/usr/share/pyshared/mdp/test/test_parallelflows.py is in python-mdp 3.3-1.
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import mdp.parallel as parallel
n = numx
def test_tasks():
"""Test parallel training and execution by running the tasks."""
flow = parallel.ParallelFlow([
mdp.nodes.SFANode(output_dim=5),
mdp.nodes.PolynomialExpansionNode(degree=3),
mdp.nodes.SFANode(output_dim=20)])
data_iterables = [[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)],
None,
[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)]]
scheduler = parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler)
# parallel execution
iterable = [n.random.random((20,10)) for _ in xrange(6)]
flow.execute(iterable, scheduler=scheduler)
def test_non_iterator():
"""Test parallel training and execution with a single array."""
flow = parallel.ParallelFlow([
mdp.nodes.SFANode(output_dim=5),
mdp.nodes.PolynomialExpansionNode(degree=3),
mdp.nodes.SFANode(output_dim=20)])
data_iterables = n.random.random((200,10))*n.arange(1,11)
scheduler = parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler)
# test execution
x = n.random.random((100,10))
flow.execute(x)
def test_multiple_schedulers():
"""Test parallel flow training with multiple schedulers."""
flow = parallel.ParallelFlow([
mdp.nodes.SFANode(output_dim=5),
mdp.nodes.PolynomialExpansionNode(degree=3),
mdp.nodes.SFANode(output_dim=20)])
data_iterables = [[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)],
None,
[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)]]
schedulers = [parallel.Scheduler(), None, parallel.Scheduler()]
flow.train(data_iterables, scheduler=schedulers)
# parallel execution
iterable = [n.random.random((20,10)) for _ in xrange(6)]
flow.execute(iterable, scheduler=parallel.Scheduler())
def test_multiple_schedulers2():
"""Test parallel flow training with multiple schedulers (part 2)."""
# now the first node is untrainable as well
flow = parallel.ParallelFlow([
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.SFANode(output_dim=5),
mdp.nodes.PolynomialExpansionNode(degree=3),
mdp.nodes.SFANode(output_dim=20)])
data_iterables = [None,
[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)],
None,
[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)]]
schedulers = [None, parallel.Scheduler(), None, parallel.Scheduler()]
flow.train(data_iterables, scheduler=schedulers)
# parallel execution
iterable = [n.random.random((20,10)) for _ in xrange(6)]
flow.execute(iterable, scheduler=parallel.Scheduler())
def test_multiphase():
"""Test parallel training and execution for nodes with multiple
training phases.
"""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = mdp.hinet.FlowNode(mdp.Flow([sfa_node, sfa2_node]))
flow = parallel.ParallelFlow([
flownode,
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.SFANode(output_dim=5)])
data_iterables = [[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)],
None,
[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)]]
scheduler = parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler)
# test normal execution
x = n.random.random([100,10])
flow.execute(x)
# parallel execution
iterable = [n.random.random((20,10)) for _ in xrange(6)]
flow.execute(iterable, scheduler=scheduler)
def test_firstnode():
"""Test special case in which the first node is untrainable.
This tests the proper initialization of the internal variables.
"""
flow = parallel.ParallelFlow([
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.SFANode(output_dim=20)])
data_iterables = [None,
n.random.random((6,20,10))]
scheduler = parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler)
def test_multiphase_checkpoints():
"""Test parallel checkpoint flow."""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = mdp.hinet.FlowNode(mdp.Flow([sfa_node, sfa2_node]))
flow = parallel.ParallelCheckpointFlow([
flownode,
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.SFANode(output_dim=5)])
data_iterables = [[n.random.random((30,10)) for _ in xrange(6)],
None,
[n.random.random((30,10)) for _ in xrange(6)]]
checkpoint = mdp.CheckpointFunction()
scheduler = parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler, checkpoints=checkpoint)
def test_nonparallel1():
"""Test training for mixture of parallel and non-parallel nodes."""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
# TODO: use a node with no parallel here
sfa2_node = mdp.nodes.CuBICANode(input_dim=8)
flownode = mdp.hinet.FlowNode(mdp.Flow([sfa_node, sfa2_node]))
flow = parallel.ParallelFlow([
flownode,
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.SFANode(output_dim=5)])
data_iterables = [[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)],
None,
[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)]]
scheduler = parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler)
# test execution
x = n.random.random([100,10])
flow.execute(x)
def test_nonparallel2():
"""Test training for mixture of parallel and non-parallel nodes."""
