private Tuple <string, string> callPersonalizationService(HttpRequestFeatures httpRequestFeatures)
{
// Generate an ID to associate with the request.
string eventId = Guid.NewGuid().ToString();
// Get the actions list to choose from personalization with their features.
IList <RankableAction> actions = GetActions();
// 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 },
new { httpRequestFeatures }
};
// Exclude an action for personalization ranking. This action will be held at its current position.
IList <string> excludeActions = new List <string> {
"juice"
};
// Rank the actions
var request = new RankRequest(actions, currentContext, excludeActions, eventId);
RankResponse response = client.Rank(request);
string rankjson = JsonConvert.SerializeObject(request, Formatting.Indented);
string rewardjson = JsonConvert.SerializeObject(response, Formatting.Indented);
return(Tuple.Create(rankjson, rewardjson));
}
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);
}
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 async Task RankNullParameters()
{
using (MockContext.Start(this.GetType()))
{
HttpMockServer.Initialize(this.GetType(), "RankNullParameters");
IPersonalizerClient client = GetClient(HttpMockServer.CreateInstance());
IList <RankableAction> actions = new List <RankableAction>();
actions.Add(new RankableAction
{
Id = "Person",
Features =
new List <object>()
{
new { videoType = "documentary", videoLength = 35, director = "CarlSagan" }, new { mostWatchedByAge = "30-35" }
}
});
var request = new RankRequest(actions);
// Action
RankResponse response = await client.RankAsync(request);
// Assert
Assert.Equal(actions.Count, response.Ranking.Count);
for (int i = 0; i < response.Ranking.Count; i++)
{
Assert.Equal(actions[i].Id, response.Ranking[i].Id);
}
}
}
public RankResponse GetRecommendations(IList <object> context, bool useTextAnalytics = false)
{
var eventId = Guid.NewGuid().ToString();
var actions = _actionsRepository.GetActions(useTextAnalytics);
var request = new RankRequest(actions, context, null, eventId);
RankResponse response = _personalizerClient.Rank(request);
return(response);
}
public RankResponse GetArenaRank(string siteId, Guid managerId, int rankType, int pageIndex, int pageSize)
{
RankResponse response = new RankResponse();
response.Data = new RankDataEntity();
try
{
var arenaList = ArenaCore.Instance.GetRankList(siteId);
var resultList = new List <BaseRankEntity>();
int index = 0;
foreach (var item in arenaList)
{
index++;
if (index > pageSize)
{
break;
}
BaseRankEntity entity = new RankArenaEntity()
{
Integral = item.Integral,
Logo = item.Logo,
ManagerId = item.ManagerId,
ZoneName = item.ZoneName,
SiteId = item.SiteId,
Rank = item.Rank,
Name = item.ZoneName + "." + item.ManagerName,
};
resultList.Add(entity);
}
response.Data.Ranks = resultList;
response.Data.RankType = rankType;
var myData = ArenaCore.Instance.GetRankInfo(managerId, siteId);
if (myData == null)
{
response.Data.MyRank = -1;
response.Data.MyData = 0;
response.Data.MyZoneName = "";
return(response);
}
response.Data.MyRank = myData.Rank;
if (response.Data.MyRank == 0)
{
response.Data.MyRank = -1;
}
response.Data.MyData = myData.Integral; //积分
response.Data.MyZoneName = myData.ZoneName + "." + myData.ManagerName;
}
catch (Exception ex)
{
SystemlogMgr.Error("获取竞技场排名", ex);
response.Code = (int)MessageCode.NbParameterError;
}
return(response);
}
public JsonResult Recommendation([FromBody] UserContext context)
{
var currentContext = CreateContext(context, context.UseUserAgent ? Request : null);
var eventId = Guid.NewGuid().ToString();
var actions = _actionsRepository.GetActions(context.UseTextAnalytics);
var request = new RankRequest(actions, currentContext, null, eventId);
RankResponse response = _client.Rank(request);
return(new JsonResult(response));
}
public async Task RankServerFeatures()
