mlfcrafter 0.1.0

ML Pipeline Automation Framework - Chain together data processing, model training, and deployment with minimal code

MLFCrafter

ML Pipeline Automation Framework - Chain together data processing, model training, and deployment with minimal code

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What is MLFCrafter?

MLFCrafter is a Python framework that simplifies machine learning pipeline creation through chainable "crafter" components. Build, train, and deploy ML models with minimal code and maximum flexibility.

Key Features

• 🔗 Chainable Architecture - Connect multiple processing steps seamlessly
• 📊 Smart Data Handling - Automatic data ingestion from CSV, Excel, JSON
• 🧹 Intelligent Cleaning - Multiple strategies for missing value handling
• 📏 Flexible Scaling - MinMax, Standard, and Robust scaling options
• 🤖 Multiple Models - Random Forest, XGBoost, Logistic Regression support
• 📈 Comprehensive Metrics - Accuracy, Precision, Recall, F1-Score
• 💾 Easy Deployment - One-click model saving with metadata
• 🔄 Context-Based - Seamless data flow between pipeline steps

Quick Start

Installation

pip install mlfcrafter

Basic Usage

from mlfcrafter import MLFChain, DataIngestCrafter, CleanerCrafter, ScalerCrafter, ModelCrafter, ScorerCrafter, DeployCrafter

# Create ML pipeline in one line
chain = MLFChain(
    DataIngestCrafter(data_path="data/iris.csv"),
    CleanerCrafter(strategy="auto"),
    ScalerCrafter(scaler_type="standard"),
    ModelCrafter(model_name="random_forest"),
    ScorerCrafter(),
    DeployCrafter()
)

# Run entire pipeline
results = chain.run(target_column="species")
print(f"Test Score: {results['test_score']:.4f}")

Advanced Configuration

chain = MLFChain(
    DataIngestCrafter(data_path="data/titanic.csv", source_type="csv"),
    CleanerCrafter(strategy="mean", str_fill="Unknown"),
    ScalerCrafter(scaler_type="minmax", columns=["age", "fare"]),
    ModelCrafter(
        model_name="xgboost",
        model_params={"n_estimators": 200, "max_depth": 6},
        test_size=0.25
    ),
    ScorerCrafter(),
    DeployCrafter(model_path="models/titanic_model.joblib")
)

results = chain.run(target_column="survived")

Components (Crafters)

DataIngestCrafter

Loads data from various file formats:

DataIngestCrafter(
    data_path="path/to/data.csv",
    source_type="auto"  # auto, csv, excel, json
)

CleanerCrafter

Handles missing values intelligently:

CleanerCrafter(
    strategy="auto",    # auto, mean, median, mode, drop, constant
    str_fill="missing", # Fill value for strings
    int_fill=0.0       # Fill value for numbers
)

ScalerCrafter

Scales numerical features:

ScalerCrafter(
    scaler_type="standard",  # standard, minmax, robust
    columns=["age", "income"]  # Specific columns or None for all numeric
)

ModelCrafter

Trains ML models:

ModelCrafter(
    model_name="random_forest",  # random_forest, xgboost, logistic_regression
    model_params={"n_estimators": 100},
    test_size=0.2,
    stratify=True
)

ScorerCrafter

Calculates performance metrics:

ScorerCrafter(
    metrics=["accuracy", "precision", "recall", "f1"]  # Default: all metrics
)

DeployCrafter

Saves trained models:

DeployCrafter(
    model_path="model.joblib",
    save_format="joblib",  # joblib or pickle
    include_scaler=True,
    include_metadata=True
)

Alternative Usage Patterns

Step-by-Step Building

chain = MLFChain()
chain.add_crafter(DataIngestCrafter(data_path="data.csv"))
chain.add_crafter(CleanerCrafter(strategy="median"))
chain.add_crafter(ModelCrafter(model_name="xgboost"))
results = chain.run(target_column="target")

Loading Saved Models

artifacts = DeployCrafter.load_model("model.joblib")
model = artifacts["model"]
metadata = artifacts["metadata"]

Requirements

• Python: 3.8 or higher
• Core Dependencies: pandas, scikit-learn, numpy, xgboost, joblib

Development

Setup Development Environment

git clone https://github.com/brkcvlk/mlfcrafter.git
cd mlfcrafter
pip install -r requirements-dev.txt
pip install -e .

Run Tests

# Run all tests
python -m pytest tests/ -v

# Run tests with coverage  
python -m pytest tests/ -v --cov=mlfcrafter --cov-report=html

# Check code quality
ruff check .

# Auto-fix code issues
ruff check --fix .

# Format code
ruff format .

Run Examples

python example.py

Documentation

Complete documentation is available at MLFCrafter Docs

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• 📖 Documentation: MLFCrafter Docs
• 🐛 Bug Reports: GitHub Issues
• 💬 Discussions: GitHub Discussions

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Made for the ML Community