Add support for summary fields #4765
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… fo.StringField 2. Index created for counts mode must have classifications.label 3. Updated docstring
brimoor
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Actionable comments posted: 1
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Files selected for processing (7)
- fiftyone/core/collections.py (6 hunks)
- fiftyone/core/dataset.py (10 hunks)
- fiftyone/core/fields.py (15 hunks)
- fiftyone/core/odm/embedded_document.py (1 hunks)
- fiftyone/core/odm/mixins.py (3 hunks)
- fiftyone/core/view.py (6 hunks)
- tests/unittests/dataset_tests.py (1 hunks)
Additional context used
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tests/unittests/dataset_tests.py
766-766: Do not assign a
lambdaexpression, use adefRewrite
to_setsas adef(E731)
766-766: Ambiguous variable name:
l(E741)
Additional comments not posted (19)
fiftyone/core/odm/embedded_document.py (1)
51-52: LGTM! The addition of the__hash__method is a useful enhancement.By implementing the
__hash__method, instances of theDynamicEmbeddedDocumentclass become hashable. This allows them to be used as keys in dictionaries or elements in sets, expanding their usability in various scenarios.The implementation, which converts the instance to a string and computes its hash value, is a common and reasonable approach. However, it's important to ensure that the string representation of the instance is unique and consistent across different instances to minimize the risk of hash collisions.
Overall, this change enhances the functionality and flexibility of the
DynamicEmbeddedDocumentclass.fiftyone/core/fields.py (5)
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26-107: LGTM!The new parameters
info_keysandcreated_afterare properly validated. The function raises appropriateValueErrors if the provided values do not meet the expected types.
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113-168: LGTM!The function correctly checks the new
info_keysandcreated_afterconstraints against the corresponding attributes of the field. It returnsFalseif any of the constraints are not met.Tools
Ruff
163-170: Return the negated condition directly
Inline condition
(SIM103)
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270-362: LGTM!The function properly incorporates the new
info_keysandcreated_afterparameters. It passes them to thevalidate_constraintsfunction for validation and includes them in thekwargsdictionary when applying the constraints to filter the schema.
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368-417: LGTM!The
flatten_schemafunction has been updated to include the newinfo_keysandcreated_afterparameters. It passes these parameters to thevalidate_constraintsfunction for validation and to the_flattenhelper function to apply the constraints while flattening the schema.
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429-467: LGTM!The
_flattenhelper function has been properly updated to handle the newinfo_keysandcreated_afterparameters. It passes these parameters to thematches_constraintsfunction to filter the fields based on the specified constraints and propagates them recursively when flattening embedded document fields.fiftyone/core/odm/mixins.py (2)
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182-206: LGTM!The new optional parameters
info_keysandcreated_afterexpand the functionality of theget_field_schemafunction, allowing for more granular control over the schema retrieval process. The implementation looks good.
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1-38: Looks good!The function correctly extracts updates for filtered list fields and generates extra updates using the element ID and array filter syntax. It also handles the case where the
$setoperator becomes empty after extracting the updates. The validation for illegal modifications to the root of filtered list fields is a nice touch.fiftyone/core/view.py (2)
918-919: LGTM!The new
info_keysandcreated_afterparameters enhance the filtering capabilities of theget_field_schemafunction by allowing users to specify required keys in the field'sinfodictionary and a minimum field creation date. This provides more control over the returned schema. The changes are backwards compatible.Also applies to: 937-940
977-978: LGTM!The new
info_keysandcreated_afterparameters enhance the filtering capabilities of theget_frame_field_schemafunction by allowing users to specify required keys in the frame field'sinfodictionary and a minimum frame field creation date. This provides more control over the returned frame schema. The changes are backwards compatible.Also applies to: 998-1001
tests/unittests/dataset_tests.py (1)
715-931: Comprehensive test for frame summaries functionality.The
test_frame_summariesmethod thoroughly tests the creation, management, and validation of frame summaries in theDatasetclass. It covers various scenarios, including:
- Generating frame summaries at dataset and frame levels with different configurations.
- Asserting the correctness of the generated summaries against expected values.
- Verifying the read-only status of summary fields.
- Checking the creation of appropriate database indexes.
- Updating dataset fields and ensuring frame summaries are updated accordingly.
- Dropping frame summaries and confirming their removal.
The test method is well-structured, follows good practices for unit testing, and provides comprehensive coverage of the frame summaries functionality.
Tools
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766-766: Do not assign a
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fiftyone/core/dataset.py (6)
1647-1658: LGTM!This is a straightforward and useful function to list the frame summaries that have been created on the dataset. Using
get_field_schemawithflat=Trueandinfo_keys=_FRAME_SUMMARY_KEYto retrieve just the relevant frame summary fields is the right approach.
1660-1738: Excellent work!This is a very comprehensive and well-designed function for creating frame summaries on a dataset. It covers all the key functionality and customization options one would expect, including:
- Handling both categorical and numeric field types
- Customizing the summary field name, sidebar group, counts, grouping, read-only status, indexing, and overwriting behavior
- Using efficient aggregation pipelines to compute the summaries
- Robust sidebar group management to ensure the summary field is added to an appropriate location
- Safely defaulting to making the summary fields read-only
The implementation looks solid and I don't see any issues. Great job!
