def feedback():
"""Feedback Service to provide user input on which false predictions this model provided."""
try:
content = request.json
logging.info("id: {} ".format(content["lad_id"]))
logging.info("anomaly: {} ".format(content["is_anomaly"]))
logging.info("notes: {} ".format(content["notes"]))
if not content["lad_id"] or not content["is_anomaly"]:
raise Exception("This service requires that you provide the id" +
", anomaly=True|False and notes are optional ")
fs = FactStore()
# Note id is the prediction id that is found in the email.
if (fs.write_feedback(predict_id=content["lad_id"],
notes=content["notes"],
anomaly_status=bool(content["is_anomaly"])) is
False):
raise Exception("Predict ID must be unique. This anomaly" +
" feedback has been reported before")
except Exception as e:
logging.info(e)
result = {"feedback_service": "failure", "error_msg": "{0}".format(e)}
return make_response(jsonify(result), 403)
else:
result = {"feedback_service": "success"}
return make_response(jsonify(result), 200)
return ""
def false_anomaly():
"""Tag false anomalies in database."""
content = request.get_json()
fs = FactStore()
id = content["predict_id"]
# Tracking event in fact-store
res = fs.write_event(content["predict_id"], content["message"],
content["score"], content["anomaly_status"])
# Returning status if this anomaly is real anomaly_status== false
return jsonify({"false_anomaly": res})
def feedback():
"""Feedback Service to provide user input on which false predictions this model provided."""
try:
content = request.json
# When deploying fact_store per customer you should set env var
customer_id = os.getenv("CUSTOMER_ID")
logging.info("id: {} ".format(content["lad_id"]))
logging.info("anomaly: {} ".format(content["is_anomaly"]))
logging.info("message: {} ".format(content["message"]))
logging.info("customer_id: {} ".format(customer_id))
if not content["lad_id"] or not content["is_anomaly"]:
raise Exception("This service requires that you provide the id" +
", anomaly=True|False and notes are optional ")
fs = FactStore()
# Record feedback
HUMAN_FEEDBACK_COUNT.labels(
customer_id=customer_id,
anomaly_status=content["is_anomaly"]).inc()
# Note id is the prediction id that is found in the email.
if (fs.write_feedback(predict_id=content["lad_id"],
message=content["message"],
anomaly_status=bool(content["is_anomaly"]),
customer_id=customer_id) is False):
raise Exception("Predict ID must be unique. This anomaly" +
" feedback has been reported before")
except Exception as e:
logging.info(e)
HUMAN_FEEDBACK_ERROR_COUNT.labels(
err_msg="failure to write to db in factstore").inc()
result = {"feedback_service": "failure", "error_msg": "{0}".format(e)}
return make_response(jsonify(result), 403)
else:
result = {"feedback_service": "success"}
return make_response(jsonify(result), 200)
return ""
def test_feedback_inserted():
"""Test inserting events into the fact_store."""
fact_store = FactStore(True)
fact_store.session.query(FeedbackModel).delete()
fact_store.write_feedback(
predict_id="a2b35c5b-016d-4e2c-8ec5-87d1b962b2f8",
notes="222JSJSJJS",
anomaly_status=True)
fact_store.write_feedback(
predict_id="18bd090d-ae27-4b19-a0db-ed5f589b4e2e",
notes="SSJJSJS",
anomaly_status=True)
fact_store.write_feedback(
predict_id="74a6b1bd-efea-4e7b-87a9-8f7330885160",
notes="AJJSJS",
anomaly_status=False)
items = fact_store.readall_feedback()
assert len(items) is 3
def test_events_inserted():
"""Test inserting feedback into the fact_store."""
fact_store = FactStore(True)
fact_store.session.query(EventModel).delete()
fact_store.write_event(predict_id="a2b35c5b-016d-4e2c-8ec5-87d1b962b2f8",
message="kssksjs",
score=3.1,
anomaly_status=True)
fact_store.write_event(predict_id="18bd090d-ae27-4b19-a0db-ed5f589b4e2e",
message="JSJSJS",
score=2.2,
anomaly_status=False)
fact_store.write_event(predict_id="74a6b1bd-efea-4e7b-87a9-8f7330885160",
message="sjsjsjsj",
score=8.3,
anomaly_status=True)
items = fact_store.session.query(EventModel).all()
assert len(items) is 3
def false_positive():
"""Service to provide list of false anomalies to be relabeled during ml training run."""
fs = FactStore()
df = fs.readall_false_positive()
return jsonify({"feedback": df})
