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Fix the Warnings in rst files when building Qlib's documentation (#1349)

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* Fix docs/advanced/alpha.rst

* Fix docs/reference/api.rst

* Fix docs/component/strategy.rst

* Fix docs/start/integration.rst

* Fix docs/component/report.rst

* Fix docs/component/data.rst

* Fix docs/component/rl/framework.rst

* Fix docs/introduction/quick.rst

* Fix docs/advanced/task_management.rst

* Fix CHANGES.rst

* Fix docs/developer/code_standard_and_dev_guide.rst

* Fix docs/hidden/client.rst

* Fix docs/component/online.rst

* Fix docs/start/getdata.rst

* Add docs/hidden to exclude patterns

* Add docs/developer/code_standard_and_dev_guide.rst to index.rst

* Change docs/developer/code_standard_and_dev_guide.rst place in index.rst
This commit is contained in:
Maxim Smolskiy
2022-11-13 17:07:08 +03:00
committed by GitHub
parent 4001a5d157
commit 82afd6a67a
16 changed files with 124 additions and 108 deletions
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34
docs/start/getdata.rst
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@@ -83,15 +83,14 @@ Load features of certain instruments in a given time range:
>> from qlib.data import D
>> instruments = ['SH600000']
>> fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head()
$close $volume Ref($close, 1) Mean($close, 3) $high-$low
instrument datetime
SH600000 2010-01-04 86.778313 16162960.0 88.825928 88.061483 2.907631
2010-01-05 87.433578 28117442.0 86.778313 87.679273 3.235252
2010-01-06 85.713585 23632884.0 87.433578 86.641825 1.720009
2010-01-07 83.788803 20813402.0 85.713585 85.645322 3.030487
2010-01-08 84.730675 16044853.0 83.788803 84.744354 2.047623
>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head().to_string()
' $close $volume Ref($close, 1) Mean($close, 3) $high-$low
... instrument datetime
... SH600000 2010-01-04 86.778313 16162960.0 88.825928 88.061483 2.907631
... 2010-01-05 87.433578 28117442.0 86.778313 87.679273 3.235252
... 2010-01-06 85.713585 23632884.0 87.433578 86.641825 1.720009
... 2010-01-07 83.788803 20813402.0 85.713585 85.645322 3.030487
... 2010-01-08 84.730675 16044853.0 83.788803 84.744354 2.047623'
Load features of certain stock pool in a given time range:
@@ -105,15 +104,14 @@ Load features of certain stock pool in a given time range:
>> expressionDFilter = ExpressionDFilter(rule_expression='$close>Ref($close,1)')
>> instruments = D.instruments(market='csi300', filter_pipe=[nameDFilter, expressionDFilter])
>> fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head()
$close $volume Ref($close, 1) Mean($close, 3) $high-$low
instrument datetime
SH600655 2010-01-04 2699.567383 158193.328125 2619.070312 2626.097738 124.580566
2010-01-08 2612.359619 77501.406250 2584.567627 2623.220133 83.373047
2010-01-11 2712.982422 160852.390625 2612.359619 2636.636556 146.621582
2010-01-12 2788.688232 164587.937500 2712.982422 2704.676758 128.413818
2010-01-13 2790.604004 145460.453125 2788.688232 2764.091553 128.413818
>> D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head().to_string()
' $close $volume Ref($close, 1) Mean($close, 3) $high-$low
... instrument datetime
... SH600655 2010-01-04 2699.567383 158193.328125 2619.070312 2626.097738 124.580566
... 2010-01-08 2612.359619 77501.406250 2584.567627 2623.220133 83.373047
... 2010-01-11 2712.982422 160852.390625 2612.359619 2636.636556 146.621582
... 2010-01-12 2788.688232 164587.937500 2712.982422 2704.676758 128.413818
... 2010-01-13 2790.604004 145460.453125 2788.688232 2764.091553 128.413818'
For more details about features, please refer `Feature API <../component/data.html>`_.

