pycosep 0.1.0

Travelling Salesman Path - Python wrapper

Travelling salesman path - Python wrapper

This Python package is based on the MATLAB implementation available at biomedical-cybernetics/travelling-salesman-path.

Article Source: Geometric separability of mesoscale patterns in embedding representation and visualization of multidimensional data and complex networks. Acevedo A, Wu Y, Traversa FL, Cannistraci CV (2024) Geometric separability of mesoscale patterns in embedding representation and visualization of multidimensional data and complex networks. PLOS Complex Systems 1(2): e0000012. https://doi.org/10.1371/journal.pcsy.0000012

Before starting

This package requires Concorde to execute calculations. Check the requirements and follow the instructions to install this dependency in your operating system.

Installation

Run the following to install:

pip install pycosep

Usage

Computing the community separability of a given embedding.

from sklearn.datasets import load_iris

from pycosep import community_separability
from pycosep.separability_variants import SeparabilityVariant

# Sample data. Details at https://scikit-learn.org/stable/datasets/toy_dataset.html
data = load_iris()

# Community separability variant
#   CPS: centroid projection separability
#   LDPS: linear discriminant projection separability
#   TSPS: travelling salesman projection separability
variant = SeparabilityVariant.TSPS

indices, _ = community_separability.compute_separability(
    embedding=data.data,
    communities=data.target,
    variant=variant)

print(f"AUC index: {indices['auc']}")
print(f"AUPR index: {indices['aupr']}")
print(f"MCC index: {indices['mcc']}")

Computing the community separability of a given embedding for 1000 permutations.

from sklearn.datasets import load_iris

from pycosep import community_separability
from pycosep.separability_variants import SeparabilityVariant

# Sample data. Details at https://scikit-learn.org/stable/datasets/toy_dataset.html
data = load_iris()

# Community separability variant
#   CPS: centroid projection separability
#   LDPS: linear discriminant projection separability
#   TSPS: travelling salesman projection separability
variant = SeparabilityVariant.LDPS

# Number of iterations for the Null model
permutations = 1000

index_permutations, _ = community_separability.compute_separability(
    embedding=data.data,
    communities=data.target,
    variant=variant,
    permutations=permutations)

auc_results = index_permutations['auc']
print(f"AUC original value:  {auc_results['original_value']}")
print(f"AUC p-value:  {auc_results['p_value']}")
print(f"AUC mean:  {auc_results['mean']}")
print(f"AUC max:  {auc_results['max']}")
print(f"AUC min:  {auc_results['min']}")
print(f"AUC standard_deviation:  {auc_results['standard_deviation']}")
print(f"AUC standard_error:  {auc_results['standard_error']}")

aupr_results = index_permutations['aupr']
print(f"AUPR original value: {aupr_results['original_value']}")
print(f"AUPR p-value: {aupr_results['p_value']}")
print(f"AUPR mean: {aupr_results['mean']}")
print(f"AUPR max: {aupr_results['max']}")
print(f"AUPR min: {aupr_results['min']}")
print(f"AUPR standard deviation: {aupr_results['standard_deviation']}")
print(f"AUPR standard error: {aupr_results['standard_error']}")

mcc_results = index_permutations['mcc']
print(f"MCC original value: {mcc_results['original_value']}")
print(f"MCC p-value: {mcc_results['p_value']}")
print(f"MCC mean: {mcc_results['mean']}")
print(f"MCC max: {mcc_results['max']}")
print(f"MCC min: {mcc_results['min']}")
print(f"MCC standard deviation: {mcc_results['standard_deviation']}")
print(f"MCC standard error: {mcc_results['standard_error']}")

Reporting an issue

For reporting problems, questions, and suggestions; please, use the Issues section.