package com.rapidminer.operator.RatingPrediction;
import java.util.List;
import com.rapidminer.data.EntityMapping;
import com.rapidminer.data.IEntityMapping;
import com.rapidminer.data.IRatings;
import com.rapidminer.data.Ratings;
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;
/**
* FactorWised Matrix Factorization operator for Rating Prediction
*
* @see com.rapidminer.operator.RatingPrediction.FWMF
* @see com.rapidminer.operator.RatingPrediction.FactorWisedMatrixFactorization
*
* @author Matej Mihelcic (Ru�er Bo�kovi� Institute)
*/
public class CF_FWMF extends Operator{
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_SCHRINKAGE = "Schrinkage";
public static final String PARAMETER_SENSIBILITY = "Sensibility";
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 static final String PARAMETER_Min="Min Rating";
public static final String PARAMETER_Range="Range";
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeInt(PARAMETER_Min, "Value of minimal rating value. Range: integer; 0-+?; default: 1", 0, Integer.MAX_VALUE, 1, false));
types.add(new ParameterTypeInt(PARAMETER_Range, "Range of possible rating values. Range: integer; 1-+?; default: 4 ; Max Rating=Min Rating+Range;", 1, Integer.MAX_VALUE, 4, false));
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_SCHRINKAGE, "Schrinkage. Range: double; 0-+?; default: 25", 0, Integer.MAX_VALUE, 25, true));
types.add(new ParameterTypeDouble(PARAMETER_SENSIBILITY, "Sensibility. Range: double; 0-+?; default: 0.00001", 0, Integer.MAX_VALUE, 0.00001, true));
types.add(new ParameterTypeInt(PARAMETER_NUM_ITER, "Number of iterations. Range: integer; 1-+?; default: 10", 1, Integer.MAX_VALUE, 10, 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 CF_FWMF(OperatorDescription description) {
super(description);
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, "user identification", Ontology.ATTRIBUTE_VALUE));
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, "item identification", Ontology.ATTRIBUTE_VALUE));
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, "label", Ontology.ATTRIBUTE_VALUE));
getTransformer().addRule(new ExampleSetPassThroughRule(exampleSetInput, exampleSetOutput, SetRelation.EQUAL) {
});
getTransformer().addRule(new GenerateNewMDRule(exampleSetOutput1, new MetaData(RatingPredictor.class)) {
});
}
@Override
public void doWork() throws OperatorException {
ExampleSet exampleSet = exampleSetInput.getData();
IEntityMapping user_mapping=new EntityMapping();
IEntityMapping item_mapping=new EntityMapping();
IRatings training_data=new Ratings();
if (exampleSet.getAttributes().getSpecial("user identification") == null) {
throw new UserError(this,105);
}
if (exampleSet.getAttributes().getSpecial("item identification") == null) {
throw new UserError(this, 105);
}
if (exampleSet.getAttributes().getLabel() == 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();
Attribute ui=Att.getLabel();
for (Example example : exampleSet) {
double j=example.getValue(u);
int uid=user_mapping.ToInternalID((int) j);
j=example.getValue(i);
int iid=item_mapping.ToInternalID((int) j);
double r=example.getValue(ui);
training_data.Add(uid, iid, r);
}
FactorWisedMatrixFactorization recommendAlg=new FactorWisedMatrixFactorization();
recommendAlg.user_mapping=user_mapping;
recommendAlg.item_mapping=item_mapping;
recommendAlg.NumFactors=getParameterAsInt("Num Factors");
recommendAlg.NumIter=getParameterAsInt("Iteration number");
recommendAlg.InitMean=getParameterAsDouble("Initial mean");
recommendAlg.InitStdev=getParameterAsDouble("Initial stdev");
recommendAlg.Shrinkage=getParameterAsDouble("Schrinkage");
recommendAlg.Sensibility=getParameterAsDouble("Sensibility");
recommendAlg.SetMinRating(getParameterAsInt("Min Rating"));
recommendAlg.SetMaxRating(recommendAlg.GetMinRating()+getParameterAsInt("Range"));
recommendAlg.SetRatings(training_data);
recommendAlg.Train();
exampleSetOutput.deliver(exampleSet);
exampleSetOutput1.deliver(recommendAlg);
}
}