2026-2050: LGTM!This function properly deletes a frame summary field from the dataset. Unsetting the read-only status before deleting is the correct approach, and the early return if the field doesn't exist is a nice optimization.
2052-2087: Looks good!This is a handy function to identify frame summaries that may be outdated due to modifications made to the source frames or samples since the summary was created. Comparing the creation timestamp of the summary to the last modification timestamp of the frames/samples is a sensible approach.
Properly handling both frame-level and sample-level fields is important since frame summaries can be created from either.
No issues found - good work!
Line range hint
2088-2097: LGTM!This is a straightforward extension of the existing
_add_implied_sample_fieldto allow adding implied frame fields too.The check that the dataset supports frame fields is good defensive programming. Reloading the dataset to update its schema after adding the field is the right step.
Looks good!
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2099-2114: Looks good to me!This function extends
_merge_sample_field_schemato allow merging frame field schemas into the dataset as well.The check that the dataset supports frame fields is a good safeguard. Only calling
_reload()if the schema actually expanded is a nice optimization to avoid unnecessary work.No issues found, good implementation!
Tools
Ruff
48-48:
fiftyone.core.odm.dataset.SampleFieldDocumentimported but unusedRemove unused import:
fiftyone.core.odm.dataset.SampleFieldDocument(F401)
fiftyone/core/collections.py (2)
1406-1407: LGTM!The new
info_keysandcreated_afterparameters provide useful ways to filter the returned schema based on field metadata and creation time.
1449-1450: Looks good!The
info_keysandcreated_afterparameters have been added toget_frame_field_schemaas well, providing a consistent way to filter frame-level fields across the API.
brimoor
changed the title
| for field_name in update_fields: | ||
| dataset.update_summary_field(field_name) | ||
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| update_fields = dataset.check_summary_fields() |
| field_name, | ||
| fof.ListField, | ||
| subfield=fof.EmbeddedDocumentField, | ||
| embedded_doc_type=foo.DynamicEmbeddedDocument, |
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Noob question - what is dynamic embedded document?
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Haha nice, the Fiftyone nested document doll ~
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Thanks @brimoor - this is awesome! I have a couple of thoughts:
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Actionable comments posted: 1
Review details
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Files selected for processing (2)
- docs/source/user_guide/app.rst (2 hunks)
- docs/source/user_guide/using_datasets.rst (1 hunks)
Additional comments not posted (6)
docs/source/user_guide/app.rst (4)
366-394: Excellent addition of the dataset filtering feature!The new "Filtering sample fields" section provides clear instructions on using the App's UI to conveniently filter datasets by various fields.
Automatically making custom attributes filterable is a great usability enhancement. The ability to convert filters to view stages also seems very handy.
Overall, this feature will make it much easier to quickly drill down into datasets of interest. Nice work!
512-518: Resolved: Unassigned fields behavior is documentedThe note on lines 512-518 directly addresses the suggestion from the previous comment.
It clearly states that fields not explicitly assigned to any sidebar group will be dynamically added to default groups in the App at runtime.
This provides the missing information and nicely complements the sidebar groups documentation. No further clarification is needed.
395-445: Great guidance on optimizing filters with indexes!The new section provides clear recommendations and code examples for leveraging dataset indexes to speed up sidebar filters in the App.
The advice to create separate indexes for group datasets to optimize filters in both "group" and "slice" modes is very helpful.
Mentioning the use of summary fields for efficient filtering on video datasets is also a nice tip.
Let's verify that the recommended indexes are being created properly:
#!/bin/bash # Description: Verify that the recommended indexes are created. # Test: Search for the `ground_truth.detections.label` index rg --type python -A 5 $'dataset.create_index("ground_truth.detections.label")' # Test: Search for the compound index on `group.name` and `detections.detections.label` rg --type python -A 5 $'dataset.create_index([("group.name", 1), ("detections.detections.label", 1)])'
451-511: Sidebar groups customization is a great addition!The new "Sidebar groups" section thoroughly explains the feature for visually creating, modifying, and persisting custom sidebar groups in the App.
Being able to configure sidebar groups programmatically through the dataset's App config is also very useful for more advanced customization.
The code examples are clear and easy to follow.
One suggestion for improvement:
It would be helpful to clarify what happens to dataset fields that are not explicitly assigned to any sidebar group. Are they automatically added to a default group?
Let's check if this behavior is documented anywhere:
#!/bin/bash # Description: Check for documentation on behavior of fields not assigned to sidebar groups. # Test: Search for any comments related to the behavior of unassigned fields rg --type python -i $'fields that do not appear in the dataset\'s sidebar_groups'docs/source/user_guide/using_datasets.rst (2)
1636-1679: LGTM!This is a clear and informative introduction to summary fields in FiftyOne. The explanations of the concept and benefits are easy to understand. The examples showing how to create summary fields for categorical and numeric input fields are helpful.
