Add kale naming APIs

.pre-commit-config.yaml

@@ -1,47 +1,45 @@
 repos:
--   repo: https://github.com/datarootsio/databooks
-    rev: 1.3.7
-    hooks:
-    -   id: databooks-meta
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.4.0
-    hooks:
-    -   id: check-added-large-files
-        args: ['--maxkb=300']
-    -   id: check-byte-order-marker
-    -   id: check-case-conflict
-    -   id: check-merge-conflict
-    -   id: end-of-file-fixer
-    -   id: forbid-new-submodules
-    -   id: mixed-line-ending
-    -   id: trailing-whitespace
-    -   id: debug-statements
-    -   id: check-yaml
-        exclude: .conda/meta.yaml
-    -   id: requirements-txt-fixer
--   repo: https://github.com/pycqa/flake8.git
-    rev: 6.1.0
-    hooks:
-    -   id: flake8
-        additional_dependencies: [flake8-print, "importlib-metadata<5.0.0"]
-        args: [--config=setup.cfg]
-        exclude: ^examples/
--   repo: https://github.com/psf/black
-    rev: 23.11.0
-    hooks:
-    -   id: black
-        language_version: python3
-        additional_dependencies: ['click==8.0.4']
--   repo: https://github.com/pycqa/isort
-    rev: 5.11.2
-    hooks:
-      - id: isort
-        name: isort
-        entry: python -m isort
-        args: [--settings-path, ./pyproject.toml]
-        language: system
-        types: [python]
--   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.5.0
-    hooks:
-    -   id: mypy
+    - repo: https://github.com/datarootsio/databooks
+      rev: 1.3.7
+      hooks:
+          - id: databooks-meta
+    - repo: https://github.com/pre-commit/pre-commit-hooks
+      rev: v3.4.0
+      hooks:
+          - id: check-added-large-files
+            args: ["--maxkb=300"]
+          - id: check-byte-order-marker
+          - id: check-case-conflict
+          - id: check-merge-conflict
+          - id: end-of-file-fixer
+          - id: forbid-new-submodules
+          - id: mixed-line-ending
+          - id: trailing-whitespace
+          - id: debug-statements
+          - id: check-yaml
+            exclude: .conda/meta.yaml
+          - id: requirements-txt-fixer
+    - repo: https://github.com/pycqa/flake8.git
+      rev: 6.1.0
+      hooks:
+          - id: flake8
+            additional_dependencies: [flake8-print, "importlib-metadata<5.0.0"]
+            args: [--config=setup.cfg]
+            exclude: ^examples/
+    - repo: https://github.com/psf/black
+      rev: 23.11.0
+      hooks:
+          - id: black
+            language_version: python3
+            additional_dependencies: ["click==8.0.4"]
+    - repo: https://github.com/pycqa/isort
+      rev: 5.13.2
+      hooks:
+          - id: isort
+            name: isort
+            args: [--settings-path, ./pyproject.toml]
+            types: [python]
+    - repo: https://github.com/pre-commit/mirrors-mypy
+      rev: v1.5.0
+      hooks:
+          - id: mypy

README.md

@@ -1,40 +1,32 @@
-# Kale-Linear
+<p align="center">
+<img src="https://github.com/pykale/linear/raw/main/docs/images/kalelinear.jpg" width="60%" alt="kalelinear logo" />
+</p>
 
-Kale-Linear is a Python library for non-deep, knowledge-aware machine learning
-from multiple sources, domains, or views. It provides NumPy-based methods for
-leveraging related data distributions and structural assumptions, including
-transfer learning, domain adaptation, manifold regularization, and group-aware
-learning, through a scikit-learn style API.
+<!-- [![tests](https://github.com/pykale/linear/workflows/test/badge.svg)](https://github.com/pykale/linear/actions/workflows/test.yml) -->
+<!-- [![codecov](https://codecov.io/gh/pykale/linear/branch/main/graph/badge.svg?token=jmIYPbA2le)](https://codecov.io/gh/pykale/linear) -->
+[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/pykale/linear/blob/main/LICENSE)
+[![Python](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue)](https://www.python.org)
+[![PyPI version](https://img.shields.io/pypi/v/kalelinear?color=blue)](https://pypi.org/project/kalelinear/)
+[![PyPI downloads](https://pepy.tech/badge/kalelinear)](https://pepy.tech/project/kalelinear)
 
-The package is part of the PyKale ecosystem and focuses on classical linear and
-kernel methods that are useful when data are structured by domain labels,
-covariates, side information, or unlabeled target samples.
 
