semvec/pypi-packages huggingface dataset:
embarrassingly 0.0.6
Pimp your objective function for faster, robust optimization
License: MIT

# embarrassingly

Embarrassingly obvious (in retrospect) ways to hack objective functions before you send them to optimization routines. 
See [blog article](https://www.microprediction.com/blog/robust-optimization) for motivation and explanation

![](https://i.imgur.com/pvcS5AX.png)

### Install 

    pip install embarrassingly 

### Example 1 : Parallel objective computation 

See [optuna_parallel.py](https://github.com/microprediction/embarrassingly/blob/main/examples/optuna_parallel.py)

    from embarrassingly.parallel import Parallel
    import optuna

    def pre_objective(worker, trial):
        print('Hi this is worker ' + str(worker))
        x = [trial.suggest_float('x' + str(i), 0, 1) for i in range(3)]
        return x[0] + x[1] * x[2]
    
    def test_optuna():
        objective = Parallel(pre_objective, num_workers=7)
        study = optuna.create_study()
        study.optimize(objective, n_trials=15, n_jobs=7)

### Example 2 : Plateau finding

See [underpromoted_shgo.py](https://github.com/microprediction/embarrassingly/blob/main/examples/underpromoted_shgo.py)

    from scipy.optimize import shgo
    from embarrassingly.underpromoted import plateaudinous, Underpromoted2d
    
    bounds = [(-1 ,1) ,(-1 ,1)]
    f = plateaudinous
    res1 = shgo(func=f, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})
    print('Minimum at '+str(res1.x))

    f_tilde = Underpromoted2d(f, bounds=bounds, radius=0.05)
    res1 = shgo(func=f_tilde, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})
    print('Landed at '+str(res1.x))

    

### Example 3 : Expensive functions 

See [shy_shgo.py](https://github.com/microprediction/embarrassingly/blob/main/examples/shy_shgo.py)

    def slow_and_pointless(x):
    """ Example of a function with varying computation time """
        r = np.linalg.norm(x)
        quad = (0.5*0.5-r*r)/(0.5*0.5)
        compute_time = max(0,0.5*quad+x[0])
        time.sleep(compute_time)
        return schwefel([1000*x[0],980*x[1]])[0]
    
    # Save time by making it a "shy" objective function
    bounds = [(-0.5, 0.5), (-0.5, 0.5)]
    SAP = Shy(slow_and_pointless, bounds=bounds, t_unit=0.01, d_unit=0.3)
    from scipy.optimize import minimize
    res = scipy.optimize.shgo(func=SAP, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})
certspotter 1.0.12
sslmate CertSpotter API
License: MIT

[![CircleCI](https://circleci.com/gh/fartbagxp/certspotter-api.svg?style=svg)](https://circleci.com/gh/fartbagxp/certspotter-api)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](http://makeapullrequest.com)
[![MIT License](https://img.shields.io/github/license/dawnlabs/carbon.svg)](https://github.com/dawnlabs/carbon/blob/master/LICENSE)
[![codecov](https://img.shields.io/codecov/c/github/fartbagxp/certspotter-api?style=flat-square)](https://codecov.io)

# Goal

This is a simple python library for utilizing sslmate's certspotter v1 API.

## How to use this library

Installation:

```python
pip install certspotter
```

```python
from certspotter.api import CertSpotter

api = CertSpotter("api key")
subdomains, retryinsec = api.getdomains("example.com")
```

## Development

1. Install [Python 3.7+](https://www.python.org/downloads/).
1. Install [pyenv](https://github.com/pyenv/pyenv).
1. Git clone the repository: `git clone git@github.com:fartbagxp/certspotter-api.git`
1. Setup python development environment by running: `pipenv shell && pipenv install`
1. Get an API key from certspotter.
1. Run unit test: `cd certspotter-api/src && python -m unittest discover -v`

## Deploy new version

1. Update `VERSION` in [setup.py](setup.py).
1. Run `git tag <VERSION>`.
1. Run git push --tags.

## Helpful Documentation

[The Certspotter V1 API](https://sslmate.com/certspotter/api/docs-v1)
skoobpy 0.1.3
extracts user's desired books from Skoob.com.br
License: MIT

# 📘🐍 Skoobpy 

[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)

Gives you methods to extract the users' data from [Skoob](https://www.skoob.com.br/).

## ⚙️ Installation

You can install Skoobpy from [PyPI](https://pypi.org/project/skoobpy/):

```bash
pip install skoobpy
```

## 💻 How to Use

You can run it as a module in a command line as `skoobpy` followed by an `user_id`. It is going to generate a csv file named `books_user_id.csv` with all informations of user's desired books.

    $ python skoobpy <user_id>

## 📑 Release History

* 0.1.0
    * First version - extract users' desired books in CSV file

## ⚖️ License

Distributed under the MIT license. See [`LICENSE`](https://github.com/Diegoslourenco/skoopy/blob/main/LICENSE) for more information.

## 🚧🚀 Contributing


Feel free to contribute with anything.

[In case of any doubt, see this.](https://github.com/firstcontributions/first-contributions)

To start with development, don't forget to install:

```python
$ pip install -r requirements.txt
```
synbio-jecalles 0.0.1
Tools for doing synthetic biology
License: MIT
Requires Python: >3.8

# synbio: a simple python package for doing synthetic biology
![GitHub](https://img.shields.io/github/license/jecalles/synbio)
![Travis (.org)](https://img.shields.io/travis/jecalles/synbio)
This is a homebrewed package for doing synthetic biology. It provides Python classes representing common primitives in biology.

## Documentation
There is none, yet. Email me (callesjonathan@gmail.com) if you have questions

## Dependencies
There are none, yet. The goal is to keep this package written in pure Python for simplicity.

## Installation
Run the following commands

```bash
git clone https://github.com/jecalles/synbio.git
pip install ./synbio
```