1757-1810: Looks good!This section provides a nice set of examples demonstrating how to manage summary fields:
- Listing summary fields with
list_summary_fields()- Checking if summary fields need updating with
check_summary_fields()- Updating summary fields with
update_summary_field()- Deleting summary fields with
delete_summary_field()anddelete_summary_fields()The explanations are clear and the code snippets are easy to understand.
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Thank you for the detailed review @minhtuev 🙌🏼 |
If a sidebar group becomes empty, it will remain in the App sidebar with an |
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Actionable comments posted: 1
Outside diff range and nitpick comments (2)
fiftyone/core/dataset.py (2)
1660-1734: Add a code comment explaining the purpose of_SUMMARY_FIELD_KEY.The
create_summary_fieldfunction looks good overall and handles the creation and population of summary fields based on the provided configuration.One suggestion is to add a code comment explaining the purpose and usage of the
_SUMMARY_FIELD_KEYinfo key, which is used to identify and store metadata about summary fields. This will make the code more self-explanatory and easier to maintain.
1915-1917: Add a comment explaining the purpose of unwinding list fields.Consider adding a code comment to explain why the pipeline unwinds list fields using the
$unwindstage. This will make it clearer to future readers that unwinding is necessary to handle summary fields that are nested within list fields in the dataset.
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- fiftyone/core/dataset.py (12 hunks)
Additional comments not posted (7)
fiftyone/core/dataset.py (7)
1647-1658: LGTM!The
list_summary_fieldsfunction looks good. It retrieves the summary fields by filtering the dataset's field schema for fields with the_SUMMARY_FIELD_KEYinfo key, sorts the field names, and returns them as a list.
1894-2037: Efficient use of aggregation pipelines to compute summary field values.The
_populate_summary_fieldfunction makes good use of MongoDB aggregation pipelines to efficiently compute the summary field values based on the provided configuration. The pipelines handle various scenarios, such as categorical and numeric fields, sample-level and frame-level fields, and optional grouping and counting.Using aggregation pipelines allows for optimized computation of the summaries directly in the database, avoiding the need to load all the data into memory.
2038-2050: Effective use of last modified timestamps to determine summary field updates.The
check_summary_fieldsfunction implements a useful mechanism to determine which summary fields may need to be updated based on the last modified timestamps of the samples and frames in the dataset.By comparing the timestamps, the function identifies summary fields that were last generated before the most recent modifications to the dataset, indicating that they might be outdated and need to be refreshed.
This approach helps optimize the summary field update process by only updating the necessary fields instead of regenerating all summaries unconditionally.
2086-2107: Appropriate handling of updating a specific summary field.The
update_summary_fieldfunction provides a straightforward way to update a single summary field based on the current values of its source field.The function takes the necessary steps to ensure data integrity and consistency:
- It reloads the dataset to mitigate potential concurrency issues.
- It validates that the provided field name corresponds to an existing summary field.
- It updates the
last_modified_attimestamp to reflect the update operation.- It saves the updated field document, allowing the read-only status to be overridden.
- It calls the
_populate_summary_fieldmethod to recompute the summary field values.Overall, the function handles the update process effectively and ensures that the summary field is refreshed with the latest data.
2109-2120: Straightforward wrapper function for deleting a summary field.The
delete_summary_fieldfunction serves as a simple wrapper around the_delete_summary_fieldsmethod, providing a convenient way to delete a single summary field from all samples in the dataset.It directly passes the provided
field_nameanderror_levelarguments to the underlying method, allowing for consistent behavior and error handling.The function's purpose is clear, and its implementation is concise and readable.
2122-2133: Straightforward wrapper function for deleting multiple summary fields.The
delete_summary_fieldsfunction serves as a simple wrapper around the_delete_summary_fieldsmethod, providing a convenient way to delete multiple summary fields from all samples in the dataset.It allows for flexibility by accepting either a single field name or an iterable of field names through the
field_namesargument. This enables the caller to delete one or more summary fields in a single operation.The function directly passes the
field_namesanderror_levelarguments to the underlying method, ensuring consistent behavior and error handling.Overall, the function's purpose is clear, and its implementation is concise and readable.
2135-2155: Robust implementation for deleting summary fields with error handling.The
_delete_summary_fieldsfunction provides a robust implementation for deleting summary fields from all samples in the dataset. It handles various scenarios and incorporates error handling based on the providederror_level.Key aspects of the function:
- It converts the
field_namesargument to a list, allowing for consistent handling of single field names and iterables.- It validates each field name to ensure it corresponds to an existing summary field. If a field is not a valid summary field, it handles the error based on the
error_levelusing thefou.handle_errorfunction.- It sets the
read_onlyattribute of
* Added support for summary fields which allow the users to collect frame-level information at the sample level * Added documentation and unit tests for adding, querying, and deleting summary fields * Updated retrieving field schema by info_keys and created_after * Updated drop_index to not throw error if the index does not exist Co-authored-by: brimoor <[email protected]> Co-authored-by: Stuart Wheaton <[email protected]> Co-authored-by: minhtuevo <[email protected]>
What changes are proposed in this pull request?
Adds support for creating and managing summary fields on datasets.
How is this patch tested? If it is not, please explain why.
Example usage
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New Features
Bug Fixes
drop_indexmethod.Tests