use of org.knime.base.node.mine.treeensemble2.model.AbstractTreeEnsembleModel.TreeType in project knime-core by knime.
the class RegressionTreeModel method load.
/**
* Loads and returns new ensemble model, input is NOT closed afterwards.
*
* @param in ...
* @param exec ...
* @return ...
* @throws IOException ...
* @throws CanceledExecutionException ...
*/
public static RegressionTreeModel load(final InputStream in, final ExecutionMonitor exec, final TreeBuildingInterner treeBuildingInterner) throws IOException, CanceledExecutionException {
// wrapping the argument (zip input) stream in a buffered stream
// reduces read operation from, e.g. 42s to 2s
TreeModelDataInputStream input = new TreeModelDataInputStream(new BufferedInputStream(new NonClosableInputStream(in)));
int version = input.readInt();
if (version > 20140201) {
throw new IOException("Tree Ensemble version " + version + " not supported");
}
TreeType type = TreeType.load(input);
TreeMetaData metaData = TreeMetaData.load(input);
boolean isRegression = metaData.isRegression();
TreeModelRegression model;
try {
model = TreeModelRegression.load(input, metaData, treeBuildingInterner);
if (input.readByte() != 0) {
throw new IOException("Model not terminated by 0 byte");
}
} catch (IOException e) {
throw new IOException("Can't read tree model. " + e.getMessage(), e);
}
// does not close the method argument stream!!
input.close();
return new RegressionTreeModel(metaData, model, type);
}
use of org.knime.base.node.mine.treeensemble2.model.AbstractTreeEnsembleModel.TreeType in project knime-core by knime.
the class TreeEnsembleModel method load.
public static TreeEnsembleModel load(final InputStream in) throws IOException {
// wrapping the argument (zip input) stream in a buffered stream
// reduces read operation from, e.g. 42s to 2s
TreeModelDataInputStream input = new TreeModelDataInputStream(new BufferedInputStream(new NonClosableInputStream(in)));
int version = input.readInt();
if (version > 20160114) {
throw new IOException("Tree Ensemble version " + version + " not supported");
}
byte ensembleType;
if (version == 20160114) {
ensembleType = input.readByte();
} else {
ensembleType = 'r';
}
TreeType type = TreeType.load(input);
TreeMetaData metaData = TreeMetaData.load(input);
int nrModels = input.readInt();
boolean containsClassDistribution;
if (version == 20121019) {
containsClassDistribution = true;
} else {
containsClassDistribution = input.readBoolean();
}
input.setContainsClassDistribution(containsClassDistribution);
AbstractTreeModel[] models = new AbstractTreeModel[nrModels];
boolean isRegression = metaData.isRegression();
if (ensembleType != 'r') {
isRegression = true;
}
final TreeBuildingInterner treeBuildingInterner = new TreeBuildingInterner();
for (int i = 0; i < nrModels; i++) {
AbstractTreeModel singleModel;
try {
singleModel = isRegression ? TreeModelRegression.load(input, metaData, treeBuildingInterner) : TreeModelClassification.load(input, metaData, treeBuildingInterner);
if (input.readByte() != 0) {
throw new IOException("Model not terminated by 0 byte");
}
} catch (IOException e) {
throw new IOException("Can't read tree model " + (i + 1) + "/" + nrModels + ": " + e.getMessage(), e);
}
models[i] = singleModel;
}
TreeEnsembleModel result;
switch(ensembleType) {
case 'r':
result = new TreeEnsembleModel(metaData, models, type, containsClassDistribution);
break;
case 'g':
result = new GradientBoostingModel(metaData, models, type, containsClassDistribution);
break;
case 't':
result = new GradientBoostedTreesModel(metaData, models, type, containsClassDistribution);
break;
case 'm':
result = new MultiClassGradientBoostedTreesModel(metaData, models, type, containsClassDistribution);
break;
default:
throw new IllegalStateException("Unknown ensemble type: '" + (char) ensembleType + "'");
}
result.loadData(input);
// does not close the method argument stream!!
input.close();
return result;
}
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