:py:mod:`airflow.providers.google.cloud.utils.mlengine_operator_utils`
======================================================================

.. py:module:: airflow.providers.google.cloud.utils.mlengine_operator_utils

.. autoapi-nested-parse::

   This module contains helper functions for MLEngine operators.


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


Functions
~~~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.utils.mlengine_operator_utils.create_evaluate_ops


Attributes
~~~~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.utils.mlengine_operator_utils.T


.. py:data:: T
   

.. py:function:: create_evaluate_ops(task_prefix, data_format, input_paths, prediction_path, metric_fn_and_keys, validate_fn, batch_prediction_job_id = None, region = None, project_id = None, dataflow_options = None, model_uri = None, model_name = None, version_name = None, dag = None, py_interpreter='python3')

   Creates Operators needed for model evaluation and returns.

   It gets prediction over inputs via Cloud ML Engine BatchPrediction API by
   calling MLEngineBatchPredictionOperator, then summarize and validate
   the result via Cloud Dataflow using DataFlowPythonOperator.

   For details and pricing about Batch prediction, please refer to the website
   https://cloud.google.com/ml-engine/docs/how-tos/batch-predict
   and for Cloud Dataflow, https://cloud.google.com/dataflow/docs/

   It returns three chained operators for prediction, summary, and validation,
   named as ``<prefix>-prediction``, ``<prefix>-summary``, and ``<prefix>-validation``,
   respectively.
   (``<prefix>`` should contain only alphanumeric characters or hyphen.)

   The upstream and downstream can be set accordingly like:

   .. code-block:: python

       pred, _, val = create_evaluate_ops(...)
       pred.set_upstream(upstream_op)
       ...
       downstream_op.set_upstream(val)

   Callers will provide two python callables, metric_fn and validate_fn, in
   order to customize the evaluation behavior as they wish.

   - metric_fn receives a dictionary per instance derived from json in the
     batch prediction result. The keys might vary depending on the model.
     It should return a tuple of metrics.
   - validation_fn receives a dictionary of the averaged metrics that metric_fn
     generated over all instances.
     The key/value of the dictionary matches to what's given by
     metric_fn_and_keys arg.
     The dictionary contains an additional metric, 'count' to represent the
     total number of instances received for evaluation.
     The function would raise an exception to mark the task as failed, in a
     case the validation result is not okay to proceed (i.e. to set the trained
     version as default).

   Typical examples are like this:

   .. code-block:: python

       def get_metric_fn_and_keys():
           import math  # imports should be outside of the metric_fn below.

           def error_and_squared_error(inst):
               label = float(inst["input_label"])
               classes = float(inst["classes"])  # 0 or 1
               err = abs(classes - label)
               squared_err = math.pow(classes - label, 2)
               return (err, squared_err)  # returns a tuple.

           return error_and_squared_error, ["err", "mse"]  # key order must match.


       def validate_err_and_count(summary):
           if summary["err"] > 0.2:
               raise ValueError("Too high err>0.2; summary=%s" % summary)
           if summary["mse"] > 0.05:
               raise ValueError("Too high mse>0.05; summary=%s" % summary)
           if summary["count"] < 1000:
               raise ValueError("Too few instances<1000; summary=%s" % summary)
           return summary

   For the details on the other BatchPrediction-related arguments (project_id,
   job_id, region, data_format, input_paths, prediction_path, model_uri),
   please refer to MLEngineBatchPredictionOperator too.

   :param task_prefix: a prefix for the tasks. Only alphanumeric characters and
       hyphen are allowed (no underscores), since this will be used as dataflow
       job name, which doesn't allow other characters.

   :param data_format: either of 'TEXT', 'TF_RECORD', 'TF_RECORD_GZIP'

   :param input_paths: a list of input paths to be sent to BatchPrediction.

   :param prediction_path: GCS path to put the prediction results in.

   :param metric_fn_and_keys: a tuple of metric_fn and metric_keys:

       - metric_fn is a function that accepts a dictionary (for an instance),
         and returns a tuple of metric(s) that it calculates.

       - metric_keys is a list of strings to denote the key of each metric.

   :param validate_fn: a function to validate whether the averaged metric(s) is
       good enough to push the model.

   :param batch_prediction_job_id: the id to use for the Cloud ML Batch
       prediction job. Passed directly to the MLEngineBatchPredictionOperator as
       the job_id argument.

   :param project_id: the Google Cloud project id in which to execute
       Cloud ML Batch Prediction and Dataflow jobs. If None, then the `dag`'s
       `default_args['project_id']` will be used.

   :param region: the Google Cloud region in which to execute Cloud ML
       Batch Prediction and Dataflow jobs. If None, then the `dag`'s
       `default_args['region']` will be used.

   :param dataflow_options: options to run Dataflow jobs. If None, then the
       `dag`'s `default_args['dataflow_default_options']` will be used.

   :param model_uri: GCS path of the model exported by Tensorflow using
       ``tensorflow.estimator.export_savedmodel()``. It cannot be used with
       model_name or version_name below. See MLEngineBatchPredictionOperator for
       more detail.

   :param model_name: Used to indicate a model to use for prediction. Can be
       used in combination with version_name, but cannot be used together with
       model_uri. See MLEngineBatchPredictionOperator for more detail. If None,
       then the `dag`'s `default_args['model_name']` will be used.

   :param version_name: Used to indicate a model version to use for prediction,
       in combination with model_name. Cannot be used together with model_uri.
       See MLEngineBatchPredictionOperator for more detail. If None, then the
       `dag`'s `default_args['version_name']` will be used.

   :param dag: The `DAG` to use for all Operators.

   :param py_interpreter: Python version of the beam pipeline.
       If None, this defaults to the python3.
       To track python versions supported by beam and related
       issues check: https://issues.apache.org/jira/browse/BEAM-1251

   :returns: a tuple of three operators, (prediction, summary, validation)
   :rtype: tuple(DataFlowPythonOperator, DataFlowPythonOperator,
                 PythonOperator)