update docs

Author: antoinedemathelin

adapt/feature_based/_deep.py

@@ -562,7 +562,10 @@ class DANN(BaseDeepFeature):
     classification DA but it could be widen to other task in
     **supervised** DA straightforwardly.
     
-    .. image:: ../_static/images/dann.png
+    .. figure:: ../_static/images/dann.png
+        :align: center
+        
+        DANN architecture (source: [1])
     
     Parameters
     ----------
@@ -811,7 +814,10 @@ class ADDA(BaseDeepFeature):
     classification DA but it could be widen to other task in **supervised**
     DA straightforwardly.
     
-    .. image:: ../_static/images/adda.png
+    .. figure:: ../_static/images/adda.png
+        :align: center
+        
+        Overview of the ADDA approach (source: [1])
     
     Parameters
     ----------
@@ -1305,7 +1311,10 @@ class DeepCORAL(BaseDeepFeature):
     Notice that DeepCORAL only uses labeled source and unlabeled target
     data. It belongs then to "unsupervised" domain adaptation methods.
     
-    .. image:: ../_static/images/deepcoral.png
+    .. figure:: ../_static/images/deepcoral.png
+        :align: center
+        
+        DeepCORAL architecture (source: [1])
     
     Parameters
     ----------

adapt/feature_based/_fe.py

@@ -90,8 +90,8 @@ class FE:
     .. [1] `[1] <https://arxiv.org/pdf/0907.1815\
 .pdf>`_ Daume III, H. "Frustratingly easy domain adaptation". In ACL, 2007.
 
-    Note
-    ----
+    Notes
+    -----
     FE can be used for multi-source DA by giving list of source data
     for arguments Xs, ys of fit method : Xs = [Xs1, Xs2, ...],
     ys = [ys1, ys2, ...]

adapt/parameter_based/_regular.py

@@ -431,6 +431,12 @@ class RegularTransferNN:
     neural network through the ``lambdas`` parameter.
     Some layers can also be frozen during training via
     the ``training`` parameter.
+    
+    .. figure:: ../_static/images/regulartransfer.png
+        :align: center
+        
+        Transferring parameters of a CNN pretrained on Imagenet
+        (source: [2])
 
     Parameters
     ----------
@@ -482,6 +488,32 @@ class RegularTransferNN:
         history of the losses and metrics across the epochs
         of the network training.
         
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import tensorflow as tf
+    >>> from adapt.parameter_based import RegularTransferNN
+    >>> np.random.seed(0)
+    >>> tf.random.set_seed(0)
+    >>> Xs = np.random.randn(50) * 0.1
+    >>> Xs = np.concatenate((Xs, Xs + 1.))
+    >>> Xt = np.random.randn(100) * 0.1
+    >>> ys = (np.array([-0.2 * x if x<0.5 else 1. for x in Xs])
+    ...       + 0.1 * np.random.randn(100))
+    >>> yt = 0.75 * Xt + 0.1 * np.random.randn(100)
+    >>> model = tf.keras.Sequential()
+    >>> model.add(tf.keras.layers.Dense(1))
+    >>> model.compile(optimizer="adam", loss="mse")
+    >>> model.predict(Xt.reshape(-1,1))
+    >>> model.fit(Xs.reshape(-1, 1), ys, epochs=300, verbose=0)
+    >>> np.abs(model.predict(Xt).ravel() - yt).mean()
+    0.48265...
+    >>> rt = RegularTransferNN(model, lambdas=0.01, random_state=0)
+    >>> rt.fit(Xt[:10], yt[:10], epochs=300, verbose=0)
+    >>> rt.predict(Xt.reshape(-1, 1))
+    >>> np.abs(rt.predict(Xt).ravel() - yt).mean()
+    0.23114...
+        
     See also
     --------
     RegularTransferLR, RegularTransferLC

docs/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 5cef7bb97a2b4045581541c4416376d8
+config: d55b6138e9bd8e9e2ea847d162fd62a2
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/_images/adda.png

@@ -55,6 +55,7 @@ and **target** distributions. The **task** is then learned in this **encoded fea
    :toctree: generated/
    :template: class.rst
 
+   feature_based.BaseDeepFeature
    feature_based.FE
    feature_based.CORAL
    feature_based.DeepCORAL
@@ -156,3 +157,5 @@ This module contains utility functions used in the previous modules.
    utils.GradientHandler
    utils.make_classification_da
    utils.make_regression_da
+
+:ref:`Two_moons.ipynb`

docs/_images/dann.png

@@ -16,18 +16,24 @@
 	.. autosummary::
 
 		~ADDA.__init__
+		~ADDA.create_model
 		~ADDA.fit
 		~ADDA.fit_source
 		~ADDA.fit_target
+		~ADDA.get_loss
+		~ADDA.get_metrics
 		~ADDA.predict
 		~ADDA.predict_disc
 		~ADDA.predict_features
 
 
 	.. automethod:: __init__
+	.. automethod:: create_model
 	.. automethod:: fit
 	.. automethod:: fit_source
 	.. automethod:: fit_target
+	.. automethod:: get_loss
+	.. automethod:: get_metrics
 	.. automethod:: predict
 	.. automethod:: predict_disc
 	.. automethod:: predict_features