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Fix doc bugs

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Jactus
2020-12-30 17:33:06 +08:00
committed by you-n-g
parent afcd91a2d0
commit 46c8d791ac
3 changed files with 12 additions and 5 deletions
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1
docs/component/data.rst
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@@ -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. - ``RobustZScoreNorm``: `processor` that applies robust z-score normalization.
- ``CSZScoreNorm``: `processor` that applies cross sectional z-score normalization. - ``CSZScoreNorm``: `processor` that applies cross sectional z-score normalization.
- ``CSRankNorm``: `processor` that applies cross sectional rank 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>`_). 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>`_).

5
docs/conf.py
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@@ -226,3 +226,8 @@ epub_exclude_files = ["search.html"]
autodoc_member_order = "bysource" autodoc_member_order = "bysource"
autodoc_default_flags = ["members"] autodoc_default_flags = ["members"]
autodoc_default_options = {
"members": True,
"member-order": "bysource",
"special-members": "__init__",
}

11
qlib/model/base.py
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@@ -30,11 +30,6 @@ class Model(BaseModel):
The attribute names of learned model should `not` start with '_'. So that the model could be The attribute names of learned model should `not` start with '_'. So that the model could be
dumped to disk. 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`: The following code example shows how to retrieve `x_train`, `y_train` and `w_train` from the `dataset`:
.. code-block:: Python .. code-block:: Python
@@ -53,6 +48,12 @@ class Model(BaseModel):
except KeyError as e: except KeyError as e:
w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index) 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) 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() raise NotImplementedError()