Fix issue where pd.read_json converts strings to dates #4902
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Signed-off-by: Wendy Hu <wendy.hu@databricks.com>
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Looks good, thanks for the fix @wentinghu! 2 comments:
- I don't think we should change behavior when model has no schema.
- Can you add a short test?
mlflow/utils/proto_json_utils.py
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| orient=pandas_orient, | ||
| dtype=False, | ||
| precise_float=precise_float, | ||
| convert_dates=False, |
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Hmm, I am. not 100% if we want to change the behavior when the model has no schema. It could be a breaking change.
Signed-off-by: Wendy Hu <wendy.hu@databricks.com>
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Signed-off-by: Wendy Hu wendy.hu@databricks.com
What changes are proposed in this pull request?
This fixes a bug during schema enforcement where date-like strings are cast to datetime objects when converting json to dataframes
How is this patch tested?
unit tests
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/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