Metadata-Version: 2.4
Name: compileml
Version: 0.8.0
Summary: Compile tree-ensemble models into deterministic, auditable, integer-exact decision artifacts — score, calibration, risk bands, and reason codes in one portable JSON, runnable with zero ML dependencies.
Author-email: Carlos Ortiz <orgoca@gmail.com>
License: Apache-2.0
Project-URL: Homepage, https://github.com/orgoca/CompileML
Project-URL: Documentation, https://orgoca.github.io/CompileML
Project-URL: Repository, https://github.com/orgoca/CompileML
Project-URL: Issues, https://github.com/orgoca/CompileML/issues
Project-URL: Changelog, https://github.com/orgoca/CompileML/blob/main/CHANGELOG.md
Keywords: credit-risk,model-deployment,explainability,determinism,scorecard,adverse-action,model-governance,xgboost,lightgbm,cobol
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Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
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Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Office/Business :: Financial
Classifier: Typing :: Typed
Requires-Python: >=3.10
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License-File: LICENSE
License-File: NOTICE
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Requires-Dist: scikit-learn>=1.3
Provides-Extra: xgboost
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Dynamic: license-file

# CompileML

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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.22242231.svg)](https://doi.org/10.5281/zenodo.22242231)

**Keep the predictive power of a tree ensemble. Deploy it as transparent decision logic with no model runtime.**

## Why this exists

CompileML started in credit risk, where two schools of thought meet and rarely agree.

Risk practitioners build scorecards. A scorecard is transparent, reproducible, and deploys anywhere; it is trusted because a validator can check every point by hand. It also leaves predictive power on the table. Data scientists build tree ensembles. An ensemble predicts better, then arrives with a Python environment, a serving stack, post-hoc explanations, and a model no validator can reproduce independently.

Each side is right about what the other gives up. That leaves teams a bad choice: the model they can defend, or the model that performs.

The choice is not unique to credit. It appears wherever a model makes a decision about an individual case and has to answer for it — to a regulator, an auditor, a customer, a clinician — or has to run somewhere the data-science stack does not. Fraud and anti-money-laundering alerts, insurance underwriting and claims, eligibility screening and clinical decision support all meet it. The details differ; the choice is the same.

CompileML removes that choice.

Train the strongest teacher you can: XGBoost, LightGBM, NN. CompileML projects its predictive structure into a shallow, integer-valued whitebox and packages the complete decision into one hashed artifact: score, calibrated probability, risk bands, reason codes, attribution.

Then the teacher is discarded. It was a means, not a deliverable, and it never reaches production.

What ships instead is decision logic built from integer addition, comparison, and table lookup. It runs under CompileML's standard-library-only Python implementation, an ordinary application, a small Lambda, a batch job or you export it as standalone SQL or COBOL and run it directly where the decision already happens. No XGBoost in production, no scikit-learn, no scoring service to operate. In exported form there is no model runtime at all.

This is not a lighter way to serve the black box. The black box was used as a teacher and then compiled out of the system.

That buys three things, and I would not trade any one of them for the other two: the artifact reproduces to the integer on any machine, it explains itself by arithmetic rather than approximation, and it lands where the decision already runs. The sections below are how each one is enforced.

The benchmark puts the cost at about 2% of the teacher's Gini. In exchange, at whitebox depth two or less, every decision reconstructs exactly from a printed scorecard table, and every explanation adds back to the production score with nothing left over.

The examples in this repository come from credit, where the project began and where lenders must explain every adverse decision, reproduce it for a validator, and often run it on systems that predate Python. [Where it fits](docs/concepts/where-it-fits.md) maps the vocabulary to other domains and says where the fit is weaker.

