Model api

[chewy-zlai]

Summary

Checklist

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

Summary by CodeRabbit

• New Features

  • Introduced a new Model function for constructing machine learning model objects with customizable parameters.
  • Added a ModelType class to encapsulate different model types such as XGBoost and PyTorch.
  • New model configuration quickstart.test.v1 for defining output schemas and model parameters.

• Configurations

  • New JSON configuration files for the quickstart.test.v1 model, specifying output schema and source details.

• Tests

  • Added tests for setting up training datasets and integrating with machine learning models.

[coderabbitai]

Walkthrough

The changes in this pull request introduce a new Model function and a ModelType class in the model.py file, enhancing model creation capabilities. Additionally, a new JSON configuration file for the model quickstart.test.v1 is added, defining its schema and parameters. Updates in the test.py file focus on setting up a training dataset for machine learning, encapsulating event sources and queries.

Changes

File Path	Change Summary
api/py/ai/chronon/model.py	Added Model function and ModelType class for model creation.
api/py/test/sample/models/quickstart/test.py	Added classes Source, EventSource, Query, and Model, function select, and variables source, v1 for training setup.
api/py/test/sample/production/models/quickstart/test.v1	New JSON configuration for model quickstart.test.v1.

Poem

In fields of code, we hop and play,
New models bloom, bright as day.
With data streams, we group and join,
Each change a step, our skills we hone.
From merchants' tales to users' spree,
A rabbit's work is never done, you see! 🐇✨

---

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[coderabbitai]

Actionable comments posted: 29

🧹 Outside diff range and nitpick comments (25)

api/py/test/sample/models/quickstart/test.py (2)

7-18: LGTM: Well-structured source definition with clear documentation.

The Source object is well-defined and includes a helpful comment explaining its purpose in the training set. The structure using nested EventSource and Query objects is clear and logical.

Consider adding error handling or validation for the table name and column names to ensure they exist in the database. This could prevent runtime errors if the schema changes or if there's a typo in the names.

---

1-20: Overall structure is good, but consider expanding functionality.

The file provides a solid foundation for setting up a machine learning model with a defined data source. However, it appears to be incomplete as a standalone example or test file. Consider the following suggestions:

1. Add actual training steps for the model.
2. Include model evaluation or prediction functionality.
3. If this is meant to be a test file (as suggested by the filename), add proper test cases using a testing framework like pytest.
4. Provide more comprehensive comments or docstrings explaining the purpose and usage of this setup.

These additions would make the file more self-contained and useful as either an example or a test suite.

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

19-19: Add documentation for modelType value.

The modelType is set to 1, but without context, it's not clear what this value represents. Consider adding a comment or documentation explaining the meaning of different modelType values.

Could you provide documentation or a brief explanation of what modelType 1 represents?

api/py/test/sample/joins/risk/user_transactions.py (2)

8-16: LGTM: Source object is well-configured. Consider adding a docstring.

The source_users Source object is correctly set up to fetch user data. The use of a Query object for flexible data selection is a good practice.

Consider adding a brief docstring to explain the purpose of this Source object and any important details about the data it's fetching. This would improve code readability and maintainability.

---

18-21: LGTM: Join object is well-structured. Consider refactoring for readability.

The txn_join Join object effectively combines user data with transaction and merchant data using multiple JoinPart objects. This allows for complex data relationships, which is good.

Consider the following improvements:

1. Break down the long line (line 20) for better readability. You can use Python's line continuation or create a list of JoinPart objects.
2. Add a docstring explaining the purpose of this join and how it should be used.

Here's a suggested refactor:

# Define JoinPart objects
join_parts = [
    JoinPart(group_by=txn_group_by_user, prefix="user"),
    JoinPart(group_by=txn_group_by_merchant, prefix="merchant"),
    JoinPart(group_by=user_group_by, prefix="user"),
    JoinPart(group_by=merchant_group_by, prefix="merchant")
]

txn_join = Join(
    left=source_users,
    right_parts=join_parts
)

# Add a docstring explaining the purpose and usage of txn_join

This refactoring improves readability and makes it easier to modify the join structure in the future if needed.

api/py/test/sample/group_bys/risk/merchant_data.py (3)

1-9: Remove unused imports to improve code cleanliness.

The following imports are currently unused in the file:

• Aggregation
• Operation
• Window
• TimeUnit

Consider removing these imports to keep the code clean and improve readability. If you plan to use these in the future, you can add them back when needed.

Apply this diff to remove the unused imports:

 from ai.chronon.api.ttypes import Source, EntitySource
 from ai.chronon.query import Query, select
-from ai.chronon.group_by import (
-    GroupBy,
-    Aggregation,
-    Operation,
-    Window,
-    TimeUnit
-)
+from ai.chronon.group_by import GroupBy

🧰 Tools

🪛 Ruff

5-5: ai.chronon.group_by.Aggregation imported but unused

Remove unused import

(F401)

---

6-6: ai.chronon.group_by.Operation imported but unused

Remove unused import

(F401)

---

7-7: ai.chronon.group_by.Window imported but unused

Remove unused import

(F401)

---

8-8: ai.chronon.group_by.TimeUnit imported but unused

Remove unused import

(F401)

---

11-13: Enhance the docstring for better clarity.

