semvec/pypi-packages huggingface dataset:
midone 1.0.5
Online autonomous time series prediction of near martingales
License: MIT

# Mid+One ![tests](https://github.com/microprediction/midone/actions/workflows/tests.yml/badge.svg)

Utilities to help build `Attackers` that detect small departures from martingality in time-series. 

### Contest entry notebook examples

Play the game at [CrunchDAO.com](https://www.crunchdao.com/)! You might win some cash or even have ongoing upside. 

| Contest notebook | Description |
| --- | --- |
| [Mean reversion attacker](https://github.com/crunchdao/quickstarters/blob/master/competitions/mid-one/quickstarters/mean_reversion_attacker/mean_reversion_attacker.ipynb) | Illustrates use of the `Attacker` class|
| [Momentum attacker](https://github.com/crunchdao/quickstarters/blob/master/competitions/mid-one/quickstarters/momentum_attacker/momentum_attacker.ipynb) | Illustrates use of running calculations |
| [Regression Attacker](https://github.com/crunchdao/quickstarters/blob/master/competitions/mid-one/quickstarters/regression_attacker/regression_attacker.ipynb) | Illustrates running regression pattern |


### About
This package is intended to make life simpler for those participating in an ongoing tournament at [CrunchDAO.com](https://www.crunchdao.com), although it hopefully has independent interest. For instance, you might build on `Attacker` or examples herein to help find signal in your model residuals.  

 - See the README in [attacker.md](https://github.com/microprediction/midone/blob/main/midone/attackers/attacker.md).  
 - It's also highly recommended to read the [FAQ.md](https://github.com/microprediction/midone/blob/main/midone/attackers/FAQ.md).


### Install 
Stable:

     pip install midone 

Latest:

     pip install git+https://github.com/microprediction/midone.git
 
### Getting help

See the  [discord channel](https://discord.gg/NuqJTcYQ2J) and [CrunchDAO.com](https://www.crunchdao.com).
melanoma-segmentation 1.4.0
Melanoma segmentation and classification
License: MIT
Keywords: melanoma segmentation classification

# Melanoma Segmentation and Classification
![Comparison](https://github.com/user-attachments/assets/c12f67fe-77d2-442a-8605-73e2e357470f)

This package/repository provides a comprehensive solution for melanoma segmentation and classification using deep learning techniques. The core of this project involves training and evaluating convolutional neural networks (CNNs) for accurately identifying and segmenting skin lesions in dermatoscopic images.

### Features:
- **Segmentation Models**: Includes implementations of various U-Net based models like R2U-Net and TransUNet with enhancements such as attention mechanisms and transformer blocks to achieve accurate segmentation of skin lesions.
- **Dataset Preparation**: Automates the splitting of large dermatoscopic datasets into training, validation, and testing sets using customizable transformations for data augmentation, ensuring robust training.
- **Custom Metrics and Evaluation**: Implements metrics such as Dice coefficient, Intersection over Union (IoU), accuracy, and recall to measure the modelâ€™s performance in segmentation tasks.
- **Visualization Tools**: Offers functionality to visualize images alongside ground truth and predicted segmentation masks, making it easier to analyze model performance.
- **Comprehensive Data Processing Pipeline**: Integrates data loading, preprocessing, model training, and evaluation in a unified pipeline using PyTorch, facilitating reproducibility and scalability.
- **Trained_models**: Trained models that can be used to compare metrics, there are multiple of them accesible as you can see inside `evaluate.py`
### Dependencies:
The project uses various dependencies such as `PyTorch`, `torchvision`, and `albumentations` for image augmentation and preprocessing, as well as pre-trained models for enhanced feature extraction. The package also leverages `numpy`, `pandas`, and `scikit-learn` for numerical operations and data handling.

### Use Case:
This project is intended for researchers and developers aiming to build or extend segmentation models for medical image analysis, specifically for tasks related to skin lesion identification and segmentation. It is well-suited for experimentation with dermatoscopic datasets and contributes to research efforts in melanoma detection.

### Package Link:
[melanoma-segmentation Package](https://test.pypi.org/project/melanoma-segmentation/)

### References:
- **Visualization**: Based on code by *an-eve* from the ISIC 2016 Lesion Segmentation Challenge [GitHub link](https://github.com/an-eve/ISIC-2016-lesion-segmentation-challenge/tree/main).
- **TransUNet Design**: Inspired by *TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation* by Shahzaib Iqbal et al. (2024). DOI: [arXiv link](https://arxiv.org/abs/2408.09687).
- **Data Augmentation**: Inspired by the top-ranked solution of the SIIM-ISIC Melanoma Classification Challenge [GitHub link](https://github.com/haqishen/SIIM-ISIC-Melanoma-Classification-1st-Place-Solution).
npt-promote 0.2
Mypy plugin to add type promotions between NumPy and builtin data types.
License: MIT
Requires Python: >=3.8
Keywords: mypy, typing, numpy

# npt-promote
Mypy plugin to add type promotions between NumPy and builtin data types.

The main use case for this plugin is to enable generic use of `bool`, `int` and `float`
type annotations with NumPy arrays, e.g.

``` python
import numpy as np
import numpy.typing as npt

x: npt.NDArray[float] = np.array((42.0))
```

## Installation

Dependencies:
- [mypy](https://github.com/python/mypy)
- [NumPy](https://github.com/numpy/numpy)

Install it with:

``` bash
python -m pip install npt-promote
```

Alternatively, add `npt-promote` as a project dependency wherever `mypy` is used,
e.g. as an optional dev requirement in `pyproject.toml`:

``` toml
[project.optional-dependencies]
dev = ["mypy", "npt-promote"]
```

## Usage

To enable the plugin, it must be added to your project's mypy configuration file
along with NumPy's mypy plugin. E.g. add the following to `pyproject.toml`:

``` toml
[tool.mypy]
plugins = [
  'numpy.typing.mypy_plugin',
  'npt_promote',
]
```

## pre-commit
To use the plugin with `mypy` as a `pre-commit` hook, it must be added as a
dependency, e.g. add the following to `.pre-commit-config.yaml`:
``` yaml
- repo: https://github.com/pre-commit/mirrors-mypy
  hooks:
  - id: mypy
    additional_dependencies: [
      "npt-promote"
    ]
```

## Testing

First, install `npt-promote` with dev requirements:
``` bash
python -m pip install npt-promote[dev]
```

To run the tests, execute:
``` bash
pytest
```