This is an automated email from the ASF dual-hosted git repository. rzo1 pushed a commit to branch OPENNLP-1643-Remove-inconsistent-Training-Parameter-Definitions in repository https://gitbox.apache.org/repos/asf/opennlp.git
commit 577608f064d4cce96c5de544efa0046cb16ecb31 Author: Richard Zowalla <[email protected]> AuthorDate: Wed Nov 6 15:43:59 2024 +0100 OPENNLP-1643 - Remove inconsistent Training Parameter Definitions --- .../ml/AbstractEventModelSequenceTrainer.java | 3 +- .../opennlp/tools/ml/AbstractEventTrainer.java | 6 +-- .../java/opennlp/tools/ml/AbstractTrainer.java | 31 ++++++-------- .../main/java/opennlp/tools/ml/TrainerFactory.java | 25 ++++++----- .../java/opennlp/tools/ml/maxent/GISTrainer.java | 4 +- .../tools/ml/model/AbstractDataIndexer.java | 4 -- .../opennlp/tools/ml/model/OnePassDataIndexer.java | 4 +- .../opennlp/tools/ml/model/TwoPassDataIndexer.java | 4 +- .../SimplePerceptronSequenceTrainer.java | 5 ++- .../opennlp/tools/util/TrainingParameters.java | 49 ++++------------------ .../tools/doccat/DocumentCategorizerNBTest.java | 5 +-- .../java/opennlp/tools/ml/TrainerFactoryTest.java | 4 +- .../opennlp/tools/ml/maxent/GISIndexingTest.java | 23 +++++----- .../tools/ml/maxent/MaxentPrepAttachTest.java | 11 +++-- .../tools/ml/maxent/RealValueModelTest.java | 3 +- .../tools/ml/maxent/ScaleDoesntMatterTest.java | 3 +- .../ml/maxent/io/RealValueFileEventStreamTest.java | 3 +- .../maxent/quasinewton/NegLogLikelihoodTest.java | 3 +- .../ml/maxent/quasinewton/QNPrepAttachTest.java | 19 ++++----- .../tools/ml/maxent/quasinewton/QNTrainerTest.java | 3 +- .../ml/naivebayes/NaiveBayesCorrectnessTest.java | 3 +- .../naivebayes/NaiveBayesModelReadWriteTest.java | 3 +- .../ml/naivebayes/NaiveBayesPrepAttachTest.java | 11 +++-- .../NaiveBayesSerializedCorrectnessTest.java | 3 +- .../ml/perceptron/PerceptronPrepAttachTest.java | 33 +++++++-------- 25 files changed, 109 insertions(+), 156 deletions(-) diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractEventModelSequenceTrainer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractEventModelSequenceTrainer.java index b233df23..cdbd267f 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractEventModelSequenceTrainer.java +++ b/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractEventModelSequenceTrainer.java @@ -22,6 +22,7 @@ import java.io.IOException; import opennlp.tools.ml.model.Event; import opennlp.tools.ml.model.MaxentModel; import opennlp.tools.ml.model.SequenceStream; +import opennlp.tools.util.TrainingParameters; /** * A basic {@link EventModelSequenceTrainer} implementation that processes {@link Event events}. @@ -39,7 +40,7 @@ public abstract class AbstractEventModelSequenceTrainer extends AbstractTrainer validate(); MaxentModel model = doTrain(events); - addToReport(AbstractTrainer.TRAINER_TYPE_PARAM, EventModelSequenceTrainer.SEQUENCE_VALUE); + addToReport(TrainingParameters.TRAINER_TYPE_PARAM, EventModelSequenceTrainer.SEQUENCE_VALUE); return model; } diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractEventTrainer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractEventTrainer.java index 9ea5ddce..19b3ba6e 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractEventTrainer.java +++ b/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractEventTrainer.java @@ -57,8 +57,8 @@ public abstract class AbstractEventTrainer extends AbstractTrainer implements Ev trainingParameters.put(AbstractDataIndexer.SORT_PARAM, isSortAndMerge()); // If the cutoff was set, don't overwrite the value. - if (trainingParameters.getIntParameter(CUTOFF_PARAM, -1) == -1) { - trainingParameters.put(CUTOFF_PARAM, 5); + if (trainingParameters.getIntParameter(TrainingParameters.CUTOFF_PARAM, -1) == -1) { + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, TrainingParameters.CUTOFF_DEFAULT_VALUE); } DataIndexer indexer = DataIndexerFactory.getDataIndexer(trainingParameters, reportMap); @@ -77,7 +77,7 @@ public abstract class AbstractEventTrainer extends AbstractTrainer implements Ev } MaxentModel model = doTrain(indexer); - addToReport(AbstractTrainer.TRAINER_TYPE_PARAM, EventTrainer.EVENT_VALUE); + addToReport(TrainingParameters.TRAINER_TYPE_PARAM, EventTrainer.EVENT_VALUE); return model; } diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractTrainer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractTrainer.java index f7bc777a..54e315c8 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractTrainer.java +++ b/opennlp-tools/src/main/java/opennlp/tools/ml/AbstractTrainer.java @@ -26,16 +26,6 @@ import opennlp.tools.util.TrainingParameters; public abstract class AbstractTrainer implements Trainer { - public static final String ALGORITHM_PARAM = "Algorithm"; - - public static final String TRAINER_TYPE_PARAM = "TrainerType"; - - public static final String CUTOFF_PARAM = "Cutoff"; - public static final int CUTOFF_DEFAULT = 5; - - public static final String ITERATIONS_PARAM = "Iterations"; - public static final int ITERATIONS_DEFAULT = 100; - protected TrainingParameters trainingParameters; protected Map<String,String> reportMap; @@ -66,24 +56,27 @@ public abstract class AbstractTrainer implements Trainer { } /** - * @return Retrieves the configured {@link #ALGORITHM_PARAM} value. + * @return Retrieves the configured {@link TrainingParameters#ALGORITHM_PARAM} value. */ public String getAlgorithm() { - return trainingParameters.getStringParameter(ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); + return trainingParameters.getStringParameter(TrainingParameters.ALGORITHM_PARAM, + GISTrainer.MAXENT_VALUE); } /** - * @return