public async Task Predict()
{
var contents = File.ReadAllText(PredictionModelWrapper.GetFilePath(@"test.csv"));
var data = FileUtil.Read(contents);
var plainClassifiers = new List <KeyValuePair <string, TextClassificationResult> >();
foreach (var item in data)
{
var result = await Matcher.Match(item.Description);
if (result.Classifier != null)
{
var kvp = new KeyValuePair <string, TextClassificationResult>(item.SubCategory, result);
plainClassifiers.Add(kvp);
}
}
var correct = plainClassifiers.Count(x => x.Key == x.Value.Classifier.SubCategory);
var over = (double)plainClassifiers.Count;
var percentage = correct / over;
Console.WriteLine(percentage.ToString("P5"));
percentage.Should().BeGreaterThan(.3, percentage.ToString("P5"));
}
public static async Task <IActionResult> Run([HttpTrigger(AuthorizationLevel.Function, "get", "post", Route = null)]
HttpRequest req, TraceWriter log)
{
var r = new StreamReader(req.Body);
log.Info("Prediction trigger function started...");
var content = await r.ReadToEndAsync();
log.Info(content);
//if (typeof(Microsoft.ML.Runtime.Data.LoadTransform) == null ||
// typeof(Microsoft.ML.Runtime.Learners.LinearClassificationTrainer) == null ||
// typeof(Microsoft.ML.Runtime.Internal.CpuMath.SseUtils) == null)
//{
// log.Info("Assemblies are NOT loaded correctly");
// return new BadRequestObjectResult("ML model failed to load");
//}
var request = JsonConvert.DeserializeObject <PredictionRequest>(content);
var model = await PredictionModel.ReadAsync <BankStatementLineItem, PredictedLabel>(PredictionModelWrapper.GetModel());
var predicted = model.Predict(BankStatementLineItem.ToBankStatementLineItem(request));
//return predicted != null
// ? (ActionResult) new OkObjectResult(predicted.SubCategory)
// : new BadRequestObjectResult("prediction failed");
return(new BadRequestObjectResult("no dice"));
}