{
using (MockContext.Start(this.GetType().FullName))
{
HttpMockServer.Initialize(this.GetType().FullName, "RankServerFeatures");
IPersonalizerClient client = GetClient(HttpMockServer.CreateInstance());
IList <object> contextFeatures = new List <object>()
{
new { Features = new { day = "tuesday", time = "night", weather = "rainy" } },
new { Features = new { userId = "1234", payingUser = true, favoriteGenre = "documentary", hoursOnSite = 0.12, lastwatchedType = "movie" } }
};
IList <RankableAction> actions = new List <RankableAction>();
actions.Add(new RankableAction
{
Id = "Person1",
Features =
new List <object>()
{
new { videoType = "documentary", videoLength = 35, director = "CarlSagan" }, new { mostWatchedByAge = "30-35" }
}
});
actions.Add(new RankableAction
{
Id = "Person2",
Features =
new List <object>()
{
new { videoType = "documentary", videoLength = 35, director = "CarlSagan" }, new { mostWatchedByAge = "40-45" }
}
});
IList <string> excludeActions = new List <string> {
"Person1"
};
string eventId = "123456789";
var request = new RankRequest(actions, contextFeatures, excludeActions, eventId);
// Action
RankResponse response = await client.RankAsync(request);
// Assert
Assert.Equal(eventId, response.EventId);
Assert.Equal(actions.Count, response.Ranking.Count);
for (int i = 0; i < response.Ranking.Count; i++)
{
Assert.Equal(actions[i].Id, response.Ranking[i].Id);
}
}
}
public async Task <Dictionary <Guid, RankStats> > GetRanksAsync(Platform platform, Region region, Season season, params Guid[] userIds)
{
RankResponse response = await RankRequests.GetRank(this, platform, region, season, userIds);
return(response.Players.ToDictionary(kv => Guid.Parse(kv.Key), kv => new RankStats
{
Abandons = kv.Value.Abandons,
Losses = kv.Value.Losses,
Mmr = kv.Value.Mmr,
Rank = kv.Value.Rank,
SeasonId = kv.Value.Season,
Wins = kv.Value.Wins,
MaxMmr = kv.Value.MaxMmr,
MaxRank = kv.Value.MaxRank,
Region = FormatUtility.RegionFromString(kv.Value.Region) ?? throw new FormatException("Unknown region format")
}));
public void Train(TrainingCase[] cases)
{
if (cases != null)
{
foreach (TrainingCase testCase in cases)
{
Console.WriteLine($"{testCase.Name}:");
string lessonId = Guid.NewGuid().ToString();
var request = new RankRequest(Actions, testCase.Features, testCase.Exclude, lessonId, false);
RankResponse response = Client.Rank(request);
double reward = 0.0;
if (response.RewardActionId.Equals(testCase.Expected))
{
reward = 1.0;
}
Client.Reward(response.EventId, new RewardRequest(reward));
}
}
}
public async Task <IActionResult> GetSearchMethodForRank(RankRequest request, string Name)
{
var result = new RankResponse();
var rank = await _context.Ranks.Take(request.Quantity).Where(c => c.Name.StartsWith(Name) || c.Name.Contains(Name) || c.Name.EndsWith(Name)).Select(p => new Ranks {
RankId = p.Id, Name = p.Name
}).ToListAsync();
if (rank.Count == 0)
{
result.Code = -100;
result.Message = "Can't get products with given parameters.";
return(Ok(result));
}
result.Code = 100;
result.Message = "Success";
result.Ranks = rank;
return(Ok(result));
}
public async Task <IActionResult> GetRank(RankRequest request)
{
var result = new RankResponse();
var rank = await _context.Ranks.Skip(request.Skip).Take(request.Quantity).Select(p => new Ranks {
RankId = p.Id, Name = p.Name
}).ToListAsync();
if (rank.Count == 0)
{
result.Code = -100;
result.Message = "Can't get products with given parameters.";
return(Ok(result));
}
result.Code = 100;
result.Message = "Success";
result.Ranks = rank;
return(Ok(result));
}
private static void RunTasteTest(PersonalizerService personalizer)
{
IList <RankableAction> actions = new List <RankableAction>
{
new RankableAction
{
Id = "pasta",
Features =
new List <object>()
{
new { taste = "salty", spiceLevel = "medium" }, new { nutritionLevel = 5, cuisine = "italian" }
}
},
