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_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 = "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.endpoints), 0) endpoint_name = "classifier" algorithm_object = FasttextClassifier() algorithm_name = "fasttext" algorithm_status = "production" algorithm_version = "0.0.1" algorithm_owner = "" algorithm_description = "Fasttext with simple pre-processing" algorithm_code = inspect.getsource(FasttextClassifier) # add to registry registry.add_algorithm(endpoint_name, algorithm_object, algorithm_name, algorithm_status, algorithm_version, algorithm_owner, algorithm_description, algorithm_code)
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)
def test_registry(self): registry = MLRegistry() self.assertEqual(len(registry.endpoints), 0) endpoint_name = "rate_classifier" algorithm_object = LstmClassifier() algorithm_name = "lstm" algorithm_status = "production" algorithm_version = "0.0.1" algorithm_owner = "Ashish" algorithm_description = "Lstm simple pre-preocessing" algorithm_code = inspect.getsource(LstmClassifier) # 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 = "phising_classifier" algorithm_object = PhisingClassifier() algorithm_name = "phising svm" algorithm_status = "production" algorithm_version = "0.0.1" algorithm_owner = "gemastik" algorithm_description = "Phising Detection using SVM Classifier" algorithm_code = inspect.getsource(PhisingClassifier) # 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 = "movie_rec" algorithm_object = ContentRec() algorithm_name = "content-based recommendation" algorithm_status = "production" algorithm_version = "0.0.2" algorithm_owner = "James" algorithm_description = "Content-Based Recommendation based on Movie Genres" algorithm_code = inspect.getsource(ContentRec) # 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 = "nails_segmenter" algorithm_object = NailsSegmenter() algorithm_name = "segmentation" algorithm_status = "production" algorithm_version = "0.0.1" algorithm_owner = "xyz" algorithm_description = "-" algorithm_code = inspect.getsource(NailsSegmenter) # 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 = "Gunosy_classifier" algorithm_object = NaiveBayes() algorithm_name = "Naive Bayes" algorithm_status = "production" algorithm_version = "1.0" algorithm_owner = "Tung Dang" algorithm_description = "Navie Bayes with NLP to classify news article" algorithm_code = inspect.getsource(NaiveBayes) # 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 = "posture_detector" algorithm_object = DarkflowDetector( pbpath="D:\code\Yangpyong_Detecting_Model/tiny-yolo-voc-custom.pb", metapath= "D:\code\Yangpyong_Detecting_Model/tiny-yolo-voc-custom.meta") algorithm_name = "darkflow" algorithm_status = "production" algorithm_version = "0.0.1" algorithm_owner = "Shawn" algorithm_description = "Object Detecting and Image Saving by Darkflow" algorithm_code = inspect.getsource(DarkflowDetector) # 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 = "rf-classifier-personal loan" algorithm_object = RandomForestClassifier() algorithm_name = "randomforrest" algorithm_status = "production" algorithm_code = inspect.getsource(RandomForestClassifier) algorithm_owner = "starboy" algorithm_description = "Random Forest with simple pre- and post-processing" registry.add_algorithm(endpoint_name, algorithm_object, algorithm_name, algorithm_status, algorithm_owner, algorithm_description, algorithm_code) self.assertEqual(len(registry.endpoints), 1) # input_data={ # "Age" :25, # "Experience":1, # "Income":49, # "Family":4, # "CCAvg":1.6, # "Mortgage":0, # "Securities Account":1, # "CD Amount":0, # "Online":0, # "Credit Card":0, # "Zips":911, # "ed1":1, # "ed2":0, # "ed3":0 # } # alg=RandomForestClassifier() # response=alg.predict(input_data) # print(response)
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 apps.ml.registry import MLRegistry from apps.ml.posture_detector.darkflow import DarkflowDetector try: registry = MLRegistry() # create ML registry # Random Forest classifier df = DarkflowDetector( pbpath="D:\code\Yangpyong_Detecting_Model/tiny-yolo-voc-custom.pb", metapath="D:\code\Yangpyong_Detecting_Model/tiny-yolo-voc-custom.meta") # add to ML registry registry.add_algorithm( endpoint_name="posture_detector", algorithm_object=df, algorithm_name="darkflow", algorithm_status="production", algorithm_version="0.0.1", owner="Shawn", algorithm_description="Object Detecting and Image Saving by Darkflow", algorithm_code=inspect.getsource(DarkflowDetector))
""" 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 apps.ml.registry import MLRegistry from apps.ml.movie_rec.content_rec import ContentRec from apps.ml.movie_rec.title_rec import TitleRec try: # create ML registry registry = MLRegistry() # content based recommendation cr = ContentRec() # add to ML registry registry.add_algorithm(endpoint_name="movie_rec", algorithm_object=cr, algorithm_name="content-based recommendation", algorithm_status="production", algorithm_version="0.0.2", owner="James", algorithm_description="Content-Based Recommendation based on Movie Genres", algorithm_code=inspect.getsource(ContentRec)) # title based recommendation tr = TitleRec() # add to ML registry
