semvec/pypi-packages huggingface dataset:
flask-batteries 0.0.0
A simple command line tool for bootstrapping flask applications.
Requires Python: >=3.6

*\~work in progress\~*

# Flask-Batteries

An opinionated command line tool for bootstrapping Flask applications with less boiler-plate code.

Inspired by Ruby on Rails.


## Installing
This has not been deployed to PYPI yet, as it is still under active development. 

To install, clone the repo and install using pip 
```bash
git clone git@github.com:graydenshand/flask_batteries.git
pip install ./flask_batteries # path to source code
```

## Usage
Currently there are just two commands:
```bash
flask_batteries new <name>
flask_batteries destroy <name>
```
The `new` command creates a Flask app in a new directory with the specified name.

The `destroy` command will recursively erase a directory with the specified name. Be careful with this, as it will erase any directory you specify without checking to make sure it's actually a Flask-Batteries project. 

## Tests
Tests are run with [tox](https://tox.readthedocs.io/en/latest/) against python 3.9. 

From the root directory of the project, simply call:
```bash
tox
```

## Roadmap
* Finish building the basic template. Add SQLAlchemy (Flask-SQLAlchemy) + Alembic (Flask-Migrate). 
* Extend Flask's CLI with a set of commands for quickly generating and destroying assets. E.g. `flask g route login` might generate a view function, map it to a url, generate a template, and generate a test.
* Add an `install` command for installing common flask extensions.
* Add an `--api` option to the `new` command to install `Flask-Restful` and `Marshmallow` and generate a blueprint for API resources.
* Add an `--spa` option to the `new` command to set up a Node JS project and generate a "catch all" client blueprint for rendering a JS Single Page Application (SPA). Possibly integrate with `create-react-app` and/or Vue's hello world example.
* Add a `--websocket` option to the `new` command to install `Flask-SocketIO` and use its event loop instead of standard flask server.
* Add a `--skip-db` option to the `new` command to not install SQLAlchemy.
* Implement a flexible uploads system that can be configured to work with multiple back ends
* ...and more
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offline-evaluation 0.0.6
Implementations of common offline policy evaluation methods.
Requires Python: >=3.6

# Offline policy evaluation
[![PyPI version](https://badge.fury.io/py/offline-evaluation.svg)](https://badge.fury.io/py/offline-evaluation) [![](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

Implementations and examples of common offline policy evaluation methods in Python. For more information on offline policy evaluation see this [tutorial](https://edoconti.medium.com/offline-policy-evaluation-run-fewer-better-a-b-tests-60ce8f93fa15).

## Installation
`pip install offline-evaluation`

## Usage
```
from ope.methods import doubly_robust
```

Get some historical logs generated by a previous policy:
```
df = pd.DataFrame([
	{"context": {"p_fraud": 0.08}, "action": "blocked", "action_prob": 0.90, "reward": 0},
	{"context": {"p_fraud": 0.03}, "action": "allowed", "action_prob": 0.90, "reward": 20},
	{"context": {"p_fraud": 0.02}, "action": "allowed", "action_prob": 0.90, "reward": 10},
	{"context": {"p_fraud": 0.01}, "action": "allowed", "action_prob": 0.90, "reward": 20},     
	{"context": {"p_fraud": 0.09}, "action": "allowed", "action_prob": 0.10, "reward": -20},
	{"context": {"p_fraud": 0.40}, "action": "allowed", "action_prob": 0.10, "reward": -10},
 ])
```
Define a function that computes `P(action | context)` under the new policy:
```
def action_probabilities(context):
    epsilon = 0.10
    if context["p_fraud"] > 0.10:
        return {"allowed": epsilon, "blocked": 1 - epsilon}    
    return {"allowed": 1 - epsilon, "blocked": epsilon}
```
Conduct the evaluation:
```
doubly_robust.evaluate(df, action_probabilities)
> {'expected_reward_logging_policy': 3.33, 'expected_reward_new_policy': -28.47}
```
This means the new policy is significantly worse than the logging policy.  Instead of A/B testing this new policy online, it would be better to test some other policies offline first.

See examples for more detailed tutorials.

## Supported methods

- [x] Inverse propensity scoring
- [x] Direct method
- [x] Doubly robust ([paper](https://arxiv.org/abs/1503.02834))