semvec/pypi-packages huggingface dataset:
ztkn 0.1.0
CLI Tool for Zettelkasten Graph Visualization

# ztkn

## Quickstart

```sh
wget https://raw.githubusercontent.com/aerithnetzer/ztkn/refs/heads/main/install.sh
sudo chmod +x install.sh
sudo sh install.sh
```

## What this thing does

Parses a directory of `.md` files, create a network graph of wikilinks using `networkx` and creates an interactive graph in web browser using `pyvis` and `flask`.

## Motivation

Tools such as [foam](https://github.com/foambubble/foambubble.github.io) and amethyst create interactivate graphs but that are highly-integrated features that are difficult to use outside of their respective application ecosystems. This allows you to interact with your second brain, even if you are on neovim, emacs, or helix.

## Installation

Clone repository to `~/` and set the `src.py` to your PATH. 

## How to use

`ztkn ~/path/to/your/vault`

## TODOs

- [x] Add preview of text when hovering over a node
- [ ] When double clicking on a node, open the node's file in a the system's preffered editor of markdown files
- [ ] Add a search bar to search for nodes
- [ ] Advanced metrics for the graph
  - [ ] Centrality of a given node
  - [ ] Betweenness of a given node(s)
keel-ds 0.2.5
KeelDS is a package to load some datasets from KEEL repository with some normalizations and with split and discretization options.
Requires Python: <4.0,>=3.10

# KeelDS

## KeelDS: A Python package for loading datasets from KEEL repository

KeelDS is a Python package that provides easy access to datasets from the [KEEL repository](https://sci2s.ugr.es/keel/datasets.php), a popular source for machine learning datasets. This package simplifies the process of loading KEEL datasets, offering options for cross-validation and discretization.

### Features

- Load KEEL datasets with a single line of code
- Access datasets pre-split into train and test sets
- Discretization option using the Fayyad algorithm (MDLP)
- Support for both balanced and imbalanced datasets
- Easy integration with machine learning workflows

### Installation

#### Dependencies

- Python (>= 3.12)
- pandas (>= 2.2.2)

You can install KeelDS using pip:

```bash
pip install keel-ds
```

### Usage

Here's a simple example of how to use KeelDS with a machine learning model:

```python
from keel_ds import load_data
import numpy as np
from catboost import CatBoostClassifier

file_name = 'iris'
folds = load_data(file_name)

evaluations = []
for x_train, y_train, x_test, y_test in folds:
    model = CatBoostClassifier(verbose=False)
    model.fit(x_train, y_train)
    evaluation = model.score(x_test, y_test)
    evaluations.append(evaluation)

print(np.mean(evaluations))  # Output: 0.933333333333
```