cf_engine
========================================

.. py:module:: evalml.automl.engine.cf_engine

.. autoapi-nested-parse::

   Custom CFClient API to match Dask's CFClient and allow context management.



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

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

.. autoapisummary::

   evalml.automl.engine.cf_engine.CFClient
   evalml.automl.engine.cf_engine.CFComputation
   evalml.automl.engine.cf_engine.CFEngine





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: CFClient(pool)

   Custom CFClient API to match Dask's CFClient and allow context management.

   :param pool: The resource pool to execute the futures work on.
   :type pool: cf.ThreadPoolExecutor or cf.ProcessPoolExecutor




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.automl.engine.cf_engine.CFClient.close
      evalml.automl.engine.cf_engine.CFClient.is_closed
      evalml.automl.engine.cf_engine.CFClient.submit

   .. py:method:: close(self)

      Closes the underlying Executor.


   .. py:method:: is_closed(self)
      :property:

      Property that determines whether the Engine's Client's resources are closed.


   .. py:method:: submit(self, *args, **kwargs)

      Pass through to imitate Dask's Client API.



.. py:class:: CFComputation(future)



   A Future-like wrapper around jobs created by the CFEngine.

   :param future: The concurrent.futures.Future that is desired to be executed.
   :type future: cf.Future




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.automl.engine.cf_engine.CFComputation.cancel
      evalml.automl.engine.cf_engine.CFComputation.done
      evalml.automl.engine.cf_engine.CFComputation.get_result
      evalml.automl.engine.cf_engine.CFComputation.is_cancelled

   .. py:method:: cancel(self)

      Cancel the current computation.

      :returns:

                False if the call is currently being executed or finished running
                  and cannot be cancelled.  True if the call can be canceled.
      :rtype: bool


   .. py:method:: done(self)

      Returns whether the computation is done.


   .. py:method:: get_result(self)

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

      :raises Exception: If computation fails. Returns traceback.
      :raises cf.TimeoutError: If computation takes longer than default timeout time.
      :raises cf.CancelledError: If computation was canceled before completing.

      :returns: The result of the requested job.


   .. py:method:: is_cancelled(self)
      :property:

      Returns whether computation was cancelled.



.. py:class:: CFEngine(client=None)



   The concurrent.futures (CF) engine.

   :param client: If None, creates a threaded pool for processing. Defaults to None.
   :type client: None or CFClient




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.automl.engine.cf_engine.CFEngine.close
      evalml.automl.engine.cf_engine.CFEngine.is_closed
      evalml.automl.engine.cf_engine.CFEngine.setup_job_log
      evalml.automl.engine.cf_engine.CFEngine.submit_evaluation_job
      evalml.automl.engine.cf_engine.CFEngine.submit_scoring_job
      evalml.automl.engine.cf_engine.CFEngine.submit_training_job

   .. py:method:: close(self)

      Function to properly shutdown the Engine's Client's resources.


   .. py:method:: is_closed(self)
      :property:

      Property that determines whether the Engine's Client's resources are shutdown.


   .. 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)

      Send evaluation job to cluster.

      :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:

                An object wrapping a reference to a future-like computation
                    occurring in the resource pool
      :rtype: CFComputation


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

      Send scoring job to cluster.

      :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: Objectives to score on.
      :type objectives: list[ObjectiveBase]

      :returns:

                An object wrapping a reference to a future-like computation
                    occurring in the resource pool.
      :rtype: CFComputation


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

      Send training job to cluster.

      :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

      :returns:

                An object wrapping a reference to a future-like computation
                    occurring in the resource pool
      :rtype: CFComputation