driconfig 0.2.0

Pydantic-ish YAML configuration management.

A Pydantic-ish way to manage your project's YAML configurations.

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Documentation: https://dribia.github.io/driconfig

Source Code: https://github.com/dribia/driconfig

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The usage of YAML files to store configurations and parameters is widely accepted in the Python community, especially in Data Science environments.

DriConfig provides a clean interface between your Python code and these YAML configuration files.

It is heavily based on Pydantic's Settings Management, preserving its core functionalities and advantages.

Key features

• Subclassing the DriConfig class we create an interface to any YAML configuration file.
• Our project's configurations are then attributes of this class.
• They are automatically filled with the values in the YAML configuration file.
• We can define complex configuration structures using Pydantic models.
• We preserve Pydantic's type casting and validation!

Example

Let's say we have a YAML configuration file config.yaml with the following data:

# config.yaml
model_parameters:
  eta: 0.2
  gamma: 2
  lambda: 1

date_interval:
  start: 2021-01-01
  end: 2021-12-31

Then we can parse with driconfig as follows:

from datetime import date
from typing import Dict

from driconfig import DriConfig
from pydantic import BaseModel


class DateInterval(BaseModel):
  """Model for the `date_interval` configuration."""
  start: date
  end: date

  
class AppConfig(DriConfig):
   """Interface for the config/config.yaml file."""

   class Config:
       """Configure the YAML file location."""

       config_folder = "."
       config_file_name = "config.yaml"

   model_parameters: Dict[str, float]
   date_interval: DateInterval

config = AppConfig()
print(config.json(indent=4))
"""
{
    "model_parameters": {
        "eta": 0.2,
        "gamma": 2.0,
        "lambda": 1.0
    },
    "date_interval": {
        "start": "2021-01-01",
        "end": "2021-12-31"
    }
}
"""