Relax the transformation contract

Oct 13, 2025 by orovajewels.com 20 views

Problem Description

In the context of time series data transformation, certain operations may generate new instances from the input data. This is particularly relevant in scenarios such as reconciliation and data augmentation. However, the current transformation contract restricts the number of instances after transformation, which can lead to unnecessary test cases and limitations in data processing.

Solution Overview

The proposed solution aims to relax the panel-to-panel transformation contract to allow for a different number of instances after transformation. This change will enable more flexibility in data processing and reduce the need for redundant test cases.

Current Limitations

The current transformation contract is restrictive in that it assumes a one-to-one correspondence between the input and output instances. This is evident in the following test case:

if X_scitype == "Panel" and Xt_scitype == "Panel":
      assert X_metadata["n_instances"] == Xt_metadata["n_instances"]

This test case is necessary to ensure that the transformation contract is upheld, but it can be avoided if the contract is relaxed to allow for different numbers of instances.

Proposed Solution

To address the current limitations, we propose the following changes:

Relax the transformation contract: Modify the transformation contract to allow for a different number of instances after transformation.
Add a new tag: Introduce a new tag to indicate that the transformation is capable of generating a different number of instances.
Update test cases: Update test cases to reflect the relaxed transformation contract and the new tag.

Benefits of the Proposed Solution

The proposed solution offers several benefits, including:

Increased flexibility: The relaxed transformation contract allows for more flexibility in data processing, enabling users to perform a wider range of operations on their data.
Reduced test cases: By avoiding the need for redundant test cases, users can reduce their testing burden and focus on more critical aspects of their data processing pipeline.
Improved data augmentation: The ability to generate a different number of instances after transformation enables more effective data augmentation, which can improve the accuracy and robustness of machine learning models.

Implementation Details

To implement the proposed solution, we will need to make the following changes:

Modify the transformation contract: Update the transformation contract to allow for a different number of instances after transformation.
Implement the new tag: Introduce a new tag to indicate that the transformation is capable of generating a different number of instances.
Update test cases: Update test cases to reflect the relaxed transformation contract and the new tag.

Example Use Cases

The proposed solution will be particularly useful in scenarios such as:

Reconciliation: The ability to generate a different number of instances after transformation enables more effective reconciliation, which can improve the accuracy and robustness of machine learning models.
Data augmentation: The relaxed transformation contract allows for more effective data augmentation, which can improve the accuracy and robustness of machine learning models.

Conclusion

In conclusion, the proposed solution aims to relax the panel-to-panel transformation contract to allow for a different number of instances after transformation. This change will enable more flexibility in data processing, reduce the need for redundant test cases, and improve data augmentation. We believe that this solution will be particularly useful in scenarios such as reconciliation and data augmentation, and we look forward to implementing it in the near future.

Additional Context

Q: What is the current limitation of the panel-to-panel transformation contract?

A: The current transformation contract assumes a one-to-one correspondence between the input and output instances. This means that the number of instances after transformation must be the same as the number of instances before transformation.

Q: Why is this limitation a problem?

A: This limitation can lead to unnecessary test cases and limitations in data processing. For example, some unit tests have a test case that checks if the number of instances after transformation is the same as the number of instances before transformation. This test case can be avoided if the transformation contract is relaxed to allow for different numbers of instances.

Q: What is the proposed solution to this limitation?

A: The proposed solution is to relax the panel-to-panel transformation contract to allow for a different number of instances after transformation. This change will enable more flexibility in data processing and reduce the need for redundant test cases.

Q: How will the proposed solution be implemented?

A: To implement the proposed solution, we will need to make the following changes:

Modify the transformation contract: Update the transformation contract to allow for a different number of instances after transformation.
Implement the new tag: Introduce a new tag to indicate that the transformation is capable of generating a different number of instances.
Update test cases: Update test cases to reflect the relaxed transformation contract and the new tag.

Q: What are the benefits of the proposed solution?

A: The proposed solution offers several benefits, including:

Increased flexibility: The relaxed transformation contract allows for more flexibility in data processing, enabling users to perform a wider range of operations on their data.
Reduced test cases: By avoiding the need for redundant test cases, users can reduce their testing burden and focus on more critical aspects of their data processing pipeline.
Improved data augmentation: The ability to generate a different number of instances after transformation enables more effective data augmentation, which can improve the accuracy and robustness of machine learning models.

Q: What are some example use cases for the proposed solution?

A: The proposed solution will be particularly useful in scenarios such as:

Reconciliation: The ability to generate a different number of instances after transformation enables more effective reconciliation, which can improve the accuracy and robustness of machine learning models.
Data augmentation: The relaxed transformation contract allows for more effective data augmentation, which can improve the accuracy and robustness of machine learning models.

Q: What is the current status of the proposed solution?

A: The proposed solution is currently in the discussion phase. We are working on implementing the necessary changes to the transformation contract and test cases.

Q: How can I provide feedback on the proposed solution?

A: We welcome feedback from the community on the proposed solution. You can provide feedback by commenting on this issue or by reaching out to us directly.

Q: What is the expected timeline for implementing the proposed solution?

A: We expect to implement the proposed solution in the near future. The exact timeline will depend on the complexity of the changes and the availability of resources.

Q: How will the proposed solution be tested?

A: We will test the proposed solution thoroughly to ensure that it meets the required standards. This will include writing new test cases and updating existing test cases to reflect the relaxed transformation contract and the new tag.

Q: What are the potential risks associated with the proposed solution?

A: The proposed solution carries some risks, including:

Changes to existing code: The proposed solution will require changes to existing code, which can be time-consuming and error-prone.
Impact on existing tests: The proposed solution may impact existing tests, which can require updates to ensure that they continue to function correctly.

Q: How will the potential risks be mitigated?

A: We will mitigate the potential risks associated with the proposed solution by:

Thoroughly testing the changes: We will test the changes thoroughly to ensure that they meet the required standards.
Updating existing tests: We will update existing tests to reflect the relaxed transformation contract and the new tag.
Providing clear documentation: We will provide clear documentation on the changes and their impact on existing code and tests.