA-IIS   |   .117 ± .015   |   .419 ± .034   |   .875 ± .086   |   .070 ± .012   |   .934 ± .012   |   .851 ± .016   |
|  AA-kNN   |   .114 ± .017   |   .410 ± .050   |   .843 ± .113   |   .071 ± .023   |   .934 ± .018   |   .855 ± .021   |
|   AA-BP   |   .130 ± .017   |   .510 ± .054   |   1.05 ± .124   |   .113 ± .030   |   .908 ± .019   |   .824 ± .022   |
| PT-Bayes  |   .121 ± .016   |   .430 ± .035   |   .904 ± .086   |   .074 ± .014   |   .930 ± .016   |   .846 ± .016   |
|  PT-SVM   |   .127 ± .017   |   .457 ± .039   |   .935 ± .074   |   .086 ± .016   |   .920 ± .014   |   .839 ± .015   |

## Requirements

```
Cython
matplotlib
numba
numpy
qpsolvers
quadprog
scikit-fuzzy
scikit-learn
scipy
tensorflow
tensorflow-probability
```

PyLDL

PyLDL 최신버젼 다운로드

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PyLDL

PyLDL 최신버젼 다운로드

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