def setUp(self):
self.data = data.fromfile(testfile('testdata10.txt'))
self.neteval = evaluator.NetworkEvaluator(
self.data,
network.fromdata(self.data),
prior.UniformPrior(self.data.variables.size))
self.neteval.network.edges.add_many([(1,0),(2,0),(3,0)])
def setUp(self):
a,b,c,d,e = 0,1,2,3,4
self.data = data.fromfile(testfile('testdata9.txt'))
self.net = network.fromdata(self.data)
self.net.edges.add_many([(a,c), (b,c), (c,d), (c,e)])
self.neteval1 = self.neteval_type(self.data, self.net, max_iterations="10*n**2")
def test_config2(self):
try:
config.read(testfile('config2.txt'))
except:
assert True
else:
assert False
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 test_arity_checking2():
try:
# arity specified is MORE than number of unique values. this is ok.
dataset = data.fromfile(testfile('testdata7.txt'))
except:
assert False
assert [v.arity for v in dataset.variables] == [3,4,3,6]
def test_arity_checking():
try:
# arity specified is less than number of unique values!!
dataset = data.fromfile(testfile('testdata6.txt'))
except data.IncorrectArityError:
assert True
else:
assert False
def setUp(self):
dat = data.fromfile(testfile("testdata5.txt"))
dat.discretize()
g = greedy.GreedyLearner(dat, max_iterations=100)
g.run()
self.result = g.result
self.tempdir = tempfile.mkdtemp()
self.result.tohtml(self.tempdir)
def setUp(self):
self.data = data.fromfile(testfile('testdata4.txt')) # no tab before variable names
self.expected_observations = N.array([[0, 0], [1, 1], [1,2]])
self.expected_missing = N.array([[0, 0], [0, 0], [0, 0]], dtype=bool)
self.expected_interventions = N.array([[1, 1], [0, 1], [0, 0]], dtype=bool)
self.expected_varnames = ['shh', 'ptchp']
self.expected_samplenames = ['sample1', 'sample2', 'sample3']
self.expected_arities = [2,3]
self.expected_dtype = N.dtype(int)
def setUp(self):
self.data = data.fromfile(testfile('testdata2.txt'))
self.expected_observations = N.array([[ 0. , 0. , 1.25, 0. ],
[ 1. , 1. , 1.1 , 1. ],
[ 1. , 2. , 0.45, 1. ]])
self.expected_dtype = N.dtype(float) # because one continuous variable
self.expected_varnames = ['shh', 'ptchp', 'smo', 'outcome']
self.expected_interventions = N.array([[ True, True, False, False],
[False, True, False, False],
[False, False, False, False]], dtype=bool)
self.expected_missing = N.array([[False, False, False, False],
[False, False, False, False],
[False, False, False, False]], dtype=bool)
self.expected_arities = [2, 3, -1, 2]
def setUp(self):
self.data = data.fromfile(testfile('testdata1.txt'))
self.expected_observations = N.array([[ 2.5, 0. , 1.7],
[ 1.1, 1.7, 2.3],
[ 4.2, 999.3, 12. ]])
self.expected_dtype = N.dtype(float)
self.expected_varnames = ['var1', 'var2', 'var3']
self.expected_missing = N.array([[False, True, False],
[False, False, False],
[False, False, False]], dtype=bool)
self.expected_interventions = N.array([[ True, True, False],
[False, True, False],
[False, False, False]], dtype=bool)
self.expected_arities = [-1,-1,-1]
def setup(self):
self.tempdir = tempfile.mkdtemp()
self.outdir = os.path.join(self.tempdir, 'result')
htmlreport_config = textwrap.dedent("""
[data]
filename = %s
[result]
format = html
outdir = %s
""" % (testfile("testdata12.txt"), self.outdir))
configfile = os.path.join(self.tempdir, "config.txt")
with file(configfile, 'w') as f:
f.write(htmlreport_config)
def setUp(self):
self.data = data.fromfile(testfile('testdata5m.txt'))
self.data.discretize()
self.expected_original = \
N.array([[ 1.2, 1.4, 2.1, 2.2, 1.1],
[ 2.3, 1.1, 2.1, 3.2, 1.3],
[ 3.2, 0. , 1.2, 2.5, 1.6],
[ 4.2, 2.4, 3.2, 2.1, 2.8],
[ 2.7, 1.5, 0. , 1.5, 1.1],
[ 1.1, 2.3, 2.1, 1.7, 3.2],
