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()
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")