Cron Tasks #42
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Cron Tasks #42
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Co-authored-by: Megaklis Vasilakis <megaklis.vasilakis@gmail.com> Co-authored-by: Stavros Theocharis <stavrostheocharis@users.noreply.github.com>
gcharis
reviewed
Nov 14, 2022
| "class2" | ||
| ] | ||
| assert multi_metrics["accuracy"] == 0.6 | ||
| assert multi_metrics.accuracy == 0.6 |
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How can we verify this number?
This question is more generic:
We should distinguish the tests that assert the results from the libraries used in our pipelines. Testing a pipeline does not necessarily mean testing the value that occurs.
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This is a nice discussion in general. Testing in ML may sometimes be hard. For such cases as the above you mention we can by ourselves do the calculations and check if the result is the same as the one indicated by the pipeline.
sinnec
commented
Nov 14, 2022
src/cron_tasks/monitoring_metrics.py
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| data_drif_report = run_data_drift_pipeline(training_df, inference_df) | ||
| # TODO: Fix pipeline to return a DataDriftTable first | ||
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| new_drifting_metric = entities.DriftingMetric( |
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Shouldn't this be DriftingMetric from schemas?
Co-authored-by: John Zaras <sinnec@users.noreply.github.com> Co-authored-by: Charis Giaralis <gcharis@users.noreply.github.com> Co-authored-by: Stavros Theocharis <stavrostheocharis@users.noreply.github.com>
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Co-authored-by: Megaklis Vasilakis megaklis.vasilakis@gmail.com
Co-authored-by: Stavros Theocharis stavrostheocharis@users.noreply.github.com