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.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.Ontology;
/**
* Weighted Matrix Factorization operator for Item Recommendation
*
* @see com.rapidminer.ItemRecommendation.WRMatrixFactorization
* @see com.rapidminer.ItemRecommendation.WRMF
*
* @author Matej Mihelcic (Ru�er Bo�kovi� Institute)
*/
public class WRMatrixFactorization extends Operator {
public static final String PARAMETER_K = "k";
private InputPort exampleSetInput = getInputPorts().createPort("example set");
private OutputPort exampleSetOutput1 = getOutputPorts().createPort("Model");
private OutputPort exampleSetOutput = getOutputPorts().createPort("example set");
public static final String PARAMETER_NUM_FACTORS = "Num Factors";
public static final String PARAMETER_REGULARIZATION="Regularization";
public static final String PARAMETER_CPOS="C position";
public static final String PARAMETER_NUM_ITER="Iteration number";
public static final String PARAMETER_INIT_MEAN="Initial mean";
public static final String PARAMETER_INIT_STDEV="Initial stdev";
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeInt(PARAMETER_NUM_FACTORS, "Number of latent factors. Range: integer; 1-+?; default: 10", 1, Integer.MAX_VALUE, 10, true));
types.add(new ParameterTypeDouble(PARAMETER_REGULARIZATION, "Value of regularization parameter. Range: double; 0-+?; default: 0.015", 0, Double.MAX_VALUE, 0.015, true));
types.add(new ParameterTypeDouble(PARAMETER_CPOS, "C position: the weight/confidence that is put on positive observations. Range: double; 0-+?; default: 1", 0, Double.MAX_VALUE, 1, true));
types.add(new ParameterTypeInt(PARAMETER_NUM_ITER, "Number of iterations. Range: integer; 1-+?; default: 30", 1, Integer.MAX_VALUE, 30, false));
types.add(new ParameterTypeDouble(PARAMETER_INIT_MEAN, "Initial mean. Range: double; 0-+?; default: 0", 0, Double.MAX_VALUE, 0, true));
types.add(new ParameterTypeDouble(PARAMETER_INIT_STDEV, "Initial stdev. Range: double; 0-+?; default: 0.1", 0, Double.MAX_VALUE, 0.1, true));
return types;
}
/**
* Constructor
*/
public WRMatrixFactorization(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();
}
WRMF recommendAlg=new WRMF();
recommendAlg.num_factors=getParameterAsInt("Num Factors");
recommendAlg.NumIter=getParameterAsInt("Iteration number");
recommendAlg.InitMean=getParameterAsDouble("Initial mean");
recommendAlg.InitStdev=getParameterAsDouble("Initial stdev");
recommendAlg.c_pos=getParameterAsDouble("C position");
recommendAlg.regularization=getParameterAsDouble("Regularization");
recommendAlg.SetFeedback(training_data);
recommendAlg.user_mapping=user_mapping;
recommendAlg.item_mapping=item_mapping;
recommendAlg.Train();
exampleSetOutput.deliver(exampleSet);
exampleSetOutput1.deliver(recommendAlg);
}
}