Merge pull request #7 from edahelsinki/update_references

Update references

.github/workflows/python-pytest.yml

@@ -16,7 +16,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v2

CITATIONS.bib

@@ -1,13 +1,27 @@
 @article{bjorklund2022slisemap,
-  title = {{SLISEMAP}: Supervised dimensionality reduction through local explanations},
+  title      = {SLISEMAP: supervised dimensionality reduction through local explanations},
   shorttitle = {{SLISEMAP}},
-  author = {Bj{\"o}rklund, Anton and M{\"a}kel{\"a}, Jarmo and Puolam{\"a}ki, Kai},
-  year = {2022},
-  journal = {arXiv:2201.04455 [cs]},
-  eprint = {2201.04455},
-  eprinttype = {arxiv},
+  issn       = {1573-0565},
+  url        = {https://doi.org/10.1007/s10994-022-06261-1},
+  doi        = {10.1007/s10994-022-06261-1},
+  number     = {arXiv:2201.04455},
+  journal    = {Machine Learning},
+  author     = {Bj{\"o}rklund, Anton and M{\"a}kel{\"a}, Jarmo and Puolam{\"a}ki, Kai},
+  year       = {2022},
+  month      = {Nov},
+  language   = {en}
+}
+
+@article{bjorklund2022slisemaparxiv,
+  title        = {{SLISEMAP}: Supervised dimensionality reduction through local explanations},
+  shorttitle   = {{SLISEMAP}},
+  author       = {Bj{\"o}rklund, Anton and M{\"a}kel{\"a}, Jarmo and Puolam{\"a}ki, Kai},
+  year         = {2022},
+  journal      = {arXiv:2201.04455 [cs]},
+  eprint       = {2201.04455},
+  eprinttype   = {arxiv},
   primaryclass = {cs},
-  url = {http://arxiv.org/abs/2201.04455},
-  doi = {10.48550/ARXIV.2201.04455},
-  publisher = {arXiv}
+  url          = {http://arxiv.org/abs/2201.04455},
+  doi          = {10.48550/ARXIV.2201.04455},
+  publisher    = {arXiv}
 }

README.md

@@ -1,4 +1,4 @@
-[![PyPI](https://img.shields.io/pypi/v/slise)](https://pypi.org/project/slisemap/)
+[![PyPI](https://img.shields.io/pypi/v/slisemap)](https://pypi.org/project/slisemap/)
 [![Documentation](https://github.com/edahelsinki/slisemap/actions/workflows/python-docs.yml/badge.svg)](https://edahelsinki.github.io/slisemap/slisemap/)
 [![Tests](https://github.com/edahelsinki/slisemap/actions/workflows/python-pytest.yml/badge.svg)](https://github.com/edahelsinki/slisemap/actions/workflows/python-pytest.yml)
 [![Licence: MIT](https://img.shields.io/github/license/edahelsinki/slisemap)](https://github.com/edahelsinki/slisemap/blob/master/LICENSE)
@@ -8,14 +8,14 @@
 
 SLISEMAP is a supervised dimensionality reduction method, that takes data, in the form of vectors, and predictions from a *black box* regression or classification model as input. SLISEMAP then simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby. The explanations consist of *white box* models that locally approximate the *black box* model.
 
-SLISEMAP is implemented in *Python* using *PyTorch* for efficient optimisation, and optional GPU-acceleration. For more information see the [full paper](https://arxiv.org/abs/2201.04455), the [demo paper](https://github.com/edahelsinki/slisemap/blob/main/examples/demo_paper.pdf), the [demonstration video](https://youtu.be/zvcFYItwRlQ) ([slides](https://github.com/edahelsinki/slisemap/blob/main/examples/demo_presentation.pdf)), the [examples](https://github.com/edahelsinki/slisemap/tree/main/examples), or the [documentation](https://edahelsinki.github.io/slisemap/slisemap).
+SLISEMAP is implemented in *Python* using *PyTorch* for efficient optimisation, and optional GPU-acceleration. For more information see the [full paper](https://doi.org/10.1007/s10994-022-06261-1) ([arXiv](https://arxiv.org/abs/2201.04455)), the [demo paper](https://github.com/edahelsinki/slisemap/blob/main/examples/demo_paper.pdf), the [demo video](https://youtu.be/zvcFYItwRlQ) ([slides](https://github.com/edahelsinki/slisemap/blob/main/examples/demo_presentation.pdf)), the [examples](https://github.com/edahelsinki/slisemap/tree/main/examples), or the [documentation](https://edahelsinki.github.io/slisemap/slisemap).
 
