getml.feature_learning

This module contains relational learning algorithms to learn features from relational data or time series.

Note:

All feature learners need to be passed to Pipeline.

Classes

FastPropModel(aggregation, loss_function, …)	Generates simple features based on propositionalization.
FastPropTimeSeries(aggregation, …)	Generates simple features based on propositionalization.
MultirelModel(aggregation, allow_sets, …)	Feature learning based on Multi-Relational Decision Tree Learning.
MultirelTimeSeries(aggregation, allow_sets, …)	Feature learning for time series based on multi-relational decision tree learning.
RelboostModel(allow_null_weights, delta_t, …)	Feature learning based on Gradient Boosting.
RelboostTimeSeries(allow_null_weights, …)	Feature learning for time series based on Gradient Boosting.
RelMTModel(allow_avg, delta_t, gamma, …)	Feature learning based on relational linear model trees.
RelMTTimeSeries(allow_avg, delta_t, gamma, …)	Feature learning based on relational linear model trees.

Submodules

aggregations

This module contains all possible aggregations to be used with MultirelModel, MultirelTimeSeries, FastPropModel, FastPropTimeSeries, Mapping.