| 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. |
Estimate online covariance with precise
precise provides sklearn-style estimators with a single partial_fit contract. Pure numpy.
pip install precise
The pattern
import numpy as np
from precise import EwaCovariance
est = EwaCovariance(r=0.05)
for y in stream:
est.partial_fit(y)
est.covariance_
est.correlation_
est.precision_
est.location_
est.n_samples_
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.
Choosing the class
all_estimators() lists every estimator; estimator_from_name("LedoitWolfCovariance") looks one up.
Sensible defaults by situation:
- general / recency-weighted:
EwaCovariance(r=...)
- many variables relative to samples (p/n large) or ill-conditioned:
LedoitWolfCovariance, OASCovariance, ShrunkCovariance, FactorCovariance
- heavy tails / outliers:
HuberCovariance, TylerCovariance
- regime changes:
AdaptiveEwaCovariance, DCCCovariance
- you don't know: use the choose-covariance-estimator skill (
suggest(X)).
Notes
- All estimators are truly online — constant work per observation, no growing buffers.
- State is a JSON-able dict:
est.get_state() / est.set_state(s) for mid-stream checkpointing.
covariance_ is always symmetric PSD; don't hand-symmetrize or clip it yourself.- Named series with a changing universe? Use the keyed-dynamic-universe skill instead.