Retrieves the configured {@link #CUTOFF_PARAM} value. + * @return Retrieves the configured {@link TrainingParameters#CUTOFF_PARAM} value. */ public int getCutoff() { - return trainingParameters.getIntParameter(CUTOFF_PARAM, CUTOFF_DEFAULT); + return trainingParameters.getIntParameter(TrainingParameters.CUTOFF_PARAM, + TrainingParameters.CUTOFF_DEFAULT_VALUE); } /** - * @return Retrieves the configured {@link #ITERATIONS_PARAM} value. + * @return Retrieves the configured {@link TrainingParameters#ITERATIONS_PARAM} value. */ public int getIterations() { - return trainingParameters.getIntParameter(ITERATIONS_PARAM, ITERATIONS_DEFAULT); + return trainingParameters.getIntParameter(TrainingParameters.ITERATIONS_PARAM, + TrainingParameters.ITERATIONS_DEFAULT_VALUE); } /** @@ -97,8 +90,10 @@ public abstract class AbstractTrainer implements Trainer { // should validate if algorithm is set? What about the Parser? try { - trainingParameters.getIntParameter(CUTOFF_PARAM, CUTOFF_DEFAULT); - trainingParameters.getIntParameter(ITERATIONS_PARAM, ITERATIONS_DEFAULT); + trainingParameters.getIntParameter(TrainingParameters.CUTOFF_PARAM, + TrainingParameters.CUTOFF_DEFAULT_VALUE); + trainingParameters.getIntParameter(TrainingParameters.ITERATIONS_PARAM, + TrainingParameters.ITERATIONS_DEFAULT_VALUE); } catch (NumberFormatException e) { throw new IllegalArgumentException(e); } diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/TrainerFactory.java b/opennlp-tools/src/main/java/opennlp/tools/ml/TrainerFactory.java index 9e46367a..b47e3a75 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/ml/TrainerFactory.java +++ b/opennlp-tools/src/main/java/opennlp/tools/ml/TrainerFactory.java @@ -59,7 +59,7 @@ public class TrainerFactory { /** * Determines the {@link TrainerType} based on the - * {@link AbstractTrainer#ALGORITHM_PARAM} value. + * {@link TrainingParameters#ALGORITHM_PARAM} value. * * @param trainParams - A mapping of {@link TrainingParameters training parameters}. * @@ -67,7 +67,7 @@ public class TrainerFactory { */ public static TrainerType getTrainerType(TrainingParameters trainParams) { - String algorithmValue = trainParams.getStringParameter(AbstractTrainer.ALGORITHM_PARAM,null); + String algorithmValue = trainParams.getStringParameter(TrainingParameters.ALGORITHM_PARAM,null); // Check if it is defaulting to the MAXENT trainer if (algorithmValue == null) { @@ -122,7 +122,7 @@ public class TrainerFactory { * Retrieves a {@link SequenceTrainer} that fits the given parameters. * * @param trainParams The {@link TrainingParameters} to check for the trainer type. - * Note: The entry {@link AbstractTrainer#ALGORITHM_PARAM} is used + * Note: The entry {@link TrainingParameters#ALGORITHM_PARAM} is used * to determine the type. * @param reportMap A {@link Map} that shall be used during initialization of * the {@link SequenceTrainer}. @@ -132,7 +132,7 @@ public class TrainerFactory { */ public static SequenceTrainer getSequenceModelTrainer( TrainingParameters trainParams, Map<String, String> reportMap) { - String trainerType = trainParams.getStringParameter(AbstractTrainer.ALGORITHM_PARAM,null); + String trainerType = trainParams.getStringParameter(TrainingParameters.ALGORITHM_PARAM,null); if (trainerType != null) { final SequenceTrainer trainer; @@ -153,7 +153,7 @@ public class TrainerFactory { * Retrieves an {@link EventModelSequenceTrainer} that fits the given parameters. * * @param trainParams The {@link TrainingParameters} to check for the trainer type. - * Note: The entry {@link AbstractTrainer#ALGORITHM_PARAM} is used + * Note: The entry {@link TrainingParameters#ALGORITHM_PARAM} is used * to determine the type. * @param reportMap A {@link Map} that shall be used during initialization of * the {@link EventModelSequenceTrainer}. @@ -163,7 +163,7 @@ public class TrainerFactory { */ public static <T> EventModelSequenceTrainer<T> getEventModelSequenceTrainer( TrainingParameters trainParams, Map<String, String> reportMap) { - String trainerType = trainParams.getStringParameter(AbstractTrainer.ALGORITHM_PARAM,null); + String trainerType = trainParams.getStringParameter(TrainingParameters.ALGORITHM_PARAM,null); if (trainerType != null) { final EventModelSequenceTrainer<T> trainer; @@ -184,7 +184,7 @@ public class TrainerFactory { * Retrieves an {@link EventTrainer} that fits the given parameters. * * @param trainParams The {@link TrainingParameters} to check for the trainer type. - * Note: The entry {@link AbstractTrainer#ALGORITHM_PARAM} is used + * Note: The entry {@link TrainingParameters#ALGORITHM_PARAM} is used * to determine the type. If the type is not defined, the * {@link GISTrainer#MAXENT_VALUE} will be used. * @param reportMap A {@link Map} that shall be used during initialization of @@ -197,7 +197,7 @@ public class TrainerFactory { // if the trainerType is not defined -- use the GISTrainer. String trainerType = trainParams.getStringParameter( - AbstractTrainer.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); + TrainingParameters.