static void Main(string[] args)
{
//These are all the properties necessary to perform model retraining.
//See the github repo for graphics and details
//See https://github.com/jmarymee/convirga
//
//First we create an object to store all of the config parameters.
MLRetrainerLib.RetrainerLib.MLRetrainConfig configobj = new MLRetrainerLib.RetrainerLib.MLRetrainConfig();
//We first add the parameters for the retrainer experiment. These are found in the Azure Studio. You must first publish your
//training experiement as a retraining endpoint. Today is it called a 'predictive experiment' in Azure ML Studio
//Once created it will have at least two output nodes; one for the newly updated/trained model (the iLearner) and a metrics/accuracy scores output
//You can then publish as a web service endpoint and obtain the two following values:
configobj.mlretrainerurl = Properties.Settings.Default.mlretrainermodelurl;
configobj.mlretrainerkey = Properties.Settings.Default.mlretrainerkey;
//In order to get these setup you MUST go to the Azure Portal (currently the legacy one), go to the ML workspace and go to Web Services. Go to the
//Predictive experiment endpoint (not the training one) and create another endpoint. The reason for this is that you cannot deploy a new model to the initial, default
//endpoint. Once you create the additional endpoint here, you can first locate the API key on the lower right side (that will be your endpoint key)
//and then find and click on Update Resource. That will show you the endpoint URL. You can find the endpoint name in the sample C# code. BE SURE that you
//copy it exactly as it is in the C# example. If it is mismatched, it will cause a failure when you attempt to programmatically deply the retrained model
configobj.publishendpointurl = Properties.Settings.Default.enpointurl;
configobj.publishendpointkey = Properties.Settings.Default.endpointkey;
configobj.publishendpointname = Properties.Settings.Default.endpointname;
configobj.publishendpoint2url = Properties.Settings.Default.endpoint2url;
configobj.publishendpoint2key = Properties.Settings.Default.endpoint2key;
//These are somewhat arbitrary. Essentially it's just a place to store the retrained models and CSV files with the scoring/accuracy daata.
//You should manually created this storage area in Azure Blobs and then use the url, key and container name in the params below.
configobj.mlretrainerstoragename = Properties.Settings.Default.mlstoragename;
configobj.mlretrainerstoragekey = Properties.Settings.Default.mlstoragekey;
configobj.mlretrainercontainername = Properties.Settings.Default.mlstoragecontainer;
//This only takes one object but it must be prefilled first based on the notes above.
//there are some internal activities that take place when you instantiate the object. One is that it will attempt to load
//scoring data from the last time you ran this model retraining. If it doesn't exist, then it still creates an internal object but
//it only has one entry in the Dictionary with a text string indicating no scoring data.
MLRetrainerLib.RetrainerLib retrainer = new MLRetrainerLib.RetrainerLib(configobj);
//You can change the date run of the SQL query in order to get a daily trained model
//retrainer.UpdateSQLQueryForNewDate("2015-10-01");
//Upload new training set
retrainer.UploadNewTrainingFileToStorage(@"C:\Users\jmarymee\Downloads\AIC-Dataset.csv");
//Used to get a blob handle to the retraining blob. Not necessary IF you uploaded a fresh set. If you did that then behind the scenes the lib grabbed a reference to the blob
//But if you ALREADY uploaded a new set to your configured blob storage (using another tool) then this can be used to grab the Azure blob handle the retrainer needs.
retrainer.GetRetrainingBlob("AIC-Dataset.csv");
//This is for display. it allows a person to view the results of the last model training.
//This List is stored initially in the Library upon object instatiation. If there is no previous retrainging then there will only be one entry in the Dictionary.
//This example is here only so that you can comoare the last result with the new result after retraining.
//Console.WriteLine("Results of last training: ");
//foreach(var val in retrainer.lastScores)
//{
// Console.WriteLine("Rating Name: {0} : Value: {1}", val.Key, val.Value.ToString());
//}
//Retraining a model takes two steps; queue up the job then start the job. One must save the jobID in order to start the job
//STEP ONE: Assuming your config params are correct, you queue up a retraining job using this call. Be sure to save the jobID or else you won't be able to start
//the job. If you don't start the job it will be automatically deleted after a few minutes (per the ML doc)
//The Dictionary of parms setup below are used when you are 'steering' the retraining - such as algorithmn parms, SQL queryies etc.
