circover 0.2.0

NHOP metric, geometry-preserving seed selection, circular oversampling (GVM-CO, LRE-CO, LS-CO), and degradation-recovery benchmark

circover

NHOP metric, geometry-preserving seed selection, circular oversampling, and controlled degradation benchmark for imbalanced classification.

From the thesis: "From Distributional Similarity to Causal Imbalance: NHOP, Circular Oversampling, and a Controlled Degradation Study" — Parsa Hajiannejad, Università degli Studi di Milano, 2025.

Install

pip install circover

Modules

Class	Description
NHOP	Normalised Histogram Overlap Percentage metric
GeometricSeedSelector	Geometry-preserving seed selection (NHOP + AGTP + JSD + Z)
GVMCO	Gravity-biased Von Mises Circular Oversampling
LRECO	Local Region Estimation Circular Oversampling (Voronoi-constrained)
LSCO	Layered Segmental Circular Oversampling
DegradationBench	Controlled degradation-and-recovery benchmark

Quick start

import circover as cc

# NHOP: measure how faithfully synthetic data reproduces the original distribution
nhop = cc.NHOP(n_bins=30)
nhop.score(X_original, X_synthetic)           # scalar in [0, 1]
nhop.score_per_feature(X_original, X_synth)   # per-feature array
nhop.tv_per_feature(X_original, X_synth)      # TV distance = 1 - NHOP

# Geometry-preserving seed selection
selector = cc.GeometricSeedSelector(n_seeds=20, random_state=42)
seed_indices, score = selector.select(X_minority)

# Circular oversamplers — drop-in replacements for SMOTE
from imblearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier

pipe = Pipeline([
    ("over", cc.GVMCO(random_state=42)),   # or LRECO, LSCO
    ("clf",  RandomForestClassifier()),
])
pipe.fit(X_train, y_train)

Degradation-and-Recovery Benchmark

Run any oversampler (or pipeline) through a controlled degradation protocol to measure its recovery power:

bench = cc.DegradationBench(steps=10, metric="f1", cv=5, random_state=42)
results = bench.run(pipe, X, y)          # DataFrame: degradation, score, n_minority
bench.plot(results)                      # recovery curve with ARI annotation
ari = cc.DegradationBench.area_recovery_index(results)   # scalar summary

The DegradationBench:

1. Removes minority samples in steps equal increments (0% → 100%)
2. Evaluates the estimator via cross-validation at each level
3. Computes the Area Recovery Index (ARI) = ∫ score(δ) dδ — higher = better recovery

Compare multiple methods by their ARI:

results_smote = bench.run(smote_pipe, X, y)
results_gvm   = bench.run(gvm_pipe,   X, y)

ari_smote = cc.DegradationBench.area_recovery_index(results_smote)
ari_gvm   = cc.DegradationBench.area_recovery_index(results_gvm)
print(f"SMOTE ARI: {ari_smote:.3f}   GVM-CO ARI: {ari_gvm:.3f}")

Key parameters

cc.GVMCO(
    n_clusters=5,       # K-Means clusters on minority class
    k_neighbors=5,      # k-NN graph for circle formation
    kappa_max=4.0,      # max Von Mises concentration
    use_pca=True,       # False = native-dimension mode
    random_state=42,
)

cc.NHOP(n_bins=30)      # histogram bins B (default 30, stable range: 20-50)

cc.DegradationBench(
    steps=10,           # number of degradation levels
    metric="f1",        # sklearn scoring string
    cv=5,               # cross-validation folds
    random_state=42,
)

All oversamplers are compatible with imbalanced-learn pipelines and sklearn cross-validation.