/**
* Searches the attribute subset space using a genetic algorithm.
*
* @param ASEval the attribute evaluator to guide the search
* @param data the training instances.
* @return an array (not necessarily ordered) of selected attribute indexes
* @throws Exception if the search can't be completed
*/
@Override
public int[] search(ASEvaluation ASEval, Instances data) throws Exception {
m_best = null;
m_generationReports = new StringBuffer();
if (!(ASEval instanceof SubsetEvaluator)) {
throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!");
}
if (ASEval instanceof UnsupervisedSubsetEvaluator) {
m_hasClass = false;
} else {
m_hasClass = true;
m_classIndex = data.classIndex();
}
SubsetEvaluator ASEvaluator = (SubsetEvaluator) ASEval;
m_numAttribs = data.numAttributes();
m_startRange.setUpper(m_numAttribs - 1);
if (!(getStartSet().equals(""))) {
m_starting = m_startRange.getSelection();
}
// initial random population
m_lookupTable = new Hashtable<BitSet, GABitSet>(m_lookupTableSize);
m_random = new Random(m_seed);
m_population = new GABitSet[m_popSize];
// set up random initial population
initPopulation();
evaluatePopulation(ASEvaluator);
populationStatistics();
scalePopulation();
checkBest();
m_generationReports.append(populationReport(0));
boolean converged;
for (int i = 1; i <= m_maxGenerations; i++) {
generation();
evaluatePopulation(ASEvaluator);
populationStatistics();
scalePopulation();
// find the best pop member and check for convergence
converged = checkBest();
if ((i == m_maxGenerations) || ((i % m_reportFrequency) == 0) || (converged == true)) {
m_generationReports.append(populationReport(i));
if (converged == true) {
break;
}
}
}
return attributeList(m_best.getChromosome());
}
/**
* Sets the format of the input instances.
*
* @param instanceInfo an Instances object containing the input instance structure (any instances
* contained in the object are ignored - only the structure is required).
* @return true if the outputFormat may be collected immediately
* @throws Exception if the format couldn't be set successfully
*/
public boolean setInputFormat(Instances instanceInfo) throws Exception {
super.setInputFormat(instanceInfo);
m_Insert.setUpper(instanceInfo.numAttributes());
Instances outputFormat = new Instances(instanceInfo, 0);
Attribute newAttribute = null;
switch (m_AttributeType) {
case Attribute.NUMERIC:
newAttribute = new Attribute(m_Name);
break;
case Attribute.NOMINAL:
newAttribute = new Attribute(m_Name, m_Labels);
break;
case Attribute.STRING:
newAttribute = new Attribute(m_Name, (FastVector) null);
break;
case Attribute.DATE:
newAttribute = new Attribute(m_Name, m_DateFormat);
break;
default:
throw new IllegalArgumentException("Unknown attribute type in Add");
}
if ((m_Insert.getIndex() < 0) || (m_Insert.getIndex() > getInputFormat().numAttributes())) {
throw new IllegalArgumentException("Index out of range");
}
outputFormat.insertAttributeAt(newAttribute, m_Insert.getIndex());
setOutputFormat(outputFormat);
// all attributes, except index of added attribute
// (otherwise the length of the input/output indices differ)
Range atts = new Range(m_Insert.getSingleIndex());
atts.setInvert(true);
atts.setUpper(outputFormat.numAttributes() - 1);
initOutputLocators(outputFormat, atts.getSelection());
return true;
}
/**
* Searches the attribute subset space by best first search
*
* @param ASEval the attribute evaluator to guide the search
* @param data the training instances.
* @return an array (not necessarily ordered) of selected attribute indexes
* @throws Exception if the search can't be completed
*/
public int[] search(ASEvaluation ASEval, Instances data) throws Exception {
m_totalEvals = 0;
if (!(ASEval instanceof SubsetEvaluator)) {
throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!");
}
if (ASEval instanceof UnsupervisedSubsetEvaluator) {
m_hasClass = false;
} else {
m_hasClass = true;
m_classIndex = data.classIndex();
}
SubsetEvaluator ASEvaluator = (SubsetEvaluator) ASEval;
m_numAttribs = data.numAttributes();
int i, j;
int best_size = 0;
int size = 0;
int done;
int sd = m_searchDirection;
BitSet best_group, temp_group;
int stale;
double best_merit;
double merit;
boolean z;
boolean added;
Link2 tl;
Hashtable lookup = new Hashtable(m_cacheSize * m_numAttribs);
int insertCount = 0;
int cacheHits = 0;
LinkedList2 bfList = new LinkedList2(m_maxStale);
best_merit = -Double.MAX_VALUE;
stale = 0;
best_group = new BitSet(m_numAttribs);
m_startRange.setUpper(m_numAttribs - 1);
if (!(getStartSet().equals(""))) {
m_starting = m_startRange.getSelection();
}
// If a starting subset has been supplied, then initialise the bitset
if (m_starting != null) {
for (i = 0; i < m_starting.length; i++) {
if ((m_starting[i]) != m_classIndex) {
best_group.set(m_starting[i]);
}
}
best_size = m_starting.length;
m_totalEvals++;
} else {
if (m_searchDirection == SELECTION_BACKWARD) {
setStartSet("1-last");
m_starting = new int[m_numAttribs];
// init initial subset to all attributes
for (i = 0, j = 0; i < m_numAttribs; i++) {
if (i != m_classIndex) {
best_group.set(i);
m_starting[j++] = i;
}
}
best_size = m_numAttribs - 1;
m_totalEvals++;
}
}
// evaluate the initial subset
best_merit = ASEvaluator.evaluateSubset(best_group);
// add the initial group to the list and the hash table
Object[] best = new Object[1];
best[0] = best_group.clone();
bfList.addToList(best, best_merit);
BitSet tt = (BitSet) best_group.clone();
String hashC = tt.toString();
lookup.put(hashC, new Double(best_merit));
while (stale < m_maxStale) {
added = false;
if (m_searchDirection == SELECTION_BIDIRECTIONAL) {
// bi-directional search
done = 2;
sd = SELECTION_FORWARD;
} else {
done = 1;
}
// finished search?
