Comments (4)
You can follow Custom Data Validation which guides on implementation of custom data validation.
Thank you!
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You can follow Custom Data Validation which guides on implementation of custom data validation.
Thank you!
Sorry, the question is about how to extend statistics, not validation. As I understand, custom data validation is used for extending the monitoring indicators.
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Hi - If you set is_categorical to true in the schema for a given numeric feature, and pass that schema when you generate statistics with TFDV, TFDV will calculate top-k and unique stats for that feature.
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Closing this due to inactivity. Please take a look into the answers provided above, feel free to reopen and post your comments(if you still have queries on this). Thank you!
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Related Issues (20)
- Support for manual numerical distribution constraints in schema/anomalies HOT 5
- Dependency Issues HOT 4
- Dipslay schema and stats in dashboard HOT 2
- The potential security vulnerability on the joblib library HOT 2
- hot key issue HOT 4
- The latest numpy release 1.24.0 broke TFDV HOT 3
- Installation of tensorflow data validation still failing for mac m1/m2 chips HOT 3
- Request to update source distributions in pypi repository HOT 3
- Error building data-validation HOT 5
- Remove pyarrow dependency upper bound cap HOT 1
- Error installing tensorflow_data_validation HOT 7
- Descriptor can not be created directly
- Python 3.10 Support HOT 7
- Lack of understandable documentation for Custom Data Validation HOT 6
- Use all the CPU available on a single node for `generate_statistics_from_tfrecord` HOT 4
- EVA HOT 3
- Update pyarrow version range to address vulnerability CVE-2023-47248 HOT 3
- Installation of tensorflow data validation still failing for mac m2 chips? HOT 1
- Upgrade pandas version HOT 2
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