pdstools.utils.datasets¶

Attributes¶

ADMTreesModel

Functions¶

`cdh_sample`(→ pdstools.adm.ADMDatamart.ADMDatamart)	Import a sample dataset from the CDH Sample application
`sample_trees`()	Load the anonymized AGB sample model (100 trees, with sampleCount).
`sample_value_finder`(...)	Import a sample dataset of a Value Finder simulation
`sample_explanations`(→ pdstools.explanations.Explanations)	Import a sample set of pre-aggregated AGB global explanations.
`dq_sample`(→ pdstools.data_quality.TopicDataQuality)	Load the built-in smalltalk sample dataset for Topic Data Quality.

Module Contents¶

ADMTreesModel = None¶

cdh_sample(query: pdstools.utils.types.QUERY | None = None) → pdstools.adm.ADMDatamart.ADMDatamart¶

Import a sample dataset from the CDH Sample application

Parameters:: query (QUERY | None, optional) – An optional query to apply to the data, by default None
Returns:: The ADM Datamart class populated with CDH Sample data
Return type:: ADMDatamart

sample_trees()¶

Load the anonymized AGB sample model (100 trees, with sampleCount).

Returns:: An ADMTreesModel loaded from the bundled data/agb/ModelExportWithSampleCount.json file (dev environment) or from the canonical GitHub raw URL (installed package).
Return type:: ADMTreesModel

sample_value_finder(threshold: float | None = None) → pdstools.valuefinder.ValueFinder.ValueFinder¶

Import a sample dataset of a Value Finder simulation

This simulation was ran on a stock CDH Sample system.

Parameters:: threshold (float | None, optional) – Optional override of the propensity threshold in the system, by default None
Returns:: The Value Finder class populated with the Value Finder simulation data
Return type:: ValueFinder

sample_explanations(*, model_name: str = 'AdaptiveBoostCT', from_date: datetime.datetime | None = None, to_date: datetime.datetime | None = None) → pdstools.explanations.Explanations¶

Import a sample set of pre-aggregated AGB global explanations.

These were generated from a stock CDH Sample system. The aggregates are fetched from the pdstools repository over HTTPS, so this works from an installed package as well as from a repository checkout.

Parameters:

model_name (str, keyword-only, default "AdaptiveBoostCT") – Name of the model rule. Used for report metadata only.
from_date (datetime | None, keyword-only, optional) – Start of the reporting window. Defaults to a week before to_date.
to_date (datetime | None, keyword-only, optional) – End of the reporting window. Defaults to today.

Returns:

The Explanations class populated with the sample aggregates.

Return type:

Explanations

dq_sample(*, similarity_threshold: float = 0.8) → pdstools.data_quality.TopicDataQuality¶

Load the built-in smalltalk sample dataset for Topic Data Quality.

Returns a ready-to-use TopicDataQuality instance with embeddings, UMAP, and similarity already computed.

Parameters:: similarity_threshold (float, default 0.8) – Topic pairs above this TF-IDF cosine similarity are flagged.
Returns:: A fully-initialized instance with precomputed results.
Return type:: TopicDataQuality