feature-fabrica 1.3.1

⚙️ The Framework to Simplify and Scale Feature Engineering ⚙️

For data scientists, ML engineers, and AI researchers who want to simplify feature engineering, manage complex dependencies, and boost productivity.

Introduction

Feature Fabrica is an open-source Python library designed to improve engineering practices and transparency in feature engineering. It allows users to define features declaratively using YAML, manage dependencies between features, and apply complex transformations in a scalable and convenient manner.

By providing a structured approach to feature engineering, Feature Fabrica aims to save time, reduce errors, and enhance the transparency and reproducibility of your machine learning workflows. Whether you're working on small projects or managing large-scale pipelines, Feature Fabrica is designed to meet your needs.

Key Features

• 📝 Declarative Feature Definitions: Define features, data types, and dependencies using a simple YAML configuration.
• 🔄 Transformations: Apply custom transformations to raw features to derive new features.
• 🔗 Dependency Management: Automatically handle dependencies between features.
• ✔️ Pydantic Validation: Ensure data types and values conform to expected formats.
• 🛡️ Fail-Fast with Beartype: Catch type-related errors instantly during development, ensuring your transformations are robust.
• 🚀 Scalability: Designed to scale from small projects to large machine learning pipelines.
• 🔧 Hydra Integration: Leverage Hydra for configuration management, enabling flexible and dynamic configuration of transformations.

🛠️ Quick Start

Installation

To install Feature Fabrica, simply run:

pip install feature-fabrica

Defining Features in YAML

Features are defined in a YAML file. Here’s an example:

feature_a:
  description: "Raw feature A"
  data_type: "float32"

feature_b:
  description: "Raw feature B"
  data_type: "float32"

feature_c:
  description: "Derived feature C"
  data_type: "float32"
  dependencies: ["feature_a", "feature_b"]
  transformation:
    sum_fn:
      _target_: feature_fabrica.transform.SumReduce
      iterable: ["feature_a", "feature_b"]
    scale_feature:
      _target_: feature_fabrica.transform.ScaleFeature
      factor: 0.5

Creating and Using Transformations

You can define custom transformations by subclassing the Transformation class:

from typing import Union
import numpy as np
from beartype import beartype
from numpy.typing import NDArray
from feature_fabrica.transform import Transformation
from feature_fabrica.transform.utils import NumericArray, NumericValue


class ScaleFeature(Transformation):
    def __init__(self, factor: float):
        super().__init__()
        self.factor = factor

    @beartype
    def execute(self, data: NumericArray | NumericValue) -> NumericArray | NumericValue:
        return np.multiply(data, self.factor)

Compiling and Executing Features

To compile and execute features:

import numpy as np
from feature_fabrica.core import FeatureManager

data = {
    "feature_a": np.array([10.0], dtype=np.float32),
    "feature_b": np.array([20.0], dtype=np.float32),
}
feature_manager = FeatureManager(
    config_path="../examples", config_name="basic_features"
)
results = feature_manager.compute_features(data)
print(results["feature_c"])  # 0.5 * (10 + 20) = 15.0
print(results.feature_c)  # 0.5 * (10 + 20) = 15.0

Visualize Features and Dependencies

Track & trace Transformation Chains

import numpy as np
from feature_fabrica.core import FeatureManager

data = {
    "feature_a": np.array([10.0], dtype=np.float32),
    "feature_b": np.array([20.0], dtype=np.float32),
}
feature_manager = FeatureManager(
    config_path="../examples", config_name="basic_features"
)
results = feature_manager.compute_features(data)
print(feature_manager.features.feature_c.get_transformation_chain())
# Transformation Chain: (Transformation: sum_fn, Value: 30.0 Time taken: 9.5367431640625e-07 seconds) -> (Transformation: scale_feature, Value: 15.0, Time taken:  9.5367431640625e-07 seconds)

Visualize Dependencies

from feature_fabrica.core import FeatureManager

feature_manager = FeatureManager(
    config_path="../examples", config_name="basic_features"
)
feature_manager.get_visual_dependency_graph()

Contributing

We welcome contributions to Feature Fabrica! If you have ideas for new features, improvements, or if you'd like to report issues, feel free to open a pull request or an issue on GitHub.

How to Contribute

1. Fork the repository to your own GitHub account.
2. Clone your fork locally.
3. Create a new branch for your feature or fix.
4. Commit your changes with a clear and concise message.
5. Push to the branch.
6. Open a pull request from your fork to the original repository.

We look forward to your contributions! 😄