-## Features
+`kalelinear` is a Python library for learning harmonized or individualized models from multi-source/multi-view data in linear or reproducing kernel Hilbert spaces (RKHS). It provides NumPy-based methods for leveraging related data distributions and structural assumptions, including transfer learning, domain adaptation, manifold regularization, and group-aware learning, through a [`scikit-learn`](https://github.com/scikit-learn/scikit-learn) style API.
+
+The package is part of the [PyKale](https://github.com/pykale/pykale) ecosystem and focuses on linear and kernel methods for data characterized by covariates (e.g., domain labels, group labels, side information), unlabeled target samples, or tensor structures.
+
+## Methods and features
 
 - Transformer models for learning feature embeddings:
-  - Multilinear Principal Component Analysis (MPCA): Lu et al., 2008
-    [[IEEE](https://ieeexplore.ieee.org/abstract/document/4359192)]
-  - Transfer Component Analysis (TCA): Pan et al., 2009
-    [[paper](http://www.aaai.org/ocs/index.php/IJCAI/IJCAI-09/paper/download/294/962)]
-  - Joint Distribution Adaptation (JDA): Long et al., 2013
-    [[paper](http://openaccess.thecvf.com/content_iccv_2013/papers/Long_Transfer_Feature_Learning_2013_ICCV_paper.pdf)]
-  - Balanced Distribution Adaptation (BDA): Wang et al., 2017
-    [[paper](http://jd92.wang/assets/files/a08_icdm17.pdf)]
-  - Maximum Independence Domain Adaptation (MIDA): Yan et al., 2017
-    [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7815350)]
-- Estimator models for classification and adaptation:
-  - Manifold Regularization Learning Framework (LapSVM, LapRLS): Belkin et al.,
-    2006 [[paper](http://www.jmlr.org/papers/v7/belkin06a.html)]
-  - Adaptation Regularization Learning Framework (ARSVM, ARRLS): Long et al.,
-    2014 [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6550016)]
-  - Covariate Independence Regularized Learning Framework (CoIRSVM, CoIRLS):
-    Zhou et al., 2020
-    [[paper](https://aaai.org/ojs/index.php/AAAI/article/view/6179)],
-    Zhou, 2022 [[thesis](https://etheses.whiterose.ac.uk/id/eprint/31044/)]
-  - Group-specific Discriminant Analysis (GSDA): Zhou et al., 2025
-    [[paper](https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giaf082/8244707)],
-    Zhou, 2022 [[thesis](https://etheses.whiterose.ac.uk/id/eprint/31044/)]
+  - Multilinear Principal Component Analysis (MPCA) [[1](#references)]
+  - Transfer Component Analysis (TCA) [[2](#references)]
+  - Joint Distribution Adaptation (JDA) [[3](#references)]
+  - Balanced Distribution Adaptation (BDA) [[4](#references)]
+  - Maximum Independence Domain Adaptation (MIDA) [[5](#references)]
+- Estimator models for classification:
+  - Manifold Regularization Learning Framework (LapSVM, LapRLS) [[6](#references)]
+  - Adaptation Regularization Learning Framework (ARSVM, ARRLS) [[7](#references)]
+  - Covariate Independence Regularized Learning Framework (CoIRSVM, CoIRLS) [[8](#references)][[9](#references)]
+  - Group-specific Discriminant Analysis (GSDA) [[9](#references)][[10](#references)]
 - NumPy-compatible inputs and outputs.
 - scikit-learn style `fit`, `transform`, `predict`, `fit_transform`, and
   `fit_predict` workflows where applicable.
@@ -54,7 +46,7 @@ Install from a local checkout for development:
 pip install -e ".[dev]"
 ```
 
-Kale-Linear requires Python 3.10 or later. Core dependencies include:
+`kalelinear` requires Python 3.10 or later. Core dependencies include:
 
 - [NumPy](http://www.numpy.org/)
 - [SciPy](https://www.scipy.org/)
@@ -198,8 +190,29 @@ From the root of the repository, run the following commands in your terminal:
