Categorization

Jobs

Tasks related to running jobs for Tamr Categorization projects

tamr_toolbox.project.categorization.jobs.run(project, *, run_apply_feedback=False)

Run the project

Parameters

• project (CategorizationProject) – The target categorization project

• run_apply_feedback (bool) – Whether train should be called on the categorization model

Return type

List[Operation]

Returns

The operations that were run

tamr_toolbox.project.categorization.jobs.update_unified_dataset(project)

Updates the unified dataset for a categorization project

Parameters

project (CategorizationProject) – Target categorization project

Return type

List[Operation]

Returns

The operations that were run

tamr_toolbox.project.categorization.jobs.apply_feedback_and_update_results(project)

Trains the model and updates the categorization predictions of a categorization project

Parameters

project (CategorizationProject) – Target categorization project

Return type

List[Operation]

Returns

The operations that were run

tamr_toolbox.project.categorization.jobs.update_results_only(project)

Updates the categorization predictions based on the existing model of a categorization project

Parameters

project (CategorizationProject) – Target categorization project

Return type

List[Operation]

Returns

The operations that were run

Metrics

Tasks related to metrics for Tamr Categorization projects

tamr_toolbox.project.categorization.metrics.get_tier_confidence(project, *, tier=-1, allow_dataset_refresh=False)

Extracts tier-specific average confidence from a Tamr internal dataset <unified dataset name>_classifications_average_confidences in a dictionary

Parameters

• project (Project) – Tamr project object

• tier (int) – integer specifying the tier to extract the average confidence; default value will return the average confidence at all leaf categories

• allow_dataset_refresh (bool) – if True, allows running a job to refresh dataset to make it streamable

Return type

Dict[str, Any]

Returns

dictionary - keys are category paths, joined by ‘|’ if multi-level taxonomy and values are average confidence of the corresponding keys

Raises

• RuntimeError – if dataset is not streamable and allow_dataset_refresh is False;

• TypeError – if tier is not of type int; or if the project type is not classification

• ValueError – if tier is less than -1 or equal to 0

Schema

tamr_toolbox.project.categorization.schema.map_attribute(project, *, source_attribute_name, source_dataset_name, unified_attribute_name)

Maps source_attribute in source_dataset to unified_attribute in unified_dataset. If the mapping already exists it will log a warning and return the existing AttributeMapping from the project’s collection.

Parameters

• source_attribute_name (str) – Source attribute name to map

• source_dataset_name (str) – Source dataset containing the source attribute

• unified_attribute_name (str) – Unified attribute to which to map the source attribute

• project (Project) – The project in which to perform the mapping

Return type

AttributeMapping

Returns

AttributeMapping that was created

Raises

ValueError – if input variables source_attribute_name or source_dataset_name or unified_attribute_name are set to empty strings; or if the dataset source_dataset_name is not found on Tamr; or if source_attribute_name is missing from the attributes of source_attribute_name

tamr_toolbox.project.categorization.schema.unmap_attribute(project, *, source_attribute_name, source_dataset_name, unified_attribute_name)

Unmaps a source attribute.

Parameters

• source_attribute_name (str) – the name of the source attribute to unmap

• source_dataset_name (str) – the name of the source dataset containing that source attribute

• unified_attribute_name (str) – the unified attribute from which to unmap

• project (Project) – the project in which to unmap the attribute

Return type

None

Returns

None

tamr_toolbox.project.categorization.schema.bootstrap_dataset(project, *, source_dataset, force_add_dataset_to_project=False)

Bootstraps a dataset (i.e. maps all source columns to themselves)

Parameters

• source_dataset (Dataset) – the source dataset (a Dataset object not a string)

• project (Project) – the project to do the mapping ing

• force_add_dataset_to_project (bool) – boolean whether to add the dataset to the project if it is not already a part of it

Return type

List[AttributeMapping]

Returns

List of the AttributeMappings generated

Raises

RuntimeError – if source_dataset is not part of the given project, set ‘force_add_dataset_to_project’ flag to True to automatically add it

tamr_toolbox.project.categorization.schema.unmap_dataset(project, *, source_dataset, remove_dataset_from_project=False, skip_if_missing=False)

Wholly unmaps a dataset and optionally removes it from a project.

Parameters

• source_dataset (Dataset) – the source dataset (Dataset object not a string) to unmap

• project (Project) – the project in which to unmap the dataset

• remove_dataset_from_project (bool) – boolean to also remove the dataset from the project

• skip_if_missing (bool) – boolean to skip if dataset is not in project. If set to false and dataset is not in project will raise a RuntimeError

Return type

None

Returns

None

Raises

RuntimeError – if source_dataset is not in project and skip_if_missing not set to True

Transformations

tamr_toolbox.project.categorization.transformations.InputTransformation(transformation, datasets=<factory>)

A transformation scoped to input datasets

Version:

Requires Tamr 2020.009.0 or later

Parameters

• transformation (str) – The text of a transformations script

• datasets (List[Dataset]) – The list of input datasets that the script should be applied to

tamr_toolbox.project.categorization.transformations.TransformationGroup(input_scope=<factory>, unified_scope=<factory>)

A group of input transformations and unified transformations

Version:

Requires Tamr 2020.009.0 or later

Parameters

• input_scope (List[InputTransformation]) – A list of transformation to apply to input datasets

• unified_scope (List[str]) – A list of transformation scripts to apply to the unified dataset

tamr_toolbox.project.categorization.transformations.get_all(project)

Get the transformations of a Project

Version:

Requires Tamr 2020.009.0 or later

Parameters

project (Project) – Project containing transformations

Return type

TransformationGroup

Returns

All input transformations and unified transformations of a project

tamr_toolbox.project.categorization.transformations.set_all(project, tx, *, allow_overwrite=True)

Set the transformations of a Project

Version:

Requires Tamr 2020.009.0 or later

Parameters

• project (Project) – Project to place transformations within

• tx (TransformationGroup) – Transformations to put into project

• allow_overwrite – Whether existing transformations can be overwritten

Return type

Response

Returns

Response object created when transformations of a project are replaced

Raises

• RuntimeError – if allow_overwrite is set to False but transformations already exists in project

• ValueError – if provided tx are invalid

tamr_toolbox.project.categorization.transformations.get_all_unified(project)

Get the unified transformations of a Project

Version:

Requires Tamr 2020.009.0 or later

Parameters

project (Project) – Project containing transformations

Return type

List[str]

Returns

All unified transformations of a project

tamr_toolbox.project.categorization.transformations.set_all_unified(project, tx, *, allow_overwrite=True)

Set the unified transformations of a Project. Any input transformations will not be altered

Version:

Requires Tamr 2020.009.0 or later

Parameters

• project (Project) – Project to place transformations within

• tx (List[str]) – Unified transformations to put into project

• allow_overwrite – Whether existing unified transformations can be overwritten

Return type

Response

Returns

Response object created when transformations of a project are replaced

Raises

RuntimeError – if allow_overwrite is set to False but transformations already exists in project