Python Package for the creation and analysis of similarity networks.
Install latest release from PyPi with pip
pip install simnetpy
The source code for this project can be installed using git and pip.
Clone repository
git clone https://github.com/amarnane/simnetpy.git
cd simnetpy
pip install .
To remove the package simply use
pip uninstall simnetpy
To install in developer mode (have changes in source code update without reinstallation) add -e flag
pip install -e .
There is one dependency that cannot be installed through pip - Graph-Tool. This is a result of it's underlying c++ dependencies.
The simplest method for python users is to make use of a conda environment, install this package using the commands above and install graph-tool using conda-forge
conda install -c conda-forge graph-tool
Note: this will not work on Windows. Alternative (conda independent) solutions can be found on the Graph Tool Website
import simnet as sn
import numpy as np
# create mixed guassian data with 100 nodes, 2 dimensions and 3 equally sized clusters.
N = 100
sizes=np.array([34,33,33])
d = 2
dataset = sn.datasets.mixed_multi_guassian(len(sizes), d, N, sizes=sizes)
# calculate pairwise similarity
S = sn.pairwise_sim(dataset.X, metric='euclidean', norm=True)
# Create igraph Igraph from matrix
gg = sn.network_from_sim_mat(S, method='knn', K=10)
# print graph stats
print(gg.graph_stats())
# true cluster quality
cqual_ytrue = sn.clustering.cluster_quality(gg, dataset.y)
print(cqual_ytrue)
# cluster
ylabels = sn.clustering.spectral_clustering(gg, laplacian='lrw')
# cluster accuracy
cacc = sn.clustering.cluster_accuracy(dataset.y, ylabels)
print(cacc)
# predicted cluster quality
cqual = sn.clustering.cluster_quality(gg, ylabels)
print(cqual)
