semvec/pypi-packages huggingface dataset:
scikit-MTR 0.1.0
A sklearn-compatible framework for multi-task regression in Python.
Requires Python: >=3.6

# 🛠 scikit-mtr: Multi-Task Regression in Python

`scikit-mtr` provides a framework for multi-task regression using popular regression algorithms and introduces a
stacking method to combine different regressors for enhanced performance.

## 🌟 Features

- 📊 Support for multiple regressors: Decision Trees, Linear Regression, Random Forests, Extra Trees, MLP, and MOKP.
- 🏆 Stacking method to combine predictions from various regressors.

## 📦 Installation

```bash
pip install scikit-mtr
```

## ⚡ Quick Start

Get up and running with `scikit-mtr` in a flash:

```python
from sklearn.model_selection import train_test_split

from scikit_mtr.multi_output_tools import multi_output_regressor, load_data_by_sklearn

params = {
    'regressor': 'LR',
    'dataset': 41467,
    'random_seed': 0
}

# Load and split data
X, y = load_data_by_sklearn(params['dataset'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=params['random_seed'])

# Initialize regressor
model = multi_output_regressor(params['regressor'])
model.fit(X_train, y_train)

# 🎯 Make predictions
y_pred_test = model.predict(X_test)
```

## 📚 Using Stacking

`scikit-mtr` introduces a powerful stacking method where base regressors' predictions serve as features for
meta-regressors:

```python
from scikit_mtr.multi_output_stacking import MultiTargetRegressorStacking

base_regressors = [LinearRegression() for _ in range(y_train.shape[1])]
meta_regressors = [LinearRegression() for _ in range(y_train.shape[1])]

stacker = MultiTargetRegressorStacking(base_regressors, meta_regressors)
stacker.fit(X_train, y_train)
stacked_predictions = stacker.predict(X_test)
print(stacked_predictions)
```

## 🧪 Supported Regressors

Currently, the following regressors are in our arsenal:

- `DT`: 🌲 Decision Tree
- `LR`: 📈 Linear Regression
- `RF`: 🌳 Random Forest
- `ET`: 🍃 Extra Trees
- `MLP`: 🧠 Multi-layer Perceptron
- `MOKP`: 🔮 MOKP Regressor
biopytk 0.2.2
Personal Toolkit for Bioinformatics and Computational Biology pipelines. (WIP)
Requires Python: >=3.11


# BioPyTK

Personal Toolkit for Bioinformatics and Computational Biology pipelines.
Latest release: **v0.1.2**

## In this README:

- [Features](#features)
- [Usage](#usage)
  - [Initial setup](#initial-setup)
  - [Examples](#examples)
- [FAQ](#faq)

## Features

This package contains the following modules:

⚙️ [bio_seq](https://github.com/Steve-Cheney/biopytk/blob/main/src/biopytk/bio_seq.py)
- DNA/RNA sequencing tools.

||||
|--|--|--|
| `__init__()` | `printSeq()` | `nucFrequencyDict()`|
| `validateSeq()` | `percentGC()` | `transcribe()`|
| `dnaCompliment()` | `rnaCompliment()` | `dna_reverseCompliment()`|
| `rna_reverseCompliment()` | `getBasePairs()` | `hammingDist()`|
| `seqCompare()` | `translate()` | `seqSummary()`|
| `get_reading_frames()` | `getAllORFProteins()` | `globalAlign()` |

⚙️ [aa_seq](https://github.com/Steve-Cheney/biopytk/blob/main/src/biopytk/aa_seq.py)
- Amino Acid & Polypeptide analysis tools.

	`getProteinsFromRF()`
	
⚙️ [fasta_tk](https://github.com/Steve-Cheney/biopytk/blob/main/src/biopytk/fasta_tk.py)
- FASTA file analysis tools.
	- Supports: `.fasta`, `.fa` file types.
	- More support to come in later releases

||||
|--|--|--|
| `percentGC_fasta()` | `gcContentFromFASTA()` | `getMaxGCFromFASTA()`|
| `dictFromFASTA()` | `parseFASTA()` | `dfFromFASTA()`|

## Usage

### Initial setup

1. cd to your desired install directory and use [pip](https://pypi.org/project/pip/) to install the package.

   
    ```
    pip install biopytk
    ```
    ***OR***
2. Navigate to the [PyPi BioPyTK download page](https://pypi.org/project/biopytk/#files) and install manually.
		*Need help installing packages? [Click here](https://packaging.python.org/en/latest/tutorials/installing-packages/).* 
### Examples
*Coming soon!*

## FAQ

#### Can I use this package for my own work?
Absolutely! Please see the [License](#License) information below.
#### Will this package work with older versions of Python?
It is recommended to use ``>Python 3.11.1`` as this was the version the package was built on. If future Python releases cause dependency issues, patches will be released.
#### Why are you making this if [BioPython](https://biopython.org/) exists?
This is a personal project to practice bioinformatics tooling creation, package management, and overall explore bioinformatics. This is in no way intended to take over large open-source projects, but rather act as a sandbox for personal research and development!

## License
Steve-Cheney/biopytk is licensed under  the [MIT License](https://github.com/Steve-Cheney/biopytk/blob/main/LICENSE).

Copyright (c) 2023 Stephen Cheney
gabrielribalves-mifflin 0.1.4
Reward Point System for Dunder Mifflin

# Projeto Dundie Rewards

Nós fomos contratados pela Dunder Mifflin, grande fabricante de papéis para desenvolver um sistema
de recompensas para seus funcionários.

Michael, o gerente da empresa quer aumentar a motivação dos funcionários oferecendo um sistema
de pontos que os funcionários podem acumular de acordo com as suas metas atingidas, bônus oferecidos
pelo gerente e os funcionários podem também trocam pontos entre si.

O funcionário pode uma vez a cada ano resgatar seus pontos em um cartão de crédito para gastar onde
quiserem.

Acordamos em contrato que o MVP (Minimum Viable Product) será uma versão para ser executada no terminal
e que no futuro terá também as interfaces OI, web e API.

Os dados dos funcionários atuais serão fornecidos em um arquivo que pode ser no formato .csv ou .json
e este mesmo arquivo poderá ser usado para versões futuras. `Nome, Depto, Cargo, Email`
numan 1.1.1
numerosity analysis package
Requires Python: >=3.10

# numan : Numerosity analysis
Numan is a Python library for a project aiming to identify the neural correlates of numerosity
abilities in zebrafish. The broad project aims to test the hypothesis that the ability to represent numerosity has an evolutionarily conserved neural basis and to identify the cellular and molecular processes involved. In particular, using a 2P Light-Sheet microscope, we recorded the whole-brain GCaMP activity in zebrafish larvae in response to a change in the number of visual stimuli and we aim to find a set of neurons/activity patterns that is characteristic of a specific number presented to the fish.

Numan contains only the analysis tools and relies on [vodex](https://github.com/LemonJust/vodex) for data management.

<p align="center">
  <img src="img/cover.JPG" alt="cover" width="600"/>
</p>
Schematic of the analysis pipeline used for numerosity-stimuli experiments. The image shows an experiment with two conditions: visual stimuli "2" and "5".

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install numan.

```bash
pip install numan
```

## Usage

Please see notebooks/examples on [github](https://github.com/LemonJust/numan).

## Contributing
Pull requests are welcome, but for now it's only me working on this project, so it might take me some time. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)