def test_2parameters_random(self):
experiment = read_text_file('data/text/two_parameter_1.txt')
modeler = MultiParameterModeler()
measurements = experiment.measurements[(Callpath('reg'),
Metric('metr'))]
for _ in range(len(measurements)):
shuffle(measurements)
f_msm = modeler.find_first_measurement_points(measurements)
self.assertEqual(len(f_msm), 2)
self.assertSetEqual(
set(m.coordinate for m in f_msm[0]), {
Coordinate(20),
Coordinate(30),
Coordinate(40),
Coordinate(50),
Coordinate(60)
})
self.assertSetEqual(
set(m.coordinate for m in f_msm[1]), {
Coordinate(1),
Coordinate(2),
Coordinate(3),
Coordinate(4),
Coordinate(5)
})
def test_3parameters_reversed(self):
experiment = read_text_file('data/text/three_parameter_1.txt')
modeler = MultiParameterModeler()
measurements = experiment.measurements[(Callpath('reg'),
Metric('metr'))]
measurements = list(reversed(measurements))
f_msm = modeler.find_best_measurement_points(measurements)
self.assertEqual(len(f_msm), 3)
self.assertListEqual([m.coordinate for m in f_msm[0]], [
Coordinate(60),
Coordinate(50),
Coordinate(40),
Coordinate(30),
Coordinate(20)
])
self.assertListEqual([m.coordinate for m in f_msm[1]], [
Coordinate(5),
Coordinate(4),
Coordinate(3),
Coordinate(2),
Coordinate(1)
])
self.assertListEqual([m.coordinate for m in f_msm[2]], [
Coordinate(500),
Coordinate(400),
Coordinate(300),
Coordinate(200),
Coordinate(100)
])
def test_3parameters_bands(self):
experiment = read_jsonlines_file(
'data/jsonlines/matrix_3p_bands.jsonl')
modeler = MultiParameterModeler()
measurements = experiment.measurements[(Callpath('<root>'),
Metric('metr'))]
f_msm = modeler.find_best_measurement_points(measurements)
self.assertEqual(len(f_msm), 3)
self.assertListEqual([m.coordinate for m in f_msm[0]], [
Coordinate(1),
Coordinate(2),
Coordinate(3),
Coordinate(4),
Coordinate(5)
])
self.assertListEqual([0] + [1] * 4, [m.mean for m in f_msm[0]])
self.assertListEqual([m.coordinate for m in f_msm[1]], [
Coordinate(1),
Coordinate(2),
Coordinate(3),
Coordinate(4),
Coordinate(5)
])
self.assertListEqual([0.5] + [2.5] * 4, [m.mean for m in f_msm[1]])
self.assertListEqual([m.coordinate for m in f_msm[2]], [
Coordinate(1),
Coordinate(2),
Coordinate(3),
Coordinate(4),
Coordinate(5)
])
self.assertListEqual([0] + [4] * 4, [m.mean for m in f_msm[2]])
def test_modeling_4p(self):
exponents = [(0, 1, 1), (0, 1, 2), (1, 4, 0), (1, 3, 0), (1, 4, 1),
(1, 3, 1), (1, 4, 2), (1, 3, 2), (1, 2, 0), (1, 2, 1),
(1, 2, 2), (2, 3, 0), (3, 4, 0), (2, 3, 1), (3, 4, 1),
(4, 5, 0), (2, 3, 2), (3, 4, 2), (1, 1, 0), (1, 1, 1),
(1, 1, 2), (5, 4, 0), (5, 4, 1), (4, 3, 0), (4, 3, 1),
(3, 2, 0), (3, 2, 1), (3, 2, 2), (5, 3, 0), (7, 4, 0),
(2, 1, 0), (2, 1, 1), (2, 1, 2), (9, 4, 0), (7, 3, 0),
(5, 2, 0), (5, 2, 1), (5, 2, 2), (8, 3, 0), (11, 4, 0),
(3, 1, 0), (3, 1, 1)]
points = np.array(
list(zip(*itertools.product([2, 4, 8, 10, 12], repeat=4))))
for expo1, expo2, expo3, expo4 in zip(exponents, exponents[1:],
exponents[2:], exponents[3:]):
termX = CompoundTerm.create(*expo1)
termY = CompoundTerm.create(*expo2)
termZ = CompoundTerm.create(*expo3)
termW = CompoundTerm.create(*expo4)
term = MultiParameterTerm((0, termX), (1, termY), (2, termZ),