While the current docstring provides a basic description, it could be more informative. Consider expanding it to include:

1. The specific metrics being aggregated
2. The purpose of this aggregation in the context of the application
3. Any important details about the data source or aggregation process

This will help other developers understand the purpose and functionality of this module more easily.

Here's a suggested improvement:

"""
This GroupBy aggregates metrics about a merchant's previous transactions in various time windows.

It processes raw purchase events to calculate aggregated metrics such as:
- Total transaction volume
- Average transaction amount
- Number of unique customers

These metrics are used for risk assessment and merchant profiling in our payment processing system.

The data is sourced from daily batch updates of the merchant transaction log.
"""

---

15-23: Approve source definition and suggest error handling.

The source_merchants definition looks good. It correctly sets up a Source object with an EntitySource, pointing to the appropriate snapshot table and selecting relevant fields.

However, consider adding error handling or validation to ensure the data source is available and contains the expected fields. This could prevent runtime errors if the data source is unavailable or has changed.

Consider wrapping the Source creation in a try-except block and adding a validation function. Here's an example:

def validate_source(source):
    # Add validation logic here
    pass

try:
    source_merchants = Source(
        entities=EntitySource(
            snapshotTable="data.merchants",
            query=Query(
                selects=select("merchant_id", "account_age", "zipcode", "is_big_merchant", "country", "account_type", "preferred_language"),
            )
        )
    )
    validate_source(source_merchants)
except Exception as e:
    logging.error(f"Failed to create or validate source_merchants: {str(e)}")
    # Handle the error appropriately

api/py/test/sample/group_bys/risk/user_data.py (3)

1-9: Consider removing unused imports.

The following imports are currently unused in the file:

• Aggregation
• Operation
• Window
• TimeUnit

If these are not intended for immediate future use, consider removing them to keep the imports clean and improve code readability.

Apply this diff to remove the unused imports:

 from ai.chronon.group_by import (
     GroupBy,
-    Aggregation,
-    Operation,
-    Window,
-    TimeUnit
 )

🧰 Tools

🪛 Ruff

5-5: ai.chronon.group_by.Aggregation imported but unused

Remove unused import

(F401)

---

6-6: ai.chronon.group_by.Operation imported but unused

Remove unused import

(F401)

---

7-7: ai.chronon.group_by.Window imported but unused

Remove unused import

(F401)

---

8-8: ai.chronon.group_by.TimeUnit imported but unused

Remove unused import

(F401)

---

11-13: Enhance the docstring with more specific information.

While the current docstring provides a basic description, it could be more informative. Consider expanding it to include:

• The specific metrics being aggregated
• The time windows used for aggregation (if any)
• The purpose or use case for this aggregation

This will help other developers understand the functionality and purpose of this module more clearly.

Here's a suggested improvement:

"""
This GroupBy aggregates metrics about a user's previous purchases in various time windows.
It includes metrics such as account age, balance, credit score, and device usage,
which can be used for risk assessment or user profiling purposes.
"""

---

15-23: LGTM! Consider clarifying the comment about the snapshot table.

The implementation of source_users looks good. It correctly defines a Source with an EntitySource pointing to the "data.users" snapshot table and selects relevant user attributes.

Consider updating the comment on line 18 to clarify the nature of the snapshot table:

-        snapshotTable="data.users", # This points to the log table in the warehouse with historical purchase events, updated in batch daily
+        snapshotTable="data.users", # This points to the snapshot table in the warehouse with historical user data, updated in batch daily

This change more accurately reflects that it's a user data table, not specifically a purchase events log.

api/py/test/sample/teams.json (2)

60-63: LGTM! Consider adding more details to the description.

The addition of the "risk" section is consistent with the existing structure and appears to be correctly implemented.

Consider expanding the description to provide more context about the specific proof of concept this section is intended for. This could help other developers understand its purpose and usage better.

For example:

 "risk": {
-    "description": "Used for the proof of concept",
+    "description": "Used for the risk assessment proof of concept. Includes configurations for risk modeling and analysis.",
     "namespace": "default"
 }

---

Line range hint 1-63: Overall structure looks good. Consider documenting the file's purpose.

The addition of the "risk" section maintains the consistency of the file structure. Each section follows a similar pattern, which is good for maintainability.

Consider adding a comment at the beginning of the file to explain its purpose and how it's used in the context of the project. This would help new developers understand the significance of this configuration file.

For example, you could add:

{
  "// File purpose": "This JSON file defines configurations for various teams and environments used in the Chronon project. It includes settings for production, development, and specific use cases.",
  "default": {
    // ... existing content ...
  },
  // ... rest of the file ...
}

api/py/ai/chronon/repo/compile.py (1)

Line range hint 28-41: Summary: New Model class support added.

The changes in this file consistently introduce support for a new Model class. This includes importing the Model class, adding MODEL_FOLDER_NAME to the imports, and updating the FOLDER_NAME_TO_CLASS dictionary. These changes appear to be part of a larger feature implementation for handling Model objects.

