/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.com
*
* This program is free software: you can redistribute it and/or modify it under the terms of the
* GNU Affero General Public License as published by the Free Software Foundation, either version 3
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without
* even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License along with this program.
* If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.meta;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorChain;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.learner.CapabilityCheck;
import com.rapidminer.operator.learner.CapabilityProvider;
import com.rapidminer.operator.learner.Learner;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.LearnerPrecondition;
import com.rapidminer.operator.ports.metadata.MetaData;
import com.rapidminer.operator.ports.metadata.PassThroughRule;
import com.rapidminer.operator.ports.metadata.PredictionModelMetaData;
import com.rapidminer.operator.ports.metadata.SimplePrecondition;
import com.rapidminer.operator.ports.metadata.SubprocessTransformRule;
import com.rapidminer.tools.ParameterService;
import com.rapidminer.tools.Tools;
/**
* A <tt>MetaLearner</tt> is an operator that encapsulates one or more learning steps to build its
* model. New meta learning schemes should extend this class to support the same parameters as other
* learners. The main purpose of this class is to perform some compatibility checks.
*
* @author Ingo Mierswa
*/
public abstract class AbstractMetaLearner extends OperatorChain implements Learner {
protected final InputPort exampleSetInput = getInputPorts().createPort("training set");
private final OutputPort modelOutput = getOutputPorts().createPort("model");
private final OutputPort innerExampleSource = getSubprocess(0).getInnerSources().createPort("training set");
protected final InputPort innerModelSink = getSubprocess(0).getInnerSinks().createPort("model");
private final OutputPort exampleSetOutput = getOutputPorts().createPort("example set");
public AbstractMetaLearner(OperatorDescription description) {
super(description, "Learning Process");
exampleSetInput.addPrecondition(new LearnerPrecondition(this, exampleSetInput));
innerModelSink.addPrecondition(new SimplePrecondition(innerModelSink, new PredictionModelMetaData(
PredictionModel.class, new ExampleSetMetaData())));
getTransformer().addRule(new PassThroughRule(exampleSetInput, innerExampleSource, true) {
@Override
public MetaData modifyMetaData(MetaData unmodifiedMetaData) {
if (unmodifiedMetaData instanceof ExampleSetMetaData) {
return modifyExampleSetMetaData((ExampleSetMetaData) unmodifiedMetaData);
}
return unmodifiedMetaData;
}
});
getTransformer().addRule(new SubprocessTransformRule(getSubprocess(0)));
getTransformer().addRule(new PassThroughRule(innerModelSink, modelOutput, true) {
@Override
public MetaData modifyMetaData(MetaData unmodifiedMetaData) {
if (unmodifiedMetaData instanceof PredictionModelMetaData) {
PredictionModelMetaData pmd = (PredictionModelMetaData) unmodifiedMetaData.clone();
return modifyGeneratedModelMetaData(pmd);
} else {
return super.modifyMetaData(unmodifiedMetaData);
}
}
});
getTransformer().addPassThroughRule(exampleSetInput, exampleSetOutput);
}
/** Modifies the meta data of the generated model. */
protected MetaData modifyGeneratedModelMetaData(PredictionModelMetaData unmodifiedMetaData) {
return unmodifiedMetaData;
}
/**
* This method can be used by subclasses to additionally change the example set meta data
* delivered to the inner learner
*/
protected MetaData modifyExampleSetMetaData(ExampleSetMetaData unmodifiedMetaData) {
return unmodifiedMetaData;
}
public InputPort getTrainingSetInputPort() {
return exampleSetInput;
}
public OutputPort getModelOutputPort() {
return modelOutput;
}
public InputPort getInnerModelSink() {
return innerModelSink;
}
/**
* Trains a model using an ExampleSet from the input. Uses the method learn(ExampleSet).
*/
@Override
public void doWork() throws OperatorException {
ExampleSet exampleSet = exampleSetInput.getData(ExampleSet.class);
// some checks
if (exampleSet.getAttributes().getLabel() == null) {
throw new UserError(this, 105, new Object[0]);
}
if (exampleSet.getAttributes().size() == 0) {
throw new UserError(this, 106, new Object[0]);
}
// check capabilities and produce errors if they are not fulfilled
CapabilityCheck check = new CapabilityCheck(this, Tools.booleanValue(
ParameterService.getParameterValue(CapabilityProvider.PROPERTY_RAPIDMINER_GENERAL_CAPABILITIES_WARN), true));
check.checkLearnerCapabilities(this, exampleSet);
Model model = learn(exampleSet);
modelOutput.deliver(model);
exampleSetOutput.deliver(exampleSet);
}
/**
* This is a convenience method to apply the inner operators and return the model which must be
* output of the last operator.
*/
protected Model applyInnerLearner(ExampleSet exampleSet) throws OperatorException {
innerExampleSource.deliver(exampleSet);
executeInnerLearner();
return innerModelSink.getData(Model.class);
}
protected void executeInnerLearner() throws OperatorException {
getSubprocess(0).execute();
}
@Override
public boolean shouldAutoConnect(OutputPort port) {
if (port == exampleSetOutput) {
return getParameterAsBoolean("keep_example_set");
} else {
return super.shouldAutoConnect(port);
}
}
/**
* Returns true if the user wants to estimate the performance (depending on a parameter). In
* this case the method getEstimatedPerformance() must also be overridden and deliver the
* estimated performance. The default implementation returns false.
*/
@Override
public boolean shouldEstimatePerformance() {
return false;
}
/**
* Returns true if the user wants to calculate feature weights (depending on a parameter). In
* this case the method getWeights() must also be overriden and deliver the calculated weights.
* The default implementation returns false.
*/
@Override
public boolean shouldCalculateWeights() {
return false;
}
/** The default implementation throws an exception. */
@Override
public PerformanceVector getEstimatedPerformance() throws OperatorException {
throw new UserError(this, 912, getName(), "estimation of performance not supported.");
}
/**
* Returns the calculated weight vectors. The default implementation throws an exception.
*/
@Override
public AttributeWeights getWeights(ExampleSet exampleSet) throws OperatorException {
throw new UserError(this, 916, getName(), "calculation of weights not supported.");
}
}