def test_vector_aggfunc_multiple_trigger(self):
"""TestAggregator: multi trigger vector aggfunc"""
def aggfunc(series):
return series.cumsum()
filters = {"result": "fire"}
event_class = self._trace.aim_and_fire
value = 1
pivot = "identifier"
trigger_fire = Trigger(self._trace, event_class, filters, value, pivot)
filters = {"result": "blank"}
value = -1
trigger_blank = Trigger(self._trace, event_class, filters, value,
pivot)
aggregator = MultiTriggerAggregator([trigger_fire, trigger_blank],
self.topology,
aggfunc=aggfunc)
# There are three "fire" in total
# The all level in topology looks like
# [[0, 1]]
result = aggregator.aggregate(level="all")
expected_result = pd.Series([1.0, 0.0, 1.0, 0.0, 1.0, 0.0],
index=pd.Index(
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]))
assert_series_equal(result[0], expected_result)
def test_default_aggfunc_multiple_trigger(self):
"""MultiTriggerAggregator with the default aggfunc"""
trigger_fire = Trigger(self._trace,
self._trace.aim_and_fire,
filters={"result": "fire"},
pivot="identifier",
value=1)
trigger_blank = Trigger(self._trace,
self._trace.aim_and_fire,
filters={"result": "blank"},
pivot="identifier",
value=2)
aggregator = MultiTriggerAggregator([trigger_fire, trigger_blank],
self.topology)
results = aggregator.aggregate(level="cpu")
expected_results = [
pd.Series([1., 2., 1., 0., 0., 0.],
index=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]),
pd.Series([0., 0., 0., 2., 1., 2.],
index=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]),
]
self.assertEqual(len(results), len(expected_results))
for result, expected in zip(results, expected_results):
assert_series_equal(result, expected)
def test_vector_aggfunc_single_trigger(self):
"""TestAggregator: 1 trigger vector aggfunc"""
def aggfunc(series):
return series.cumsum()
filters = {
"result": "fire"
}
event_class = self._trace.aim_and_fire
value = 1
pivot = "identifier"
trigger = Trigger(self._trace, event_class, filters, value, pivot)
aggregator = MultiTriggerAggregator([trigger],
self.topology,
aggfunc=aggfunc)
# There are three "fire" in total
# The all level in topology looks like
# [[0, 1]]
result = aggregator.aggregate(level="all")
expected_result = pd.Series([1.0, 1.0, 2.0, 2.0, 3.0, 3.0],
index=pd.Index([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
)
assert_series_equal(result[0], expected_result)
def test_scalar_aggfunc_single_trigger(self):
"""TestAggregator: 1 trigger scalar aggfunc"""
def aggfunc(series):
return series.sum()
filters = {"result": "fire"}
event_class = self._trace.aim_and_fire
value = 1
pivot = "identifier"
trigger = Trigger(self._trace, event_class, filters, value, pivot)
aggregator = MultiTriggerAggregator([trigger],
self.topology,
aggfunc=aggfunc)
# There are three "fire" in total
# The all level in topology looks like
# [[0, 1]]
result = aggregator.aggregate(level="all")
self.assertEqual(result, [3.0])
# There are two "fire" on the first node group and a
# a single "fire" on the second node group at the cluster
# level which looks like
# [[0], [1]]
result = aggregator.aggregate(level="cluster")
self.assertEqual(result, [2.0, 1.0])
def test_default_aggfunc_multiple_trigger(self):
"""MultiTriggerAggregator with the default aggfunc"""
trigger_fire = Trigger(self._trace, self._trace.aim_and_fire,
filters={"result": "fire"},
pivot="identifier", value=1)
trigger_blank = Trigger(self._trace, self._trace.aim_and_fire,
filters={"result": "blank"},
pivot="identifier", value=2)
aggregator = MultiTriggerAggregator([trigger_fire, trigger_blank],
self.topology)
results = aggregator.aggregate(level="cpu")
expected_results = [
pd.Series([1., 2., 1., 0., 0., 0.],
index=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]),
pd.Series([0., 0., 0., 2., 1., 2.],
index=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]),
]
self.assertEquals(len(results), len(expected_results))
for result, expected in zip(results, expected_results):
assert_series_equal(result, expected)
def _generate_matrix(self, run, reference_run, aggfunc):
"""Generate the Correlation Matrix"""
reference_aggs = []
aggs = []
for idx in range(self._dimension):
reference_aggs.append(
MultiTriggerAggregator(
sched_funcs.sched_triggers(reference_run,
self._reference_pids[idx],
trappy.sched.SchedSwitch),
self._topology, aggfunc))
aggs.append(
MultiTriggerAggregator(
sched_funcs.sched_triggers(run, self._pids[idx],
trappy.sched.SchedSwitch),
self._topology, aggfunc))
agg_pair_gen = ((r_agg, agg) for r_agg in reference_aggs
for agg in aggs)
# pylint fails to recognize numpy members.
# pylint: disable=no-member
matrix = np.zeros((self._dimension, self._dimension))
# pylint: enable=no-member
for (ref_result, test_result) in agg_pair_gen:
i = reference_aggs.index(ref_result)
j = aggs.index(test_result)
corr = Correlator(ref_result,
test_result,
corrfunc=sched_funcs.binary_correlate,
filter_gaps=True)
_, total = corr.correlate(level="cluster")
matrix[i][j] = total
return matrix
def _aggregator(self, aggfunc):
"""
Returns an aggregator corresponding to the
aggfunc, the aggregators are memoized for performance
Args:
aggfunc (function(pandas.Series)): Function parameter that
accepts a pandas.Series object and returns a vector/scalar result
"""
if aggfunc not in self._aggs.keys():
self._aggs[aggfunc] = MultiTriggerAggregator(
self._triggers, self._topology, aggfunc)
return self._aggs[aggfunc]
def test_scalar_aggfunc_single_trigger(self):
"""TestAggregator: 1 trigger scalar aggfunc"""
def aggfunc(series):
return series.sum()
filters = {
"result": "fire"
}
event_class = self._trace.aim_and_fire
value = 1
pivot = "identifier"
trigger = Trigger(self._trace,
event_class,
filters,
value,
pivot)
aggregator = MultiTriggerAggregator([trigger],
self.topology,
aggfunc=aggfunc)
# There are three "fire" in total
# The all level in topology looks like
# [[0, 1]]
result = aggregator.aggregate(level="all")
self.assertEqual(result, [3.0])
# There are two "fire" on the first node group and a
# a single "fire" on the second node group at the cluster
# level which looks like
# [[0], [1]]
result = aggregator.aggregate(level="cluster")
self.assertEqual(result, [2.0, 1.0])
def _aggregator(self, aggfunc):
"""
Return an aggregator corresponding to the
aggfunc, the aggregators are memoized for performance
:param aggfunc: Function parameter that
accepts a :mod:`pandas.Series` object and
returns a vector/scalar
:type: function(:mod:`pandas.Series`)
"""
if aggfunc not in self._aggs.keys():
self._aggs[aggfunc] = MultiTriggerAggregator(self._triggers,
self._topology,
aggfunc)
return self._aggs[aggfunc]
