Choose a covariance estimator for your data
No estimator wins everywhere, so precise recommends one from observable, truth-free features.
from precise import suggest, covariance_features
suggest(X, top=3) # -> list of estimator CLASSES, best first
covariance_features(X) # -> dict of the features behind the choice
X is 2-D (rows = observations, columns = variables). Then:
Est = suggest(X, top=1)[0]
est = Est()
est.fit(X) # or stream rows with partial_fit
cov = est.covariance_
What it keys on
covariance_features(X) returns p, n, p_over_n, effective_rank, sphericity,
condition_number, mean_abs_offdiag_corr, avg_excess_kurtosis — all computed from the sample, none
requiring the unknown truth. Internally suggest uses a frozen, numpy-only decision tree; broadly it
routes high p/n or ill-conditioned data to shrinkage/factor estimators and heavy-tailed data to robust
ones.
Guardrail (important)
suggest is a heuristic trained on synthetic regimes. In leave-one-generative-family-out tests it does
not reliably beat the single best fixed estimator on a wholly novel data-generating family — it
generalizes across samples within familiar regimes, not to arbitrarily new structure. So:
- Treat its output as a strong shortlist, not an oracle.
- If the decision matters, verify the shortlist out-of-sample on your own data with the
score-covariance-estimate skill, rather than trusting the recommendation blind.
- A well-conditioned shrinkage estimator (
LedoitWolfCovariance / OASCovariance) is a safe default
when in doubt.