Dictionary <string, string> gParms = new Dictionary <string, string>();
gParms.Add("Fraction of rows in the first output dataset", "0.5");
string jobID = retrainer.QueueRetrainingAsync(MLRetrainerLib.RetrainerLib.TRAINING_DATA_SOURCE.DATA_UPLOAD, gParms).Result;
//STEP TWO: This is how you start the retraining job
retrainer.StartRetrainingJob(jobID).Wait();
//We use this to watch the retraining so that we can decide if we want to deploy to the endpoint or not.
//We spin here on the token until we show complete
//This var is declared here and checked in the Do/While below. Of course you could also use Delegates or Threads to wait on this if the main thread has other
//work you want it do do.
MLRetrainerLib.BatchScoreStatusCode status;
//STEP THREE: Wait for retraining completion
//Here is when we spin lock until we show that the retraining job is Finished
do
{
status = retrainer.CheckJobStatus(jobID).Result;
Console.WriteLine(status.ToString());
} while (!(status == MLRetrainerLib.BatchScoreStatusCode.Finished));
//Now we look at the new (latest) results.
//These are pulled from CSV files in the configured Azure Blob Storaged
Console.WriteLine("New Scores for retraining...");
Dictionary <string, double> scores = retrainer.GetLatestRetrainedResults();
foreach (var val in scores)
{
Console.WriteLine("Rating Name: {0} : Value: {1}", val.Key, val.Value.ToString());
}
string resultOfRetrain = retrainer.GetLatestRetrainedResults(true);
Console.WriteLine(resultOfRetrain);
Console.WriteLine("Now deploying the new model to the published endpoint...");
//STEP FOUR: Check to see if the new model is more accurate.
//Here is where we compare the current result to the last result. In this scenario, we compare AUC and if we haven't at
//least seen a 20% improvement then we don't deploy the retrained model
//There are seven values to review and the API currently only allows you to select one. In this case it's AUC. The second param indicates the
//improvement amount that the retrained model should have in order to get TRUE back from the API call.
bool isModelbetter = true; //Arbitrary for testing...
//bool isModelbetter = retrainer.isUdpateModel("AUC", 0.02f);
if (!isModelbetter)
{
Console.WriteLine("No need to update endpoint. Accuracy has not improved. Press a key to end");
Console.ReadLine();
return; //if the model isn't more accurate then terminate the app by returning
}
//STEP FIVE: Deploy the updated, retrained model if you like the scores
//Here is where we deploy the model to the published endpoint IF the accuracy has met our hurdle
//You use the same jobID that you used to start the job
bool isUpdated = retrainer.UpdateModel(jobID).Result;
if (isUpdated)
{
Console.WriteLine("Successful model retraining and endpoint deployment");
}
else
{
Console.WriteLine("Something went wrong updating the model");
}
Console.WriteLine("Process has completed. Press a key to end");
Console.ReadLine();
}
static void Main(string[] args)
{
//These are all the properties necessary to perform model retraining.
//See the github repo for graphics and details
//See https://github.com/jmarymee/azuremlmodelretrainer
//
//First we create an object to store all of the config parameters.
