diff --git a/.gitignore b/.gitignore
index 3229eff..fbcd846 100644
--- a/.gitignore
+++ b/.gitignore
@@ -70,6 +70,8 @@ instance/
# Sphinx documentation
docs/_build/
+docs/source/auto_examples/
+docs/source/back_references/
# PyBuilder
target/
diff --git a/README.md b/README.md
index 6e0b803..4b44da5 100644
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IMBENS: Class-imbalanced Ensemble Learning in Python
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-**Release:
- PyPI |
- Source |
- Download |
- Changelog
- Links:
- Getting Started |
- API Reference |
- Examples |
- Related Projects |
- 知乎/Zhihu
- Paper:
- "IMBENS: Ensemble Class-imbalanced Learning in Python"
-
**
-
***IMBENS* (imported as `imbens`) is a Python library for quick implementation, modification, evaluation, and visualization of ensemble [learning from class-imbalanced data](https://github.com/ZhiningLiu1998/awesome-imbalanced-learning)**.
-Currently, IMBENS includes **[over 15 ensemble imbalanced learning algorithms](#list-of-implemented-methods) (SMOTEBoost, SMOTEBagging, RUSBoost, EasyEnsemble, BalanceCascade, SelfPacedEnsemble, etc)** and also **[19 resampling methods](https://imbalanced-ensemble.readthedocs.io/en/latest/api/sampler/api.html) (SMOTE, ADASYN, TomekLinks, etc)** from [imbalance-learn](https://imbalanced-learn.org/stable/references/index.html#api).
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-**IMBENS is featured for:**
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+Currently, IMBENS includes **[over 15 ensemble imbalanced learning algorithms](#list-of-implemented-methods) (SMOTEBoost, SMOTEBagging, RUSBoost, EasyEnsemble, SelfPacedEnsemble, etc)** and **[19 over-/under-sampling methods](https://imbalanced-ensemble.readthedocs.io/en/latest/api/sampler/api.html) (SMOTE, ADASYN, TomekLinks, etc)** from [imbalance-learn](https://imbalanced-learn.org/stable/references/index.html#api).
-
+🌈 IMBENS Highlights
-
-- 🍎 **Unified, easy-to-use APIs with [documentation](https://imbalanced-ensemble.readthedocs.io/) and [examples](https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/index.html#).**
-- 🍎 **Optimized performance with parallelization using [joblib](https://github.com/joblib/joblib).**
-- 🍎 **Powerful, customizable, interactive training [logging]((#customizing-training-log)) and [visualizer](#visualize-ensemble-classifiers).**
-- 🍎 **Full compatibility with [scikit-learn](https://scikit-learn.org/stable/) and [imbalanced-learn](https://imbalanced-learn.org/stable/).**
-- 🍎 **Off-the-shelf *multi-class* imbalanced learning by extending existing techniques.**
+- 🧑💻 **Ease-of-use:** Unified, easy-to-use APIs with [documentation](https://imbalanced-ensemble.readthedocs.io/) and [examples](https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/index.html#).
+- 🚀 **Performance:** Optimized performance with parallelization using [joblib](https://github.com/joblib/joblib).
+- 📊 **Benchmarking:** Running & comparing multiple models with our [visualizer](#visualize-ensemble-classifiers).
+- 📺 **Monitoring:** Powerful, customizable, interactive training [logging]((#customizing-training-log)).
+- 🪐 **Versatility:** Full compatibility with [scikit-learn](https://scikit-learn.org/stable/) and [imbalanced-learn](https://imbalanced-learn.org/stable/).
+- 📈 **Functionality:** Extending existing techniques from binary to ***multi-class*** setting.
-**IMBENS for class-imbalanced classification with <5 lines of code:**
+### ✂️ **Use IMBENS for class-imbalanced classification with <5 lines of code:**
```python
# Train an SPE classifier
@@ -112,10 +116,9 @@ clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
```
-**Citing IMBENS**
+### 🤗 Citing IMBENS
-The [IMBENS paper](https://arxiv.org/pdf/2111.12776.pdf) is available on arxiv.
-If you use IMBENS in a scientific publication, we would appreciate citations to the following paper:
+🍻 We appreciate your citation if you find our work helpful! The BibTeX entry:
```bib
@article{liu2023imbens,
@@ -126,15 +129,14 @@ If you use IMBENS in a scientific publication, we would appreciate citations to
}
```
-**Contributing to IMBENS**
+### 👯♂️ Contribute to IMBENS
+Join us and become a contributor!
Please refer to the [contributing guidelines](https://github.com/ZhiningLiu1998/imbalanced-ensemble/blob/main/CONTRIBUTING.md).
