def test_tc(self):
d = data.fromfile(testfile("testdata5.txt"))
d.discretize()
tasks = [greedy.GreedyLearner(d) for x in range(5)]
tc = ipy1.IPython1Controller("127.0.0.1:10113")
results = tc.run(tasks)
results = result.merge(results)
assert isinstance(results, result.LearnerResult)
def run(configfile=None):
try:
configfile = configfile or sys.argv[2]
except:
usage("Please specify a config file.")
config.read(configfile)
numtasks = config.get('learner.numtasks')
tasks = learner.fromconfig().split(numtasks)
controller = taskcontroller.fromconfig()
results = controller.run(tasks)
merged_result = result.merge(results)
if config.get('result.format') == 'html':
merged_result.tohtml()
else:
merged_result.tofile()
def run(configfile=None):
try:
configfile = configfile or sys.argv[2]
except:
usage("Please specify a config file.")
config.read(configfile)
numtasks = config.get('learner.numtasks')
tasks = learner.fromconfig().split(numtasks)
controller = taskcontroller.fromconfig()
results = controller.run(tasks)
merged_result = result.merge(results)
if config.get('result.format') == 'html':
merged_result.tohtml()
else:
merged_result.tofile()
from pebl.taskcontroller import xgrid
from pebl.test import testfile
help = """Test the Xgrid TaskController.
USAGE: test_xgrid.py configfile
You need to provide the configfile for use with XGridController.
###############################################################################
WARNING for pebl devs:
Do NOT put your configfile under svn.
It contains sensitve information.
###############################################################################
"""
if len(sys.argv) < 2:
print help
sys.exit(1)
config.read(sys.argv[1])
d = data.fromfile(testfile("testdata5.txt"))
d.discretize()
tc = xgrid.XgridController()
results = tc.run([greedy.GreedyLearner(d, max_time=10) for i in xrange(10)])
results = result.merge(results)
print results
print [r.host for r in results.runs]
#!/usr/bin/env python
# Bayesian with five greedy and 5 simulated annearling learners serially
# http://pythonhosted.org/pebl/tutorial.html
from pebl import data, result
from pebl.learner import greedy, simanneal
from pebl.taskcontroller import multiprocess
dataset = data.fromfile("pebl-tutorial-data2.txt")
learners = [ greedy.GreedyLearner(dataset, max_iterations=1000000) for i in range(5) ] + \
[ simanneal.SimulatedAnnealingLearner(dataset) for i in range(5) ]
tc = multiprocess.MultiProcessController(poolsize=2)
results = tc.run(learners)
merged_result = result.merge(results)
merged_result.tofile("example4-result")
from pebl.learner import greedy
from pebl.taskcontroller import ec2
from pebl.test import testfile
help = """Test the EC2 TaskController.
USAGE: test_ec2.py configfile
You need to provide the configfile for use with EC2Controller.
###############################################################################
WARNING for pebl devs:
Do NOT put your configfile under svn.
It contains sensitve information.
###############################################################################
"""
if len(sys.argv) < 2:
print help
sys.exit(1)
d = data.fromfile(testfile("testdata5.txt"))
d.discretize()
tc = ec2.EC2Controller(config=sys.argv[1], min_count=3)
results = tc.run([greedy.GreedyLearner(d, max_time=10) for i in xrange(10)])
results = result.merge(results)
print results
print[r.host for r in results.runs]
from pebl import data, result
from pebl.learner import greedy, simanneal
import numpy as np
from pebl.taskcontroller import multiprocess
dataset = data.fromfile("zinc.txt")
dataset.discretize()
learner1 = greedy.GreedyLearner(dataset, max_iterations=1000)
result1 = learner1.run()
learner2 = greedy.GreedyLearner(dataset, max_iterations=1000)
result2 = learner2.run()
#learners = [ greedy.GreedyLearner(dataset, max_iterations=100) for i in range(5) ] #+ \
# [ simanneal.SimulatedAnnealingLearner(dataset) for i in range(5) ]
result1.tohtml('example2.out')
result2.tohtml('example3.out')
merged_result = result.merge(result1, result2)
merged_result.tohtml("zinc",)
merged_result.tofile('example.out')
#!/usr/bin/env python
# Bayesian with five greedy and 5 simulated annearling learners serially
# http://pythonhosted.org/pebl/tutorial.html
from pebl import data, result
from pebl.learner import greedy, simanneal
dataset = data.fromfile("pebl-tutorial-data2.txt")
learners = [ greedy.GreedyLearner(dataset, max_iterations=1000000) for i in range(5) ] + \
[ simanneal.SimulatedAnnealingLearner(dataset) for i in range(5) ]
merged_result = result.merge([learner.run() for learner in learners])
merged_result.tofile("example3-result")
def test_tc(self):
results = self.tc.run(self.tasks)
results = result.merge(results)
assert isinstance(results, result.LearnerResult)
def setUp(self):
super(TestPosterior, self).setUp()
self.merged = result.merge(self.result1, self.result2)
self.posterior = self.merged.posterior
def test_merged_scores(self):
mr = result.merge([self.result1, self.result2])
assert [n.score for n in mr.networks] == [-13, -12, -11, -10.5, -8.5, -6, -5.5]
def test_merged_size1(self):
mr = result.merge(self.result1, self.result2)
len(mr.networks) == (5+3-1) # 1 duplicate network
#!/usr/bin/env python
# http://pythonhosted.org/pebl/tutorial.html
from pebl import data, result
from pebl.learner import greedy
dataset = data.fromfile("pebl-tutorial-data2.txt")
learner1 = greedy.GreedyLearner(dataset, max_iterations=1000000)
learner2 = greedy.GreedyLearner(dataset, max_time=120) # in seconds
result1 = learner1.run()
result2 = learner2.run()
merged_result = result.merge(result1, result2)
merged_result.tofile("example2-result")
