semvec/pypi-packages huggingface dataset:
mlu 0.1.0
A comprehensive Python machine learning utility application designed to simplify and enhance the machine learning workflow.
Requires Python: >=3.6

# mlu

mlu is a comprehensive Python machine learning utility application designed to simplify and enhance the machine learning workflow. Inspired by lodash's modularity and chaining features, mlu encompasses a broad array of functionalities, including data preprocessing, model building, evaluation, deployment, continuous learning, and more. Developed entirely in Python, mlu leverages the Python standard library alongside powerful libraries such as NumPy for numerical operations, making it a versatile tool for data scientists and machine learning practitioners.

## Overview

mlu is structured as a modular application, with distinct components for data transformation, array manipulation, model building, and a unique chaining mechanism for seamless operation sequences. It integrates with popular cloud platforms and supports a variety of machine learning tasks and algorithms.

## Features

- Comprehensive suite of data preprocessing functions
- Extensive model building capabilities with support for numerous algorithms
- Hyperparameter optimization for fine-tuning models
- Robust evaluation tools including metrics and visualization
- Flexible pipeline construction for streamlined workflows
- Extensibility and customization through a modular architecture
- Advanced visualization tools for insightful data and model analysis
- Integrated model deployment for putting models into production
- Continuous learning capabilities for model updating
- Scalability and performance optimization for handling large datasets
- Cloud integration and AutoML support for efficient machine learning processes

## Getting started

### Requirements

- Python 3.x
- NumPy
- Optional: Pandas, Flask

### Quickstart

1. Clone the repository to your local machine.
2. Install the required packages: `pip install numpy pandas flask`
3. Run the application: `python app.py`
4. Explore the functionalities as per the documentation provided.

### License

Copyright (c) 2024.
geometatool 0.1.5
Geospatial Metadata Toolkit

## Geospatial Metadata Toolkit (GeoMetaTool)

The Geospatial Metadata Toolkit is a Python package designed to streamline geospatial metadata operations. Whether you're working with GIS data, satellite imagery, or any other geospatial information, GeoMetaTool provides a convenient set of tools for three primary tasks: **metadata extraction**, **metadata conversion**, and **metadata validation**.

### Metadata extraction
GeoMetaTool simplifies the process of extracting essential metadata from various types of geospatial data. It allows you to access key information about spatial datasets, including details such as coordinate reference systems, temporal extents, data sources, and more. With this feature, you can save time and avoid the hassle of manually retrieving metadata from your geospatial assets. Specifically, it extract geospatial metadata in [ISO 19115-3:2023](https://www.iso.org/standard/80874.html) schema embedding [ISO 19110:2016](https://www.iso.org/standard/57303.html) as the contentInfo element. ISO 19115-3:2023 defines an integrated XML implementation of ISO 19115-1 and ISO 19115-2 while ISO 19110:2016 defines the methodology for cataloguing feature types. 

The package is currently under development to support the following geospatial data
* Vector: shp, kml, mif, gml, dxf, geojson
* Raster: tif, tiff
* Collection: gdb, gpkg

```sh
import geometatool
iso_metadata = geometatool.extract_metadata(input_file_path, default_metadata_values=[])
iso_metadata.save_to_file(output_file_path)
```

### Metadata conversion
Dealing with different metadata standards across geospatial projects can be cumbersome. GeoMetaTool offers a solution by enabling the conversion of metadata between different schemas. This is achieved through a conversion table between ISO 19115-3 and other predefined schemas. This feature promotes interoperability and consistency across diverse geospatial data sources.

The package is currently under development to support the following geospatial metadata

* XML schema: ISO 19115-3/19110, QGIS metadata
* JSON schema: NADA geospatial metadata, World Bank DDH (Development Data Hub) metadata

```sh
import geometatool
converted_metadata = geometatool.convert_metadata(input_file_path, conversion_table_file_path, target_schema_file_path)
with open(output_file_path, "w") as f:
    json.dump(converted_metadata, f, indent = 4)
```

### Metadata validation
GeoMetaTool allows you to validate the correctness of metadata syntax, ensuring that your geospatial metadata adheres to ISO 19115-3 or predefined json schema.

```sh
import geometatool

geometatool.validate_xml_ISO19115_3(instance_file_path)
# The XML is well formed.
# The instance is valid aginst the ISO 19115-3 schemas.

geometatool.validate_json(instance_file_path, schema_file_path)
# The instance is valid aginst the provided schema.
```