User guide for active learning

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

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_)