lizyml 0.4.2

Config-driven ML analysis library for regression and classification

LizyML

Config-driven ML library that unifies tune / fit / predict / evaluate / export for regression, binary classification, and multiclass classification.

Key Features

• One config, full pipeline -- A single dict/YAML/JSON drives splitting, training, tuning, evaluation, and export. No boilerplate orchestration code.
• Reproducibility by default -- Seed, split indices, params, library versions, and data fingerprint are captured automatically in every run.
• Leakage-aware CV and calibration -- OOF predictions never see their own training rows. Calibration uses cross-fit on the same outer splits. Time and group constraints propagate to inner validation.
• 8 CV strategies -- KFold, Stratified, Group, StratifiedGroup, TimeSeries, Purged TimeSeries, Group TimeSeries, and 2-axis Blocked Group KFold.
• Stable result contracts -- FitResult, PredictionResult, and artifact formats have fixed schemas. Downstream code never breaks on shape changes.
• Codegen export -- Generate standalone train.py + predict.py that run without LizyML installed.
• Optional extras -- Tuning (Optuna), SHAP explanations, Plotly visualizations, and Beta calibration (scipy) are all opt-in.

Installation

pip install lizyml

Extras

pip install 'lizyml[tuning]'        # Optuna hyperparameter search
pip install 'lizyml[explain]'        # SHAP explanations
pip install 'lizyml[plots]'          # Plotly visualizations
pip install 'lizyml[calibration]'    # Beta calibrator (scipy)
pip install 'lizyml[tuning,explain,plots,calibration]'  # all extras

Development install

git clone https://github.com/nbx-liz/LizyML.git
cd LizyML
uv sync --group dev

Quick Start

import numpy as np
import pandas as pd
from lizyml import Model

# --- Synthetic data ---
rng = np.random.default_rng(42)
n = 500
df = pd.DataFrame({
    "feat_a": rng.normal(size=n),
    "feat_b": rng.normal(size=n),
    "cat_col": rng.choice(["x", "y", "z"], size=n),
    "target": rng.normal(size=n),
})

# --- Config ---
config = {
    "config_version": 1,
    "task": "regression",
    "data": {"target": "target"},
    "features": {"categorical": ["cat_col"]},
    "split": {"method": "kfold", "n_splits": 5},
    "model": {"name": "lgbm"},
    "evaluation": {"metrics": ["rmse", "mae"]},
}

# --- Train, evaluate, predict ---
model = Model(config=config)
fit_result = model.fit(data=df)
metrics = model.evaluate()
print(metrics)  # {"raw": {"oof": {"rmse": ..., "mae": ...}, ...}}

pred = model.predict(df.drop(columns=["target"]))
print(pred.pred[:5])

# --- Save and reload ---
model.export("my_model")
loaded = Model.load("my_model")
loaded.predict(df.drop(columns=["target"]))

Configuration

LizyML accepts configs as Python dicts, JSON files, or YAML files. Environment variables override any key using the LIZYML__ prefix (e.g., LIZYML__training__seed=99).

See Config Reference for all keys, defaults, split method guides, and tuning space definitions.

Codegen Export

Generate LizyML-free scripts for production deployment:

model.export_code("deploy/my_model")

Output:

• train.py -- retrain on new data with python train.py data.csv
• predict.py -- run inference with python predict.py test.csv -o out.csv
• config.json -- all hyperparameters and feature definitions
• test_equivalence.py -- verify codegen matches Model.predict()
• artifacts/ -- model files in human-readable formats

Dependencies: only lightgbm, numpy, pandas, scikit-learn.

Architecture

LizyML uses a 5-layer architecture where dependencies flow strictly downward:

Layer 4  Facade       Model (orchestration only, no logic)
           |
Layer 3  Optional     explain / plots / persistence / codegen
           |
Layer 2  Composition  training / evaluation / tuning
           |
Layer 1  Leaf         config / data / splitters / features / estimators / metrics / calibration
           |
Layer 0  Foundation   types (FitResult, PredictionResult, ...) / exceptions / logging

Key rules:

• Downward-only dependencies (no circular imports)
• Layer 2 references Layer 1 through abstract interfaces only
• Only the Facade (Layer 4) assembles concrete classes

See ARCHITECTURE.md for full diagrams and module layout.

Design Priorities

Reproducibility -- Same config + seed = same splits, same OOF predictions, same metrics. Every run captures seed, split indices, params, library versions, and a data fingerprint.

Leakage prevention -- OOF rows are never seen during training. Calibration cross-fit reuses outer CV splits. Time and group constraints propagate to inner validation (early stopping) and calibration.

Contract stability -- FitResult, PredictionResult, and artifact formats have fixed schemas. Breaking changes require a format_version bump and migration path.

Result Objects

Object	Key fields
FitResult	oof_pred, if_pred_per_fold, metrics, models, splits, run_meta
PredictionResult	pred, proba (binary), shap_values (optional), warnings
Model Artifact	Trained models, pipeline state, calibrator, config, format_version

model.evaluate() returns structured metrics:

{
    "raw": {
        "oof": {"rmse": 0.42, "mae": 0.33},
        "if_mean": {"rmse": 0.40, "mae": 0.31},
        "if_per_fold": [...],
        "oof_coverage": 1.0,
    },
    "calibrated": {"oof": {"logloss": 0.35}},  # binary only
}

See BLUEPRINT.md for full schemas and invariants.

Roadmap

• Broader scikit-learn estimator support
• DNN backend (PyTorch)
• Multiclass calibration
• Ranking tasks
• Additional export formats (ONNX, TorchScript)

Documentation

• Config Reference -- all config keys, defaults, and split guides
• BLUEPRINT.md -- implementation specification (source of truth)
• ARCHITECTURE.md -- 5-layer architecture diagrams
• CHANGELOG.md -- release history
• HISTORY.md -- proposal and decision records

Contributing

1. Fork the repo and create a branch from develop
2. Run quality gates: uv run ruff check . && uv run mypy lizyml/ && uv run pytest
3. Open a PR against develop

License

MIT