semvec/pypi-packages huggingface dataset:
marine-omics 0.1.20
Utilities for metagoflow and marine omics demos
Requires Python: >=3.10
Keywords: marine omics, emo-bon, metagenomics, bioinformatics, data visualization, data analysis

# marine-omics

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Package of utilities for FAIR-Ease demo workflows.

These methods are currently use only for FAIR-EASE [pilot demos](https://github.com/palec87/momics-demos), but eventually they can serve for general purpose manipulation of metagenomic data, locally and in VREs.

The idea is to provide testable methods to allow as much flexibility and remixing of the functionalities provided.

## Installation

One of the dependencies is currently not released to PyPI and is in active development, therefore you need to manually install it before `marine-omics` itself:

```bash
# UDAL data query layer
pip install git+https://github.com/fair-ease/py-udal-mgo.git

pip install marine-omics
```

## European Marine Omics Biodiversity Observation Network

Specifically, we aim primarily to manipulate EMO-BON marine genomics sampling data and metadata from ENA project [PRJEB51688](https://www.ebi.ac.uk/ena/browser/view/PRJEB51688). The interactive dashboards and jupyter notebooks built on top of this repository can be found [here](https://github.com/emo-bon/momics-demos/tree/main).

The methods are mixture of statistical methods, plotting functionalities, metadata and data handling utilities and generators of `holoviz panel` widgets and panes. Experimental integration to `Galaxy` uses a wrapper around [bioblend](https://bioblend.readthedocs.io/en/latest/).
kuplift 0.0.15
A User Parameter-free Bayesian Framework for Uplift Modeling
Requires Python: >=3.9

## kuplift package

<p  align="center">
  <img src="https://raw.githubusercontent.com/UData-Orange/kuplift/main/docs/source/logo.png" width="310" />
</p>


kuplift is a _Python_ package that provides a series of uplift modeling methods based on recent research work. kuplift allows users to easily use the following algorithms:

1. Encoding data using a discretization method for treatment effect (uplift) modeling called _UMODL_.

2. OptimizedUnivariateEncoding is an optimized version of the umodl algorithm written in C++ for continuous and categorical variables.
    
3. Variable selection for uplift modeling with _UMODL-FS_.
    
4. Learning a Bayesian decision tree model for uplift modeling with _UB-DT_.
    
5. Learning a random forest model for uplift modeling with _UB-RF_.


### How to install

~~~ console
$ pip install kuplift
~~~


### User Guide

~~~ python
import kuplift as kp
import pandas as pd

df = pd.read_csv("data.csv")

# Make sure the dtype of all categorical variables is object
df = df.astype({"some_categorical_variable": object})

variables = list(df.columns[:-2])  # Last two columns are treatment and target columns

# Univariate variable transformation
ue = kp.UnivariateEncoding()
encoded_data = ue.fit_transform(df[variables], df["treatment"], df["target"])

# Univariate variable transformation optimized through the use of the C++ implementation of umodl
ue = kp.OptimizedUnivariateEncoding()
encoded_data = ue.fit_transform(df[variables], df["treatment"], df["target"])

# Feature selection
fs = kp.FeatureSelection()
important_vars = fs.filter(df[variables], df["treatment"], df["target"])

# Uplift Bayesian Decision Tree
tree = kp.BayesianDecisionTree()
tree.fit(df[variables], df["treatment"], df["target"])
preds = tree.predict(df[variables])

# Uplift Bayesian Random Forest
forest = kp.BayesianRandomForest(n_trees=4)
forest.fit(df[variables], df["treatment"], df["target"])
preds = forest.predict(df[variables])
~~~


### Examples

You can find examples in the [examples](./examples) directory.


### Documentation

Refer to the documentation at https://udata-orange.github.io/kuplift/


### Credits
kuplift has been developed at Orange Labs.


### Current contributors

Mina Rafla

Nicolas Voisine


### References

Rafla, M., Voisine, N., Crémilleux, B., & Boullé, M. (2022, September). A non-parametric bayesian approach for uplift discretization and feature selection. **_ECML PKDD 2022_**

Rafla, M., Voisine, N., & Crémilleux, B. (2023, May). Parameter-free Bayesian decision trees for uplift modeling. **_PAKDD 2023_**
autumn-py 3.1.9
A Python SDK for Autumn's REST API
Requires Python: >=3.9
Keywords: autumn, payments, stripe

# autumn-py

The Python SDK for [Autumn](https://github.com/useautumn/autumn)'s REST API.

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## Documentation

The Python SDK's documentation can be found on [ReadTheDocs](https://autumn-py.readthedocs.io).