Merge pull request #496 from GAA-UAM/issue/classifier_classes/logreg

Issue/classifier_classes/logreg
GAA-UAM · Nov 23, 2022 · 8756059 · 8756059
2 parents aee15f4 + a9e9e51
commit 8756059
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Showing 3 changed files with 31 additions and 4 deletions.
diff --git a/setup.cfg b/setup.cfg
@@ -114,7 +114,7 @@ rst-directives =
     versionadded,versionchanged,
 
 rst-roles =
-    attr,class,doc,footcite,footcite:ts,func,meth,mod,obj,ref,term,
+    attr,class,doc,footcite,footcite:ts,func,meth,mod,obj,ref,term,external:class
 
 allowed-domain-names = data, obj, result, results, val, value, values, var
 

diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py
@@ -105,7 +105,7 @@ def __init__(
         self.solver = solver
         self.max_iter = max_iter
 
-    def fit(  # noqa: D102
+    def fit(  # noqa: D102, WPS210
         self,
         X: FDataGrid,
         y: NDArrayAny,
@@ -162,7 +162,10 @@ def fit(  # noqa: D102
                 # This does not improve
                 selected_indexes = selected_indexes[:n_selected]
                 selected_values = selected_values[:, :n_selected]
-                likelihood_curves_data = likelihood_curves_data[:n_selected, t]
+                likelihood_curves_data = likelihood_curves_data[
+                    :n_selected,
+                    n_features - 1,
+                ]
                 break
 
             last_max_likelihood = max_likelihood
@@ -171,7 +174,7 @@ def fit(  # noqa: D102
             selected_values[:, n_selected] = X.data_matrix[:, tmax, 0]
 
         # fit for the complete set of points
-        mvlr.fit(selected_values, y_ind)
+        mvlr.fit(selected_values, y)
 
         self.coef_ = mvlr.coef_
         self.intercept_ = mvlr.intercept_

diff --git a/skfda/tests/test_classifier_classes.py b/skfda/tests/test_classifier_classes.py
@@ -9,8 +9,18 @@
 
 from skfda._utils._sklearn_adapter import ClassifierMixin
 from skfda.datasets import make_gaussian_process
+from skfda.exploratory.depth import ModifiedBandDepth
+from skfda.exploratory.stats.covariance import ParametricGaussianCovariance
+from skfda.misc.covariances import Gaussian
 from skfda.ml.classification import (
+    DDClassifier,
+    DDGClassifier,
+    DTMClassifier,
     KNeighborsClassifier,
+    LogisticRegression,
+    MaximumDepthClassifier,
+    NearestCentroid,
+    QuadraticDiscriminantAnalysis,
     RadiusNeighborsClassifier,
 )
 from skfda.representation import FData
@@ -20,7 +30,21 @@
 
 @pytest.fixture(
     params=[
+        DDClassifier(degree=2),
+        DDGClassifier(
+            depth_method=[("mbd", ModifiedBandDepth())],
+            multivariate_classifier=KNeighborsClassifier(),
+        ),
+        DTMClassifier(proportiontocut=0.25),
         KNeighborsClassifier(),
+        LogisticRegression(),
+        MaximumDepthClassifier(),
+        NearestCentroid(),
+        QuadraticDiscriminantAnalysis(
+            cov_estimator=ParametricGaussianCovariance(
+                Gaussian(),
+            ),
+        ),
         RadiusNeighborsClassifier(),
     ],
     ids=lambda clf: type(clf).__name__,