semvec/pypi-packages huggingface dataset:
nn-for-dummies 0.0.42
a small classfication neural network framework

# NN Framework For Dummies

*A simple neural network framework that use similar interface to TensorFlow*

#colab link

	https://colab.research.google.com/drive/1uXPiYy5kjNvUR31bzw_IrnX4zRJ6G3Wx?usp=sharing

# Installation

    $ pip install nn-for-dummies

# Usage

    model = nn.Model(
        nn.Layer(size=(4,5), activation='Relu'),
        nn.Layer(size=(5,3), activation='Relu'),
        nn.Layer(size=(3,10), activation='sigmoid'),
        nn.Layer(size=(10,6), activation='ReLU'),
        nn.Layer(size=(6,1), activation='ReLU')
    )

    # import and preprocess data
    x,label = Data.get_data("data_banknote_authentication.csv")
    x = Data.normalize(x)
    X_train, X_test, label_train, label_test = Data.split_data(x,label)

    # Train the model
    model.fit(X_train,label_train,'SGD','MSE',alpha = 0.0001,epoch = 15,graph_on = True)

    # evaluate the model
    [accuracy,f1_score,confusion_matrix] = model.evaluate(X_test,label_test,metric = ['accuracy','f1 score','confusion matrix'])
    print(f"accuracy: {accuracy}")
    print(f"f1_score: {f1_score}")
    print("confusion matrix:\n",confusion_matrix)

    model.save()
imbDRL 2021.1.26.1
Imbalanced Classification with Deep Reinforcement Learning.
Requires Python: >=3.7
Keywords: imbalanced classification,deep reinforcement learning,deep q-network,reward-function,classification,medical

# imbDRL

![GitHub Workflow Status](https://img.shields.io/github/workflow/status/Denbergvanthijs/imbDRL/Build) ![License](https://img.shields.io/github/license/Denbergvanthijs/imbDRL)

***Imbalanced Classification with Deep Reinforcement Learning.***

This repository contains an (Double) Deep Q-Network implementation of binary classification on unbalanced datasets using [TensorFlow 2.3+](https://www.tensorflow.org/) and [TF Agents 0.6+](https://www.tensorflow.org/agents). The Double DQN as published in [this paper](https://arxiv.org/abs/1509.06461) by *van Hasselt et al. (2015)* is using a custom environment based on [this paper](https://arxiv.org/abs/1901.01379) by *Lin, Chen & Qi (2019)*.

Example scripts on the [Mnist](http://yann.lecun.com/exdb/mnist/), [Fashion Mnist](https://github.com/zalandoresearch/fashion-mnist), [Credit Card Fraud](https://www.kaggle.com/mlg-ulb/creditcardfraud) and [Titanic](https://www.tensorflow.org/datasets/catalog/titanic) datasets can be found in the `./imbDRL/examples/ddqn/` folder.

## Requirements

* [Python 3.7+](https://www.python.org/)
* The required packages as listed in: `requirements.txt`
* Logs are by default saved in `./logs/`
* Trained models are by default saved in `./models/`
* Optional: `./data/` folder located at the root of this repository.
  * This folder must contain ```creditcard.csv``` downloaded from [Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud) if you would like to use the [Credit Card Fraud](https://www.kaggle.com/mlg-ulb/creditcardfraud) dataset.
  * Note: `creditcard.csv` needs to be split in a seperate train and test file. Please use the function `imbDRL.utils.split_csv`

## Getting started

Install via `pip`:

* `pip install imbDRL`

Run any of the following scripts:

* `python .\imbDRL\examples\ddqn\train_credit.py`
* `python .\imbDRL\examples\ddqn\train_famnist.py`
* `python .\imbDRL\examples\ddqn\train_mnist.py`
* `python .\imbDRL\examples\ddqn\train_titanic.py`

## TensorBoard

To enable [TensorBoard](https://www.tensorflow.org/tensorboard), run ```tensorboard --logdir logs```

## Tests and linting

Extra arguments are handled with the `./tox.ini` file.

* Pytest: `python -m pytest`
* Flake8: `flake8`
* Coverage can be found in the generated `./htmlcov` folder

## Appendix

The appendix can be found in the [imbDRLAppendix](https://github.com/Denbergvanthijs/imbDRLAppendix) repository.
fusion-vm-tf-deploy 0.1.49
A short description of the project
Requires Python: >=3.6,<4.0

[![pipeline status](https://git.datcon.co.uk/tools.core/fusion_vm_tf_deploy/badges/master/pipeline.svg)](https://git.datcon.co.uk/tools.core/fusion_vm_tf_deploy/commits/master)
[![coverage report](https://git.datcon.co.uk/tools.core/fusion_vm_tf_deploy/badges/master/coverage.svg)](https://git.datcon.co.uk/tools.core/fusion_vm_tf_deploy/commits/master)

# Fusion_vm_tf_deploy

## Installation

To install from [artifactory](https://metacom2.metaswitch.com/confluence/x/3F32AQ) you can run this command:

~~~~
pip install --index https://artifactory.metaswitch.com/api/pypi/pypi-virtual/simple fusion_vm_tf_deploy
~~~~

## Usage

@@@TODO@@@

## Development and Deployment

See the [Contribution guidelines for this project](CONTRIBUTING.md) for details on how to make changes to this library.

