def test_initialization(self):
slotmap = InputSlotInfo()
self.assertEqual(slotmap.source, "Environment")
self.assertEqual(slotmap.name, "")
with self.assertRaises(TraitError):
slotmap.name = "000"
def setUp(self):
self.registry = ProbeFactoryRegistry()
self.plugin = self.registry.plugin
self.data_values = [
DataValue(name="foo"),
DataValue(name="bar"),
DataValue(name="baz"),
]
self.slot_map = (InputSlotInfo(name="baz"), InputSlotInfo(name="bar"))
self.slots = (Slot(), Slot())
def test_data_source_run_error(self):
data_values = [DataValue(name="foo")]
self.layer.data_sources[0].input_slot_info = [
InputSlotInfo(name="foo")
]
factory = self.registry.data_source_factories[0]
factory.raises_on_data_source_run = True
with testfixtures.LogCapture() as capture:
with self.assertRaises(Exception):
self.layer.execute_layer(data_values)
capture.check(
(
"force_bdss.core.execution_layer",
"INFO",
"Evaluating for Data Source test_data_source",
),
("force_bdss.core.execution_layer", "INFO", "Passed values:"),
(
"force_bdss.core.execution_layer",
"INFO",
"0: foo = None (AVERAGE)",
),
(
"force_bdss.core.execution_layer",
"ERROR",
"Evaluation could not be performed. "
"Run method raised exception.",
),
)
def test_error_for_non_data_source(self):
data_values = [DataValue(name="foo")]
self.layer.data_sources[0].input_slot_info = [
InputSlotInfo(name="foo")
]
self.layer.data_sources[0].output_slot_info = [
OutputSlotInfo(name="one")
]
def probe_run(self, *args, **kwargs):
return ["hello"]
factory = self.registry.data_source_factories[0]
factory.run_function = probe_run
with testfixtures.LogCapture():
with self.assertRaisesRegex(
RuntimeError,
"The result list returned by DataSource "
"test_data_source"
" contains an entry that is not a DataValue."
" An entry of type <.* 'str'> was instead found"
" in position 0."
r" Fix the DataSource.run\(\) method to"
" return the appropriate entity.",
):
self.layer.execute_layer(data_values)
def from_json(cls, factory, json_data):
""" Instantiate an BaseMCOModel object from a `json_data`
dictionary and the generating `factory` object.
Parameters
----------
factory: IDataSourceFactory
Generating factory object
json_data: dict
Dictionary with a DataSourceModel serialized data
Returns
----------
layer: BaseDataSourceModel
BaseDataSourceModel instance with attributes values from
the `json_data` dict
"""
data = deepcopy(json_data)
input_slots = [InputSlotInfo(**d) for d in data["input_slot_info"]]
data["input_slot_info"] = input_slots
output_slots = [OutputSlotInfo(**d) for d in data["output_slot_info"]]
data["output_slot_info"] = output_slots
data_source = factory.create_model(data)
return data_source
def test_getstate(self):
model = DummyDataSourceModel(self.mock_factory)
self.assertDictEqual(
model.__getstate__(),
{
"id": "id",
"model_data": {
"input_slot_info": [],
"output_slot_info": []
},
},
)
model.input_slot_info = [
InputSlotInfo(name="foo"),
InputSlotInfo(name="bar"),
]
model.output_slot_info = [
OutputSlotInfo(name="baz"),
OutputSlotInfo(name="quux"),
]
self.assertDictEqual(
model.__getstate__(),
{
"id": "id",
"model_data": {
"input_slot_info": [
{
"source": "Environment",
"name": "foo"
},
{
"source": "Environment",
"name": "bar"
},
],
"output_slot_info": [{
"name": "baz"
}, {
"name": "quux"
}],
},
},
)
def test_traits_to_dict(self):
wf = self.sample_workflow()
exec_layer = wf.execution_layers[0]
exec_layer.data_sources[0].input_slot_info = [InputSlotInfo()]
datastore_list = exec_layer.__getstate__()
new_slotdata = datastore_list["data_sources"][0]["model_data"][
"input_slot_info"]
self.assertNotIn("__traits_version__", new_slotdata)
def test_execute_layer_results(self):
data_values = [
DataValue(name="foo"),
DataValue(name="bar"),
DataValue(name="baz"),
DataValue(name="quux"),
]
def run(self, *args, **kwargs):
return [DataValue(value=1), DataValue(value=2), DataValue(value=3)]
ds_factory = self.registry.data_source_factories[0]
ds_factory.input_slots_size = 2
ds_factory.output_slots_size = 3
ds_factory.run_function = run
evaluator_model = ds_factory.create_model()
evaluator_model.input_slot_info = [
InputSlotInfo(name="foo"),
InputSlotInfo(name="quux"),
]
evaluator_model.output_slot_info = [
OutputSlotInfo(name="one"),
OutputSlotInfo(name=""),
OutputSlotInfo(name="three"),
]
self.layer.data_sources = [evaluator_model]
res = self.layer.execute_layer(data_values)
self.assertEqual(len(res), 2)
