def test_equals():
"""
Verify that a parameter set and a covering array that
contains a "don't care" value is correctly converted
to a data frame, where the "don't care" value becomes
Pandas' pd.NA value.
"""
p1 = Parameter("Colour", [RED, GREEN])
p2 = Parameter("Pet", [BIRD, CAT, DOG, FISH])
p3 = Parameter("Speed", [FAST, SLOW])
p4 = Parameter("Music", [EIGHTIES, TWENTIES])
parameter_set = ParameterSet([p1, p2, p3, p4])
# Covering array from...
# generator = RecursiveGenerator(parameter_set, 2)
# covering_array = generator.generate_covering_array()
covering_array = np.array([[1, 1, 1, 1], [1, 2, 2, 1], [2, 1, 2, 1],
[2, 2, 1, 0], [2, 2, 2, 2], [1, 1, 1, 2],
[1, 3, 1, 1], [2, 3, 2, 2], [1, 4, 1, 1],
[2, 4, 2, 2]])
# configs1 = ConfigurationSet(parameter_set, covering_array)
# configs2 = ConfigurationSet(parameter_set, covering_array)
configs1 = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
configs2 = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
assert configs1 is not configs2
assert configs1 == configs2
def make_gen_request(self, data, **kwargs):
orig_df = data["existing_configurations"]
if orig_df.empty:
data["existing_configurations"] = ConfigurationSet(data_frame=orig_df)
else:
parameter_names = [parameter.name for parameter in data["parameter_set"]]
data["existing_configurations"] = ConfigurationSet(data_frame=orig_df[parameter_names])
return GenerationRequest(**data)
def test_dataframe_to_covering_array():
p1 = Parameter("Colour", [RED, GREEN])
p2 = Parameter("Pet", [BIRD, CAT, DOG, FISH])
p3 = Parameter("Speed", [FAST, SLOW])
p4 = Parameter("Music", [EIGHTIES, TWENTIES])
parameter_set = ParameterSet([p1, p2, p3, p4])
dataframe = pd.DataFrame([
[RED, BIRD, FAST, EIGHTIES],
[RED, CAT, SLOW, EIGHTIES],
[GREEN, BIRD, SLOW, EIGHTIES],
[GREEN, CAT, FAST, pd.NA],
[GREEN, CAT, SLOW, TWENTIES],
[RED, BIRD, FAST, TWENTIES],
[RED, DOG, FAST, EIGHTIES],
[GREEN, DOG, SLOW, TWENTIES],
[RED, FISH, FAST, EIGHTIES],
[GREEN, FISH, SLOW, TWENTIES],
],
columns=["Colour", "Pet", "Speed", "Music"])
covering_array = ConfigurationSet.dataframe_to_covering_array(
dataframe, parameter_set)
expected = np.array(
[[1, 1, 1, 1], [1, 2, 2, 1], [2, 1, 2, 1], [2, 2, 1, 0], [2, 2, 2, 2],
[1, 1, 1, 2], [1, 3, 1, 1], [2, 3, 2, 2], [1, 4, 1, 1], [2, 4, 2, 2]],
dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(covering_array, expected)
def test_configset_getitem():
"""
Verify that a parameter set and a covering array with
no "don't care" values present is correctly converted
to a data frame with the correct column headings and
value names.
"""
p1 = Parameter("Colour", [RED, GREEN, BLUE])
p2 = Parameter("Pet", [BIRD, CAT, DOG])
p3 = Parameter("Speed", [FAST, MEDIUM, SLOW])
p4 = Parameter("Music", [SEVENTIES, EIGHTIES, TWENTIES])
parameter_set = ParameterSet([p1, p2, p3, p4])
