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/*
* Copyright (C) 2017 University of Pittsburgh.
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* This library 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
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
* MA 02110-1301 USA
*/
package edu.cmu.tetrad.util;
import edu.cmu.tetrad.data.*;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.graph.NodeVariableType;
import edu.pitt.dbmi.data.reader.ContinuousData;
import edu.pitt.dbmi.data.reader.Data;
import edu.pitt.dbmi.data.reader.DataColumn;
import edu.pitt.dbmi.data.reader.DiscreteDataColumn;
import edu.pitt.dbmi.data.reader.covariance.CovarianceData;
import edu.pitt.dbmi.data.reader.metadata.ColumnMetadata;
import edu.pitt.dbmi.data.reader.metadata.Metadata;
import edu.pitt.dbmi.data.reader.tabular.MixedTabularData;
import edu.pitt.dbmi.data.reader.tabular.VerticalDiscreteTabularData;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Collectors;
/**
* Dec 15, 2018 11:10:30 AM
*
* @author Kevin V. Bui ([email protected])
*/
public class DataConvertUtils {
private DataConvertUtils() {
}
public static DataModel toDataModel(Data data, Metadata metadata) {
if (data instanceof ContinuousData) {
return DataConvertUtils.toContinuousDataModel((ContinuousData) data);
} else if (data instanceof VerticalDiscreteTabularData) {
return DataConvertUtils.toVerticalDiscreteDataModel((VerticalDiscreteTabularData) data, metadata);
} else if (data instanceof MixedTabularData) {
return DataConvertUtils.toMixedDataBox((MixedTabularData) data, metadata);
} else if (data instanceof CovarianceData) {
return DataConvertUtils.toCovarianceMatrix((CovarianceData) data);
} else {
return null;
}
}
public static DataModel toDataModel(Data data) {
if (data instanceof ContinuousData) {
return DataConvertUtils.toContinuousDataModel((ContinuousData) data);
} else if (data instanceof VerticalDiscreteTabularData) {
return DataConvertUtils.toVerticalDiscreteDataModel((VerticalDiscreteTabularData) data);
} else if (data instanceof MixedTabularData) {
return DataConvertUtils.toMixedDataBox((MixedTabularData) data);
} else if (data instanceof CovarianceData) {
return DataConvertUtils.toCovarianceMatrix((CovarianceData) data);
} else {
return null;
}
}
public static DataModel toCovarianceMatrix(CovarianceData dataset) {
List variables = DataConvertUtils.toNodes(dataset.getVariables());
Matrix matrix = new Matrix(dataset.getData());
int sampleSize = dataset.getNumberOfCases();
return new CovarianceMatrix(variables, matrix, sampleSize);
}
/**
* Converting using metadata
*/
public static DataModel toMixedDataBox(MixedTabularData dataset, Metadata metadata) {
int numOfRows = dataset.getNumOfRows();
DiscreteDataColumn[] columns = dataset.getDataColumns();
double[][] continuousData = dataset.getContinuousData();
int[][] discreteData = dataset.getDiscreteData();
Node[] nodes = Arrays.stream(columns)
.map(e -> e.getDataColumn().isDiscrete()
? new DiscreteVariable(e.getDataColumn().getName(), e.getCategories())
: new ContinuousVariable(e.getDataColumn().getName()))
.toArray(Node[]::new);
metadata.getInterventionalColumns().forEach(e -> {
ColumnMetadata valueColumn = e.getValueColumn();
ColumnMetadata statusColumn = e.getStatusColumn();
int valColNum = valueColumn.getColumnNumber() - 1;
int statColNum = statusColumn.getColumnNumber() - 1;
// Default NodeVariableType.DOMAIN for all variables
// Overwrite NodeVariableType to NodeVariableType.INTERVENTION_VALUE or NodeVariableType.INTERVENTION_STATUS
nodes[statColNum].setNodeVariableType(NodeVariableType.INTERVENTION_STATUS);
nodes[valColNum].setNodeVariableType(NodeVariableType.INTERVENTION_VALUE);
});
List nodeList = Arrays.asList(nodes);
return new BoxDataSet(new MixedDataBox(nodeList, numOfRows, continuousData, discreteData), nodeList);
}
public static DataModel toMixedDataBox(MixedTabularData dataset) {
int numOfRows = dataset.getNumOfRows();
DiscreteDataColumn[] columns = dataset.getDataColumns();
double[][] continuousData = dataset.getContinuousData();
int[][] discreteData = dataset.getDiscreteData();
List nodes = Arrays.stream(columns)
.map(e -> e.getDataColumn().isDiscrete()
? new DiscreteVariable(e.getDataColumn().getName(), e.getCategories())
: new ContinuousVariable(e.getDataColumn().getName()))
.collect(Collectors.toList());
return new BoxDataSet(new MixedDataBox(nodes, numOfRows, continuousData, discreteData), nodes);
}
/**
* Converting using metadata
*/
public static DataModel toVerticalDiscreteDataModel(VerticalDiscreteTabularData dataset, Metadata metatdata) {
Node[] nodes = DataConvertUtils.toNodes(dataset.getDataColumns()).toArray(new Node[0]);
metatdata.getInterventionalColumns().forEach(e -> {
ColumnMetadata valueColumn = e.getValueColumn();
ColumnMetadata statusColumn = e.getStatusColumn();
int valColNum = valueColumn.getColumnNumber() - 1;
int statColNum = statusColumn.getColumnNumber() - 1;
// Default NodeVariableType.DOMAIN for all variables
// Overwrite NodeVariableType to NodeVariableType.INTERVENTION_VALUE or NodeVariableType.INTERVENTION_STATUS
nodes[statColNum].setNodeVariableType(NodeVariableType.INTERVENTION_STATUS);
nodes[valColNum].setNodeVariableType(NodeVariableType.INTERVENTION_VALUE);
});
DataBox dataBox = new VerticalIntDataBox(dataset.getData());
List nodeList = Arrays.asList(nodes);
return new BoxDataSet(dataBox, nodeList);
}
public static DataModel toVerticalDiscreteDataModel(VerticalDiscreteTabularData dataset) {
DataBox dataBox = new VerticalIntDataBox(dataset.getData());
List variables = DataConvertUtils.toNodes(dataset.getDataColumns());
return new BoxDataSet(dataBox, variables);
}
public static DataModel toContinuousDataModel(ContinuousData dataset) {
DataBox dataBox = new DoubleDataBox(dataset.getData());
List variables = DataConvertUtils.toNodes(dataset.getDataColumns());
return new BoxDataSet(dataBox, variables);
}
public static List toNodes(List variables) {
return variables.stream()
.map(ContinuousVariable::new)
.collect(Collectors.toList());
}
public static List toNodes(DiscreteDataColumn[] columns) {
return Arrays.stream(columns)
.map(e -> new DiscreteVariable(e.getDataColumn().getName(), e.getCategories()))
.collect(Collectors.toList());
}
public static List toNodes(DataColumn[] columns) {
return Arrays.stream(columns)
.map(e -> new ContinuousVariable(e.getName()))
.collect(Collectors.toList());
}
}