new RankableAction
{
Id = "salad",
Features =
new List <object>()
{
new { taste = "salty", spiceLevel = "low" }, new { nutritionLevel = 8 }
}
}
};
string id = Guid.NewGuid().ToString();
IList <object> currentContext = new List <object>()
{
new { time = "morning" },
new { taste = "salty" }
};
IList <string> exclude = new List <string> {
"pasta"
};
var request = new RankRequest(actions, currentContext, exclude, id);
RankResponse resp = personalizer.Client.Rank(request);
Assert.AreEqual("salad", resp.RewardActionId);
}
public string getBestNewsArticleID()
{
// Create context to personalize for, from user data or contextual information
IList <object> currentContext = new List <object>()
{
new { time = "EarlyEvening" },
new { weather = "Rainy" },
new { userFavoriteTopic = "SpaceExploration" },
new { userProfile = "PayingUser" }
//Add your own features in your own schema
};
// Provide a list of possible actions -articles- to choose from
var actionsB = new RankableAction[] {
new RankableAction("news_article_1", new List <object> {
new {
topic = "politics",
breakingNews = true,
controversy = false
}
}),
new RankableAction("news_article_2", new List <object> {
new {
topic = "technology",
breakingNews = false,
publishedWithin = "week"
}
})
};
var request = new RankRequest(actions, currentContext, null, eventId);
// Call Personalizer and get the action that should be shown
RankResponse response = personalizer.Rank(request);
//Use the result given by Personalizer
return(response.RewardActionId);
}
public async Task <IActionResult> Run(
[HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "recommendation")] HttpRequest req,
ILogger log)
{
log.LogInformation("C# HTTP trigger function processed a request.");
var client = InitializePersonalizerClient();
var currentContext = GetContextFeatures(req);
var actions = await GetActions();
string eventId = Guid.NewGuid().ToString();
IList <string> excludeActions = new List <string>();
var request = new RankRequest(actions, currentContext, excludeActions, eventId);
RankResponse response = client.Rank(request);
var recommendationDetail = new RecommendationDetail();
recommendationDetail.EventId = response.EventId;
recommendationDetail.RewardActionId = response.RewardActionId;
return(new OkObjectResult(recommendationDetail));
}
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);
}
public async Task <ActionResult <RankedRecipes> > GetRecommendations()
{
try
{
if (!HttpContext.User.Identity.IsAuthenticated)
{
throw new Exception();
}
var apiKey = (await _context.AppSettings.FirstOrDefaultAsync(setting => setting.EnumCode == (int)Enums.AppSetting.PersonalizerApiKey)).Value;
var serviceEndpoint = (await _context.AppSettings.FirstOrDefaultAsync(setting => setting.EnumCode == (int)Enums.AppSetting.PersonalizerServiceEndpoint)).Value;
PersonalizerClient client = new PersonalizerClient(new ApiKeyServiceClientCredentials(apiKey))
{
Endpoint = serviceEndpoint
};
var idUser = GetUserId();
var userRecipeTags = await _context.AspNetUserRecipeTag.Where(userTag => userTag.IdUser == idUser)
.Join(
_context.RecipeTags,
userTag => userTag.IdRecipeTag,
tag => tag.Id,
(userTag, tag) => tag)
.Join(
_context.RecipeTagTypes,
tag => tag.IdRecipeTagType,
type => type.Id,
(tag, type) => new { tag, type }).ToListAsync();
var userRecipeTagDictionary = userRecipeTags.GroupBy(recipe => recipe.type).ToDictionary(mapping => mapping.Key.EnumCode, mapping => userRecipeTags.Where(tag => tag.type.Id == mapping.Key.Id).Select(tag => tag.tag.Name));
IList <object> currentContext = new List <object>()
{
new { Cuisine = userRecipeTagDictionary.ContainsKey((int)Shared.Enums.Recipe.RecipeTagType.Cuisine) ? string.Join(',', userRecipeTagDictionary[(int)Shared.Enums.Recipe.RecipeTagType.Cuisine]) : string.Empty },
