Documentation improvements

2 files changed, 65 insertions, 27 deletions

diff --git a/gensvm/core.py b/gensvm/core.py
index 45d59ad..169a30c 100644
--- a/gensvm/core.py
+++ b/gensvm/core.py
@@ -104,7 +104,7 @@ class GenSVM(BaseEstimator, ClassifierMixin):
     errors. It is this flexibility that makes it perform well on diverse 
     datasets.
 
-    The :func:`~GenSVM.fit` and :func:`~GenSVM.predict` methods of this class 
+    The :meth:`~GenSVM.fit` and :meth:`~GenSVM.predict` methods of this class 
     use the GenSVM C library for the actual computations.
 
     Parameters
@@ -123,7 +123,7 @@ class GenSVM(BaseEstimator, ClassifierMixin):
         'group' for group size correction weights (equation 4 in the paper).
 
         It is also possible to provide an explicit vector of sample weights 
-        through the :func:`~GenSVM.fit` method. If so, it will override the 
+        through the :meth:`~GenSVM.fit` method. If so, it will override the 
         setting provided here.
 
     kernel : string, optional (default='linear')
@@ -183,7 +183,7 @@ class GenSVM(BaseEstimator, ClassifierMixin):
 
     See Also
     --------
-    :class:`.GenSVMGridSearchCV`:
+    :class:`~.gridsearch.GenSVMGridSearchCV`:
         Helper class to run an efficient grid search for GenSVM.
 
 
@@ -257,8 +257,8 @@ class GenSVM(BaseEstimator, ClassifierMixin):
     def fit(self, X, y, sample_weight=None, seed_V=None):
         """Fit the GenSVM model on the given data
 
-        The model can be fit with or without a seed matrix (``seed_V``). This 
-        can be used to provide warm starts for the algorithm.
+        The model can be fit with or without a seed matrix (`seed_V`). This can 
+        be used to provide warm starts for the algorithm.
 
         Parameters
         ----------
@@ -280,14 +280,13 @@ class GenSVM(BaseEstimator, ClassifierMixin):
             <.GenSVM.combined_coef_>` attribute of a different GenSVM model.  
             This is only supported for the linear kernel.
 
-            NOTE: the size of the seed_V matrix is ``n_features+1`` by 
-            ``n_classes - 1``.  The number of columns of ``seed_V`` is leading 
-            for the number of classes in the model. For example, if ``y`` 
-            contains 3 different classes and ``seed_V`` has 3 columns, we 
-            assume that there are actually 4 classes in the problem but one 
-            class is just represented in this training data. This can be useful 
-            for problems were a certain class has only a few samples.
-
+            NOTE: the size of the seed_V matrix is `n_features+1` by `n_classes 
+            - 1`.  The number of columns of `seed_V` is leading for the number 
+              of classes in the model. For example, if `y` contains 3 different 
+              classes and `seed_V` has 3 columns, we assume that there are 
+              actually 4 classes in the problem but one class is just 
+              represented in this training data. This can be useful for 
+              problems were a certain class has only a few samples.
 
         Returns
         -------
@@ -354,7 +353,13 @@ class GenSVM(BaseEstimator, ClassifierMixin):
                     )
                 )
 
-        self.coef_, self.intercept_, self.n_iter_, self.n_support_, self.SVs_ = _fit_gensvm(
+        (
+            self.coef_,
+            self.intercept_,
+            self.n_iter_,
+            self.n_support_,
+            self.SVs_,
+        ) = _fit_gensvm(
             X,
             y,
             n_class,
diff --git a/gensvm/gridsearch.py b/gensvm/gridsearch.py
index 22125a4..550ae64 100644
--- a/gensvm/gridsearch.py
+++ b/gensvm/gridsearch.py
@@ -65,7 +65,7 @@ def _validate_param_grid(param_grid):
     """Check if the parameter values are valid
 
     This basically does the same checks as in the constructor of the 
-    :class:`core.GenSVM` class, but for the entire parameter grid.
+    :class:`~.core.GenSVM` class, but for the entire parameter grid.
 
