semvec/pypi-packages huggingface dataset:
agc 0.0.5
Python code to compute and plot (truncated, weighted) area under gain curves (agc)
License: MIT
Requires Python: >=3.6

# AGC - Area under Gain Curves
Python code to compute and plot (truncated, weighted) area under gain curves (**AGC**)

For binary classification, gain curves are a nice alternative to ROC curves in that they can naturally be **truncated** to focus on the top scoring points only.
Moreover, the data points can have **weights**. In this code, we provide two functions:

* `agc_score`: Compute the area under the gain curve (AGC) for binary labelled data
* `gain_curve`: Compute the proportion of data points and true positive rate for all thresholds, for plotting

The first function returns the **normalized area** by default (improvement over random, so this could be negative).
The functions can be imported from the supplied `agc.py` file, or installed via `pip install agc`.

## A simple example

```
## create toy binary labels and scores for illustration
labels = np.concatenate((np.repeat(1,100),np.repeat(0,900)))
scores = np.concatenate((np.random.uniform(.4,.8,100),np.random.uniform(.2,.6,900)))

## compute (normalized) area under the gain curve
print(agc_score(labels, scores))

## compute (un-normalized) area under the gain curve
print(agc_score(labels, scores, normalized=False))

## now the area for the top scoring 10% of the points
print(agc_score(labels, scores, truncate=0.1))

## or top scoring 100 points
print(agc_score(labels, scores, truncate=100))
```

## More details in Notebooks:

For a quick introduction, see the following notebook:
https://github.com/ftheberge/agc/blob/main/agc/agc_intro.ipynb
also available in markup format:
https://github.com/ftheberge/agc/blob/main/intro/agc_intro.md

For more details, see the notebook:
https://github.com/ftheberge/agc/blob/main/agc/agc.ipynb
also available in markup format:
https://github.com/ftheberge/agc/blob/main/example/agc.md
ppw0810 0.2.0
Skeleton project created by Python Project Wizard (ppw).
License: MIT
Requires Python: >=3.7.1,<4.0

# ppw0810


<p align="center">
<a href="https://pypi.python.org/pypi/ppw0810">
    <img src="https://img.shields.io/pypi/v/ppw0810.svg"
        alt = "Release Status">
</a>

<a href="https://github.com/zillionare/ppw0810/actions">
    <img src="https://github.com/zillionare/ppw0810/actions/workflows/main.yml/badge.svg?branch=release" alt="CI Status">
</a>

<a href="https://zillionare.github.io/ppw0810/">
    <img src="https://img.shields.io/website/https/zillionare.github.io/ppw0810/index.html.svg?label=docs&down_message=unavailable&up_message=available" alt="Documentation Status">
</a>

</p>


Skeleton project created by Python Project Wizard (ppw)


* Free software: MIT
* Documentation: <https://zillionare.github.io/ppw0810/>


## Features

* TODO

## Credits

This package was created with the [ppw](https://zillionare.github.io/python-project-wizard) tool. For more information, please visit the [project page](https://zillionare.github.io/python-project-wizard/).
cardo-sme-sdk 0.3.3
SME Service SDK in Python
License: MIT
Requires Python: >=3.6

# SME SDK for Python

## Getting Started

### Install

```bash
pip install -U cardo-sme-sdk
```

### Usage

```python
import sme_sdk

# Create a new api config and s3 config for later use, these objects can be created only once
# in any module and can be reused in other modules.

api_config = sme_sdk.APIConfig(
    host='http://localhost:8000',
    api_key='<your-api-key>',
)
s3_config = sme_sdk.S3Config(
    access_key_id='access_key_id',
    secret_access_key='secret_access_key',
    bucket_name='bucket_name',
    region_name='region_name',
)

# When you want to use SME API, you need to create an APIClient object using the api_config created before.
data = {'key': 'value'}
client = sme_sdk.APIClient(api_config)
s3client = sme_sdk.S3BlobStorageClient(s3_config)
result = client.create_new_batch(data, s3client)
```

- To learn more about how to use the SDK [refer to our docs](https://docs.service.cardoai.com/sme_sdk/index.html)
fitbit-downloader 0.3.0
A CLI and Python tool for downloading Fitbit data
License: MIT



[![](https://codecov.io/gh/nickderobertis/fitbit-downloader/branch/master/graph/badge.svg)](https://codecov.io/gh/nickderobertis/fitbit-downloader)
[![PyPI](https://img.shields.io/pypi/v/fitbit-downloader)](https://pypi.org/project/fitbit-downloader/)
![PyPI - License](https://img.shields.io/pypi/l/fitbit-downloader)
[![Documentation](https://img.shields.io/badge/documentation-pass-green)](https://nickderobertis.github.io/fitbit-downloader/)
![Tests Run on Ubuntu Python Versions](https://img.shields.io/badge/Tests%20Ubuntu%2FPython-3.8%20%7C%203.9%20%7C%203.10-blue)
![Tests Run on Macos Python Versions](https://img.shields.io/badge/Tests%20Macos%2FPython-3.8%20%7C%203.9%20%7C%203.10-blue)
![Tests Run on Windows Python Versions](https://img.shields.io/badge/Tests%20Windows%2FPython-3.8%20%7C%203.9%20%7C%203.10-blue)
[![Github Repo](https://img.shields.io/badge/repo-github-informational)](https://github.com/nickderobertis/fitbit-downloader/)


#  fitbit-downloader

## Overview

A CLI and Python tool for downloading Fitbit data

## Getting Started

Install `fitbit-downloader`:

```
pip install fitbit-downloader
```

A simple example:

```python
import fitbit_downloader

# Do something with fitbit_downloader
```

See a
[more in-depth tutorial here.](
https://nickderobertis.github.io/fitbit-downloader/tutorial.html
)

## Development Status

This project is currently in early-stage development. There may be
breaking changes often. While the major version is 0, minor version
upgrades will often have breaking changes.

## Developing

First, you need a couple global dependencies installed, see their documentation for details:
- [pipx](https://pypa.github.io/pipx/installation/)
- [direnv](https://direnv.net/docs/installation.html)

Then clone the repo and run `npm install` and `mvenv sync dev`. Make your changes and then run `just` to run formatting,
linting, and tests.

Develop documentation by running `just docs` to start up a dev server.

To run tests only, run `just test`. You can pass additional arguments to pytest,
e.g. `just test -k test_something`.

Prior to committing, you can run `just` with no arguments to run all the checks.

### Download Sample Responses

Run `python -m fitbit_downloader.download_samples` to output JSON files 
with the responses.

### Generate Response Models

> Note: There are currently some manual modifications to the generated files. 
> They are all marked with TODO so that we can work them into the generation.
> If you re-run the generation, you will need to manually update the files 
> to make the same changes.

Run `python -m fitbit_downloader.gen_models` to output response models 
in `fitbit_downloader.models` from the sample responses.


## Author

Created by Nick DeRobertis. MIT License.

## Links

See the
[documentation here.](
https://nickderobertis.github.io/fitbit-downloader/
)