From f724ec1e742671eadfa0f4385d5105c7b3f60fe5 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Fri, 28 Jun 2024 15:24:34 +0200
Subject: [PATCH] User guide for active learning
---
docs/source/al_description.rst | 34 +++++++++++++++++++++++
examples/user_guide/example_user_guide.py | 16 +++++++++++
2 files changed, 50 insertions(+)
diff --git a/docs/source/al_description.rst b/docs/source/al_description.rst
index c1d5e99f2..7922be0f8 100644
--- a/docs/source/al_description.rst
+++ b/docs/source/al_description.rst
@@ -25,3 +25,37 @@ The tradeoff between exploration and exploitation is defined by **tradeoff-schem
Example
=======
We take the engine from :any:`surrogate_description` and change the settings to perform sequential training.
+
+This mainly changes the ``Engine``.
+We set the ``method`` to ``'sequential'`` to indicate that active learning should be performed.
+For this example we start with the 10 initial samples from :any:`surrogate_description` and increase this iteratively to the number of samples given in ``n_max_samples``.
+The parameter ``n_new_samples`` sets the number of new samples that are chosen in each iteration, while ``mod_LOO_threshold`` sets an additional stopping condition.
+
+>>> ExpDesign.method = 'sequential'
+>>> ExpDesign.n_max_samples = 14
+>>> ExpDesign.n_new_samples = 1
+>>> ExpDesign.mod_LOO_threshold = 1e-16
+
+Here we do not set a ``tradeoff_scheme``.
+This will result in all samples being chosen based on the exploration weights.
+
+>>> ExpDesign.tradeoff_scheme = None
+
+As the proposed samples come from the exploration method, we still need to define this.
+
+>>> ExpDesign.explore_method = 'random'
+>>> ExpDesign.n_canddidate = 1000
+>>> ExpDesign.n_cand_groups = 4
+
+For the exploitation method we use a variance-based method, as no data is given.
+
+>>> ExpDesign.exploit_method = 'VarOptDesign'
+>>> ExpDesign.util_func = 'EIGF'
+
+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/examples/user_guide/example_user_guide.py b/examples/user_guide/example_user_guide.py
index 563850ddc..0a1f5ed06 100644
--- a/examples/user_guide/example_user_guide.py
+++ b/examples/user_guide/example_user_guide.py
@@ -77,6 +77,22 @@ if __name__ == '__main__':
mean, stdev = Engine_.eval_metamodel(nsamples = 10)
mean, stdev = Engine_.MetaModel.eval_metamodel(samples)
+ #### Active learning
+ ExpDesign.method = 'sequential'
+
+ # Set the sampling parameters
+ ExpDesign.n_new_samples = 1
+ ExpDesign.n_max_samples = 14
+ ExpDesign.mod_LOO_threshold = 1e-16
+ ExpDesign.tradeoff_scheme = None
+ ExpDesign.explore_method = 'random'
+ ExpDesign.n_canddidate = 1000
+ ExpDesign.n_cand_groups = 4
+ ExpDesign.exploit_method = 'VarOptDesign'
+ ExpDesign.util_func = 'EIGF'
+ Engine_ = Engine(MetaMod, Model, ExpDesign)
+ Engine_.start_engine()
+ Engine_.train_sequential()
#### Postprocessing
PostProc = PostProcessing(Engine_)
--
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