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Score and compare covariance estimates with precise's assessor panel. Use when you need to judge an estimate out-of-sample or rank competing estimators — and especially in high dimensions, where the plain held-out likelihood is misleading.
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SOC 職業分類に基づく
Online (incremental) covariance, correlation, and precision estimation in Python — the streaming complement to sklearn.covariance. Use when code needs a covariance/correlation matrix updated per observation, recomputes np.cov/np.corrcoef in a rolling loop, must judge or compare covariance estimates, or proposes a new covariance methodology. Points to task-specific skills.
Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.
Pick which precise covariance estimator to use for a given dataset. Use when you have data X and are unsure which estimator fits its dimension, conditioning, or tail behavior. Wraps precise.suggest() and covariance_features().
Estimate a covariance / correlation / precision matrix incrementally with precise. Use when data arrives as a stream and you want the matrix updated per observation, or when you want an online (partial_fit) drop-in for sklearn.covariance, which is batch-only.
Maintain an online covariance over named series whose set changes over time (e.g. assets entering and leaving). Use when observations arrive as dicts keyed by name rather than fixed-length vectors. Wraps precise's keyed / FixedUniverse / DynamicUniverse adapters.
| name | score-covariance-estimate |
| description | Score and compare covariance estimates with precise's assessor panel. Use when you need to judge an estimate out-of-sample or rank competing estimators — and especially in high dimensions, where the plain held-out likelihood is misleading. |
from precise import all_assessors, assessor_from_name
for A in all_assessors():
s = A().score(cov, X_test=X_test, true_cov=Sigma_true) # higher = better
cov is the estimate to judge; X_test is held-out data (rows = observations); true_cov is the
population covariance (only available in simulation).needs_data and needs_truth; pass what it needs. Truth-free assessors work on
real data, truth-requiring ones (e.g. FrobeniusToTruth) only in simulation.Do not rank estimators by the held-out Gaussian log-likelihood when p is comparable to n. The
likelihood is dominated by the smallest, unidentifiable eigenvalues of the estimate; empirically it ranks
estimators below chance in that regime. Instead use inversion-free or block judges:
| Regime / goal | Use |
|---|---|
| low dimension, well-conditioned | LogLikelihood (it is optimal here) |
high dimension (p/n near 1 or larger) | BlockPseudoLikelihood, SchurLikelihood, VariogramScore, FrobeniusToTruth (sim only) |
| economic / portfolio relevance | GMVVariance (out-of-sample minimum-variance variance) |
| forecasting a variance from a noisy proxy | a QLIKE / Bregman-consistent loss, not RMSE on the proxy — RMSE on a noisy variance proxy can rank inconsistently |
SchurLikelihood(gamma=...) is a tunable bridge: gamma=1 is the full likelihood (fragile in high-d),
gamma=0 the robust block-diagonal one, interior values better-conditioned than either.