Consider the following follow-up actions:

1. Ensure that the Model class is fully implemented and tested.
2. Update any relevant documentation to reflect the addition of the Model class.
3. Review other parts of the codebase that might need to be updated to support this new functionality.
4. Add unit tests for the new Model class handling in this script.

To maintain consistency and ensure proper integration of the new Model class, consider the following:

1. Review and update the extract_and_convert function to handle Model objects if necessary.
2. Check if the _write_obj and _write_obj_as_json functions need any modifications to support Model objects.
3. Ensure that any validation logic in ChrononRepoValidator is updated to include Model object validation.

api/thrift/api.thrift (3)

425-428: Consider enum value naming consistency and future extensibility

The ModelType enum is a good addition for categorizing machine learning model types. However, there are two points to consider:

1. Enum value naming: The current values use PascalCase (XGBoost, PyTorch), which is inconsistent with other enums in the file that use UPPER_CASE. Consider changing to XGBOOST and PYTORCH for consistency.

2. Future extensibility: The enum is currently limited to two specific ML frameworks. Consider adding a more generic option like OTHER or CUSTOM to accommodate potential future model types without requiring changes to the Thrift definition.

Here's a suggested revision:

enum ModelType {
    XGBOOST = 1
    PYTORCH = 2
    CUSTOM = 3  // For future extensibility
}

---

430-436: Approve Model struct with minor suggestions

The Model struct is well-designed to encapsulate various aspects of a machine learning model. It reuses existing structures where appropriate and provides flexibility through optional fields. Here are a few suggestions for improvement:

1. Consider making modelType and metaData required fields to ensure basic information is always present.
2. The modelParams field is good for flexibility, but consider adding documentation to clarify its usage and any expected key-value pairs for different model types.
3. Consider reordering the fields for better logical grouping:
   • modelType and metaData (core information)
   • source (input)
   • outputSchema (output)
   • modelParams (configuration)

Here's a suggested revision with comments:

struct Model {
}

---

425-436: Clarify integration and usage of new Model structures

The addition of ModelType and Model structures introduces machine learning concepts to the API, which is a significant enhancement. However, there are a few points to consider regarding their integration:

1. Usage clarity: It's not immediately clear how these new structures will be used within the existing API. Consider adding comments or documentation to explain their intended use and interaction with other structures.

2. Service integration: The PR is titled "Model api", but there are no new service definitions or methods that utilize the Model struct. Consider adding or updating service definitions to show how these new structures will be accessed or manipulated through the API.

3. Consistency with existing patterns: While the new structures reuse existing types like TDataType, MetaData, and Source, ensure that their usage aligns with the patterns established in other parts of the API.

4. Version compatibility: If this is a significant change to the API, consider adding version information or migration guidelines for existing users of the API.

Could you provide more context on how these new structures will be used and integrated into the existing API? This information would help ensure that the additions are fully aligned with the API's overall architecture and design patterns.

api/py/ai/chronon/model.py (1)

30-30: Reminder: Address the TODO comment

There's a TODO comment indicating that the map should be converted to TDataType. Please ensure this is implemented to avoid potential issues later.

Would you like assistance in implementing this conversion?

api/py/test/sample/production/group_bys/risk/transaction_events.txn_group_by_merchant (1)

58-70: Consider adding comments or documentation for aggregations.

Providing explanations for each aggregation can improve readability and help future developers understand the purpose and logic behind them.

api/py/test/sample/group_bys/risk/user_transactions.py (2)

27-27: Use more descriptive variable names instead of group_by1 and group_by2

Variable names like group_by1 and group_by2 are not descriptive and can make the code harder to understand and maintain. Consider renaming them to reflect their specific purposes, such as user_transactions_group_by and merchant_transactions_group_by.

Apply this diff to rename the variables:

-# At line 27
-group_by1 = GroupBy(
+# Rename to a more descriptive name
+user_transactions_group_by = GroupBy(

 # ...

-# At line 57
-group_by2 = GroupBy(
+# Rename to a more descriptive name
+merchant_transactions_group_by = GroupBy(

Also applies to: 57-57

---

15-15: Differentiate the comments for user and merchant transaction sources

The comments at lines 15 and 45 are identical but refer to different data sources. To improve clarity, adjust the comments to accurately describe each source.

Apply this diff to revise the comments:

-# This source is raw purchase events. Every time a user makes a purchase, it will be one entry in this source.
+# This source represents raw purchase events for users. Each entry corresponds to a user's purchase.

 # ...

-# This source is raw purchase events. Every time a user makes a purchase, it will be one entry in this source.
+# This source represents raw purchase events for merchants. Each entry corresponds to a transaction with a merchant.

Also applies to: 45-45

api/py/test/sample/group_bys/risk/transaction_events.py (1)

15-15: Consolidate duplicate comments for clarity

The comments on lines 15 and 45 are identical, explaining that each source represents raw purchase events logged when a user makes a purchase. To avoid redundancy and enhance clarity, consider consolidating the comment or tailoring each to be more specific to the context.

