semvec/pypi-packages huggingface dataset:
octoflow 0.2.0
Streamlining machine learning tracking for seamless experiment management.
Requires Python: >=3.8
Keywords: machine-learning, tracking

# OctoFlow

Streamlining machine learning tracking for seamless experiment management.

## Features

* Feature 1
* Feature 2
* ...

## Development

To set up [hatch] and [pre-commit] for the first time:

1. install [hatch] globally, e.g. with [pipx], i.e. `pipx install hatch`,
2. optionally run `hatch config set dirs.env.virtual .direnv` to let [VS Code] find your virtual environments,
3. make sure `pre-commit` is installed globally, e.g. with `pipx install pre-commit`,
4. run `pre-commit install` to install [pre-commit].

A special feature that makes hatch very different from other familiar tools is that you almost never
activate, or enter, an environment. Instead, you use `hatch run env_name:command` and the `default` environment
is assumed for a command if there is no colon found. Thus you must always define your environment in a declarative
way and hatch makes sure that the environment reflects your declaration by updating it whenever you issue
a `hatch run ...`. This helps with reproducability and avoids forgetting to specify dependencies since the
hatch workflow is to specify everything directly in [pyproject.toml](pyproject.toml). Only in rare cases, you
will use `hatch shell` to enter the `default` environment, which is similar to what you may know from other tools.

To get you started, use `hatch run cov` or `hatch run no-cov` to run the unitest with or without coverage reports,
respectively. Use `hatch run lint:all` to run all kinds of typing and linting checks. Try to automatically fix linting
problems with `hatch run lint:fix` and use `hatch run docs:serve` to build and serve your documentation.
You can also easily define your own environments and commands. Check out the environment setup of hatch
in [pyproject.toml](pyproject.toml) for more commands as well as the package, build and tool configuration.

## Credits

This package was created with [The Hatchlor] project template.

[The Hatchlor]: https://github.com/florianwilhelm/the-hatchlor
[pipx]: https://pypa.github.io/pipx/
[hatch]: https://hatch.pypa.io/
[pre-commit]: https://pre-commit.com/
[VS Code]: https://code.visualstudio.com/docs/python/environments#_where-the-extension-looks-for-environments
ez-convert 0.1.1
A simple command line tool to convert files from one form to another.

# ez-cli-convert
An open-source repository based in Python for easily converting files from one form to another using the command-line interface (CLI).

## Installation

```bash
pip install ez-convert
```

## Usage

1. Copy the path of the file you wish to convert to your clipboard. (on MacOS, `option` + right-click --> `Copy ... as pathname`; on Windows, `Shift` + right-click --> `Copy as Path`)
2. Open the terminal and simply run 

```bash
ezconvert --otype {your desired output file type}
```

The script will produce a file of the desired type (*i.e.* `pdf`, `jpg`, `mp4`, `mov`, etc.) in the location of the copied file as well as the same file name. Currently, functionalities will be added as they become needed.

## Dependencies

- This tool uses `ffmpeg` under the hood for video file conversion, so you will need to have that installed on your system
- The clipboard functionality is provided by `pyperclip`
- Conversion from images to pdf and vice versa is accomplished using the `Pillow` module
machine-learning-models 0.0.2
Implementations of most popular machine learning algorithms
Requires Python: >=3.11.2

# About this repo
This repository contains my own implementations of the most popular machine learning models. It also has scripts for data preproccessing (datasets titanic and GaltonFamilies). For classification, I implemented my own class for nested cross validation. There are functions to evaluate model using confusion matrix (calculating accurancy, sensitivity, specificity, precision etc.) as well as to draw ROC curve.

# Note
All of the models and validation were implemented by myslef, without using sklearn library. They were done for learning purposes. There is a seperate file named sklearn.py, in which I focused on exploring sklearn library

# Models in this repo
1) Linear models
- Linear Regression
- Logistic Regression
2) Trees
- Regression Tree
- Decision Tree
- Random Forest
3) Boosting
- Adaboost
- Gradient boosting
- Extreme Gradient Boosting
4) SVM
- SVM primal bez jądra
- SVM dual z jądrem
5) Naive Bayes
6) K-nearest-neighbours

# Python package
This repo can be installed as a package via command
```python
pip install machine-learning-models
```
1) Using only models
```python
# Adaboost
from machine_learning.src.Adaboost.AdaboostModel import Adaboost
# XGBoost
from machine_learning.src.ExtremeGradinetBoost.XGBoostModel import XGBoost
# Gboost
from machine_learning.src.GradientBoosting.GradientBoostingModel import GBoost
# KNN
from machine_learning.src.KNearestNeighbours.KNearestNeighboursModel import KNearestNeighbours
# Linear Regression
from machine_learning.src.LinearRegression.LinearRegressionModel import LinearRegressionModel
# Logistic Regression
from machine_learning.src.LogisticRegression.LogisticRegressionModel import LogisticRegression
# Naive Bayes
from machine_learning.src.NaiveBayes.NaiveBayesModel import NaiveBayes
# Random Forest
from machine_learning.src.RandomForest.RandomForestModel import RandomForest
# DEcision and Regression Tree
from machine_learning.src.RandomForest.Tree import DecisionTree
from machine_learning.src.RegressionTree.RegressionTreeModel import RegressionTree
# SVM
from machine_learning.src.SVM.SvmModel import SVM
from machine_learning.src.SVM.SvmModel import PrimalSVM
```
#### Author
Małgorzata Grzanka