Docs update for RF replacement (#1265)

* WIP

* fit api and parallel predcting

* remnove thread lock

* implement for predict_marginalized_over_instances_batch

* add facades for pyrfr

* add pyrfr mf facade

* maint class names

* move rfr to optional requirements

* mainti README

* add tests for rf with log y

* rf trained with n_jobs=-1 instead of None

* make rf compatible with older version sklearn

* maint mypy

* update change log

* add docstrings

* fix black

* maint

* drop support for python 3.8

* maint

* black fix

* simplify pyrfr facade design

* raise errors if pyrfr is not properly installed

* maint rf

* fix pre-commit

* update rfr information for README and docs

---------

Co-authored-by: benjamc <[email protected]>

Author: dengdifan

README.md

@@ -38,8 +38,7 @@ We hope you enjoy this new user experience as much as we do. 🚀
 
 This instruction is for the installation on a Linux system, for Windows and Mac and further information see the [documentation](https://automl.github.io/SMAC3/latest/1_installation/).
 
-Create a new environment with python 3.10 and make sure swig is installed either on your system or
-inside the environment. We demonstrate the installation via anaconda in the following:
+Create a new environment with python 3.10. We demonstrate the installation via anaconda in the following:
 
 Create and activate environment:
 ```
@@ -62,7 +61,7 @@ make install-dev
 starting from 2.4.0, SMAC uses random forest from [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html)
 instead of random forest from [pyrfr](https://pypi.org/project/pyrfr/) as the default surrogate model for HPO tasks.
 However, you could still use the old pyrfr surrogate model by calling `smac.facade.old.HyperparameterOptimizationRFRFacade`
-and `smac.facade.old.MultiFidelityRFRFacade`
+and `smac.facade.old.MultiFidelityRFRFacade`.
 
 To work with pyrfr, you need to first install gcc, gxx, and swig:
 ```

docs/1_installation.md

@@ -1,12 +1,11 @@
 # Installation
 ## Requirements
 
-SMAC is written in python3 and therefore requires an environment with python>=3.8.
-Furthermore, the Random Forest used in SMAC requires SWIG as a build dependency.
+SMAC is written in python3 and therefore requires an environment with python>=3.9.
 
 !!! info 
 
-    SMAC is tested on Linux and Mac machines with python >=3.8.
+    SMAC is tested on Linux and Mac machines with python >=3.9.
 
 
 ## SetUp
@@ -18,17 +17,6 @@ conda create -n SMAC python=3.10
 conda activate SMAC
 ```
 
-Now install swig either on the system level e.g. using the following command for Linux:
-```bash
-apt-get install swig
-```
-
-Or install swig inside of an already created conda environment using:
-
-```bash
-conda install gxx_linux-64 gcc_linux-64 swig
-```
-
 ## Install SMAC
 You can install SMAC either using PyPI or Conda-forge.
 
@@ -63,7 +51,31 @@ conda install smac
 
 Read [SMAC feedstock](https://github.com/conda-forge/smac-feedstock) for more details.
 
-## Windows (native or via WSL, experimental)
+## Running SMAC with pyrfr
+starting from 2.4.0, SMAC uses random forest from [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html)
+instead of random forest from [pyrfr](https://pypi.org/project/pyrfr/) as the default surrogate model for HPO tasks.
+However, you could still use the old pyrfr surrogate model by calling `smac.facade.old.HyperparameterOptimizationRFRFacade`
+and `smac.facade.old.MultiFidelityRFRFacade`.
+
+The Random Forest used in SMAC requires SWIG as a build dependency. 
+You could install swig either on the system level e.g. using the following command for Linux:
+```bash
+apt-get install swig
+```
+
+Or install swig inside of an already created conda environment using:
+
+```bash
+conda install gxx_linux-64 gcc_linux-64 swig
+```
+
+And then install smac with the pyrfr option:
+```
+pip install smac[pyrfr]
+```
+
+
+### pyrfr on Windows (native or via WSL, experimental)
 
 SMAC can be installed under Windows in a WSL (Windows Subsystem for Linux). 
 You can find an instruction on how to do this here: [Experimental](./10_experimental.md).