semvec/pypi-packages huggingface dataset:
softlayer-zeep 5.0.0
A modern/fast Python SOAP client based on lxml / requests
License: MIT
Requires Python: >=3.6

Zeep: Python SOAP client
========================

A fast and modern Python SOAP client

Highlights:
 * Compatible with Python 3.8, 3.9, 3.10 and PyPy3
 * Build on top of lxml and requests
 * Support for Soap 1.1, Soap 1.2 and HTTP bindings
 * Support for WS-Addressing headers
 * Support for WSSE (UserNameToken / x.509 signing)
 * Support for asyncio using the httpx module
 * Experimental support for XOP messages


Please see for more information the documentation at
https://readthedocs.org/projects/python-zeep

This project is a fork of https://github.com/mvantellingen/python-zeep and exists to handle quirks in the SoftLayer API that might not be suitable to add upstream.



Installation
------------

.. code-block:: bash

    pip install softlayer-zeep


Zeep uses the lxml library for parsing xml. See https://lxml.de/installation.html for the installation requirements.

Usage
-----
.. code-block:: python

    from zeep import Client

    client = Client('tests/wsdl_files/example.rst')
    client.service.ping()


To quickly inspect a WSDL file use::

    python -m zeep <url-to-wsdl>


Please see the documentation at http://docs.python-zeep.org for more
information.


Support
=======

Open an issue here: https://github.com/softlayer/softlayer-zeep/issues

If you want to report a bug then please first read
http://docs.python-zeep.org/en/master/reporting_bugs.html

Please only report bugs and not support requests to the GitHub issue tracker.
devyl 0.0.6
Metrics evaluation for binary classification model
License: MIT
Keywords: metrics

This library used for evaluate machine learning and deep learning algorithm, still on progress and will be improved as long as there are opportunity to do it.

Binary classification algorithm target for current phase:
1. SVM
2. Logistic Regression
3. KNN
4. Decision Tree 
5. Random Forest
6. Neural Network

How to initialise this library:
1. Type 'from devyl import evaluate' to call the library
2. create the function:
ML -- variablename = evaluate.binary(algorithm, xtrain, xtest, ytrain, ytest)
DL -- variablename = evaluate.neuralnet(algorithm, xtrain, xtest, ytrain, ytest)
-- it need algorithm that already been fit with training dataset.
comparison score -- variablename = evaluate.compare(listname)
-- it need the list of the score summary of all algorithm
3. The binary and neuralnet function contains:
- plot_confusionmatrix() -- for visualise confusion matrix
- print_score(string) -- this function will calculate the accuracy score, log loss, precision, recall, f1, and roc_auc score
for training and testing dataset
- auc_plot() or auc_plot(history, epochs) for neural network 
-- this function will visualise learning curve from the model with training and testing phase comparison
- score_table -- to call score summary from the model it will return array of metrics score
4. The compare function contains:
- plot_accuracycomparison() -- visualise accuracy score comparison between training and testing
- plot_lossccomparison() -- visualise loss score comparison between training and testing
- plot_precisioncomparison() -- visualise precision score comparison between class 1 and 0
- plot_recallcomparison() -- visualise recall score comparison between class 1 and 0
- plot_ROCcomparison() -- visualise roc_auc score comparison between training and testing



Please send an email to imthedevyl@gmail.com for any feedback to improve this library