 
 ## Citation
 
 > *Björklund, A., Mäkelä, J. & Puolamäki, K. (2022).*  
 > **SLISEMAP: Supervised dimensionality reduction through local explanations**.  
-> arXiv:2201.04455 [cs], [https://arxiv.org/abs/2201.04455](https://arxiv.org/abs/2201.04455).  
+> Machine Learning, [DOI 10.1007/s10994-022-06261-1](https://doi.org/10.1007/s10994-022-06261-1)  
 
 
 ## Installation

experiments/README.md

@@ -3,4 +3,4 @@
 This directory contains the experiments for the paper.
 The scripts are also used to produce the plots and tables in the paper.
 Running all experiments sequentially would take hundreds of hours, but cached results from the experiments can be found [here](https://github.com/edahelsinki/slisemap/tree/data/results).
-The results can also be seen in [the paper](https://arxiv.org/abs/2201.04455).  
+The results can also be seen in [the paper](https://doi.org/10.1007/s10994-022-06261-1).  

mkdocs.yml

@@ -43,7 +43,9 @@ nav:
   - Links:
     - GitHub: https://github.com/edahelsinki/slisemap
     - PyPI: https://pypi.org/project/slisemap/
-    - Paper: https://arxiv.org/abs/2201.04455
+    - Paper: https://doi.org/10.1007/s10994-022-06261-1
+    - arXiv: https://arxiv.org/abs/2201.04455
+    - Demo Video: https://youtu.be/zvcFYItwRlQ
     - Demo Paper: https://github.com/edahelsinki/slisemap/blob/main/examples/demo_paper.pdf
     - Presentation: https://github.com/edahelsinki/slisemap/blob/main/examples/demo_presentation.pdf
     - Examples: https://github.com/edahelsinki/slisemap/tree/main/examples

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = slisemap
-version = 1.2.0
+version = 1.2.1
 author = Anton Björklund, Jarmo Mäkelä, and Kai Puolamäki
 author_email = [email protected], [email protected], [email protected]
 description = SLISEMAP: Combine local explanations with supervised dimensionality reduction

slisemap/__init__.py

@@ -9,15 +9,16 @@
 items with similar local explanations are projected nearby. The explanations consists of
 "white box" models that locally approximate the "black box" model.
 
-SLISEMAP uses *PyTorch* for efficient optimisation, and optional GPU-acceleration. For
-more information see the the [repository](https://github.com/edahelsinki/slisemap) or
-the [paper](https://arxiv.org/abs/2201.04455).
+SLISEMAP uses *PyTorch* for efficient optimisation, and optional GPU-acceleration.
+For more information see the the [repository](https://github.com/edahelsinki/slisemap),
+the [documentation](https://edahelsinki.github.io/slisemap/slisemap), or the
+[paper](https://doi.org/10.1007/s10994-022-06261-1).
 
 Citation
 --------
 > Björklund, A., Mäkelä, J. & Puolamäki, K. (2022).  
 > SLISEMAP: Supervised dimensionality reduction through local explanations.  
-> arXiv:2201.04455 [cs], https://arxiv.org/abs/2201.04455.  
+> Machine Learning, DOI 10.1007/s10994-022-06261-1.  
 
 
 Example Usage

slisemap/slisemap.py

@@ -364,13 +364,16 @@ def local_model(self, value: Callable[[torch.Tensor, torch.Tensor], torch.Tensor
     @property
     def local_loss(
         self,
-    ) -> Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]:
+    ) -> Callable[[torch.Tensor, torch.Tensor, Optional[torch.Tensor]], torch.Tensor]:
         # Local model loss function. Takes in Ytilde[n, n, o], Y[n, o], and B[n, q], and returns L[n, n]
         return self._local_loss
 
     @local_loss.setter
     def local_loss(
-        self, value: Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
+        self,
+        value: Callable[
+            [torch.Tensor, torch.Tensor, Optional[torch.Tensor]], torch.Tensor
+        ],
     ):
         if self._local_loss != value:
             _assert(callable(value), "local_loss must be callable", Slisemap.local_loss)
@@ -1381,8 +1384,11 @@ def plot_position(
                 loc="lower center" if inside else "upper right",
                 bbox_to_anchor=(1 - w, h * 0.35, w * 0.9, h * 0.6) if inside else None,
             )
+            marker = Line2D(
+                [], [], linestyle="None", color="#fd8431", marker="X", markersize=5
+            )
             g.add_legend(
-                {"": Line2D([], [], None, "None", "#fd8431", "X", 5)},
+                {"": marker},
                 "Selected",
                 loc="upper center" if inside else "lower right",
                 bbox_to_anchor=(1 - w, h * 0.05, w * 0.9, h * 0.3) if inside else None,

tests/test_slisemap.py

@@ -36,6 +36,24 @@ def test_lbfgs():
     assert l1 >= l2
 
 
+def test_only_B():
+    set_seed(653274)
+    sm = get_slisemap(30, 4)
+    sm.optimise(1, 3)
+    sm2 = Slisemap(
+        X=sm.get_X(intercept=False, numpy=False),
+        y=sm.get_Y(numpy=False),
+        radius=sm.radius,
+        lasso=sm.lasso,
+        intercept=sm.intercept,
+        Z0=sm.get_Z(numpy=False),
+    )
+    sm2.optimise(1, 3, only_B=True)
+    assert_allclose(sm.get_Z(), sm2.get_Z())
+    assert_approx_ge(sm.value(), sm2.value())
+    sm.lbfgs(only_B=True)
+
+
 def test_fit_new():
     sm, _ = get_slisemap2(60, 5, cheat=True, seed=239177)
     sm.lbfgs()