open-mlpipe 1.0.0

Production-level automated ML pipeline — from raw data to deployed model in one line

open-mlpipe

Production-level automated ML pipeline — from raw data to deployed model in one line.

What is open-mlpipe?

open-mlpipe is an AutoML pipeline that takes your raw dataset and automatically:

1. Loads and profiles your data (EDA)
2. Cleans duplicates, outliers, and missing values
3. Engineers features (interactions, log transforms, missingness flags)
4. Compares 14+ ML models head-to-head
5. Tunes hyperparameters with Optuna
6. Selects features via SHAP importance
7. Explains predictions with SHAP
8. Saves a production-ready inference pipeline

One command. One line of code. Production model.

Quick Start

Install

pip install open-mlpipe

CLI Usage

# Zero-touch — auto-detect everything
open-mlpipe run --data dataset.csv

# With target specified
open-mlpipe run --data dataset.csv --target price

# Config-driven
open-mlpipe run --config configs/regression.yaml

Python API

from open_mlpipe import run

# One line — full pipeline
ctx = run("dataset.csv", target="price")

# Access results
print(f"Best model: {ctx.best_model_name}")
print(f"Test R²: {ctx.metrics['test_r2']:.4f}")

# Load saved model for predictions
import joblib
model = joblib.load("artifacts/model_v1.joblib")
predictions = model.predict(new_data)

YAML Config

# configs/my_pipeline.yaml
project: my-project
task: auto
data:
  path: data.csv
  target: price
model_selection:
  candidates: [lightgbm, xgboost, random_forest, ridge]
tuning:
  enabled: true
  engine: optuna
  n_trials: 50

open-mlpipe run --config configs/my_pipeline.yaml

Features

Feature	Description
14+ Models	Ridge, Lasso, ElasticNet, DecisionTree, RandomForest, ExtraTrees, XGBoost, LightGBM, GradientBoosting, HistGradientBoosting, AdaBoost, KNN, SVM, Stacking, Voting
Auto EDA	Column hygiene, quality assessment, distributions, statistical tests, VIF
Feature Engineering	Missingness flags, interaction features, log transforms, datetime extraction
Hyperparameter Tuning	Optuna with TPE sampler, 18 model-specific search spaces
SHAP Explainability	Global feature importance, local explanations
Production Inference	Full pipeline saved (feature engineering + model) — raw data → prediction
Overfitting Detection	Automatic train/test gap analysis
Production Metrics	R², RMSE, MAE, MAPE, F1, ROC-AUC, MCC

Supported Tasks

• Regression — predict continuous values
• Classification — predict categories (binary/multiclass)

Installation Options

# Core (minimal)
pip install open-mlpipe

# With CatBoost
pip install open-mlpipe[catboost]

# With MLflow tracking
pip install open-mlpipe[mlflow]

# With deployment (FastAPI + Docker)
pip install open-mlpipe[deploy]

# Everything
pip install open-mlpipe[full]

Example Output

=== mlpipe Pipeline Runner ===
  Project:  california-housing
  Data:     california_housing.csv
  Target:   median_house_value

>> load       OK (0.1s)
>> eda        OK (0.2s)
>> clean      OK (0.0s)
>> feature_eng OK (0.0s)
>> split      OK (0.0s)
>> preprocess OK (0.0s)
>> compare    OK (25.9s)  — tested 14 models
>> tune       OK (20.2s)  — Optuna 20 trials
>> select     OK (0.0s)
>> evaluate   OK (0.0s)
>> explain    OK (3.3s)
>> save       OK (0.1s)

=== Pipeline Complete ===
  Best Model:  lightgbm
  Test R²:     0.8474
  Test MAE:    0.2924
  Test MAPE:   16.77%

Model Comparison

open-mlpipe automatically compares all models and ranks them:

Model	R²	Gap	Status
LightGBM	0.839	0.072	Best
XGBoost	0.835	0.093	Good
RandomForest	0.771	0.198	Overfit
Ridge	0.709	0.001	Stable

Project Structure

open-mlpipe/
├── src/open_mlpipe/
│   ├── cli.py              # CLI entry point
│   ├── config/             # Pydantic config + YAML resolver
│   ├── core/               # Pipeline runner, context, stages
│   ├── stages/             # 12 pipeline stages
│   ├── utils/              # I/O, feature engineering
│   └── deploy/             # FastAPI + Docker generation
├── tests/                  # 106 unit tests + 3 integration tests
├── configs/                # 5 example YAML configs
└── pyproject.toml          # Package config

Requirements

• Python >= 3.10
• See pyproject.toml for full dependency list

License

MIT License — see LICENSE for details.

Contributing

Contributions welcome! Please open an issue or PR.

Author

Yash Brahmankar