self.assertEqual(res[0].name, "one")
self.assertEqual(res[0].value, 1)
self.assertEqual(res[1].name, "three")
self.assertEqual(res[1].value, 3)
with mock.patch(
"force_bdss.data_sources.base_data_source.BaseDataSource._run",
return_value=run(None)) as mock_run:
# mock_run.side_effect = [1, 2]
self.layer.execute_layer(data_values)
mock_run.assert_called_once()
def test_data_sources(self):
wf = self.workflow
mco_factory = self.plugin.mco_factories[0]
wf.mco_model = mco_factory.create_model()
parameter_factory = mco_factory.parameter_factories[0]
wf.mco_model.parameters.append(parameter_factory.create_model())
wf.mco_model.parameters[0].name = "name"
wf.mco_model.parameters[0].type = "type"
wf.mco_model.kpis.append(KPISpecification(name='name'))
layer = ExecutionLayer()
wf.execution_layers.append(layer)
ds_factory = self.plugin.data_source_factories[0]
ds_model = ds_factory.create_model()
layer.data_sources.append(ds_model)
errors = verify_workflow(wf)
self.assertEqual(errors[0].subject, ds_model)
self.assertIn(
"The number of input slots (1 values) returned by "
"'Dummy data source' does not match the number "
"of user-defined names specified (0 values). This "
"is either a plugin error or a file error.", errors[0].local_error)
ds_model.input_slot_info.append(InputSlotInfo(name="name"))
errors = verify_workflow(wf)
self.assertEqual(errors[0].subject, ds_model)
self.assertIn(
"The number of output slots (1 values) returned by "
"'Dummy data source' does not match the number "
"of user-defined names specified (0 values). This "
"is either a plugin error or a file error.", errors[0].local_error)
ds_model.output_slot_info.append(OutputSlotInfo(name="name"))
errors = verify_workflow(wf)
self.assertEqual(len(errors), 0)
ds_model.input_slot_info[0].name = ''
errors = verify_workflow(wf)
self.assertEqual(len(errors), 1)
self.assertIn("Input slot is not named", errors[0].local_error)
ds_model.output_slot_info[0].name = ''
errors = verify_workflow(wf)
self.assertEqual(len(errors), 3)
self.assertIn("All output variables have undefined names",
errors[1].local_error)
self.assertIn("An output variable has an undefined name",
errors[2].local_error)
def test_error_for_output_slots(self):
data_values = [DataValue(name="foo")]
self.layer.data_sources[0].input_slot_info = [
InputSlotInfo(name="foo")
]
def probe_run(self, *args, **kwargs):
return ["too", "many", "data", "values"]
factory = self.registry.data_source_factories[0]
factory.run_function = probe_run
with testfixtures.LogCapture():
with self.assertRaisesRegex(
RuntimeError,
r"The number of data values \(4 values\) returned"
" by 'test_data_source' does not match the number"
r" of output slots it specifies \(1 values\)."
" This is likely a plugin error.",
):
self.layer.execute_layer(data_values)
def test_error_for_incorrect_return_type(self):
data_values = [DataValue(name="foo")]
self.layer.data_sources[0].input_slot_info = [
InputSlotInfo(name="foo")
]
def probe_run(self, *args, **kwargs):
return "hello"
factory = self.registry.data_source_factories[0]
factory.run_function = probe_run
with testfixtures.LogCapture():
with self.assertRaisesRegex(
RuntimeError,
"The run method of data source "
"test_data_source must return a list. It "
r"returned instead <.* 'str'>. Fix the run\(\)"
" method to return the appropriate entity.",
):
self.layer.execute_layer(data_values)
def test_multilayer_execution(self):
# The multilayer peforms the following execution
# layer 0: in1 + in2 | in3 + in4
# res1 res2
# layer 1: res1 + res2
# res3
# layer 2: res3 * res1
# res4
# layer 3: res4 * res2
# out1
# Final result should be
# out1 = ((in1 + in2 + in3 + in4) * (in1 + in2) * (in3 + in4)
data_values = [
DataValue(value=10, name="in1"),
DataValue(value=15, name="in2"),
DataValue(value=3, name="in3"),
DataValue(value=7, name="in4"),
]
def adder(model, parameters):
first = parameters[0].value
second = parameters[1].value
return [DataValue(value=(first + second))]
adder_factory = ProbeDataSourceFactory(
self.plugin,
input_slots_size=2,
output_slots_size=1,
run_function=adder,
)
def multiplier(model, parameters):
first = parameters[0].value
second = parameters[1].value
return [DataValue(value=(first * second))]
multiplier_factory = ProbeDataSourceFactory(
self.plugin,
input_slots_size=2,
output_slots_size=1,
run_function=multiplier,
)
mco_factory = ProbeMCOFactory(self.plugin)
mco_model = mco_factory.create_model()
