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"""
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
"""
from __future__ import annotations
import argparse
import base64
import json
import logging
import os
import apache_beam as beam
import dill
from apache_beam.coders.coders import Coder
[docs]class JsonCoder(Coder):
"""JSON encoder/decoder."""
@staticmethod
[docs] def encode(x):
"""JSON encoder."""
return json.dumps(x).encode()
@staticmethod
[docs] def decode(x):
"""JSON decoder."""
return json.loads(x)
@beam.ptransform_fn
[docs]def MakeSummary(pcoll, metric_fn, metric_keys):
"""Summary PTransform used in Dataflow."""
return (
pcoll
| "ApplyMetricFnPerInstance" >> beam.Map(metric_fn)
| "PairWith1" >> beam.Map(lambda tup: tup + (1,))
| "SumTuple" >> beam.CombineGlobally(beam.combiners.TupleCombineFn(*([sum] * (len(metric_keys) + 1))))
| "AverageAndMakeDict"
>> beam.Map(
lambda tup: dict(
[(name, tup[i] / tup[-1]) for i, name in enumerate(metric_keys)] + [("count", tup[-1])]
)
)
)
[docs]def run(argv=None):
"""Helper for obtaining prediction summary."""
parser = argparse.ArgumentParser()
parser.add_argument(
"--prediction_path",
required=True,
help=(
"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 a file"
"'prediction.summary.json'."
),
)
parser.add_argument(
"--metric_fn_encoded",
required=True,
help=(
"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))."
),
)
parser.add_argument(
"--metric_keys",
required=True,
help=(
"A comma-separated keys 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 this flag "
"shouldn't include 'count'."
),
)
known_args, pipeline_args = parser.parse_known_args(argv)
metric_fn = dill.loads(base64.b64decode(known_args.metric_fn_encoded))
if not callable(metric_fn):
raise ValueError("--metric_fn_encoded must be an encoded callable.")
metric_keys = known_args.metric_keys.split(",")
with beam.Pipeline(options=beam.pipeline.PipelineOptions(pipeline_args)) as pipe:
prediction_result_pattern = os.path.join(known_args.prediction_path, "prediction.results-*-of-*")
prediction_summary_path = os.path.join(known_args.prediction_path, "prediction.summary.json")
# This is apache-beam ptransform's convention
_ = (
pipe
| "ReadPredictionResult" >> beam.io.ReadFromText(prediction_result_pattern, coder=JsonCoder())
| "Summary" >> MakeSummary(metric_fn, metric_keys)
| "Write"
>> beam.io.WriteToText(
prediction_summary_path,
shard_name_template="", # without trailing -NNNNN-of-NNNNN.
coder=JsonCoder(),
)
)
if __name__ == "__main__":
# Dataflow does not print anything on the screen by default. Good practice says to configure the logger
# to be able to track the progress. This code is run in a separate process, so it's safe.
logging.getLogger().setLevel(logging.INFO)
run()