(3, termW))
term.coefficient = 10
function = MultiParameterFunction(term)
function.constant_coefficient = 20000
values = function.evaluate(points)
measurements = [
Measurement(Coordinate(p), None, None, v)
for p, v in zip(zip(*points), values)
]
modeler = MultiParameterModeler()
models = modeler.model([measurements])
self.assertEqual(1, len(models))
self.assertApproxFunction(function, models[0].hypothesis.function)
def test_2parameters_reversed(self):
experiment = read_text_file('data/text/two_parameter_1.txt')
modeler = MultiParameterModeler()
measurements = experiment.measurements[(Callpath('reg'),
Metric('metr'))]
measurements = list(reversed(measurements))
f_msm = modeler.find_best_measurement_points(measurements)
self.assertEqual(len(f_msm), 2)
self.assertListEqual([m.coordinate for m in f_msm[0]], [
Coordinate((60, )),
Coordinate((50, )),
Coordinate((40, )),
Coordinate((30, )),
Coordinate((20, ))
])
self.assertListEqual([m.coordinate for m in f_msm[1]], [
Coordinate((5, )),
Coordinate((4, )),
Coordinate((3, )),
Coordinate((2, )),
Coordinate((1, ))
])
def test_3parameters_sparse(self):
experiment = read_jsonlines_file('data/jsonlines/matrix_3p.jsonl')
modeler = MultiParameterModeler()
measurements = experiment.measurements[(Callpath('<root>'), Metric('metr'))]
for _ in range(len(measurements)):
shuffle(measurements)
f_msm = modeler.find_best_measurement_points(measurements)
self.assertEqual(len(f_msm), 3)
self.assertSetEqual(set(m.coordinate for m in f_msm[0]), {
Coordinate(1),
Coordinate(2),
Coordinate(3),
Coordinate(4),
Coordinate(5)
})
self.assertListEqual([1] * 5, [m.mean for m in f_msm[0]])
self.assertSetEqual(set(m.coordinate for m in f_msm[1]), {
Coordinate(1),
Coordinate(2),
Coordinate(3),
Coordinate(4),
Coordinate(5)
})
self.assertListEqual([1] * 5, [m.mean for m in f_msm[1]])
self.assertSetEqual(set(m.coordinate for m in f_msm[2]), {
Coordinate(1),
Coordinate(2),
Coordinate(3),
Coordinate(4),
Coordinate(5)
})
self.assertListEqual([1] * 5, [m.mean for m in f_msm[2]])
def test_modeling_plus(self):
exponents = [(0, 1, 1), (0, 1, 2), (1, 4, 0), (1, 3, 0), (1, 4, 1), (1, 3, 1), (1, 4, 2), (1, 3, 2),
(1, 2, 0), (1, 2, 1), (1, 2, 2), (2, 3, 0), (3, 4, 0), (2, 3, 1), (3, 4, 1), (4, 5, 0),
(2, 3, 2), (3, 4, 2), (1, 1, 0), (1, 1, 1), (1, 1, 2), (5, 4, 0), (5, 4, 1), (4, 3, 0),
(4, 3, 1), (3, 2, 0), (3, 2, 1), (3, 2, 2), (5, 3, 0), (7, 4, 0), (2, 1, 0), (2, 1, 1),
(2, 1, 2), (9, 4, 0), (7, 3, 0), (5, 2, 0), (5, 2, 1), (5, 2, 2), (8, 3, 0), (11, 4, 0),
(3, 1, 0), (3, 1, 1)]
for expo1, expo2 in zip(exponents, exponents[1:]):
termX = CompoundTerm.create(*expo1)
termY = CompoundTerm.create(*expo2)
term1 = MultiParameterTerm((0, termX))
term1.coefficient = 10
term2 = MultiParameterTerm((1, termY))
term2.coefficient = 20
function = MultiParameterFunction(term1, term2)
function.constant_coefficient = 200
points = [np.array([2, 4, 8, 16, 32, 2, 4, 8, 16, 32, 2, 4, 8, 16, 32, 2, 4, 8, 16, 32, 2, 4, 8, 16, 32]),
np.array([2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 16, 16, 16, 16, 16, 32, 32, 32, 32, 32])]
values = function.evaluate(np.array(points))
measurements = [Measurement(Coordinate(p), None, None, v) for p, v in zip(zip(*points), values)]
