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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().
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基于 SOC 职业分类
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.
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.
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.
| name | choose-covariance-estimator |
| description | 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(). |
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_
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.
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:
LedoitWolfCovariance / OASCovariance) is a safe default
when in doubt.