ce-data-preparation

Development

Validate and preprocess input data for CE pipelines, handle mixed types and categorical features, and configure encoding per ADR-002 and ADR-009.

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CE Data Preparation

You are helping users prepare their data for use with calibrated-explanations.

Required references

docs/improvement/adrs/ADR-002-validation-and-exception-design.md
docs/improvement/adrs/ADR-009-input-preprocessing-and-mapping-policy.md
src/calibrated_explanations/core/validation.py
src/calibrated_explanations/preprocessing/builtin_encoder.py

Use this skill when

A user's data has mixed types, NaN values, or categorical features.
CE raises input validation errors.
Configuring automatic encoding (auto_encode='auto').
Understanding how CE handles feature preprocessing.
Exporting or importing preprocessing mappings.

Common scenarios

1. Categorical features

CE requires numeric input by default. For categorical features:

explainer = WrapCalibratedExplainer(model, auto_encode='auto')
explainer.fit(X_train, y_train)  # auto-encoding activates here

auto_encode='auto': CE's built-in encoder handles categorical columns deterministically.
Custom preprocessor: Pass your own sklearn transformer via the preprocessor parameter.

2. Mixed types (DataFrame input)

When passing a DataFrame with mixed dtypes:

Numeric columns pass through unchanged.
Non-numeric columns require auto_encode='auto' or a custom preprocessor.
Without preprocessing, CE raises a ValidationError with actionable guidance listing the offending columns.

3. Missing values (NaN)

CE does not impute missing values by default:

Preprocess NaN values before passing to CE.
The built-in encoder does not handle NaN; use a custom preprocessor with imputation if needed.

4. Unseen categories at inference

Per ADR-009, the unseen_category_policy controls behavior:

"error" (default): Raises ValidationError on unseen categories.
"ignore": Maps unseen categories to a sentinel output with a warning.

5. Mapping export/import

After fitting, you can export and import preprocessing mappings:

mapping = explainer.export_mapping()  # JSON-safe dict
# ... save to file or transfer ...
explainer.import_mapping(mapping)     # restore on another instance

Validation error diagnostics

When CE raises input errors, check:

Column types: Are all columns numeric? If not, enable auto_encode.
Shape: Does X have the same number of features as training data?
NaN/Inf: Are there missing or infinite values?
Unseen categories: Are there categories in test data not seen during fit?

Constraints

Always fit and calibrate before explaining (CE-first invariant).
The built-in encoder is deterministic given the same stable_seed.
Mapping export produces JSON-safe primitives only; no opaque blobs.
Preprocessing does not change calibration semantics.