new { Dietary = userRecipeTagDictionary.ContainsKey((int)Shared.Enums.Recipe.RecipeTagType.Dietary) ? string.Join(',', userRecipeTagDictionary[(int)Shared.Enums.Recipe.RecipeTagType.Dietary]) : string.Empty },
new { Occasion = userRecipeTagDictionary.ContainsKey((int)Shared.Enums.Recipe.RecipeTagType.Occasion) ? string.Join(',', userRecipeTagDictionary[(int)Shared.Enums.Recipe.RecipeTagType.Occasion]) : string.Empty },
new { Meal = userRecipeTagDictionary.ContainsKey((int)Shared.Enums.Recipe.RecipeTagType.Meal) ? string.Join(',', userRecipeTagDictionary[(int)Shared.Enums.Recipe.RecipeTagType.Meal]) : string.Empty },
new { Seasonal = userRecipeTagDictionary.ContainsKey((int)Shared.Enums.Recipe.RecipeTagType.Seasonal) ? string.Join(',', userRecipeTagDictionary[(int)Shared.Enums.Recipe.RecipeTagType.Seasonal]) : string.Empty },
new { Holiday = userRecipeTagDictionary.ContainsKey((int)Shared.Enums.Recipe.RecipeTagType.Holiday) ? string.Join(',', userRecipeTagDictionary[(int)Shared.Enums.Recipe.RecipeTagType.Holiday]) : string.Empty }
};
(IEnumerable <Recipe> recipes, string selectedSearchCriteria) = await GetUserRecipes();
if (recipes != null)
{
var recipeWithTags = recipes.Join(_context.Recipe_RecipeTags,
recipe => recipe.Id,
recipeTagMapping => recipeTagMapping.IdRecipe,
(recipe, recipeTagMapping) => new { recipe, recipeTagMapping })
.Join(_context.RecipeTags,
recipeTagMapping => recipeTagMapping.recipeTagMapping.IdRecipeTag,
recipeTag => recipeTag.Id,
(recipeTagMapping, recipeTag) => new { recipeTagMapping.recipe, recipeTag })
.Join(_context.RecipeTagTypes,
tag => tag.recipeTag.IdRecipeTagType,
type => type.Id,
(tag, type) => new { tag.recipe, tag.recipeTag, type }).ToList();
var recipeWithTagsDictionary = recipeWithTags.GroupBy(recipe => recipe.recipe).ToDictionary(group => group.Key, group => recipeWithTags.Where(item => item.recipe == group.Key).Select(item => item));
IList <RankableAction> actions = recipeWithTagsDictionary.Select(recipe => new RankableAction()
{
Id = recipe.Key.Id.ToString(),
Features = new List <object>()
{
new { Cuisine = string.Join(',', recipe.Value.Where(item => item.type.EnumCode == (int)Shared.Enums.Recipe.RecipeTagType.Cuisine).Select(item => item.recipeTag.Name)) },
new { Dietary = string.Join(',', recipe.Value.Where(item => item.type.EnumCode == (int)Shared.Enums.Recipe.RecipeTagType.Dietary).Select(item => item.recipeTag.Name)) },
new { Occasion = string.Join(',', recipe.Value.Where(item => item.type.EnumCode == (int)Shared.Enums.Recipe.RecipeTagType.Occasion).Select(item => item.recipeTag.Name)) },
new { Meal = string.Join(',', recipe.Value.Where(item => item.type.EnumCode == (int)Shared.Enums.Recipe.RecipeTagType.Meal).Select(item => item.recipeTag.Name)) },
new { Seasonal = string.Join(',', recipe.Value.Where(item => item.type.EnumCode == (int)Shared.Enums.Recipe.RecipeTagType.Seasonal).Select(item => item.recipeTag.Name)) },
new { Holiday = string.Join(',', recipe.Value.Where(item => item.type.EnumCode == (int)Shared.Enums.Recipe.RecipeTagType.Holiday).Select(item => item.recipeTag.Name)) }
}
}).Take(50).ToList();
IList <string> excludeActions = new List <string>();
var eventId = Guid.NewGuid().ToString();
// Rank the actions
var request = new RankRequest(actions, currentContext, excludeActions, eventId);
RankResponse response = await client.RankAsync(request);
var rankedRecipes = await GetRecipes(response.Ranking, Convert.ToInt64(response.RewardActionId));
return(new RankedRecipes()
{
EventId = response.EventId,
RecommendedRecipeId = Convert.ToInt64(response.RewardActionId),
Recipes = rankedRecipes,
RecipeSearchCriteria = selectedSearchCriteria
});
}
return(null);
}
catch (Exception)
{
return(StatusCode(StatusCodes.Status500InternalServerError, "Error retrieving recipes"));
}
}
/// <summary>
/// Interatively asks for feature values to rank the actions. Rewards based on whether the returned action is the one the user expected.
