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Real time scoring

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

Prerequisites

Install the H2O eScorer Python client.

Import the H2O eScorer client​

import h2o_escorer
import asyncio

Add the async function main to authenticate and score​

async def main():
    client = Client()

    response = await client.realtime_scorer(
        model_name='<model_name>',
        dataset_filepath='/path/to/dataset.csv',
        skip_header=True,
        num_rows=1000,
        progress=True
    )

    await client.close()

    return response

Call the main function asynchronously​

result = asyncio.run(main())
100%|██████████| 1000/1000 [02:15<00:00,  7.39it/s]

Get the predictions​

print(result['predictions'])
              bad_loan.0           bad_loan.1 
0     0.08621099591255188   0.9137890040874481
1     0.08830222487449646   0.9116977751255035
2     0.03488980233669281   0.9651101976633072
3      0.2397906482219696   0.7602093517780304
4      0.4451645463705063   0.5548354536294937
..                    ...                  ...
995   0.15911102294921875   0.8408889770507812
996   0.03693510591983795    0.963064894080162
997   0.25428231060504913   0.7457176893949509
998    0.1850033551454544   0.8149966448545456
999   0.03011934459209442   0.9698806554079056

[1000 rows x 2 columns]

Get the number of rows scored​

print(result['rows_produced'])
1000

Get the scoring latency​

print(f'{result["time_elapsed"]}s')
135.317s
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