Fuzzy ARTMAP

The Fuzzy backend uses artlib’s Fuzzy ARTMAP implementation to build and update class-owned prototypes. It is one of the two backends that preserves learned state across partial_fit calls.

When to use it

Choose Fuzzy ARTMAP for online or continual-learning workflows with dense, bounded features when adaptive Fuzzy ART prototype geometry is appropriate. Use an offline backend when all data is available at once, sparse matrices are required, or multi-label targets are required.

ARTMAP support is optional. Install it with:

pip install "overlapindex[art]"

Invocation

Input features must already be scaled to [0, 1]. OverlapIndex applies complement coding internally before passing data to ARTMAP.

from overlapindex import OverlapIndex

oi = OverlapIndex(
    model_type="Fuzzy",
    rho=0.9,
    match_tracking="MT+",
)

for X_batch, y_batch in stream:
    oi.partial_fit(X_batch, y_batch)
    print(oi.index)

For a true single-sample stream, add_sample(x, y) updates the model and returns the current score directly.

Tuning guidance

• rho is the vigilance parameter. Higher vigilance generally creates finer prototype partitions; lower vigilance permits broader categories.

• match_tracking controls the supervised match-tracking strategy used during updates.

• Track prototype growth as well as OI when tuning vigilance. Excessively fine partitions can increase runtime and change the resolution at which overlap is measured.

ARTMAP online updates require single-label targets. Multi-label add_batch inputs are rejected, and sparse feature matrices are unsupported.