Add references and test case for partial_fit

README.md

@@ -35,6 +35,8 @@ X_obs_nc = aakr.transform(X_obs)
 
 ## References
 
+* [Assessment of Statistical and Classification Models For Monitoring EDF’s  Assets”](https://link.springer.com/chapter/10.1007/978-0-85729-320-6_52)
+
 * [A modified Auto Associative Kernel Regression method for robust signal reconstruction in nuclear power plant components](https://www.researchgate.net/publication/292538769_A_modified_Auto_Associative_Kernel_Regression_method_for_robust_signal_reconstruction_in_nuclear_power_plant_components)
 
 ---

VERSION

@@ -1 +1 @@
-0.0.1dev5
+0.0.1dev6

aakr/_aakr.py

@@ -9,19 +9,33 @@
 
 
 class AAKR(TransformerMixin, BaseEstimator):
-    """Auto Associative Kernel Regressor.
-
-    Please see the :ref:`Getting started <readme.rst>` documentation for more
-        information.
+    """Auto Associative Kernel Regression.
 
     Parameters
     ----------
     metric : str, default='euclidean'
-        Metric for calculating kernel distances.
+        Metric for calculating kernel distances, see available metrics from
+        `sklearn.metrics.pairwise_distances <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html>`_.
     bw : float, default=1.0
         Kernel bandwith parameter.
     n_jobs : int, default=-1
         The number of jobs to run in parallel.
+
+    Attributes
+    ----------
+    X_ : ndarray of shape (n_samples, n_features)
+        Historical normal condition examples given as an array.
+
+    References
+    ----------
+    .. [1] Chevalier  R., Provost  D., and Seraoui R., 2009,
+           “Assessment of Statistical and Classification Models For Monitoring
+           EDF’s  Assets”,  Sixth  American  Nuclear  Society  International
+           Topical Meeting on Nuclear Plant Instrumentation.
+    .. [2] Baraldi P., Di Maio F., Turati P., Zio E., 2014,
+           "A modified Auto Associative Kernel Regression method for robust
+           signal reconstruction in nuclear power plant components", European
+           Safety and Reliability Conference ESREL.
     """
     def __init__(self, metric='euclidean', bw=1, n_jobs=-1):
         self.metric = metric

tests/test_aakr.py

@@ -28,7 +28,7 @@ def test_aakr(data):
     assert_allclose(X_nc, X[:3])
 
 
-def test_aakr_input_shape_mismatch(data):
+def test_aakr_fit_input_shape_mismatch(data):
     X = data[0]
     aakr = AAKR().fit(X)
     assert aakr.X_.shape[1] == X.shape[1]
@@ -37,4 +37,10 @@ def test_aakr_input_shape_mismatch(data):
         aakr.transform(X[:3, :-1])
 
 
-# TODO: Test partial_fit
+def test_aakr_partial_fit_input_shape_mismatch(data):
+    X = data[0]
+    aakr = AAKR().partial_fit(X)
+    assert aakr.X_.shape[1] == X.shape[1]
+
+    with pytest.raises(ValueError, match='Shape of input is different'):
+        aakr.partial_fit(X[:, :-1])