/// </summary>
/// <param name="select">Features to use in selection process</param>
/// <param name="ignore">List of actions to ignore</param>
public void InteractiveTraining(string[] select, string[] ignore)
{
if (Actions == null || Actions.Count == 0)
{
Console.WriteLine("Nothing to select.");
return;
}
if (select == null || select.Length == 0)
{
Console.WriteLine("No features selected.");
return;
}
int lessonCount = 1;
do
{
Console.WriteLine($"Lesson {lessonCount}");
// Build context list by creating a JSON string and then convert it to a list of objects.
string[] answers = new string[select.Length];
for (int i = 0; i < select.Length; i++)
{
answers[i] = SelectFeatureInteractively(select[i]);
if (answers[i] == "Q")
{
// When null is returned the training session is over.
return;
}
}
IList <Object> contextFeatures = FeatureList(select, answers);
// Create an id for this lesson, used when setting the reward.
string lessonId = Guid.NewGuid().ToString();
// Create a list of Personalizer.Actions that should be excluded from the ranking
List <string> excludeActions = null;
if (ignore != null && ignore.Length > 0)
{
excludeActions = new List <string>(ignore);
}
// Create the rank requestr
var request = new RankRequest(Actions, contextFeatures, excludeActions, lessonId, false);
RankResponse response = null;
response = Client.Rank(request);
//response = new RankResponse();
Console.WriteLine($"Personalizer service thinks you would like to have: {response.RewardActionId}. Is this correct (y/n)?");
string answer = GetKey();
Console.WriteLine();
double reward = 0.0;
if (answer == "Y")
{
reward = 1.0;
Client.Reward(response.EventId, new RewardRequest(reward));
Console.WriteLine($"Set reward: {reward}");
}
else if (answer == "N")
{
Client.Reward(response.EventId, new RewardRequest(reward));
Console.WriteLine($"Set reward: {reward}");
}
else
{
Console.WriteLine("Entered choice is invalid. Not setting reward.");
}
} while (true);
}
private static void Main(string[] args)
{
int iteration = 1;
bool runLoop = true;
// Initialize Personalization client.
PersonalizerClient client = InitializePersonalizationClient(ServiceEndpoint);
// Initialize the RSS Feed actions provider
IActionProvider actionProvider = new RSSFeedActionProvider(new RSSParser
{
// Number of items to fetch while crawling the RSS feed
ItemLimit = 2
});
// Initialize the Cognitive Services TextAnalyticsClient for featurizing the crawled action articles
TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClient(new Uri(CognitiveTextAnalyticsEndpoint), new AzureKeyCredential(CognitiveTextAnalyticsAPIKey));
// Initialize the Cognitive Text Analytics actions featurizer
IActionFeaturizer actionFeaturizer = new CognitiveTextAnalyticsFeaturizer(textAnalyticsClient);
var newsActions = new List <RankableAction>();
foreach (var newsTopic in newsRSSFeeds)
{
Console.WriteLine($"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);
}
/// <summary>
/// Approach
/// 1. Generate actions
/// 2. Generate user contexts
/// For each user context
/// Get actions
/// For each action, calculate a score for the user
/// If the top action recommended by the service also has the top like score, send 1 back to the API.
/// Variant a: ScoreHalfRewards = false, send back 0 to the API otherwise.
/// Variant b: ScoreHalfRewards = true, if the top action recommended by the service has the second-highest like score, send 0.5 back to the API.
