semvec/pypi-packages huggingface dataset:
smartpyml 0.1.2
smartpyml: A Comprehensive Machine Learning Library
Keywords: machine learning,data science,automl,ai

smartpyml is a comprehensive machine learning library that empowers developers and data scientists to easily apply classical machine learning algorithms and time series analysis techniques. It provides a collection of user-friendly functions and tools for data preprocessing, model training, evaluation, and prediction, making it suitable for both beginners and experienced practitioners in the field of data science and artificial intelligence.

Main Features:
- A wide range of classical machine learning algorithms
- Time series analysis and forecasting capabilities
- User-friendly interfaces for easy implementation
- Data preprocessing utilities for feature engineering
- Model evaluation and performance metrics
- Support for both numerical and categorical data

- WARNING: This library is still in development and may contain bugs.
For more information and examples, visit the GitHub repository: https://github.com/srikresna/smartpyml
pydantic-settings-yaml 0.2.0
Yaml support for Pydantic settings
Requires Python: >=3.8

# pydantic-settings-yaml

***Note: 2.x needs Pydantic>2.0,<3.0. Install 1.x if you still need Pydantic 1.x.***

Yaml support for Pydantic settings. Load a yaml config file 
as nested Pydantic models. 

Allows to use <file:xxxx> placeholders in the yaml config file
for secrets. A placeholder is replaced with the contents of the
file. Paths are relative to the 'secrets_dir' setting (see below).

## Usage

$cat /config/config.yaml
```
database: 
  password: <file:database_password>
  username: my_database_username
```


$cat /secrets/database_password
```
my_secret_database_password
```


Python code example:
``` 
    from pydantic import BaseModel
    from pydantic_settings_yaml import YamlBaseSettings
    from pydantic_settings import SettingsConfigDict

    class Database(BaseModel):
        username: str
        password: str


    class Settings(YamlBaseSettings):
        database: Database

        # configure paths to secrets directory and YAML config file
        model_config = SettingsConfigDict(
            secrets_dir="/secrets", yaml_file="/config/config.yaml")

    settings = Settings()

    assert settings.dict() == {
        "database": {
            "password": "my_secret_database_password", 
            "username": "my_database_username"
        }
    }
```