def test(self):
"""
Checks whether all required attributes are set. Throws an exception
if an error was detected.
"""
MultiChoice.test(self)
if self.default_task_spec is None:
raise WorkflowException(self, 'A default output is required.')
def test(self):
"""
Checks whether all required attributes are set. Throws an exception
if an error was detected.
"""
MultiChoice.test(self)
if self.default_task_spec is None:
raise WorkflowException(self, 'A default output is required.')
def __init__(self, parent, name, **kwargs):
"""
Constructor.
parent -- a reference to the parent (TaskSpec)
name -- a name for the pattern (string)
"""
MultiChoice.__init__(self, parent, name, **kwargs)
self.default_task = None
def __init__(self, parent, name, **kwargs):
"""
Constructor.
@type parent: TaskSpec
@param parent: A reference to the parent task spec.
@type name: str
@param name: The name of the task spec.
@type kwargs: dict
@param kwargs: See L{SpiffWorkflow.specs.TaskSpec}.
"""
MultiChoice.__init__(self, parent, name, **kwargs)
self.default_task_spec = None
def __init__(self, parent, name, **kwargs):
"""
Constructor.
@type parent: TaskSpec
@param parent: A reference to the parent task spec.
@type name: str
@param name: The name of the task spec.
@type kwargs: dict
@param kwargs: See L{SpiffWorkflow.specs.TaskSpec}.
"""
MultiChoice.__init__(self, parent, name, **kwargs)
self.default_task_spec = None
def get_user_selected_model():
user_selected_model = MultiChoice(
"Select one of the following models:",
options=(dispatcher.MODELS.keys()),
)().lower()
print("[INFO]: Selected Model: {}".format(user_selected_model))
return user_selected_model
return accuracy
def test_pipeline(model_name, test_query):
tester = test_model.TestModel(model_name)
prediction = tester.predict(test_query=test_query)
return prediction
if __name__ == "__main__":
x_train, y_train, x_test, y_test = build_features(
dataset_path=os.path.join(config.DATA_PATH, "raw",
config.DATASET_NAME),
split_ratio=config.TEST_SIZE,
)
# User's Model Choice
user_selected_model = MultiChoice(
"Select one of the following models:",
options=("knn", "svm", "logisticregression", "decisiontree"),
)().lower()
print(train_pipeline(user_selected_model, x_train, y_train, x_test,
y_test))
print(test_pipeline(user_selected_model, [[4.7, 3.2, 1.3, 0.2]]))
