# convert to the appropriate objects files = map(watchFactory, files) return files def run(self): if self.watching == []: print "[watch] No files to watch." sys.exit(0) global POLLING_INTERVAL while True: time.sleep(POLLING_INTERVAL) for f in self.watching: if f.hasChanged(): verbose.changeReport(f) Popen(self.cmd) if __name__ == "__main__": w = Watcher(getArguments(POLLING_INTERVAL)) try: w.run() except KeyboardInterrupt: print "\n" sys.exit(0) except: raise
# SPDX-License-Identifier: MIT
from __future__ import print_function
import numpy as np
import timeit
from numpy.random import rand
from sklearn import linear_model
from args import getArguments, coreString
import sklearn
import bench
import argparse
argParser = argparse.ArgumentParser(prog="ridge.py",
description="sklearn ridge regression benchmark",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
args = getArguments(argParser)
REP = args.iteration if args.iteration != '?' else 10
core_number, daal_version = bench.prepare_benchmark(args)
def st_time(func):
def st_func(*args, **keyArgs):
times = []
for n in range(REP):
t1 = timeit.default_timer()
r = func(*args, **keyArgs)
t2 = timeit.default_timer()
times.append(t2-t1)
print (min(times))
return r
return st_func
key=operator.itemgetter(1),
reverse=True)
# print("\nSeeds: {}\n".format(sorted_seeds))
opt_seed = []
i = 0
while len(opt_seed) < size:
seed = int(sorted_seeds[i][0])
if (seed not in guaranteed):
opt_seed.append(seed)
i += 1
# select top k users and compute inf_score
score = inf_score_est_mp(graph, opt_seed)
file_path = "data/{}/sim/opt_seed_{}.csv".format(dataset, model)
with open(file_path, 'w') as f:
for seed in opt_seed:
f.write(str(seed))
f.write("\n")
print("Optimal seed set saved to {}".format(file_path))
msg = "Best seed set found score is: {} "
print(msg.format(score))
if __name__ == "__main__":
args = args.getArguments("SIM")
# run optimal seed size for given graph
for model in args.models:
for reach in args.reach:
run(args.dataset, model, args.simulations)
choices=['daal', 'full'],
default='daal',
help='SVD solver to use')
parser.add_argument('--n-components',
type=int,
default=None,
help='Number of components to find')
parser.add_argument('--whiten',
action='store_true',
default=False,
help='Perform whitening')
parser.add_argument('--write-results',
action='store_true',
default=False,
help='Write results to disk for verification')
args = getArguments(parser)
REP = args.iteration if args.iteration != '?' else 10
core_number, daal_version = prepare_benchmark(args)
def st_time(func):
def st_func(*args, **keyArgs):
times = []
for n in range(REP):
t1 = timeit.default_timer()
r = func(*args, **keyArgs)
t2 = timeit.default_timer()
times.append(t2 - t1)
print(min(times))
return r
import args
from research_data import import_graph_data
def run(dataset):
# step 1 load graph
graph = import_graph_data(dataset)[0]
# step 2 get keys and order them
sorted_keys = sorted(graph.keys())
# step 3 in new equivalency dict, assign index value to keys
equivalency = {}
for i in range(len(sorted_keys)):
equivalency[sorted_keys[i]] = i
# step 4 save graph back to file
file_name = "data/{}/indexed_{}_wc.inf".format(dataset)
with open(file_name, 'w') as f:
for key in graph.keys():
for neighbor in graph[key].keys():
line = str(equivalency[key]) + ' '
line += str(equivalency[neighbor]) + ' '
line += str(graph[key][neighbor]) + "\n"
f.write(line)
if __name__ == "__main__":
args = args.getArguments("idToIndex")
run(args.dataset)