MetricProof

Executable checks for the assumptions behind business metrics.

MetricProof is a small, dependency-free Python package and CLI for detecting analytical failures that ordinary schema tests can miss: a KPI calculated from the wrong denominator, customers silently lost before evaluation, segment totals that do not reconcile, and malformed retention cohorts.

It was built from a practical SaaS retention problem: a dashboard value can look plausible while still being misleading because its population or denominator changed upstream.

Why this project is different

Most data-quality tools are strong at generic checks such as nulls, types, ranges, and duplicate rows. MetricProof has a narrower purpose: it turns metric definitions into executable evidence.

• ratio_consistency recomputes a published rate from its numerator and denominator.
• population_preserved catches survivorship bias when inactive or unmatched entities disappear before analysis.
• cohort_integrity checks frozen denominators, period-zero retention, valid rates, and monotonic strict-retention curves.
• reconciliation proves that headline and segmented totals tie out.
• unique_grain and numeric_range protect the table structure and metric bounds supporting those business assertions.

The checks are deliberately narrow. They complement schema validation by testing whether a published analytical result still matches its declared population, grain, and calculation.

Quick start

MetricProof requires Python 3.11 or newer and has no runtime dependencies.

python -m pip install metricproof
metricproof --help
metricproof --version

To run the included SaaS retention audit:

git clone https://github.com/AtomicGlance/metricproof.git
cd metricproof
metricproof audit examples/retention_contract.json

Expected result:

MetricProof: SaaS retention metric audit
Result: PASS | 6 passed, 0 failed, 0 errors

[PASS ] one-headline-per-month (unique_grain)
        1 rows match the declared grain.
[PASS ] activation-rate-bounds (numeric_range)
        All 1 'activation_rate' values are within range.
[PASS ] activation-rate-recomputes (ratio_consistency)
        All 1 reported rates recompute correctly.
[PASS ] segment-mrr-ties-to-headline (reconciliation)
        The two sum aggregates reconcile.
[PASS ] eligible-population-survives (population_preserved)
        Evaluated population preserves 100.0% of baseline entities.
[PASS ] retention-cohorts-are-valid (cohort_integrity)
        All 2 cohort(s) preserve their analytical contract.

Run the intentionally broken example to see the audit expose four subtle failures:

metricproof audit examples/broken_retention_contract.json

The command exits with 0 when critical checks pass, 1 when a critical check fails, and 2 when the contract or input cannot be read. That makes it useful as a lightweight CI quality gate.

Analytical contract

Checks live in a readable JSON contract. Dataset paths are resolved relative to the contract file.

{
  "title": "SaaS retention metric audit",
  "datasets": {
    "headline": "data/headline.csv",
    "baseline": "data/baseline_accounts.csv",
    "evaluated": "data/evaluated_accounts.csv"
  },
  "checks": [
    {
      "id": "activation-rate-recomputes",
      "type": "ratio_consistency",
      "dataset": "headline",
      "numerator": "activated_accounts",
      "denominator": "eligible_accounts",
      "rate": "activation_rate"
    },
    {
      "id": "eligible-population-survives",
      "type": "population_preserved",
      "baseline": "baseline",
      "evaluated": "evaluated",
      "key": "account_id"
    }
  ]
}

Reports can be emitted for people or automation:

metricproof audit examples/retention_contract.json --format json
metricproof audit examples/retention_contract.json \
  --format markdown --output audit-report.md

JSON and Markdown reports also record a dataset inventory: the contract-relative source path, row count, file size, and SHA-256 fingerprint. This makes an audit reproducible and lets a reviewer confirm which exact extracts produced the reported result, even when the source files are regenerated later.

Python API

from metricproof import check_population_preserved

eligible = [{"account_id": "A01"}, {"account_id": "A02"}]
evaluated = [{"account_id": "A01"}]

result = check_population_preserved(
    eligible,
    evaluated,
    key="account_id",
)

assert result.status == "fail"
assert result.evidence == [
    {"key": "A02", "issue": "missing from evaluated population"}
]

Inputs are sequences of dictionaries, so the package works with standard CSV and JSON data and can also accept records from a dataframe:

rows = dataframe.to_dict(orient="records")

Where it fits

MetricProof is useful after analytical tables or dashboard extracts have been produced and before their metrics are published. A JSON contract can run locally, in a scheduled pipeline, or in CI and return a non-zero exit code when a critical analytical assumption fails.

It does not replace source-system validation. Instead, it checks the layer between clean source data and a trustworthy reported metric.

Design boundaries

MetricProof intentionally stays small:

• CSV and JSON are supported directly; warehouse connections are out of scope.
• Strict retention is expected to be monotonic. Set "monotonic": false for rolling or resurrection-style retention.
• The package validates supplied analytical outputs; it does not calculate product KPIs or replace source-system tests.
• Version 0.1.1 adds reproducible dataset provenance while keeping the API deliberately small.

Development

$env:PYTHONPATH = (Resolve-Path src).Path
python -m unittest discover -s tests -v
python -m metricproof audit examples/retention_contract.json
python -m metricproof audit examples/broken_retention_contract.json

On macOS or Linux, use export PYTHONPATH="$PWD/src" instead.

License

MIT