Merge pull request #84 from antoinedemathelin/master

docs: Add doc ULSIF, RULSIF, IWN, IWC, PRED...

Author: antoinedemathelin

.github/workflows/check-docs.yml

@@ -18,7 +18,7 @@ jobs:
       run: |
         sudo apt install pandoc
         python -m pip install --upgrade pip
-        pip install jinja2==3.0.3 sphinx numpydoc nbsphinx sphinx_gallery sphinx_rtd_theme ipython
+        pip install jinja2==3.0.3 sphinx==4.4.0 numpydoc==1.2 nbsphinx==0.8.8 sphinx_gallery==0.10.1 sphinx_rtd_theme==1.0.0 ipython==8.0.1
     - name: Install adapt dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/publish-doc-to-remote.yml

@@ -18,7 +18,7 @@ jobs:
       run: |
         sudo apt install pandoc
         python -m pip install --upgrade pip
-        pip install jinja2==3.0.3 sphinx numpydoc nbsphinx sphinx_gallery sphinx_rtd_theme ipython
+        pip install jinja2==3.0.3 sphinx==4.4.0 numpydoc==1.2 nbsphinx==0.8.8 sphinx_gallery==0.10.1 sphinx_rtd_theme==1.0.0 ipython==8.0.1
     - name: Install adapt dependencies
       run: |
         python -m pip install --upgrade pip

adapt/feature_based/_cdan.py

@@ -34,8 +34,9 @@ def _get_default_classifier(name=None, state=None):
 @make_insert_doc(["encoder"])
 class CDAN(BaseAdaptDeep):
     """
-    CDAN (Conditional Adversarial Domain Adaptation) is an
-    unsupervised domain adaptation method on the model of the 
+    CDAN: Conditional Adversarial Domain Adaptation
+    
+    CDAN is an unsupervised domain adaptation method on the model of the 
     :ref:`DANN <adapt.feature_based.DANN>`. In CDAN the discriminator
     is conditioned on the prediction of the task network for
     source and target data. This should , in theory, focus the

adapt/feature_based/_mcd.py

@@ -14,8 +14,10 @@
 @make_insert_doc(["encoder", "task"])
 class MCD(BaseAdaptDeep):
     """
-    MCD: Maximum Classifier Discrepancy is a feature-based domain adaptation
-    method originally introduced for unsupervised classification DA.
+    MCD: Maximum Classifier Discrepancy
+    
+    MCD is a feature-based domain adaptation method originally introduced
+    for unsupervised classification DA.
     
     The goal of MCD is to find a new representation of the input features which
     minimizes the discrepancy between the source and target domains 

adapt/feature_based/_mdd.py

@@ -14,8 +14,10 @@
 @make_insert_doc(["encoder", "task"])
 class MDD(BaseAdaptDeep):
     """
-    MDD: Margin Disparity Discrepancy is a feature-based domain adaptation
-    method originally introduced for unsupervised classification DA.
+    MDD: Margin Disparity Discrepancy
+    
+    MDD is a feature-based domain adaptation method originally introduced
+    for unsupervised classification DA.
     
     The goal of MDD is to find a new representation of the input features which
     minimizes the disparity discrepancy between the source and target domains 

adapt/feature_based/_tca.py

@@ -50,7 +50,7 @@ class TCA(BaseAdaptEstimator):
     
     References
     ----------
-    .. [1] `[1] <https://www.cse.ust.hk/~qyang/Docs/2009/TCA.pdf>` S. J. Pan, \
+    .. [1] `[1] <https://www.cse.ust.hk/~qyang/Docs/2009/TCA.pdf>`_ S. J. Pan, \
 I. W. Tsang, J. T. Kwok and Q. Yang. "Domain Adaptation via Transfer Component \
 Analysis". In IEEE transactions on neural networks 2010
     """

adapt/feature_based/_wdgrl.py

@@ -13,8 +13,9 @@
 @make_insert_doc(["encoder", "task", "discriminator"])
 class WDGRL(BaseAdaptDeep):
     """
-    WDGRL (Wasserstein Distance Guided Representation Learning) is an
-    unsupervised domain adaptation method on the model of the 
+    WDGRL: Wasserstein Distance Guided Representation Learning
+    
+    WDGRL is an unsupervised domain adaptation method on the model of the 
     :ref:`DANN <adapt.feature_based.DANN>`. In WDGRL the discriminator
     is used to approximate the Wasserstein distance between the
     source and target encoded distributions in the spirit of WGAN.

adapt/instance_based/_balancedweighting.py

@@ -10,7 +10,7 @@ class BalancedWeighting(BaseAdaptEstimator):
     """
     BW : Balanced Weighting
     
-    Fit the estimator on source and target labeled data
+    Fit the estimator :math:`h` on source and target labeled data
     according to the modified loss:
     
     .. math::
@@ -26,7 +26,7 @@ class BalancedWeighting(BaseAdaptEstimator):
     
     Parameters
     ----------
-    gamma : float
+    gamma : float (default=0.5)
         ratio between 0 and 1 correspond to the importance
         given to the target labeled data. When `ratio=1`, the
         estimator is only fitted on target data. `ratio=0.5`

adapt/instance_based/_iwc.py

@@ -28,7 +28,7 @@ class IWC(BaseAdaptEstimator):
     
     .. math::
     
-    w(x) = \frac{1}{P(x \in Source)} - 1
+        w(x) = \\frac{1}{P(x \in Source)} - 1
 
     Parameters
     ----------

adapt/instance_based/_iwn.py

@@ -47,11 +47,12 @@ def weighted_mmd(Xs, Xt, weights, gamma=1.):
     return mxx + myy -2*mxy
 
 
-@make_insert_doc(["estimator", "weighter"], supervised=True)
+@make_insert_doc(["estimator", "weighter"])
 class IWN(BaseAdaptDeep):
     """
-    IWN : Importance Weighting Network is an instance-based method for
-    unsupervised domain adaptation<.
+    IWN : Importance Weighting Network
+    
+    IWN is an instance-based method for unsupervised domain adaptation.
     
     The goal of IWN is to reweight the source instances in order to
     minimize the Maximum Mean Discreancy (MMD) between the reweighted
@@ -107,7 +108,7 @@ class IWN(BaseAdaptDeep):
     
     References
     ----------
-    .. [1] `[1] <https://arxiv.org/pdf/2209.04215.pdff>`_ A. de Mathelin, F. Deheeger, \
+    .. [1] `[1] <https://arxiv.org/pdf/2209.04215.pdf>`_ A. de Mathelin, F. Deheeger, \
 M. Mougeot and N. Vayatis "Fast and Accurate Importance Weighting for \
 Correcting Sample Bias" In ECML-PKDD, 2022.
     """