semvec/pypi-packages huggingface dataset:
mkdocs-yamp 0.1.2
Yet Another Multirepo Plugin for MkDocs. Adds additional repositories to a directory within the docs directory to be referenced in the navigation
Requires Python: >=2.7
Keywords: mkdocs

# Yet Another Multirepo Plugin

<div>
<a href="https://pypi.org/project/mkdocs-yamp/">
  <img src="https://img.shields.io/pypi/v/mkdocs-yamp?color=blue&style=flat-square">
</a>
<img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/mkdocs-yamp?style=flat-square">
</div>

This plugin allows users to define external repositories for integration into the MkDocs site.

Declared repositories are added to a subdirectory within your docs directory.

Users can then reference files within those repositories from their own navigation.

## Config

```yaml
plugins:
  - yamp:
    # directory within docs dir to add content
    temp_dir: "repos"
    # delete docs/{temp_dir} after build||serve?
    #  default: true
    cleanup: true
    # delete docs/{temp_dir} at the beginning of the
    # mkdocs invocation.
    start_fresh: true
    # declare a list of repositories or directories to add
    # to docs/{temp_dir}
    #   default: []
    repos:
      # the git repository URL to clone
    - url: "https://github.com/some-user/some-repo"
      # a list of globs to checkout
      # if empty or not provided, the entire repository is cloned
      # default: [ ]
      include: [ "README.md", "docs/index.md"]
      # the branch of the repository to clone
      branch: "main"

      # alternatively, you can provide a path.
      # a symlink will be created within docs/{temp_dir}
    - path: "../some-other-directory"
```

## Example Usage

```yaml
plugins:
  - search: {}
  - yamp:
      repos:
        - url: https://github.com/steven-terrana/mkdocs-b
          branch: develop
        - url: https://github.com/steven-terrana/mkdocs-a

# Page Tree
nav:
  - Home: 
    - index.md
    - repos/local-dir/README.md
  - Concepts:
    - concepts/index.md
    - repos/mkdocs-b/README.md
    - repos/mkdocs-a/docs/concepts/concept.md
```
htmlit 0.1.9
Description

# htmlgen

![](rsc/ex.png)
![](rsc/ex2.png)

As easy as 
```python
from htmlit import HtmlGenerator

gen = HtmlGenerator()

with gen.row as r:
    r(0).markdown("# Hello\n- List 1\n- *List 2*")
    r(0).latex(rf"\sum_i^n {__file__[10]}\left(i\right) = \mathbf(E)")
    r(0).markdown("That was an equation, like this one: $$x^2$$")
    r(1).image(rsc.img)
```



## Install
```bash
pip install htmlit
pip install -e <path-to-repo>/htmlgen
pip install git+ssh://git@github.com/GaetanDesrues/htmlgen.git
```
SkOpts 0.0.1
Hyperparameters tuning of machine learning models provided by sklearn library using optuna
Requires Python: >=3.8

# SkOpts
Library providing high-level functions for easy hyperparameters tuning of models using Optuna.<br /><br />
This repository contains the project of a library that offers ready-to-use functions to optimize the hyperparameters of some of models provided by the Sklearn API, XGBoost, LightGBM and CatBoost.

Code snippet to optimize a classification model:

```
from Tune_Xgboost import XGB_tuner

XGB_tuned=XGB_tuner(X=Xtrain,y=y_train,
                    scoring_metric='roc_auc',
                    n_trials=100,
                    N_folds=5,
                    direction='maximize',
                    stratify=True,
                    problem_type='classification')
```
Code snippet to optimize regression model:

```
from Tune_Xgboost import XGB_tuner

XGB_tuned=XGB_tuner(X=Xtrain,y=y_train,
                    scoring_metric='neg_mean_squared_error',
                    n_trials=100,
                    N_folds=5,
                    direction='minimize',
                    problem_type='regression')
```

| Parameter  | Usage|
| ------------- | ------------- |
| **"X"**| Training dataset without target variable.|
|**"y"**| Target variable.|
| **"scoring_metric"**  | Metric to optimize.<br/>  |
| **"n_trials"** | Number of trials to execute optimization.<br/>  |
| **"N_folds"** | Number of folds for cross validation.<br/> |
| **"direction"**  | Equals "maximize" or "minimize".<br/>  |
| **"problem_type"**  | Equals "classification" or "regression".<br/>  |
| **"stratify"** | Stratify cv splits based on target distribuition [True or False]<br/>  |

