Python Client History

3.8

3.8.3

• Bug Fixes

  • Fixed pydantic validation error for Webhook object.

3.8.2

• Modifications

  • Improved log message for timeout failure on how timeout can be increased.

3.8.1

• Bug Fixes

  • Added missing support for specifying category while creating Alert Rule.

3.8.0

• New Features

  • Added a new update method to the AlertRule object, allowing updates to the following fields: warning_threshold, critical_threshold, and evaluation_delay.

  • Added ability to create alert rules with Fiddler-determined automatic thresholds.

• Modifications

  • Project Deletion Uses v3 API:

    • project.delete() now utilizes the v3 API for deleting projects.

    • The method signature remains unchanged.

3.7

3.7.1

• Modifications

  • Connection Timeout Settings: You can now configure network timeout settings when initializing the Python client. The new timeout parameter in init() accepts:

    • A single number (in seconds) to set the connection timeout

    • A tuple of two numbers (in seconds) to set both connection and read timeouts separately

3.7.0

Release highlights:

• Robustness via retrying: this release introduces a persistent HTTP request retrying strategy to enhance fault tolerance in view of transient network problems and retryable HTTP request errors. You can take control of the maximum duration for which an HTTP request is retried by setting the environment variable FIDDLER_CLIENT_RETRY_MAX_DURATION_SECONDS.

• AWS SageMaker authentication support: to enable that, install version 2.236.0+ of the AWS Python SageMaker SDK. Then, before calling init(), set the environment variable AWS_PARTNER_APP_AUTH to true and set AWS_PARTNER_APP_ARN/AWS_PARTNER_APP_URL to meaningful values.
• Logging improvements: messages are now emitted to stderr instead of stdout. Only if the calling context does not configure a root logger this library will actively declare a handler for its own log messages (this automation can be disabled by setting auto_attach_log_handler=False during init()).

Compatibility changes:

• Pydantic 2.x is now supported (and compatibility with Pydantic 1.x has been retained).

• Support for Python 3.8 has been dropped.

API surface additions:

• Introduced Project.get_or_create() to reduce code required for instantiating a project.

• Introduced model.remove_column() to allow for removing a column from a model object.

Fixes:

• A transient error during a job status update does not prematurely terminate waiting for a job anymore.

• GET requests do not contain the Content-Type header anymore.

3.6

3.6.0

• Removed

  • The get_slice and download_slice methods are removed. Please use download_data to retrieve some data.

  • The get_mutual_info method is removed.

  • The SqlSliceQueryDataSource option is removed from explain, feature impact and importance. Please use the DatasetDataSource instead or the UI.

3.5

3.5.0

• New Features

  • New download_data method, to download a slice of data given an environment, time range and segment. Resulted file can be downloaded either as a CSV or a Parquet file.

3.4

3.4.0

• Removed

  • The get_fairness method is removed. Please use charts and custom metrics to track / compute fairness metrics on your model.

3.3

3.3.2

• Modifications

  • Fixed the error while setting notification config for alert rule.

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3.3.1

• Modifications

  • Added validation while adding notifications to alert rules.

  • Upgraded dependencies to resolve known vulnerabilities - deepdiff, mypy, pytest, pytest-mock, python-decouple, types-requests and types-simplejson.

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3.3.0

• New Features

  • Introduced upload_feature_impact() method to upload or update feature impact manually.

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3.2

3.2.0

• New Features

  • Introduced evaluation delay in Alerts Rule.

    • Optional evaluation_delay parameter added to AlertRule.__init__ method.

    • It is used to introduce a delay in the evaluation of the alert.

• Modifications

  • Fix windows file permission error bug with publish method.

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3.1

3.1.2

• Modifications

  • Adds support to get schema of Column object by fdl.Column

3.1.1

• Modifications

  • Updated pydantic and typing-extensions dependencies to support Python 3.12.

3.1.0

• New Features

  • Introduced the native support for model versions.

    • Optional version parameter added to Model, Model.from_data, Model.from_name methods.

    • New duplicate() method to seamlessly create new version from existing model.

    • Optional name parameter added to Model.list to offer the ability to list all the versions of a model.

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3.0

3.0.5

• New Features

  • Allowed usage of group_by() to form the grouped data for ranking models.

3.0.4

• Modifications

  • Return Job in ModelDeployment update.

3.0.3

• New Features

  • Added Webhook.from_name()

• Modifications

  • Import path fix for packtools.

3.0.2

• Modifications

  • Fix pydantic issue with typing-extensions versions > 4.5.0

3.0.1

• New Features

  • General

    • Moving all functions of client to an Object oriented approach

    • Methods return resource object or a deserialized object wherever possible.

    • Support to search model, project, dataset, baselines by their names using from_name() method.

    • List methods will return iterator which handles pagination internally.

  • Data

    • Concept of environments was introduced.

    • Ability to download slice data to a parquet file.

    • Publish dataframe as stream instead of batch.

    • New methods for baselines.

    • Multiple datasets can be added to a single model. Ability to choose which dataset to use for computing feature impact / importance, surrogate generation etc.

    • Model can be added without dataset.

    • Ability to generate schema for a model.

    • Model delete is async and returns job details.

    • Added cached properties for model: datasets, model_deployment.

  • Alerts

    • New methods for alerts: enable_notification, disable_notification, set_notification_config and get_notification_config.

  • Explainability

    • New methods in explainability: precompute_feature_impact, precompute_feature_importance, get_precomputed_feature_importance, get_precomputed_feature_impact, precompute_predictions.

    • Decoupled model artifact / surrogate upload and feature impact / importance pre-computation.

• Modifications

  • All IDs will be UUIDs instead of strings

  • Dataset delete is not allowed anymore