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); final EventTrainer trainer; if (BUILTIN_TRAINERS.containsKey(trainerType)) { @@ -216,7 +216,8 @@ public class TrainerFactory { public static boolean isValid(TrainingParameters trainParams) { // TODO: Need to validate all parameters correctly ... error prone?! - String algorithmName = trainParams.getStringParameter(AbstractTrainer.ALGORITHM_PARAM,null); + String algorithmName = trainParams.getStringParameter(TrainingParameters.ALGORITHM_PARAM, + null); // If a trainer type can be determined, then the trainer is valid! if (algorithmName != null && @@ -227,8 +228,10 @@ public class TrainerFactory { try { // require that the Cutoff and the number of iterations be an integer. // if they are not set, the default values will be ok. - trainParams.getIntParameter(AbstractTrainer.CUTOFF_PARAM, 0); - trainParams.getIntParameter(AbstractTrainer.ITERATIONS_PARAM, 0); + trainParams.getIntParameter(TrainingParameters.CUTOFF_PARAM, + TrainingParameters.CUTOFF_DEFAULT_VALUE); + trainParams.getIntParameter(TrainingParameters.ITERATIONS_PARAM, + TrainingParameters.ITERATIONS_DEFAULT_VALUE); } catch (NumberFormatException e) { return false; diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/GISTrainer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/GISTrainer.java index caa0248c..d2eabeb9 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/GISTrainer.java +++ b/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/GISTrainer.java @@ -287,8 +287,8 @@ public class GISTrainer extends AbstractEventTrainer { int cutoff) throws IOException { DataIndexer indexer = new OnePassDataIndexer(); TrainingParameters indexingParameters = new TrainingParameters(); - indexingParameters.put(GISTrainer.CUTOFF_PARAM, cutoff); - indexingParameters.put(GISTrainer.ITERATIONS_PARAM, iterations); + indexingParameters.put(TrainingParameters.CUTOFF_PARAM, cutoff); + indexingParameters.put(TrainingParameters.ITERATIONS_PARAM, iterations); Map<String, String> reportMap = new HashMap<>(); indexer.init(indexingParameters, reportMap); indexer.index(eventStream); diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/model/AbstractDataIndexer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/model/AbstractDataIndexer.java index 017574eb..16fa0243 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/ml/model/AbstractDataIndexer.java +++ b/opennlp-tools/src/main/java/opennlp/tools/ml/model/AbstractDataIndexer.java @@ -30,7 +30,6 @@ import java.util.Objects; import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.util.InsufficientTrainingDataException; import opennlp.tools.util.ObjectStream; import opennlp.tools.util.TrainingParameters; @@ -45,9 +44,6 @@ public abstract class AbstractDataIndexer implements DataIndexer { private static final Logger logger = LoggerFactory.getLogger(AbstractDataIndexer.class); - public static final String CUTOFF_PARAM = AbstractTrainer.CUTOFF_PARAM; - public static final int CUTOFF_DEFAULT = AbstractTrainer.CUTOFF_DEFAULT; - public static final String SORT_PARAM = "sort"; public static final boolean SORT_DEFAULT = true; diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/model/OnePassDataIndexer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/model/OnePassDataIndexer.java index 8ed24de6..71d29199 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/ml/model/OnePassDataIndexer.java +++ b/opennlp-tools/src/main/java/opennlp/tools/ml/model/OnePassDataIndexer.java @@ -28,6 +28,7 @@ import org.slf4j.LoggerFactory; import opennlp.tools.util.ObjectStream; import opennlp.tools.util.ObjectStreamUtils; +import opennlp.tools.util.TrainingParameters; /** * A {@link DataIndexer} for maxent model data which handles cutoffs for uncommon @@ -48,7 +49,8 @@ public class OnePassDataIndexer extends AbstractDataIndexer { */ @Override public void index(ObjectStream<Event> eventStream) throws IOException { - int cutoff = trainingParameters.getIntParameter(CUTOFF_PARAM, CUTOFF_DEFAULT); + int cutoff = trainingParameters.getIntParameter(TrainingParameters.CUTOFF_PARAM, + TrainingParameters.CUTOFF_DEFAULT_VALUE); boolean sort = trainingParameters.getBooleanParameter(SORT_PARAM, SORT_DEFAULT); long start = System.currentTimeMillis(); diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/model/TwoPassDataIndexer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/model/TwoPassDataIndexer.java index 0e49a4bd..005d7663 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/ml/model/TwoPassDataIndexer.java +++ b/opennlp-tools/src/main/java/opennlp/tools/ml/model/TwoPassDataIndexer.java @@ -37,6 +37,7 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; import opennlp.tools.util.ObjectStream; +import opennlp.tools.util.TrainingParameters; /** * Collecting event and context counts by making two passes over the events. @@ -61,7 +62,8 @@ public class TwoPassDataIndexer extends AbstractDataIndexer { */ @Override public void index(ObjectStream<Event> eventStream) throws IOException { - int cutoff = trainingParameters.getIntParameter(CUTOFF_PARAM, CUTOFF_DEFAULT); + int cutoff = trainingParameters.getIntParameter(TrainingParameters.CUTOFF_PARAM, + TrainingParameters.CUTOFF_DEFAULT_VALUE); boolean sort = trainingParameters.getBooleanParameter(SORT_PARAM, SORT_DEFAULT); logger.info("Indexing events with TwoPass using cutoff of {}", cutoff); diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/perceptron/SimplePerceptronSequenceTrainer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/perceptron/SimplePerceptronSequenceTrainer.java index 92c2f48e..a58bdbde 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/ml/perceptron/SimplePerceptronSequenceTrainer.java +++ b/opennlp-tools/src/main/java/opennlp/tools/ml/perceptron/SimplePerceptronSequenceTrainer.java @@ -36,6 +36,7 @@ import opennlp.tools.ml.model.OnePassDataIndexer; import opennlp.tools.ml.model.Sequence; import opennlp.tools.ml.model.SequenceStream; import opennlp.tools.ml.model.SequenceStreamEventStream; +import opennlp.tools.util.TrainingParameters; /** * Trains {@link PerceptronModel models} with sequences using the perceptron algorithm. @@ -145,7 +146,7 @@ public class SimplePerceptronSequenceTrainer extends AbstractEventModelSequenceT * * @param iterations The number of iterations to use for training. * @param sequenceStream The {@link SequenceStream<Event>} used as data input. - * @param cutoff The {{@link #CUTOFF_PARAM}} value to use for training. + * @param cutoff The {{@link TrainingParameters#CUTOFF_PARAM}} value to use for training. * @param useAverage Whether to use 'averaging', or not. * @return A valid, trained {@link AbstractModel perceptron model}. */ @@ -154,7 +155,7 @@ public class SimplePerceptronSequenceTrainer extends AbstractEventModelSequenceT this.iterations = iterations; this.sequenceStream = sequenceStream; - trainingParameters.put(AbstractDataIndexer.CUTOFF_PARAM, cutoff); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, cutoff); trainingParameters.put(AbstractDataIndexer.SORT_PARAM, false); DataIndexer di = new OnePassDataIndexer(); di.init(trainingParameters, reportMap); diff --git a/opennlp-tools/src/main/java/opennlp/tools/util/TrainingParameters.java b/opennlp-tools/src/main/java/opennlp/tools/util/TrainingParameters.java index 824d6a04..91c5033a 100644 --- a/opennlp-tools/src/main/java/opennlp/tools/util/TrainingParameters.java +++ b/opennlp-tools/src/main/java/opennlp/tools/util/TrainingParameters.java @@ -35,14 +35,21 @@ import opennlp.tools.ml.EventTrainer; */ public class TrainingParameters { - // TODO: are them duplicated? public static final String ALGORITHM_PARAM = "Algorithm"; public static final String TRAINER_TYPE_PARAM = "TrainerType"; public static final String ITERATIONS_PARAM = "Iterations"; public static final String CUTOFF_PARAM = "Cutoff"; public static final String THREADS_PARAM = "Threads"; + + /** + * The default number of iterations is 100. + */ public static final int ITERATIONS_DEFAULT_VALUE = 100; + + /** + * The default cut off value is 5. + */ public static final int CUTOFF_DEFAULT_VALUE = 5; private final Map<String, Object> parameters = new TreeMap<>(String.CASE_INSENSITIVE_ORDER); @@ -96,21 +103,6 @@ public class TrainingParameters { return (String)parameters.get(ALGORITHM_PARAM); } - private static String getStringValue(Object value) { - if (value instanceof Integer) { - return Integer.toString((Integer)value); - } - else if (value instanceof Double) { - return Double.toString((Double)value); - } - else if (value instanceof Boolean) { - return Boolean.toString((Boolean)value); - } - else { - return (String)value; - } - } - /** * @param namespace The name space to filter or narrow the search space. May be {@code null}. * @@ -252,31 +244,6 @@ public class TrainingParameters { putIfAbsent(null, key, value); } - /** - * Puts a {@code value} into the current {@link TrainingParameters} under a certain {@code key}, - * if the value was not present before. - * The {@code namespace} can be used to prefix the {@code key}. - * - * @param namespace A prefix to declare or use a name space under which {@code key} shall be put. - * May be {@code null}. - * @param key The identifying key to put or retrieve a {@code value} with. - * @param value The {@link Boolean} parameter to put into this {@link TrainingParameters} instance. - */ - public void putIfAbsent(String namespace, String key, boolean value) { - parameters.putIfAbsent(getKey(namespace, key), value); - } - - /** - * Puts a {@code value} into the current {@link TrainingParameters} under a certain {@code key}, - * if the value was not present before. - * - * @param key The identifying key to put or retrieve a {@code value} with. - * @param value The {@link Boolean} parameter to put into this {@link TrainingParameters} instance. - */ - public void putIfAbsent(String key, boolean value) { - putIfAbsent(null, key, value); - } - /** * Puts a {@code value} into the current {@link TrainingParameters} under a certain {@code key}. * If the value was present before, the previous value will be overwritten with the specified one. diff --git a/opennlp-tools/src/test/java/opennlp/tools/doccat/DocumentCategorizerNBTest.java b/opennlp-tools/src/test/java/opennlp/tools/doccat/DocumentCategorizerNBTest.java index 4c3fd562..b822ec5a 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/doccat/DocumentCategorizerNBTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/doccat/DocumentCategorizerNBTest.java @@ -24,7 +24,6 @@ import java.util.SortedMap; import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.Test; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.naivebayes.NaiveBayesTrainer; import opennlp.tools.util.ObjectStream; import opennlp.tools.util.ObjectStreamUtils; @@ -44,9 +43,9 @@ public class DocumentCategorizerNBTest { new DocumentSample("0", new String[] {"x", "y", "z", "7", "8"})); TrainingParameters params = new TrainingParameters(); - params.put(TrainingParameters.ITERATIONS_PARAM, 100); + params.put(TrainingParameters.ITERATIONS_PARAM, TrainingParameters.ITERATIONS_DEFAULT_VALUE); params.put(TrainingParameters.CUTOFF_PARAM, 0); - params.put(AbstractTrainer.ALGORITHM_PARAM, NaiveBayesTrainer.NAIVE_BAYES_VALUE); + params.put(TrainingParameters.ALGORITHM_PARAM, NaiveBayesTrainer.NAIVE_BAYES_VALUE); DoccatModel model = DocumentCategorizerME.train("x-unspecified", samples, params, new DoccatFactory()); diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/TrainerFactoryTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/TrainerFactoryTest.java index 9e6c8e0d..a8f1224a 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/TrainerFactoryTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/TrainerFactoryTest.java @@ -63,7 +63,7 @@ public class TrainerFactoryTest { @Test void testIsSequenceTrainerTrue() { - mlParams.put(AbstractTrainer.ALGORITHM_PARAM, + mlParams.put(TrainingParameters.ALGORITHM_PARAM, SimplePerceptronSequenceTrainer.PERCEPTRON_SEQUENCE_VALUE); TrainerType trainerType = TrainerFactory.getTrainerType(mlParams); @@ -73,7 +73,7 @@ public class TrainerFactoryTest { @Test void testIsSequenceTrainerFalse() { - mlParams.put(AbstractTrainer.