Poc model

[chewy-zlai]

Summary

A model to use in the POC. It uses the join as a source.

Checklist

• Added Unit Tests
• Covered by existing CI
• Integration tested
• Documentation update

Summary by CodeRabbit

• New Features
  • Introduced a new risk transaction model for enhanced data analysis and risk assessment.
  • Added structured JSON schema for the risk transaction model, including metadata and join configurations.
• Documentation
  • Updated documentation to reflect the new model and its functionalities.

[coderabbitai]

Walkthrough

The changes introduce a new file transaction_model.py that establishes a model for risk analysis, specifically for user transaction data. It incorporates components from the ai.chronon library to create a Source object linked to a JoinSource, defining a join operation for training data. Additionally, a structured JSON schema for the model is defined in transaction_model.v1, detailing the output schema and join configurations for user and merchant data aggregation.

Changes

File Path	Change Summary
api/py/test/sample/models/risk/transaction_model.py	Introduced classes: Source, JoinSource, Query, and Model. Added variables: source and v1.
api/py/test/sample/production/models/risk/transaction_model.v1	Added structured JSON schema for the risk transaction model, including join configurations and metadata.

Possibly related PRs

• Model api #27: The changes in model.py involve the Model class and its parameters, which are directly related to the Model instance created in transaction_model.py, indicating a shared functionality in model construction and configuration.

Suggested reviewers

• nikhil-zlai

Poem

In the fields where data grows,
A model blooms, as knowledge flows.
With joins and queries, we take our stand,
To analyze risks across the land.
So hop along, let’s celebrate,
For insights gained, we can’t be late! 🐇✨

---

🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

• Review comments: Directly reply to a review comment made by CodeRabbit. Example:
  • I pushed a fix in commit <commit_id>, please review it.
  • Generate unit testing code for this file.
  • Open a follow-up GitHub issue for this discussion.
• Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
  • @coderabbitai generate unit testing code for this file.
  • @coderabbitai modularize this function.
• PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
  • @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
  • @coderabbitai read src/utils.ts and generate unit testing code.
  • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
  • @coderabbitai help me debug CodeRabbit configuration file.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

• @coderabbitai pause to pause the reviews on a PR.
• @coderabbitai resume to resume the paused reviews.
• @coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
• @coderabbitai full review to do a full review from scratch and review all the files again.
• @coderabbitai summary to regenerate the summary of the PR.
• @coderabbitai resolve resolve all the CodeRabbit review comments.
• @coderabbitai configuration to show the current CodeRabbit configuration for the repository.
• @coderabbitai help to get help.

Other keywords and placeholders

• Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
• Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
• Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (.coderabbit.yaml)

• You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
• Please see the configuration documentation for more information.
• If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

• Visit our Documentation for detailed information on how to use CodeRabbit.
• Join our Discord Community to get help, request features, and share feedback.
• Follow us on X/Twitter for updates and announcements.

[coderabbitai]

Actionable comments posted: 5

🧹 Outside diff range and nitpick comments (2)

api/py/test/sample/models/risk/transaction_model.py (2)

8-18: Consider expanding the query selection.

The source definition is well-structured and clearly documented. However, the query only selects the "user_id" field. Depending on the requirements of your risk analysis model, you might want to consider including more relevant fields in the query selection.

If additional fields are needed for the risk analysis, consider modifying the query like this:

 query=Query(
-    selects=select("user_id"),
+    selects=select("user_id", "transaction_amount", "transaction_date"),
 )

Adjust the fields based on your specific requirements for the risk analysis model.

---

1-20: Overall implementation looks good, but some PR objectives are missing.

The implementation of the transaction model for risk analysis is concise and well-structured. It effectively uses the ai.chronon library components to set up a source with a join operation and create a model instance.

However, there are a few items from the PR objectives that are not addressed in this file:

1. Unit tests are not present. Consider adding unit tests to ensure the model behaves as expected.
2. Documentation updates are not visible. It would be beneficial to add more detailed documentation about how to use this model and its expected inputs and outputs.
3. Integration testing is not evident. Ensure that this model integrates well with the rest of the system.

Would you like assistance in generating unit tests or documentation for this model? I can help create a basic structure for these if needed.

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between c33893e and fb56853.

📒 Files selected for processing (2)

• api/py/test/sample/models/risk/transaction_model.py (1 hunks)
• api/py/test/sample/production/models/risk/transaction_model.v1 (1 hunks)

🧰 Additional context used

🔇 Additional comments (3)

api/py/test/sample/models/risk/transaction_model.py (2)

1-5: LGTM: Import statements are appropriate.

The import statements are well-structured and import the necessary components from the ai.chronon library and the required join definition. This sets up the foundation for the model creation.

---

20-20: Verify if DOUBLE output is sufficient for the risk analysis.

The model creation is concise and uses appropriate components. The use of XGBoost is a good choice for many risk analysis tasks. However, consider if a single DOUBLE output is sufficient for your specific risk analysis requirements.

Please confirm that a single DOUBLE output meets the needs of your risk analysis model. If your model needs to output multiple values or categorical data, you might need to adjust the output schema. For example:

# For multiple numerical outputs
outputSchema=TDataType(DataKind.ARRAY, subtype=TDataType(DataKind.DOUBLE))

# For categorical output
outputSchema=TDataType(DataKind.STRING)

api/py/test/sample/production/models/risk/transaction_model.v1 (1)

1-267: Overall structure and configuration look solid

The model is well-defined with comprehensive join configurations and aggregations tailored for risk assessment. The use of prefixes and key columns is appropriate, and the selection of aggregation windows aligns with typical risk analysis periods.