Fix some warnings in data-mining-library docs

Orange/evaluation/scoring.py

@@ -103,6 +103,7 @@ def compute_score(self, results):
 class F1(Score):
     """
     ${sklpar}
+
     Parameters
     ----------
     results : Orange.evaluation.Results
@@ -144,6 +145,7 @@ def compute_score(self, results):
 class AUC(Score):
     """
     ${sklpar}
+
     Parameters
     ----------
     results : Orange.evaluation.Results
@@ -200,6 +202,7 @@ def compute_score(self, results, target=None):
 class LogLoss(Score):
     """
     ${sklpar}
+
     Parameters
     ----------
     results : Orange.evaluation.Results

Orange/evaluation/testing.py

@@ -308,11 +308,12 @@ def get_augmented_data(self, model_names, include_attrs=True, include_prediction
     def fit(self, train_data, test_data=None):
         """Fits `self.learners` using folds sampled from the provided data.
 
-        Args:
-            train_data (Table): table to sample train folds
-            test_data (Optional[Table]): tap to sample test folds
-                of None then `train_data` will be used
-
+        Parameters
+        ----------
+        train_data : Table
+            table to sample train folds
+        test_data : Optional[Table]
+            tap to sample test folds of None then `train_data` will be used
         """
         test_data = test_data or train_data
         self.setup_indices(train_data, test_data)

Orange/regression/tree.py

@@ -26,17 +26,23 @@ class TreeLearner(Learner):
 
     If the tree is not binary, it can contain zero-branches.
 
-    Args:
-        binarize: if `True` the inducer will find optimal split into two
-            subsets for values of discrete attributes. If `False` (default),
-            each value gets its branch.
-        min_samples_leaf: the minimal number of data instances in a leaf
-        min_samples_split: the minimal number of data instances that is split
-            into subgroups
-        max_depth: the maximal depth of the tree
-
-    Returns:
-        instance of OrangeTreeModel
+    Parameters
+    ----------
+    binarize
+        if `True` the inducer will find optimal split into two
+        subsets for values of discrete attributes. If `False` (default),
+        each value gets its branch.
+    min_samples_leaf
+        the minimal number of data instances in a leaf
+    min_samples_split
+        the minimal number of data instances that is split
+        into subgroups
+    max_depth
+        the maximal depth of the tree
+
+    Returns
+    -------
+    instance of OrangeTreeModel
     """
     __returns__ = TreeModel
 
@@ -57,10 +63,11 @@ def __init__(
     def _select_attr(self, data):
         """Select the attribute for the next split.
 
-        Returns:
-            tuple with an instance of Node and a numpy array indicating
-            the branch index for each data instance, or -1 if data instance
-            is dropped
+        Returns
+        -------
+        tuple with an instance of Node and a numpy array indicating
+        the branch index for each data instance, or -1 if data instance
+        is dropped
         """
         # Prevent false warnings by pylint
         attr = attr_no = None

doc/data-mining-library/source/conf.py

@@ -154,7 +154,7 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+#html_static_path = ['_static']
 
 # Add any extra paths that contain custom files (such as robots.txt or
 # .htaccess) here, relative to this directory. These files are copied

doc/data-mining-library/source/reference/data.variable.rst

@@ -87,7 +87,7 @@ Base class
     .. automethod:: str_val
     .. automethod:: to_val
     .. automethod:: val_from_str_add
-    .. automethod:: compute_value
+    .. autoattribute:: compute_value
 
     Method `compute_value` is usually invoked behind the scenes in
     conversion of domains::