open-mlpipe 1.0.1

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.

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pip install open-mlpipe · 14+ Models · Auto EDA · Optuna Tuning · SHAP Explainability · Production Ready

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Quick Start · Features · Models · API · CLI · Config · Examples

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⚡ Quick Start

Install

pip install open-mlpipe

One Line. Done.

from open_mlpipe import run

# Full pipeline — data to production model
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)

CLI

# 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

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🎯 What It Does

Raw Data → EDA → Clean → Feature Eng → Split → Preprocess
→ Compare 14 Models → Tune → Select → Evaluate → Explain → Save

All automatic. All production-ready. One command.

Stage	What Happens
📊 Load & EDA	Auto-detect task, profile data, statistical tests
🧹 Clean	Duplicates, outliers, missing values, ID columns
🔧 Feature Eng	Interactions, log transforms, missingness flags, datetime
✂️ Split	Stratified train/test split
⚙️ Preprocess	Impute, scale, encode (ColumnTransformer)
🏆 Compare	14+ models head-to-head with cross-validation
🎛️ Tune	Optuna hyperparameter optimization
📈 Select	SHAP-based feature importance
✅ Evaluate	R², RMSE, MAE, MAPE, F1, ROC-AUC, MCC
💡 Explain	SHAP summary, dependence, waterfall plots
💾 Save	Full inference pipeline (feature_eng + model)

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🤖 Supported Models

Linear

Model	Type
Ridge	Regression
Lasso	Regression
ElasticNet	Regression
LinearRegression	Regression
LogisticRegression	Classification

Tree & Ensemble

Model	Type
DecisionTree	Both
RandomForest	Both
ExtraTrees	Both

Boosting

Model	Type
XGBoost	Both
LightGBM	Both
GradientBoosting	Both
HistGradientBoosting	Both
AdaBoost	Both

Instance & Probabilistic

Model	Type
KNN	Both
SVM	Both
NaiveBayes	Classification

Ensemble

Model	Type
Stacking	Both
Voting	Both

Auto-selected based on data characteristics and task type.

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🐍 Python API

Simple Usage

from open_mlpipe import run

ctx = run("data.csv", target="price")

Advanced Usage

from open_mlpipe import PipelineConfig, PipelineRunner
from open_mlpipe.config.resolver import build_level1_config

# Build config
config = build_level1_config("data.csv", target="price")
config.project = "my-project"
config.tuning.enabled = True
config.tuning.n_trials = 50

# Run pipeline
runner = PipelineRunner(config)
ctx = runner.run()

# Access everything
print(ctx.best_model_name)  # "lightgbm"
print(ctx.metrics["test_r2"])  # 0.847
print(ctx.metrics["test_mape"])  # 16.77

Config-Driven

from open_mlpipe import run_config

ctx = run_config("configs/regression.yaml")

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💻 CLI Usage

Commands

# Full pipeline
open-mlpipe run --data dataset.csv

# With options
open-mlpipe run --data dataset.csv --target price --project my-project

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

# EDA only (no training)
open-mlpipe profile --data dataset.csv

Options

--data, -d       Path to data file (CSV, Parquet, Excel)
--target, -t     Target column name (auto-detected if not given)
--level, -l      Automation level (1=zero-touch, 2=config-driven)
--config, -c     Path to YAML config file
--project, -p    Project name
--deploy         Generate deployment artifacts

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📋 YAML Config

# configs/regression.yaml
project: insurance-charges
task: auto
data:
  path: data/insurance.csv
  target: charges
  test_size: 0.2

model_selection:
  candidates: [lightgbm, xgboost, random_forest, ridge]
  scoring: [r2, neg_mean_absolute_error]
  ranking_primary: r2

tuning:
  enabled: true
  engine: optuna
  n_trials: 50
  timeout: 600

feature_selection:
  enabled: true
  method: shap_importance
  min_importance: 0.01

evaluation:
  explainability: true
  shap_plots: [summary, dependence, waterfall]

Run it:

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

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📊 Example Output

=== open-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%

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🏗️ Architecture

open-mlpipe/
├── src/open_mlpipe/
│   ├── cli.py              # CLI entry point
│   ├── config/             # Pydantic config + YAML resolver
│   ├── core/               # Pipeline runner, context, stages
│   ├── stages/             # 12 pipeline stages
│   │   ├── load.py         # Data loading
│   │   ├── eda.py          # Exploratory data analysis
│   │   ├── clean.py        # Data cleaning
│   │   ├── feature_eng.py  # Feature engineering
│   │   ├── split.py        # Train/test split
│   │   ├── preprocess.py   # Preprocessing pipeline
│   │   ├── compare.py      # Model comparison
│   │   ├── tune.py         # Hyperparameter tuning
│   │   ├── select.py       # Feature selection
│   │   ├── evaluate.py     # Model evaluation
│   │   ├── explain.py      # SHAP explainability
│   │   └── save.py         # Model saving
│   ├── 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

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📦 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]

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🧪 Testing

# Run all tests
pytest tests/

# Run unit tests only
pytest tests/ -m "not slow"

# Run with coverage
pytest tests/ --cov=open_mlpipe

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repo
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

MIT License — see LICENSE for details.

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🔗 Links

Resource	Link
PyPI	pypi.org/project/open-mlpipe
GitHub	github.com/loisekk/open-mlpipe
Issues	github.com/loisekk/open-mlpipe/issues

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Built with ❤️ by Yash Brahmankar

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