    sphinx-build -b html docs/source docs/build/html
    ```
 
+# References
+
+[1] Lu, H., Plataniotis, K.N. and Venetsanopoulos, A.N., 2008. [MPCA: Multilinear principal component analysis of tensor objects](https://ieeexplore.ieee.org/abstract/document/4359192/). _IEEE Transactions on Neural Networks_, 19(1), pp.18-39.
+
+[2] Pan, S.J., Tsang, I.W., Kwok, J.T. and Yang, Q., 2011. [Domain adaptation via transfer component analysis](https://dl.acm.org/doi/abs/10.1109/TNN.2010.2091281). _IEEE Transactions on Neural Networks_, 22(2), p.199-210.
+
+[3] Long, M., Wang, J., Ding, G., Sun, J. and Yu, P.S., 2013. [Transfer feature learning with joint distribution adaptation.](https://openaccess.thecvf.com/content_iccv_2013/papers/Long_Transfer_Feature_Learning_2013_ICCV_paper.pdf) In _Proceedings of the IEEE International Conference on Computer Vision_ (pp. 2200-2207).
+
+[4] Wang, J., Chen, Y., Hao, S., Feng, W. and Shen, Z., 2017, November. [Balanced distribution adaptation for transfer learning](https://ieeexplore.ieee.org/document/8215613). In _2017 IEEE International Conference on Data Mining (ICDM)_ (pp. 1129-1134). IEEE.
+
+[5] Yan, K., Kou, L. and Zhang, D., 2017. [Learning domain-invariant subspace using domain features and independence maximization](https://ieeexplore.ieee.org/document/7815350). _IEEE Transactions on Cybernetics_, 48(1), pp.288-299.
+
+[6] Belkin, M., Niyogi, P. and Sindhwani, V., 2006. Manifold regularization: [A geometric framework for learning from labeled and unlabeled examples](https://www.jmlr.org/papers/v7/belkin06a.html). _Journal of Machine Learning Research_, 7(11).
+
+[7] Long, M., Wang, J., Ding, G., Pan, S.J. and Yu, P.S., 2013. [Adaptation regularization: A general framework for transfer learning](https://ieeexplore.ieee.org/abstract/document/6550016/). _IEEE Transactions on Knowledge and Data Engineering_, 26(5), pp.1076-1089.
+
+[8] Zhou, S., Li, W., Cox, C. and Lu, H., 2020, April. [Side information dependence as a regularizer for analyzing human brain conditions across cognitive experiments](https://ojs.aaai.org/index.php/AAAI/article/view/6179). In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 34, No. 04, pp. 6957-6964).
+
+[9] Zhou, S., 2022. [Interpretable Domain-Aware Learning for Neuroimage Classification](https://etheses.whiterose.ac.uk/id/eprint/31044/) (Doctoral dissertation, University of Sheffield).
+
+[10] Zhou, S., Luo, J., Jiang, Y., Wang, H., Lu, H. and Gong, G., 2025. [Group-specific discriminant analysis enhances detection of sex differences in brain functional network lateralization](https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giaf082/8244707). _GigaScience_, 14, p.giaf082.
 
-## Related Projects
+## Other open domain adaptation repositories
 
 - [POT: Python Optimal Transport](https://github.com/rflamary/POT)
 - [Everything about Transfer Learning](https://github.com/jindongwang/transferlearning)
@@ -211,4 +224,4 @@ From the root of the repository, run the following commands in your terminal:
 
 ## License
 
-Kale-Linear is released under the MIT License. See [LICENSE](LICENSE) for details.
+`kalelinear` is released under the MIT License. See [LICENSE](LICENSE) for details.

docs/source/api.rst

@@ -6,6 +6,8 @@ API Reference
 This page is kept for existing links. The API reference is now split into the
 following pages:
 
+* :doc:`api_embed`
 * :doc:`api_transformers`
+* :doc:`api_predict`
 * :doc:`api_estimators`
 * :doc:`api_utilities`

docs/source/api_embed.rst

@@ -0,0 +1,37 @@
+Embed
+=====
+
+.. currentmodule:: kalelinear.embed
+
+This compatibility module mirrors the PyKale-style import path while exposing
+the same transformer classes as :mod:`kalelinear.transformer`.
+
