grid_search_tuner
=========================================

.. py:module:: evalml.tuners.grid_search_tuner

.. autoapi-nested-parse::

   Grid Search Optimizer, which generates all of the possible points to search for using a grid.



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

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

.. autoapisummary::

   evalml.tuners.grid_search_tuner.GridSearchTuner





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: GridSearchTuner(pipeline_hyperparameter_ranges, n_points=10, random_seed=0)



   Grid Search Optimizer, which generates all of the possible points to search for using a grid.

   :param pipeline_hyperparameter_ranges: a set of hyperparameter ranges corresponding to a pipeline's parameters
   :type pipeline_hyperparameter_ranges: dict
   :param n_points: The number of points to sample from along each dimension
                    defined in the ``space`` argument. Defaults to 10.
   :type n_points: int
   :param random_seed: Seed for random number generator. Unused in this class, defaults to 0.
   :type random_seed: int

   .. rubric:: Examples

   >>> tuner = GridSearchTuner({'My Component': {'param a': [0.0, 10.0], 'param b': ['a', 'b', 'c']}}, n_points=5)
   >>> proposal = tuner.propose()
   ...
   >>> assert proposal.keys() == {'My Component'}
   >>> assert proposal['My Component'] == {'param a': 0.0, 'param b': 'a'}

   Determines points using a grid search approach.

   >>> for each in range(5):
   ...     print(tuner.propose())
   {'My Component': {'param a': 0.0, 'param b': 'b'}}
   {'My Component': {'param a': 0.0, 'param b': 'c'}}
   {'My Component': {'param a': 10.0, 'param b': 'a'}}
   {'My Component': {'param a': 10.0, 'param b': 'b'}}
   {'My Component': {'param a': 10.0, 'param b': 'c'}}




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.tuners.grid_search_tuner.GridSearchTuner.add
      evalml.tuners.grid_search_tuner.GridSearchTuner.get_starting_parameters
      evalml.tuners.grid_search_tuner.GridSearchTuner.is_search_space_exhausted
      evalml.tuners.grid_search_tuner.GridSearchTuner.propose

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

      Not applicable to grid search tuner as generated parameters are not dependent on scores of previous parameters.

      :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


   .. 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)

      Checks if it is possible to generate a set of valid parameters. Stores generated parameters in ``self.curr_params`` to be returned by ``propose()``.

      :returns: If no more valid parameters exists in the search space, return False.
      :rtype: bool

      :raises NoParamsException: If a search space is exhausted, then this exception is thrown.


   .. py:method:: propose(self)

      Returns parameters from _grid_points iterations.

      If all possible combinations of parameters have been scored, then ``NoParamsException`` is raised.

      :returns: proposed pipeline parameters
      :rtype: dict