semvec/pypi-packages huggingface dataset:
gh-bin 2.86.0
A thin wrapper to distribute https://github.com/cli/cli via pip.
License: MIT
Requires Python: >=3.7

# gh-bin

This project is part of the [pybin family of packages](https://github.com/justin-yan/pybin/tree/main/tools), which are generally permissively-licensed binary tools that have been re-packaged to be distributable via python's PyPI infrastructure using `pip install $TOOLNAME-bin`.

This is *not* affiliated with the upstream project found at https://github.com/cli/cli, and is merely a repackaging of their releases for installation through PyPI.  If the upstream project wants to officially release their tool on PyPI, please just reach out and we will happily transfer the project ownership over.

We attempt to reflect the license of the upstream tool on the releases in PyPI, but double-check at the upstream before use.

## Packaging Details

This project was inspired by how [Maturin packages rust binaries](https://www.maturin.rs/bindings#bin).  The key observation is that in the wheel format, the [distribution-1.0.data/scripts/ directory is copied to bin](https://packaging.python.org/en/latest/specifications/binary-distribution-format/#installing-a-wheel-distribution-1-0-py32-none-any-whl), which means we can leverage this to seamlessly copy binaries onto a user's PATH.  Combined with Python's platform-specific wheels, this allows us to somehwat use pip as a "cross-platform package manager" for distributing single-binary CLI applications.
smart-open-oss 0.0.1
Defensive package registration.
License: MIT
Requires Python: >=3.6

# smart-open-oss

**NOTICE: Defensive Package Registration**

To provide internal services and prevent new enterprise users from accidentally installing the wrong package, this package is treated as a placeholder package and a friendly reminder is given during installation.

## Contact

For inquiries about this package, please contact the insiders.
surf-tls 0.1.0
An async HTTP library based on surf (Go) with TLS fingerprinting support
License: MIT
Requires Python: >=3.10

# 🔥 Surf TLS

[![Python 3.10](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/release/python-310/)

Surf TLS is an advanced async HTTP library based on [surf](https://github.com/enetx/surf) (Go HTTP client) with TLS fingerprinting support.

## Installation

```bash
pip install surf-tls
```

The library will automatically download the correct binary for your platform on first use. No manual building required!

### Features
- [x] Auto-updating: Downloads latest binaries from GitHub releases
- [x] Async support
- [x] Proxy support
- [x] Custom JA3 string
- [x] Custom JA3 Hello ID
- [x] Custom H2 settings
- [x] Custom header order
- [x] HTTP/1.1, HTTP/2, and HTTP/3 support
- [x] `requests`'s `history` support
- [x] `requests`'s `allow_redirects` support
- [x] much more...

## Quick Start

```python
import asyncio
import surf_tls

async def main():
    # Auto-download latest binaries (optional, happens automatically)
    await surf_tls.download_if_necessary()
    
    # Or check for updates
    await surf_tls.update_if_necessary()
    
    session = surf_tls.Session(ja3_hello_id="chrome_144")
    res = await session.get("https://www.example.com/")
    print(res.text)

asyncio.run(main())
```

## Local Development

If you want to build locally for development:

```bash
# Build the shared library
./build.sh

# Install in development mode
pip install -e .
```

## Examples

Example 1 - Basic usage:

```python
import surf_tls

async def main():
    session = surf_tls.Session(
        ja3_hello_id="chrome_144"
    )
    res = await session.get(
        "https://www.example.com/",
        headers={
            "key1": "value1",
        },
        proxy="http://user:password@host:port"
    )
    print(res.text)

asyncio.run(main())
```

Example 2 - Custom JA3:

```python
import surf_tls

async def main():
    session = surf_tls.Session(
        ja3_string="771,4865-4866-4867-49195-49199-49196-49200-52393-52392-49171-49172-156-157-47-53,0-23-65281-10-11-35-16-5-13-18-51-45-43-27-17513,29-23-24,0",
        h2_settings={
            "HEADER_TABLE_SIZE": 65536,
            "MAX_CONCURRENT_STREAMS": 1000,
            "INITIAL_WINDOW_SIZE": 6291456,
            "MAX_HEADER_LIST_SIZE": 262144
        },
        header_order=[
            "accept",
            "user-agent",
            "accept-encoding",
            "accept-language"
        ]
    )

    res = await session.post(
        "https://www.example.com/",
        headers={
            "key1": "value1",
        },
        proxy="http://user:password@host:port"
    )
    print(res.text)

asyncio.run(main())
```

## Acknowledgments

Big shout out to [enetx](https://github.com/enetx) for open sourcing [surf](https://github.com/enetx/surf), an excellent Go HTTP client library.
venn-abers 1.5.0
Venn-ABERS calibration package
License: MIT
Keywords: Probabilistic classification, calibration

[![python](https://img.shields.io/badge/Python-3.11-3776AB.svg?style=flat&logo=python&logoColor=white)](https://www.python.org)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![Static Badge](https://img.shields.io/badge/Probabilistic_Calibration-blue)
![GitHub Repo stars](https://img.shields.io/github/stars/ip200/venn-abers)




# Venn-ABERS calibration
This library contains the Python implementation of Venn-ABERS calibration for binary and multiclass classification problems.

### Installation
```commandline
pip install venn-abers
```
The method can be applied on top of an underlying scikit-learn algorithm.
### Example Usage
```python
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB

from venn_abers import VennAbersCalibrator

X, y = make_classification(n_samples=1000, n_classes=3, n_informative=10)
X_train, X_test, y_train, y_test = train_test_split(X, y)

clf = GaussianNB()

# Define Venn-ABERS calibrator
va = VennAbersCalibrator(estimator=clf, inductive=True, cal_size=0.2, random_state=101)

# Fit on the training set
va.fit(X_train, y_train)

# Generate probabilities and class predictions on the test set
p_prime = va.predict_proba(X_test)
y_pred = va.predict(X_test)
```


### Examples
Further examples can be found in the github repository https://github.com/ip200/venn-abers in the `examples` folder:

- [simple_classification.ipynb](https://github.com/ip200/venn-abers/blob/main/examples/simple_classification.ipynb) for a simple example in the binary classification setting.
- [multiclass_classification.ipynb](https://github.com/ip200/venn-abers/blob/main/examples/multiclass_classification.ipynb) for the multiclass setting.
- [comparison_with_existing.ipynb](https://github.com/ip200/venn-abers/blob/main/examples/comparison_with_existing.ipynb) for the comparison with a previous github implementation


### Citation
If you find this library useful please consider citing:

- Vovk, Vladimir, Ivan Petej and Valentina Fedorova. "Large-scale probabilistic predictors with and without guarantees of validity." Advances in Neural Information Processing Systems 28 (2015) (arxiv version https://arxiv.org/pdf/1511.00213.pdf)
- Vovk, Vladimir, Ivan Petej. "Venn-Abers predictors". Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence (2014) (arxiv version https://arxiv.org/abs/1211.0025)
- Manokhin, Valery. "Multi-class probabilistic classification using inductive and cross Venn–Abers predictors." Conformal and Probabilistic Prediction and Applications, PMLR, 2017.