# Covering array from...
# generator = RecursiveGenerator(parameter_set, 2)
# covering_array = generator.generate_covering_array()
covering_array = np.array([[1, 1, 1, 1], [1, 2, 2, 2], [1, 3, 3, 3],
[2, 1, 2, 3], [2, 2, 3, 1], [2, 3, 1, 2],
[3, 1, 3, 2], [3, 2, 1, 3], [3, 3, 2, 1]])
configurations = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
expected = pd.Series({
"Colour": GREEN,
"Pet": BIRD,
"Speed": MEDIUM,
"Music": TWENTIES,
})
actual = configurations[3]
assert pd.Series.equals(actual, expected)
def configs_with_none(red, green, blue, bird, cat, dog, fast, medium, slow,
seventies, eighties, twenties):
existing_configs = pd.DataFrame(
[
[red, bird, pd.NA, seventies],
[green, dog, fast, pd.NA],
[blue, pd.NA, fast, twenties],
[pd.NA, dog, medium, seventies],
],
columns=["Colour", "Pet", "Speed", "Music"])
return ConfigurationSet(data_frame=existing_configs)
def test_to_dict():
p1 = Parameter("Colour", [RED, GREEN])
p2 = Parameter("Pet", [CAT, DOG])
p3 = Parameter("Speed", [FAST, SLOW])
parameter_set = ParameterSet([p1, p2, p3])
# Covering array from...
# generator = RecursiveGenerator(parameter_set, 2)
# covering_array = generator.generate_covering_array()
covering_array = np.array([[1, 1, 1], [1, 2, 2], [2, 1, 2], [2, 2, 1]])
configurations = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
expected = {
"configurations": [
{
"Colour": "Red",
"Pet": "Cat",
"Speed": "Fast"
},
{
"Colour": "Red",
"Pet": "Dog",
"Speed": "Slow"
},
{
"Colour": "Green",
"Pet": "Cat",
"Speed": "Slow"
},
{
"Colour": "Green",
"Pet": "Dog",
"Speed": "Fast"
},
]
}
actual = configurations.to_dict()
assert actual == expected
def post(self):
post_data = request.get_json() # @UndefinedVariable
gen_req = GENERATE_REQUEST_SCHEMA.load(post_data)
generator = gen_req.construct_generator()
covering_array = generator.generate_covering_array()
configurations = ConfigurationSet(parameter_set=gen_req.parameter_set,
covering_array=covering_array)
response_content = {
'message': 'test configurations generated',
'configuration_set': CONFIGURATIONS_SCHEMA.dump(configurations),
'parameter_set_url': url_for('.parameter_set'),
}
return response_content
def complete_configs(red, green, blue, bird, cat, dog, fast, medium, slow,
seventies, eighties, twenties):
existing_configs = pd.DataFrame(
[
[red, bird, fast, seventies],
[red, cat, medium, eighties],
[red, dog, slow, twenties],
[green, bird, medium, twenties],
[green, cat, slow, seventies],
[green, dog, fast, eighties],
[blue, bird, slow, eighties],
[blue, cat, fast, twenties],
[blue, dog, medium, seventies],
],
columns=["Colour", "Pet", "Speed", "Music"])
return ConfigurationSet(data_frame=existing_configs)
def test_configset_iterate():
"""
Verify that a parameter set and a covering array with
no "don't care" values present is correctly converted
to a data frame with the correct column headings and
value names.
"""
p1 = Parameter("Colour", [RED, GREEN])
p2 = Parameter("Pet", [CAT, DOG])
p3 = Parameter("Speed", [FAST, SLOW])
parameter_set = ParameterSet([p1, p2, p3])
# Covering array from...
# generator = RecursiveGenerator(parameter_set, 2)
# covering_array = generator.generate_covering_array()
covering_array = np.array([[1, 1, 1], [1, 2, 2], [2, 1, 2], [2, 2, 1]])
configurations = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
expected = [
pd.Series({
"Colour": RED,
"Pet": CAT,
"Speed": FAST,
}),
pd.Series({
"Colour": RED,
"Pet": DOG,
"Speed": SLOW,
}),
pd.Series({
"Colour": GREEN,
"Pet": CAT,
"Speed": SLOW,
}),
pd.Series({
"Colour": GREEN,
"Pet": DOG,
"Speed": FAST,
})
]
for index, actual in enumerate(configurations):
assert pd.Series.equals(actual, expected[index])
def test_generate_configurations_with_dont_care():
"""
Verify that a parameter set and a covering array that
contains a "don't care" value is correctly converted
to a data frame, where the "don't care" value becomes
Pandas' pd.NA value.
