static void SimulateRandomUserPreferences()
{
for (int i = 0; i < 1000; i++)
{
Random random = new Random(DateTime.Now.Millisecond);
IList <RankableAction> actions = GetActions();
PersonalizerClient client = InitializePersonalizerClient(ServiceEndpoint);
IList <object> currentContext = new List <object>()
{
new { professiom = random.Next(1, 5) },
new { preference = random.Next(1, 3) }
};
IList <string> excludeActions = new List <string> {
"http://codestories.gr/index.php/2018/10/21/297/"
};
// Generate an ID to associate with the request.
string eventId = Guid.NewGuid().ToString();
var request = new RankRequest(actions, currentContext, excludeActions, eventId);
RankResponse response = client.Rank(request);
client.Reward(response.EventId, new RewardRequest(random.Next(0, 2)));
}
}
public float SimulateEvent()
{
IList <RankableAction> actions = GetActions();
int userId = 1;
UserSimulator sim = new UserSimulator(userId, rand);
var currentContext = GetSimulatedContext(userId);
string eventId = Guid.NewGuid().ToString();
var request = new RankRequest(actions, currentContext, null, eventId);
RankResponse response = client.Rank(request);
//RankResponse response = new RankResponse();
float reward = 0f;
string simulationResponse = sim.ReturnSimulatedAction(currentContext);
Console.WriteLine("For Context {2}: Personalizer suggested {0}, simulation chose {1}", response.RewardActionId, simulationResponse, sim.GetKey((FoodContext)currentContext[0]));
if (response.RewardActionId == simulationResponse)
{
reward = 1f;
}
// Send the reward for the action based on user response.
client.Reward(response.EventId, new RewardRequest(reward));
return(reward);
}
public IActionResult PostReward([FromRoute] string eventId, [FromBody] RewardRequest reward)
{
try
{
personalizerClient?.Reward(eventId, reward);
return(Ok());
}
catch (Exception e)
{
return(BadRequest(e.Message));
}
}
public string PostReward([FromRoute] string eventId, [FromBody] RewardRequest rewardRequest)
{
try
{
client.Reward(eventId, rewardRequest);
}
catch (Exception e)
{
return(e.ToString());
}
return("204: No content (Success!)");
}
static void Main(string[] args)
{
int iteration = 1;
bool runLoop = true;
// Get the actions list to choose from personalizer with their features.
IList <RankableAction> actions = GetActions();
// Initialize Personalizer client.
PersonalizerClient client = InitializePersonalizerClient(ServiceEndpoint);
do
{
Console.WriteLine("\nIteration: " + iteration++);
// Get context information from the user.
string timeOfDayFeature = GetUsersTimeOfDay();
string tasteFeature = GetUsersTastePreference();
// Create current context from user specified data.
IList <object> currentContext = new List <object>()
{
new { time = timeOfDayFeature },
new { taste = tasteFeature }
};
// Exclude an action for personalizer ranking. This action will be held at its current position.
IList <string> excludeActions = new List <string> {
"juice"
};
// Generate an ID to associate with the request.
string eventId = Guid.NewGuid().ToString();
// Rank the actions
var request = new RankRequest(actions, currentContext, excludeActions, eventId);
RankResponse response = client.Rank(request);
Console.WriteLine("\nPersonalizer service thinks you would like to have: " + response.RewardActionId + ". Is this correct? (y/n)");
float reward = 0.0f;
string answer = GetKey();
if (answer == "Y")
{
reward = 1;
Console.WriteLine("\nGreat! Enjoy your food.");
}
else if (answer == "N")
{
reward = 0;
Console.WriteLine("\nYou didn't like the recommended food choice.");
}
else
{
Console.WriteLine("\nEntered choice is invalid. Service assumes that you didn't like the recommended food choice.");
}
Console.WriteLine("\nPersonalizer service ranked the actions with the probabilities as below:");
foreach (var rankedResponse in response.Ranking)
{
Console.WriteLine(rankedResponse.Id + " " + rankedResponse.Probability);
}
// Send the reward for the action based on user response.
client.Reward(response.EventId, new RewardRequest(reward));
Console.WriteLine("\nPress q to break, any other key to continue:");
runLoop = !(GetKey() == "Q");
} while (runLoop);
}
private static void Main(string[] args)
{
int iteration = 1;
bool runLoop = true;
// Initialize Personalization client.
