adadmire 1.0.14

Functions for detecting anomalies in tabular datasets using Mixed Graphical Models.

adadmire

Functions for detecting anomalies in molecular data sets using Mixed Graphical Models.

Installation

Enter the following commands in a shell like bash, zsh or powershell:

pip install -U adadmire

Usage

The usage example in this section requires that you first download the data files from the data folder. For a description of the contents of this folder, see section Data of the adadmire documentation site.

from adadmire import admire, penalty
import numpy as np

# Load example data
X = np.load('data/Feist_et_al/scaled_data_raw.npy') # continuous data
D = np.load('data/Feist_et_al/pheno.npy') # discrete data
levels = np.load('data/Feist_et_al/levels.npy') # levels of discrete variables

# Define lambda sequence of penalty values
lam = penalty(X, D, min= -2.25, max = -1.5, step =0.25)

# Get anomalies in continuous and discrete data
X_cor, n_cont, position_cont, D_cor, n_disc, position_disc = admire(X, D, levels, lam)
print(X_cor) # corrected X
print(n_cont) # number of continuous anomalies
print(position_cont) # position in X
print(D_cor) # corrected D
print(n_disc) # number of discrete anomalies
print(position_disc) # position in D

You can find more usage examples in the Usage section of adadmire's documentation site.

Documentation

You can find the full documentation for adadmire at spang-lab.github.io/adadmire. Amongst others, it includes chapters about:

• Installation
• Usage
• Modules
• Contributing
• Testing
• Documentation