138
docs/start/integration.rst
View File

@@ -21,84 +21,88 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
- ``Qlib`` passes the initialized parameters to the \_\_init\_\_ method.
- The hyperparameters of model in the configuration must be consistent with those defined in the `__init__` method.
- Code Example: In the following example, the hyperparameters of model in the configuration file should contain parameters such as `loss:mse`.
.. code-block:: Python
def __init__(self, loss='mse', **kwargs):
if loss not in {'mse', 'binary'}:
raise NotImplementedError
self._scorer = mean_squared_error if loss == 'mse' else roc_auc_score
self._params.update(objective=loss, **kwargs)
self._model = None
.. code-block:: Python
def __init__(self, loss='mse', **kwargs):
if loss not in {'mse', 'binary'}:
raise NotImplementedError
self._scorer = mean_squared_error if loss == 'mse' else roc_auc_score
self._params.update(objective=loss, **kwargs)
self._model = None
- Override the `fit` method
- ``Qlib`` calls the fit method to train the model.
- The parameters must include training feature `dataset`, which is designed in the interface.
- The parameters could include some `optional` parameters with default values, such as `num_boost_round = 1000` for `GBDT`.
- Code Example: In the following example, `num_boost_round = 1000` is an optional parameter.
.. code-block:: Python
def fit(self, dataset: DatasetH, num_boost_round = 1000, **kwargs):
.. code-block:: Python
# prepare dataset for lgb training and evaluation
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
def fit(self, dataset: DatasetH, num_boost_round = 1000, **kwargs):
# Lightgbm need 1D array as its label
if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
y_train, y_valid = np.squeeze(y_train.values), np.squeeze(y_valid.values)
else:
raise ValueError("LightGBM doesn't support multi-label training")
# prepare dataset for lgb training and evaluation
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
dtrain = lgb.Dataset(x_train.values, label=y_train)
dvalid = lgb.Dataset(x_valid.values, label=y_valid)
# Lightgbm need 1D array as its label
if y_train.values.ndim == 2 and y_train.values.shape[1] == 1:
y_train, y_valid = np.squeeze(y_train.values), np.squeeze(y_valid.values)
else:
raise ValueError("LightGBM doesn't support multi-label training")
# fit the model
self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
valid_sets=[dtrain, dvalid],
valid_names=["train", "valid"],
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
evals_result=evals_result,
**kwargs
)
dtrain = lgb.Dataset(x_train.values, label=y_train)
dvalid = lgb.Dataset(x_valid.values, label=y_valid)
# fit the model
self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
valid_sets=[dtrain, dvalid],
valid_names=["train", "valid"],
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
evals_result=evals_result,
**kwargs
)
- Override the `predict` method
- The parameters must include the parameter `dataset`, which will be userd to get the test dataset.
- Return the `prediction score`.
- Please refer to `Model API <../reference/api.html#module-qlib.model.base>`_ for the parameter types of the fit method.
- Code Example: In the following example, users need to use `LightGBM` to predict the label(such as `preds`) of test data `x_test` and return it.
.. code-block:: Python
def predict(self, dataset: DatasetH, **kwargs)-> pandas.Series:
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
return pd.Series(self.model.predict(x_test.values), index=x_test.index)
.. code-block:: Python
def predict(self, dataset: DatasetH, **kwargs)-> pandas.Series:
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
return pd.Series(self.model.predict(x_test.values), index=x_test.index)
- Override the `finetune` method (Optional)
- This method is optional to the users. When users want to use this method on their own models, they should inherit the ``ModelFT`` base class, which includes the interface of `finetune`.
- The parameters must include the parameter `dataset`.
- Code Example: In the following example, users will use `LightGBM` as the model and finetune it.
.. code-block:: Python
def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
# Based on existing model and finetune by train more rounds
dtrain, _ = self._prepare_data(dataset)
self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
init_model=self.model,
valid_sets=[dtrain],
valid_names=["train"],
verbose_eval=verbose_eval,
)
.. code-block:: Python
def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
# Based on existing model and finetune by train more rounds
dtrain, _ = self._prepare_data(dataset)
self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
init_model=self.model,
valid_sets=[dtrain],
valid_names=["train"],
verbose_eval=verbose_eval,
)
Configuration File
==================
@@ -107,21 +111,21 @@ The configuration file is described in detail in the `Workflow <../component/wor
- Example: The following example describes the `model` field of configuration file about the custom lightgbm model mentioned above, where `module_path` is the module path, `class` is the class name, and `args` is the hyperparameter passed into the __init__ method. All parameters in the field is passed to `self._params` by `\*\*kwargs` in `__init__` except `loss = mse`.
.. code-block:: YAML
.. code-block:: YAML
model:
class: LGBModel
module_path: qlib.contrib.model.gbdt
args:
loss: mse
colsample_bytree: 0.8879
learning_rate: 0.0421
subsample: 0.8789
lambda_l1: 205.6999
lambda_l2: 580.9768
max_depth: 8
num_leaves: 210
num_threads: 20
model:
class: LGBModel
module_path: qlib.contrib.model.gbdt
args:
loss: mse
colsample_bytree: 0.8879
learning_rate: 0.0421
subsample: 0.8789
lambda_l1: 205.6999
lambda_l2: 580.9768
max_depth: 8
num_leaves: 210
num_threads: 20
Users could find configuration file of the baselines of the ``Model`` in ``examples/benchmarks``. All the configurations of different models are listed under the corresponding model folder.