def singlecore(filename, delta, popsize, generations):
"""Run serially"""
#set up the evo strategy
best_list, mut_list = [], []
evo = popstrat.Evostrategy(5000, popsize)
children = evo.iterate(evo.pop)
for i in range(generations):
for child in children:
results, conclist = run_grn(child['genome'], delta)
bestidx = results.index(max(results))
child['fitness'] = results[bestidx]
print "fitness:", child['fitness']
children = evo.iterate(children)
bestgenome = evo.pop[-1]['genome']
results, conclist = run_grn(bestgenome, delta)
filename = "best_gen_" + str(i)
graph.plot_2d(conclist, filename)
if evo.adaptive:
evo.adapt_mutation()
best_list.append(evo.pop[-1]['fitness'])
mut_list.append(evo.mut_rate)
print "best overall fitness", evo.pop[-1]['fitness']
graph.plot_2d([best_list], 'bestfit')
graph.plot_2d([mut_list], 'mutrate')
def multicore():
delta = 20
#set up the evo strategy
best_list, mut_list = [], []
evo = popstrat.Evostrategy(5000, 50)
children = evo.iterate(evo.pop)
nodes = ("*",)
job_server = pp.Server(8, ppservers=nodes)
print "Starting pp with", job_server.get_ncpus(), "workers"
start_time = time.time()
for i in range(50):
run_time = time.time()
jobs = [(child, job_server.submit(run_grn,
(child['genome'],
delta),
(),
("grn","numpy","math")))
for child in children]
for child, result in jobs:
results, conclist = result()
bestidx = results.index(max(results))
child['fitness'] = results[bestidx]
#plotting the best with colors
children = evo.iterate(children)
bestgenome = evo.pop[-1]['genome']
bestresult, conclist = run_grn(bestgenome, delta)
bestidx = bestresult.index(max(bestresult))
filename = "best_gen_"+str("%03d" % i)
print filename
colors = []
simplist = []
for idx, result in enumerate(bestresult):
if idx == len(bestresult)-1:
simplist.append(conclist[idx])
colors.append('k')
elif idx == bestidx:
colors.append('g')
simplist.append(conclist[idx])
# elif result == 0:
# colors.append('b')
# else:
# colors.append('r')
graph.plot_2d(simplist, filename, colors)
print "gen:", evo.gen_count, "fitness:", evo.pop[-1]['fitness']
if evo.adaptive:
evo.adapt_mutation()
best_list.append(evo.pop[-1]['fitness'])
mut_list.append(evo.mut_rate)
mutfile = open('mutrate.txt','a')
mutfile.write(str(mut_list)+'\n')
mutfile.close()
def multicore(filename, delta, popsize, generations):
#set up the evo strategy
evo = popstrat.Evostrategy(5000, popsize)
children = evo.iterate(evo.pop)
nodes = ("*", )
job_server = pp.Server(8, ppservers=nodes)
print "Starting pp with", job_server.get_ncpus(), "workers"
start_time = time.time()
for i in range(generations):
run_time = time.time()
jobs = [(child,
job_server.submit(run_grn, (child['genome'], delta), (),
("cgrn as grn", "numpy", "math")))
for child in children]
for child, result in jobs:
results, conclist = result()
bestidx = results.index(max(results))
child['fitness'] = results[bestidx]
#plotting the best with colors
children = evo.iterate(children)
bestgenome = evo.pop[-1]['genome']
bestresult, conclist = run_grn(bestgenome, delta)
bestidx = bestresult.index(max(bestresult))
colors = []
simplist = []
for idx, result in enumerate(bestresult):
if idx == len(bestresult) - 1:
simplist.append(conclist[idx])
colors.append('k')
elif idx == bestidx:
colors.append('g')
simplist.append(conclist[idx])
else:
simplist.append(conclist[idx])
colors.append('r')
graph.plot_2d(simplist, filename, colors)
print "gen:", evo.gen_count, "fitness:", evo.pop[-1]['fitness']
if evo.adaptive:
evo.adapt_mutation()
res_file = open(filename, "a")
res_file.write(str(evo.pop[-1]) + '\n')
res_file.close()
def main():
import time
if(len(sys.argv) != 2):
print "Usage: " + sys.argv[0] + " <rseed>"
sys.exit(1)
seed = int(sys.argv[1])
random.seed(seed)
#read in the compustat data for given companies
delta = 20
companies = []
company_list = ['ARCHER-DANIELS-MIDLAND CO', 'ARTHUR J GALLAGHER & CO',
'ASCENA RETAIL GROUP INC', 'ASTEC INDUSTRIES INC', 'ASTORIA FINANCIAL CORP',
'BLACKBAUD INC', 'BMC SOFTWARE INC','BOSTON BEER INC -CL A', 'BRADY CORP',
