/// <summary>
/// Load Model from given file.
/// </summary>
private void LoadModel()
{
this.predictor = PredictorUtils.LoadPredictor <float[]>(out dataModel, out dataStats, modelFile);
if (predictor == null)
{
throw new Exception("Predictor is not a binary classifier");
}
}
public void FactoryExampleTest()
{
var dataset = TestDatasets.adultText;
string dataFilename = GetDataPath(dataset.trainFilename);
string testDataFilename = GetDataPath(dataset.testFilename);
///********* Training a model *******//
string modelFilename = Path.GetTempFileName();
TLCArguments cmd = new TLCArguments();
Assert.True(CmdParser.ParseArguments(dataset.extraSettings, cmd));
cmd.command = Command.Train;
cmd.modelfile = modelFilename;
cmd.datafile = dataFilename;
cmd.instancesSettings = dataset.settings;
cmd.classifierName = TestLearners.linearSVM.Trainer;
RunExperiments.Run(cmd);
// Load and make predictions with a previously saved model.
IDataModel dataModel;
IDataStats dataStats;
var predictor = (IDistributionPredictor <Instance, Float, Float>)PredictorUtils.LoadPredictor(
out dataModel, out dataStats, modelFilename);
var instanceFactory = ReflectionUtilsOld.CreateInstanceOld <IInstanceFactory, SignatureInstances>(
cmd.instancesClass, cmd.instancesSettings, null, dataModel);
bool headerSkip = true;
List <Float> outputs = new List <Float>();
List <Float> probabilities = new List <Float>();
using (StreamReader reader = new StreamReader(testDataFilename))
{
List <string> features = new List <string>();
string text;
long line = 0;
while ((text = reader.ReadLine()) != null)
{
++line;
if (string.IsNullOrWhiteSpace(text))
{
continue;
}
string[] cols = text.Split(',');
Assert.True(cols.Length == 15);
if (headerSkip)
{
// skip header line
headerSkip = false;
continue;
}
features.Clear();
// Add in the "max dimensionality"
features.Add("15");
for (int col = 0; col < cols.Length; col++)
{
string s = cols[col].Trim();
switch (col)
{
case 0:
case 2:
case 4:
case 10:
case 11:
case 12:
case 14:
// numeric feature or label -- add if non-zero
Float val = InstancesUtils.FloatParse(s);
if (val == 0) // Beware of NaNs - they should be recorded!
{
continue;
}
break;
}
features.Add(col + ":" + s);
}
Instance instance = instanceFactory.ProduceInstance(line, features.ToArray());
Float rawOutput, probability;
probability = predictor.PredictDistribution(instance, out rawOutput);
outputs.Add(rawOutput);
probabilities.Add(probability);
}
}
List <Float> originalOutputs = new List <Float>();
List <Float> originalProbabilities = new List <Float>();
var env = new LocalEnvironment(SysRandom.Wrap(RunExperiments.GetRandom(cmd)));
Instances instances = RunExperiments.CreateTestData(cmd, testDataFilename, dataModel, null, env);
foreach (Instance instance in instances)
{
Float rawOutput, probability;
probability = predictor.PredictDistribution(instance, out rawOutput);
originalOutputs.Add(rawOutput);
originalProbabilities.Add(probability);
}
CollectionAssert.Equal(outputs, originalOutputs);
CollectionAssert.Equal(probabilities, originalProbabilities);
File.Delete(modelFilename);
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 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();
}