semvec/pypi-packages huggingface dataset:
snippy-cli 1.0.0
A CLI tool for managing code snippets
Requires Python: >=3.6

Here’s the **`README.md`** file as a text block for your "Snippy" project:


# 📝 Snippy - A Simple CLI for Managing Code Snippets

Snippy is a lightweight command-line interface (CLI) tool for managing code snippets efficiently. With Snippy, you can create, edit, copy, and delete snippets right from your terminal. It's designed to help developers quickly store and retrieve useful code snippets, all while keeping things simple and straightforward.

## 🚀 Features
- 📂 **Create, list, and manage snippets** easily.
- ✏️ **Open snippets** in your preferred text editor.
- 📋 **Copy snippets** directly to the clipboard.
- 🗑️ **Delete snippets** when they are no longer needed.

## 📦 Installation

To install Snippy, follow these steps:

1. **Clone the repository**:
   ```bash
   git clone https://github.com/TejasBaid/snippy-cli.git
   cd snippy-cli
   ```

2. **Install the package**:
   ```bash
   pip install .
   ```

> **Note**: Make sure you have Python 3.x and `pip` installed.

## 🛠️ Usage

Once installed, Snippy can be used directly from the terminal using the `snip` command.

### Example Commands:

- **Create a new snippet**:
   ```bash
   snip create my_snippet
   ```
   This command opens your default text editor to create a snippet. The snippet will be saved in the `Snippets/` directory.

- **List all snippets**:
   ```bash
   snip list
   ```

- **Open a snippet for editing**:
   ```bash
   snip open my_snippet
   ```

- **Copy a snippet to the clipboard**:
   ```bash
   snip copy my_snippet
   ```

- **Delete a snippet**:
   ```bash
   snip delete my_snippet
   ```

- **Display help**:
   ```bash
   snip help
   ```

## 📖 Available Commands

| Command                      | Description                                         |
|-------------------------------|-----------------------------------------------------|
| `snip create <snippet_name>`   | Create a new snippet                                |
| `snip list`                    | List all snippets                                   |
| `snip open <snippet_name>`     | Open an existing snippet for editing                |
| `snip copy <snippet_name>`     | Copy the snippet's content to the clipboard         |
| `snip delete <snippet_name>`   | Delete a snippet                                    |
| `snip help`                    | Display this help message                           |

## 🤝 Contributing

Feel free to submit pull requests, open issues, or suggest new features! Contributions are welcome.

## 📜 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgements

- Thanks to the open-source community for the inspiration and support.
```

You can directly copy and paste this into your `README.md` file for your GitHub repository. Let me know if you need any further changes!
galileo-forecast 0.1.4
Thompson Sampling using bootstrap sampling
Requires Python: <4.0,>=3.10

# Galileo Forecast

Galileo Forecast is a Python package that implements Thompson Sampling. It provides a flexible wrapper that can be used with various base model classes.

## Installation

You can install Galileo Forecast using pip:

```bash
pip install galileo-forecast
```

## Usage

To use Galileo Forecast, you need to create a wrapper for your base model class. Here's an example with LightGBM:

```python
from galileo_forecast import ThompsonSamplingWrapper
from lightgbm import LGBMClassifier

# make classification data, us sklearn make_classification
from sklearn.datasets import make_classification

# sample data with low hit rate
X, y = make_classification(n_samples=1000, n_features=10, n_informative=1, n_redundant=1, n_clusters_per_class=1, class_sep=0.1)

# create a wrapper for the LightGBM model  
wrapper = ThompsonSamplingWrapper(base_model_class=LGBMClassifier, num_models=10)

# fit the wrapper
wrapper.fit(X, y)

# get the predicted probabilities for the positive class
selected_model_indices, sampled_probabilities = wrapper.predict_proba(X)

# get the fancy output dataframe - contains sampled probabilities, the sampled model and the greedy model, etc.
print(wrapper.get_fancy_output_df().head())

```

## Demo

The demo folder contains Jupyter notebooks that shows how to use the package.