Fix accuracy score feature name in model validation #6729
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jerrylian-db
commented
Sep 7, 2022
| ) | ||
| # If you would like to catch model validation failures, you can add try except clauses around | ||
| # the mlflow.evaluate() call and catch the ModelValidationFailedException, imported at the top | ||
| # of this file. |
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Getting rid of catching model validation exceptions so that we can catch future model validation breaking changes.
jerrylian-db
commented
Sep 7, 2022
| @@ -1092,11 +1092,14 @@ def evaluate( | |||
| if evaluator_config.get("_disable_candidate_model", False): | |||
| evaluation_result = EvaluationResult(metrics=dict(), artifacts=dict()) | |||
| else: | |||
| if baseline_model: | |||
| _logger.info("Evaluating candidate model:") | |||
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When I was looking at the model evaluation logs, I didn't know whether they belonged to the candidate or the baseline model. I'm adding these logs so that it would be easier for users to make that discernment.
dbczumar
approved these changes
Sep 7, 2022
nnethery
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to nnethery/mlflow
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Feb 1, 2024
* wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
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Signed-off-by: Jerry Liang jerry.liang@databricks.com
Related Issues/PRs
#6593
What changes are proposed in this pull request?
#6593 broke the model validation Python example. This PR fixes that example and adds some usability improvements to model validation. It also makes some doc fixes.
How is this patch tested?
Does this PR change the documentation?
Detailslink on thePreview docscheck.See that accuracy_score has been updated in the mlflow.evaluate() API docs.
Release Notes
Is this a user-facing change?
(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/artifacts: Artifact stores and artifact loggingarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesarea/examples: Example codearea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models: MLmodel format, model serialization/deserialization, flavorsarea/pipelines: Pipelines, Pipeline APIs, Pipeline configs, Pipeline Templatesarea/projects: MLproject format, project running backendsarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra: MLflow Tracking server backendarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows: Windows supportLanguage
language/r: R APIs and clientslanguage/java: Java APIs and clientslanguage/new: Proposals for new client languagesIntegrations
integrations/azure: Azure and Azure ML integrationsintegrations/sagemaker: SageMaker integrationsintegrations/databricks: Databricks integrationsHow should the PR be classified in the release notes? Choose one:
rn/breaking-change- The PR will be mentioned in the "Breaking Changes" sectionrn/none- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/feature- A new user-facing feature worth mentioning in the release notesrn/bug-fix- A user-facing bug fix worth mentioning in the release notesrn/documentation- A user-facing documentation change worth mentioning in the release notes