def main(
date,
aws_access_key_id,
aws_secret_access_key,
bucket,
prefix,
elastic_net_param,
reg_param,
min_installed_addons,
client_sample_date_from,
sample_rate,
):
print("Sampling clients since {}".format(client_sample_date_from))
# Clobber the AWS access credentials
os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id
os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key
ctx = default_context()
APP_NAME = "TaarEnsemble"
conf = SparkConf().setAppName(APP_NAME)
spark = SparkSession.builder.config(conf=conf).getOrCreate()
taar_training = extract(
spark, client_sample_date_from, min_installed_addons, sample_rate
)
coefs = transform(ctx, spark, taar_training, reg_param, elastic_net_param)
load(coefs, date, prefix, bucket)
def create_recommendation_manager():
root_ctx = default_context()
pf = ProfileFetcher(root_ctx)
pf.set_client(ProfileController(root_ctx, "us-west-2", "taar_addon_data_20180206"))
root_ctx["profile_fetcher"] = pf
r_factory = recommenders.RecommenderFactory(root_ctx.child())
root_ctx["recommender_factory"] = r_factory
rm = recommenders.RecommendationManager(root_ctx.child())
return rm
def create_recommendation_manager():
root_ctx = default_context()
client = ProfileController('us-west-2', 'taar_addon_data_20180206')
pf = ProfileFetcher(client)
root_ctx['profile_fetcher'] = pf
r_factory = recommenders.RecommenderFactory(root_ctx.child())
root_ctx['recommender_factory'] = r_factory
rm = recommenders.RecommendationManager(root_ctx.child())
return rm
def load_recommenders(ctx):
from taar.recommenders import LocaleRecommender
from taar.recommenders import SimilarityRecommender
from taar.recommenders import CollaborativeRecommender
ctx = default_context()
reload_configuration()
lr = LocaleRecommender(ctx)
sr = SimilarityRecommender(ctx)
cr = CollaborativeRecommender(ctx)
return {LOCALE: lr, COLLABORATIVE: cr, SIMILARITY: sr}
def check_proxy_manager(PROXY_MANAGER):
if PROXY_MANAGER.getResource() is None:
ctx = default_context()
profile_fetcher = ProfileFetcher(ctx)
ctx["profile_fetcher"] = profile_fetcher
# Lock the context down after we've got basic bits installed
root_ctx = ctx.child()
r_factory = recommenders.RecommenderFactory(root_ctx)
root_ctx["recommender_factory"] = r_factory
instance = recommenders.RecommendationManager(root_ctx.child())
PROXY_MANAGER.setResource(instance)
return PROXY_MANAGER.getResource()
def recommendations(uuid_client_id):
"""Return a list of recommendations provided a telemetry client_id."""
# Use the module global PROXY_MANAGER
global PROXY_MANAGER
# Coerce the uuid.UUID type into a string
client_id = str(uuid_client_id)
branch = request.args.get('branch', '')
extra_data = {'branch': branch}
locale = request.args.get('locale', None)
if locale is not None:
extra_data['locale'] = locale
platform = request.args.get('platform', None)
if platform is not None:
extra_data['platform'] = platform
if PROXY_MANAGER.getResource() is None:
ctx = default_context()
profile_fetcher = ProfileFetcher(ctx)
ctx['profile_fetcher'] = profile_fetcher
# Lock the context down after we've got basic bits installed
root_ctx = ctx.child()
r_factory = recommenders.RecommenderFactory(root_ctx)
root_ctx['recommender_factory'] = r_factory
instance = recommenders.RecommendationManager(root_ctx.child())
PROXY_MANAGER.setResource(instance)
instance = PROXY_MANAGER.getResource()
recommendations = instance.recommend(client_id=client_id,
limit=TAAR_MAX_RESULTS,
extra_data=extra_data)
# Strip out weights from TAAR results to maintain compatibility
# with TAAR 1.0
jdata = {"results": [x[0] for x in recommendations]}
response = app.response_class(response=json.dumps(jdata),
status=200,
mimetype='application/json')
return response
from funcsigs import signature as inspect_sig
import funcsigs
from taar.schema import INTERVENTION_A
from taar.schema import INTERVENTION_B
from taar.schema import INTERVENTION_CONTROL
from taar.context import default_context
from .lazys3 import LazyJSONLoader
import random
# We need to build a default logger for the schema validation as there
# is no class to bind to yet.
ctx = default_context()
schema_logger = ctx[IMozLogging].get_logger('taar.schema_validate')
TEST_CLIENT_IDS = [
'00000000-0000-0000-0000-000000000000',
'11111111-1111-1111-1111-111111111111',
'22222222-2222-2222-2222-222222222222',
'33333333-3333-3333-3333-333333333333'
]
EMPTY_TEST_CLIENT_IDS = [
'00000000-aaaa-0000-0000-000000000000',
'11111111-aaaa-1111-1111-111111111111',
'22222222-aaaa-2222-2222-222222222222',
'33333333-aaaa-3333-3333-333333333333'
]
def __init__(self, *args, **kwargs):
self._ctx = default_context()
self._ctx["profile_fetcher"] = kwargs["profile_fetcher"]
super(ProfileFetcherEnabledRecommendationManager,
self).__init__(args, kwargs)