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Version: v0.11.x

Batch scoring

This example demonstrates how to use the eScorer Python client for batch scoring. You can download the complete example here.

Prerequisites

Install the H2O eScorer Python client.

Batch scoring with the Python client​

After authenticating the client, you can use the batch_scorer method to perform batch scoring. The batch_scorer method takes the model name and the properties file path as arguments. The properties file contains the configuration for the batch scoring job. The batch_job object is returned, which can be used to monitor the progress of the batch scoring job.

Note

For information on how to autogenerate and populate a properties file to configure batch scoring, see Batch scoring configuration and usage.

batch_job = await client.batch_scorer(
    model_name='<model_name>',
    properties_filepath='/path/to/file.properties',
    verbose=True,
)

Get job ID​

batch_job.id
09e5042c-096a-4ee7-9bd6-182ef7457298

Check if the job is complete​

await batch_job.is_complete()
True

Get the logs​

await batch_job.get_logs()
****** AWS *******
Thread-3 BAD DATA Too Many Features ROW: = 31487,3500, 36 months,7.74,109.27,9,31200,"Not,Verified",MN,5.73,,1092,9.9,12 Len:15 Features:13 Offset: 1
Thread-3 Model has 13 features but after parsing feature count is 15 check field seperator or maybe the data contains a default seperator.
Total selected rows 39029 Total Read time (ms) 16056
Thread-1 Rows Read 19644 Scored 19644 Error 0 Queue Empty true
Thread-3 Rows Read 19385 Scored 19384 Error 1 Queue Empty true
Upload of file escorer/predictions-2024-05-28-07-51-58.csv to S3 completed
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