⇅ 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 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| +-----+ +---+
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.