MLRetrainerLib.RetrainerLib.MLRetrainConfig configobj = new MLRetrainerLib.RetrainerLib.MLRetrainConfig();
//We first add the parameters for the retrainer experiment. These are found in the Azure Studio. You must first publish your
//training experiement as a retraining endpoint. Today is it called a 'predictive experiment' in Azure ML Studio
//Once created it will have at least two output nodes; one for the newly updated/trained model (the iLearner) and a metrics/accuracy scores output
//You can then publish as a web service endpoint and obtain the two following values:
configobj.mlretrainerurl = Properties.Settings.Default.mlretrainermodelurl;
configobj.mlretrainerkey = Properties.Settings.Default.mlretrainerkey;
//In order to get these setup you MUST go to the Azure Portal (currently the legacy one), go to the ML workspace and go to Web Services. Go to the
//Predictive experiment endpoint (not the training one) and create another endpoint. The reason for this is that you cannot deploy a new model to the initial, default
//endpoint. Once you create the additional endpoint here, you can first locate the API key on the lower right side (that will be your endpoint key)
//and then find and click on Update Resource. That will show you the endpoint URL. You can find the endpoint name in the sample C# code. BE SURE that you
//copy it exactly as it is in the C# example. If it is mismatched, it will cause a failure when you attempt to programmatically deply the retrained model
configobj.publishendpointurl = Properties.Settings.Default.enpointurl;
configobj.publishendpointkey = Properties.Settings.Default.endpointkey;
configobj.publishendpointname = Properties.Settings.Default.endpointname;
//These are somewhat arbitrary. Essentially it's just a place to store the retrained models and CSV files with the scoring/accuracy daata.
//You should manually created this storage area in Azure Blobs and then use the url, key and container name in the params below.
configobj.mlretrainerstoragename = Properties.Settings.Default.mlstoragename;
configobj.mlretrainerstoragekey = Properties.Settings.Default.mlstoragekey;
configobj.mlretrainercontainername = Properties.Settings.Default.mlstoragecontainer;
//This only takes one object but it must be prefilled first based on the notes above.
//there are some internal activities that take place when you instantiate the object. One is that it will attempt to load
//scoring data from the last time you ran this model retraining. If it doesn't exist, then it still creates an internal object but
//it only has one entry in the Dictionary with a text string indicating no scoring data.
MLRetrainerLib.RetrainerLib retrainer = new MLRetrainerLib.RetrainerLib(configobj);
//Retraining a model takes two steps; queue up the job then start the job. One must save the jobID in order to start the job
//STEP ONE: Assuming your config params are correct, you queue up a retraining job using this call. Be sure to save the jobID or else you won't be able to start
//the job. If you don't start the job it will be automatically deleted after a few minutes (per the ML doc)
Dictionary <string, string> gParms = new Dictionary <string, string>();
gParms.Add("Database query", "select * from TblVCCompanies");
gParms.Add("Fraction of rows in the first output dataset", "0.7");
gParms.Add("L1 regularization weight", "1");
//string jobID = retrainer.QueueRetrainingAsync(MLRetrainerLib.RetrainerLib.TRAINING_DATA_SOURCE.CLOUD_HOSTED, "select top(2000) * from TblVCCompanies").Result;
string jobID = retrainer.QueueRetrainingAsync(MLRetrainerLib.RetrainerLib.TRAINING_DATA_SOURCE.CLOUD_HOSTED, null).Result;
//STEP TWO: This is how you start the retraining job
retrainer.StartRetrainingJob(jobID).Wait();
//We use this to watch the retraining so that we can decide if we want to deploy to the endpoint or not.
//We spin here on the token until we show complete
//This var is declared here and checked in the Do/While below. Of course you could also use Delegates or Threads to wait on this if the main thread has other
//work you want it do do.
MLRetrainerLib.BatchScoreStatusCode status;
//STEP THREE: Wait for retraining completion
//Here is when we spin lock until we show that the retraining job is Finished
do
{
status = retrainer.CheckJobStatus(jobID).Result;
Console.WriteLine(status.ToString());
} while (!(status == MLRetrainerLib.BatchScoreStatusCode.Finished));
//Now we look at the new (latest) results.
//These are pulled from CSV files in the configured Azure Blob Storaged
Console.WriteLine("New Scores for retraining...");
//Dictionary<string, double> scores = retrainer.GetLatestRetrainedResults();
//foreach (var val in scores)
//{
// Console.WriteLine("Rating Name: {0} : Value: {1}", val.Key, val.Value.ToString());
//}
//string resultOfRetrain = retrainer.GetLatestRetrainedResults(true);
//Console.WriteLine(resultOfRetrain);
}