For example:

• At line 15:

  # Source of raw purchase events for users. Each entry corresponds to a user's purchase.

• At line 45:

  # Source of raw purchase events for merchants. Each entry corresponds to a purchase associated with a merchant.

Also applies to: 45-45

api/py/test/sample/production/joins/risk/user_transactions.txn_join (1)

36-45: Consistency: Include the 'online' field in all 'metaData' sections

Some metaData sections lack the online field. For consistency and clarity, include the online status in all metaData entries.

If online defaults to a specific value when omitted, please disregard this comment.

Also applies to: 106-115, 176-184, 213-221

api/py/test/sample/production/group_bys/risk/transaction_events.txn_group_by_user (2)

3-15: Ensure consistent naming conventions in keys

There is a mix of camelCase (e.g., metaData, customJson) and snake_case/lowercase keys (e.g., tableProperties, outputNamespace, offlineSchedule). Consistency in naming improves readability and maintainability. Consider standardizing the key naming convention throughout the configuration.

---

14-14: Review the offlineSchedule frequency

The offlineSchedule is set to "@daily". Confirm that this scheduling aligns with data freshness requirements and system capacity. If more frequent updates are needed, consider adjusting the schedule accordingly.

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between 52a2ed4 and 0f57ce8.

📒 Files selected for processing (16)

• api/py/ai/chronon/model.py (1 hunks)
• api/py/ai/chronon/repo/init.py (1 hunks)
• api/py/ai/chronon/repo/compile.py (2 hunks)
• api/py/test/sample/group_bys/risk/merchant_data.py (1 hunks)
• api/py/test/sample/group_bys/risk/transaction_events.py (1 hunks)
• api/py/test/sample/group_bys/risk/user_data.py (1 hunks)
• api/py/test/sample/group_bys/risk/user_transactions.py (1 hunks)
• api/py/test/sample/joins/risk/user_transactions.py (1 hunks)
• api/py/test/sample/models/quickstart/test.py (1 hunks)
• api/py/test/sample/production/group_bys/risk/transaction_events.txn_group_by_merchant (1 hunks)
• api/py/test/sample/production/group_bys/risk/transaction_events.txn_group_by_user (1 hunks)
• api/py/test/sample/production/joins/risk/user_transactions.txn_join (1 hunks)
• api/py/test/sample/production/models/quickstart/test.v1 (1 hunks)
• api/py/test/sample/teams.json (1 hunks)
• api/thrift/api.thrift (1 hunks)
• docker-init/compose.yaml (2 hunks)

✅ Files skipped from review due to trivial changes (1)

• api/py/ai/chronon/repo/init.py

🧰 Additional context used

🪛 Ruff

api/py/ai/chronon/model.py

2-2: ai.chronon.utils imported but unused

Remove unused import: ai.chronon.utils

(F401)

---

3-3: logging imported but unused

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---

4-4: inspect imported but unused

Remove unused import: inspect

(F401)

---

5-5: json imported but unused

Remove unused import: json

(F401)

---

6-6: typing.List imported but unused

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6-6: typing.Optional imported but unused

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6-6: typing.Union imported but unused

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6-6: typing.Dict imported but unused

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6-6: typing.Callable imported but unused

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(F401)

---

6-6: typing.Tuple imported but unused

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---

18-18: Do not use mutable data structures for argument defaults

Replace with None; initialize within function

(B006)

api/py/test/sample/group_bys/risk/merchant_data.py

5-5: ai.chronon.group_by.Aggregation imported but unused

Remove unused import

(F401)

---

6-6: ai.chronon.group_by.Operation imported but unused

Remove unused import

(F401)

---

7-7: ai.chronon.group_by.Window imported but unused

Remove unused import

(F401)

---

8-8: ai.chronon.group_by.TimeUnit imported but unused

Remove unused import

(F401)

api/py/test/sample/group_bys/risk/user_data.py

5-5: ai.chronon.group_by.Aggregation imported but unused

Remove unused import

(F401)

---

6-6: ai.chronon.group_by.Operation imported but unused

Remove unused import

(F401)

---

7-7: ai.chronon.group_by.Window imported but unused

Remove unused import

(F401)

---

8-8: ai.chronon.group_by.TimeUnit imported but unused

Remove unused import

(F401)

🪛 yamllint

docker-init/compose.yaml

[error] 2-2: syntax error: mapping values are not allowed here

(syntax)

🔇 Additional comments (26)

api/py/test/sample/models/quickstart/test.py (2)

1-4: LGTM: Import statements are appropriate and well-organized.

The import statements are concise and relevant to the functionality being implemented in this file. They cover the necessary components for defining a model, source, and query.

---

20-20: Please clarify model configuration and next steps.

The Model object is instantiated with the defined source, which is good. However, there are a few points that need clarification:

1. The outputSchema is empty. Is this intentional? If so, please add a comment explaining why.
2. XGBoost is chosen as the model type. Could you provide a brief explanation for this choice?
3. Are there any additional setup or training steps that should be included in this file?

