public void SimpleExampleTest()
{
RunMTAThread(() =>
{
string dataFilename = GetDataPath(TestDatasets.msm.trainFilename);
///********* Training a model *******//
// assume data is in memory in matrix/vector form. Sparse format also supported.
Float[][] data;
Float[] labels;
// below just reads some actual data into these arrays
PredictionUtil.ReadInstancesAsArrays(dataFilename, out data, out labels);
// Create an Instances dataset.
ListInstances instances = new ListInstances();
for (int i = 0; i < data.Length; i++)
{
instances.AddInst(data[i], labels[i]);
}
instances.CopyMetadata(null);
// Create a predictor and specify some non-default settings
var sub = new SubComponent <ITrainer <Instances, IPredictor <Instance, Float> >, SignatureOldBinaryClassifierTrainer>(
"FastRank", "nl=5 lr =0.25 iter= 20");
var trainer = sub.CreateInstance(new TrainHost(new Random(1), 0));
// Train a predictor
trainer.Train(instances);
var predictor = trainer.CreatePredictor();
///********* Several ways to save models. Only binary can be used to-reload in TLC. *******//
// Save the model in internal binary format that can be used for loading it.
string modelFilename = Path.GetTempFileName();
PredictorUtils.Save(modelFilename, predictor, instances, null);
// Save the model as a plain-text description
string modelFilenameText = Path.GetTempFileName();
PredictorUtils.SaveText(predictor, instances.Schema.FeatureNames, modelFilenameText);
// Save the model in Bing's INI format
string modelFilenameIni = Path.GetTempFileName();
PredictorUtils.SaveIni(predictor, instances.Schema.FeatureNames, modelFilenameIni);
///********* Loading and making predictions with a previously saved model *******//
// Note: there are several alternative ways to construct instances
// For example, see FactoryExampleTest below that demonstrates named-feature : value pairs.
// Load saved model
IDataModel dataModel;
IDataStats dataStats;
var pred = PredictorUtils.LoadPredictor <Float>(out dataModel, out dataStats, modelFilename);
var dp = pred as IDistributionPredictor <Instance, Float, Float>;
// Get predictions for instances
Float[] probPredictions = new Float[instances.Count];
Float[] rawPredictions = new Float[instances.Count];
Float[] rawPredictions1 = new Float[instances.Count];
for (int i = 0; i < instances.Count; i++)
{
probPredictions[i] = dp.PredictDistribution(instances[i], out rawPredictions[i]);
rawPredictions1[i] = dp.Predict(new Instance(data[i]));
}
Float[] bulkPredictions = ((IBulkPredictor <Instance, Instances, Float, Float[]>)pred).BulkPredict(instances);
Assert.Equal(rawPredictions.Length, bulkPredictions.Length);
Assert.Equal(rawPredictions.Length, rawPredictions1.Length);
for (int i = 0; i < rawPredictions.Length; i++)
{
Assert.Equal(rawPredictions[i], bulkPredictions[i]);
}
for (int i = 0; i < rawPredictions.Length; i++)
{
Assert.Equal(rawPredictions[i], rawPredictions1[i]);
}
//test new testers
{
var results = new ClassifierTester(new ProbabilityPredictorTesterArguments()).Test(pred, instances);
// Get metric names and print them alongside numbers
for (int i = 0; i < results.Length; i++)
{
Log("{0,-30} {1}", results[i].Name, results[i].Value);
}
// sanity check vs. original predictor
var results2 = new ClassifierTester(new ProbabilityPredictorTesterArguments()).Test(predictor, instances);
Assert.Equal(results.Length, results2.Length);
for (int i = 0; i < results.Length; i++)
{
Assert.Equal(results[i].Name, results2[i].Name);
Assert.Equal(results[i].Value, results2[i].Value);
}
}
File.Delete(modelFilename);
File.Delete(modelFilenameText);
File.Delete(modelFilenameIni);
});
Done();
}
[Ignore] // REVIEW: OVA no longer implements BulkPredict.
public void MulticlassExampleTest()
{
string dataFilename = GetDataPath(TestDatasets.msm.trainFilename);
///********* Training a model *******//
// assume data is in memory in matrix/vector form. Sparse format also supported.
