public void GitHubUserGetUserIssuesTestSuccess()
{
// npm
const string githubUsername = "******";
var data = new GitHubIssueData();
var repos = data.GetUserIssues(githubUsername);
foreach (var githubRepo in repos)
{
// flexbox, chrome dev, remy sharp
Debug.WriteLine("github: " + githubRepo.Title);
}
Assert.IsNotEmpty(repos);
}
private static void PredictIssue()
{
//Load the saved model into your application
ITransformer loadedModel = _mlContext.Model.Load(_modelPath, out var modelInputSchema);
//Add a GitHub issue to test the trained model's prediction
GitHubIssueData singleIssue = new GitHubIssueData()
{
Title = "Entity Framework crashes", Description = "When connecting to the database, EF is crashing"
};
_predEngine = _mlContext.Model.CreatePredictionEngine <GitHubIssueData, IssuePrediction>(loadedModel);
var prediction = _predEngine.Predict(singleIssue);
Console.WriteLine($"=============== Single Prediction - Result: {prediction.Area} ===============");
}
/// <summary>
/// Returns the model.
/// </summary>
/// <param name="trainingDataView"></param>
/// <param name="pipeline"></param>
/// <returns></returns>
public static IEstimator <ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator <ITransformer> pipeline)
{
var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
Console.WriteLine("------transforming the dataset and applying the training.--------");
_trainedModel = trainingPipeline.Fit(trainingDataView);//Fit()method trains your model by transforming the dataset and applying the training.
_predEngine = _mlContext.Model.CreatePredictionEngine <GitHubIssueData, IssuePrediction>(_trainedModel);
GitHubIssueData issue = new GitHubIssueData()
{
Title = "WebSockets communication is slow in my machine",
Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."
};
var prediction = _predEngine.Predict(issue);// Predict() function makes a prediction on a single row of data:
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
return(trainingPipeline);
}
public void GitHubUserHaveRepositoryTestSuccess()
{
var data = new GitHubIssueData();
var repos = data.GetUserRepos(this.userName);
Assert.IsNotEmpty(repos);
}
private void ButtonUpdate_Click(object sender, RoutedEventArgs e)
{
var gi = new GitHubIssueData();
var repos = gi.GetUserRepos("kedde");
MessageBox.Show("Update data grid with issues: " + repos.Count);
}