parameter_factory = mco_factory.parameter_factories[0]
mco_model.parameters = [
parameter_factory.create_model({"name": "in1"}),
parameter_factory.create_model({"name": "in2"}),
parameter_factory.create_model({"name": "in3"}),
parameter_factory.create_model({"name": "in4"}),
]
mco_model.kpis = [KPISpecification(name="out1")]
wf = Workflow(
mco_model=mco_model,
execution_layers=[
ExecutionLayer(),
ExecutionLayer(),
ExecutionLayer(),
ExecutionLayer(),
],
)
# Layer 0
model = adder_factory.create_model()
model.input_slot_info = [
InputSlotInfo(name="in1"),
InputSlotInfo(name="in2"),
]
model.output_slot_info = [OutputSlotInfo(name="res1")]
wf.execution_layers[0].data_sources.append(model)
model = adder_factory.create_model()
model.input_slot_info = [
InputSlotInfo(name="in3"),
InputSlotInfo(name="in4"),
]
model.output_slot_info = [OutputSlotInfo(name="res2")]
wf.execution_layers[0].data_sources.append(model)
# layer 1
model = adder_factory.create_model()
model.input_slot_info = [
InputSlotInfo(name="res1"),
InputSlotInfo(name="res2"),
]
model.output_slot_info = [OutputSlotInfo(name="res3")]
wf.execution_layers[1].data_sources.append(model)
# layer 2
model = multiplier_factory.create_model()
model.input_slot_info = [
InputSlotInfo(name="res3"),
InputSlotInfo(name="res1"),
]
model.output_slot_info = [OutputSlotInfo(name="res4")]
wf.execution_layers[2].data_sources.append(model)
# layer 3
model = multiplier_factory.create_model()
model.input_slot_info = [
InputSlotInfo(name="res4"),
InputSlotInfo(name="res2"),
]
model.output_slot_info = [OutputSlotInfo(name="out1")]
wf.execution_layers[3].data_sources.append(model)
kpi_results = wf.execute(data_values)
self.assertEqual(1, len(kpi_results))
self.assertEqual(8750, kpi_results[0].value)
def test_kpi_specification_adherence(self):
# Often the user may only wish to treat a subset of DataSource
# output slots as KPIs. This test makes sure they get what they
# ask for!
# keep input DataValues constant
data_values = [
DataValue(value=99, name="in1"),
DataValue(value=1, name="in2"),
]
# dummy addition DataSource(a, b) that also returns its inputs
# [a, b, a+b]
def adder(model, parameters):
first = parameters[0].value
second = parameters[1].value
return [
DataValue(value=first),
DataValue(value=second),
DataValue(value=(first + second)),
]
adder_factory = ProbeDataSourceFactory(
self.plugin,
input_slots_size=2,
output_slots_size=3,
run_function=adder,
)
mco_factory = ProbeMCOFactory(self.plugin)
parameter_factory = mco_factory.parameter_factories[0]
mco_model = mco_factory.create_model()
# DataSourceModel stats constant throughout
model = adder_factory.create_model()
model.input_slot_info = [
InputSlotInfo(name="in1"),
InputSlotInfo(name="in2"),
]
model.output_slot_info = [
OutputSlotInfo(name="out1"),
OutputSlotInfo(name="out2"),
OutputSlotInfo(name="out3"),
]
# test Parameter and KPI spec that follows DataSource slots
# exactly
mco_model.parameters = [
parameter_factory.create_model({"name": "in1"}),
parameter_factory.create_model({"name": "in2"}),
]
mco_model.kpis = [
KPISpecification(name="out1"),
KPISpecification(name="out2"),
KPISpecification(name="out3"),
]
# need to make a new workflow for each KPISpecification
wf = Workflow(mco_model=mco_model, execution_layers=[ExecutionLayer()])
wf.execution_layers[0].data_sources.append(model)
kpi_results = wf.execute(data_values)
self.assertEqual(len(kpi_results), 3)
self.assertEqual(kpi_results[0].value, 99)
self.assertEqual(kpi_results[1].value, 1)
self.assertEqual(kpi_results[2].value, 100)
self.assertEqual(kpi_results[0].name, "out1")
self.assertEqual(kpi_results[1].name, "out2")
self.assertEqual(kpi_results[2].name, "out3")
# now test all possible combinations of KPISpecification, including
# those with KPIs repeated, and empty KPI specification
import itertools
out_options = [("out1", 99), ("out2", 1), ("out3", 100)]
for num_outputs in range(len(out_options) + 2, 0, -1):
for spec in itertools.permutations(out_options, r=num_outputs):
mco_model.kpis = [
KPISpecification(name=opt[0]) for opt in spec
]
wf = Workflow(
mco_model=mco_model, execution_layers=[ExecutionLayer()]
)
wf.execution_layers[0].data_sources.append(model)
kpi_results = wf.execute(data_values)
self.assertEqual(len(kpi_results), num_outputs)
for i in range(num_outputs):
self.assertEqual(kpi_results[i].name, spec[i][0])
self.assertEqual(kpi_results[i].value, spec[i][1])