Improve statsmodels autologging metrics

[WeichenXu123]

Signed-off-by: Weichen Xu weichen.xu@databricks.com

Improve statsmodels autologging metrics
(1) We only log basic metrics of:
[
'aic', 'bic', 'centered_tss', 'condition_number', 'df_model', 'df_resid', 'ess', 'f_pvalue',
'fvalue', 'llf', 'mse_model', 'mse_resid', 'mse_total', 'rsquared', 'rsquared_adj', 'scale',
'ssr', 'uncentered_tss'
]

(2) log the model.summary() as a text artifact.
Example of artifact display:

How is this patch tested?

Unit tests.

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

(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 logging
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

[dbczumar]

LGTM with a couple small comments. Thanks @WeichenXu123 !

[harupy]

LGTM, once #4942 (comment) is addressed!

[harupy]

Suggested change

	# because we patch metric property to make it raise error, OLSResults.summary internally
	# call metric property will also raise error, so also patch OLSResults.summary
	# Prevent `OLSResults.summary` from calling `fvalue` and `f_pvalue` that raise an exception.