ice-skaters 0.1.0

skaters on a river: calibrated forecast features for streaming ML pipelines. Each numeric stream becomes what the forecaster expected and how surprising the value was.

ice-skaters

skaters on a river: calibrated forecast features for streaming ML pipelines.

Every numeric stream is replaced by two scalars from its own online Laplace forecaster: the predictive mean (what the forecaster expected this value to be) and the standardized surprise z (how unexpected the actual value was, bounded near |z| = 7 by construction). The mean carries the level, the z carries the news. A wild observation can move the pair only so far, and that bounded influence is where the robustness comes from.

pip install ice-skaters

from river import datasets, linear_model, metrics, preprocessing
from ice_skaters import LaplaceFeatures, LaplaceTarget

model = LaplaceTarget(
    regressor=preprocessing.TargetStandardScaler(
        regressor=LaplaceFeatures()
        | preprocessing.StandardScaler()
        | linear_model.LinearRegression()))

mae = metrics.MAE()
for x, y in datasets.TrumpApproval():
    pred = model.predict_one(x)
    mae.update(y, pred if pred is not None else 0.0)
    model.learn_one(x, y)

LaplaceFeatures is a river Transformer for the input streams. LaplaceTarget wraps any regressor, in the style of TargetStandardScaler, to add the target's own (mean, surprise) pair, which a transformer cannot do since it never sees y. The target itself stays raw. Both estimators pipe, pickle and deep-copy like any river estimator. Non-numeric values pass through untouched, and NaN is imputed by the forecast itself with z = 0: the model receives "expected value, no news" instead of a poisoned pipeline.

Why

On TrumpApproval with river's recommended pipeline (progressive validation MAE, burn-in 100, examples/trump_approval.py):

	clean	2% corrupted readings
StandardScaler pipeline	0.328	0.597
+ Laplace front-end	0.382	0.407

The front-end pays a small toll on clean data and holds its footing when the inputs misbehave. In controlled simulation the same substitution beats raw features, a running z-score, a median/MAD winsorizer and a Huberised loss 30/30 seeds under every contamination type tested, at a small clean-data toll. Full protocols, numbers and the study design live in the timemachines repo, benchmarks/RESULTS.md section 6.

Boundaries, stated plainly

• Distance-based learners (KNN) do not benefit: neighbour averaging is already spike-robust and the extra dimensions degrade the metric.
• Entity-interleaved streams (many units multiplexed into one key) want per-entity bodies; a single body per key is handicapped there.
• If your heavy tails are signal rather than noise, taming them costs accuracy. Whether the extremes are informative decides the coordinates.

Relation to the stack

skaters does one thing: fast univariate distributional forecasting, stdlib-only, and this package is deliberately a thin adapter over it. timemachines builds anomaly detection on the same calibrated surprise streams. ice-skaters is the bridge from those streams to river's estimator protocol, and nothing more.