Scoring Pandas Data Frames

Do parallelized scoring of a data frame in mini-batches against a MLOps deployment.

Prerequisites

• h2o_mlops_scoring_client-*-py3-none-any.whl file or access to the Python Package Index (PyPI)

Setup

Install the h2o_mlops_scoring_client with pip.

Example Usage

import h2o_mlops_scoring_client
import pandas

Choose the MLOps scoring endpoint.

MLOPS_ENDPOINT_URL = "https://model.internal.dedicated.h2o.ai/d4d36117-c94a-4182-8b75-5f5abbd1c28b/model/score"

Get a data frame to use along with a unique ID column used to identify each score.

DATA_FRAME = pandas.read_csv("/Users/jgranados/datasets/BNPParibas.csv")
ID_COLUMN = "ID"

And now we score.

pandas_df = h2o_mlops_scoring_client.score_data_frame(
    mlops_endpoint_url=MLOPS_ENDPOINT_URL,
    id_column=ID_COLUMN,
    data_frame=DATA_FRAME,
)

23/08/21 14:23:58 INFO h2o_mlops_scoring_client: Connecting to H2O.ai MLOps scorer at 'https://model.internal.dedicated.h2o.ai/d4d36117-c94a-4182-8b75-5f5abbd1c28b/model/score'
23/08/21 14:23:59 INFO h2o_mlops_scoring_client: Starting scoring data frame
23/08/21 14:25:11 INFO h2o_mlops_scoring_client: Scoring complete
23/08/21 14:25:11 INFO h2o_mlops_scoring_client: Total run time: 0:01:13
23/08/21 14:25:11 INFO h2o_mlops_scoring_client: Scoring run time: 0:01:12

Optionally merge the scores into the original data frame.

DATA_FRAME.merge(pandas_df, on=ID_COLUMN)

	ID	target	v1	v2	v3	v4	v5	v6	v7	v8	...	v124	v125	v126	v127	v128	v129	v130	v131	target.0	target.1
0	3	1	1.335739	8.727474	C	3.921026	7.915266	2.599278	3.176895	0.012941	...	0.035754	AU	1.804126	3.113719	2.024285	0	0.636365	2.857144	0.116770	0.883230
1	4	1	NaN	NaN	C	NaN	9.191265	NaN	NaN	2.301630	...	0.598896	AF	NaN	NaN	1.957825	0	NaN	NaN	0.298435	0.701565
2	5	1	0.943877	5.310079	C	4.410969	5.326159	3.979592	3.928571	0.019645	...	0.013452	AE	1.773709	3.922193	1.120468	2	0.883118	1.176472	0.154390	0.845610
3	6	1	0.797415	8.304757	C	4.225930	11.627438	2.097700	1.987549	0.171947	...	0.002267	CJ	1.415230	2.954381	1.990847	1	1.677108	1.034483	0.042505	0.957495
4	8	1	NaN	NaN	C	NaN	NaN	NaN	NaN	NaN	...	NaN	Z	NaN	NaN	NaN	0	NaN	NaN	0.057625	0.942375
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
114316	228708	1	NaN	NaN	C	NaN	NaN	NaN	NaN	NaN	...	NaN	AL	NaN	NaN	NaN	0	NaN	NaN	0.108165	0.891835
114317	228710	1	NaN	NaN	C	NaN	NaN	NaN	NaN	NaN	...	NaN	E	NaN	NaN	NaN	1	NaN	NaN	0.038374	0.961626
114318	228711	1	NaN	NaN	C	NaN	10.069277	NaN	NaN	0.323324	...	0.156764	Q	NaN	NaN	2.417606	2	NaN	NaN	0.053958	0.946042
114319	228712	1	NaN	NaN	C	NaN	10.106144	NaN	NaN	0.309226	...	0.490658	BW	NaN	NaN	3.526650	0	NaN	NaN	0.220766	0.779234
114320	228713	1	1.619763	7.932978	C	4.640085	8.473141	2.351470	2.826766	3.479754	...	3.135205	V	1.943149	4.385553	1.604493	0	1.787610	1.386138	0.129088	0.870912

114321 rows × 135 columns

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