[doc] Add auto-generated docs

README.md

@@ -11,13 +11,13 @@ Here’s what tune-sklearn has to offer:
  * **Framework support**: tune-sklearn is used primarily for tuning Scikit-Learn models, but it also supports and provides examples for many other frameworks with Scikit-Learn wrappers such as Skorch (Pytorch) [[example](https://github.com/ray-project/tune-sklearn/blob/master/examples/torch_nn.py)], KerasClassifier (Keras) [[example](https://github.com/ray-project/tune-sklearn/blob/master/examples/keras_example.py)], and XGBoostClassifier (XGBoost) [[example](https://github.com/ray-project/tune-sklearn/blob/master/examples/xgbclassifier.py)].
  * **Scale up**: Tune-sklearn leverages [Ray Tune](http://tune.io/), a library for distributed hyperparameter tuning, to parallelize cross validation on multiple cores and even multiple machines without changing your code.
 
-Check out our [API Documentation](https://docs.ray.io/en/master/tune/api_docs/sklearn.html) and [Walkthrough](https://docs.ray.io/en/master/tune/tutorials/tune-sklearn.html) (for `master` branch).
+Check out our [API Documentation](docs) and [Walkthrough](https://docs.ray.io/en/master/tune/examples/tune-sklearn.html) (for `master` branch).
 
 ## Installation
 
 ### Dependencies
 - numpy (>=1.16)
-- [ray](http://docs.ray.io/)
+- [ray](http://docs.ray.io/) (>=2.7.0)
 - scikit-learn (>=0.23)
 
 ### User Installation
@@ -48,10 +48,10 @@ See [the Ray documentation for an overview of available stoppers](https://docs.r
 
 ## Examples
 
-#### TuneGridSearchCV
+#### [TuneGridSearchCV](docs/tune_gridsearch.md)
 To start out, it’s as easy as changing our import statement to get Tune’s grid search cross validation interface, and the rest is almost identical!
 
-`TuneGridSearchCV` accepts dictionaries in the format `{ param_name: str : distribution: list }` or a list of such dictionaries, just like scikit-learn's `GridSearchCV`. The distribution can also be the output of Ray Tune's [`tune.grid_search`](https://docs.ray.io/en/master/tune/api_docs/search_space.html#grid-search-api).
+`TuneGridSearchCV` accepts dictionaries in the format `{ param_name: str : distribution: list }` or a list of such dictionaries, just like scikit-learn's `GridSearchCV`. The distribution can also be the output of Ray Tune's [`tune.grid_search`](https://docs.ray.io/en/master/tune/api/search_space.html).
 
 ```python
 # from sklearn.model_selection import GridSearchCV
@@ -110,11 +110,11 @@ accuracy = np.count_nonzero(np.array(pred) == np.array(y_test)) / len(pred)
 print("Sklearn Accuracy:", accuracy)
 ```
 
-#### TuneSearchCV
+#### [TuneSearchCV](docs/tune_search.md)
 
 `TuneSearchCV` is an upgraded version of scikit-learn's `RandomizedSearchCV`.
 
-It also provides a wrapper for several search optimization algorithms from Ray Tune's [`tune.suggest`](https://docs.ray.io/en/master/tune/api_docs/suggestion.html), which in turn are wrappers for other libraries. The selection of the search algorithm is controlled by the `search_optimization` parameter. In order to use other algorithms, you need to install the libraries they depend on (`pip install` column). The search algorithms are as follows:
+It also provides a wrapper for several search optimization algorithms from Ray Tune's [searchers](https://docs.ray.io/en/master/tune/api/suggestion.html), which in turn are wrappers for other libraries. The selection of the search algorithm is controlled by the `search_optimization` parameter. In order to use other algorithms, you need to install the libraries they depend on (`pip install` column). The search algorithms are as follows:
 
 | Algorithm          | `search_optimization` value | Summary                | Website                                                 | `pip install`              |
 |--------------------|-----------------------------|------------------------|---------------------------------------------------------|--------------------------|
@@ -126,7 +126,7 @@ It also provides a wrapper for several search optimization algorithms from Ray T
 
 All algorithms other than RandomListSearcher accept parameter distributions in the form of dictionaries in the format `{ param_name: str : distribution: tuple or list }`.
 
