Add GFK and API map

README.md

@@ -20,6 +20,7 @@ The package is part of the [PyKale](https://github.com/pykale/pykale) ecosystem
     - Multilinear Principal Component Analysis (`MPCA`) [[1](#references)]
   - Transferable / generalizable feature extraction across domains or groups:
     - Correlation Alignment (`CORAL`) [[13](#references)]
+    - Geodesic Flow Kernel (`GFK`) [[14](#references)]
     - Transfer Component Analysis (`TCA`) [[2](#references)]
     - Joint Distribution Adaptation (`JDA`) [[3](#references)]
     - Balanced Distribution Adaptation (`BDA`) [[4](#references)]
@@ -38,6 +39,10 @@ The package is part of the [PyKale](https://github.com/pykale/pykale) ecosystem
 - scikit-learn style `fit`, `transform`, `predict`, `fit_transform`, and `fit_predict` workflows where applicable.
 - Most methods accept additional `covariates` — e.g., domain or group labels — alongside `X` and `y`, with optional one-hot encoding for categorical values; multiblock transformers (CIFE, AJIVE) take `groups` to specify block membership.
 
+<p align="center">
+<img src="https://github.com/pykale/linear/raw/main/docs/images/api_map.jpg" width="100%" alt="KaleLinear API map: transformers (embed) and estimators (predict) arranged by common vs. specific learning" />
+</p>
+
 KaleLinear requires Python 3.10 or later. Core dependencies include:
 
 - [NumPy](http://www.numpy.org/)
@@ -140,6 +145,8 @@ Worked examples for the main transformers and estimators are collected in
 
 [13] Sun, B., Feng, J. and Saenko, K., 2016. [Return of frustratingly easy domain adaptation](https://ojs.aaai.org/index.php/AAAI/article/view/10306). In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 30, No. 1, pp. 2058-2065).
 
+[14] Gong, B., Shi, Y., Sha, F. and Grauman, K., 2012. [Geodesic flow kernel for unsupervised domain adaptation](https://ieeexplore.ieee.org/document/6247911). In _IEEE Conference on Computer Vision and Pattern Recognition_ (pp. 2066-2073).
+
 ## Other open domain adaptation repositories
 
 - [POT: Python Optimal Transport](https://github.com/rflamary/POT)

docs/source/api_transformers.rst

@@ -31,6 +31,11 @@ The transformer classes are also available through the PyKale-compatible alias
    :undoc-members:
    :show-inheritance:
 
+.. autoclass:: GFK
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 .. autoclass:: MIDA
    :members:
    :undoc-members:

docs/source/introduction.rst

@@ -14,7 +14,7 @@ covariates, side information, or unlabeled target samples.
 Main Features
 -------------
 
-* Transformer models for learning feature embeddings: CORAL, MPCA, TCA, JDA,
+* Transformer models for learning feature embeddings: CORAL, GFK, MPCA, TCA, JDA,
   BDA, MIDA, CIFE, and AJIVE.
 * Estimator models for classification and adaptation: LapSVM, LapRLS, ARSVM,
   ARRLS, CoIRSVM, CoIRLS, and GSDA.

docs/source/usage.rst

@@ -6,7 +6,7 @@ Usage
 Domain Adaptation Transformers
 ------------------------------
 
-TCA, JDA, BDA, and CORAL take all samples in a single input array and receive
+TCA, JDA, BDA, CORAL, and GFK take all samples in a single input array and receive
 domain labels through ``covariates``. Use ``target_covariate`` to identify
 which domain label is the target domain. CORAL is asymmetric: after fitting, it
 aligns source samples to the target covariance and centers target samples.

kalelinear/embed.py

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

kalelinear/transformer/__init__.py

@@ -1,6 +1,7 @@
 from kalelinear.transformer._ajive import AJIVE
 from kalelinear.transformer._cife import CIFE
 from kalelinear.transformer._coral import CORAL
+from kalelinear.transformer._gfk import GFK
 from kalelinear.transformer._jda import BDA, JDA
 from kalelinear.transformer._mida import MIDA
 from kalelinear.transformer._mpca import MPCA
@@ -11,6 +12,7 @@
     "JDA",
     "BDA",
     "CORAL",
+    "GFK",
     "MIDA",
     "MPCA",
     "CIFE",