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); + mlParams.put(TrainingParameters.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); TrainerType trainerType = TrainerFactory.getTrainerType(mlParams); Assertions.assertNotEquals(TrainerType.EVENT_MODEL_SEQUENCE_TRAINER, trainerType); } diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/GISIndexingTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/GISIndexingTest.java index 912d30f5..fa1f18cf 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/GISIndexingTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/GISIndexingTest.java @@ -27,7 +27,6 @@ import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.Test; import opennlp.tools.ml.AbstractEventTrainer; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.EventTrainer; import opennlp.tools.ml.TrainerFactory; import opennlp.tools.ml.maxent.quasinewton.QNTrainer; @@ -64,7 +63,7 @@ public class GISIndexingTest { void testGISTrainSignature1() throws IOException { try (ObjectStream<Event> eventStream = createEventStream()) { TrainingParameters params = ModelUtil.createDefaultTrainingParameters(); - params.put(AbstractTrainer.CUTOFF_PARAM, 1); + params.put(TrainingParameters.CUTOFF_PARAM, 1); EventTrainer trainer = TrainerFactory.getEventTrainer(params, null); @@ -79,7 +78,7 @@ public class GISIndexingTest { void testGISTrainSignature2() throws IOException { try (ObjectStream<Event> eventStream = createEventStream()) { TrainingParameters params = ModelUtil.createDefaultTrainingParameters(); - params.put(AbstractTrainer.CUTOFF_PARAM, 1); + params.put(TrainingParameters.CUTOFF_PARAM, 1); params.put("smoothing", true); EventTrainer trainer = TrainerFactory.getEventTrainer(params, null); @@ -95,8 +94,8 @@ public class GISIndexingTest { try (ObjectStream<Event> eventStream = createEventStream()) { TrainingParameters params = ModelUtil.createDefaultTrainingParameters(); - params.put(AbstractTrainer.ITERATIONS_PARAM, 10); - params.put(AbstractTrainer.CUTOFF_PARAM, 1); + params.put(TrainingParameters.ITERATIONS_PARAM, 10); + params.put(TrainingParameters.CUTOFF_PARAM, 1); EventTrainer trainer = TrainerFactory.getEventTrainer(params, null); @@ -111,8 +110,8 @@ public class GISIndexingTest { void testGISTrainSignature4() throws IOException { try (ObjectStream<Event> eventStream = createEventStream()) { TrainingParameters params = ModelUtil.createDefaultTrainingParameters(); - params.put(AbstractTrainer.ITERATIONS_PARAM, 10); - params.put(AbstractTrainer.CUTOFF_PARAM, 1); + params.put(TrainingParameters.ITERATIONS_PARAM, 10); + params.put(TrainingParameters.CUTOFF_PARAM, 1); GISTrainer trainer = (GISTrainer) TrainerFactory.getEventTrainer(params, null); trainer.setGaussianSigma(0.01); @@ -129,8 +128,8 @@ public class GISIndexingTest { try (ObjectStream<Event> eventStream = createEventStream()) { TrainingParameters params = ModelUtil.createDefaultTrainingParameters(); - params.put(AbstractTrainer.ITERATIONS_PARAM, 10); - params.put(AbstractTrainer.CUTOFF_PARAM, 1); + params.put(TrainingParameters.ITERATIONS_PARAM, 10); + params.put(TrainingParameters.CUTOFF_PARAM, 1); params.put("smoothing", false); EventTrainer trainer = TrainerFactory.getEventTrainer(params, null); @@ -146,7 +145,7 @@ public class GISIndexingTest { // by default we are using GIS/EventTrainer/Cutoff of 5/100 iterations parameters.put(TrainingParameters.ITERATIONS_PARAM, 10); parameters.put(AbstractEventTrainer.DATA_INDEXER_PARAM, AbstractEventTrainer.DATA_INDEXER_ONE_PASS_VALUE); - parameters.put(AbstractEventTrainer.CUTOFF_PARAM, 1); + parameters.put(TrainingParameters.CUTOFF_PARAM, 1); // note: setting the SORT_PARAM to true is the default, so it is not really needed parameters.put(AbstractDataIndexer.SORT_PARAM, true); @@ -168,7 +167,7 @@ public class GISIndexingTest { parameters.put(TrainingParameters.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); parameters.put(AbstractEventTrainer.DATA_INDEXER_PARAM, AbstractEventTrainer.DATA_INDEXER_TWO_PASS_VALUE); - parameters.put(AbstractEventTrainer.CUTOFF_PARAM, 2); + parameters.put(TrainingParameters.CUTOFF_PARAM, 2); trainer = TrainerFactory.getEventTrainer(parameters, new HashMap<>()); Assertions.assertEquals("opennlp.tools.ml.maxent.quasinewton.QNTrainer", trainer.getClass().getName()); @@ -186,7 +185,7 @@ public class GISIndexingTest { // set the cutoff to 1 for this test. TrainingParameters parameters = new TrainingParameters(); - parameters.put(AbstractDataIndexer.CUTOFF_PARAM, 1); + parameters.put(TrainingParameters.CUTOFF_PARAM, 1); // test with a 1 pass data indexer... parameters.put(AbstractEventTrainer.DATA_INDEXER_PARAM, AbstractEventTrainer.DATA_INDEXER_ONE_PASS_VALUE); diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/MaxentPrepAttachTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/MaxentPrepAttachTest.java index 9cc31704..3d379780 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/MaxentPrepAttachTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/MaxentPrepAttachTest.java @@ -24,7 +24,6 @@ import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; import opennlp.tools.ml.AbstractEventTrainer; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.EventTrainer; import opennlp.tools.ml.PrepAttachDataUtil; import opennlp.tools.ml.TrainerFactory; @@ -43,7 +42,7 @@ public class MaxentPrepAttachTest { @BeforeEach void initIndexer() { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); trainingParameters.put(AbstractDataIndexer.SORT_PARAM, false); testDataIndexer = new TwoPassDataIndexer(); testDataIndexer.init(trainingParameters, new HashMap<>()); @@ -76,10 +75,10 @@ public class MaxentPrepAttachTest { void testMaxentOnPrepAttachDataWithParams() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); trainParams.put(AbstractEventTrainer.DATA_INDEXER_PARAM, AbstractEventTrainer.DATA_INDEXER_TWO_PASS_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); MaxentModel model = trainer.train(PrepAttachDataUtil.createTrainingStream()); @@ -91,7 +90,7 @@ public class MaxentPrepAttachTest { void testMaxentOnPrepAttachDataWithParamsDefault() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); MaxentModel model = trainer.train(PrepAttachDataUtil.createTrainingStream()); @@ -102,7 +101,7 @@ public class MaxentPrepAttachTest { @Test void testMaxentOnPrepAttachDataWithParamsLLThreshold() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, GISTrainer.MAXENT_VALUE); trainParams.put(GISTrainer.LOG_LIKELIHOOD_THRESHOLD_PARAM, 5.); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/RealValueModelTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/RealValueModelTest.java index 28b98f5a..a26b1ac4 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/RealValueModelTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/RealValueModelTest.java @@ -24,7 +24,6 @@ import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.model.DataIndexer; import opennlp.tools.ml.model.FileEventStream; import opennlp.tools.ml.model.OnePassRealValueDataIndexer; @@ -39,7 +38,7 @@ public class RealValueModelTest { @BeforeEach void initIndexer() { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); testDataIndexer = new OnePassRealValueDataIndexer(); testDataIndexer.init(trainingParameters, new HashMap<>()); } diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/ScaleDoesntMatterTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/ScaleDoesntMatterTest.java index 5869562b..466ff6aa 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/ScaleDoesntMatterTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/ScaleDoesntMatterTest.java @@ -24,7 +24,6 @@ import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.EventTrainer; import opennlp.tools.ml.TrainerFactory; import opennlp.tools.ml.model.DataIndexer; @@ -45,7 +44,7 @@ public class ScaleDoesntMatterTest { @BeforeEach void initIndexer() { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 0); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 0); testDataIndexer = new OnePassRealValueDataIndexer(); testDataIndexer.init(trainingParameters, new HashMap<>()); } diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/io/RealValueFileEventStreamTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/io/RealValueFileEventStreamTest.java index 08f2e8e5..a5a64d7d 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/io/RealValueFileEventStreamTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/io/RealValueFileEventStreamTest.java @@ -24,7 +24,6 @@ import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.model.DataIndexer; import opennlp.tools.ml.model.OnePassRealValueDataIndexer; import opennlp.tools.ml.model.RealValueFileEventStream; @@ -37,7 +36,7 @@ public class RealValueFileEventStreamTest { @BeforeEach void initIndexer() { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); indexer = new OnePassRealValueDataIndexer(); indexer.init(trainingParameters, new HashMap<>()); } diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihoodTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihoodTest.java index 7a031310..155aed22 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihoodTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihoodTest.java @@ -27,7 +27,6 @@ import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.model.DataIndexer; import opennlp.tools.ml.model.OnePassRealValueDataIndexer; import opennlp.tools.ml.model.RealValueFileEventStream; @@ -42,7 +41,7 @@ public class NegLogLikelihoodTest { @BeforeEach void initIndexer() { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); testDataIndexer = new OnePassRealValueDataIndexer(); testDataIndexer.init(trainingParameters, new HashMap<>()); } diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNPrepAttachTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNPrepAttachTest.java index 82d8afb4..5dd7c2cf 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNPrepAttachTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNPrepAttachTest.java @@ -23,7 +23,6 @@ import java.util.HashMap; import org.junit.jupiter.api.Test; import opennlp.tools.ml.AbstractEventTrainer; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.PrepAttachDataUtil; import opennlp.tools.ml.TrainerFactory; import opennlp.tools.ml.model.AbstractDataIndexer; @@ -39,7 +38,7 @@ public class QNPrepAttachTest { void testQNOnPrepAttachData() throws IOException { DataIndexer indexer = new TwoPassDataIndexer(); TrainingParameters indexingParameters = new TrainingParameters(); - indexingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + indexingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); indexingParameters.put(AbstractDataIndexer.SORT_PARAM, false); indexer.init(indexingParameters, new HashMap<>()); indexer.index(PrepAttachDataUtil.createTrainingStream()); @@ -53,7 +52,7 @@ public class QNPrepAttachTest { void testQNOnPrepAttachDataWithParamsDefault() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); MaxentModel model = TrainerFactory.getEventTrainer(trainParams, null) .train(PrepAttachDataUtil.createTrainingStream()); @@ -65,10 +64,10 @@ public class QNPrepAttachTest { void testQNOnPrepAttachDataWithElasticNetParams() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); trainParams.put(AbstractEventTrainer.DATA_INDEXER_PARAM, AbstractEventTrainer.DATA_INDEXER_TWO_PASS_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); trainParams.put(QNTrainer.L1COST_PARAM, 0.25); trainParams.put(QNTrainer.L2COST_PARAM, 1.0D); @@ -82,10 +81,10 @@ public class QNPrepAttachTest { void testQNOnPrepAttachDataWithL1Params() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); trainParams.put(AbstractEventTrainer.DATA_INDEXER_PARAM, AbstractEventTrainer.DATA_INDEXER_TWO_PASS_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); trainParams.put(QNTrainer.L1COST_PARAM, 1.0D); trainParams.put(QNTrainer.L2COST_PARAM, 0D); @@ -99,10 +98,10 @@ public class QNPrepAttachTest { void testQNOnPrepAttachDataWithL2Params() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); trainParams.put(AbstractEventTrainer.DATA_INDEXER_PARAM, AbstractEventTrainer.DATA_INDEXER_TWO_PASS_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); trainParams.put(QNTrainer.L1COST_PARAM, 0D); trainParams.put(QNTrainer.L2COST_PARAM, 1.0D); @@ -116,7 +115,7 @@ public class QNPrepAttachTest { void testQNOnPrepAttachDataInParallel() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE); trainParams.put(QNTrainer.THREADS_PARAM, 2); MaxentModel model = TrainerFactory.getEventTrainer(trainParams, null) diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNTrainerTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNTrainerTest.java index 8751c02a..ac323f1a 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNTrainerTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNTrainerTest.java @@ -27,7 +27,6 @@ import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.model.AbstractModel; import opennlp.tools.ml.model.BinaryFileDataReader; import opennlp.tools.ml.model.DataIndexer; @@ -46,7 +45,7 @@ public class QNTrainerTest { @BeforeEach void initIndexer() { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); testDataIndexer = new OnePassRealValueDataIndexer(); testDataIndexer.init(trainingParameters, new HashMap<>()); } diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesCorrectnessTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesCorrectnessTest.java index 0aacb370..2c837744 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesCorrectnessTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesCorrectnessTest.java @@ -27,7 +27,6 @@ import org.junit.jupiter.params.ParameterizedTest; import org.junit.jupiter.params.provider.Arguments; import org.junit.jupiter.params.provider.MethodSource; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.model.AbstractDataIndexer; import opennlp.tools.ml.model.DataIndexer; import opennlp.tools.ml.model.Event; @@ -45,7 +44,7 @@ public class NaiveBayesCorrectnessTest extends AbstractNaiveBayesTest { @BeforeEach void initIndexer() throws IOException { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); trainingParameters.put(AbstractDataIndexer.SORT_PARAM, false); testDataIndexer = new TwoPassDataIndexer(); testDataIndexer.init(trainingParameters, new HashMap<>()); diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesModelReadWriteTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesModelReadWriteTest.java index 6c135c9d..2aabbfc3 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesModelReadWriteTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesModelReadWriteTest.java @@ -27,7 +27,6 @@ import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.model.AbstractDataIndexer; import opennlp.tools.ml.model.AbstractModel; import opennlp.tools.ml.model.DataIndexer; @@ -44,7 +43,7 @@ public class NaiveBayesModelReadWriteTest extends AbstractNaiveBayesTest { @BeforeEach void initIndexer() throws IOException { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); trainingParameters.put(AbstractDataIndexer.SORT_PARAM, false); testDataIndexer = new TwoPassDataIndexer(); testDataIndexer.init(trainingParameters, new HashMap<>()); diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesPrepAttachTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesPrepAttachTest.java index 954aa840..aea7e4b2 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesPrepAttachTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesPrepAttachTest.java @@ -24,7 +24,6 @@ import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.BeforeEach; import org.junit.jupiter.api.Test; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.EventTrainer; import opennlp.tools.ml.PrepAttachDataUtil; import opennlp.tools.ml.TrainerFactory; @@ -52,7 +51,7 @@ public class NaiveBayesPrepAttachTest { @Test void testNaiveBayesOnPrepAttachData() throws IOException { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); trainingParameters.put(AbstractDataIndexer.SORT_PARAM, false); DataIndexer testDataIndexer = new TwoPassDataIndexer(); testDataIndexer.init(trainingParameters, new HashMap<>()); @@ -66,8 +65,8 @@ public class NaiveBayesPrepAttachTest { @Test void testNaiveBayesOnPrepAttachDataUsingTrainUtil() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, NaiveBayesTrainer.NAIVE_BAYES_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, NaiveBayesTrainer.NAIVE_BAYES_VALUE); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); MaxentModel model = trainer.train(trainingStream); @@ -78,8 +77,8 @@ public class NaiveBayesPrepAttachTest { @Test void testNaiveBayesOnPrepAttachDataUsingTrainUtilWithCutoff5() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, NaiveBayesTrainer.NAIVE_BAYES_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 5); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, NaiveBayesTrainer.NAIVE_BAYES_VALUE); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 5); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); MaxentModel model = trainer.train(trainingStream); diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesSerializedCorrectnessTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesSerializedCorrectnessTest.java index e0abd823..65869813 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesSerializedCorrectnessTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/naivebayes/NaiveBayesSerializedCorrectnessTest.java @@ -35,7 +35,6 @@ import org.junit.jupiter.params.ParameterizedTest; import org.junit.jupiter.params.provider.Arguments; import org.junit.jupiter.params.provider.MethodSource; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.model.AbstractDataIndexer; import opennlp.tools.ml.model.DataIndexer; import opennlp.tools.ml.model.Event; @@ -52,7 +51,7 @@ public class NaiveBayesSerializedCorrectnessTest extends AbstractNaiveBayesTest @BeforeEach void initIndexer() throws IOException { TrainingParameters trainingParameters = new TrainingParameters(); - trainingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); trainingParameters.put(AbstractDataIndexer.SORT_PARAM, false); testDataIndexer = new TwoPassDataIndexer(); testDataIndexer.init(trainingParameters, new HashMap<>()); diff --git a/opennlp-tools/src/test/java/opennlp/tools/ml/perceptron/PerceptronPrepAttachTest.java b/opennlp-tools/src/test/java/opennlp/tools/ml/perceptron/PerceptronPrepAttachTest.java index 41c50dee..1b3db1a8 100644 --- a/opennlp-tools/src/test/java/opennlp/tools/ml/perceptron/PerceptronPrepAttachTest.java +++ b/opennlp-tools/src/test/java/opennlp/tools/ml/perceptron/PerceptronPrepAttachTest.java @@ -28,7 +28,6 @@ import java.util.Map; import org.junit.jupiter.api.Assertions; import org.junit.jupiter.api.Test; -import opennlp.tools.ml.AbstractTrainer; import opennlp.tools.ml.EventTrainer; import opennlp.tools.ml.PrepAttachDataUtil; import opennlp.tools.ml.TrainerFactory; @@ -47,7 +46,7 @@ public class PerceptronPrepAttachTest { void testPerceptronOnPrepAttachData() throws IOException { TwoPassDataIndexer indexer = new TwoPassDataIndexer(); TrainingParameters indexingParameters = new TrainingParameters(); - indexingParameters.put(AbstractTrainer.CUTOFF_PARAM, 1); + indexingParameters.put(TrainingParameters.CUTOFF_PARAM, 1); indexingParameters.put(AbstractDataIndexer.SORT_PARAM, false); indexer.init(indexingParameters, new HashMap<>()); indexer.index(PrepAttachDataUtil.createTrainingStream()); @@ -59,8 +58,8 @@ public class PerceptronPrepAttachTest { void testPerceptronOnPrepAttachDataWithSkippedAveraging() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); trainParams.put("UseSkippedAveraging", true); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); @@ -72,9 +71,9 @@ public class PerceptronPrepAttachTest { void testPerceptronOnPrepAttachDataWithTolerance() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); - trainParams.put(AbstractTrainer.ITERATIONS_PARAM, 500); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.ITERATIONS_PARAM, 500); trainParams.put("Tolerance", 0.0001d); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); @@ -86,9 +85,9 @@ public class PerceptronPrepAttachTest { void testPerceptronOnPrepAttachDataWithStepSizeDecrease() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); - trainParams.put(AbstractTrainer.ITERATIONS_PARAM, 500); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.ITERATIONS_PARAM, 500); trainParams.put("StepSizeDecrease", 0.06d); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); @@ -100,8 +99,8 @@ public class PerceptronPrepAttachTest { void testModelSerialization() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); trainParams.put("UseSkippedAveraging", true); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); @@ -124,8 +123,8 @@ public class PerceptronPrepAttachTest { @Test void testModelEquals() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); trainParams.put("UseSkippedAveraging", true); EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, null); @@ -139,10 +138,10 @@ public class PerceptronPrepAttachTest { @Test void verifyReportMap() throws IOException { TrainingParameters trainParams = new TrainingParameters(); - trainParams.put(AbstractTrainer.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); - trainParams.put(AbstractTrainer.CUTOFF_PARAM, 1); + trainParams.put(TrainingParameters.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE); + trainParams.put(TrainingParameters.CUTOFF_PARAM, 1); // Since we are verifying the report map, we don't need to have more than 1 iteration - trainParams.put(AbstractTrainer.ITERATIONS_PARAM, 1); + trainParams.put(TrainingParameters.ITERATIONS_PARAM, 1); trainParams.put("UseSkippedAveraging", true); Map<String, String> reportMap = new HashMap<>();