To ensure this is the only place where the model is defined, let's run the following script:

This will help us verify if there are any other places where the model is defined, which might provide more context or configuration.

api/py/test/sample/production/models/quickstart/test.v1 (4)

2-18: Consider adding documentation for numeric values and verify parameter data types.

The outputSchema structure looks correct, but there are a couple of points to consider:

1. The use of numeric values for "kind" (11 for outputSchema, 7 for dataType) might be confusing without proper documentation. Consider adding comments or documentation explaining what these values represent.

2. Both "key" and "value" parameters have the same dataType (kind: 7). While this might be intentional, it's worth verifying if this is the desired configuration.

Could you please confirm if the dataType (kind: 7) is correct for both "key" and "value" parameters? If documentation for these numeric values exists, please provide a reference.

---

20-27: Metadata structure looks good. Verify the "default" namespace.

The metaData section is well-structured and includes relevant information. However, please verify if using "default" as the outputNamespace is intentional and appropriate for this model.

Could you confirm if "default" is the correct outputNamespace for this model? If it's a placeholder, consider replacing it with the actual intended namespace.

---

28-39: Clarify the query structure and setups configuration.

The source section is structured correctly, but there are two points that need clarification:

1. The query currently selects only the "user_id" field. Is this intentional, or should more fields be included?

2. The setups array is empty. If this is intentional, it's fine. However, if additional setup is required for this model, consider adding the necessary configuration.

Could you please confirm if the current query structure and empty setups array are intentional? If not, what additional fields or setups should be included?

---

40-40: Clarify the empty modelParams section.

The modelParams section is currently empty. This could mean either that no parameters are needed for this model, or that the configuration is incomplete.

Could you please confirm if the empty modelParams is intentional? If parameters are needed for this model, please add them to this section.

api/py/test/sample/joins/risk/user_transactions.py (2)

1-6: LGTM: Imports are well-organized and relevant.

The imports are logically structured and include all necessary modules for the functionality being implemented. Good job on following import best practices.

---

1-21: Overall assessment: Well-structured implementation with room for minor improvements.

This new file introduces a solid foundation for managing user transaction data through structured join operations. The code is logically organized and uses appropriate data structures from the Chronon API.

Key points:

1. Imports are well-organized and relevant.
2. The Source object for user data is correctly configured.
3. The Join object effectively combines user, transaction, and merchant data.

Suggestions for improvement:

1. Add docstrings to explain the purpose and usage of the source_users and txn_join objects.
2. Refactor the Join object creation for better readability, breaking down the long line of JoinPart objects.
3. Consider adding comments or documentation to explain the overall purpose of this file in the context of the larger system.

These improvements will enhance the maintainability and readability of the code, making it easier for other developers to understand and work with this module in the future.

api/py/test/sample/group_bys/risk/merchant_data.py (2)

1-29: Summary of review for merchant_data.py

1. The file successfully sets up a data source for merchant transaction data and initializes a GroupBy operation.
2. Main areas for improvement:
   • Remove unused imports to enhance code cleanliness.
   • Expand the docstring to provide more context about the module's purpose and functionality.
   • Consider adding error handling for the data source creation.
   • Define aggregations for the GroupBy operation or explain why they are intentionally left undefined.
3. The file appears to be part of a larger system for processing merchant data, possibly for risk assessment purposes.

Overall, the structure is good, but some refinements will improve its functionality and maintainability.

🧰 Tools

🪛 Ruff

5-5: ai.chronon.group_by.Aggregation imported but unused

Remove unused import

(F401)

---

6-6: ai.chronon.group_by.Operation imported but unused

Remove unused import

(F401)

---

7-7: ai.chronon.group_by.Window imported but unused

Remove unused import

(F401)

---

8-8: ai.chronon.group_by.TimeUnit imported but unused

Remove unused import

(F401)

---

25-29: ⚠️ Potential issue

Clarify the purpose of merchant_group_by without aggregations.

The merchant_group_by object is correctly set up to group by merchant_id, but the aggregations parameter is set to None. This means no actual aggregations are being performed, which is unusual for a GroupBy operation.

Is this intentional? If so, consider adding a comment explaining why. If not, you should define the aggregations you want to perform.

Here's an example of how you might define aggregations:

merchant_group_by = GroupBy(
    sources=[source_merchants],
    keys=["merchant_id"],
    aggregations=[
        Aggregation(operation=Operation.COUNT, alias="transaction_count"),
        Aggregation(operation=Operation.SUM, field="transaction_amount", alias="total_volume"),
        Aggregation(operation=Operation.AVG, field="transaction_amount", alias="avg_transaction_amount"),
    ]
)

To verify the intended usage of GroupBy, let's check for other occurrences in the codebase:

docker-init/compose.yaml (1)

Line range hint 1-58: Summary of changes and their impact

The changes made to this Docker Compose file are minor but beneficial:

1. The project name has been updated to follow a more standard naming convention, which improves consistency and readability.
2. A new dependency has been added to ensure that the base service starts before the app service, which should improve the reliability of the application startup process.

These changes should not introduce any breaking changes to the existing setup and generally improve the configuration.

However, please ensure that all merge conflicts are resolved before merging this pull request.

