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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: rank_actions_and_reward_events.py
DESCRIPTION:
This sample demos sending a rank and reward call to personalizer
USAGE: python 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_SINGLE_SLOT"]
except KeyError:
print("PERSONALIZER_ENDPOINT_SINGLE_SLOT must be set.")
sys.exit(1)
try:
api_key = os.environ["PERSONALIZER_API_KEY_SINGLE_SLOT"]
except KeyError:
print("PERSONALIZER_API_KEY_SINGLE_SLOT must be set.")
sys.exit(1)
client = PersonalizerClient(endpoint, AzureKeyCredential(api_key))
# The list of actions to be ranked with metadata associated for each action.
actions = [
{
"id": "Video1",
"features": [
{"videoType": "documentary", "videoLength": 35, "director": "CarlSagan"},
{"mostWatchedByAge": "50-55"},
],
},
{
"id": "Video2",
"features": [
{"videoType": "movie", "videoLength": 120, "director": "StevenSpielberg"},
{"mostWatchedByAge": "40-45"},
],
},
]
# Context of the user to which the action must be presented.
context_features = [
{"currentContext": {"day": "tuesday", "time": "night", "weather": "rainy"}},
{
"userContext": {
"payingUser": True,
"favoriteGenre": "documentary",
"hoursOnSite": 0.12,
"lastWatchedType": "movie",
},
},
]
request = {
"actions": actions,
"contextFeatures": context_features,
}
print("Sending rank request")
rank_response = client.rank(request)
print(
"Rank returned response with event id {} and recommended {} as the best action".format(
rank_response.get("eventId"), rank_response.get("rewardActionId")
)
)
# 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. So we associate a reward of 1.
print("Sending reward event")
client.reward(rank_response.get("eventId"), {"value": 1.0})
print("Completed sending reward response")
if __name__ == "__main__":
main()
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