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# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: multi_slot_rank_actions_and_reward_events.py
DESCRIPTION:
This sample demos sending a rank and reward call to personalizer for multi-slot configuration.
USAGE: python multi_slot_rank_actions_and_reward_events.py
Environment variables PERSONALIZER_ENDPOINT and PERSONALIZER_API_KEY must be set as per your personalizer instance.
"""
import os
import sys
from azure.ai.personalizer import PersonalizerClient
from azure.core.credentials import AzureKeyCredential
def main():
try:
endpoint = os.environ["PERSONALIZER_ENDPOINT_MULTI_SLOT"]
except KeyError:
print("PERSONALIZER_ENDPOINT_MULTI_SLOT must be set.")
sys.exit(1)
try:
api_key = os.environ["PERSONALIZER_API_KEY_MULTI_SLOT"]
except KeyError:
print("PERSONALIZER_API_KEY_MULTI_SLOT must be set.")
sys.exit(1)
client = PersonalizerClient(endpoint, AzureKeyCredential(api_key))
# We want to rank the actions for two slots.
slots = [
{
"id": "Main Article",
"baselineAction": "NewsArticle",
"positionFeatures": [{"Size": "Large", "Position": "Top Middle"}],
},
{
"id": "Side Bar",
"baselineAction": "SportsArticle",
"positionFeatures": [{"Size": "Small", "Position": "Bottom Right"}],
},
]
# The list of actions to be ranked with metadata associated for each action.
actions = [
{"id": "NewsArticle", "features": [{"type": "News"}]},
{"id": "SportsArticle", "features": [{"type": "Sports"}]},
{"id": "EntertainmentArticle", "features": [{"type": "Entertainment"}]},
]
# Context of the user to which the action must be presented.
context_features = [
{"user": {"profileType": "AnonymousUser", "latLong": "47.6,-122.1"}},
{"environment": {"dayOfMonth": "28", "monthOfYear": "8", "weather": "Sunny"}},
{"device": {"mobile": True, "windows": True}},
{"recentActivity": {"itemsInCart": 3}},
]
request = {
"slots": slots,
"actions": actions,
"contextFeatures": context_features,
}
print("Sending multi-slot rank request")
rank_response = client.rank_multi_slot(request)
print("Rank returned response with event id {} and recommended the following:".format(rank_response.get("eventId")))
for slot in rank_response.get("slots"):
print("Action: {} for slot: {}".format(slot.get("rewardActionId"), slot.get("id")))
# The event response will be determined by how the user interacted with the action that was presented to them.
# Let us say that they like the action presented to them for the Main Article slot. So we associate a reward of 1.
print("Sending reward event for Main Article slot")
client.reward_multi_slot(rank_response.get("eventId"), {"reward": [{"slotId": "Main Article", "value": 1.0}]})
print("Completed sending reward response")
if __name__ == "__main__":
main()
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