LargeVis_run.py

#!/usr/bin/env python

import LargeVis
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--fea', default=1, type=int, help='whether to visualize high-dimensional feature vectors or networks')
parser.add_argument('--input', default='', help='input file', required=True)
parser.add_argument('--output', default='', help='output file', required=True)
parser.add_argument('--outdim', default=-1, type=int, help='output dimensionality')
parser.add_argument('--threads', default=-1, type=int, help='number of training threads')
parser.add_argument('--samples', default=-1, type=int, help='number of training mini-batches')
parser.add_argument('--prop', default=-1, type=int, help='number of propagations')
parser.add_argument('--alpha', default=-1, type=float, help='learning rate')
parser.add_argument('--trees', default=-1, type=int, help='number of rp-trees')
parser.add_argument('--neg', default=-1, type=int, help='number of negative samples')
parser.add_argument('--neigh', default=-1, type=int, help='number of neighbors in the NN-graph')
parser.add_argument('--gamma', default=-1, type=float, help='weight assigned to negative edges')
parser.add_argument('--perp', default=-1, type=float, help='perplexity for the NN-grapn')

args = parser.parse_args()

if args.fea == 1:
    LargeVis.loadfile(args.input)
else:
    LargeVis.loadgraph(args.input)

Y = LargeVis.run(args.outdim, args.threads, args.samples, args.prop, args.alpha, args.trees, args.neg, args.neigh, args.gamma, args.perp)

LargeVis.save(args.output)