菜单
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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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.
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().
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 | estimate-online-covariance |
| description | 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. |
precise provides sklearn-style estimators with a single partial_fit contract. Pure numpy.
pip install precise
import numpy as np
from precise import EwaCovariance # exponentially weighted; recency-biased
est = EwaCovariance(r=0.05) # r in (0,1]; larger = faster forgetting
for y in stream: # y is a 1-D array (one observation)
est.partial_fit(y)
est.covariance_ # (d, d) ndarray, symmetric and PSD by construction
est.correlation_ # unit-diagonal correlation
est.precision_ # inverse covariance (when well-conditioned)
est.location_ # running mean
est.n_samples_ # observations seen
fit(X) is the batch drop-in (X is 2-D, rows = observations); it resets then replays rows, so
it matches sklearn.covariance's call shape.
all_estimators() lists every estimator; estimator_from_name("LedoitWolfCovariance") looks one up.
Sensible defaults by situation:
EwaCovariance(r=...)LedoitWolfCovariance, OASCovariance, ShrunkCovariance, FactorCovarianceHuberCovariance, TylerCovarianceAdaptiveEwaCovariance, DCCCovariancesuggest(X)).est.get_state() / est.set_state(s) for mid-stream checkpointing.covariance_ is always symmetric PSD; don't hand-symmetrize or clip it yourself.