## Quick example

Train however you want. The example below uses a strong model as a teacher and distills it into a shallow whitebox:

```python
from compileml.compile import train_whitebox
from compileml.bands import monotone_quantile_bands
from compileml.artifact import build_artifact, save_artifact

whitebox, fidelity = train_whitebox(
    X_train,
    teacher.predict_proba(X_train)[:, 1],
)

latent = whitebox.predict(X_train).clip(0, 1)

bands = monotone_quantile_bands(
    latent,
    y_train,
    n_bands=10,
)

artifact = build_artifact(
    whitebox,
    feature_names,
    baseline=medians,
    band_edges=bands,
    calibration_latent=latent,
    calibration_y=y_train,
    reasons=REASON_DICTIONARY,
)

save_artifact(artifact, "decision.json")
```

Production does not need the training stack:

```python
from compileml.runtime import load_artifact, decide

artifact = load_artifact("decision.json")
decision = decide(artifact, applicant_row)

# {
#   "band": "G07",
#   "pd": 0.1284,
#   "latent_int": 146,
#   "reasons_negative": [
#       {
#           "code": "HIGH_UTILIZATION",
#           "message": "…",
#           "impact_int": 56,
#       }
#   ],
#   "reasons_positive": [...],
#   "artifact_hash": "84372c36…",
# }
```

The runtime imports nothing outside the Python standard library.

Or skip the Python runtime entirely:

```bash
compileml export decision.json --target sql   --out scorer.sql
compileml export decision.json --target cobol --out scorer.cob
```

## The three objections, answered

I have watched good models become much less impressive on the way to production.

The model starts in Python. Someone rewrites it in SQL. Someone else builds the bands in a spreadsheet. Calibration lives in another script. Reason codes are produced through a separate explanation process. Six months later, everybody is discussing "the model," but they are no longer talking about exactly the same thing.

Each of the three standing objections has a structural answer.

### Stability: scores drift

Floating-point arithmetic is not a reassuring foundation for a decision that must be reproduced across languages and systems. Small differences in accumulation, precision, or implementation can move a score near a boundary.

A credit score should be a fact, not a distribution over environments.

CompileML quantizes model leaves once, at compile time. After that, scoring is integer addition, banding is integer comparison, and calibration is integer table lookup. Recalibration refits probabilities while the model and band edges stay byte-identical, so updating a PD table cannot move a single account between bands.

### Explainability: explanations do not reconcile

Post-hoc explainers are useful, but an explanation of a regulated decision should not merely resemble the decision.

CompileML computes attribution from the compiled model in integer units. The feature impacts, baseline, and residual satisfy a reconciliation identity that validation can add back independently.

For whiteboxes of depth two or less, the pairwise decomposition is complete and the residual is exactly zero. The runtime refuses to emit an explanation that fails to reconcile.

### Deployability: the deployment stack is not the modeling stack

Banks and other large institutions run important decisions on SQL systems, core platforms, and mainframes. Requiring the entire training environment in production is often unrealistic and sometimes unnecessary.

CompileML moves complexity to compile time and leaves production with a small, explicit artifact.

## It compiles to a scorecard

At whitebox depth ≤ 2, the compiled artifact collapses into a classic points scorecard — exactly, not as an approximation.

```bash
compileml scorecard decision.json --format csv --out scorecard.csv
```

Depth 1 produces the familiar form: per feature, a bin and its points. Depth 2 adds explicit pairwise interaction grids over the union of the relevant thresholds. Points are the artifact's own integers, and the identity

```text
base_points + Σ main_effect(x) + Σ interaction(x) == score
```

holds bit-for-bit on every row. `score_from_scorecard()` re-derives any production decision from the printed tables alone, and the test suite asserts it.

Hand the CSV to a validator and they can reproduce production scores in a spreadsheet. Above depth 2 no clean scorecard exists, and the tool raises instead of approximating — the same boundary as exact attribution, for the same reason.

Expect a depth-2 scorecard to be mostly interaction grids. A tree whose two splits use different features contributes a grid rather than a main effect, and boosting seldom spends both splits on one feature: the fairness notebook's 40-tree model on the 23-feature UCI panel compiles to 55 grids and no main effects at all. It is still exact, but it is not the one-table-per-feature card many validation teams expect. If yours does, compile at `max_depth=1` and measure the fidelity that costs with `sweep_whitebox`.