# TODO: use a node with no parallel here
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = mdp.hinet.FlowNode(mdp.Flow([sfa_node, sfa2_node]))
flow = parallel.ParallelFlow([
flownode,
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.SFANode(output_dim=5)])
data_iterables = [[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)],
None,
[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)]]
scheduler = parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler)
# test execution
x = n.random.random([100,10])
flow.execute(x)
def test_nonparallel3():
"""Test training for non-parallel nodes."""
# TODO: use a node with no parallel here
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
# TODO: use a node with no parallel here
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flow = parallel.ParallelFlow([sfa_node, sfa2_node])
data_iterables = [[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)],
[n.random.random((30,10))*n.arange(1,11)
for _ in xrange(6)]]
scheduler = parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler)
while flow.is_parallel_training:
results = []
while flow.task_available():
task = flow.get_task()
results.append(task())
flow.use_results(results)
# test execution
x = n.random.random([100,10])
flow.execute(x)
def test_train_purge_nodes():
"""Test that FlowTrainCallable correctly purges nodes."""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = mdp.hinet.FlowNode(mdp.Flow([sfa_node,
mdp.nodes.IdentityNode(),
sfa2_node]))
data = n.random.random((30,10))
mdp.activate_extension("parallel")
try:
clbl = mdp.parallel.FlowTrainCallable(flownode)
flownode = clbl(data)
finally:
mdp.deactivate_extension("parallel")
assert flownode._flow[1].__class__.__name__ == "_DummyNode"
def test_execute_fork():
"""Test the forking of a node based on use_execute_fork."""
class _test_ExecuteForkNode(mdp.nodes.IdentityNode):
# Note: The explicit signature is important to preserve the dim
# information during the fork.
def __init__(self, input_dim=None, output_dim=None, dtype=None):
self.n_forks = 0
self.n_joins = 0
super(_test_ExecuteForkNode, self).__init__(input_dim=input_dim,
output_dim=output_dim,
dtype=dtype)
class Parallel_test_ExecuteForkNode(parallel.ParallelExtensionNode,
_test_ExecuteForkNode):
def _fork(self):
self.n_forks += 1
return self._default_fork()
def _join(self, forked_node):
self.n_joins += forked_node.n_joins + 1
def use_execute_fork(self):
return True
try:
n_chunks = 6
## Part 1: test execute fork during flow training
data_iterables = [[n.random.random((30,10)) for _ in xrange(n_chunks)],
None,
[n.random.random((30,10)) for _ in xrange(n_chunks)],
None]
flow = parallel.ParallelFlow([mdp.nodes.PCANode(output_dim=5),
_test_ExecuteForkNode(),
mdp.nodes.SFANode(),
_test_ExecuteForkNode()])
scheduler = parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler)
for node in flow:
if isinstance(node, _test_ExecuteForkNode):
assert node.n_forks == 2 * n_chunks + 2
assert node.n_joins == 2 * n_chunks
# reset the counters to prepare the execute test
node.n_forks = 0
node.n_joins = 0
## Part 2: test execute fork during flow execute
data_iterable = [n.random.random((30,10)) for _ in xrange(n_chunks)]
flow.execute(data_iterable, scheduler=scheduler)
for node in flow:
if isinstance(node, _test_ExecuteForkNode):
assert node.n_forks == n_chunks
assert node.n_joins == n_chunks
finally:
# unregister the testing class
del mdp.get_extensions()["parallel"][_test_ExecuteForkNode]
scheduler.shutdown()
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