def test_get_suggestion(self, get_architecture, problem_type):
# Return value (architectures) for the various trials.
get_architecture.side_effect = [
np.array([1, 2, 3, 4]),
np.array([2, 3, 4, 1]),
np.array([3, 4, 1, 2]),
np.array([4, 1, 2, 3]),
np.array([1, 1, 1, 1]),
np.array([2, 2, 2, 2]),
np.array([3, 3, 3, 3]),
np.array([2, 3, 2, 3]),
np.array([3, 4, 3, 4])
]
algorithm = categorical_harmonica.Harmonica(test_utils.create_spec(
problem_type,
blocks_to_use=[
"FIXED_CHANNEL_CONVOLUTION_16", "FIXED_CHANNEL_CONVOLUTION_32",
"FIXED_CHANNEL_CONVOLUTION_64", "CONVOLUTION_3X3"
],
min_depth=4),
num_random_samples=10,
seed=73)
output_architecture, _ = algorithm.get_suggestion(
_create_trials(), hp.HParams())
expected_output = [3, 3, 3, 3]
if problem_type == phoenix_spec_pb2.PhoenixSpec.CNN:
expected_output += [34]
self.assertAllEqual(expected_output, output_architecture)
def test_translate_architecture_to_assignment(self):
algorithm = categorical_harmonica.Harmonica(
test_utils.create_spec(phoenix_spec_pb2.PhoenixSpec.DNN,
blocks_to_use=[
"FIXED_CHANNEL_CONVOLUTION_16",
"FIXED_CHANNEL_CONVOLUTION_32",
"FIXED_CHANNEL_CONVOLUTION_64",
"CONVOLUTION_3X3"
],
min_depth=4))
assignment = algorithm.translate_architecture_to_feature_assignment(
np.array([2, 3, 4]))
expected_output = [[
1.00000000e+00, 6.12323400e-17, -1.00000000e+00, -1.83697020e-16,
1.00000000e+00, -1.00000000e+00, 1.00000000e+00, -1.00000000e+00,
1.00000000e+00, -1.83697020e-16, -1.00000000e+00, 5.51091060e-16,
0.00000000e+00, -1.00000000e+00, 3.67394040e-16, 1.00000000e+00
],
[
0.00000000e+00, 1.00000000e+00, 1.22464680e-16,
-1.00000000e+00, 0.00000000e+00, 1.22464680e-16,
-2.44929360e-16, 3.67394040e-16, 0.00000000e+00,
-1.00000000e+00, 3.67394040e-16, 1.00000000e+00,
1.00000000e+00, -1.83697020e-16,
-1.00000000e+00, 5.51091060e-16
]]
for i in range(len(assignment)):
self.assertAllAlmostEqual(expected_output[i], assignment[i])
def test_polynomial_expansion(self):
algorithm = categorical_harmonica.Harmonica(
test_utils.create_spec(phoenix_spec_pb2.PhoenixSpec.DNN))
feature_extender = PolynomialFeatures(2, interaction_only=True)
self.assertAllEqual([[1, 0, 1, 0], [1, 0.5, 1, 0.5]],
algorithm._get_polynomial_expansion(
feature_extender, np.array([[0, 1], [0.5,
1]])))
def test_get_good_architecture_with_relevant_variables(self):
algorithm = categorical_harmonica.Harmonica(test_utils.create_spec(
phoenix_spec_pb2.PhoenixSpec.CNN,
blocks_to_use=[
"FIXED_CHANNEL_CONVOLUTION_16", "FIXED_CHANNEL_CONVOLUTION_32",
"FIXED_CHANNEL_CONVOLUTION_64", "CONVOLUTION_3X3"
],
min_depth=2),
degree=2,
seed=73)
expected_output = [3, 1, 34]
feature_extender = PolynomialFeatures(2, interaction_only=True)
self.assertAllEqual(
expected_output,
algorithm._get_good_architecture(
feature_extender, 20, np.array([0, 0.5, 0.6, -0.2] + [0] * 33),
set([3, 3, 3, 3])))
def test_get_good_architecture(self, problem_type):
algorithm = categorical_harmonica.Harmonica(test_utils.create_spec(
problem_type,
blocks_to_use=[
"FIXED_CHANNEL_CONVOLUTION_16", "FIXED_CHANNEL_CONVOLUTION_32",