from apps.ml.income_classifier.random_forest import RandomForestClassifier from apps.ml.registry import MLRegistry import inspect import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'server.settings') application = get_wsgi_application() # ML registry try: # create ML registry registry = MLRegistry() # 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="production", algorithm_version="0.0.1a", owner="Daycu_", algorithm_description="description", algorithm_code=inspect.getsource(RandomForestClassifier)) 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', 'server.settings') application = get_wsgi_application() import inspect from apps.ml.registry import MLRegistry from apps.ml.income_classifier.random_forest import RandomForestClassifier from apps.ml.playstyle_classifier.nnclassifier import NNClassifier 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="production", # algorithm_version="0.0.1", # owner="Piotr", # algorithm_description="Random Forest with simple pre- and post-processing", # algorithm_code=inspect.getsource(RandomForestClassifier)) nn = NNClassifier() # add to ML registry registry.add_algorithm( endpoint_name="chess_playstyle_classifier",
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 apps.ml.registry import MLRegistry from apps.ml.income_classifer_example.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_classifier', algorithm_object=rf, algorithm_name='random forest', algorithm_status='production', algorithm_version='0.0.1', owner='JR', algorithm_code=inspect.getsource(RandomForestClassifier), algorithm_description= 'Random Forest with simple pre- and post-processing') except Exception as e:
from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'ml_service.settings') application = get_wsgi_application() import json import joblib import inspect from apps.ml.registry import MLRegistry from apps.ml.income_classifier.random_forest import RandomForestClassifier from apps.ml.income_classifier.etclassifier import ExtraTreeClassifier try: registry = MLRegistry() rf = RandomForestClassifier() registry.add_algorithm(endpoint_name='income_classifier', algorithm_object=rf, algorithm_name='random forest', algorithm_status='production', algorithm_version='1.0.0', owner='admin', description='Random Forest Income Classifier', algorithm_code=inspect.getsource(RandomForestClassifier) ) et = ExtraTreeClassifier() registry.add_algorithm(endpoint_name='income_classifier', algorithm_object= et, algorithm_name='extratreeclassifier', algorithm_status='production',
# file backend/server/server/wsgi.py 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 apps.ml.registry import MLRegistry from apps.ml.income_classifier.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_classifier", algorithm_object=rf, algorithm_name="random forest", algorithm_status="production", algorithm_version="0.0.1", owner="Mohammed Samir Mahmoud", 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))
For more information on this file, see https://docs.djangoproject.com/en/3.1/howto/deployment/wsgi/ """ from apps.ml.income_classifier.random_forest import RandomForestClassifier from apps.ml.income_classifier.extra_trees import ExtraTreesClassifier from apps.ml.registry import MLRegistry import inspect import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'server.settings') application = get_wsgi_application() try: registry = MLRegistry() rf = RandomForestClassifier() et = ExtraTreesClassifier() registry.add_algorithm(endpoint_name="income_classifier", algorithm_object=rf, algorithm_name="random forest", algorithm_status="production", algorithm_version="0.0.1", owner="Josh", algorithm_description="Random forest with simple pre- and post processing", algorithm_code=inspect.getsource(RandomForestClassifier)) registry.add_algorithm(endpoint_name="income_classifier", algorithm_object=et,
""" 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 apps.ml.registry import MLRegistry from apps.ml.income_classifier.random_forest import RandomForestClassifier try: registry = MLRegistry() #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='production', algorithm_version='0.0.1', owner='edgarbasto', algorithm_description='Random forest income classifier.', algorithm_code=inspect.getsource(RandomForestClassifier)) 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', 'server.settings') application = get_wsgi_application() import inspect from apps.ml.registry import MLRegistry from apps.ml.Gunosy_classifier.NaiveBayes import NaiveBayes try: registry = MLRegistry() nb = NaiveBayes() # add to ML registry registry.add_algorithm( endpoint_name="Gunosy_classifier", algorithm_object=nb, algorithm_name="Naive Bayes", algorithm_status="production", algorithm_version="1.0", owner="Tung Dang", algorithm_description="Navie Bayes with NLP to classify news article", algorithm_code=inspect.getsource(NaiveBayes)) except Exception as e: print("Exception while loading the algorithm to the registry", str(e))
