public async Task <IActionResult> CreateModel([FromBody] CreateMachineLearningModel request)
{
Guid trainingId = Guid.NewGuid();
Guid correlationId = Guid.NewGuid();
await _bus.Publish(new StartTraining(
id : request.TargetFolderId,
parentId : request.TargetFolderId,
sourceBlobId : request.SourceBlobId,
scaler : request.Scaler ?? "",
sourceBucket : request.SourceBucket,
correlationId : correlationId,
userId : request.UserId,
sourceFileName : request.SourceFileName,
methods : request.Methods,
className : request.TrainingParameter,
subSampleSize : request.SubSampleSize,
testDataSize : request.TestDataSize,
kFold : request.KFold,
fingerprints : request.Fingerprints == null ? new List <Dictionary <string, object> >() : request.Fingerprints.Select(f => new Dictionary <string, object>()
{
{ "Type", f.Type },
{ "Size", f.Size },
{ "Radius", f.Radius }
}),
optimize : request.Optimize,
hyperParameters : request.HyperParameters,
dnnLayers : request.DnnLayers,
dnnNeurons : request.DnnNeurons
));
return(Accepted(new { modelFolderId = request.TargetFolderId, correlationId = correlationId }));
}
public static async Task <HttpResponseMessage> MachineLearningTrain(this OsdrWebClient client,
Guid sourceBlobId, string sourceBucket, Guid userId, Guid parentId, bool optimize)
{
var folderId = Guid.NewGuid();
var createMLModel = new CreateMachineLearningModel
{
TargetFolderId = folderId,
SourceBlobId = sourceBlobId,
Scaler = "some sting named as Scaler",
SourceBucket = sourceBucket,
UserId = userId,
SourceFileName = "combined lysomotrophic.sdf",
Methods = new List <string> {
"NaiveBayes"
},
TrainingParameter = "Soluble",
SubSampleSize = 1,
TestDataSize = new decimal(.1),
KFold = 2,
ModelType = "Classification",
Fingerprints = new List <Fingerprint>
{
new Fingerprint {
Type = "ecfp",
Radius = 2,
Size = 512
}
},
Optimize = optimize
};
var postParameters = JsonConvert.SerializeObject(createMLModel);
var response = await client.PostData("/api/machinelearning/models", postParameters);
return(response);
}
