nomoselect 0.2.0

Task-aware subspace selection with exact diagnostics. Built on nomogeo.

nomoselect

Task-aware dimensionality reduction with exact diagnostics.

PCA keeps what is biggest. nomoselect keeps what matters.

Documentation: docs.nomogenetics.com/nomoselect

What it does

nomoselect finds the lowest-dimensional view of your data that preserves the structure you actually care about. It tells you exactly what was kept and what was lost — not as a heuristic, but as an exact certificate.

Install

pip install nomoselect

Requires Python >= 3.10, numpy, scikit-learn, and nomogeo >= 0.4.0.

Quickstart

from sklearn.datasets import load_iris
from nomoselect import GeometricSubspaceSelector, ObserverReport

X, y = load_iris(return_X_y=True)

# Fit: 2 dimensions, preserving class separation
sel = GeometricSubspaceSelector(n_components=2, task="fisher")
X_proj = sel.fit_transform(X, y)

# Diagnostics: what was kept?
report = ObserverReport.from_selector(sel)
print(report.summary())

Output:

nomoselect report  (2 dimensions kept)

  Target structure retained : 100.0%
  Target structure lost     :   0.0%

  Per-class retention:
    0                     retained 100.0%  (weight=0.333)
    1                     retained 100.0%  (weight=0.333)
    2                     retained 100.0%  (weight=0.333)

  Note: these diagnostics are exact for the task you
  declared.  They do not prove the task itself is valid.
  Cross-validation or permutation tests are still needed
  to confirm that the structure is real.

Task families

nomoselect supports multiple ways to declare what structure matters:

Task	What it preserves	When to use
"fisher"	Sample-weighted class separation	Default; equivalent to Fisher/LDA
"equal_weight"	Equal importance per class	Unbalanced class sizes
"minority"	Inverse-frequency weighting	Rare-class detection
"pairwise"	Every class pair equally	Fine-grained separation

Results

Task-aware observer beats PCA consistently:

Dataset	Advantage over PCA
Iris	+0.32
Wine	+1.00 (maximum possible)
Breast Cancer	+0.15
Digits	+0.09

The biggest gains appear when variance and task structure point in different directions. On well-separated data where PCA already aligns with the class boundary, both methods agree.

Beyond selection: diagnostics

from nomoselect import RegularisationAudit, DimensionCostLadder

# Is my result stable across regularisation choices?
audit = RegularisationAudit.run(X, y, n_components=2)
print(audit.summary())

# How many dimensions do I actually need?
ladder = DimensionCostLadder.build(X, y)
print(ladder.summary())

How it relates to PCA and LDA

nomoselect includes Fisher/LDA as a special case. When task="fisher" and regularisation is matched, it recovers the same subspace as sklearn's LinearDiscriminantAnalysis. The difference is that nomoselect:

• tells you exactly how much target structure each dimension captures
• lets you declare alternative tasks (equal-weight, minority, pairwise)
• provides exact certificates for what was kept and what leaked
• gives you a task-aware rank selection diagram instead of a scree plot

For high-dimensional data (features >> samples), apply PCA pre-reduction first, then nomoselect.

License

BSD-3-Clause. See LICENSE.