🧰 Tools

🪛 yamllint

[error] 2-2: syntax error: mapping values are not allowed here

(syntax)

api/py/test/sample/teams.json (1)

Line range hint 1-63: Request for clarification on PR checklist and unit tests

The changes to this configuration file look good, but I have a few questions regarding the PR checklist and the mentioned unit tests:

1. The PR description mentions that unit tests were added. Could you clarify where these unit tests are located, as they're not visible in this file?
2. The PR checklist indicates that integration testing and documentation updates are not completed. Do you plan to address these in a future commit, or should they be part of this PR?

To help verify the existence of the mentioned unit tests, you could run the following command:

This will list any new or modified test files in the PR, which might help locate the mentioned unit tests.

api/py/ai/chronon/repo/compile.py (3)

41-41: LGTM. Verify MODEL_FOLDER_NAME usage in the script.

The addition of MODEL_FOLDER_NAME: Model to the FOLDER_NAME_TO_CLASS dictionary is correct and consistent with the previous changes. This allows the script to handle Model objects similarly to other object types.

To ensure the MODEL_FOLDER_NAME is being used correctly in the script, please run the following:

#!/bin/bash
# Description: Verify the usage of MODEL_FOLDER_NAME in the script.

# Test: Search for MODEL_FOLDER_NAME usage
rg --type python 'MODEL_FOLDER_NAME'

---

30-30: LGTM. Verify MODEL_FOLDER_NAME constant.

The addition of MODEL_FOLDER_NAME to the import statement is correct and consistent with the changes described in the PR summary. This suggests a new folder structure or configuration related to the Model class.

To ensure the MODEL_FOLDER_NAME constant is defined correctly, please run the following script:

#!/bin/bash
# Description: Verify the definition of MODEL_FOLDER_NAME constant.

# Test: Search for MODEL_FOLDER_NAME definition
rg --type python 'MODEL_FOLDER_NAME\s*='

---

28-28: LGTM. Verify Model class usage.

The addition of Model to the import statement is correct and consistent with the changes described in the PR summary. This suggests new functionality related to the Model class.

To ensure the Model class is being used correctly, please run the following script:

✅ Verification successful

Verified Model class usage.

All usages of the Model class have been confirmed and align with the intended functionality.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Verify the usage of the Model class in the codebase.

# Test: Search for Model class usage
rg --type python 'Model(?:\s*\(|\s*=|\s*:)'

Length of output: 73

---

Script:

#!/bin/bash
# Description: Verify the usage of the Model class in the codebase.

# Test: Search for Model class usage
rg --type py 'Model(?:\s*\(|\s*=|\s*:)'

Length of output: 400

api/py/test/sample/production/group_bys/risk/transaction_events.txn_group_by_merchant (4)

33-33: Verify that "merchant_id" is an appropriate key column.

Ensure that "merchant_id" uniquely identifies records for aggregation and aligns with the data model.

---

38-38: Ensure operation codes correspond to intended aggregation functions.

Operation codes 6 and 7 are used in the aggregations. Verify that these codes correctly represent the intended operations according to your system's specifications.

Also applies to: 61-61

---

13-13: Ensure the "team" field value is valid and consistent.

The "team" field is set to "risk". Please verify that this value matches existing team names in your system and follows naming conventions.

Run the following script to list existing team names:

#!/bin/bash
# Description: List all unique team names in the codebase.

# Extract team names from existing configuration files.
fd --type f --extension json | xargs jq -r '.metaData.team' | sort | uniq

---

17-30: Confirm that the event source and selected columns are correct.

Please verify that "data.txn_events" is the correct source table and that the selected columns ("merchant_id", "transaction_amount", "transaction_type") exist in that table.

Run the following script to confirm the table and column existence:

api/py/test/sample/production/joins/risk/user_transactions.txn_join (1)

65-100: Verify correctness of aggregation configurations

Ensure that the aggregation operations and windows are correctly configured to meet the desired analytics requirements.

Run the following script to check for potential issues:

This script will list all unique combinations of inputColumn and operation used in the aggregations. Review the output to confirm that all operations are correctly applied to the intended columns.

Also applies to: 135-170

api/py/test/sample/production/group_bys/risk/transaction_events.txn_group_by_user (6)

22-24: Ensure all necessary columns are selected in query.selects

The selects section includes user_id, transaction_amount, and transaction_type. Verify that all necessary columns for the aggregations and key computations are included. Missing columns could cause issues during data processing.

---

32-34: Validate the keyColumns configuration

The keyColumns field specifies "user_id". Ensure that this key uniquely identifies records and is suitable for grouping transactions by user.

---

38-38: Verify that operation code 6 corresponds to the intended aggregation

The operation field is set to 6. Ensure that this operation code correctly maps to the desired aggregation function in your system's specification.

Run the following script to confirm the operation code:

#!/bin/bash
# Description: Verify that operation code 6 is defined and corresponds to the correct aggregation.

# Test: Search for the definition of operation code 6.
rg --type json --pcre2 '"operation_code"\s*:\s*6' docs/aggregation_operations/

---

42-55: Validate timeUnit values in window configurations

The timeUnit fields have values 0 and 1. Ensure these values correctly correspond to the intended time units (e.g., seconds, minutes, hours, days) according to your system's specifications.

