semvec/pypi-packages huggingface dataset:
HighlanderML 1.0.1.1.8
Highlander project
Keywords: python,machine learning,Weather forecasting,Milk,Climat change


# Highlander Project

This repository contains all the information for the Machine learning data analyses used in the Highlander project

## Dependencies

The following dependencies are required to perform the analysis in Python:

```
pandas>=1.3.4 
numpy>=1.21.2
scikit-learn>=1.0
h2o>=3.38.0.1
```
The main functions are grouped into two files:

```
RFE_module.py
H2O_module.py
```

## Modules and functions

Two modules are required to perform the analyses in the highlander_script/ directory. The RFE_module.py perform a Recursive Feature Elimination reducing the input variables. The user can define the number of features to maintain. The discarded features are the most uninformative and redundant in the dataset. Consequently, this step can reduce the variables, allowing a more precise and quick analysis using the Machine Learning algorithms. It can be considered a feature selection before analyzing the data. The function name is: "RFE_highlander()".
The H2O_module.py contains several functions to perform the Machine Learning analyses. In detail:

<ul>
<li> It searches for the best algorithm to perform the prediction; the best algorithm is chosen using a set of different models (best_model)</li>
<li> Once the best model is selected, the best hyper-parameters are tuned using a grid search approach (grid_search_gbm)</li>
<li> The relative importance of all the variables in the prediction is then evaluated (varimp)</li> 
<li> The model is then trained and tested, and the accuracy is evaluated using the Mean Absolute Error metric (mae_nf)</li>
<li> The subset of the most important variables is identified. This step an be considered a feature selection after the data analysis (varimp)</li>
<li> The SHAP algorithm is then used to evaluate the contribution and the explanation of each feature to the classification (shap)</li>
<li> The explaination of the model is useful to evaluate the contribution of the feature to the model (explain_model)</li>
</ul>

## Tutorial

The whole pipeline to perform and end-to-end data analysis is reported as a Python notebook file ("Highlander_ML_notebook.ipynb") in the "Notebook" directory.
pynfftls 1.6
Fast Lomb-Scargle periodogram using Non-equispaced Fast Fourier Transform (NFFT)  by B. Leroy

PyNFFTls 

This Python module provides the Fast Lomb-Scargle periodogram
developed by B. Leroy (2012, Astron. Astrophys. 545, A50)

It is based on the Non-equispaced Fast Fourier Transform (NFFTn http://www-user.tu-chemnitz.de/~potts/nfft/) as well as the FFTW3 library (http://www.fftw.org/).
Both librairies must be installed.

Calling sequence:
    (f,p) = period(t,y,ofac,hifac)

For more details, see the associated documentation
For a complete example, see nfftls_test.py


This Python module also provides the following methods:
- the Non-equidistant Fast Fourier Transform (NFFT) of a time series: (f,A) = nfft(t,y,p,d). For more details, see the associated documentation
- the Discrete  Fourier Transform  (DFT) of a time series: A = dft(t,y,f). For more details, see the associated documentation

For a complete example, see nfftls_test2.py

Change history:
1.6 (29/11/2020): module made compatible with NFFT version 3.5.3
1.5 (2/02/2020): module interface is now based on Cython, module now compatible with python 3
1.4 (11/04/2019): interface of nfft() changed, this function can now compute an over-sampled fourier transform
1.3 (10/04/2019):  correct a bug that lead to over-estimate the frequency by a relative factor of 1e-5
1.2 (4/06/2013): 
1.1 (18/01/2013):
1.0 (3/01/2013): initial version

R. Samadi, LESIA (http://lesia.obspm.fr), Observatoire de Paris, 22 Dec. 2012
googledrive-files 0.1.1
Simple Google Drive API wrapper
Requires Python: >=3.5

# pyGoogleDriveFiles

Python module 'googledrive-files' is
a simple wrapper for the files Resource of Google Drive API.

Features:
* List files and folders.
* Create, read, write, and delete files.


## Requirement

* Python 3.5 or above


## Installation

```sh
pip install googledrive-files
```


## Basic Usage

```python
>>> from googledrive import Service
>>> with Service(credentials).files() as gdrive:
...   file_content = gdrive.read(['dirA', 'subdir1'], 'filename')
...   gdrive.write(['dirB', 'subdir2'], 'filename.txt',
...                file_content, 'text/plain')
```


## Documentation

Documentation is
[available online](https://googledrive-files.readthedocs.io/).
combat 0.3.3
pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods
Requires Python: >=3.6

# pyComBat

pyComBat [1] is a Python 3 implementation of ComBat [2], one of the most widely used tool for correcting technical biases, called batch effects, in microarray expression data.

More detailed documentation can be found at [this address](https://epigenelabs.github.io/pyComBat/).

## TO DO

## Minimum dependencies

We list here the versions of the packages that have been used for development/testing of pyComBat, as well as for writing the documentation.

### pyComBat dependencies

* python 3.6

* numpy 1.18.5

* mpmath 1.1.0

* pandas 0.24.2

* patsy 0.5.1

### Documentation

* sphinx 2.1.2

## Usage example

### Installation

You can install pyComBat directly with:

```python
pip install combat
```

You can upgrade pyComBat to its latest version with:

```python
pip install combat --upgrade
```

### Running pyComBat

The simplest way of using pyComBat is to first import it, and then simply use the pycombat function with default parameters:

```python
from combat.pycombat import pycombat
data_corrected = pycombat(data,batch)
```

* data: The expression matrix as a dataframe. It contains the information about the gene expression (rows) for each sample (columns).

* batch: List of batch indexes. The batch list describes the batch for each sample. The list of batches contains as many elements as the number of columns in the expression matrix.

## How to contribute

Please refer to [CONTRIBUTING.md](https://github.com/epigenelabs/pyComBat/blob/master/CONTRIBUTING.md) to learn more about the contribution guidelines.

## References

[1] Behdenna A, Haziza J, Azencot CA and Nordor A. (2020) pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. bioRxiv doi: 10.1101/2020.03.17.995431

[2] Johnson W E, et al. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–127