semvec/pypi-packages huggingface dataset:
shpark-module 0.0.1
Modules related to preprocessing, crawling, collection, and preprocessing

# 🤖 AI Module

### 👉🏻 데이터 수집, 전처리 관련 패키지 등록
<br>
<br>


## 👉🏻 tree
 * [src]
   * []
   * []
 * [README.md]
 * [setup.py]
darkfly 0.1
530+ tools ready for intall

<!--
https://pypi.org/project/darkfly
-->

[![](https://img.shields.io/pypi/pyversions/readme-generator.svg?longCache=True)](https://pypi.org/project/readme-generator/)

#### Installation :
```
pip install darkfly && darkfly
```

#### Features :
+   **`darkfly`**, ** 500+ termux tools installer **


#### How to install :
```
$ pkg install python -y
$ pip install darkfly
$ darkfly
```
#### Run :
```
$ darkfly
```

#### Disclaimer :
```
We are not responsible for your loss by this tool.
this tool is made for Education/Learning Purpose.
Dont harm anyone using this tool.
```

#### About Us :
```
Created by Parixit Sutariya
Github : github.com/Bhai4You
Blogspot : https://bhai4you.blogspot.com
matrix-admin-sdk 0.2.0b0
Async wrapper for matrix.org admin API
Requires Python: >=3.8,<3.11
Keywords: sdk,matrix,synapse

# Matrix Admin Sdk

[![PyPI version](https://badge.fury.io/py/matrix-admin-sdk.svg)](https://badge.fury.io/py/matrix-admin-sdk)

Async wrapper for matrix.org admin API


## Installation
```shell
pip install matrix-admin-sdk
```

## Usage
Documentations [here](https://dmitriiweb.github.io/matrix-admin-sdk/)

### Quick Start
```python
import asyncio

import httpx

from matrix_admin_sdk import MatrixAdminClient
from matrix_admin_sdk.endpoints.v1 import EditRoomMembership


async def main():
    admin_key = "admin_key"
    http_client = httpx.AsyncClient()
    server_url = "https://matrix.server.com"

    admin_client = MatrixAdminClient(http_client, admin_key, server_url)

    api = EditRoomMembership(admin_client)
    res = await api.join_user_to_room("room_id", "user_id")


if __name__ == "__main__":
    asyncio.run(main())
```
sparseutils 0.1.0
Utilities for interacting with sparse files
Keywords: sparse

sparseutils
-----------

A collection of utilities for interacting with sparse files.

Installation
------------

You can install sparseutils with pip,

    % sudo pip3 install sparseutils

alternatively you can clone the [source](https://git.sr.ht/~richardipsum/sparseutils)
and then run pip from within the git repo:

    % sudo pip3 install .

you can also run the tests from within the git repo using tox:

    % tox

Contributing
------------

Please feel welcome to submit patches and bug reports via email
to my public-inbox ~richardipsum/public-inbox@lists.sr.ht
alternatively patches may be submitted in private using the address
found in the git log.

See https://git-send-email.io/ for details on contributing
via email with git.

The archive of my public-inbox can be found at
https://lists.sr.ht/~richardipsum/public-inbox.
bestree 0.1.0
A Python package for finding the best decision tree parameters.
Keywords: decision tree,decision tree parameters,best tree parameters


# bestree

Under construction. 
---
Developed by CodingLive from ProtDos (c) 2022

##Installation
```bash
pip install bestree
```

### Requirements
- Python (>= 3.8)
- NumPy (>= 1.17.3)
- ScikitLearn
- Pandas

## Usage

### All best features
```python
from bestree import best_all
from sklearn.tree import DecisionTreeClassifier

from sklearn.datasets import make_blobs # To create random X and y data
X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)

criterion, max_depths, split, state = best_all(X, y)

clf = DecisionTreeClassifier(max_depth=max_depths, criterion=criterion, min_samples_split=split, random_state=state)
# your script
```

### Choosing the best criterion
```python
from bestree import best_criterion #For pandas objects
from bestree import best_criterion_norm #For other objects
from sklearn.tree import DecisionTreeClassifier

from sklearn.datasets import make_blobs # To create random X and y data
X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)

criterion = best_criterion_norm(X, y)
print(criterion)

clf = DecisionTreeClassifier(criterion=criterion)
# your script
```

### Choosing the best max_depth value
```python
from bestree import best_depth
from sklearn.tree import DecisionTreeClassifier

from sklearn.datasets import make_blobs # To create random X and y data
X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)

max_depth = best_depth(X, y, criterion="gini")

clf = DecisionTreeClassifier(max_depth=max_depth)
# your script
```

### Choosing the best splitter
```python
from bestree import best_splitter
from sklearn.tree import DecisionTreeClassifier

from sklearn.datasets import make_blobs # To create random X and y data
X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)

max_depths = 3
criterion = "gini"

split = best_splitter(X, y, max_depths, criterion=criterion)

clf = DecisionTreeClassifier(min_samples_split=split)
# your script
```

### Choosing the best state
```python
from bestree import best_state
from sklearn.tree import DecisionTreeClassifier

from sklearn.datasets import make_blobs # To create random X and y data
X, y = make_blobs(n_samples=100, n_features=2, centers=3, random_state=0)

max_depths = 3
criterion = "gini"

state = best_state(X, y, max_depths, criterion=criterion)

clf = DecisionTreeClassifier(random_state=state)
# your script
```

## Help & Support
Communication:
- Mail: rootcode@duck.com
- Website: https://protdos.com
- Discord: https://discord.gg/Xtk4XbwW4Z
- Reddit: https://reddit.com/u/ProtDos
- Twitter: https://twitter.com/ProtDos

## Conclusion
Thanks for everybody that supported me (nobody)