Run the following script to check the timeUnit mappings:

#!/bin/bash
# Description: Verify that timeUnit values 0 and 1 are correctly defined.

# Test: Search for the definitions of timeUnit values.
rg --type json --pcre2 '"timeUnit_definitions"' docs/time_units/

Also applies to: 65-67

---

19-19: Confirm existence of the source table data.txn_events

The configuration references data.txn_events as the source table. Ensure that this table exists and is accessible.

Run the following script to check the table's existence:

#!/bin/bash
# Description: Verify that the source table data.txn_events exists.

# Test: Search for the table definition.
rg --type sql --pcre2 'CREATE\s+TABLE\s+data\.txn_events' schemas/

---

61-61: Verify that operation code 7 corresponds to the intended aggregation

The operation field is set to 7. Confirm that this operation code aligns with the correct aggregation function as per your system's documentation.

Run the following script to confirm the operation code:

[nikhil-zlai]

code rabbit left some pretty good comments I think. ignore the comments on generated json files though.

[coderabbitai]

Actionable comments posted: 4

🧹 Outside diff range and nitpick comments (1)

api/py/ai/chronon/model.py (1)

30-30: Address TODO comment

There's a TODO comment that needs to be addressed:

# Todo: convert map to Tdata type

This comment suggests that there might be additional work needed to convert the map to a Tdata type. Could you provide more context on what needs to be done here? I can assist in implementing the conversion if you provide more details.

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between 0f57ce8 and 846bce5.

📒 Files selected for processing (2)

• api/py/ai/chronon/model.py (1 hunks)
• api/py/test/sample/production/models/quickstart/test.v1 (1 hunks)

🚧 Files skipped from review as they are similar to previous changes (1)

• api/py/test/sample/production/models/quickstart/test.v1

🧰 Additional context used

🪛 Ruff

api/py/ai/chronon/model.py

2-2: ai.chronon.utils imported but unused

Remove unused import: ai.chronon.utils

(F401)

---

3-3: logging imported but unused

Remove unused import: logging

(F401)

---

4-4: inspect imported but unused

Remove unused import: inspect

(F401)

---

5-5: json imported but unused

Remove unused import: json

(F401)

---

6-6: typing.List imported but unused

Remove unused import

(F401)

---

6-6: typing.Optional imported but unused

Remove unused import

(F401)

---

6-6: typing.Union imported but unused

Remove unused import

(F401)

---

6-6: typing.Dict imported but unused

Remove unused import

(F401)

---

6-6: typing.Callable imported but unused

Remove unused import

(F401)

---

6-6: typing.Tuple imported but unused

Remove unused import

(F401)

---

18-18: Do not use mutable data structures for argument defaults

Replace with None; initialize within function

(B006)

🔇 Additional comments (2)

api/py/ai/chronon/model.py (2)

1-36: Overall assessment of the Model API implementation

The introduction of the Model function and ModelType class provides a good foundation for the model API. However, there are several areas for improvement:

1. Removing unused imports will enhance code clarity.
2. Converting ModelType to an Enum will provide better structure and type safety.
3. Fixing the typo in ModelType ensures correctness.
4. Addressing the mutable default argument issue in the Model function signature improves reliability.
5. Adding error handling and input validation will make the API more robust.
6. The TODO comment needs to be addressed to ensure completeness.

These changes will significantly improve the quality and reliability of the model API. Once these issues are addressed, consider adding unit tests to verify the functionality of the Model function with various input scenarios.

The overall structure and logic of the API are sound, and with the suggested improvements, it will be a solid foundation for the project.

🧰 Tools

🪛 Ruff

2-2: ai.chronon.utils imported but unused

Remove unused import: ai.chronon.utils

(F401)

---

3-3: logging imported but unused

Remove unused import: logging

(F401)

---

4-4: inspect imported but unused

Remove unused import: inspect

(F401)

---

5-5: json imported but unused

Remove unused import: json

(F401)

---

6-6: typing.List imported but unused

Remove unused import

(F401)

---

6-6: typing.Optional imported but unused

Remove unused import

(F401)

---

6-6: typing.Union imported but unused

Remove unused import

(F401)

---

6-6: typing.Dict imported but unused

Remove unused import

(F401)

---

6-6: typing.Callable imported but unused

Remove unused import

(F401)

---

6-6: typing.Tuple imported but unused

Remove unused import

(F401)

---

18-18: Do not use mutable data structures for argument defaults

Replace with None; initialize within function

(B006)

---

10-10: ⚠️ Potential issue

Fix typo in 'PyTorth' to 'PyTorch'

There's a typo in the ModelType class definition.

Apply this diff to correct the typo:

-    PyTorth = ttypes.ModelType.PyTorch
+    PyTorch = ttypes.ModelType.PyTorch

This correction ensures consistency and prevents potential errors.

Likely invalid or redundant comment.

