sequential_engine
================================================

.. py:module:: evalml.automl.engine.sequential_engine

.. autoapi-nested-parse::

   A Future-like api for jobs created by the SequentialEngine, an Engine that sequentially computes the submitted jobs.



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

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

.. autoapisummary::

   evalml.automl.engine.sequential_engine.SequentialComputation
   evalml.automl.engine.sequential_engine.SequentialEngine





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: SequentialComputation(work, **kwargs)



   A Future-like api for jobs created by the SequentialEngine, an Engine that sequentially computes the submitted jobs.

   In order to separate the engine from the AutoMLSearch loop, we need the sequential computations to behave the same
   way as concurrent computations from AutoMLSearch's point-of-view. One way to do this is by delaying the computation
   in the sequential engine until get_result is called. Since AutoMLSearch will call get_result only when the
   computation is "done", by always returning True in done() we make sure that get_result is called in the order that
   the jobs are submitted. So the computations happen sequentially!

   :param work: Computation that should be done by the engine.
   :type work: callable




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.automl.engine.sequential_engine.SequentialComputation.cancel
      evalml.automl.engine.sequential_engine.SequentialComputation.done
      evalml.automl.engine.sequential_engine.SequentialComputation.get_result

   .. py:method:: cancel(self)

      Cancel the current computation.


   .. py:method:: done(self)

      Whether the computation is done.

      :returns: Always returns True.
      :rtype: bool


   .. py:method:: get_result(self)

      Gets the computation result. Will block until the computation is finished.

      :raises Exception: If computation fails. Returns traceback.

      :returns: Computation results.



.. py:class:: SequentialEngine



   The default engine for the AutoML search.

   Trains and scores pipelines locally and sequentially.




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.automl.engine.sequential_engine.SequentialEngine.close
      evalml.automl.engine.sequential_engine.SequentialEngine.setup_job_log
      evalml.automl.engine.sequential_engine.SequentialEngine.submit_evaluation_job
      evalml.automl.engine.sequential_engine.SequentialEngine.submit_scoring_job
      evalml.automl.engine.sequential_engine.SequentialEngine.submit_training_job

   .. py:method:: close(self)

      No-op.


   .. py:method:: setup_job_log()
      :staticmethod:

      Set up logger for job.


   .. py:method:: submit_evaluation_job(self, automl_config, pipeline, X, y, X_holdout=None, y_holdout=None)

      Submit a job to evaluate a pipeline.

      :param automl_config: Structure containing data passed from AutoMLSearch instance.
      :param pipeline: Pipeline to evaluate.
      :type pipeline: pipeline.PipelineBase
      :param X: Input data for modeling.
      :type X: pd.DataFrame
      :param y: Target data for modeling.
      :type y: pd.Series
      :param X_holdout: Holdout input data for holdout scoring.
      :type X_holdout: pd.Series
      :param y_holdout: Holdout target data for holdout scoring.
      :type y_holdout: pd.Series

      :returns: Computation result.
      :rtype: SequentialComputation


   .. py:method:: submit_scoring_job(self, automl_config, pipeline, X, y, objectives, X_train=None, y_train=None)

      Submit a job to score a pipeline.

      :param automl_config: Structure containing data passed from AutoMLSearch instance.
      :param pipeline: Pipeline to train.
      :type pipeline: pipeline.PipelineBase
      :param X: Input data for modeling.
      :type X: pd.DataFrame
      :param y: Target data for modeling.
      :type y: pd.Series
      :param X_train: Training features. Used for feature engineering in time series.
      :type X_train: pd.DataFrame
      :param y_train: Training target. Used for feature engineering in time series.
      :type y_train: pd.Series
      :param objectives: List of objectives to score on.
      :type objectives: list[ObjectiveBase]

      :returns: Computation result.
      :rtype: SequentialComputation


   .. py:method:: submit_training_job(self, automl_config, pipeline, X, y)

      Submit a job to train a pipeline.

      :param automl_config: Structure containing data passed from AutoMLSearch instance.
      :param pipeline: Pipeline to evaluate.
      :type pipeline: pipeline.PipelineBase
      :param X: Input data for modeling.
      :type X: pd.DataFrame
      :param y: Target data for modeling.
      :type y: pd.Series

      :returns: Computation result.
      :rtype: SequentialComputation