package com.rapidminer.ItemRecommendation;
import java.util.List;
import com.rapidminer.data.EntityMapping;
import com.rapidminer.data.IEntityMapping;
import com.rapidminer.data.IPosOnlyFeedback;
import com.rapidminer.data.PosOnlyFeedback;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeRole;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.ExampleSetPassThroughRule;
import com.rapidminer.operator.ports.metadata.ExampleSetPrecondition;
import com.rapidminer.operator.ports.metadata.GenerateNewMDRule;
import com.rapidminer.operator.ports.metadata.MetaData;
import com.rapidminer.operator.ports.metadata.SetRelation;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.Ontology;
/**
* ItemKnn operator
*
* @see com.rapidminer.ItemRecommendation.UserKnn
* @see com.rapidminer.ItemRecommendation._userKnn
* @see com.rapidminer.ItemRecommendation.WeightedUserKnn
*
* @author Matej Mihelcic (Ru�er Bo�kovi� Institute)
*/
public class UserKnn extends Operator {
public static final String PARAMETER_K = "k";
public static final String PARAMETER_WEIGHTED="Weighted Knn";
private InputPort exampleSetInput = getInputPorts().createPort("example set");
private OutputPort exampleSetOutput1 = getOutputPorts().createPort("Model");
private OutputPort exampleSetOutput = getOutputPorts().createPort("example set");
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeInt(PARAMETER_K, "The used number of nearest neighbors. Range: integer; 1-+?; default: 80", 1, Integer.MAX_VALUE, 80, false));
types.add(new ParameterTypeBoolean(PARAMETER_WEIGHTED, "Use weighted Knn. Range: boolean; default: false", false, false));
return types;
}
/**
*
*
* Constructor
*/
public UserKnn(OperatorDescription description) {
super(description);
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, "user identification", Ontology.ATTRIBUTE_VALUE));
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, "item identification", Ontology.ATTRIBUTE_VALUE));
getTransformer().addRule(new ExampleSetPassThroughRule(exampleSetInput, exampleSetOutput, SetRelation.EQUAL) {
});
getTransformer().addRule(new GenerateNewMDRule(exampleSetOutput1, new MetaData(ItemRecommender.class)) {
});
}
@Override
public void doWork() throws OperatorException {
ExampleSet exampleSet = exampleSetInput.getData();
IPosOnlyFeedback training_data=new PosOnlyFeedback();
IEntityMapping user_mapping=new EntityMapping();
IEntityMapping item_mapping=new EntityMapping();
if (exampleSet.getAttributes().getSpecial("user identification") == null) {
throw new UserError(this,105);
}
if (exampleSet.getAttributes().getSpecial("item identification") == null) {
throw new UserError(this, 105);
}
Attributes Att = exampleSet.getAttributes();
AttributeRole ur=Att.getRole("user identification");
Attribute u=ur.getAttribute();
AttributeRole ir=Att.getRole("item identification");
Attribute i=ir.getAttribute();
for (Example example : exampleSet) {
double j=example.getValue(u);
int uid=(int) j;
j=example.getValue(i);
int iid=(int) j;
training_data.Add(user_mapping.ToInternalID(uid), item_mapping.ToInternalID(iid));
checkForStop();
}
boolean isW = getParameterAsBoolean("Weighted Knn");
_userKnn recommendAlg;
if(isW==false)
recommendAlg=new _userKnn() ;
else recommendAlg=new WeightedUserKnn();
int K=getParameterAsInt("k");
recommendAlg.setK(K);
recommendAlg.SetFeedback(training_data);
recommendAlg.user_mapping=user_mapping;
recommendAlg.item_mapping=item_mapping;
checkForStop();
recommendAlg.Train();
checkForStop();
exampleSetOutput.deliver(exampleSet);
exampleSetOutput1.deliver(recommendAlg);
}
}