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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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().
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.