semvec/pypi-packages huggingface dataset:
iris-validation 0.0.0
A package for interactive all-in-one graphical validation of 3D protein model iterations
Requires Python: >=2.7, <3

# Iris-Validation
A Python package for interactive all-in-one graphical validation of 3D protein model iterations.

## metrics
Calculates comprehensive per-residue metrics from a model (and reflections) file.

## interface
Generates the HTML validation report.

## utils
A number of functions that are utilised extensively within the library and are also useful on their own.


# Iris-Tools
Companion scripts for the main module that include tests, timings, and the scripts used to generate data for the metrics module.


# Installation
The package can now be installed via pip, with the command `pip install iris-validation`.
For now, the best way to install and run Iris is to use the Python environment that comes with CCP4, by running `ccp4-python -m pip install iris-validation`
You may first need to install pip, by downloading the install script from https://bootstrap.pypa.io/get-pip.py and running `ccp4-python get-pip.py`
This is because Clipper-Python, upon which Iris depends, is currently available in a number of different forms, most of which are broken in some way or another. As a consequece, only the Python 2 version of Iris is available. This will be remedied soon.
iris-validation-wrochira 0.0.0
A package for interactive all-in-one graphical validation of 3D protein model iterations
Requires Python: >=2.7, <3

# Iris-Validation
A Python package for interactive all-in-one graphical validation of 3D protein model iterations.

## metrics
Calculates comprehensive per-residue metrics from a model (and reflections) file.

## interface
Generates the HTML validation report.

## utils
A number of functions that are utilised extensively within the library and are also useful on their own.


# Iris-Tools
Companion scripts for the main module that include tests, timings, and the scripts used to generate data for the metrics module.


# Installation
The package can now be installed via pip, with the command `pip install iris-validation`.
For now, the best way to install and run Iris is to use the Python environment that comes with CCP4, by running `ccp4-python -m pip install iris-validation`
You may first need to install pip, by downloading the install script from https://bootstrap.pypa.io/get-pip.py and running `ccp4-python get-pip.py`
This is because Clipper-Python, upon which Iris depends, is currently available in a number of different forms, most of which are broken in some way or another. As a consequece, only the Python 2 version of Iris is available. This will be remedied soon.
bestvarspk 0.3
Bestvars feature_selection methods

<h1>Best Variables for classification and regression models</h1>

The objective of this packege is to simplify the usage of methods to make feature selection.

The Package **bestvarspk** is a Python module for machine learning built based on top of sklearn feature_selection and is distributed under the  license.

The project was started in 2020 by Gutelvam as a Udacity Nanodegree of project.

<h4>Installation</h4>

***Dependencies***

bestvars_pk requires:

        -Python (>= 3.6)

        -NumPy (>= 1.13.3)

        -SciPy (>= 0.19.1)

        -joblib (>= 0.11)

        -threadpoolctl (>= 2.0.0)

        -scikit-learn (>=0.23.1)

        -matplotlib(>=3.2.2)

        -seaborn(>=0.10.1)

        -pandas(>=1.0.3)

***User installation***


If you already have a working installation of scikit-learn, the easiest way to install is using pip:
 >!pip install bestvarspk

<h2>How to use</h2>

        1. Instantiate an objet 'Selection'
                from bestvarspk.Variables_selection import Selection
                obj = Selecton(df, target)
where:

**df** is a dataframe 

**target** is a string of target column name    

        2. Use methods available.

                obj.corr_features()

                obj.importance_features()

                obj.rfe_features()

obs: Anytime you can check for help(?) to check docstrings.