org.tribuo.classification.example

Class LabelledDataGenerator

java.lang.Object

org.tribuo.classification.example.LabelledDataGenerator

public final class LabelledDataGenerator
extends Object

Generates three example train and test datasets, used for unit testing. They don't necessarily have sensible classification boundaries, it's for testing the machinery rather than accuracy.

Method Summary

Modifier and Type	Method and Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	binarySparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	binarySparseTrainTest(double negate) Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	denseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	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 classes, {Foo,Bar,Baz,Quux}.
static Example<Label>	emptyExample() Generates an example with no features.
static Example<Label>	invalidSparseExample() Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	sparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	sparseTrainTest(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

denseTrainTest

public static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>> denseTrainTest()

denseTrainTest

public static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>> 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 classes, {Foo,Bar,Baz,Quux}.

Parameters:

negate - Supply -1.0 to insert some negative values into the dataset.

Returns:

A pair of datasets.

sparseTrainTest

public static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>> sparseTrainTest()

sparseTrainTest

public static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>> sparseTrainTest(double negate)

Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data. It has the same 4 classes {Foo,Bar,Baz,Quux}.

Parameters:

negate - Supply -1.0 to negate some values in this dataset.

Returns:

A pair of train and test datasets.

binarySparseTrainTest

public static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>> binarySparseTrainTest()

binarySparseTrainTest

public static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>> binarySparseTrainTest(double negate)

Generates a pair of datasets with sparse features and unknown features in the test data. Has binary labels {Foo,Bar}.

Parameters:

negate - Supply -1.0 to negate some values in this dataset.

Returns:

A pair of train and test datasets.

invalidSparseExample

public static Example<Label> invalidSparseExample()

Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class. This should make the example empty at prediction time.

Returns:

An example with features {1:1.0,5:5.0,8:8.0}.

emptyExample

public static Example<Label> emptyExample()

Generates an example with no features.

Returns:

An example with no features.