docs-archive/apache-airflow-providers-google/8.4.0/_sources/_api/airflow/providers/google/cloud/utils/mlengine_prediction_summary/index.rst.txt

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

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

.. autoapi-nested-parse::

   A template called by DataFlowPythonOperator to summarize BatchPrediction.

   It accepts a user function to calculate the metric(s) per instance in
   the prediction results, then aggregates to output as a summary.

   It accepts the following arguments:

   - ``--prediction_path``:
     The GCS folder that contains BatchPrediction results, containing
     ``prediction.results-NNNNN-of-NNNNN`` files in the json format.
     Output will be also stored in this folder, as 'prediction.summary.json'.
   - ``--metric_fn_encoded``:
     An encoded function that calculates and returns a tuple of metric(s)
     for a given instance (as a dictionary). It should be encoded
     via ``base64.b64encode(dill.dumps(fn, recurse=True))``.
   - ``--metric_keys``:
     A comma-separated key(s) of the aggregated metric(s) in the summary
     output. The order and the size of the keys must match to the output
     of metric_fn.
     The summary will have an additional key, 'count', to represent the
     total number of instances, so the keys shouldn't include 'count'.


   Usage example:

   .. code-block: python

       from airflow.providers.google.cloud.operators.dataflow import DataflowCreatePythonJobOperator


       def get_metric_fn():
           import math  # all imports must be outside of the function to be passed.
           def metric_fn(inst):
               label = float(inst["input_label"])
               classes = float(inst["classes"])
               prediction = float(inst["scores"][1])
               log_loss = math.log(1 + math.exp(
                   -(label * 2 - 1) * math.log(prediction / (1 - prediction))))
               squared_err = (classes-label)**2
               return (log_loss, squared_err)
           return metric_fn
       metric_fn_encoded = base64.b64encode(dill.dumps(get_metric_fn(), recurse=True))
       DataflowCreatePythonJobOperator(
           task_id="summary-prediction",
           py_options=["-m"],
           py_file="airflow.providers.google.cloud.utils.mlengine_prediction_summary",
           options={
               "prediction_path": prediction_path,
               "metric_fn_encoded": metric_fn_encoded,
               "metric_keys": "log_loss,mse"
           },
           dataflow_default_options={
               "project": "xxx", "region": "us-east1",
               "staging_location": "gs://yy", "temp_location": "gs://zz",
           }
       ) >> dag

   When the input file is like the following::

       {"inputs": "1,x,y,z", "classes": 1, "scores": [0.1, 0.9]}
       {"inputs": "0,o,m,g", "classes": 0, "scores": [0.7, 0.3]}
       {"inputs": "1,o,m,w", "classes": 0, "scores": [0.6, 0.4]}
       {"inputs": "1,b,r,b", "classes": 1, "scores": [0.2, 0.8]}

   The output file will be::

       {"log_loss": 0.43890510565304547, "count": 4, "mse": 0.25}

   To test outside of the dag:

   .. code-block:: python

       subprocess.check_call(
           [
               "python",
               "-m",
               "airflow.providers.google.cloud.utils.mlengine_prediction_summary",
               "--prediction_path=gs://...",
               "--metric_fn_encoded=" + metric_fn_encoded,
               "--metric_keys=log_loss,mse",
               "--runner=DataflowRunner",
               "--staging_location=gs://...",
               "--temp_location=gs://...",
           ]
       )

   .. spelling::

       pcoll



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

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.utils.mlengine_prediction_summary.JsonCoder



Functions
~~~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.utils.mlengine_prediction_summary.MakeSummary
   airflow.providers.google.cloud.utils.mlengine_prediction_summary.run



.. py:class:: JsonCoder

   JSON encoder/decoder.

   .. py:method:: encode(x)
      :staticmethod:

      JSON encoder.


   .. py:method:: decode(x)
      :staticmethod:

      JSON decoder.



.. py:function:: MakeSummary(pcoll, metric_fn, metric_keys)

   Summary PTransform used in Dataflow.


.. py:function:: run(argv=None)

   Helper for obtaining prediction summary.