[coderabbitai]

Actionable comments posted: 1

🧹 Outside diff range and nitpick comments (1)

api/py/ai/chronon/model.py (1)

17-20: LGTM: Input validation looks good, consider adding modelType check

The input validation for source and outputSchema is well implemented. It addresses the previous suggestion for error handling and input validation.

Consider adding a similar check for modelType:

if not isinstance(modelType, ModelType):
    raise ValueError("modelType must be an instance of ModelType")

This additional check would ensure all input parameters are properly validated.

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between 846bce5 and 73c4fd8.

📒 Files selected for processing (3)

• api/py/ai/chronon/model.py (1 hunks)
• api/py/test/sample/models/quickstart/test.py (1 hunks)
• api/py/test/sample/production/models/quickstart/test.v1 (1 hunks)

🚧 Files skipped from review as they are similar to previous changes (2)

• api/py/test/sample/models/quickstart/test.py
• api/py/test/sample/production/models/quickstart/test.v1

🧰 Additional context used

🪛 Ruff

api/py/ai/chronon/model.py

15-15: Do not use mutable data structures for argument defaults

Replace with None; initialize within function

(B006)

🔇 Additional comments (2)

api/py/ai/chronon/model.py (2)

1-6: LGTM: Import and ModelType class look good

The import statement and the ModelType class implementation are correct. The class is appropriately defined as a regular class instead of an Enum, which aligns with the Thrift code requirements.

---

22-27: LGTM: Model object creation and return look good

The creation of the MetaData object and the return statement constructing the ttypes.Model object are implemented correctly. All necessary parameters are included in the Model object creation.

[coderabbitai]

Actionable comments posted: 1

🧹 Outside diff range and nitpick comments (3)

api/py/ai/chronon/model.py (3)

4-6: Add a docstring to explain the purpose of the ModelType class.

While the ModelType class seems to be a wrapper for ttypes.ModelType, its purpose and benefits over using ttypes.ModelType directly are not immediately clear. Consider adding a docstring to explain why this wrapper class is necessary and how it should be used.

---

9-9: Clarify or remove the comment about S3 path.

The comment "Name must match S3 path that we expose if you're uploading trained models?" is unclear and not enforced in the code. If this is an important constraint, consider adding validation logic. Otherwise, it might be better to remove or clarify this comment to avoid confusion.

---

17-20: Enhance error messages in type checking.

While the type checking is good, the error messages could be more informative. Consider including the actual type received in the error message. For example:

if not isinstance(source, ttypes.Source):
    raise ValueError(f"Invalid source type. Expected ttypes.Source, got {type(source)}")
if not isinstance(outputSchema, ttypes.TDataType):
    raise ValueError(f"outputSchema must be a TDataType. Got {type(outputSchema)}")

This will make debugging easier for users of this function.

📜 Review details

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Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between 73c4fd8 and 459da08.

📒 Files selected for processing (1)

• api/py/ai/chronon/model.py (1 hunks)

🔇 Additional comments (1)

api/py/ai/chronon/model.py (1)

1-2: LGTM: Imports are clean and appropriate.

The imports are concise and relevant to the functionality in the file. Good job on keeping only the necessary imports.

[nikhil-zlai]

LGTM with two comments

[coderabbitai]

Actionable comments posted: 0

🧹 Outside diff range and nitpick comments (2)

api/py/ai/chronon/model.py (2)

10-17: Consider clarifying the comment and improving type hints

The function signature looks good overall, but there are a couple of points to consider:

1. The comment about the name matching the S3 path is unclear. Consider elaborating on this requirement or removing the comment if it's not necessary.

2. The name parameter is optional (has a default value of None) but doesn't use the Optional type hint. For consistency with modelParams, consider updating it to:

name: Optional[str] = None

---

18-27: Good input validation and handling, with a minor improvement suggestion

The input validation and handling look good, addressing previous review comments. The conversion of outputSchema and initialization of modelParams are well implemented.

To improve the code slightly and address the static analysis hint, consider merging the isinstance checks for outputSchema:

if not isinstance(outputSchema, (ttypes.TDataType, int)):
    raise ValueError("outputSchema must be a TDataType or DataKind")

This change makes the code more concise without affecting its functionality.

🧰 Tools

🪛 Ruff

20-20: Multiple isinstance calls for outputSchema, merge into a single call

Merge isinstance calls for outputSchema

(SIM101)

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between 12c24ff and b8d7792.

📒 Files selected for processing (1)

• api/py/ai/chronon/model.py (1 hunks)

🧰 Additional context used

🪛 Ruff

api/py/ai/chronon/model.py

20-20: Multiple isinstance calls for outputSchema, merge into a single call

Merge isinstance calls for outputSchema

(SIM101)

🔇 Additional comments (2)

api/py/ai/chronon/model.py (2)

1-7: Imports and ModelType class look good

The imports are concise and appropriate for the file's content. The ModelType class is well-defined and matches the expected structure.

---

29-34: Return statement looks good, with a note on name usage

The creation of metaData and return of the Model object are implemented correctly.

Note: The name parameter is used to create the MetaData object without checking if it's None. While a past review comment indicates this can be ignored, it's worth being aware of this in case it causes issues in the future.