> ## Documentation Index
> Fetch the complete documentation index at: https://fpde-80-mintlify-f19c7fde.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Reproducibility checklist

> Record the environment, data, model, and FPDE settings behind an experiment

Use this checklist when reporting FPDE experiments, publishing results, or comparing attribution settings across runs.

## Package and environment

* Record the FPDE package version.
* Record the Python version. FPDE requires Python 3.12 or newer.
* Record dependency versions, especially NumPy and scikit-learn.
* State whether FPDE was installed from PyPI or from a local checkout.
* Preserve the commit hash when using a local checkout.

## Data and preprocessing

* Identify the dataset and any dataset version or download date.
* Record the train, validation, and test split procedure.
* Record all random seeds used for splitting, model fitting, and sampling.
* Describe feature preprocessing, including scaling, encoding, imputation, and feature selection.
* Confirm that FPDE receives data in the same feature space used by the classifier.
* Record feature names and feature order.

## Model

* Name the classifier and implementation library.
* Record model hyperparameters.
* Record the fitted model version or artifact location.
* Confirm that the model exposes `predict_proba` and `classes_` when using `FPDEEngine`, grid search, lambda selection, or perturbation curves.
* Report model performance on the evaluation split used for explanations.

## FPDE configuration

* Record the FPDE mode: Diff-FPDE, Cos-FPDE, or Hyb-FPDE.
* Record `lambda_hyb` for Hyb-FPDE.
* Record whether `lambda_hyb` was fixed manually or selected with `FPDEEngine.select_lambda`.
* Record the `lambda_hyb_grid` used for validation selection.
* Record `normalize`, usually `"l1"` or `"none"`.
* Record `anchor_strategy`, usually `"mean"`, `"zero"`, or `"none"`.
* Record `eps` for cosine computations.
* Record the baseline vector used for deletion and insertion curves.
* Record the validation fractions used by `select_lambda` or `perturbation_curves`.

## Prototype state

* Record how prototypes were built. FPDE currently provides class-mean prototypes.
* Record the training data used to build prototypes.
* Record class labels and label ordering.
* If you use `prepare_fpde_context`, preserve the context-generation inputs.

## Explanation outputs

* Save raw attribution vectors before visualization normalization.
* Save target and rival labels for each explanation.
* Save evidence values and exactness residuals.
* Save `positive_score` and `negative_score` when comparing contrasts.
* Save grid-search rows or validation-selection rows when using automated selection.

## Validation and sanity checks

* Run the test suite with `python -m pytest`.
* Verify that attribution shapes match the number of features.
* Check that attribution values are finite.
* Check that target and rival labels are different for multiclass explanations.
* Inspect deletion and insertion curves for a representative sample.
* Confirm that repeated runs with fixed seeds are deterministic.

## Minimal reporting template

```text theme={null}
FPDE version:
Python version:
Model:
Dataset:
Preprocessing:
Train/validation/test split:
Prototype construction:
FPDE mode:
lambda_hyb:
normalize:
anchor_strategy:
eps:
baseline:
Validation fractions:
Random seeds:
Repository commit:
```