ml-labs 0.6.0

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ml-labs

A structured machine learning experimentation framework for building, managing, and evaluating ML pipelines with cross-validation, caching, and multi-framework support.

Installation

pip install ml-labs

With optional dependencies:

pip install ml-labs[xgboost]    # XGBoost support
pip install ml-labs[lightgbm]   # LightGBM support
pip install ml-labs[catboost]   # CatBoost support
pip install ml-labs[shap]       # SHAP value analysis
pip install ml-labs[polars]     # Polars DataFrame support
pip install ml-labs[all]        # All optional dependencies

Key Features

• Pipeline: DAG-based node graph for defining ML workflows with stages (data transformation) and heads (model prediction)
• Experimenter: Experiment execution engine with LRU caching, state management, and error resilience
• Trainer: Cross-validation training pipeline with split management
• Collectors: Extensible data collection — metrics, stacking outputs, model attributes, SHAP values, raw outputs
• Adapters: Unified interface for scikit-learn, XGBoost, LightGBM, CatBoost, and Keras
• Data Flexibility: Support for pandas, polars, cuDF, and NumPy arrays

Architecture Overview

Pipeline          Define node graphs (stages + heads) with groups and edges
    │
Experimenter      Execute pipelines, manage cache and state
    │
  ├── ExpObj      Per-node build/experiment objects (StageObj, HeadObj)
  ├── Trainer     Cross-validation training with split management
  └── Collector   Collect metrics, predictions, model attributes, SHAP values

Node State Model:

init ──→ built ──→ finalized
  │
  └──→ error ──→ (reset) ──→ init

Quick Start

from mllabs import Experimenter, Connector, MetricCollector

exp = Experimenter(data=df, path="exp/my_experiment")

p = exp.pipeline
p.set_grp("scale", role="stage", processor="StandardScaler")
p.set_grp("model", role="head", processor="RandomForestClassifier",
          parent="scale", edges={"X": [(None, None)], "y": [(None, "target")]})

p.set_node("rf_default", grp="model")

exp.build(["rf_default"])
exp.exp(["rf_default"])

mc = MetricCollector("accuracy", Connector(), output_var="prediction",
                     metric_func=lambda y, pred: (y == pred).mean())
exp.add_collector(mc)
exp.collect(mc)

print(mc.get_metrics(["rf_default"]))

Requirements

• Python >= 3.10
• pandas >= 1.5
• numpy >= 1.23
• scikit-learn >= 1.2
• cachetools >= 5.0

License

PolyForm Noncommercial 1.0.0 — free for non-commercial use.