private void EndToEnd(MwtExplorer <SimpleContext> mwtt, IExplorer <SimpleContext> explorer, TestRecorder <SimpleContext> recorder)
{
uint numActions = 10;
Random rand = new Random();
List <float> rewards = new List <float>();
for (int i = 0; i < 1000; i++)
{
Feature[] f = new Feature[rand.Next(800, 1201)];
for (int j = 0; j < f.Length; j++)
{
f[j].Id = (uint)(j + 1);
f[j].Value = (float)rand.NextDouble();
}
SimpleContext c = new SimpleContext(f);
mwtt.ChooseAction(explorer, i.ToString(), c);
rewards.Add((float)rand.NextDouble());
}
var testInteractions = recorder.GetAllInteractions();
Interaction[] partialInteractions = new Interaction[testInteractions.Count];
for (int i = 0; i < testInteractions.Count; i++)
{
partialInteractions[i] = new Interaction()
{
ApplicationContext = new OldSimpleContext(testInteractions[i].Context.GetFeatures(), null),
ChosenAction = testInteractions[i].Action,
Probability = testInteractions[i].Probability,
Id = testInteractions[i].UniqueKey
};
}
MwtRewardReporter mrr = new MwtRewardReporter(partialInteractions);
for (int i = 0; i < partialInteractions.Length; i++)
{
Assert.AreEqual(true, mrr.ReportReward(partialInteractions[i].GetId(), rewards[i]));
}
Interaction[] completeInteractions = mrr.GetAllInteractions();
MwtOptimizer mop = new MwtOptimizer(completeInteractions, numActions);
string modelFile = "model";
mop.OptimizePolicyVWCSOAA(modelFile);
Assert.IsTrue(System.IO.File.Exists(modelFile));
float evaluatedValue = mop.EvaluatePolicyVWCSOAA(modelFile);
Assert.IsFalse(float.IsNaN(evaluatedValue));
System.IO.File.Delete(modelFile);
}
public static void Run()
{
string interactionFile = "serialized.txt";
MwtLogger logger = new MwtLogger(interactionFile);
MwtExplorer mwt = new MwtExplorer("test", logger);
uint numActions = 10;
float epsilon = 0.2f;
uint tau = 0;
uint bags = 2;
float lambda = 0.5f;
int policyParams = 1003;
CustomParams customParams = new CustomParams()
{
Value1 = policyParams, Value2 = policyParams + 1
};
/*** Initialize Epsilon-Greedy explore algorithm using a default policy function that accepts parameters ***/
mwt.InitializeEpsilonGreedy <int>(epsilon, new StatefulPolicyDelegate <int>(SampleStatefulPolicyFunc), policyParams, numActions);
/*** Initialize Epsilon-Greedy explore algorithm using a stateless default policy function ***/
//mwt.InitializeEpsilonGreedy(epsilon, new StatelessPolicyDelegate(SampleStatelessPolicyFunc), numActions);
/*** Initialize Tau-First explore algorithm using a default policy function that accepts parameters ***/
//mwt.InitializeTauFirst<CustomParams>(tau, new StatefulPolicyDelegate<CustomParams>(SampleStatefulPolicyFunc), customParams, numActions);
/*** Initialize Tau-First explore algorithm using a stateless default policy function ***/
//mwt.InitializeTauFirst(tau, new StatelessPolicyDelegate(SampleStatelessPolicyFunc), numActions);
/*** Initialize Bagging explore algorithm using a default policy function that accepts parameters ***/
//StatefulPolicyDelegate<int>[] funcs =
//{
// new StatefulPolicyDelegate<int>(SampleStatefulPolicyFunc),
// new StatefulPolicyDelegate<int>(SampleStatefulPolicyFunc2)
//};
//int[] parameters = { policyParams, policyParams };
//mwt.InitializeBagging<int>(bags, funcs, parameters, numActions);
/*** Initialize Bagging explore algorithm using a stateless default policy function ***/
//StatelessPolicyDelegate[] funcs =
//{
// new StatelessPolicyDelegate(SampleStatelessPolicyFunc),
// new StatelessPolicyDelegate(SampleStatelessPolicyFunc2)
//};
//mwt.InitializeBagging(bags, funcs, numActions);
/*** Initialize Softmax explore algorithm using a default policy function that accepts parameters ***/
//mwt.InitializeSoftmax<int>(lambda, new StatefulScorerDelegate<int>(SampleStatefulScorerFunc), policyParams, numActions);
/*** Initialize Softmax explore algorithm using a stateless default policy function ***/
//mwt.InitializeSoftmax(lambda, new StatelessScorerDelegate(SampleStatelessScorerFunc), numActions);
FEATURE[] f = new FEATURE[2];
f[0].X = 0.5f;
f[0].Index = 1;
f[1].X = 0.9f;
f[1].Index = 2;
string otherContext = "Some other context data that might be helpful to log";
CONTEXT context = new CONTEXT(f, otherContext);
UInt32 chosenAction = mwt.ChooseAction(context, "myId");
INTERACTION[] interactions = mwt.GetAllInteractions();
mwt.Unintialize();
MwtRewardReporter mrr = new MwtRewardReporter(interactions);
string joinKey = "myId";
float reward = 0.5f;
if (!mrr.ReportReward(joinKey, reward))
{
throw new Exception();
}
MwtOptimizer mot = new MwtOptimizer(interactions, numActions);
float eval1 = mot.EvaluatePolicy(new StatefulPolicyDelegate <int>(SampleStatefulPolicyFunc), policyParams);
mot.OptimizePolicyVWCSOAA("model_file");
float eval2 = mot.EvaluatePolicyVWCSOAA("model_file");
Console.WriteLine(chosenAction);
Console.WriteLine(interactions);
logger.Flush();
// Create a new logger to read back interaction data
logger = new MwtLogger(interactionFile);
INTERACTION[] inters = logger.GetAllInteractions();
// Load and save reward data to file
string rewardFile = "rewards.txt";
RewardStore rewardStore = new RewardStore(rewardFile);
rewardStore.Add(new float[2] {
1.0f, 0.4f
});
rewardStore.Flush();
// Read back reward data
rewardStore = new RewardStore(rewardFile);
float[] rewards = rewardStore.GetAllRewards();
}