semvec/pypi-packages huggingface dataset:
coxbuild 0.1.6
A tiny build automation tool.
Requires Python: >=3.10

![](https://socialify.git.ci/StardustDL/coxbuild/image?description=1&font=Bitter&forks=1&issues=1&language=1&owner=1&pulls=1&stargazers=1&theme=Light)

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Coxbuild is a tiny python-script-based build automation tool, an alternative to make, psake and so on.

> [Documents](https://stardustdl.github.io/coxbuild/)

> [References](https://stardustdl.github.io/coxbuild/api/)

> [Test Coverage Report](https://stardustdl.github.io/coxbuild/cov/)

> [Extension Gallery](https://github.com/StardustDL/coxbuild-ext-gallery/)

![](https://raw.githubusercontent.com/StardustDL/coxbuild/master/docs/assets/images/demo.gif)

Supported features:

- Task
- Dependency
- Pre / Post condition
- Lifecycle hooks
  - Setup / Teardown
  - Before / After
- Event-based build as a long-run service
- Multiple schema sources
  - File `file://`
  - Url `url://`
  - Source code `src://`
  - Module `mod://`
  - Gallery `ext://`

Extensions:

- Python
- Shell
- Git
- Node.js
- .NET
- Gradle

## Install

```sh
pip install coxbuild
```

## Try

```sh
# Version

coxbuild --version

# Hello World

cb -u https://raw.githubusercontent.com/StardustDL/coxbuild/master/demo/hello.py

cb -e hello

# Life Cycle

cb -u https://cdn.jsdelivr.net/gh/StardustDL/coxbuild@master/demo/lifecycle.py

# Event-based build

cb -u https://cdn.jsdelivr.net/gh/StardustDL/coxbuild@master/demo/event.py

# Interactive

cb -i src://interative
```

## Getting Started

> Coxbuild build itself by itself, see [here](https://github.com/StardustDL/coxbuild/blob/master/buildcox.py) for details.

1. Write Schema (buildcox.py)

```python
@task
def pre():
    print("pre task")

@depend(pre)
@task
def default():
    pass
```

2. Run

```sh
coxbuild

# or a shortcut

cb
```

## Extensions

We provide a few extensions with coxbuild release package, in `coxbuild.extensions` module. We also provide a gallery of extensions in [exts](https://github.com/StardustDL/coxbuild-ext-gallery/).

> Visit [here](https://stardustdl.github.io/coxbuild/#/extensions/README) for more details.
discordpy-bot-cli 7.0
CLI for helping in making Discord bots easily
Requires Python: >=3.6

# discordpy-bot-cli

discordpy-cli is a CLI that helps in making Discord bots easily. It is used with discord.py and can be installed with PIP.

## Features

* Auto check-and-update. No manual updation.
* Variations in project making: .env and no .env file projects.
* Hosting: Git and Heroku, and Github and Heroku.
* Commands to simplify tasks.

## Installation

To install discordpy-bot-cli use this command in terminal:

```
pip install discordpy-bot-cli
```

## Commands

This CLI has 4 commands.

`init`: Initializes a Discord bot project.

`cog`: Creates a cog file and adds setup.

`deploy`: Deploys the bot (Use with Git and Heroku).

`wrap`: Creates a requirements file (Use with Github and Heroku).

And to use these, type `dcpy` followed by the command.

To know more about commands and options, do `dcpy --help`.
covidactnow 1.1.76
A wrapper allowing users to get specific state-based COVID statistics from the Covid Act Now database.
Keywords: python,covid,rest,information,wrapper

# Covid Act Now Database Wrapper

This package is an extremely bare-bones wrapper around Covid Act Now's database of COVID-19 related information. View the PyPi project [here](https://pypi.org/project/covidactnow/), and the original post page [here](https://covidinfo.preritdas.com). 

Usage is simple. Install the package with `pip install covidactnow`.

Instantiate a user object with your API key as an argument. You can easily generate an API key [here](https://covidactnow.org/data-api).

```python
import covidactnow

api = covidactnow.User(api_key = 'yourapikey')

washingtonInfectionRate = api.infRate('WA')
massachussettsVaxRate = api.vaxRate('MA')

print(f"{washingtonInfectionRate = }")
print(f"{massachussettsVaxRate = }")
```
This will result (with different data, of course):
```
washingtonInfectionRate = 1.16
massachussettsVaxRate = 76.3
```

To view the docstring (explainer) of a particular function, use `covidactnow.User.infRate.__doc__` where `infRate` can be repalced with any other function. For example, running `print(covidactnow.User.freeBedPercentage.__doc__)` will return: `Get's the percentage of free hospital beds by state.` Depending on your IDE, hovering over a function in the editor will show the function's docstring (works in VS Code).

```python
import covidactnow

if __name__ == "__main__":
    print(covidactnow.User.infRate.__doc__)
    print(covidactnow.User.freeBedPercentage.__doc__)

""" Result:

>>> Gets today's COVID infection rate by state.
>>> Get's the percentage of free hospital beds by state.

"""

----
For more information on how the wrapper works, particularly how it was redesigned to be package-friendly, read the "Updated for Distribution" section of this [page](https://covidinfo.preritdas.com). Note that this version has been updated to get data from within a `User` class allowing individual API keys to be used (as opposed to defining a state as an object and defining statistics as object attributes).
ml-holmes 0.0.1
A machine learning benchmark on tabular data
Requires Python: >=3.6

This project aims to develop a diverse and detailed ML benchmark on tabular data for reproducible research.

Installation: `!pip install ml-holmes`

Usage: `see guide.ipynb`

------------------------

Planned contributions:
	1. Provide a diverse collection of datasets on tabular data
	2. Provide a detailed description for each dataset
	3. Provide a library for downloading these datasets
	4. Provide an online evaluation system based on this collection
	5. Provide a detailed information about each model predictions
	6. Provide realistic data with missing features, outliers, and data shift
Purpose:
	1. Compare and provide in-depth analysis for a lot of existing methods
	2. Provide a reproducible code for a lot of existing methods
	3. Facilitate development and evaluation of new methods

------------------------

Versions: TODO

------------------------

Dataset subsets:
	Main subset
	Subset with data shift
	Subset with toy model tasks
	Subsets with fine-tuning and representation learning?
	
Dataset types:
	Classification (2-50000 classes), regression
	Balanced and imbalanced, different metrics (MSE, MAPE, F1, ROC AUC, ...)
	Small and large (optionally: make small by truncating large)
	With and without outliers and missing features
	With different type of features (quantitative, categorial, ordinal, sparse binary, bag-of-words, TF-IDF, text/image embeddings)
	Diverse: economical, biological, synthetic, time series etc.

Dataset examples:
	model parameters -> validation metrics

Model(s) + dataset = 
	accuracies
	partial dependency plots (1D, 2D, between 2 or 3 examples)
	stratified accuracies (on subsets)
	OOD accuracies (and optionally confidences)
	correlation between models
	ensemble accuracies
	accuracy dependencies: from iteration, number of train examples
	cross validation score?
	reproducible code
	fine-tuning accuracy
	measure bias and variance
	representation learning?

Evaluation method:
	choose feature preprocessing method and hyperparameters
	provide concise and reproducible code (same for every dataset)
	provide best hyperparameters for every dataset?
	provide tags (auto feature selection, autoML, confidence, ...)
	- method should optionally return confidences, accuracy per epoch
	system will automatically make predictions on test data

Try:
	- GBDT, DL models, SVM, models from sklearn, bayesian neural networks
	- SP-models
	- multitask tabular pretraining
	- language models on numerical data
	- generative FT-transformers