## What the artifact guarantees

Given the same artifact and the same input values, CompileML produces the same governed integer outputs across supported runtimes.

The repository tests this rather than asking you to take it on faith:

* SQL output is executed in SQLite and compared row by row with the Python runtime.
* Generated COBOL is compiled and run in CI, then checked against the reference implementation.
* The same seeded artifact is built on Linux, macOS, and Windows and the hashes are compared.
* Committed reference decisions are replayed on every OS and Python version in the matrix.
* Attribution is added back to the decision during validation.
* Recalibration tests verify that the model and band edges remain unchanged.
* Scorecard tables are re-summed against the runtime's own integers.
* The standard-library-only runtime is enforced by inspecting its imports.

The artifact includes a SHA-256 hash. Loaders verify it by default and reject a document whose contents no longer match the stored hash. This detects modification or corruption; it is an integrity check, not a cryptographic signature of who produced the artifact.

## Measured performance

The committed benchmark uses deterministic synthetic credit data with 40,000 rows and 23 features. It runs on a consumer laptop through the pure-Python runtime.

You can reproduce every number from an editable install of the checked-out source:

```bash
pip install -e .
python benchmarks/run_benchmarks.py
```

`results.json` records the CompileML version it measured, so a run against an older installed copy cannot pass for a measurement of the current code.

| Metric                                     |                      Value |
| ------------------------------------------ | -------------------------: |
| Teacher Gini, 300-tree GBM                 |                      0.667 |
| **Compiled integer artifact Gini**         | **0.653 — 97.9% retained** |
| Band-ordinal Gini, 10 bands                |     0.647 — 97.0% retained |
| Spearman correlation, teacher vs. artifact |                      0.977 |
| Score + band + calibrated PD               |         **0.03 ms median** |
| Score + band + calibrated PD, p95          |                    0.04 ms |
| Full explained decision, 120 trees         |             0.62 ms median |
| Full explained decision, p95               |                    0.84 ms |
| Band assignment alone                      |                     0.2 µs |
| Artifact size                              |                      97 KB |
| Identical hash on rebuild                  |                        Yes |

That 2% of Gini is the price of everything above it. It is stated rather than hidden, and it is reproducible on your own data with `compileml.tune.sweep_whitebox`.

One honest qualification: scoring is very fast; full explanation costs more.

The exact pairwise decomposition is aggregated per tree, which makes it `O(trees)` and independent of how many features the model has. A depth-2 tree is walked at most eight times however wide the model is. A perturbation-based derivation instead scores the whole ensemble `2 + p + p(p−1)/2` times.

On the benchmark's 120-tree ensemble, attribution alone:

| features | perturbation | per tree | tree walks, perturbation | tree walks, per tree |
| -------: | -----------: | -------: | -----------------------: | -------------------: |
| 8 | 0.94 ms | 0.62 ms | 4,560 | 792 |
| 23 | 6.79 ms | 0.70 ms | 33,360 | 912 |
| 50 | 32.26 ms | 0.78 ms | 153,240 | 944 |
| 100 | 131.31 ms | 0.76 ms | 606,240 | 936 |

The walk counts are exact and hold on any machine; the milliseconds belong to one laptop. Cost follows walks, and walks follow tree structure rather than width: the sweep's models are fitted to a target that uses every feature, so more of their trees split on three distinct features than the headline model's do, which is why attribution alone at 23 features here costs slightly more than the full decision in the table above. At eight features the difference between the two derivations is modest. At a hundred it is the difference between a quadratic cost and a flat one. It is exact rather than sampled, and both derivations reach identical integers on every timed row.

In practice: explain everything. Under a millisecond per decision is real-time for credit decisioning — the bureau pull costs more — and complete attribution on every decision is what turns portfolio questions (marginal analysis, driver drift, fairness cuts) into census facts instead of sample estimates. It also means every production decision carries its own explanation in the record, computed at decision time under the same artifact hash.

