def test_rf_algorithm(self):
my_alg = RandomForestClassifier()
response = my_alg.compute_prediction(self.input_data)
self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual('<=50K', response['label'])
def test_rf_algorithm(self):
input_data = {
"gender": "male",
"race/ethnicity": "group A",
"parental level of education": "bachelor's degree",
"lunch": "standard",
"math score": 99,
"reading score": 99,
"writing score": 99
}
my_alg = RandomForestClassifier()
response = my_alg.compute_prediction(input_data)
self.assertNotEqual('OK', response['status'])
self.assertFalse('label' in response)
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 = "Piotr"
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)
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 = "v1"
algorithm_description = (
"A random forest is a meta estimator that fits a number of decision tree classifiers "
"on various sub-samples of the dataset and uses averaging to improve the predictive "
"accuracy and control over-fitting. ")
algorithm_code = inspect.getsource(RandomForestClassifier)
# add to registry
registry.add_algorithm(
endpoint_name,
algorithm_object,
algorithm_name,
algorithm_status,
algorithm_version,
algorithm_description,
algorithm_code,
)
# there should be one endpoint available
self.assertEqual(len(registry.endpoints), 1)
def test_rf_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 = RandomForestClassifier()
response = my_alg.compute_prediction(input_data)
self.assertEqual("OK", response["status"])
self.assertTrue("label" in response)
self.assertEqual("Approved", response["label"])
def test_rf_algorithm(self):
input_data = {
"Age": 37,
"Workclass": "Private",
"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,
"Country": "United-States"
}
my_alg = RandomForestClassifier()
response = my_alg.compute_prediction(input_data)
self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual(False, response['label'])
def test_rf_algorithm(self):
input_data = {"age": 37,
"workclass": "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 = RandomForestClassifier()
response = my_alg.compute_prediction(input_data)
self.assertEqual("OK", response['status'])
self.assertTrue("label" in response)
self.assertEqual("<=50k", response["label"])
def test_rf_algorithm(self):
input_data = {
"age": 37,
"workclass": "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 = RandomForestClassifier()
response = my_alg.compute_prediction(input_data)
self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual('<=50K', response['label'])
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 = "Piotr"
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)
def test_rf_algorithm(self):
input_data = {
"age": 48,
"workclass": "Private",
"fnlwgt": 171095,
"education": "Assoc-acdm",
"education-num": 12,
"marital-status": "Divorced",
"occupation": "Exec-managerial",
"relationship": "Unmarried",
"race": "White",
"sex": "Female",
"capital-gain": 0,
"capital-loss": 0,
"hours-per-week": 40,
"native-country": "England"
}
my_alg = RandomForestClassifier()
response = my_alg.compute_prediction(input_data)
print(response)
self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual('<=50K', response['label'])
def test_registry(self):
registry = MLRegistry()
self.assertEqual(len(registry.endpoint), 0)
endpoint_name = "income_classifier"
algorithm_object = RandomForestClassifier()
algorithm_name = "Random Forest"
algorithm_status = "production"
algorithm_version = "1.0"
owner = "admin"
description = "Adding Random Forest Income Classifier"
algorithm_code = inspect.getsource(RandomForestClassifier)
registry.add_algorithm(endpoint_name, algorithm_object, algorithm_name,
algorithm_status, algorithm_version, owner,
description, algorithm_code)
self.assertEqual(len(registry.endpoint), 1)
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 = "Islambek" algorithm_description = "RF al with pre post proc" algorithm_code = inspect.getsource(RandomForestClassifier) registry.add_algorithm(endpoint_name, algorithm_object, algorithm_name, algorithm_status, algorithm_version, algorithm_owner, algorithm_description, algorithm_code) self.assertEqual(len(registry.endpoints), 1)
def test_registry(self):
registry = MLRegistry()
self.assertEqual(len(registry.newsite), 0)
newsite_name = "income_classifier"
algorithm_object = RandomForestClassifier()
algorithm_name = "random forest"
algorithm_status = "production"
algorithm_version = "0.0.1"
algorithm_owner = "Piotr"
algorithm_description = "Random Forest with simple pre- and post-processing"
algorithm_code = inspect.getsource(RandomForestClassifier)
registry.add_algorithm(newsite_name, algorithm_object, algorithm_name,
algorithm_status, algorithm_version,
algorithm_owner, algorithm_description,
algorithm_code)
self.assertEqual(len(registry.newsite), 1)
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mysite.settings')
application = get_wsgi_application()
# ML registry
import inspect
from apps.ml.registry import MLRegistry
from apps.ml.income_classifier.random_forest import RandomForestClassifier
from apps.ml.income_classifier.extra_trees import ExtraTreesClassifier # import ExtraTrees ML algorithm
from apps.ml.profile_classifier.random_forestN import RandomForestClassifierN
from apps.ml.profile_classifier.extra_treesN import ExtraTreesClassifierN # import ExtraTrees ML algorithm
try:
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(
