The git repository for DSBox primitives related to featurization is located here. The git repository containing DSBox cleaning related primitives is here.
Generate features using pre-trained ResNet50 deep neural network. Use hyperparameter layer_index to select the network layer to use for featurization.
Generate features using pre-trained VGG16 deep neural network. Use hyperparameter layer_index to select the network layer to use for featurization.
Reads in image files and generates a tensor that suitable as input to d3m.primitives.dsbox.ResNet50ImageFeature and d3m.primitives.dsbox.Vgg16ImageFeature.
Performs forecasting of one timeseries using recursive neural network.
Performs forecasting of one timeseries using AutoArima.
Performs forecasting of one timeseries using Group Up.
Generate features of multiple timeseries by random projecting the timeseries matrix into lower dimendions.
Reads in timeseries csv files and generate output List that is suitable as input to d3m.primitives.dsbox.RandomProjectionTimeSeriesFeaturization.
Automatically detect foriegn key relationships among multiple tables, and join the tables into one table using aggregation.
This an identity function primitive that returns the input dataframe as output. This useful for bypassing a step in a pipeline without having to modify the pipeline structure.