PersonalizerClient client = InitializePersonalizationClient(ServiceEndpoint);
// Initialize the RSS Feed actions provider
IActionProvider actionProvider = new RSSFeedActionProvider(new RSSParser
{
// Number of items to fetch while crawling the RSS feed
ItemLimit = 2
});
// Initialize the Cognitive Services TextAnalyticsClient for featurizing the crawled action articles
TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClient(new Uri(CognitiveTextAnalyticsEndpoint), new AzureKeyCredential(CognitiveTextAnalyticsAPIKey));
// Initialize the Cognitive Text Analytics actions featurizer
IActionFeaturizer actionFeaturizer = new CognitiveTextAnalyticsFeaturizer(textAnalyticsClient);
var newsActions = new List <RankableAction>();
foreach (var newsTopic in newsRSSFeeds)
{
Console.WriteLine($"Fetching Actions for: {newsTopic.Key} from {newsTopic.Value}");
IList <CrawlAction> crawlActions = actionProvider.GetActionsAsync(newsTopic.Value).Result.ToList();
Console.WriteLine($"Fetched {crawlActions.Count} actions");
actionFeaturizer.FeaturizeActionsAsync(crawlActions).ConfigureAwait(false);
Console.WriteLine($"Featurized actions for {newsTopic.Key}");
// Generate a rankable action for each crawlAction and add the news topic as additional feature
newsActions.AddRange(crawlActions.Select(a =>
{
a.Features.Add(new { topic = newsTopic.Key });
return((RankableAction)a);
}).ToList());
}
do
{
Console.WriteLine("Iteration: " + iteration++);
// Get context information from the user.
string username = GetUserName();
string timeOfDay = GetUsersTimeOfDay();
string location = GetLocation();
// Create current context from user specified data.
IList <object> currentContext = new List <object>()
{
new { username },
new { timeOfDay },
new { location }
};
// Id to associate with the request
string eventId = Guid.NewGuid().ToString();
// Rank the actions
var request = new RankRequest(newsActions, currentContext, null, eventId);
RankResponse response = client.Rank(request);
var recommendedAction = newsActions.Where(a => a.Id.Equals(response.RewardActionId)).FirstOrDefault();
Console.WriteLine("Personalization service thinks you would like to read: ");
Console.WriteLine("Id: " + recommendedAction.Id);
JsonSerializerOptions options = new JsonSerializerOptions
{
WriteIndented = true
};
Console.WriteLine("Features : " + JsonSerializer.Serialize(recommendedAction.Features, options));
Console.WriteLine("Do you like this article ?(y/n)");
float reward = 0.0f;
string answer = GetKey();
if (answer == "Y")
{
reward = 1;
Console.WriteLine("Great!");
}
else if (answer == "N")
{
reward = 0;
Console.WriteLine("You didn't like the recommended news article.");
}
else
{
Console.WriteLine("Entered choice is invalid. Service assumes that you didn't like the recommended news article.");
}
Console.WriteLine("Personalization service ranked the actions with the probabilities as below:");
Console.WriteLine("{0, 10} {1, 0}", "Probability", "Id");
var rankedResponses = response.Ranking.OrderByDescending(r => r.Probability);
foreach (var rankedResponse in rankedResponses)
{
Console.WriteLine("{0, 10} {1, 0}", rankedResponse.Probability, rankedResponse.Id);
}
// Send the reward for the action based on user response.
client.Reward(response.EventId, new RewardRequest(reward));
Console.WriteLine("Press q to break, any other key to continue:");
runLoop = !(GetKey() == "Q");
} while (runLoop);
}
static void Main(string[] args)
{
//SimulateRandomUserPreferences();
int iteration = 1;
bool runLoop = true;
// Get the actions list to choose from personalizer with their features.
IList <RankableAction> actions = GetActions();
// Initialize Personalizer client.
PersonalizerClient client = InitializePersonalizerClient(ServiceEndpoint);
do
{
Console.WriteLine("\nIteration: " + iteration++);
// Get context information from the user.
string userProfession = GetUserProfession();
string UserPreference = GetUsersPreference();
// Create current context from user specified data.
IList <object> currentContext = new List <object>()
{
new { professiom = userProfession },
new { preference = UserPreference }
};
// Exclude an action for personalizer ranking. This action will be held at its current position.
// This simulates a business rule to force the action "juice" to be ignored in the ranking.
// As juice is excluded, the return of the API will always be with a probability of 0.
IList <string> excludeActions = new List <string> {
"http://codestories.gr/index.php/2018/10/21/297/"
};
// Generate an ID to associate with the request.
string eventId = Guid.NewGuid().ToString();
// Rank the actions
var request = new RankRequest(actions, currentContext, excludeActions, eventId);
RankResponse response = client.Rank(request);
Console.WriteLine("\nPersonalizer service thinks you should read this: " + response.RewardActionId + ". Is this correct? (y/n)");
float reward = 0.0f;
string answer = GetKey();
if (answer == "Y")
{
reward = 1;
Console.WriteLine("\nGreat! Enjoy this article.");
}
else if (answer == "N")
{
reward = 0;
Console.WriteLine("\nSorry, but try again we can do better!");
}
else
{
Console.WriteLine("\nEntered choice is invalid. Service assumes that you didn't like the article.");
}
Console.WriteLine("\nPersonalizer service ranked the actions with the probabilities as below:");
foreach (var rankedResponse in response.Ranking)
{
Console.WriteLine(rankedResponse.Id + " " + rankedResponse.Probability);
}
// Send the reward for the action based on user response.
