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.
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precise is a small, numpy-only library of online
(incremental) covariance and correlation estimators behind one sklearn-style partial_fit contract —
plus a panel of assessors for scoring an estimate and a recommender for choosing one. It is the streaming
complement to sklearn.covariance, whose estimators are batch-only.
pip install precise
from precise import EwaCovariance
est = EwaCovariance(r=0.05)
for y in stream: # y is one observation (1-D)
est.partial_fit(y)
est.covariance_ # symmetric PSD; also .correlation_ / .precision_ / .location_
np.cov / np.corrcoef,
pandas .rolling().cov()) — that is O(window) per step; precise updates in O(1)–O(d²);partial_fit covariance where sklearn.covariance only offers batch fit;Fetch the relevant one for copy-pasteable code and guardrails:
In high dimensions (variables comparable to observations), do not rank covariance estimates by the held-out Gaussian log-likelihood — it is dominated by unidentifiable small eigenvalues and ranks below chance. Use inversion-free / block judges instead (see the scoring skill). Background: https://precise.microprediction.org/papers/schur-likelihood/.
Docs https://precise.microprediction.org · PyPI https://pypi.org/project/precise/ · Repo https://github.com/microprediction/precise.
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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.
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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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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.
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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.
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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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