     """
     # the conditions that the parameters must satisfy
@@ -169,16 +169,24 @@ def _format_results(
             score_time = 0
 
             if return_train_score:
-                train_pred = predictions[cv_idx != test_idx,]
-                y_train = true_y[cv_idx != test_idx,]
+                train_pred = predictions[
+                    cv_idx != test_idx,
+                ]
+                y_train = true_y[
+                    cv_idx != test_idx,
+                ]
                 train_score, score_t = _wrap_score(
                     train_pred, y_train, scorers, is_multimetric
                 )
                 score_time += score_t
                 ret.append(train_score)
 
-            test_pred = predictions[cv_idx == test_idx,]
-            y_test = true_y[cv_idx == test_idx,]
+            test_pred = predictions[
+                cv_idx == test_idx,
+            ]
+            y_test = true_y[
+                cv_idx == test_idx,
+            ]
             test_score, score_t = _wrap_score(
                 test_pred, y_test, scorers, is_multimetric
             )
@@ -232,7 +240,7 @@ def _fit_grid_gensvm(
     Returns
     -------
     cv_results_ : dict
-        The cross validation results. See :func:`~GenSVMGridSearchCV.fit`.
+        The cross validation results. See :meth:`~GenSVMGridSearchCV.fit`.
 
     """
 
@@ -349,7 +357,7 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin):
 
         The refitted estimator is made available at the `:attr:best_estimator_ 
         <.GenSVMGridSearchCV.best_estimator_>` attribute and allows the user to 
-        use the :func:`~GenSVMGridSearchCV.predict` method directly on this 
+        use the :meth:`~GenSVMGridSearchCV.predict` method directly on this 
         :class:`.GenSVMGridSearchCV` instance.
 
         Also for multiple metric evaluation, the attributes :attr:`best_index_ 
@@ -623,7 +631,7 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin):
                 and not self.best_params_["kernel"] == "linear"
                 and not "gamma" in self.best_params_
             ):
-                self.best_params_["gamma"] = 1. / X.shape[1]
+                self.best_params_["gamma"] = 1.0 / X.shape[1]
             self.best_estimator_ = GenSVM(**self.best_params_)
             # y_orig because GenSVM fit must know the conversion for predict to
             # work correctly
@@ -711,9 +719,24 @@ def load_grid_tiny():
     """
 
     pg = [
-        {"p": [2.0], "kappa": [5.0], "lmd": [pow(2, -16)], "weights": ["unit"]},
-        {"p": [2.0], "kappa": [5.0], "lmd": [pow(2, -18)], "weights": ["unit"]},
-        {"p": [2.0], "kappa": [0.5], "lmd": [pow(2, -18)], "weights": ["unit"]},
+        {
+            "p": [2.0],
+            "kappa": [5.0],
+            "lmd": [pow(2, -16)],
+            "weights": ["unit"],
+        },
+        {
+            "p": [2.0],
+            "kappa": [5.0],
+            "lmd": [pow(2, -18)],
+            "weights": ["unit"],
+        },
+        {
+            "p": [2.0],
+            "kappa": [0.5],
+            "lmd": [pow(2, -18)],
+            "weights": ["unit"],
+        },
         {
             "p": [2.0],
             "kappa": [5.0],
@@ -726,7 +749,12 @@ def load_grid_tiny():
             "lmd": [pow(2, -18)],
             "weights": ["unit"],
         },
-        {"p": [2.0], "kappa": [5.0], "lmd": [pow(2, -14)], "weights": ["unit"]},
+        {
+            "p": [2.0],
+            "kappa": [5.0],
+            "lmd": [pow(2, -14)],
+            "weights": ["unit"],
+        },
         {
             "p": [2.0],
             "kappa": [0.5],
@@ -739,7 +767,12 @@ def load_grid_tiny():
             "lmd": [pow(2, -18)],
             "weights": ["unit"],
         },
-        {"p": [2.0], "kappa": [0.5], "lmd": [pow(2, -16)], "weights": ["unit"]},
+        {
+            "p": [2.0],
+            "kappa": [0.5],
+            "lmd": [pow(2, -16)],
+            "weights": ["unit"],
+        },
         {
             "p": [2.0],
             "kappa": [0.5],