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Fix tests broken in pandas 2.0 #8577
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schema = _infer_schema(pd.Series(np.array([True, None], dtype=object))) | ||
assert schema == Schema([ColSpec(DataType.string)]) |
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Documentation preview for d063993 will be available here when this CircleCI job completes successfully. More info
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Looks good, but can we also take a look at
FAILED tests/pyfunc/test_pyfunc_schema_enforcement.py::test_column_schema_enforcement - TypeError: Cannot cast DatetimeArray to dtype datetime64[D]
i think it may be related to these other changes.
Signed-off-by: harupy <hkawamura0130@gmail.com>
Signed-off-by: harupy <hkawamura0130@gmail.com>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
We've been running tests with
pandas < 2.0
unknowingly because ofsktime
. The latest version ofsktime
no longer pins pandas to< 2.0
. Tests run withpandas 2.0.x
now. This change broke a few tests. This PR fixes them.How is this patch tested?
Does this PR change the documentation?
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/recipes
: Recipes, Recipe APIs, Recipe configs, Recipe 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