def load_rest():
User.load_to_cache()
# Load main models
Popularity.load_to_cache()
TensorCoFi.load_to_cache()
if "recommendation.language" in settings.INSTALLED_APPS:
from recommendation.language.models import Region
Region.load_to_cache()
if "recommendation.diversity" in settings.INSTALLED_APPS:
from recommendation.diversity.models import ItemGenre, Genre
Genre.load_to_cache()
ItemGenre.load_to_cache()
def setup_class(cls, *args, **kwargs):
"""
Put elements in db
"""
for app in ITEMS:
Item.objects.create(**app)
for u in USERS:
user = User.objects.create(external_id=u["external_id"])
for i in u["items"]:
Inventory.objects.create(user=user, item=Item.get_item_by_external_id(i))
TensorCoFi.train_from_db()
Popularity.train_from_db()
TensorCoFi.load_to_cache()
Popularity.load_to_cache()
def test_popularity_score_against_testfm(self):
"""
[recommendation.models.TensorCoFi] Test popularity scores with test.fm benchmark
"""
evaluator = Evaluator()
training, testing = testfm.split.holdoutByRandom(self.df, 0.9)
items = training.item.unique()
tc = Popularity(len(items))
ptc = TFMPopularity()
tc.fit(training)
ptc.fit(training)
tc_score = evaluator.evaluate_model(tc, testing, all_items=items)[0]
ptc_score = evaluator.evaluate_model(ptc, testing, all_items=items)[0]
assert abs(tc_score-ptc_score) < .1, \
"Popularity score is not close enough to testfm benchmark (%.3f != %.3f)" % (tc_score, ptc_score)
def setup_class(cls, *args, **kwargs):
"""
Put elements in db
"""
path = resource_filename(recommendation.__name__, "/")
fill.FillTool({"items": True, "--mozilla": True, "prod": True}).load()
fill.FillTool({"users": True, "--mozilla": True, "<path>": path+"data/user"}).load()
modelcrafter.main("train", "popularity")
modelcrafter.main("train", "tensorcofi")
# Load user and items
Item.load_to_cache()
User.load_to_cache()
# Load main models
Popularity.load_to_cache()
TensorCoFi.load_to_cache()
cls.client = Client()
def get_alternative_recommendation(self, user):
"""
Return the popular items
:return: list
"""
return Popularity.get_model().recommendation
import os
if "DJANGO_SETTINGS_MODULE" not in os.environ:
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "recommendation.default_settings")
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
from django.conf import settings
from recommendation.models import Item, User, TensorCoFi, Popularity
# Load user and items
Item.load_to_cache()
User.load_to_cache()
# Load main models
Popularity.load_to_cache()
TensorCoFi.load_to_cache()
if "recommendation.language" in settings.INSTALLED_APPS:
from recommendation.language.models import Locale, Region
Locale.load_to_cache()
Region.load_to_cache()
#if "recommendation.simple_logging" in recommendation.settings.INSTALLED_APPS:
# print("Loading logs to cache...")
# from recommendation.simple_logging.models import LogEntry
# LogEntry.load_to_cache()
# print("done!")
if "DJANGO_SETTINGS_MODULE" not in os.environ:
os.environ.setdefault("DJANGO_SETTINGS_MODULE",
"recommendation.default_settings")
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
from django.conf import settings
from recommendation.models import Item, User, TensorCoFi, Popularity
# Load user and items
Item.load_to_cache()
User.load_to_cache()
# Load main models
Popularity.load_to_cache()
TensorCoFi.load_to_cache()
if "recommendation.language" in settings.INSTALLED_APPS:
from recommendation.language.models import Locale, Region
Locale.load_to_cache()
Region.load_to_cache()
#if "recommendation.simple_logging" in recommendation.settings.INSTALLED_APPS:
# print("Loading logs to cache...")
# from recommendation.simple_logging.models import LogEntry
# LogEntry.load_to_cache()
# print("done!")
if "recommendation.diversity" in settings.INSTALLED_APPS:
