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fix_typo (#790)

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Signed-off-by: unknown <lv.linlang@qq.com>
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SunsetWolf
2021-12-31 22:14:47 +08:00
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56 changed files with 92 additions and 92 deletions
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docs/hidden/tuner.rst
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@@ -31,7 +31,7 @@ Let's see an example,
First make sure you have the latest version of `qlib` installed.
Then, you need to privide a configuration to setup the experiment.
Then, you need to provide a configuration to setup the experiment.
We write a simple configuration example as following,
.. code-block:: YAML
@@ -217,13 +217,13 @@ The tuner pipeline contains different tuners, and the `tuner` program will proce
Each part represents a tuner, and its modules which are to be tuned. Space in each part is the hyper-parameters' space of a certain module, you need to create your searching space and modify it in `/qlib/contrib/tuner/space.py`. We use `hyperopt` package to help us to construct the space, you can see the detail of how to use it in https://github.com/hyperopt/hyperopt/wiki/FMin .
- model
You need to provide the `class` and the `space` of the model. If the model is user's own implementation, you need to privide the `module_path`.
You need to provide the `class` and the `space` of the model. If the model is user's own implementation, you need to provide the `module_path`.
- trainer
You need to proveide the `class` of the trainer. If the trainer is user's own implementation, you need to privide the `module_path`.
You need to provide the `class` of the trainer. If the trainer is user's own implementation, you need to provide the `module_path`.
- strategy
You need to provide the `class` and the `space` of the strategy. If the strategy is user's own implementation, you need to privide the `module_path`.
You need to provide the `class` and the `space` of the strategy. If the strategy is user's own implementation, you need to provide the `module_path`.
- data_label
The label of the data, you can search which kinds of labels will lead to a better result. This part is optional, and you only need to provide `space`.
@@ -273,7 +273,7 @@ You need to use the same dataset to evaluate your different `estimator` experime
About the data and backtest
~~~~~~~~~~~~~~~~~~~~~~~~~~~
`data` and `backtest` are all same in the whole `tuner` experiment. Different `estimator` experiments must use the same data and backtest method. So, these two parts of config are same with that in `estimator` configuration. You can see the precise defination of these parts in `estimator` introduction. We only provide an example here.
`data` and `backtest` are all same in the whole `tuner` experiment. Different `estimator` experiments must use the same data and backtest method. So, these two parts of config are same with that in `estimator` configuration. You can see the precise definition of these parts in `estimator` introduction. We only provide an example here.
.. code-block:: YAML