-Tuples represent real distributions and should be two-element or three-element, in the format `(lower_bound: float, upper_bound: float, Optional: "uniform" (default) or "log-uniform")`. Lists represent categorical distributions. [Ray Tune Search Spaces](https://docs.ray.io/en/master/tune/api_docs/search_space.html) are also supported and provide a rich set of potential distributions. Search spaces allow for users to specify complex, potentially nested search spaces and parameter distributions. Furthermore, each algorithm also accepts parameters in their own specific format. More information in [Tune documentation](https://docs.ray.io/en/master/tune/api_docs/suggestion.html).
+Tuples represent real distributions and should be two-element or three-element, in the format `(lower_bound: float, upper_bound: float, Optional: "uniform" (default) or "log-uniform")`. Lists represent categorical distributions. [Ray Tune Search Spaces](https://docs.ray.io/en/master/tune/api/search_space.html) are also supported and provide a rich set of potential distributions. Search spaces allow for users to specify complex, potentially nested search spaces and parameter distributions. Furthermore, each algorithm also accepts parameters in their own specific format. More information in [Tune documentation](https://docs.ray.io/en/master/tune/api/suggestion.html).
 
 Random Search (default) accepts dictionaries in the format `{ param_name: str : distribution: list }` or a list of such dictionaries, just like scikit-learn's `RandomizedSearchCV`.
 
@@ -191,5 +191,16 @@ Tune-sklearn also supports the use of other machine learning libraries such as P
 * [Pytorch (Skorch)](https://github.com/ray-project/tune-sklearn/blob/master/examples/torch_nn.py)
 * [XGBoost](https://github.com/ray-project/tune-sklearn/blob/master/examples/xgbclassifier.py)
 
+## [Documentation](docs)
+
+See the auto-generated docs [here](docs).
+
+These are generated by `lazydocs` and should be updated on every release:
+
+```bash
+pip install lazydocs
+lazydocs /path/to/tune-sklearn/tune-sklearn --src-base-url="https://github.com/ray-project/tune-sklearn/blob/master" --overview-file="README.md"
+```
+
 ## More information
 [Ray Tune](https://docs.ray.io/en/latest/tune/index.html)

docs/.pages

@@ -0,0 +1,4 @@
+title: API Reference
+nav:
+    - Overview: README.md
+    - ...