Note: This library was generated from the centrally owned [template package](https://git.datcon.co.uk/tools.core/python-template-library).
pyshaker 1.4.2
Distributed data-plane performance testing tool

Shaker
======

**The distributed data-plane testing tool built for OpenStack.**

Shaker wraps around popular system network testing tools like
`iperf <https://iperf.fr/>`_, `iperf3 <https://iperf.fr/>`_
and netperf (with help of `flent <https://flent.org/>`_).
Shaker is able to deploy OpenStack instances and networks in different
topologies. Shaker scenario specifies the deployment and list of tests
to execute. Additionally tests may be tuned dynamically in command-line.

Features
--------
* User-defined topology via Heat templates
* Simultaneously test execution on multiple instances
* Interactive report with stats and charts
* Built-in SLA verification

Deployment Requirements
-----------------------
* Shaker server routable from OpenStack cloud
* Admin-user access to OpenStack API is preferable

Run in Python Environment
-------------------------

.. code-block:: bash

   $ pip install pyshaker
   $ . openrc
   $ shaker-image-builder
   $ shaker --server-endpoint <host:port> --scenario <scenario> --report <report.html>``

where:
    * ``host`` and ``port`` - host and port of machine where Shaker is deployed
    * ``scenario`` - the scenario to execute, e.g. `openstack/perf_l2` (
      `catalog <http://pyshaker.readthedocs.io/en/latest/catalog.html>`_)
    * ``<report.html>`` - file to store the final report

Full list of parameters is available in `documentation <http://pyshaker.readthedocs.io/en/latest/tools.html#shaker>`_.


Shaker in Container
-------------------

Shaker is available as container at Docker Hub at
`performa/shaker <https://hub.docker.com/r/performa/shaker/>`_

.. code-block:: bash

    $ docker run -p <port>:<port> -v <artifacts-dir>:/artifacts performa/shaker --scenario <scenario> --server-endpoint <host:port>
      --os-auth-url <os-auth-url> --os-username <os-username> --os-password <os-password> --os-project-name <os-project-name>

where:
 * ``host`` and ``port`` - host and port on machine where Shaker is deployed
 * ``artifacts-dir`` - where to store report and raw result
 * ``scenario`` - the scenario to execute, e.g. `openstack/perf_l2` (
   `catalog <http://pyshaker.readthedocs.io/en/latest/catalog.html>`_)
 * ``os-XXX`` - OpenStack cloud credentials


Links
-----
* PyPi - https://pypi.org/project/pyshaker/
* Docker - https://hub.docker.com/r/performa/shaker/
* Docs - https://pyshaker.readthedocs.io/
* Bugtracker - https://launchpad.net/shaker
pointwise-libs 0.0.7
Helper functions for my political science research
Requires Python: >=3.6

# pointwise-libs
library functions for pointwise polisci research

## How to build package

First, ensure that there's a `.pypirc` in your home directory `~/`
that looks like this:

```
[pypi]
    username = __token__
    password = pypi-GET_API_TOKEN_FROM_PYPI_ACCOUNT
```

Then build with

```
python3 -m pip install --user --upgrade setuptools wheel
python3 -m pip install --user --upgrade twine
rm -r dist
# Increment the version in `setup.py`
python3 setup.py sdist bdist_wheel
twine upload dist/*
```
kimdb 0.0.16
kimdb
Requires Python: >=3.4

# kimdb

![PyPI - version](https://img.shields.io/pypi/v/kimdb?style=flat-square)
![PyPI - license](https://img.shields.io/pypi/l/kimdb?label=package%20license&style=flat-square)
![PyPI - python version](https://img.shields.io/pypi/pyversions/kimdb?logo=pypi&style=flat-square)
![PyPI - downloads](https://img.shields.io/pypi/dm/kimdb?logo=pypi&style=flat-square)

![GitHub - last commit](https://img.shields.io/github/last-commit/kkristof200/py_imdb?style=flat-square)
![GitHub - commit activity](https://img.shields.io/github/commit-activity/m/kkristof200/py_imdb?style=flat-square) 

![GitHub - code size in bytes](https://img.shields.io/github/languages/code-size/kkristof200/py_imdb?style=flat-square)
![GitHub - repo size](https://img.shields.io/github/repo-size/kkristof200/py_imdb?style=flat-square)
![GitHub - lines of code](https://img.shields.io/tokei/lines/github/kkristof200/py_imdb?style=flat-square)

![GitHub - license](https://img.shields.io/github/license/kkristof200/py_imdb?label=repo%20license&style=flat-square)

## Description

IMDB scraper

## Install

~~~~bash
pip install kimdb
# or
pip3 install kimdb
~~~~

## Usage

~~~~python
from kimdb import IMDB

imdb = IMDB(debug=True)

top_movies = imdb.top_movies()
top_movies[0].jsonprint()

top_series = imdb.top_series()
top_series[0].jsonprint()

most_popular_movies = imdb.most_popular_movies()
most_popular_movies[0].jsonprint()

most_popular_series = imdb.most_popular_movies()
most_popular_series[0].jsonprint()

title = imdb.get_title(top_movies[0].id)
title.jsonprint()

list = imdb.get_list(title.lists[0].id)
list.jsonprint()

user = imdb.get_user(list.owner.id)
user.jsonprint()

name = imdb.get_name(title.actors[0].id)
name.jsonprint()
~~~~ 

## Dependencies

[beautifulsoup4](https://pypi.org/project/beautifulsoup4), [jsoncodable](https://pypi.org/project/jsoncodable), [kcu](https://pypi.org/project/kcu), [ksimpleapi](https://pypi.org/project/ksimpleapi), [noraise](https://pypi.org/project/noraise), [requests](https://pypi.org/project/requests)