[ 2.3, 1.1, 4.3, 2.3, 1.1],
[ 3.2, 2.6, 1.9, 1.7, 1.1],
[ 2.1, 1.5, 3. , 1.4, 1.1],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ]])
self.expected_discretized = \
N.array([[0, 1, 1, 1, 0],
[1, 0, 1, 2, 1],
[2, 0, 0, 2, 2],
[2, 2, 2, 1, 2],
[1, 1, 0, 0, 0],
[0, 2, 1, 0, 2],
[1, 0, 2, 2, 0],
[2, 2, 0, 0, 0],
[0, 1, 2, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
self.expected_arities = [3,3,3,3,3]
self.expected_missing = N.array([[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[False, False, False, False, False],
[True , True , True , True , True ],
[True , True , True , True , True ],
[True , True , True , True , True ]],
dtype=bool)
def setUp(self):
self.data = data.fromfile(testfile('testdata5.txt'))
self.data.discretize()
self.expected_original = \
N.array([[ 1.2, 1.4, 2.1, 2.2, 1.1],
[ 2.3, 1.1, 2.1, 3.2, 1.3],
[ 3.2, 0. , 1.2, 2.5, 1.6],
[ 4.2, 2.4, 3.2, 2.1, 2.8],
[ 2.7, 1.5, 0. , 1.5, 1.1],
[ 1.1, 2.3, 2.1, 1.7, 3.2],
[ 2.3, 1.1, 4.3, 2.3, 1.1],
[ 3.2, 2.6, 1.9, 1.7, 1.1],
[ 2.1, 1.5, 3. , 1.4, 1.1]])
self.expected_discretized = \
N.array([[0, 1, 1, 1, 0],
[1, 0, 1, 2, 1],
[2, 0, 0, 2, 2],
[2, 2, 2, 1, 2],
[1, 1, 0, 0, 0],
[0, 2, 1, 0, 2],
[1, 0, 2, 2, 0],
[2, 2, 0, 0, 0],
[0, 1, 2, 0, 0]])
self.expected_arities = [3,3,3,3,3]
def setUp(self):
self.data = data.fromfile(testfile('testdata5.txt'))
self.data.discretize(excludevars=[0,1])
self.expected_original = \
N.array([[ 1.2, 1.4, 2.1, 2.2, 1.1],
[ 2.3, 1.1, 2.1, 3.2, 1.3],
[ 3.2, 0. , 1.2, 2.5, 1.6],
[ 4.2, 2.4, 3.2, 2.1, 2.8],
[ 2.7, 1.5, 0. , 1.5, 1.1],
[ 1.1, 2.3, 2.1, 1.7, 3.2],
[ 2.3, 1.1, 4.3, 2.3, 1.1],
[ 3.2, 2.6, 1.9, 1.7, 1.1],
[ 2.1, 1.5, 3. , 1.4, 1.1]])
self.expected_discretized = \
N.array([[ 1.2, 1.4, 1. , 1. , 0. ],
[ 2.3, 1.1, 1. , 2. , 1. ],
[ 3.2, 0. , 0. , 2. , 2. ],
[ 4.2, 2.4, 2. , 1. , 2. ],
[ 2.7, 1.5, 0. , 0. , 0. ],
[ 1.1, 2.3, 1. , 0. , 2. ],
[ 2.3, 1.1, 2. , 2. , 0. ],
[ 3.2, 2.6, 0. , 0. , 0. ],
[ 2.1, 1.5, 2. , 0. , 0. ]])
self.expected_arities = [-1,-1,3,3,3]
def setUp(self):
self.data = data.fromfile(testfile('testdata10.txt'))
self.ne = evaluator.SmartNetworkEvaluator(
self.data,
network.fromdata(self.data))
def test_config1(self):
config.read(testfile('config1.txt'))
def setUp(self):
self.data = data.fromfile(testfile("testdata5.txt")).subset(samples=range(5))
self.data.discretize()
self.learner = self.learnertype(self.data)
def setUp(self):
config.set("evaluator.missingdata_evaluator", self.missing_evaluator)
self.data = data.fromfile(testfile("testdata13.txt"))
self.learner = self.learnertype(self.data)
def setUp(self):
config.set("evaluator.missingdata_evaluator", "exact")
self.data = data.fromfile(testfile("testdata13.txt")).subset(samples=range(5))
self.learner = greedy.GreedyLearner(self.data, max_iterations=10)
def setUp(self):
d = data.fromfile(testfile("testdata5.txt"))
d.discretize()
self.tc = self.tctype(*self.args)
self.tasks = [greedy.GreedyLearner(d, max_iterations=100) for i in xrange(6)]
def setUp(self):
d = data.fromfile(testfile("testdata5.txt"))
d.discretize()
self.proc = subprocess.Popen("ipcluster -n 2 </dev/null 1>&0 2>&0", shell=True)
time.sleep(5)
def setUp(self):
self.data = data.fromfile(testfile('testdata5.txt'))
self.data.discretize()
def setUp(self):
self.data = data.fromfile(testfile('testdata10.txt'))
self.neteval = evaluator.NetworkEvaluator(self.data, network.fromdata(self.data))
self.neteval.network.edges.add_many([(1,0), (2,0), (3,0)]) # {1,2,3} --> 0
def setUp(self):
self.data = data.fromfile(testfile("greedytest1-200.txt"))
from pebl2.taskcontroller import xgrid
from pebl2.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]