modeler = MultiParameterModeler()
models = modeler.model([measurements])
self.assertEqual(1, len(models))
self.assertApproxFunction(function, models[0].hypothesis.function)
def test_matrix_3p(self):
experiment = read_jsonlines_file('data/jsonlines/matrix_3p.jsonl')
modeler = MultiParameterModeler()
modeler.single_parameter_point_selection = 'all'
# initialize model generator
model_generator = ModelGenerator(experiment, modeler)
# create models from data
self.assertWarns(UserWarning, model_generator.model_all)
def test_complete_matrix_2p(self):
experiment = read_jsonlines_file('data/jsonlines/complete_matrix_2p.jsonl')
modeler = MultiParameterModeler()
modeler.single_parameter_point_selection = 'all'
# initialize model generator
model_generator = ModelGenerator(experiment, modeler)
# create models from data
model_generator.model_all()
def deserialize_MultiParameterModelGenerator(exp, supports_sparse, ioHelper):
userName = ioHelper.readString()
# read the options
generate_strategy = ioHelper.readString()
use_median = False
# convert generate model options to enum
if generate_strategy == "GENERATE_MODEL_MEAN":
use_median = False
elif generate_strategy == "GENERATE_MODEL_MEDIAN":
use_median = True
else:
logging.error("Invalid ModelOptions found in File.")
with ioHelper.begin_transaction():
min_number_points = ioHelper.readInt()
single_strategy = ioHelper.readString()
add_points = ioHelper.readInt()
number_add_points = ioHelper.readInt()
multi_strategy = ioHelper.readString()
# convert ints to bool values
use_add_points = bool(add_points)
decode_strategy(multi_strategy, single_strategy, supports_sparse)
return ModelGenerator(exp, MultiParameterModeler(), userName, use_median)
def test_3parameters_bands_incomplete(self):
experiment = read_jsonlines_file('data/jsonlines/matrix_3p_bands_incomplete.jsonl')
modeler = MultiParameterModeler()
measurements = experiment.measurements[(Callpath('<root>'), Metric('metr'))]
f_msm = modeler.find_best_measurement_points(measurements)
self.assertEqual(len(f_msm), 3)
self.assertListEqual([m.coordinate for m in f_msm[0]], [
Coordinate(c) for c in [1, 3, 4, 5, 6]
])
self.assertListEqual([0] + [1] * 4, [m.mean for m in f_msm[0]])
self.assertListEqual([m.coordinate for m in f_msm[1]], [
Coordinate(c) for c in range(1, 5 + 1)
])
self.assertListEqual([0] + [2] * 4, [m.mean for m in f_msm[1]])
self.assertListEqual([m.coordinate for m in f_msm[2]], [
Coordinate(c) for c in range(1, 5 + 1)
])
self.assertListEqual([0] + [4] * 4, [m.mean for m in f_msm[2]])
measurements.reverse()
f_msm = modeler.find_best_measurement_points(measurements)
self.assertEqual(len(f_msm), 3)
self.assertListEqual([m.coordinate for m in f_msm[0]], [
Coordinate(c) for c in reversed([1, 3, 4, 5, 6])
])
self.assertListEqual([1] * 4 + [0], [m.mean for m in f_msm[0]])
self.assertListEqual([m.coordinate for m in f_msm[1]], [
Coordinate(c) for c in [6, 5, 4, 3, 2]
])
self.assertListEqual([3] * 5, [m.mean for m in f_msm[1]])
self.assertListEqual([m.coordinate for m in f_msm[2]], [
Coordinate(c) for c in reversed(range(1, 5 + 1))
])
self.assertListEqual([4] * 4 + [0], [m.mean for m in f_msm[2]])
