def setUp(self):
enum = ScoringFunctionComponentNameEnum()
predictive_property = create_predictive_property_component_regression()
activity = create_activity_component_regression()
qed_score = ComponentParameters(component_type=enum.QED_SCORE,
name="qed_score_name",
weight=1.,
smiles=[],
model_path="",
specific_parameters={})
custom_alerts = ComponentParameters(component_type=enum.CUSTOM_ALERTS,
name="custom_alerts_name",
weight=1.,
smiles=["CCCOOO"],
model_path="",
specific_parameters={})
matching_substructure = ComponentParameters(
component_type=enum.MATCHING_SUBSTRUCTURE,
name="matching_substructure_name",
weight=1.,
smiles=["*CCC"],
model_path="",
specific_parameters={})
self.sf_instance = CustomSum(parameters=[
activity, qed_score, custom_alerts, matching_substructure,
predictive_property
])
def setUpClass(self):
csp_enum = ComponentSpecificParametersEnum()
transf_type = TransformationTypeEnum()
sf_enum = ScoringFunctionComponentNameEnum()
activity = create_activity_component_regression()
activity.specific_parameters[
csp_enum.TRANSFORMATION_TYPE] = transf_type.DOUBLE_SIGMOID
activity.specific_parameters[csp_enum.COEF_DIV] = 100.
activity.specific_parameters[csp_enum.COEF_SI] = 150.
activity.specific_parameters[csp_enum.COEF_SE] = 150.
off_activity = create_offtarget_activity_component_regression()
delta_params = {
"high": 3.0,
"k": 0.25,
"low": 0.0,
"transformation": True,
"transformation_type": "sigmoid"
}
selectivity = ComponentParameters(
component_type=sf_enum.SELECTIVITY,
name="desirability",
weight=1.,
smiles=[],
model_path="",
specific_parameters={
"activity_model_path": activity.model_path,
"offtarget_model_path": off_activity.model_path,
"activity_specific_parameters":
activity.specific_parameters.copy(),
"offtarget_specific_parameters":
off_activity.specific_parameters,
"delta_transformation_parameters": delta_params
})
qed_score = ComponentParameters(component_type=sf_enum.QED_SCORE,
name="qed_score",
weight=1.,
smiles=[],
model_path="",
specific_parameters={})
matching_substructure = ComponentParameters(
component_type=sf_enum.MATCHING_SUBSTRUCTURE,
name="matching_substructure",
weight=1.,
smiles=["[*]n1cccc1CC"],
model_path="",
specific_parameters={})
custom_alerts = create_custom_alerts_configuration()
self.sf_state = CustomProduct(parameters=[
activity, selectivity, qed_score, matching_substructure,
custom_alerts
])
def setUp(self):
self.rm_enums = RunningModeEnum()
self.cs_enum = ComponentSpecificParametersEnum()
self.tf_enum = TransformationTypeEnum()
self.sfc_enum = ScoringFunctionComponentNameEnum()
self.lm_enum = LoggingModeEnum()
self.parameters = create_activity_component_regression()
log_conf = BaseLoggerConfiguration(
sender="http://10.59.162.10:8081",
recipient=self.lm_enum.LOCAL,
logging_path=f"{MAIN_TEST_PATH}/log",
job_name="model_validation_test",
job_id="1")
self.configuration_envelope = GeneralConfigurationEnvelope(
parameters=vars(self.parameters),
logging=vars(log_conf),
run_type=self.rm_enums.VALIDATION,
version="2.0")
def setUpClass(self):
csp_enum = ComponentSpecificParametersEnum()
transf_type = TransformationTypeEnum()
enum = ScoringFunctionComponentNameEnum()
delta_params = {
"high": 3.0,
"k": 0.25,
"low": 0.0,
"transformation": True,
"transformation_type": "sigmoid"
}
activity = create_activity_component_regression()
activity.specific_parameters[
csp_enum.TRANSFORMATION_TYPE] = transf_type.DOUBLE_SIGMOID
activity.specific_parameters[csp_enum.COEF_DIV] = 100.
activity.specific_parameters[csp_enum.COEF_SI] = 150.
activity.specific_parameters[csp_enum.COEF_SE] = 150.
off_activity = create_offtarget_activity_component_classification()
selectivity = ComponentParameters(
component_type=enum.SELECTIVITY,
name="desirability",
weight=1.,
smiles=[],
model_path="",
specific_parameters={
"activity_model_path":
activity.model_path,
"offtarget_model_path":
off_activity.model_path,
"activity_specific_parameters":
activity.specific_parameters.copy(),
"offtarget_specific_parameters":
off_activity.specific_parameters.copy(),
"delta_transformation_parameters":
delta_params
})
self.component = SelectivityComponent(parameters=selectivity)
def setUp(self):
csp_enum = ComponentSpecificParametersEnum()
ts_parameters = create_activity_component_regression()
ts_parameters.specific_parameters[csp_enum.TRANSFORMATION] = False
# unzip the model for loading
if not os.path.isdir(MAIN_TEST_PATH):
os.makedirs(MAIN_TEST_PATH)
tmp_model_path = os.path.join(
MAIN_TEST_PATH,
os.path.splitext(os.path.basename(SAS_MODEL_PATH))[0])
with gzip.open(SAS_MODEL_PATH, "rb") as f_in:
with open(tmp_model_path, "wb") as f_out:
shutil.copyfileobj(f_in, f_out)
ts_parameters.model_path = tmp_model_path
self.query_smiles = ['n1cccc2ccccc12']
self.query_mols = [
chem_smiles.to_mol(smile) for smile in self.query_smiles
]
self.component = SASComponent(ts_parameters)
def setUpClass(self):
enum = ScoringFunctionComponentNameEnum()
activity = create_activity_component_regression()
qed_score = ComponentParameters(component_type=enum.QED_SCORE,
name="qed_score_name",
weight=1.,
smiles=[],
model_path="",
specific_parameters={})
custom_alerts = create_custom_alerts_configuration()
matching_substructure = ComponentParameters(
component_type=enum.MATCHING_SUBSTRUCTURE,
name="matching_substructure_name",
weight=1.,
smiles=["*CCC"],
model_path="",
specific_parameters={})
self.sf_state = CustomProduct(parameters=[
activity, qed_score, custom_alerts, matching_substructure
])