+.. autoclass:: MPCA
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: TCA
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: JDA
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: BDA
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: MIDA
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:

docs/source/api_predict.rst

@@ -0,0 +1,49 @@
+Predict
+=======
+
+.. currentmodule:: kalelinear.predict
+
+This compatibility module mirrors the PyKale-style import path while exposing
+the same estimator classes as :mod:`kalelinear.estimator`.
+
+.. autoclass:: LapSVM
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: LapRLS
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: ARSVM
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: ARRLS
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: CoIRSVM
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: CoIRLS
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:
+
+.. autoclass:: GSDA
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :no-index:

docs/source/index.rst

@@ -17,7 +17,9 @@ Kale-Linear API
 .. toctree::
    :maxdepth: 2
 
+   api_embed
    api_transformers
+   api_predict
    api_estimators
    api_utilities
 
@@ -31,7 +33,8 @@ Project
 .. toctree::
    :maxdepth: 1
 
-   GitHub repository <https://github.com/pykale/kale-linear>
+   Read the Docs (PyKale subproject) <https://pykale.readthedocs.io/projects/linear/>
+   GitHub repository <https://github.com/pykale/linear>
 
 Indices and Tables
 ------------------

kalelinear/__init__.py

@@ -1,9 +1,19 @@
 """
 kalelinear.
 
-A Transfer Learning Python package
+Learning harmonized or individualized models from multi-source/multi-view data in linear or reproducing kernel Hilbert spaces (RKHS).
 """
 
-__version__ = "0.0.1"
-__author__ = "Shuo Zhou"
-__credits__ = "Machine Learning Group, Department of Computer Science, the University of Sheffield"
+from importlib import import_module
+
+__version__ = "0.1.0a1"
+
+__all__ = ["transformer", "estimator", "embed", "predict"]
+
+
+def __getattr__(name):
+    if name in __all__:
+        module = import_module(f"{__name__}.{name}")
+        globals()[name] = module
+        return module
+    raise AttributeError(f"module '{__name__}' has no attribute '{name}'")

kalelinear/embed.py

@@ -0,0 +1,5 @@
+"""Embedding models exposed with a PyKale-style API."""
+
+from kalelinear.transformer import BDA, JDA, MIDA, MPCA, TCA
+
+__all__ = ["TCA", "JDA", "BDA", "MIDA", "MPCA"]

kalelinear/predict.py

@@ -0,0 +1,5 @@
+"""Predictive models exposed with a PyKale-style API."""
+
+from kalelinear.estimator import ARRLS, ARSVM, CoIRLS, CoIRSVM, GSDA, LapRLS, LapSVM
+
+__all__ = ["ARSVM", "ARRLS", "CoIRSVM", "CoIRLS", "GSDA", "LapSVM", "LapRLS"]

setup.py

@@ -70,12 +70,14 @@ def read(*names, **kwargs):
     name="kalelinear",
     version="0.1.0a1",
     description="Non-deep knowledge-aware machine learning from multiple sources/views in Python",
-    url="https://github.com/pykale/kale-linear",
+    long_description=read("README.md"),
+    long_description_content_type="text/markdown",
+    url="https://github.com/pykale/linear",
     author="The PyKale team",
     author_email="pykale-group@sheffield.ac.uk",
     project_urls={
-        "Bug Tracker": "https://github.com/pykale/kale-linear/issues",
-        "Source": "https://github.com/pykale/kale-linear",
+        "Bug Tracker": "https://github.com/pykale/linear/issues",
+        "Source": "https://github.com/pykale/linear",
     },
     license="MIT License",
     packages=find_packages(exclude=("tests*", "examples*", "docs*")),

tests/__init__.py

@@ -1 +1 @@
-"""Test package for kale-linear."""
+"""Test package for kalelinear."""