-## Table of Contents
+📚 Table of Contents
-- [Table of Contents](#table-of-contents)
- [Installation](#installation)
-- [Highlights](#highlights)
- [List of implemented methods](#list-of-implemented-methods)
- [5-min Quick Start with IMBENS](#5-min-quick-start-with-imbens)
- [A minimal working example](#a-minimal-working-example)
@@ -176,7 +178,7 @@ imbalanced-ensemble requires following dependencies:
- [tqdm](https://tqdm.github.io/) (>=4.50.2)
-## Highlights
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+.. .. raw:: html
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+..
.. raw:: html
-
- [Github]
- [Gallery]
- [PyPI]
- [Changelog]
- [Source]
- [Download]
- [知乎/Zhihu]
- [中文README]
- [arXiv]
+
-**Date**: |today| **Version**: |version|
+**IMBENS** (imported as ``imbens``) is a Python library for quick implementation, modification, evaluation, and visualization of ensemble `learning from class-imbalanced data `_.
+Currently, IMBENS includes `over 15 ensemble imbalanced learning algorithms <#list-of-implemented-methods>`_ (SMOTEBoost, SMOTEBagging, RUSBoost, EasyEnsemble, SelfPacedEnsemble, etc) and `19 over-/under-sampling methods `_ (SMOTE, ADASYN, TomekLinks, etc) from `imbalance-learn `_.
-**Paper**: `IMBENS: Ensemble Class-imbalanced Learning in Python `_
+🌈 **IMBENS Highlights**
-**Citing US**
+- 🧑💻 **Ease-of-use:** Unified, easy-to-use APIs with `documentation `_ and `examples `_.
+- 🚀 **Performance:** Optimized performance with parallelization using `joblib `_.
+- 📊 **Benchmarking:** Running & comparing multiple models with our `visualizer <#visualize-ensemble-classifiers>`_.
+- 📺 **Monitoring:** Powerful, customizable, interactive training `logging <#customizing-training-log>`_.
+- 🪐 **Versatility:** Full compatibility with `scikit-learn `_ and `imbalanced-learn `_.
+- 📈 **Functionality:** Extending existing techniques from binary to **multi-class** setting.
-If you find IMBENS helpful in your work or research, we would greatly appreciate citations to the following paper [`PDF <(https://arxiv.org/pdf/2111.12776.pdf>`_]::
-
- @article{liu2023imbens,
- title={IMBENS: Ensemble Class-imbalanced Learning in Python},
- author={Liu, Zhining and Kang, Jian and Tong, Hanghang and Chang, Yi},
- journal={arXiv preprint arXiv:2111.12776},
- year={2023}
- }
+✂️ **Use IMBENS for class-imbalanced classification with <5 lines of code:**
+
+.. code-block:: python
+ # Train an SPE classifier
+ from imbens.ensemble import SelfPacedEnsembleClassifier
+ clf = SelfPacedEnsembleClassifier(random_state=42)
+ clf.fit(X_train, y_train)
-**imbalanced-ensemble** (IMBENS, imported as ``imbens``) is a Python toolbox
-for quick implementation, modification, evaluation, and visualization of ensemble learning
-algorithms for class-imbalanced data.
-It was built on the basis of `scikit-learn `__
-and `imbalanced-learn `__.
-IMBENS includes more than 15 ensemble imbalanced learning (EIL) algorithms, from the
-classical SMOTEBoost (2003) and RUSBoost (2010) to recent SPE (2020), from resampling-based
-methods to cost-sensitive ensemble learning.
-
-**IMBENS is featured for:**
-
-- Unified, easy-to-use APIs, detailed `documentation `_ and `examples `_.
-- Capable for out-of-the-box *multi-class* imbalanced (long-tailed) learning.
-- Optimized performance with parallelization when possible using
- `joblib `__.
-- Powerful, customizable, interactive training logging and visualizer.
-- Full compatibility with other popular packages like
- `scikit-learn `__ and
- `imbalanced-learn `__.
+ # Predict with an SPE classifier
+ y_pred = clf.predict(X_test)
+
+🤗 **Citing IMBENS**
+
+🍻 We appreciate your citation if you find our work helpful! The BibTeX entry:
+
+.. code-block:: bibtex
+
+ @article{liu2023imbens,
+ title={IMBENS: Ensemble Class-imbalanced Learning in Python},
+ author={Liu, Zhining and Kang, Jian and Tong, Hanghang and Chang, Yi},
+ journal={arXiv preprint arXiv:2111.12776},
+ year={2023}
+ }
**API Demo:**
diff --git a/readthedocs.yml b/readthedocs.yml
index 774ea5f..cf4c10c 100644
--- a/readthedocs.yml
+++ b/readthedocs.yml
@@ -3,10 +3,15 @@
version: 2
+# Set the OS, Python version and other tools you might need
+build:
+ os: ubuntu-22.04
+ tools:
+ python: "3.11"
+
sphinx:
configuration: docs/source/conf.py
python:
- version: 3.8
install:
- - requirements: docs/requirements.txt
+ - requirements: docs/source/doc_requirements.txt