Make example input and PyFuncInput support csc csr matrix#5016
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WeichenXu123 merged 16 commits intoDec 7, 2021
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Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
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tomasatdatabricks
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Looks good to me in general. One aspect I am not sure if I understand is - do we store the example as dense matrix? I think we should have a way to store it as sparse matrix and load it back as sparse matrix.
| else: | ||
| elif isinstance(input_tensor, np.ndarray): | ||
| return {"inputs": input_tensor.tolist()} | ||
| else: |
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- can we do
elif isinstance(input_tesnor,csr_matrix, csc_matrix)? - does this mean we store sparse input as dense vector? or is this stored as array of indices and array of values?
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I update code to store sparse input as data/indices/indptr vectors.
I also split csc/csr saving code out of the ndarray saving code.
harupy
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Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
tomasatdatabricks
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Thanks for the changes! The looks good in great.
I left a minor comment about the example type string for sparse matrices but it's only a suggestion. I am also ok with it as is.
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
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What changes are proposed in this pull request?
Make example input and PyFuncInput support csc csr matrix
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