def test_registry(self):
registry = MLRegistry()
self.assertEqual(len(registry.endpoints), 0)
endpoint_name = "classifier"
algorithm_object = RandomForestClassifier()
algorithm_name = "random forest"
algorithm_status = "production"
algorithm_version = "0.0.1"
algorithm_owner = "Kami"
algorithm_description = "Random Forest with simple pre- and post-processing"
registry.add_algorithm(endpoint_name, algorithm_object, algorithm_name,
algorithm_status, algorithm_version,
algorithm_owner, algorithm_description)
print(registry)
self.assertEqual(len(registry.endpoints), 1)
def test_registry(self):
registry = MLRegistry()
self.assertEqual(len(registry.endpoints), 0)
algorithm_object = BookRatePredictor()
algorithm_name = "xgboost"
algorithm_version = "1.0.1"
algorithm_owner = "from kpi import iasa"
algorithm_description = "Book rate estimator"
algorithm_code = inspect.getsource(BookRatePredictor)
# add to registry
registry.add_algorithm(algorithm_name,
algorithm_version, algorithm_owner,
algorithm_description, algorithm_code, algorithm_object)
# 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 = RandomForest()
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(RandomForest)
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 = "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_et_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 = ExtraTreesClassifier()
response = my_alg.compute_prediction(input_data)
self.assertEqual('OK', response['status'])
self.assertTrue('label' in response)
self.assertEqual('<=50K', response['label'])
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'server.settings')
application = get_wsgi_application()
# ML registry
from ml.registry import MLRegistry
from ml.classifier.random_forest import RandomForestClassifier
try:
# create ML registry
registry = MLRegistry()
# Random Forest classifier
rf = RandomForestClassifier()
# add to ML registry
registry.add_algorithm(
endpoint_name="classifier",
algorithm_object=rf,
algorithm_name="random forest",
algorithm_status="production",
algorithm_version="0.0.1",
owner="Kami",
algorithm_description=
"Random Forest with simple pre- and post-processing")
except Exception as e:
print("Exception while loading the algorithms to the registry,", str(e))
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'recommendations.settings')
application = get_wsgi_application()
from ml.registry import MLRegistry
# from ml.knn import KNN
from ml.bert import BERT
from utils import custom_logging
try:
registry = MLRegistry()
# knn = KNN('/code/ml/knn/')
# registry.add_algorithm("knn", knn)
bert = BERT()
registry.add_algorithm("bert", bert)
print('ke')
except Exception as e:
print("Exception while loading the algorithms to the registry,", str(e))
https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'server.settings')
application = get_wsgi_application()
import inspect
from ml.registry import MLRegistry
from ml.income.random_forest import RandomForestClassifier
try:
registry = MLRegistry() # create ML registry
# Random Forest classifier
rf = RandomForestClassifier()
# add to ML registry
registry.add_algorithm(endpoint_name="income",
algorithm_object=rf,
algorithm_name="random forest",
algorithm_status="production",
algorithm_version="0.0.1",
owner="Piotr",
algorithm_description="Random Forest with simple pre- and post-processing",
algorithm_code=inspect.getsource(RandomForestClassifier))
except Exception as e:
print("Exception while loading the algorithms to the registry,", str(e))
https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'server.settings')
application = get_wsgi_application()
# ML registry
import inspect
from ml.registry import MLRegistry
from ml.income_classifier.random_forest import RandomForest
from ml.income_classifier.extra_trees import ExtraTrees
try:
registry = MLRegistry() #Creates ML registry
rf = RandomForest()
registry.add_algorithm(endpoint_name="income_classifier", algorithm_object=rf,
algorithm_name="random forest",
algorithm_status="production",
algorithm_version="0.0.1",
owner="Gabriel",
algorithm_description="Random Forest with simple pre- and post-processing",
algorithm_code=inspect.getsource(RandomForest))
et = ExtraTrees()
registry.add_algorithm(endpoint_name="income_classifier", algorithm_object=et,
algorithm_name="extra trees",
algorithm_status="testing",
algorithm_version="0.0.1",
owner="Gabriel",
algorithm_description="Extra Trees with simple pre- and post-processing",
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'coursework.settings')
application = get_wsgi_application()
import inspect
from ml.registry import MLRegistry
from ml.classifier.log import Models
from ml.classifier.ada import Ada
try:
registry = MLRegistry() # create ML registry
# Random Forest classifier
rf = Models()
# add to ML registry
registry.add_algorithm(endpoint_name="classifier",
algorithm_object=rf,
algorithm_name="Models",
algorithm_status="production",
algorithm_version="0.0.1",
owner="Piotr",
algorithm_description="Models with simple pre- and post-processing",
algorithm_code=inspect.getsource(Models))
# Extra Trees classifier
et =Ada()
# add to ML registry
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'server.settings')
application = get_wsgi_application()
# ML registry
import inspect
from ml.registry import MLRegistry
from ml.predictor.predictor import BookRatePredictor
try:
registry = MLRegistry() # create ML registry
# Random Forest classifier
pred = BookRatePredictor()
# add to ML registry
registry.add_algorithm(algorithm_object=pred,
algorithm_name="xgboost",
algorithm_version="1.0.1",
algorithm_owner="from kpi import iasa",
algorithm_description="Book rate estimator",
algorithm_code=inspect.getsource(BookRatePredictor))
except Exception as e:
print("Exception while loading the algorithms to the registry,", str(e))