Float[][] data;
Float[] labels;
// below just reads some actual data into these arrays
PredictionUtil.ReadInstancesAsArrays(dataFilename, out data, out labels);
// Create an Instances dataset.
ListInstances instances = new ListInstances();
for (int i = 0; i < data.Length; i++)
{
instances.AddInst(data[i], labels[i]);
}
instances.CopyMetadata(null);
// Create a predictor and specify some non-default settings
var args = new OVA.OldArguments();
PredictionUtil.ParseArguments(args, "p=FR ps=iter:20");
ITrainer <Instances, IPredictor <Instance, Float[]> > trainer = new OVA(args, new TrainHost(new Random(1), 0));
// Train a predictor
trainer.Train(instances);
var predictor = trainer.CreatePredictor();
///********* Several ways to save models. Only binary can be used to-reload in TLC. *******//
// Save the model in internal binary format that can be used for loading it.
string modelFilename = Path.GetTempFileName();
PredictorUtils.Save(modelFilename, predictor, instances, null);
// Save the model as a plain-text description
string modelFilenameText = Path.GetTempFileName();
PredictorUtils.SaveText(predictor, instances.Schema.FeatureNames, modelFilenameText);
// Save the model in Bing's INI format
string modelFilenameIni = Path.GetTempFileName();
PredictorUtils.SaveIni(predictor, instances.Schema.FeatureNames, modelFilenameIni);
///********* Loading and making predictions with a previously saved model *******//
// Note: there are several alternative ways to construct instances
// For example, see FactoryExampleTest below that demonstrates named-feature : value pairs.
// Load saved model
IDataModel dataModel;
IDataStats dataStats;
var pred = PredictorUtils.LoadPredictor <Float[]>(out dataModel, out dataStats, modelFilename);
// Get predictions for instances
Float[][] predictions = new Float[instances.Count][];
for (int i = 0; i < instances.Count; i++)
{
predictions[i] = pred.Predict(instances[i]);
}
// REVIEW: This looks like it wasn't doing what was expected - OVA didn't override
// BulkPredict, so this wasn't using FastRank's BulkPredict.
Float[][] bulkPredictions = ((IBulkPredictor <Instance, Instances, Float[], Float[][]>)pred).BulkPredict(instances);
Assert.Equal(predictions.Length, bulkPredictions.Length);
for (int i = 0; i < predictions.Length; i++)
{
Assert.Equal(predictions[i].Length, bulkPredictions[i].Length);
for (int j = 0; j < predictions[i].Length; j++)
{
Assert.Equal(predictions[i][j], bulkPredictions[i][j]);
}
}
//test new testers
{
var results = new MulticlassTester(new MulticlassTesterArguments()).Test(pred, instances);
// Get metric names and print them alongside numbers
for (int i = 0; i < results.Length; i++)
{
Log("{0,-30} {1}", results[i].Name, results[i].Value);
}
// sanity check vs. original predictor
var results2 = new MulticlassTester(new MulticlassTesterArguments()).Test(predictor, instances);
Assert.Equal(results.Length, results2.Length);
for (int i = 0; i < results.Length; i++)
{
Assert.Equal(results[i].Name, results2[i].Name);
Assert.Equal(results[i].Value, results2[i].Value);
}
}
File.Delete(modelFilename);
File.Delete(modelFilenameText);
File.Delete(modelFilenameIni);
Done();
}
public ColInfoEx(ArgumentsBase args)
{
Separators = PredictionUtil.SeparatorFromString(args.TermSeparators);
Contracts.CheckUserArg(Utils.Size(Separators) > 0, nameof(args.TermSeparators));
}
public ColInfoEx(Arguments args, int iinfo)
{
Separators = PredictionUtil.SeparatorFromString(args.Column[iinfo].TermSeparators ?? args.TermSeparators);
Contracts.CheckUserArg(Utils.Size(Separators) > 0, nameof(args.TermSeparators));
}
