semi-supervised-gmm 0.1.2

Semi-supervised Gaussian mixture classifier with a weighted unlabeled likelihood

semi_supervised_gmm

Semi-supervised Gaussian mixture classification with a weighted unlabeled likelihood.

Companion to the paper:

Semi-Supervised Generative Classification via a Weighted Unlabeled Likelihood (under review)

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Overview

Many classification problems have cheap features but expensive labels. This package fits a two-component Gaussian mixture model by maximising a weighted log-likelihood:

J(θ) = ℓ_sup(θ)  +  λ · ℓ_unl(θ)

where ℓ_sup is the supervised log-likelihood over labeled data and ℓ_unl is the unlabeled marginal mixture log-likelihood. The scalar λ controls how much the unlabeled corpus influences the fit. EM yields closed-form updates at every iteration.

The central contribution is treating λ as an object of study in its own right:

• λ = 0 recovers purely supervised MLE.
• λ > 0 borrows geometric structure from the unlabeled distribution.
• Pre-fitting diagnostic A(0): a computable score whose sign predicts whether adding unlabeled data will improve or degrade the estimator — before any semi-supervised fitting is done.

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Tutorial notebook

An end-to-end walkthrough using sklearn.datasets.load_breast_cancer() (569 samples, 30 features):

jupyter notebook notebooks/tutorial.ipynb

Covers all four estimators, the λ path, the pre-fitting diagnostic, and a comparative AUROC summary. All plots use a dark theme consistent with the paper's visual style.

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Installation

# from repo root
pip install -e .

Dependencies: numpy, scipy, scikit-learn (base classes only)

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Quick start

import numpy as np
from semi_supervised_gmm import SemiSupervisedGMM, make_semi_supervised

rng = np.random.default_rng(0)

# Generate data: y=1 (positive), y=0 (negative), y=-1 (unlabeled)
X_pos = rng.multivariate_normal([2, 0], np.eye(2), 30)
X_neg = rng.multivariate_normal([-2, 0], np.eye(2), 30)
X_u   = rng.multivariate_normal([0, 0], np.eye(2), 300)

X, y = make_semi_supervised(
    np.vstack([X_pos, X_neg]),
    np.array([1]*30 + [0]*30),
    X_u,
)

# Fit
model = SemiSupervisedGMM(lambda_=1.0).fit(X, y)

# Predict
proba  = model.predict_proba(X_test)   # shape (n, 2): [P(y=0|x), P(y=1|x)]
labels = model.predict(X_test)
auc    = model.score(X_test, y_test)   # AUROC

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Results

Benchmark results

AUROC averaged over 10 random seeds ± std. covariance_type="ledoit_wolf", StandardScaler.
All four semi-supervised variants shown at N_labeled=40 (20 per class); Parkinsons at N_labeled=20 (15-per-class test due to small positive class).

Dataset	d	N	N_lab	Supervised	Semi (λ=1)	Learned-λ	Local-λ	Δ best
Breast Cancer	30	569	40	0.869±0.049	0.971±0.018	0.960±0.034	0.973±0.018	+0.105
Ionosphere	34	351	80	0.825±0.126	0.957±0.012	0.952±0.022	0.956±0.012	+0.132
Heart Disease	13	270	40	0.780±0.106	0.874±0.047	0.831±0.096	0.872±0.049	+0.094
Parkinsons	22	195	20	0.785±0.065	0.808±0.046	0.815±0.058	0.811±0.043	+0.030
Sonar	60	208	40	0.926±0.000	0.741±0.131	0.767±0.139	0.740±0.130	−0.159

When it helps: In the label-scarce regime (10–40 labeled samples per class), all three semi-supervised variants consistently improve AUROC by +0.03 to +0.13 on datasets where the Gaussian mixture structure is reasonable (Breast Cancer, Ionosphere, Heart Disease). The choice of variant matters little — Semi(λ=1) and Local-λ are typically tied for best.

When it doesn't: Sonar (d/N_per_class = 6) is the clear failure case. The feature-to-label ratio is far above the threshold identified in the paper's high-dimensional alignment analysis: unlabeled data estimate a 60-dimensional covariance that doesn't align with the labeled decision boundary, and ‖g₀‖ is large, correctly flagging the diagnostic as "unreliable".

Stability bonus: At Ionosphere N_labeled=80, the supervised estimator is highly variable (std=0.126); semi-supervised stabilises it to std=0.012 — a variance-reduction effect separate from the bias improvement.

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Four estimators

All estimators follow the sklearn interface: fit / predict / predict_proba / score. Use y = -1 as the unlabeled sentinel, matching sklearn.semi_supervised.LabelPropagation.