client.Reward(response.EventId, new RewardRequest(reward));
Console.WriteLine("\nPress q to break, any other key to continue:");
runLoop = !(GetKey() == "Q");
} while (runLoop);
}
// </classVariables>
// <mainLoop>
static void Main(string[] args)
{
int iteration = 1;
bool runLoop = true;
IList <PersonalizerRankableAction> actions = GetActions();
PersonalizerClient client = InitializePersonalizerClient(new Uri(ServiceEndpoint));
do
{
Console.WriteLine("\nIteration: " + iteration++);
string timeOfDayFeature = GetUsersTimeOfDay();
string tasteFeature = GetUsersTastePreference();
IList <object> currentContext = new List <object>()
{
new { time = timeOfDayFeature },
new { taste = tasteFeature }
};
IList <string> excludeActions = new List <string> {
"juice"
};
string eventId = Guid.NewGuid().ToString();
var rankOptions = new PersonalizerRankOptions(actions, currentContext, excludeActions, eventId);
PersonalizerRankResult result = client.Rank(rankOptions);
Console.WriteLine("\nPersonalizer service thinks you would like to have: " + result.RewardActionId + ". Is this correct? (y/n)");
float reward = 0.0f;
string answer = GetKey();
if (answer == "Y")
{
reward = 1f;
Console.WriteLine("\nGreat! Enjoy your food.");
}
else if (answer == "N")
{
reward = 0f;
Console.WriteLine("\nYou didn't like the recommended food choice.");
}
else
{
Console.WriteLine("\nEntered choice is invalid. Service assumes that you didn't like the recommended food choice.");
}
Console.WriteLine("\nPersonalizer service ranked the actions with the probabilities as below:");
foreach (var rankedResponse in result.Ranking)
{
Console.WriteLine(rankedResponse.Id + " " + rankedResponse.Probability);
}
client.Reward(result.EventId, reward);
Console.WriteLine("\nPress q to break, any other key to continue:");
runLoop = !(GetKey() == "Q");
} while (runLoop);
}
static void Main(string[] args)
{
int iteration = 1;
bool runLoop = true;
string csvPath = Environment.GetFolderPath(Environment.SpecialFolder.MyDocuments);
string csvFile = System.IO.Path.Combine(csvPath, "PersonalizeDocs/Volkswagen-Models.csv");
List <List <string> > csvFileRead = readCSV(csvFile);
//printCSV(csvFileRead);
// Get the actions list to choose from personalizer with their features.
IList <RankableAction> actions = GetActions(csvFileRead);
//foreach (RankableAction s in actions){ Console.WriteLine(s.Id);
//foreach (object i in s.Features) Console.WriteLine(i);}
// Initialize Personalizer client.
PersonalizerClient client = InitializePersonalizerClient(ServiceEndpoint);
do
{
Console.WriteLine("\nIteration: " + iteration++);
// Get context information from the user.
string typeOfCarFeature = GetUsersCarChoice();
string personalityFeature = GetUsersCarFeatures();
// Create current context from user specified data.
string feat1 = csvFileRead[0][1];
string feat2 = csvFileRead[0][2];
//Console.WriteLine(feat1 + " " + feat2);
IList <object> currentContext = new List <object>()
{
new { feat1 = typeOfCarFeature },
new { feat2 = personalityFeature }
}; //to print
//foreach (object k in currentContext){Console.WriteLine(k);}
// Exclude an action for personalizer ranking. This action will be held at its current position.
IList <string> excludeActions = new List <string> {
"juice"
};
// Generate an ID to associate with the request.
string eventId = Guid.NewGuid().ToString();
// Rank the actions
var request = new RankRequest(actions, currentContext, excludeActions, eventId);
RankResponse response = client.Rank(request);
Console.WriteLine("\nPersonalizer service thinks you would like to have: " + response.RewardActionId + ". Is this correct? (y/n)");
float reward = 0.0f;
string answer = GetKey();
if (answer == "Y")
{
reward = 1;
Console.WriteLine("\nGreat! Enjoy your car.");
}
else if (answer == "N")
{
reward = 0;
Console.WriteLine("\nYou didn't like the recommended car.");
}
else
{
Console.WriteLine("\nEntered choice is invalid. Service assumes that you didn't like the recommended car.");
}
Console.WriteLine("\nPersonalizer service ranked the actions with the probabilities as below:");
foreach (var rankedResponse in response.Ranking)
{
Console.WriteLine(rankedResponse.Id + " " + rankedResponse.Probability);
}
// Send the reward for the action based on user response.
client.Reward(response.EventId, new RewardRequest(reward));
Console.WriteLine("\nPress q to break, any other key to continue:");
runLoop = !(GetKey() == "Q");
} while (runLoop);
}