/// </summary>
/// <param name="endpoint"></param>
/// <param name="apiKey"></param>
/// <param name="scoreHalfRewards"></param>
/// <returns></returns>
public async Task <List <SegmentScore> > ProcessAsync(string endpoint, string apiKey, bool scoreHalfRewards, int howManyActions, int howManyUserContexts, int howManyUsersPerSegment, int segmentPauseMilliseconds)
{
this.HowManyActions = howManyActions;
this.HowManyUserContexts = howManyUserContexts;
this.HowManyUsersPerSegment = howManyUsersPerSegment;
this.SegmentPauseMilliseconds = segmentPauseMilliseconds;
List <SegmentScore> result = new List <SegmentScore>();
int overallCounter = 0;
SegmentScore segmentScore = new SegmentScore();
segmentScore.Segment = 1;
using (PersonalizerClient client = InitializePersonalizerClient(endpoint, apiKey))
{
// Iterate through each context - i.e. each interaction where we want to get a ranked list of choices
foreach (var context in UserContexts)
{
overallCounter++;
segmentScore.Count++;
// Each interaction (context + choices -> ranked choices -> user behavior -> send feedback) requires a unique ID to correlate throughout
string eventId = Guid.NewGuid().ToString();
var rankingRequest = new RankRequest(this.Actions, context, null, eventId, false);
RankResponse response = await client.RankAsync(rankingRequest);
// These are our calcs for points, for THIS user/context, for each of the actions
IDictionary <string, int> actionScoresForContext = this.GetActionScoresForContext(context);
// Using THIS user/context's points for each action, calculate a reward based on whether the API "got it right"
double reward = CalculateReward(actionScoresForContext, response.RewardActionId, scoreHalfRewards);
Console.WriteLine($"Iteration {overallCounter} = reward {reward}");
// Send feedback to the API - for this event (ranking interaction), what is the reward we calculated
await client.RewardAsync(response.EventId, new RewardRequest(reward));
// We are tracking segments of users so we can more clearly track reward accumulation / API performance
// Manage this segment's rewards here
segmentScore.TotalReward += reward;
if (reward > 0)
{
if (reward == 1)
{
segmentScore.CountRewardFull++;
}
else if (reward < 1)
{
segmentScore.CountRewardHalf++;
}
}
if (segmentScore.Count % this.HowManyUsersPerSegment == 0)
{
result.Add(segmentScore);
int newSegment = segmentScore.Segment + 1;
segmentScore = new SegmentScore()
{
Segment = newSegment
};
// As we complete each segment of users let's pause for a time to allow the API to retrain
// This is artificial so we can watch the API's performance improve in this contrived app
// In the real world we wouldn't do this since ongoing usage would be far lengthier than a quick sample app like this
if (overallCounter < this.HowManyUserContexts)
{
Console.WriteLine();
Console.WriteLine("Sleeping for service training...");
Thread.Sleep(this.SegmentPauseMilliseconds);
Console.WriteLine("Completed sleep, continuing");
Console.WriteLine();
}
}
}
}
return(result);
}
public static void Run([CosmosDBTrigger(
databaseName: "changefeedlabdatabase",
collectionName: "changefeeddemocollection",
ConnectionStringSetting = "DBconnection",
LeaseCollectionName = "leases")] IReadOnlyList <Document> input, ILogger log)
{
// Get the actions list to choose from personalizer with their features.
IList <RankableAction> actions = GetActions();
string apikey = "API key here";
string endpoint = "URL to cognitive service";
PersonalizerClient client = InitializePersonalizerClient(endpoint, apikey);
// Iterate through modified documents from change feed.
foreach (var doc in input)
{
if (doc.GetPropertyValue <string>("Action") == "Viewed")
{
// Get context information.
string itemCategoryFeature = doc.GetPropertyValue <string>("Item");
if (itemCategoryFeature.StartsWith("W"))
{
itemCategoryFeature = "Women";
}
else
{
itemCategoryFeature = "Men";
}
string itemPriceFeature = doc.GetPropertyValue <string>("Price");
// Create current context from user specified data.
IList <object> currentContext = new List <object>()
{
new { category = itemCategoryFeature },
new { price = itemPriceFeature }
};
// 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 ranked the actions with the probabilities as below:");
foreach (var rankedResponse in response.Ranking)
{
log.LogInformation($"Id: {rankedResponse.Id}, Probability: {rankedResponse.Probability}");
}
// Send the reward for the action based on user response.
//client.Reward(response.EventId, new RewardRequest(reward));
}
/*
* "CartID": 4673,
* "Action": "Purchased",
* "Item": "Women's Flannel Shirt",
* "Price": 19.99,
* */
}
}
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);
}
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);
}
