tuner
=============================

.. py:module:: evalml.tuners.tuner

.. autoapi-nested-parse::

   Base Tuner class.



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

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

.. autoapisummary::

   evalml.tuners.tuner.Tuner





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: Tuner(pipeline_hyperparameter_ranges, random_seed=0)



   Base Tuner class.

   Tuners implement different strategies for sampling from a search space. They're used in EvalML to search the space of pipeline hyperparameters.

   :param pipeline_hyperparameter_ranges: a set of hyperparameter ranges corresponding to a pipeline's parameters.
   :type pipeline_hyperparameter_ranges: dict
   :param random_seed: The random state. Defaults to 0.
   :type random_seed: int




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.tuners.tuner.Tuner.add
      evalml.tuners.tuner.Tuner.get_starting_parameters
      evalml.tuners.tuner.Tuner.is_search_space_exhausted
      evalml.tuners.tuner.Tuner.propose

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

      Register a set of hyperparameters with the score obtained from training a pipeline with those hyperparameters.

      :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


   .. 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)
      :abstractmethod:

      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