Metadata-Version: 2.3
Name: samesame
Version: 0.3.2
Summary: Low-level statistical tests for comparing source and target score distributions.
Keywords: drift detection,data monitoring,dataset shift,data validation,model validation,noninferiority tests,covariate balance,statistical tests
Author: Vathy M. Kamulete
Author-email: Vathy M. Kamulete <vathymut@gmail.com>
License:                    GNU LESSER GENERAL PUBLIC LICENSE
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Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Typing :: Typed
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Operating System :: OS Independent
Requires-Dist: numpy>=1.21
Requires-Dist: scipy>=1.15
Requires-Dist: scikit-learn
Requires-Dist: samesame[docs,test] ; extra == 'dev'
Requires-Dist: mkdocs-material ; extra == 'docs'
Requires-Dist: mkdocstrings-python ; extra == 'docs'
Requires-Dist: numpydoc ; extra == 'docs'
Requires-Dist: pandas ; extra == 'docs'
Requires-Dist: uv>=0.11.14 ; extra == 'test'
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: pytest-cov ; extra == 'test'
Requires-Dist: ruff ; extra == 'test'
Maintainer: Vathy M. Kamulete
Maintainer-email: Vathy M. Kamulete <vathymut@gmail.com>
Requires-Python: >=3.12
Provides-Extra: dev
Provides-Extra: docs
Provides-Extra: test
Description-Content-Type: text/markdown

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<!-- markdownlint-disable MD033 -->

# samesame

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> Same, same but different ...

`samesame` compares a reference group with a new group and tells you whether the new group looks
different, and whether it moved in a worse direction.

In the package, the reference group is called **source** and the new group is called **target**.
That could mean training vs production data, a baseline batch vs a fresh batch, or one segment vs
another.

The package is built around two practical questions:

- Did anything change?
- Did the change point in a worse direction?

You answer those questions with the signal that matches your use case: predicted risk, model
confidence, prediction error, or a classifier score used to compare two datasets.

## Start here

- Start with [Detect a distribution shift](examples/tutorials/detect-distribution-shift.md) if you want to know whether two datasets differ at all.
- Continue to [Check whether a shift is harmful](examples/tutorials/check-shift-harm.md) when you know what "worse" means for your signal.
- Use [Adjust for covariate shift with importance weights](examples/tutorials/adjust-for-covariate-shift.md) when source and target have different feature coverage and you want to focus on their overlap.

## Quick example

```python
import numpy as np
import samesame as ss

rng = np.random.default_rng(123_456)
source_scores = rng.normal(size=600)
target_scores = rng.normal(size=600)

shift = ss.shift.detect_shift(source_scores, target_scores)
harm = ss.shift.detect_harm(
    source_scores,
    target_scores,
    direction="higher-is-worse",
)

print(f"Shift p-value: {shift.pvalue:.4f}")
print(f"Harm  p-value: {harm.pvalue:.4f}")
```

A small p-value from `detect_shift(...)` means the groups differ.
A small p-value from `detect_harm(...)` means the target group also moved in the declared worse
direction.

## Common signals

Choose the signal that matches the decision you need to make:

- **Predicted risk** when higher values already mean higher business risk.
- **Prediction error** when labels are available and you want to measure accuracy directly.
- **Confidence score** when you want to monitor certainty rather than business impact.
- **Domain-classifier score** when your goal is to detect distribution shift between datasets.

The package does not force one interpretation on you. It gives you a small set of tests you can
reuse across these settings.

## Why it works well in practice

`samesame` is statistically grounded, but the working model is simple:

1. Build a numeric signal for source and target.
2. Test for any change with `ss.shift.detect_shift(...)`.
3. Test for directional harm with `ss.shift.detect_harm(...)` when direction matters.

Both tests are permutation-based, which keeps the assumptions light. When source and target differ
in feature support, `ss.weights.from_domain_probabilities(...)` lets you focus the test on the
region where the two groups are genuinely comparable.

## Pick a guide

- [Monitor predicted credit risk](examples/credit/monitor-credit-risk.md) for a label-free business-risk workflow.
- [Monitor model confidence](examples/credit/monitor-model-confidence.md) when confidence matters more than the raw prediction.
- [Monitor prediction errors once labels arrive](examples/credit/monitor-prediction-errors.md) for direct accuracy monitoring.
- [Focus harmful-shift testing on shared support](examples/weighting/source-reweighting.md) when source contains outliers that are irrelevant for deployment.
- [Restrict testing to common support on both sides](examples/weighting/double-weighting.md) when both groups contain low-overlap outliers.

## Installation

```bash
python -m pip install samesame
```
