From e7f44b322ff176f85682bfa16dd62abc8e251a0e Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Thu, 18 Jul 2024 21:53:03 +0200
Subject: [PATCH] [docs] SeqDesign on webpage
---
docs/source/al_description.rst | 5 ++---
docs/source/surrogate_description.rst | 7 +------
2 files changed, 3 insertions(+), 9 deletions(-)
diff --git a/docs/source/al_description.rst b/docs/source/al_description.rst
index ac52ef0a6..4b3ce9859 100644
--- a/docs/source/al_description.rst
+++ b/docs/source/al_description.rst
@@ -2,10 +2,10 @@ Active learning: iteratively expanding the training set
*******************************************************
Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.
+The relevant functions are contained in the class :any:`bayesvalidrox.surrogate_models.sequential_design.SequentialDesign` and :any:`bayesvalidrox.surrogate_models.exploration.Exploration`.
.. warning::
- The active learning methods are currently being reworked.
- This should not change the function call ``Engine.train_sequential()``, but will change the associated class structures.
+ Exploration with 'voronoi' is disabled for release v1.1.0!
.. image:: ../diagrams/active_learning_reduced.png
:width: 550
@@ -54,6 +54,5 @@ Once all properties are set, we can assemble the engine and start it.
This time we use ``train_sequential``.
>>> Engine_ = Engine(MetaMod, Model, ExpDesign)
->>> Engine_.start_engine()
>>> Engine_.train_sequential()
\ No newline at end of file
diff --git a/docs/source/surrogate_description.rst b/docs/source/surrogate_description.rst
index 0d17b06ce..2a1da4b3f 100644
--- a/docs/source/surrogate_description.rst
+++ b/docs/source/surrogate_description.rst
@@ -85,14 +85,9 @@ For this the sampling method should be set to 'user' and our samples given as ``
>>> ExpDesign.sampling_method = 'user'
>>> ExpDesign.root_samples = samples
-Now we create an engine object with the model, experimental design and surrogate model.
-With the function ``start_engine`` the engine performs its preparations for training.
+Now we create an engine object with the model, experimental design and surrogate model and run the training.
>>> Engine_ = Engine(MetaMod, Model, ExpDesign)
->>> Engine_.start_engine()
-
-Then we train the surrogate model.
-
>>> Engine.train_normal()
We can evaluate the trained surrogate model in two ways, via the engine, or directly.
--
GitLab