Batch re-explanation over an entire book used to be the one place the cost bit, and wide feature sets made it worse. Per-tree aggregation removed both.

## Choosing the configuration

The two capacity knobs are not symmetric, and this is the single most useful thing to know before tuning.

`n_estimators` buys fidelity at a linear cost in artifact size and explanation time, and costs nothing else. Determinism, portability, and exact attribution are unaffected at any tree count.

`max_depth` buys fidelity per tree, but above 2 it takes the exactness guarantee with it: attribution residuals appear and no clean scorecard exists.

Spend on trees. Be stingy with depth.

Both are measurable rather than guessable:

```python
from compileml.tune import sweep_whitebox, sweep_bands

sweep_whitebox(X, teacher_latent, y, X_val=X_val, y_val=y_val, teacher_latent_val=t_val)
sweep_bands(latent, y, k_grid=(4, 6, 8, 10, 12, 16))
```

For banding, `band_efficiency()` reports what the ladder discards — the Gini gap against the continuous score — and, per band, whether the score still ranks risk internally. A band that can still separate outcomes is a refinement opportunity; a band that cannot is a band you have used up. The [tuning guide](docs/howto/tuning.md) walks through both.

## What CompileML is not

CompileML is not a new training framework. Use XGBoost, LightGBM, scikit-learn, or another teacher that can be distilled into the supported whitebox representation.

It is not a promise that your data pipelines are identical. Determinism means:

```text
same input values + same artifact = same governed outputs
```

Producing the same input values across systems remains the caller's responsibility.

It is also not a compliance certification. No library can certify an institution's model, data, policy language, or governance process.

CompileML is infrastructure intended to make those things inspectable instead of asking validators to trust a chain of separate implementations.

## Reason codes belong to the institution

CompileML can determine which features moved a decision and by how much. It cannot decide how your institution should explain that result to a customer.

That language is policy, not mathematics.

You provide the reason dictionary:

```python
REASON_DICTIONARY = {
    "BILLS_PAID_LATE": {
        "code": "LATE_PAYMENTS",
        "negative": "Recent payments were made after their due date.",
        "positive": "Consistent on-time payment history.",
    },

    # Add one entry per feature.
    # `suppress: True` hides policy-masked features.
}
```

Features without an entry still work, but they receive generic fallback messages. CompileML measures reason coverage, records it in the artifact, warns when coverage is incomplete, and can make full coverage a validation requirement.

The tooling should not quietly pretend that generic feature names are suitable adverse-action notices.

## Seeing what the model did

The visualization package draws the outputs emitted by `decide()`. It does not independently recompute the model or explanation.

That matters: the chart cannot disagree with the deployed decision because both come from the same payload.

```python
from compileml.viz import (
    waterfall,
    decision_drivers,
    band_conditioned_decision_drivers,
    band_ladder,
)

waterfall(
    decide(artifact, row, include_contributions=True)
)

decision_drivers(sample_decisions, y=y_sample)
band_conditioned_decision_drivers(sample_decisions, y=y_sample)
band_ladder(score_decisions, y_sample)
```

The image at the top of this README was rendered by `waterfall_svg()` from the repository's committed reference artifact. That renderer also uses only the standard library.

## Validation

Run the validation framework against a holdout set:

```bash
compileml validate artifact.json \
  --csv holdout.csv \
  --y-col DEFAULT
```

It checks:

1. artifact integrity;
2. explanation reconciliation;
3. fidelity to the source model;
4. band coverage, score resolution, and band efficiency;
5. bad-rate monotonicity;
6. band-ladder churn;
7. explanation stability;
8. reason-code coverage;
9. declared monotone directions, re-verified against the shipped trees;
10. that the artifact out-scores a reference model on the same data.

These checks run against the compiled artifact through the same runtime used for production decisions. There is no separate notebook implementation allowed to become "almost the same" over time.

The command exits with `0` or `1`, so it can gate deployment in CI.