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 apps.ml.registry import MLRegistry from apps.ml.recommendation_model.rec_model import RandomForestClassifier try: registry = MLRegistry() # create ML registry # Random Forest classifier rf = RandomForestClassifier() # add to ML registry registry.add_algorithm( endpoint_name="recommend_sci", algorithm_object=rf, algorithm_name="science_concierge", algorithm_status="production", algorithm_version="0.0.1", owner="Aditya", algorithm_description="Recommendation based on likes", algorithm_code=inspect.getsource(RandomForestClassifier)) except Exception as e: print("Exception while loading the algorithms to the registry,", str(e))
For more information on this file, see 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 apps.ml.registry import MLRegistry from apps.ml.crime_classifier.random_forest import RandomForestClassifier try: registry = MLRegistry() # create ML registry # Random Forest classifier rf = RandomForestClassifier() # add to ML registry registry.add_algorithm(endpoint_name="crime_classifier", algorithm_object=rf, algorithm_name="random forest", algorithm_status="production", algorithm_version="0.2", owner="dataDetective", algorithm_description="Crime Prediction model using Boston Dataset with Accuracy 50%", algorithm_code=inspect.getsource(RandomForestClassifier)) except Exception as e: print("Exception while loading the algorithms to the registry,", str(e))
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() import inspect from apps.ml.registry import MLRegistry from apps.ml.classifier.rf import RandomForestClassifier registry = MLRegistry() try: endpoint_name = "rf-PL-hardpunish" algorithm_object = RandomForestClassifier("Web_rf_3.joblib") algorithm_name = "randomforrest-3" algorithm_status = "production" algorithm_code = inspect.getsource(RandomForestClassifier) algorithm_owner = "starboy" algorithm_description = "Random Forest with class weight ration 10" # registry.isbefore(endpoint_name,algorithm_name,algorithm_status,algorithm_owner,algorithm_description) registry.add_algorithm(endpoint_name, algorithm_object, algorithm_name, algorithm_status, algorithm_owner, algorithm_description, algorithm_code) print("Success 3") except Exception as e:
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 apps.ml.registry import MLRegistry from apps.ml.nails_segmentation.nails_seg import NailsSegmenter try: registry = MLRegistry() # create ML registry # Nails Segmentation model ns = NailsSegmenter() # add to ML registry registry.add_algorithm(endpoint_name="nails_segmenter", algorithm_object=ns, algorithm_name="segmentation", algorithm_status="production", algorithm_version="0.0.1", owner="xyz", algorithm_description="--", algorithm_code=inspect.getsource(NailsSegmenter)) except Exception as e: print("Exception while loading the algorithms to the registry, oui", str(e))
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 apps.ml.registry import MLRegistry from apps.ml.income_classifier.MLP import MLP # from apps.ml.income_classifier.extra_trees import ExtraTreesClassifier # import ExtraTrees ML algorithm try: registry = MLRegistry() # create ML registry # Random Forest classifier MLP = MLP() # add to ML registry registry.add_algorithm( endpoint_name="income_classifier", algorithm_object=MLP, algorithm_name="Multi Layer Perceptron", algorithm_status="production", algorithm_version="0.0.1", owner="Piotr", algorithm_description= "Random Forest with simple pre- and post-processing", algorithm_code=inspect.getsource(MLP)) # Extra Trees classifier # et = ExtraTreesClassifier()
from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'djangoProject.settings') application = get_wsgi_application() 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 from apps.ml.income_classifier.logistic_regression import LogisticRegressionClassifier from apps.ml.income_classifier.naive_bayes import NaiveBayesClassifier from apps.ml.income_classifier.voting_classifier import VotingClassifier 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="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)) # Extra Trees classifier et = ExtraTreesClassifier()
https://docs.djangoproject.com/en/dev/howto/deployment/wsgi/ """ 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()
For more information on this file, see https://docs.djangoproject.com/en/2.1/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 apps.ml.registry import MLRegistry from apps.ml.classifier.fasttext import FasttextClassifier try: registry = MLRegistry() # create ML registry ft = FasttextClassifier() # add to ML registry registry.add_algorithm(endpoint_name="classifier", algorithm_object=ft, algorithm_name="fasttext", algorithm_status="production", algorithm_version="0.0.1", owner="", algorithm_description="Fasttext with simple pre-processing", algorithm_code=inspect.getsource(FasttextClassifier)) except Exception as e: print("Exception while loading the algorithms to the registry,", str(e))