/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package opennlp.tools.ml.model;
import java.text.DecimalFormat;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import java.util.Objects;
public abstract class AbstractModel implements MaxentModel {
/** Mapping between predicates/contexts and an integer representing them. */
protected Map<String, Integer> pmap;
/** The names of the outcomes. */
protected String[] outcomeNames;
/** Parameters for the model. */
protected EvalParameters evalParams;
/** Prior distribution for this model. */
protected Prior prior;
public enum ModelType { Maxent,Perceptron,MaxentQn,NaiveBayes }
/** The type of the model. */
protected ModelType modelType;
/**
* @deprecated this will be removed in 1.8.1, pmap should be private
*
* @param params
* @param predLabels
* @param pmap
* @param outcomeNames
*/
@Deprecated
public AbstractModel(Context[] params, String[] predLabels,
Map<String, Integer> pmap, String[] outcomeNames) {
this.pmap = pmap;
this.outcomeNames = outcomeNames;
this.evalParams = new EvalParameters(params,outcomeNames.length);
}
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames) {
init(predLabels, outcomeNames);
this.evalParams = new EvalParameters(params, outcomeNames.length);
}
private void init(String[] predLabels, String[] outcomeNames) {
this.pmap = new HashMap<>(predLabels.length);
for (int i = 0; i < predLabels.length; i++) {
pmap.put(predLabels[i], i);
}
this.outcomeNames = outcomeNames;
}
/**
* Return the name of the outcome corresponding to the highest likelihood
* in the parameter ocs.
*
* @param ocs A double[] as returned by the eval(String[] context)
* method.
* @return The name of the most likely outcome.
*/
public final String getBestOutcome(double[] ocs) {
int best = 0;
for (int i = 1; i < ocs.length; i++)
if (ocs[i] > ocs[best]) best = i;
return outcomeNames[best];
}
public ModelType getModelType() {
return modelType;
}
/**
* Return a string matching all the outcome names with all the
* probabilities produced by the <code>eval(String[] context)</code>
* method.
*
* @param ocs A <code>double[]</code> as returned by the
* <code>eval(String[] context)</code>
* method.
* @return String containing outcome names paired with the normalized
* probability (contained in the <code>double[] ocs</code>)
* for each one.
*/
public final String getAllOutcomes(double[] ocs) {
if (ocs.length != outcomeNames.length) {
return "The double array sent as a parameter to GISModel.getAllOutcomes() " +
"must not have been produced by this model.";
}
else {
DecimalFormat df = new DecimalFormat("0.0000");
StringBuilder sb = new StringBuilder(ocs.length * 2);
sb.append(outcomeNames[0]).append("[").append(df.format(ocs[0])).append("]");
for (int i = 1; i < ocs.length; i++) {
sb.append(" ").append(outcomeNames[i]).append("[").append(df.format(ocs[i])).append("]");
}
return sb.toString();
}
}
/**
* Return the name of an outcome corresponding to an int id.
*
* @param i An outcome id.
* @return The name of the outcome associated with that id.
*/
public final String getOutcome(int i) {
return outcomeNames[i];
}
/**
* Gets the index associated with the String name of the given outcome.
*
* @param outcome the String name of the outcome for which the
* index is desired
* @return the index if the given outcome label exists for this
* model, -1 if it does not.
**/
public int getIndex(String outcome) {
for (int i = 0; i < outcomeNames.length; i++) {
if (outcomeNames[i].equals(outcome))
return i;
}
return -1;
}
public int getNumOutcomes() {
return evalParams.getNumOutcomes();
}
/**
* Provides the fundamental data structures which encode the maxent model
* information. This method will usually only be needed by
* GISModelWriters. The following values are held in the Object array
* which is returned by this method:
* <ul>
* <li>index 0: opennlp.tools.ml.maxent.Context[] containing the model
* parameters
* <li>index 1: java.util.Map containing the mapping of model predicates
* to unique integers
* <li>index 2: java.lang.String[] containing the names of the outcomes,
* stored in the index of the array which represents their
* unique ids in the model.
* </ul>
*
* @return An Object[] with the values as described above.
*/
public final Object[] getDataStructures() {
Object[] data = new Object[3];
data[0] = evalParams.getParams();
data[1] = pmap;
data[2] = outcomeNames;
return data;
}
@Override
public int hashCode() {
return Objects.hash(pmap, Arrays.hashCode(outcomeNames), evalParams, prior);
}
@Override
public boolean equals(Object obj) {
if (obj == this) {
return true;
}
if (obj instanceof AbstractModel) {
AbstractModel model = (AbstractModel) obj;
return pmap.equals(model.pmap) && Objects.deepEquals(outcomeNames, model.outcomeNames)
&& evalParams.equals(model.evalParams) && Objects.equals(prior, model.prior);
}
return false;
}
}