.. _ExplorationObjective:


Exploration objective
---------------------


The objective is to learn the global behaviour of a  target variable by exploring regions with large information gain. It implements two exploration strategies:  

:uncertainty: Samples are targeted that have a large prediction uncertainty.   That is, the posterior of the target variable has a large variance.  

:surprise: This strategy only works in conjunction with neural network   ensembles. It considers the gradient of the weights of the output layer   (feature layer) with respect to the loss :math:`L(\mathbf{p}) = \sum_i   \left(nn_i(\mathbf{p}) - \overline{y}(\mathbf{p})\right)^2`, where   :math:`nn_i(\mathbf{p})` is the prediction of the :math:`i`-th ensemble   member and :math:`\overline{y}(\mathbf{p})` is the fantasy of the function   value (i.e. the average of the ensemble members). The strategy targets   parameters with maximal gradient magnitude, :math:`\Vert \nabla_\theta   L(\mathbf{p}) \Vert_2`, where :math:`\theta` denotes the weights of the   output layer.  

If using a neural network ensemble, the ``'surprise'`` strategy offers often offers more informed sampling leading to slightly more accurate predictions.

.. _ActiveLearning.Explorer.name:


name (str)
""""""""""
   The name of the objective. 

   Default: ``'objective'``

.. _ActiveLearning.Explorer.variable:


variable (str)
""""""""""""""
   The name of a single-output variable  that specifies the objective value. 

   Default: The first  variable in the list of all variables.

.. _ActiveLearning.Explorer.penalize_boundaries:


penalize_boundaries (bool)
""""""""""""""""""""""""""
   If true, the uncertainty is penalized by a small distance to the domain boundary. 

   Default: ``False``

.. _ActiveLearning.Explorer.min_uncertainty:


min_uncertainty (float)
"""""""""""""""""""""""
   The study is stopped if the uncertainty (square root of variance) of the objective at the last 5 sampling points was below ``min_uncertainty``. 

   Default: ``0.0``

.. _ActiveLearning.Explorer.strategy:


strategy (str)
""""""""""""""
   Exploration strategy. If ``strategy='surprise'`` all input surrogates must be neural network ensembles. 

   Default: ``'uncertainty'``
   Choices: ``'uncertainty'``, ``'surprise'``.