The **'scoring_metric'** parameter takes the same values from sklearn API (link of available list: https://scikit-learn.org/stable/modules/model_evaluation.html)
quotescli 0.2.0
A Python CLI tool for displaying Quotes
Keywords: Quotes,Python,scraping,notification,cli


***
# QuotesPy
QuotesPy is an open source program that displays a random quote from a celebritie as a notification
***
# installation
  - build from source :
```
  git clone https://github.com/0RaMsY0/QuotesPy
  cd QuotesPy
  python setup.py
```
- install using pip :
``` cli
    pip install quotescli
```
***
# Usage
  
  | *arguments*    | Arguments form | *usage*     |
  |:--------------:|:--------------:|:---------:|
  | -h             |  --help | show help |
  | -sn            |  --search-by-name | search for the quote of someone by his name|
  | -st            |  --search-by-topic | search for the quote of a topic |
  | -T             |  --Topic | display all the availables Topics |
  | -t             |  --time | set a time to display the quote |
  | -pr            |  --print-quote | print the quote into the console (in default it will be shown as a notification) |
  ---
  <h2> Examples :</h2>
   - search by name (-sn).
  <img src="https://github.com/0RaMsY0/QuotesPy/blob/main/quotescli/examples/search_by_name-pr.png" alt="search by name (-sn)"/>
   - search by Topic (-st)
  <img src="https://github.com/0RaMsY0/QuotesPy/blob/main/quotescli/examples/serach_by_topic.png" alt="search by Topic (-st)"/>
   - this is how the quote well be displaied (as a notification) if the -pr is not used
  <img src="https://github.com/0RaMsY0/QuotesPy/blob/main/quotescli/examples/show_as_notification.png" alt="this is how the quote well be displaied (as a notification) if the -pr is not used"/>
   - the diferents Topics that you can search for quote about them (use -T to diplay them)
  <img src="https://github.com/0RaMsY0/QuotesPy/blob/main/quotescli/examples/show_topics.png" alt="the diferents Topics that you can search for quote about them (use -T to diplay them)"/>
 
# ressourses

 QuotesPy uses https://www.brainyquote.com/ to scrape search resultes and displays it to the user
  by a notification or directly to the terminal
DescomplicandoACria-oDePacotes 0.0.1
DIO project
Requires Python: >=3.8

# package_name

Description. 
The package package_name is used to:
	- 
	-

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install package_name
```

## Usage

```python
from package_name.module1_name import file1_name
file1_name.my_function()
```

## Author
Ana Capeletti 

## License
[MIT](https://choosealicense.com/licenses/mit/)
mlknn 0.0.1
A KNN algorithm
Keywords: python,video,stream,video stream,camera stream,sockets

<ul>
<li>Your program will be invoked as follows:</li>
<li>knn_classify <training_file> <test_file> <k></li>
<li>The arguments provide to the program the following information:</li>
<li>The first argument, <training_file>, is the path name of the training file, where the training data is stored.</li>
<li>The path name can specify any file stored on the local computer.</li>
<li>The second argument, <test_file>, is the path name of the test file, where the test data is stored.</li>
<li>The path name can specify any file stored on the local computer.</li>
<li>The third argument specifies the value of k for the k-nearest neighbor classifier.</li>
<li>Files used will be any of the files in the UCI datasets directory</li>
<li>A description of the datasets and the file format can be found on below link.</li>
</ul>