semvec/pypi-packages huggingface dataset:
pairwiseprediction 0.240311.7
Handle a regression problem inducing the model on pairs A,B of instances
Requires Python: >=3.10,<4.0

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# pairwiseprediction
[Latest Release](https://pypi.org/project/pairwiseprediction) |
[Current Code](https://github.com/davips/pairwiseprediction) |
[API Documentation](https://davips.github.io/pairwiseprediction)

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Handle a regression problem inducing the model on pairs A,B of instances.

Last column of `X` is the continuous target. `y` is ignored.

Four modes are hipotetically possible, although only mode 'b' below is provided by now to ease compatibility with sklearn:
* a classification algorithm is trained to tell when instance A has higher target than instance B
  * 1) the prediction is based on interpolation which is the type of result expected from a regression
  * 2) the interpolated value can be converted to a hard prediction through binarization
* a regression algorithm is trained to predict the difference between target values of A,B
  * 3) the prediction is the value provided directly by the regressor
  * 4) the regression value can be converted to a hard prediction through binarization
nickgenfast 0.0.3
A random nickname generator.
Requires Python: >=3.6

# Random Name Generator

This Python package generates random names by combining adjectives, verbs, and nouns from predefined word lists.

## Installation

To install this package, you can use pip:

## Usage

Here's an example of how to use the package:

```python
from nickgenfast import generate_nick_name

# Generate a random name
random_name = generate_nick_name()
print(random_name)
```
wstats 0.0.99
An implementation of a few weighted statistics

# Weighted statistics

## What?
We've re-implemented some weighted statistics inspired by the weights R package (https://github.com/cran/weights/tree/master).
At the moment, it's just the weighted t-test, but this may expand.

## How?
```python
import numpy as np
from wstats import wtd_t_test

# Example data for testing
x = np.random.normal(0, 1, 100)
y = np.random.normal(0.5, 1, 100)
wx = np.random.rand(100)
wy = np.random.rand(100)

# Execute tests
# Without bootstrapping
result_no_boot = wtd_t_test(x, y, wx, wy, alternative="two-sided", bootse=False)
# With bootstrapping
result_boot = wtd_t_test(x, y, wx, wy, alternative="two-sided", bootse=True, bootn=int(1e4))

(result_no_boot, result_boot)

```
cloudytab 0.1.0
Ultra-lightweight python wrapper for output files from the Cloudy astrophysical plasma simulation code
Requires Python: >=3.8

# cloudytab
Ultra-lightweight python wrapper for output files from the [Cloudy](https://nublado.org) astrophysical plasma simulation code

## Example of usage

```python
from cloudytab import CloudyModel
m = CloudyModel("myfolder/mymodel")
```

`m.files` contains a list of all the files that were found:
```python
['myfolder/mymodel.in', 'myfolder/mymodel.ovr', ...]
```

`m.data` contains dict of `astropy.Table`, one for each save file: 
```
{'ovr': <Table length=289> ..., ...}
```

`m.io['in']` and `m.io['out']` contain the input and output streams

`m.skipped` contains a dict of each extension that was skipped with its reason.
yolo-data-augmentor 0.1
Offers a solution for augmenting datasets for YOLOv8 and YOLOv5

# YOLOv5/YOLOv8 Data Augmentation with Albumentations
This GitHub repository offers a solution for augmenting datasets for YOLOv8 and YOLOv5 using the Albumentations library. Albumentations is a Python package designed for image augmentation, providing a simple and flexible approach to perform various image transformations. For more detail you can refer my medium [article](https://medium.com/red-buffer/apply-data-augmentation-on-yolov5-yolov8-dataset-958e89d4bc5d).

## Input 
![input image](input-ds/images/image_1.jpg)
## Output
![input label](bb_image/image_1_aug_out.png)

## Directories description
- **input-ds** contain the input of YOLOv8 and YOLOv5 which are following directories.
    - Images directory contains the images
    - labels directory contains the .txt files. Each .txt file contains the normalized bounding boxes in a following format.
- **out-aug-ds** contain the augmented output contains following directories.
    - Images directory contains the augmented images.
    - labels directory contains the augmented labels.
- **bb_image** This directory contains images with bounding boxes for visualizing the results of augmented data. This is for validation; bounding boxes should be correctly drawn on the objects of interest.
- **utils.py**: Contains all user-defined helper function.
- **main.py**: Contains Yolo dataset augmentor pipeline
- **CONSTANT.yaml** contain following contants need to update on according to your case.
    - inp_img_pth for input images path
    - inp_lab_pth for input labels path
    - out_img_pth for output image path
    - out_lab_pth for output labels path
    - transformed_file_name: use to name augmented output to differentiate from other input dataset.
    - CLASSES: list of input class name according to class number. 
## Usage
- step to apply augmentation on your own dataset.
    - Create Virtual Environment.
    - Install requirements using 
        ```
        pip install -r requirements.txt
        ```
    - Provide the input and output path in **CONSTANT.yaml** file.
    - Update the name of transformed_file_name in CONSTANT.yaml otherwise code will overwrite last augmentations.
    - Provide the list of name of  classes in CONSTANT.yaml in a same sequence as used to assign class numbers in the YOLO dataset labeling.
        - For example, if you provided a class list as ```['obj1', 'obj2', 'obj3']```, the class number used for 'obj1' in the label file should be 0, similarly for 'obj2', the class number should be 1, and so on.
    - Run the pipeline using 
        ```
        python3 run.py
        ```