⇅ Bag Features

Bag Features Preprocessing

Bag features are expected to be provided as a string of elements separated by whitespace, e.g. "elem5 elem0 elem5 elem1". Bags are similar to set features, the only difference being that elements may appear multiple times. The bag feature encoder outputs a matrix, similar to a set encoder, except each element of the matrix is a float value representing the frequency of the respective element in the bag. Embeddings are aggregated by summation, weighted by the frequency of each element.

Bag Input Features and Encoders

Bag features have only one encoder type available.

Embed Weighted Encoder

The embed weighted encoder first transforms the element frequency vector to sparse integer lists, which are then mapped to either dense or sparse embeddings (one-hot encodings). Lastly, embeddings are aggregated as a weighted sum where each embedding is multiplied by its respective element's frequency. Inputs are of size b while outputs are of size b x h where b is the batch size and h is the dimensionality of the embeddings.

The parameters are the same used for set input features except for reduce_output which should not be used because the weighted sum already acts as a reducer.

+---+
|0.0|          +-----+
|1.0|   +-+    |emb 0|   +-----------+
|1.0|   |0|    +-----+   |Weighted   |
|0.0+--->1+---->emb 1+--->Sum        +->
|0.0|   |5|    +-----+   |Operation  |
|2.0|   +-+    |emb 5|   +-----------+
|0.0|          +-----+
+---+

The encoder parameters specified at the feature level are:

• tied (default null): name of another input feature to tie the weights of the encoder with. It needs to be the name of a feature of the same type and with the same encoder parameters.

Example bag feature entry in the input features list:

name: bag_column_name
type: bag
representation: dense
tied: null

Bag Output Features and Decoders

There is no bag decoder available yet.

Bag Features Metrics

As there is no decoder there is also no metric available yet for bag features.