/// <summary>
/// Maps Custom Text test example and prediction response
/// </summary>
/// <param name="documentText"></param>
/// <param name="labeledExample"></param>
/// <param name="predictionResponse"></param>
/// <param name="modelsDictionary"></param>
/// <returns>Testing Example model used by nuget</returns>
public static TestingExample CreateTestExample(string documentText, Example labeledExample, CustomTextPredictionResponse predictionResponse, Dictionary <string, string> modelsDictionary)
{
var PredictionData = MapCutomTextPredictionResponse(predictionResponse);
PredictionObject groundTruth = MapCustomTextLabeledExample(modelsDictionary, labeledExample);
return(new TestingExample
{
Text = documentText,
LabeledData = groundTruth,
PredictedData = PredictionData
});
}
/// <summary>
/// Map CustomText labeled example
/// </summary>
/// <param name="modelsDictionary"></param>
/// <param name="e"></param>
/// <returns>PredictionObject used by evaluation nuget</returns>
private static PredictionObject MapCustomTextLabeledExample(Dictionary <string, string> modelsDictionary, Example e)
{
var actualClassNames = e.ClassificationLabels?.Where(c => c.Label == true).Select(c => modelsDictionary[c.ModelId]).ToList();
actualClassNames = actualClassNames ?? new List <string>();
var groundTruth = new PredictionObject
{
Classification = actualClassNames,
Entities = MapCustomTextLabeledEntities(e.MiniDocs, modelsDictionary)
};
return(groundTruth);
}
void UpdatePrediction(PredictionObject obj)
{
obj.Update();
////Day Start////
Label_Full_Day_Average.Text = obj.GetFullDayAverage().ToString("F8") + " btc";
Label_Full_Day_Median.Text = obj.GetFullDayMedian().ToString("F8") + " btc";
Label_Hour_Day_Average.Text = obj.GetHourDayAverage().ToString("F8") + " btc";
Label_Hour_Day_Median.Text = obj.GetHourDayMedian().ToString("F8") + " btc";
////Day End////
////Week Start////
Label_Full_Week_Average.Text = obj.GetFullWeekAverage().ToString("F8") + " btc";
Label_Full_Week_Median.Text = obj.GetFullWeekMedian().ToString("F8") + " btc";
Label_Hour_Week_Average.Text = obj.GetHourWeekAverage().ToString("F8") + " btc";
Label_Hour_Week_Median.Text = obj.GetHourWeekMedian().ToString("F8") + " btc";
////Week End////
////Month Start////
Label_Full_Month_Average.Text = obj.GetFullMonthAverage().ToString("F8") + " btc";
Label_Full_Month_Median.Text = obj.GetFullMonthMedian().ToString("F8") + " btc";
Label_Hour_Month_Average.Text = obj.GetHourMonthAverage().ToString("F8") + " btc";
Label_Hour_Month_Median.Text = obj.GetHourMonthMedian().ToString("F8") + " btc";
////Month End////
}
public static async Task <string> MakePredictionRequest(string ImageUrl, ITurnContext <IMessageActivity> turnContext, CancellationToken cancellationToken)
{
string url = "https://uksouth.api.cognitive.microsoft.com/customvision/v3.0/Prediction/1660c786-db2d-4626-b37b-37486b2fe7eb/classify/iterations/Iteration5/image";
using (var client = new HttpClient())
{
// Request headers - replace this example key with your valid Prediction-Key.
client.DefaultRequestHeaders.Add("Prediction-Key", "<PREDICTION KEY>");
HttpResponseMessage response;
byte[] byteData;
// Request body. Try this sample with a locally stored image.
using (var webClient = new WebClient())
{
byteData = webClient.DownloadData(ImageUrl);
}
using (var content = new ByteArrayContent(byteData))
{
content.Headers.ContentType = new MediaTypeHeaderValue("application/octet-stream");
response = await client.PostAsync(url, content);
var responseStr = await response.Content.ReadAsStringAsync();
PredictionObject rootObject = JsonConvert.DeserializeObject <PredictionObject>(responseStr);
var mainTag = rootObject.predictions.First().tagName;
foreach (var prediction in rootObject.predictions)
{
string output = prediction.tagName + "-confidence: " + prediction.probability;
//await turnContext.SendActivityAsync(MessageFactory.Text(output), cancellationToken);
}
return(mainTag);
}
}
}
public static TheoryData EvaluateModelTestData()
{
var predictionData = new PredictionObject
{
Classification = new List <string> {
"testClass"
},
Entities = new List <Entity>()
{
new Entity
{
Name = "testEntity",
StartPosition = 1,
EndPosition = 20,
Children = new List <Entity>()
{
new Entity
{
Name = "testChildEntity",
StartPosition = 4,
EndPosition = 10,
Children = null
}
}
}
}
};
var wrongPrediction = new PredictionObject
{
Classification = new List <string> {
"wrongClass"
},
Entities = new List <Entity>()
{
new Entity
{
Name = "wrongEntity",
StartPosition = 5,
EndPosition = 8,
Children = new List <Entity>()
{
new Entity
{
Name = "testWrongChildEntity",
StartPosition = 4,
EndPosition = 10,
Children = null
}
}
}
}
};
var perfectScore = 1.0;
var worstScore = 0.0;
return(new TheoryData <List <TestingExample>, double, int, int>
{
{
new List <TestingExample> {
new TestingExample
{
PredictedData = predictionData,
LabeledData = predictionData
}
},
perfectScore,
2,
1
},
{
new List <TestingExample> {
new TestingExample
{
PredictedData = wrongPrediction,
LabeledData = predictionData
}
},
worstScore,
4,
2
}
});
}