def test_registry(self):
registry = MLRegistry()
self.assertEqual(len(registry.endpoints), 0)
endpoint_name = "income_classifier"
algorithm_object = RandomForestClassifier()
algorithm_name = "random forest"
algorithm_status = "production"
algorithm_version = "0.0.1"
algorithm_owner = "Alex"
algorithm_description = "Random Forest with simple pre- and post-processing"
algorithm_code = inspect.getsource(RandomForestClassifier)
# add to registry
registry.add_algorithm(endpoint_name, algorithm_object, algorithm_name,
algorithm_status, algorithm_version,
algorithm_owner, algorithm_description,
algorithm_code)
# there should be one endpoint available
self.assertEqual(len(registry.endpoints), 1)
endpoint_name = "income_classifier"
algorithm_object = ExtraTreesClassifier()
algorithm_name = "extra trees"
algorithm_status = "production"
algorithm_version = "0.0.1"
algorithm_owner = "Alex"
algorithm_description = "Extra Trees with simple pre- and post-processing"
algorithm_code = inspect.getsource(ExtraTreesClassifier)
# add to registry
registry.add_algorithm(endpoint_name, algorithm_object, algorithm_name,
algorithm_status, algorithm_version,
algorithm_owner, algorithm_description,
algorithm_code)
# there should be one endpoint available
self.assertEqual(len(registry.endpoints), 2)
def test_et_algorithm(self):
input_data = {
"Gender": "Male",
"Married": "Yes",
"Dependents": 2,
"Education": "Graduate",
"Self_Employed": "Yes",
"ApplicantIncome": 5849,
"CoapplicantIncome": 6000,
"LoanAmount": 120,
"Loan_Amount_Term": 360,
"Credit_History": 1,
"Property_Area": "Urban",
}
my_alg = ExtraTreesClassifier()
response = my_alg.compute_prediction(input_data)
self.assertEqual("OK", response["status"])
self.assertTrue("label" in response)
self.assertEqual("Approved", response["label"])
def test_et_algorithm(self):
input_data = {
"age": 37,
"workplace": "Private",
"fnlwgt": 34146,
"education": "HS-grad",
"education-num": 9,
"marital-status": "Married-civ-spouse",
"occupation": "Craft-repair",
"relationship": "Husband",
"race": "White",
"sex": "Male",
"capital-gain": 0,
"capital-loss": 0,
"hours-per-week": 68,
"native-country": "United-States"
}
my_alg = ExtraTreesClassifier()
response = my_alg.compute_prediction(input_data)
self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual('<=50K', response['label'])
registry = MLRegistry() # create ML registry
# Random Forest classifier
rf = RandomForestClassifier()
# add to ML registry
registry.add_algorithm(
endpoint_name="income_classifier",
algorithm_object=rf,
algorithm_name="random forest",
algorithm_status="ab_testing",
algorithm_version="0.0.1",
owner="Bilal Fourka",
algorithm_description=
"Random Forest with simple pre- and post-processing",
algorithm_code=inspect.getsource(RandomForestClassifier))
# Extra Trees classifier
et = ExtraTreesClassifier()
# add to ML registry
registry.add_algorithm(
endpoint_name="income_classifier",
algorithm_object=et,
algorithm_name="extra trees",
algorithm_status="ab_testing",
algorithm_version="0.0.1",
owner="Bilal Fourka",
algorithm_description=
"Extra Trees with simple pre- and post-processing",
algorithm_code=inspect.getsource(RandomForestClassifier))
rfN = RandomForestClassifierN()
# add to ML registry
registry.add_algorithm(
endpoint_name="profile_classifier",
def test_et_algorithm(self):
my_alg = ExtraTreesClassifier()
response = my_alg.compute_prediction(self.input_data)
self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual('<=50K', response['label'])
