/*
* Licensed 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 com.facebook.presto.ml;
import com.google.common.base.Throwables;
import com.google.common.io.Files;
import libsvm.svm;
import libsvm.svm_model;
import libsvm.svm_node;
import libsvm.svm_parameter;
import libsvm.svm_problem;
import java.io.File;
import java.io.IOException;
import java.util.List;
import java.util.SortedMap;
import static com.google.common.base.Preconditions.checkNotNull;
public abstract class AbstractSvmModel
implements Model
{
protected svm_model model;
protected AbstractSvmModel()
{
}
protected AbstractSvmModel(svm_model model)
{
this.model = checkNotNull(model, "model is null");
}
@Override
public byte[] getSerializedData()
{
File file = null;
try {
// libsvm doesn't have a method to serialize the model into a buffer, so write it out to a file and then read it back in
file = File.createTempFile("svm", null);
svm.svm_save_model(file.getAbsolutePath(), model);
return Files.toByteArray(file);
}
catch (IOException e) {
throw Throwables.propagate(e);
}
finally {
if (file != null) {
file.delete();
}
}
}
@Override
public void train(Dataset dataset)
{
svm_parameter param = new svm_parameter();
// default values
param.svm_type = getLibsvmType();
param.kernel_type = svm_parameter.LINEAR;
param.degree = 3;
param.gamma = 0;
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 100;
param.C = 1;
param.eps = 0.1;
param.p = 0.1;
param.shrinking = 1;
param.probability = 0;
param.nr_weight = 0;
param.weight_label = new int[0];
param.weight = new double[0];
svm_problem problem = toSvmProblem(dataset);
//TODO: we should probably run this in another thread, and put a bound on the running time
model = svm.svm_train(problem, param);
}
protected abstract int getLibsvmType();
private static svm_problem toSvmProblem(Dataset dataset)
{
svm_problem problem = new svm_problem();
List<Double> labels = dataset.getLabels();
problem.l = labels.size();
problem.y = new double[labels.size()];
for (int i = 0; i < labels.size(); i++) {
problem.y[i] = labels.get(i);
}
problem.x = new svm_node[labels.size()][];
for (int i = 0; i < dataset.getDatapoints().size(); i++) {
problem.x[i] = toSvmNodes(dataset.getDatapoints().get(i));
}
return problem;
}
protected static svm_node[] toSvmNodes(FeatureVector features)
{
svm_node[] nodes = new svm_node[features.size()];
int i = 0;
// Features map is sorted, so we can just flatten it to a list for libsvm
for (SortedMap.Entry<Integer, Double> feature : features.getFeatures().entrySet()) {
nodes[i] = new svm_node();
nodes[i].index = feature.getKey();
nodes[i].value = feature.getValue();
i++;
}
return nodes;
}
}