abacusai.feature
Classes
A nested feature in a feature group |
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A point-in-time feature description |
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A feature in a feature group |
Module Contents
- class abacusai.feature.NestedFeature(client, name=None, selectClause=None, featureType=None, featureMapping=None, dataType=None, sourceTable=None, originalName=None)
Bases:
abacusai.return_class.AbstractApiClassA nested feature in a feature group
- Parameters:
client (ApiClient) – An authenticated API Client instance
name (str) – The unique name of the column
selectClause (str) – The sql logic for creating this feature’s data
featureType (str) – Feature Type of the Feature
featureMapping (str) – The Feature Mapping of the feature
dataType (str) – Data Type of the Feature
sourceTable (str) – The source table of the column
originalName (str) – The original name of the column
- __repr__()
Return repr(self).
- class abacusai.feature.PointInTimeFeature(client, historyTableName=None, aggregationKeys=None, timestampKey=None, historicalTimestampKey=None, lookbackWindowSeconds=None, lookbackWindowLagSeconds=None, lookbackCount=None, lookbackUntilPosition=None, expression=None, groupName=None)
Bases:
abacusai.return_class.AbstractApiClassA point-in-time feature description
- Parameters:
client (ApiClient) – An authenticated API Client instance
historyTableName (str) – The name of the history table. If not specified, the current table is used for a self-join.
aggregationKeys (list[str]) – List of keys to use for joining the historical table and performing the window aggregation.
timestampKey (str) – Name of feature which contains the timestamp value for the point-in-time feature.
historicalTimestampKey (str) – Name of feature which contains the historical timestamp.
lookbackWindowSeconds (float) – If window is specified in terms of time, the number of seconds in the past from the current time for the start of the window.
lookbackWindowLagSeconds (float) – Optional lag to offset the closest point for the window. If it is positive, the start of the window is delayed. If it is negative, we are looking at the “future” rows in the history table.
lookbackCount (int) – If window is specified in terms of count, the start position of the window (0 is the current row).
lookbackUntilPosition (int) – Optional lag to offset the closest point for the window. If it is positive, the start of the window is delayed by that many rows. If it is negative, we are looking at those many “future” rows in the history table.
expression (str) – SQL aggregate expression which can convert a sequence of rows into a scalar value.
groupName (str) – The group name this point-in-time feature belongs to.
- __repr__()
Return repr(self).
- class abacusai.feature.AbstractApiClass(client, id)
- __eq__(other)
Return self==value.
- _get_attribute_as_dict(attribute)
- class abacusai.feature.Feature(client, name=None, selectClause=None, featureMapping=None, sourceTable=None, originalName=None, usingClause=None, orderClause=None, whereClause=None, featureType=None, dataType=None, detectedFeatureType=None, detectedDataType=None, columns={}, pointInTimeInfo={})
Bases:
abacusai.return_class.AbstractApiClassA feature in a feature group
- Parameters:
client (ApiClient) – An authenticated API Client instance
name (str) – The unique name of the column
selectClause (str) – The sql logic for creating this feature’s data
featureMapping (str) – The Feature Mapping of the feature
sourceTable (str) – The source table of the column
originalName (str) – The original name of the column
usingClause (str) – Nested Column Using Clause
orderClause (str) – Nested Column Ordering Clause
whereClause (str) – Nested Column Where Clause
featureType (str) – Feature Type of the Feature
dataType (str) – Data Type of the Feature
detectedFeatureType (str) – The detected feature type of the column
detectedDataType (str) – The detected data type of the column
columns (NestedFeature) – Nested Features
pointInTimeInfo (PointInTimeFeature) – Point in time column information
- __repr__()
Return repr(self).