skopt_tuner
===================================

.. py:module:: evalml.tuners.skopt_tuner

.. autoapi-nested-parse::

   Bayesian Optimizer.



Module Contents
---------------

Classes Summary
~~~~~~~~~~~~~~~

.. autoapisummary::

   evalml.tuners.skopt_tuner.SKOptTuner





Attributes Summary
~~~~~~~~~~~~~~~~~~~

.. autoapisummary::

   evalml.tuners.skopt_tuner.logger


Contents
~~~~~~~~~~~~~~~~~~~
.. py:data:: logger
   

   

.. py:class:: SKOptTuner(pipeline_hyperparameter_ranges, random_seed=0)



   Bayesian Optimizer.

   :param pipeline_hyperparameter_ranges: A set of hyperparameter ranges corresponding to a pipeline's parameters.
   :type pipeline_hyperparameter_ranges: dict
   :param random_seed: The seed for the random number generator. Defaults to 0.
   :type random_seed: int

   .. rubric:: Examples

   >>> tuner = SKOptTuner({'My Component': {'param a': [0.0, 10.0], 'param b': ['a', 'b', 'c']}})
   >>> proposal = tuner.propose()
   ...
   >>> assert proposal.keys() == {'My Component'}
   >>> assert proposal['My Component'] == {'param a': 5.928446182250184, 'param b': 'c'}

   Determines points using a Bayesian Optimizer approach.

   >>> for each in range(7):
   ...     print(tuner.propose())
   {'My Component': {'param a': 8.57945617622757, 'param b': 'c'}}
   {'My Component': {'param a': 6.235636967859724, 'param b': 'b'}}
   {'My Component': {'param a': 2.9753460654447235, 'param b': 'a'}}
   {'My Component': {'param a': 2.7265629458011325, 'param b': 'b'}}
   {'My Component': {'param a': 8.121687287754932, 'param b': 'b'}}
   {'My Component': {'param a': 3.927847961008298, 'param b': 'c'}}
   {'My Component': {'param a': 3.3739616041726843, 'param b': 'b'}}




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.tuners.skopt_tuner.SKOptTuner.add
      evalml.tuners.skopt_tuner.SKOptTuner.get_starting_parameters
      evalml.tuners.skopt_tuner.SKOptTuner.is_search_space_exhausted
      evalml.tuners.skopt_tuner.SKOptTuner.propose

   .. py:method:: add(self, pipeline_parameters, score)

      Add score to sample.

      :param pipeline_parameters: A dict of the parameters used to evaluate a pipeline
      :type pipeline_parameters: dict
      :param score: The score obtained by evaluating the pipeline with the provided parameters
      :type score: float

      :returns: None

      :raises Exception: If skopt tuner errors.
      :raises ParameterError: If skopt receives invalid parameters.


   .. py:method:: get_starting_parameters(self, hyperparameter_ranges, random_seed=0)

      Gets the starting parameters given the pipeline hyperparameter range.

      :param hyperparameter_ranges: The custom hyperparameter ranges passed in during search. Used to determine the starting parameters.
      :type hyperparameter_ranges: dict
      :param random_seed: The random seed to use. Defaults to 0.
      :type random_seed: int

      :returns: The starting parameters, randomly chosen, to initialize a pipeline with.
      :rtype: dict


   .. py:method:: is_search_space_exhausted(self)

      Optional. If possible search space for tuner is finite, this method indicates whether or not all possible parameters have been scored.

      :returns: Returns true if all possible parameters in a search space has been scored.
      :rtype: bool


   .. py:method:: propose(self)

      Returns a suggested set of parameters to train and score a pipeline with, based off the search space dimensions and prior samples.

      :returns: Proposed pipeline parameters.
      :rtype: dict