SupervisedGMM

Purely supervised MLE (λ = 0). Ignores unlabeled observations.

from semi_supervised_gmm import SupervisedGMM

model = SupervisedGMM().fit(X, y)

SemiSupervisedGMM

Fixed global λ. Grid-search on a validation set via fit_cv.

from semi_supervised_gmm import SemiSupervisedGMM

# Fixed lambda
model = SemiSupervisedGMM(lambda_=2.0).fit(X, y)

# Grid-searched lambda
model = SemiSupervisedGMM().fit_cv(X, y, X_val, y_val,
                                   lam_grid=np.logspace(-2, 2, 20))
print(model.lambda_used_)   # selected value

Compatible with GridSearchCV:

from sklearn.model_selection import GridSearchCV

gs = GridSearchCV(SemiSupervisedGMM(),
                  {"lambda_": [0.1, 0.5, 1.0, 2.0, 5.0]},
                  scoring="accuracy")
gs.fit(X_labeled, y_labeled)

LearnedLambdaGMM

Learns λ by gradient ascent on validation log-likelihood (IFT-based, as described in the paper).

from semi_supervised_gmm import LearnedLambdaGMM

model = LearnedLambdaGMM(lambda_init=1.0, n_steps=10).fit(
    X, y, X_val=X_val, y_val=y_val
)
print(model.lambda_)   # learned value

LocalLambdaGMM

Per-point confidence-weighted λ: λ(x) = λ · max(γ(x), 1−γ(x))^α. Downweights ambiguous unlabeled points.

from semi_supervised_gmm import LocalLambdaGMM

model = LocalLambdaGMM(lambda_=1.0, alpha=1.0).fit(X, y)

---

Pre-fitting diagnostic: should you use unlabeled data?

The alignment coefficient A(0) is computable before any semi-supervised fitting. Its sign predicts whether unlabeled data will help:

from semi_supervised_gmm import SemiSupervisedGMM

result = SemiSupervisedGMM.alignment_score(X_train, y_train, X_val, y_val)
# {
#   "A0":             float,           # alignment coefficient
#   "g0_norm":        float,           # ||g_0|| score residual norm
#   "recommendation": "use"|"discard"|"unreliable",
#   "n_unlabeled":    int
# }

if result["recommendation"] == "use":
    model = SemiSupervisedGMM(lambda_=1.0).fit(X_train, y_train)
else:
    model = SupervisedGMM().fit(X_train, y_train)

Interpretation:

• A(0) > 0 → unlabeled geometry is aligned with the classification task → use semi-supervised learning
• A(0) < 0 → unlabeled geometry is misaligned → stick with supervised MLE
• g0_norm large (> 15) → Gaussian assumption likely violated → diagnostic unreliable

In the small-labeled-data regime (where semi-supervised learning is most consequential), the sign of A(0) achieves 65–72% decision accuracy with mean regret below 0.01 AUROC.

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Covariance options

All estimators accept covariance_type:

Value	When to use
"full" (default)	N ≫ d; unrestricted covariance
"diag"	d > N or when features are approximately independent
"ledoit_wolf"	d ≈ N; shrinkage toward scaled identity (requires sklearn)

model = SemiSupervisedGMM(lambda_=1.0, covariance_type="ledoit_wolf").fit(X, y)

---

Convenience: split-data interface

All estimators expose fit_semi for callers who already have labeled and unlabeled data in separate arrays:

model = SemiSupervisedGMM(lambda_=1.0).fit_semi(X_labeled, y_labeled, X_unlabeled)

---

Persistence

model.save("model.pkl")
loaded = SemiSupervisedGMM.load("model.pkl")

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Replicating paper results

All paper figures and tables can be regenerated using the production package:

# All experiments
python3 run_paper_experiments.py --experiments all

# Individual experiments
python3 run_paper_experiments.py --experiments e1 e3 e10

Experiments available: e1 (diagnostic validity), e2 (lambda learning), e3 (misspecification), e4 (bias-variance), e5 (local lambda trajectory), e6 (lambda parameter path), e7 (decision accuracy), e8 (confidence-weighting ablation), e9 (regime grid), e10 (high-dimensional geometry), pr (parameter recovery), sens (sensitivity).

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Algorithmic notes

The implementation improves on the reference code in three ways:

1. Cholesky caching. Each E-step uses a single Cholesky factorisation per class per iteration (the reference performs two O(d³) factorisations: slogdet + solve). ~1.5× E-step speedup.

2. Precomputed labeled scatter. X_pos^T X_pos and X_pos.sum(0) are computed once outside the EM loop. The centered scatter is recovered via:

(X − μ)ᵀ(X − μ) = XᵀX − outer(Σx, μ) − outer(μ, Σx) + N·outer(μ, μ)

Note: the simpler form XᵀX − N·outer(μ,μ) only holds when μ = x̄. In EM, μ_new includes unlabeled contributions, so the full identity is required.

3. Analytic Fisher for A(0). The reference computes the Fisher information matrix via numerical Jacobians — O(N·d⁴). The production implementation uses the closed-form mean-block Fisher:

F_μ = Σ⁻¹ · S_sample · Σ⁻¹

Cost: O(N·d²). At d=50 this is a ~5000× reduction and eliminates floating-point error that degrades diagnostic accuracy at high dimension.

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Package structure

semi_supervised_gmm/
    _params.py        GMMParams dataclass
    _em.py            Pure NumPy EM loop, E/M-step, posterior (zero sklearn)
    _lambda.py        grid_search_lambda, gradient_lambda
    _diagnostics.py   alignment_A0, score_residual_g0 — analytic Fisher
    _data.py          encode_labels: y=-1 sentinel → (X_pos, X_neg, X_u)
    _base.py          BaseGMM: sklearn mixins, predict*, score, persistence
    _estimators.py    Four estimator classes
    exceptions.py     ConvergenceWarning, InsufficientLabeledDataError

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Running tests

python3 -m pytest tests/ -q

61 tests: unit tests for EM numerics, data encoding, and diagnostics; integration tests for all four estimators including numerical agreement with the reference implementation, GridSearchCV compatibility, and save/load roundtrip.