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new cfb6c57c OPENNLP-1643 - Remove inconsistent Training Parameter
Definitions (#682)
cfb6c57c is described below
commit cfb6c57c3920e4a8d9e8bad6d3a0b3276959dc52
Author: Richard Zowalla <[email protected]>
AuthorDate: Fri Nov 8 06:28:35 2024 +0100
OPENNLP-1643 - Remove inconsistent Training Parameter Definitions (#682)
---
.../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 | 24 ++++++----------
.../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(+), 131 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..37f5bd92 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}.
*
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<>();