'BRIGGS & STRATTON']
database = csvreader.CSVReader('compustat.csv')
for company in company_list:
companies.append(database.get_company(company))
#set up the evo strategy
best_list, mut_list = [], []
evo = popstrat.Evostrategy(5000,50)
children = evo.iterate(evo.pop)
for i in range(50):
for child in children:
start_time = time.time()
fitness = run_grn(child['genome'], companies, delta)
child['fitness'] = fitness
print "fitness:",child['fitness'],"time taken", str(time.time()-start_time)
children = evo.iterate(children)
if evo.adaptive:
evo.adapt_mutation()
best_list.append(evo.pop[-1]['fitness'])
mut_list.append(evo.mut_rate)
graph.plot_2d([best_list], 'bestfit')
graph.plot_2d([mut_list], 'mutrate')
def multicore(filename, syncsize, offset, delta, popsize, generations):
""" Uses parallel python to evaluate, PP can also be used to
distribute evaluations to machines accross the network"""
#set up the evo strategy
evo = popstrat.Evostrategy(5000, popsize)
children = evo.iterate(evo.pop)
nodes = ("*", )
job_server = pp.Server(ncpus=8, ppservers=nodes)
print "Starting pp with", job_server.get_ncpus(), "workers"
for _ in range(generations):
jobs = [(child,
job_server.submit(run_grn,
(child['genome'], delta, syncsize, offset),
(), ("cgrn as grn", "numpy", "math")))
for child in children]
for child, result in jobs:
results, conclist = result()
bestidx = results.index(max(results))
child['fitness'] = results[bestidx]
print "gen:", evo.gen_count, "fitness:", evo.pop[-1]['fitness']
children = evo.iterate(children)
if evo.adaptive:
evo.adapt_mutation()
res_file = open(filename, "a")
res_file.write(str(evo.pop[-1]) + '\n')
res_file.close()
restopname = filename + ".rest"
#plotting the best with colors
bestgenome = evo.pop[-1]['genome']
bestresult, conclist = run_grn(bestgenome, delta, syncsize, offset,
restopname)
bestidx = bestresult.index(max(bestresult))
fitness = round(max(bestresult), 1)
colors = []
for idx, result in enumerate(bestresult):
# draw the input in black
if idx == len(bestresult) - 1:
colors.append('k')
# draw the best in green
elif idx == bestidx:
colors.append('g')
# draw p_genes in red
elif result > 0:
colors.append('r')
# draw TFs in blue
else:
colors.append('b')
print "colors", colors
graphname = filename.split('/')[2]
graphname = graphname[:-4] + "-F" + str(fitness)
graph.plot_2d(conclist, graphname, colors, (0, 1))
def main():
import time
if(len(sys.argv) != 2):
print "Usage: " + sys.argv[0] + " <rseed>"
sys.exit(1)
seed = int(sys.argv[1])
random.seed(seed)
delta = 20
filename = "results-"+str(seed)+".dat"
#read in the compustat data for given companies
companies = []
company_list = ['ARCHER-DANIELS-MIDLAND CO', 'ARTHUR J GALLAGHER & CO',
'ASCENA RETAIL GROUP INC', 'ASTEC INDUSTRIES INC',
'ASTORIA FINANCIAL CORP', 'BLACKBAUD INC',
'BMC SOFTWARE INC','BOSTON BEER INC -CL A',
'BRADY CORP', 'BRIGGS & STRATTON']
database = csvreader.CSVReader('compustat.csv')
for company in company_list:
companies.append(database.get_company(company))
#set up the evo strategy
best_list, mut_list = [], []
evo = popstrat.Evostrategy(5000, 50)
children = evo.iterate(evo.pop)
nodes = ("*",)
job_server = pp.Server(0, ppservers=nodes)
print "Starting pp with", job_server.get_ncpus(), "workers"
start_time = time.time()
for i in range(50):
run_time = time.time()
jobs = [(child, job_server.submit(run_grn,
(child['genome'],
companies,
delta),
(),
("grn",)))
for child in children]
for child, result in jobs:
child['fitness'], child['testfit'] = result()
children = evo.iterate(children)
print "gen", evo.gen_count, "time taken", str(time.time()-run_time), "total time", str(time.time()-start_time)
if evo.adaptive:
evo.adapt_mutation()
res_file = open(filename,"a")
print "best fitness:", evo.pop[-1]['fitness'], evo.pop[-1]['testfit']
res_file.write(str(evo.pop[-1])+'\n')
res_file.close()
best_list.append(evo.pop[-1]['fitness'])
mut_list.append(evo.mut_rate)
mutfile = open('mutrate.txt','a')
mutfile.write(str(mut_list)+'\n')
mutfile.close()