public override void OnJungleClear()
{
var minions = EntityManager.MinionsAndMonsters.Monsters.Where(t => t.IsValidTarget(1000));
var jungleclear = Features.Find(f => f.NameFeature == "Jungle Clear");
if (minions == null || !minions.Any(t => t.IsValidTarget(1000)) || jungleclear.SliderValue("jungleclear.mana") > Player.Instance.ManaPercent)
{
return;
}
var Q = ((Spell.Chargeable)Spells[0]);
var W = ((Spell.Skillshot)Spells[1]);
var E = ((Spell.Skillshot)Spells[2]);
if (jungleclear.IsChecked("jungleclear.q") && Q.IsReady())
{
var bestFarmPostion = PredictionUtil.GetBestLineFarmLocation(minions.Select(o => o.ServerPosition.To2D()).ToList(), Q.Width, Q.MaximumRange);
if (!Q.IsCharging)
{
Q.StartCharging();
}
else if (Q.Range == Q.MaximumRange)
{
if (Q.Cast(minions.FirstOrDefault().ServerPosition))
{
}
}
else
{
if (bestFarmPostion.Position.Distance(Player.Instance) > Q.Range && Q.IsCharging)
{
return;
}
Q.Cast(bestFarmPostion.Position.To3D());
}
}
if (Q.IsCharging)
{
return;
}
if (jungleclear.IsChecked("jungleclear.w") && W.IsReady())
{
var bestFarmPosition =
PredictionUtil.GetBestCircularFarmLocation(minions.Select(o => o.ServerPosition.To2D()).ToList(),
W.Width, W.Range);
W.Cast(bestFarmPosition.Position.To3D());
}
if (jungleclear.IsChecked("jungleclear.e") && E.IsReady() && E.IsInRange(minions.FirstOrDefault()))
{
var predictionE = E.GetPrediction(minions.FirstOrDefault());
if (predictionE.HitChancePercent >= 85)
{
E.Cast(predictionE.CastPosition);
}
}
}
/*
* Spells[0] = Q
* Spells[1] = W
* Spells[2] = E
* Spells[3] = R
*/
public override void OnCombo()
{
var combo = Features.Find(f => f.NameFeature == "Combo");
var comboMisc = Features.Find(f => f.NameFeature == "ComboMisc");
if (R.IsReady() && combo.IsChecked("combo.r"))
{
var targetR = TargetSelector.GetTarget(R.Range, DamageType.Mixed);
if (targetR != null && targetR.IsValidTarget(R.Range))
{
if (Player.Instance.Distance(targetR) > comboMisc.SliderValue("combo.misc.minDistance") && targetR != null && targetR.IsValidTarget() && DamageUtil.Killable(targetR, SpellSlot.R, 89))
{
var predictionR = R.GetPrediction(targetR);
if (predictionR.HitChance >= HitChance.High)
{
R.Cast(predictionR.CastPosition);
}
else
{
var castPosition = Prediction.Position.PredictUnitPosition(targetR, (int)Math.Round(PredictionUtil.GetArrivalTime(Player.Instance.Distance(targetR), 0.5f, R.Speed)));
R.Cast(castPosition.To3D());
}
}
}
}
var target = TargetSelector.GetTarget(Q.Range, DamageType.Mixed);
if (target == null || !target.IsValidTarget(Q.Range) || !target.IsValidTargetUtil())
{
return;
}
if (combo.IsChecked("combo.q") && Q.IsReady())
{
if (comboMisc.IsChecked("combo.misc.q"))
{
castQ = true;
}
else
{
var qPredict = Q.GetPrediction(target);
if (qPredict.HitChancePercent >= 75)
{
if (Q.Cast(qPredict.CastPosition))
{
castQ = false;
}
}
}
}
if (combo.IsChecked("combo.w") && W.IsReady() && W.IsInRange(target))
{
var wPredict = W.GetPrediction(target);
if (wPredict.HitChancePercent >= 85)
{
W.Cast(wPredict.CastPosition);
}
}
if (combo.IsChecked("combo.e") && E.IsReady() && E.IsInRange(target))
{
if (target.CountEnemiesInRange(700) < target.CountAlliesInRange(475) && DamageUtil.Killable(target, SpellSlot.E))
{
E.Cast(target.ServerPosition);
}
}
}
public ColInfoEx(Arguments args, int iinfo)
{
// Use character array separator if any otherwise default to the comma separated string
Separators = args.CharArrayTermSeparators ?? PredictionUtil.SeparatorFromString(args.Column[iinfo].TermSeparators ?? args.TermSeparators);
Contracts.CheckUserArg(Utils.Size(Separators) > 0, nameof(args.TermSeparators));
}
