Package | Description |
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org.tribuo.anomaly |
Provides classes and infrastructure for anomaly detection problems.
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org.tribuo.anomaly.evaluation |
Evaluation classes for anomaly detection.
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org.tribuo.anomaly.example |
Provides a anomaly data generator used for testing implementations.
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org.tribuo.anomaly.libsvm |
Provides an interface to LibSVM for anomaly detection problems.
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Modifier and Type | Field and Description |
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static Event |
AnomalyFactory.ANOMALOUS_EVENT
The anomalous event.
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static Event |
AnomalyFactory.EXPECTED_EVENT
The expected event.
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static Event |
AnomalyFactory.UNKNOWN_EVENT
The unknown event.
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Modifier and Type | Method and Description |
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Event |
Event.copy() |
<V> Event |
AnomalyFactory.generateOutput(V label) |
Event |
ImmutableAnomalyInfo.getOutput(int id) |
Event |
AnomalyFactory.getUnknownOutput() |
Modifier and Type | Method and Description |
---|---|
ImmutableOutputInfo<Event> |
AnomalyFactory.constructInfoForExternalModel(Map<Event,Integer> mapping) |
ImmutableOutputInfo<Event> |
AnomalyInfo.generateImmutableOutputInfo() |
MutableOutputInfo<Event> |
AnomalyFactory.generateInfo() |
MutableOutputInfo<Event> |
AnomalyInfo.generateMutableOutputInfo() |
Set<Event> |
AnomalyInfo.getDomain()
Returns the set of possible
Event s. |
Evaluator<Event,AnomalyEvaluation> |
AnomalyFactory.getEvaluator() |
Iterator<com.oracle.labs.mlrg.olcut.util.Pair<Integer,Event>> |
ImmutableAnomalyInfo.iterator() |
Modifier and Type | Method and Description |
---|---|
boolean |
Event.fullEquals(Event o) |
int |
ImmutableAnomalyInfo.getID(Event output) |
void |
MutableAnomalyInfo.observe(Event output) |
Modifier and Type | Method and Description |
---|---|
ImmutableOutputInfo<Event> |
AnomalyFactory.constructInfoForExternalModel(Map<Event,Integer> mapping) |
Modifier and Type | Method and Description |
---|---|
MetricTarget<Event> |
AnomalyMetric.getTarget() |
Modifier and Type | Method and Description |
---|---|
org.tribuo.anomaly.evaluation.AnomalyMetric.Context |
AnomalyMetric.createContext(Model<Event> model,
List<Prediction<Event>> predictions) |
org.tribuo.anomaly.evaluation.AnomalyMetric.Context |
AnomalyMetric.createContext(Model<Event> model,
List<Prediction<Event>> predictions) |
protected org.tribuo.anomaly.evaluation.AnomalyMetric.Context |
AnomalyEvaluator.createContext(Model<Event> model,
List<Prediction<Event>> predictions) |
protected org.tribuo.anomaly.evaluation.AnomalyMetric.Context |
AnomalyEvaluator.createContext(Model<Event> model,
List<Prediction<Event>> predictions) |
protected AnomalyEvaluation |
AnomalyEvaluator.createEvaluation(org.tribuo.anomaly.evaluation.AnomalyMetric.Context context,
Map<MetricID<Event>,Double> results,
EvaluationProvenance provenance) |
protected Set<AnomalyMetric> |
AnomalyEvaluator.createMetrics(Model<Event> model) |
Constructor and Description |
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AnomalyMetric(MetricTarget<Event> target,
String name,
ToDoubleBiFunction<MetricTarget<Event>,org.tribuo.anomaly.evaluation.AnomalyMetric.Context> impl)
Creates an anomaly detection metric, with a specific name, using the supplied evaluation function.
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AnomalyMetric(MetricTarget<Event> target,
String name,
ToDoubleBiFunction<MetricTarget<Event>,org.tribuo.anomaly.evaluation.AnomalyMetric.Context> impl)
Creates an anomaly detection metric, with a specific name, using the supplied evaluation function.
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Modifier and Type | Method and Description |
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.denseTrainTest()
Makes a simple dataset for training and testing.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.denseTrainTest()
Makes a simple dataset for training and testing.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.denseTrainTest(double negate)
Generates a train/test dataset pair which is dense in the features,
each example has 4 features,{A,B,C,D}, and there are 4 clusters,
{0,1,2,3}.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.denseTrainTest(double negate)
Generates a train/test dataset pair which is dense in the features,
each example has 4 features,{A,B,C,D}, and there are 4 clusters,
{0,1,2,3}.
|
static Example<Event> |
AnomalyDataGenerator.emptyExample()
Generates an example with no features.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.gaussianAnomaly()
Generates two datasets, one without anomalies drawn from a single gaussian
and the second drawn from a mixture of two gaussians, with the second tagged
anomalous.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.gaussianAnomaly()
Generates two datasets, one without anomalies drawn from a single gaussian
and the second drawn from a mixture of two gaussians, with the second tagged
anomalous.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.gaussianAnomaly(long size,
double fractionAnomalous)
Generates two datasets, one without anomalies drawn from a single gaussian
and the second drawn from a mixture of two gaussians, with the second tagged
anomalous.
|
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.gaussianAnomaly(long size,
double fractionAnomalous)
Generates two datasets, one without anomalies drawn from a single gaussian
and the second drawn from a mixture of two gaussians, with the second tagged
anomalous.
|
static Example<Event> |
AnomalyDataGenerator.invalidSparseExample()
Generates an example with the feature ids 1,5,8, which does not intersect with the
ids used elsewhere in this class.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.sparseTrainTest()
Makes a simple dataset for training and testing.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.sparseTrainTest()
Makes a simple dataset for training and testing.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.sparseTrainTest(double negate)
Generates a pair of datasets, where the features are sparse,
and unknown features appear in the test data.
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static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>> |
AnomalyDataGenerator.sparseTrainTest(double negate)
Generates a pair of datasets, where the features are sparse,
and unknown features appear in the test data.
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Modifier and Type | Method and Description |
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protected LibSVMModel<Event> |
LibSVMAnomalyTrainer.createModel(ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<Event> outputIDInfo,
List<libsvm.svm_model> models) |
Prediction<Event> |
LibSVMAnomalyModel.predict(Example<Event> example) |
LibSVMModel<Event> |
LibSVMAnomalyTrainer.train(Dataset<Event> dataset,
Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance) |
Modifier and Type | Method and Description |
---|---|
protected double |
LibSVMAnomalyTrainer.extractOutput(Event output)
Converts an output into a double for use in training.
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Modifier and Type | Method and Description |
---|---|
protected LibSVMModel<Event> |
LibSVMAnomalyTrainer.createModel(ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<Event> outputIDInfo,
List<libsvm.svm_model> models) |
protected com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]> |
LibSVMAnomalyTrainer.extractData(Dataset<Event> data,
ImmutableOutputInfo<Event> outputInfo,
ImmutableFeatureMap featureMap) |
protected com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]> |
LibSVMAnomalyTrainer.extractData(Dataset<Event> data,
ImmutableOutputInfo<Event> outputInfo,
ImmutableFeatureMap featureMap) |
Prediction<Event> |
LibSVMAnomalyModel.predict(Example<Event> example) |
LibSVMModel<Event> |
LibSVMAnomalyTrainer.train(Dataset<Event> dataset,
Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance) |
Constructor and Description |
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LibSVMAnomalyTrainer(SVMParameters<Event> parameters)
Creates a one-class LibSVM trainer using the supplied parameters.
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