private static List <ConversationInput> LoadFromXml()
{
var result = new List <ConversationInput>();
var document = new XmlDocument();
document.Load("questions.xml");
var posts = document.LastChild.LastChild.ChildNodes;
int i = 0;
foreach (XmlNode post in posts)
{
try
{
i++;
var inputRow = new ConversationInput();
inputRow.Description = post.FirstChild.Value;
inputRow.Area = post.Attributes["class"].Value;
//inputRow.Title = post.FirstChild.Value;
//inputRow.ClassificationOutput = (uint) (post.Attributes["class"].Value.Contains("Question") ? 1 : 0);
result.Add(inputRow);
}
catch (Exception)
{
}
if (i == 1000)
{
break;
}
}
return(result);
}
public static void PredictIssue()
{
// <SnippetLoadModel>
ITransformer loadedModel = _mlContext.Model.Load(_modelPath, out var modelInputSchema);
// </SnippetLoadModel>
// <SnippetAddTestIssue>
ConversationInput singleIssue = new ConversationInput()
{
Description = "When connecting to the database, EF is crashing"
};
//ConversationInput singleIssue = new ConversationInput() { Description = "What is your name"};
// </SnippetAddTestIssue>
//Predict label for single hard-coded issue
// <SnippetCreatePredictionEngine>
_predEngine = _mlContext.Model.CreatePredictionEngine <ConversationInput, SentenceClassifiedOutput>(loadedModel);
// </SnippetCreatePredictionEngine>
// <SnippetPredictIssue>
var prediction = _predEngine.Predict(singleIssue);
// </SnippetPredictIssue>
// <SnippetDisplayResults>
//Console.WriteLine($"=============== Single Prediction - Result: {prediction.Area} ===============");
// </SnippetDisplayResults>
}
private void btnTestSentence_Click(object sender, EventArgs e)
{
var _mlContext = new MLContext(seed: 0);
ITransformer _trainedModel = _mlContext.Model.Load("model.zip", out DataViewSchema inputSchema);
PredictionEngine <ConversationInput, SentenceClassifiedOutput> _predEngine = _mlContext.Model.CreatePredictionEngine <ConversationInput, SentenceClassifiedOutput>(_trainedModel);
ConversationInput issue = new ConversationInput()
{
Description = txtInput.Text
};
var prediction = _predEngine.Predict(issue);
txtOutPut.Text = prediction.Area;
}
public static IEstimator <ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator <ITransformer> pipeline)
{
// STEP 3: Create the training algorithm/trainer
// Use the multi-class SDCA algorithm to predict the label using features.
//Set the trainer/algorithm and map label to value (original readable state)
// <SnippetAddTrainer>
var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// </SnippetAddTrainer>
// STEP 4: Train the model fitting to the DataSet
//Console.WriteLine($"=============== Training the model ===============");
// <SnippetTrainModel>
_trainedModel = trainingPipeline.Fit(trainingDataView);
// </SnippetTrainModel>
//Console.WriteLine($"=============== Finished Training the model Ending time: {DateTime.Now.ToString()} ===============");
// (OPTIONAL) Try/test a single prediction with the "just-trained model" (Before saving the model)
//Console.WriteLine($"=============== Single Prediction just-trained-model ===============");
// Create prediction engine related to the loaded trained model
// <SnippetCreatePredictionEngine1>
_predEngine = _mlContext.Model.CreatePredictionEngine <ConversationInput, SentenceClassifiedOutput>(_trainedModel);
// </SnippetCreatePredictionEngine1>
// <SnippetCreateTestIssue1>
ConversationInput issue = new ConversationInput()
{
//Title = "WebSockets communication is slow in my machine",
Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."
};
// </SnippetCreateTestIssue1>
// <SnippetPredict>
var prediction = _predEngine.Predict(issue);
// </SnippetPredict>
// <SnippetOutputPrediction>
//Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
// </SnippetOutputPrediction>
// <SnippetReturnModel>
return(trainingPipeline);
// </SnippetReturnModel>
}