## Independent evaluation

[@deburky](https://github.com/deburky) evaluated CompileML 0.4.3 on the AWS Fraud Detector sample data with a CatBoost teacher — a different domain, teacher and dataset from anything in this repository — and published the code and notes in [deburky/compileml-fraud-scoring](https://github.com/deburky/compileml-fraud-scoring).

On that data:

* Gini retention of 98.5% and 96.9% at depth 2, on two datasets;
* all ten validation checks passing on both artifacts;
* the SQL export, executed in SQLite, matching the Python runtime on every holdout row;
* scorecard re-sums matching the production score on every row;
* the runtime scoring with no NumPy, pandas, scikit-learn or CatBoost loaded, including from a SageMaker serving image built without them;
* recalibration after a fourfold drop in base rate leaving the model and band edges byte-identical, with zero band churn.

It found two bugs, both fixed in 0.5.0: invalid SQL for a single-band artifact ([#40](https://github.com/orgoca/CompileML/issues/40), fixed by [@tote10](https://github.com/tote10)) and band builders emitting edges the build then rejected on low base rates ([#41](https://github.com/orgoca/CompileML/issues/41)).

Its notes also recorded five usability observations, all addressed in 0.5.2: a latent-range warning that blamed margin-space models even for `train_whitebox` output; no pointer onward when certified banding finds a single band; a documented recalibration metadata key that did not match the one written; an undocumented macOS dependency of the XGBoost and LightGBM extras; and nothing warning that a depth-2 scorecard is mostly interaction grids.

It did not exercise the COBOL export (no compiler was installed), determinism across operating systems (CI checks that separately), or anything released after 0.4.3.

## Citing

Every release is archived on Zenodo. Cite the concept DOI — it always
resolves to the newest version:

> Ortiz, C. *CompileML*. https://doi.org/10.5281/zenodo.22242231

To pin a specific version, use that release's own DOI from its
[Zenodo record](https://doi.org/10.5281/zenodo.22242231). GitHub's **Cite this repository**
button reads [CITATION.cff](CITATION.cff) and will format it for you.

## Install

```bash
pip install compileml
```

Optional teacher integrations:

```bash
pip install compileml[xgboost]
pip install compileml[lightgbm]
```

On macOS both libraries load the OpenMP runtime. If importing either fails while loading its shared library, install it with `brew install libomp`. That is a requirement of XGBoost and LightGBM, not of CompileML, whose runtime needs neither.

Visualization dependencies:

```bash
pip install compileml[viz]
```

The compile side depends on NumPy and scikit-learn. The `compileml.runtime` package uses only the Python standard library.

If necessary, the runtime directory can be vendored into a constrained environment:

```text
src/compileml/runtime/
```

## Documentation

* [Quickstart](docs/quickstart.md)
* [Where it fits](docs/concepts/where-it-fits.md)
* [FAQ](docs/faq.md)
* [Tuning the compilation](docs/howto/tuning.md)
* [Artifact specification](docs/ARTIFACT_SPEC.md)
* [Reason codes](docs/howto/reason-codes.md)
* [Recalibration without band churn](docs/howto/recalibrate.md)
* [Deploying to Python, SQL, and COBOL](docs/howto/deploy.md)
* [Validation framework](docs/howto/validate.md)
* [Fairness audit](docs/howto/fairness.md)
* [Monitoring a deployed artifact](docs/howto/monitor.md)
* [Visualization](docs/howto/visualize.md)
* [Executable notebooks](examples/)

## Roadmap

Recently shipped:

* exact attribution aggregated per tree, so explaining a decision no longer grows with feature count, and a fairness audit in `compileml.fairness` (0.5);
* exact drift decomposition, band calibration and baseline staleness in `compileml.monitor` (0.6);
* the calibrated PD and reason codes from the COBOL and SQL exports (0.7);
* retention by segment and across PD cutoff ranges, and weighting a whitebox toward the segment that pays (0.8).

The current priorities are:

* add Java and C exporters;
* add an optional NumPy batch scorer.

## License

Apache-2.0.

Copyright 2026 Carlos Ortiz.