docs/README.md

@@ -0,0 +1,37 @@
+<!-- markdownlint-disable -->
+
+# API Overview
+
+## Modules
+
+- [`list_searcher`](./list_searcher.md#module-list_searcher): Helper class to support passing a
+- [`tune_basesearch`](./tune_basesearch.md#module-tune_basesearch): Parent class for a cross-validation interface
+- [`tune_gridsearch`](./tune_gridsearch.md#module-tune_gridsearch): Class for doing grid search over lists of hyperparameters
+- [`tune_search`](./tune_search.md#module-tune_search): Class for cross-validation over distributions of hyperparameters
+- [`utils`](./utils.md#module-utils)
+
+## Classes
+
+- [`list_searcher.ListSearcher`](./list_searcher.md#class-listsearcher): Custom search algorithm to support passing in a list of
+- [`list_searcher.RandomListSearcher`](./list_searcher.md#class-randomlistsearcher): Custom search algorithm to support passing in a list of
+- [`tune_basesearch.TuneBaseSearchCV`](./tune_basesearch.md#class-tunebasesearchcv): Abstract base class for TuneGridSearchCV and TuneSearchCV
+- [`tune_gridsearch.TuneGridSearchCV`](./tune_gridsearch.md#class-tunegridsearchcv): Exhaustive search over specified parameter values for an estimator.
+- [`tune_search.TuneSearchCV`](./tune_search.md#class-tunesearchcv): Generic, non-grid search on hyper parameters.
+- [`utils.EarlyStopping`](./utils.md#class-earlystopping): An enumeration.
+
+## Functions
+
+- [`tune_basesearch.resolve_early_stopping`](./tune_basesearch.md#function-resolve_early_stopping)
+- [`utils.check_error_warm_start`](./utils.md#function-check_error_warm_start)
+- [`utils.check_is_pipeline`](./utils.md#function-check_is_pipeline)
+- [`utils.check_partial_fit`](./utils.md#function-check_partial_fit)
+- [`utils.check_warm_start_ensemble`](./utils.md#function-check_warm_start_ensemble)
+- [`utils.check_warm_start_iter`](./utils.md#function-check_warm_start_iter)
+- [`utils.get_early_stop_type`](./utils.md#function-get_early_stop_type)
+- [`utils.is_tune_grid_search`](./utils.md#function-is_tune_grid_search): Checks if obj is a dictionary returned by tune.grid_search.
+- [`utils.resolve_logger_callbacks`](./utils.md#function-resolve_logger_callbacks)
+
+
+---
+
+_This file was automatically generated via [lazydocs](https://github.com/ml-tooling/lazydocs)._

docs/list_searcher.md

@@ -0,0 +1,141 @@
+<!-- markdownlint-disable -->
+
+<a href="https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/list_searcher.py#L0"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
+
+# <kbd>module</kbd> `list_searcher`
+Helper class to support passing a list of dictionaries for hyperparameters 
+    -- Anthony Yu and Michael Chau 
+
+
+
+---
+
+<a href="https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/list_searcher.py#L11"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
+
+## <kbd>class</kbd> `ListSearcher`
+Custom search algorithm to support passing in a list of dictionaries to TuneGridSearchCV 
+
+<a href="https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/list_searcher.py#L17"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
+
+### <kbd>method</kbd> `__init__`
+
+```python
+__init__(param_grid)
+```
+
+
+
+
+
+
+---
+
+#### <kbd>property</kbd> metric
+
+The training result objective value attribute. 
+
+---
+
+#### <kbd>property</kbd> mode
+
+Specifies if minimizing or maximizing the metric. 
+
+
+
+---
+
+<a href="https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/list_searcher.py#L25"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
+
+### <kbd>method</kbd> `on_trial_complete`
+
+```python
+on_trial_complete(**kwargs)
+```
+
+
+
+
+
+---
+
+<a href="https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/list_searcher.py#L21"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
+
+### <kbd>method</kbd> `suggest`
+
+```python
+suggest(trial_id)
+```
+
+
+
+
+
+
+---
+
+<a href="https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/list_searcher.py#L29"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
+
+## <kbd>class</kbd> `RandomListSearcher`
+Custom search algorithm to support passing in a list of dictionaries to TuneSearchCV for randomized search 
+
+<a href="https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/list_searcher.py#L35"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
+
+### <kbd>method</kbd> `__init__`
+
+```python
+__init__(param_grid)
+```
+
+
+
+
+
+
+---
+
+#### <kbd>property</kbd> metric
+
+The training result objective value attribute. 
+
+---
+
+#### <kbd>property</kbd> mode
+
+Specifies if minimizing or maximizing the metric. 
+
+
+
+---
+
+<a href="https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/list_searcher.py#L55"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
+
+### <kbd>method</kbd> `on_trial_complete`
+
+```python
+on_trial_complete(**kwargs)
+```
+
+
+
+
+
+---
+
+<a href="https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/list_searcher.py#L39"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
+
+### <kbd>method</kbd> `suggest`
+
+```python
+suggest(trial_id)
+```
+
+
+
+
+
+
+
+
+---
+
+_This file was automatically generated via [lazydocs](https://github.com/ml-tooling/lazydocs)._