trustgfs 0.1.0

TRUST-GFS feature selection for tabular classification

trustgfs

trustgfs packages the TRUST-GFS feature selector for tabular classification.

It supports two input modes:

1. fit(X_train, y_train)

   • the package creates an outer train/validation split internally
   • then runs TRUST-GFS on the recombined outer split, matching the locked script behavior

2. fit(X_train, y_train, X_val, y_val)

   • the package directly combines train and validation
   • then runs TRUST-GFS on that combined data

The implementation preserves the locked TRUST-GFS-only logic, including:

• Stage 1 bootstrap evidence scoring
• null-threshold zoning into Strong / Mid / Weak
• Soft-Weak rescue against Strong anchors
• Stage 2 occlusion energy/support analysis
• feasible add-on pool selection
• genetic search over add-on subsets
• final selected subset output

Installation

Local editable install

pip install -e .

From GitHub later

pip install "trustgfs @ git+https://github.com/YOUR_USERNAME/trustgfs.git"

From PyPI later

pip install trustgfs

Quick usage

from trustgfs import TRUSTGFSSelector

selector = TRUSTGFSSelector(feature_names=feature_names)
selector.fit(X_train, y_train)

print(selector.selected_features_)
X_selected = selector.transform(X_train)

With explicit validation data:

from trustgfs import TRUSTGFSSelector

selector = TRUSTGFSSelector(feature_names=feature_names)
selector.fit(X_train, y_train, X_val=X_val, y_val=y_val)

print(selector.selected_features_)

Convenience function

from trustgfs import run_trust_gfs

selected_idx, info = run_trust_gfs(
    X_train=X_train,
    y_train=y_train,
    feature_names=feature_names,
)

This prints only:

• TRUST_GFS_STAGE1_FLOW_COUNTS
• TRUST_GFS_STAGE1_GROUP_FEATURES
• TRUST_GFS_FINAL_SELECTED_FEATURES

Notes

• For exact old TRUST-GFS behavior, y should already be integer-encoded as 0..C-1.
• transform() returns the selected columns from the given matrix.
• The package does not include the older benchmark feature selectors or model-comparison loop.