"FIXED_CHANNEL_CONVOLUTION_64", "CONVOLUTION_3X3"
],
min_depth=2),
degree=2,
seed=73)
expected_output = [3, 3]
if problem_type == phoenix_spec_pb2.PhoenixSpec.CNN:
expected_output += [34]
feature_extender = PolynomialFeatures(2, interaction_only=True)
self.assertAllEqual(
expected_output,
algorithm._get_good_architecture(
feature_extender, 10, np.array([0, 0.5, 0.6, -0.2] + [0] * 33),
None))
def test_extract_relevant_variables_indices(self):
algorithm = categorical_harmonica.Harmonica(test_utils.create_spec(
phoenix_spec_pb2.PhoenixSpec.CNN,
blocks_to_use=[
"FIXED_CHANNEL_CONVOLUTION_16", "FIXED_CHANNEL_CONVOLUTION_32",
"FIXED_CHANNEL_CONVOLUTION_64", "CONVOLUTION_3X3"
],
min_depth=4),
degree=2)
feature_extender = PolynomialFeatures(2, interaction_only=True)
algorithm._get_polynomial_expansion(feature_extender,
np.array([[1, 2, 3, 4]]))
output = algorithm._extract_relevant_variables_indices(
feature_extender, np.array([1, 1, 1] + [0] * 7 + [1]))
logging.info(output)
self.assertAllEqual(output, set([0, 1, 2, 3]))
output = algorithm._extract_relevant_variables_indices(
feature_extender, np.array([1, 0, 0] + [0] * 7 + [1]))
self.assertAllEqual(output, set([2, 3]))
output = algorithm._extract_relevant_variables_indices(
feature_extender, np.array([1, 0, 0] + [0] * 7 + [0]))
self.assertEmpty(output)
def test_batch_sample(self, get_architecture):
algorithm = categorical_harmonica.Harmonica(test_utils.create_spec(
phoenix_spec_pb2.PhoenixSpec.DNN,
blocks_to_use=[
"FIXED_CHANNEL_CONVOLUTION_16", "FIXED_CHANNEL_CONVOLUTION_32",
"FIXED_CHANNEL_CONVOLUTION_64", "CONVOLUTION_3X3"
],
min_depth=4),
degree=1)
get_architecture.side_effect = [
np.array([1, 2, 3, 4]),
np.array([2, 3, 4, 1]),
np.array([3, 4, 1, 2]),
# Adding 34 (flatten) to see that it is ignored
np.array([4, 1, 2, 34, 3]),
np.array([1, 1, 1, 1]),
np.array([2, 2, 2, 2]),
np.array([3, 3, 3, 3]),
np.array([2, 3, 2, 3]),
np.array([3, 4, 3, 4])
]
x, y = algorithm.batch_sample(_create_trials())
# Check first and last assignments
self.assertAllAlmostEqual([
1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 6.12323400e-17, -1.00000000e+00, -1.83697020e-16,
1.00000000e+00, -1.00000000e+00, 1.00000000e+00, -1.00000000e+00,
1.00000000e+00, -1.83697020e-16, -1.00000000e+00, 5.51091060e-16
], x[0])
self.assertAllAlmostEqual([
0.00000000e+00, 1.22464680e-16, -2.44929360e-16, 3.67394040e-16,
0.00000000e+00, -1.00000000e+00, 3.67394040e-16, 1.00000000e+00,
0.00000000e+00, 1.22464680e-16, -2.44929360e-16, 3.67394040e-16,
0.00000000e+00, -1.00000000e+00, 3.67394040e-16, 1.00000000e+00
], x[17])
self.assertAllEqual([
0.97, 0., 0.94, 0., 0.7, 0., 0.72, 0., 0.3, 0., 0.79, 0., 0.39, 0.,
0.9, 0., 0.19, 0.
], y)
def test_not_enough_data_get_suggestion(self, spec, initial_architecture,
expected_architecture):
algorithm = categorical_harmonica.Harmonica(spec)
output_architecture, _ = algorithm.get_suggestion(
[], hp.HParams(initial_architecture=initial_architecture))
self.assertAllEqual(expected_architecture, output_architecture)