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Documentation Index

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Wrap a scikit-learn estimator (or Pipeline) with a transparent proxy that opens scopes around fit, predict, transform, fit_transform, predict_proba, and score. sklearn has no callback API, so instrumentation is opt-in via explicit wrapping.

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Signature

def wrap(estimator: Any) -> Any

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Parameters

NameTypePurpose
estimatoranyAn sklearn estimator or sklearn.pipeline.Pipeline.
Any non-sklearn object passes through unchanged (documented passthrough).

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Behavior

The returned object is a thin proxy:
  • fit, predict, transform, fit_transform, predict_proba, and score are wrapped. Each opens a named scope around the call.
  • All other attribute access delegates directly to the underlying estimator. hasattr, isinstance, and pickling work normally.
  • For Pipeline, each step is wrapped recursively so per-step scopes appear as children of the pipeline’s top-level scope.

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Examples

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Single estimator

from sklearn.ensemble import RandomForestClassifier
import cirron as ci

ci.profile()
model = ci.wrap(RandomForestClassifier(n_estimators=100))

model.fit(X_train, y_train)     # opens a `fit` scope
pred = model.predict(X_test)    # opens a `predict` scope
score = model.score(X_test, y_test)

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Pipeline

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

pipe = ci.wrap(Pipeline([
    ("scale", StandardScaler()),
    ("lr",    LogisticRegression()),
]))

pipe.fit(X, y)     # fit scope at the top; each step fits inside its own scope

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Combined with ci.scope

with ci.scope("grid-search"):
    for params in param_grid:
        model = ci.wrap(RandomForestClassifier(**params))
        model.fit(X, y)
        ci.mark("score", model.score(X_val, y_val))

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Version support

Requires scikit-learn >= 1.3, the floor declared by the cirron-sdk[sklearn] extra. The proxy works with the classic estimator API (fit / predict / transform / fit_transform / predict_proba / score) and with sklearn.pipeline.Pipeline. The newer set_output API is orthogonal; wrapping does not interfere with configured output transforms.

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Why it’s opt-in

sklearn has no callback or hook API comparable to Keras callbacks or HuggingFace’s TrainerCallback. Auto-wrapping every BaseEstimator subclass would be invasive and prone to breaking user code. Explicit ci.wrap(estimator) is the least-surprise approach.

ci.scope

The primitive wrap builds on; use directly for non-sklearn code.

Profiling guide

Narrative walk-through of training instrumentation.