Update docs

docs/component/data.rst

@@ -295,6 +295,7 @@ The ``Processor`` module in ``Qlib`` is designed to be learnable and it is respo
 - ``RobustZScoreNorm``: `processor` that applies robust z-score normalization.
 - ``CSZScoreNorm``: `processor` that applies cross sectional z-score normalization.
 - ``CSRankNorm``: `processor` that applies cross sectional rank normalization.
+- ``CSZFillna``: `processor` that fills N/A values in a cross sectional way by the mean of the column.
 
 Users can also create their own `processor` by inheriting the base class of ``Processor``. Please refer to the implementation of all the processors for more information (`Processor Link <https://github.com/microsoft/qlib/blob/main/qlib/data/dataset/processor.py>`_). 
 

docs/conf.py

@@ -226,3 +226,8 @@
 
 autodoc_member_order = "bysource"
 autodoc_default_flags = ["members"]
+autodoc_default_options = {
+    "members": True,
+    "member-order": "bysource",
+    "special-members": "__init__",
+}

qlib/model/base.py

@@ -30,11 +30,6 @@ def fit(self, dataset: Dataset):
             The attribute names of learned model should `not` start with '_'. So that the model could be
             dumped to disk.
 
-        Parameters
-        ----------
-        dataset : Dataset
-            dataset will generate the processed data from model training.
-
         The following code example shows how to retrieve `x_train`, `y_train` and `w_train` from the `dataset`:
 
             .. code-block:: Python
@@ -53,6 +48,12 @@ def fit(self, dataset: Dataset):
                 except KeyError as e:
                     w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
                     w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
+
+        Parameters
+        ----------
+        dataset : Dataset
+            dataset will generate the processed data from model training.
+
         """
         raise NotImplementedError()