def _initialize_atomic(self, name, root, real_name=None, count=1):
real_name = real_name or name
root[name] = {
# streaming algorithms
"sa":
[[streaming.MinComputation(), None],
[streaming.PercentileComputation(0.5, self.iters_num), None],
[streaming.PercentileComputation(0.9, self.iters_num), None],
[streaming.PercentileComputation(0.95, self.iters_num), None],
[streaming.MaxComputation(), None],
[streaming.MeanComputation(), None],
[
streaming.MeanComputation(), lambda st, has_result:
("%.1f%%" % (st.result() * 100) if has_result else "n/a")
],
[
streaming.IncrementComputation(),
lambda st, has_result: st.result()
]],
"children":
collections.OrderedDict(),
"real_name":
real_name,
"count_per_iteration":
count
}
def test_merge(self):
single_min_algo = algo.MinComputation()
for val in six.moves.range(100):
single_min_algo.add(val)
algos = [algo.MinComputation() for _ in six.moves.range(10)]
for idx, min_algo in enumerate(algos):
for val in six.moves.range(idx * 10, (idx + 1) * 10):
min_algo.add(val)
merged_min_algo = algos[0]
for min_algo in algos[1:]:
merged_min_algo.merge(min_algo)
self.assertEqual(single_min_algo._value, merged_min_algo._value)
self.assertEqual(single_min_algo.result(), merged_min_algo.result())
def _init_columns(self):
return costilius.OrderedDict(
(("Min (sec)", streaming.MinComputation()),
("Median (sec)", streaming.PercentileComputation(50)),
("90%ile (sec)", streaming.PercentileComputation(90)),
("95%ile (sec)", streaming.PercentileComputation(95)),
("Max (sec)", streaming.MaxComputation()),
("Avg (sec)", streaming.MeanComputation()),
("Success", streaming.ProgressComputation(self.base_size)),
("Count", streaming.IncrementComputation())))
def add_iteration(self, iteration):
for name, value in self._map_iteration_values(iteration):
if name not in self._data:
self._data[name] = [
streaming.PointsSaver(),
streaming.IncrementComputation(),
streaming.MinComputation(),
streaming.MaxComputation(),
streaming.MeanComputation()
]
points, count, min_v, max_v, avg = self._data[name]
count.add()
for ins in (points, min_v, max_v, avg):
ins.add(value)
def __init__(self, *args, **kwargs):
super(MainStatsTable, self).__init__(*args, **kwargs)
iters_num = self._workload["total_iteration_count"]
for name in (self._get_atomic_names() + ["total"]):
self._data[name] = [
[streaming.MinComputation(), None],
[streaming.PercentileComputation(0.5, iters_num), None],
[streaming.PercentileComputation(0.9, iters_num), None],
[streaming.PercentileComputation(0.95, iters_num), None],
[streaming.MaxComputation(), None],
[streaming.MeanComputation(), None],
[streaming.MeanComputation(),
lambda st, has_result: ("%.1f%%" % (st.result() * 100)
if has_result else "n/a")],
[streaming.IncrementComputation(),
lambda st, has_result: st.result()]]
def add_iteration(self, iteration):
for name, value in self._map_iteration_values(iteration):
if name not in self._data:
iters_num = self._workload["total_iteration_count"]
self._data[name] = [
[streaming.MinComputation(), None],
[streaming.PercentileComputation(0.5, iters_num), None],
[streaming.PercentileComputation(0.9, iters_num), None],
[streaming.PercentileComputation(0.95, iters_num), None],
[streaming.MaxComputation(), None],
[streaming.MeanComputation(), None],
[streaming.IncrementComputation(),
lambda v, na: v.result()]]
self._data[name][-1][0].add(None)
self._data[name][-2][0].add(1)
for idx, dummy in enumerate(self._data[name][:-1]):
self._data[name][idx][0].add(value)
def _initialize_atomic(self, name, root, real_name=None, count=1):
real_name = real_name or name
root[name] = {
# streaming algorithms
"sa": [
streaming.PointsSaver(),
streaming.MinComputation(),
streaming.MaxComputation(),
streaming.MeanComputation(),
streaming.MeanComputation(),
streaming.IncrementComputation()
],
"children":
collections.OrderedDict(),
"real_name":
real_name,
"count_per_iteration":
count
}
def _init_row(self, name, iterations_count):
def round_3(stream, no_result):
if no_result:
return "n/a"
return round(stream.result(), 3)
return [("Action", name),
("Min (sec)", streaming.MinComputation(), round_3),
("Median (sec)",
streaming.PercentileComputation(0.5,
iterations_count), round_3),
("90%ile (sec)",
streaming.PercentileComputation(0.9,
iterations_count), round_3),
("95%ile (sec)",
streaming.PercentileComputation(0.95,
iterations_count), round_3),
("Max (sec)", streaming.MaxComputation(), round_3),
("Avg (sec)", streaming.MeanComputation(), round_3),
("Success", streaming.MeanComputation(),
lambda stream, no_result: "%.1f%%" % (stream.result() * 100)),
("Count", streaming.IncrementComputation(),
lambda x, no_result: x.result())]
def test_result_empty(self):
comp = algo.MinComputation()
self.assertRaises(TypeError, comp.result, 1)
self.assertIsNone(comp.result())
def test_add_raises(self):
comp = algo.MinComputation()
self.assertRaises(TypeError, comp.add)
self.assertRaises(TypeError, comp.add, None)
self.assertRaises(TypeError, comp.add, "str")
def test_add_and_result(self):
comp = algo.MinComputation()
[comp.add(i) for i in [3, 5.2, 2, -1, 1, 8, 33.4, 0, -3, 42, -2]]
self.assertEqual(-3, comp.result())
def test_result_raises(self):
comp = algo.MinComputation()
self.assertRaises(TypeError, comp.result, 1)
self.assertRaises(ValueError, comp.result)
def __init__(self):
self.min_timestamp = streaming.MinComputation()
self.max_timestamp = streaming.MaxComputation()
self.mttr = 0
self.last_error_duration = 0
self.last_iteration = None