"""
p1 = Parameter("Colour", [RED, GREEN])
p2 = Parameter("Pet", [BIRD, CAT, DOG, FISH])
p3 = Parameter("Speed", [FAST, SLOW])
p4 = Parameter("Music", [EIGHTIES, TWENTIES])
parameter_set = ParameterSet([p1, p2, p3, p4])
# Covering array from...
# generator = RecursiveGenerator(parameter_set, 2)
# covering_array = generator.generate_covering_array()
covering_array = np.array([[1, 1, 1, 1], [1, 2, 2, 1], [2, 1, 2, 1],
[2, 2, 1, 0], [2, 2, 2, 2], [1, 1, 1, 2],
[1, 3, 1, 1], [2, 3, 2, 2], [1, 4, 1, 1],
[2, 4, 2, 2]])
configurations = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
actual_configurations = configurations.configs
expected_configurations = pd.DataFrame(
[
[RED, BIRD, FAST, EIGHTIES],
[RED, CAT, SLOW, EIGHTIES],
[GREEN, BIRD, SLOW, EIGHTIES],
[GREEN, CAT, FAST, pd.NA],
[GREEN, CAT, SLOW, TWENTIES],
[RED, BIRD, FAST, TWENTIES],
[RED, DOG, FAST, EIGHTIES],
[GREEN, DOG, SLOW, TWENTIES],
[RED, FISH, FAST, EIGHTIES],
[GREEN, FISH, SLOW, TWENTIES],
],
columns=["Colour", "Pet", "Speed", "Music"])
assert actual_configurations.equals(expected_configurations)
def test_generate_configurations():
"""
Verify that a parameter set and a covering array with
no "don't care" values present is correctly converted
to a data frame with the correct column headings and
value names.
"""
p1 = Parameter("Colour", [RED, GREEN, BLUE])
p2 = Parameter("Pet", [BIRD, CAT, DOG])
p3 = Parameter("Speed", [FAST, MEDIUM, SLOW])
p4 = Parameter("Music", [SEVENTIES, EIGHTIES, TWENTIES])
parameter_set = ParameterSet([p1, p2, p3, p4])
# Covering array from...
# generator = RecursiveGenerator(parameter_set, 2)
# covering_array = generator.generate_covering_array()
covering_array = np.array([[1, 1, 1, 1], [1, 2, 2, 2], [1, 3, 3, 3],
[2, 1, 2, 3], [2, 2, 3, 1], [2, 3, 1, 2],
[3, 1, 3, 2], [3, 2, 1, 3], [3, 3, 2, 1]])
configurations = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
actual_configurations = configurations.configs
expected_configurations = pd.DataFrame(
[
[RED, BIRD, FAST, SEVENTIES],
[RED, CAT, MEDIUM, EIGHTIES],
[RED, DOG, SLOW, TWENTIES],
[GREEN, BIRD, MEDIUM, TWENTIES],
[GREEN, CAT, SLOW, SEVENTIES],
[GREEN, DOG, FAST, EIGHTIES],
[BLUE, BIRD, SLOW, EIGHTIES],
[BLUE, CAT, FAST, TWENTIES],
[BLUE, DOG, MEDIUM, SEVENTIES],
],
columns=["Colour", "Pet", "Speed", "Music"])
assert actual_configurations.equals(expected_configurations)
def make_config_set(self, data, **kwargs):
orig_df = data["configs"]
ordered_df = orig_df[data["parameter_names"]]
return ConfigurationSet(data_frame=ordered_df)
def config_set(parm_set, covering_array):
return ConfigurationSet(parameter_set=parm_set, covering_array=covering_array)