if (bfList.size() == 0) {
stale = m_maxStale;
break;
}
// copy the attribute set at the head of the list
tl = bfList.getLinkAt(0);
temp_group = (BitSet) (tl.getData()[0]);
temp_group = (BitSet) temp_group.clone();
// remove the head of the list
bfList.removeLinkAt(0);
// count the number of bits set (attributes)
int kk;
for (kk = 0, size = 0; kk < m_numAttribs; kk++) {
if (temp_group.get(kk)) {
size++;
}
}
do {
for (i = 0; i < m_numAttribs; i++) {
if (sd == SELECTION_FORWARD) {
z = ((i != m_classIndex) && (!temp_group.get(i)));
} else {
z = ((i != m_classIndex) && (temp_group.get(i)));
}
if (z) {
// set the bit (attribute to add/delete)
if (sd == SELECTION_FORWARD) {
temp_group.set(i);
size++;
} else {
temp_group.clear(i);
size--;
}
/* if this subset has been seen before, then it is already
in the list (or has been fully expanded) */
tt = (BitSet) temp_group.clone();
hashC = tt.toString();
if (lookup.containsKey(hashC) == false) {
merit = ASEvaluator.evaluateSubset(temp_group);
m_totalEvals++;
// insert this one in the hashtable
if (insertCount > m_cacheSize * m_numAttribs) {
lookup = new Hashtable(m_cacheSize * m_numAttribs);
insertCount = 0;
}
hashC = tt.toString();
lookup.put(hashC, new Double(merit));
insertCount++;
} else {
merit = ((Double) lookup.get(hashC)).doubleValue();
cacheHits++;
}
// insert this one in the list
Object[] add = new Object[1];
add[0] = tt.clone();
bfList.addToList(add, merit);
if (m_debug) {
System.out.print("Group: ");
printGroup(tt, m_numAttribs);
System.out.println("Merit: " + merit);
}
// is this better than the best?
if (sd == SELECTION_FORWARD) {
z = ((merit - best_merit) > 0.00001);
} else {
if (merit == best_merit) {
z = (size < best_size);
} else {
z = (merit > best_merit);
}
}
if (z) {
added = true;
stale = 0;
best_merit = merit;
// best_size = (size + best_size);
best_size = size;
best_group = (BitSet) (temp_group.clone());
}
// unset this addition(deletion)
if (sd == SELECTION_FORWARD) {
temp_group.clear(i);
size--;
} else {
temp_group.set(i);
size++;
}
}
}
if (done == 2) {
sd = SELECTION_BACKWARD;
}
done--;
} while (done > 0);
/* if we haven't added a new attribute subset then full expansion
of this node hasen't resulted in anything better */
if (!added) {
stale++;
}
}
m_bestMerit = best_merit;
return attributeList(best_group);
}
/**
* Sets the format of the input instances.
*
* @param instanceInfo an Instances object containing the input instance structure (any instances
* contained in the object are ignored - only the structure is required).
* @return true if the outputFormat may be collected immediately
* @throws Exception if the format couldn't be set successfully
*/
@Override
public boolean setInputFormat(Instances instanceInfo) throws Exception {
super.setInputFormat(instanceInfo);
int classIndex = instanceInfo.classIndex();
// setup the map
if (m_renameVals != null && m_renameVals.length() > 0) {
String[] vals = m_renameVals.split(",");
for (String val : vals) {
String[] parts = val.split(":");
if (parts.length != 2) {
throw new WekaException("Invalid replacement string: " + val);
}
if (parts[0].length() == 0 || parts[1].length() == 0) {
throw new WekaException("Invalid replacement string: " + val);
}
m_renameMap.put(
m_ignoreCase ? parts[0].toLowerCase().trim() : parts[0].trim(), parts[1].trim());
}
}
// try selected atts as a numeric range first
Range tempRange = new Range();
tempRange.setInvert(m_invert);
if (m_selectedColsString == null) {
m_selectedColsString = "";
}
try {
tempRange.setRanges(m_selectedColsString);
tempRange.setUpper(instanceInfo.numAttributes() - 1);
m_selectedAttributes = tempRange.getSelection();
m_selectedCols = tempRange;
} catch (Exception r) {
// OK, now try as named attributes
StringBuffer indexes = new StringBuffer();
String[] attNames = m_selectedColsString.split(",");
boolean first = true;
for (String n : attNames) {
n = n.trim();
Attribute found = instanceInfo.attribute(n);
if (found == null) {
throw new WekaException(
"Unable to find attribute '" + n + "' in the incoming instances'");
}
if (first) {
indexes.append("" + (found.index() + 1));
first = false;
} else {
indexes.append("," + (found.index() + 1));
}
}
tempRange = new Range();
tempRange.setRanges(indexes.toString());
tempRange.setUpper(instanceInfo.numAttributes() - 1);
m_selectedAttributes = tempRange.getSelection();
m_selectedCols = tempRange;
}
ArrayList<Attribute> attributes = new ArrayList<Attribute>();
for (int i = 0; i < instanceInfo.numAttributes(); i++) {
if (m_selectedCols.isInRange(i)) {
if (instanceInfo.attribute(i).isNominal()) {
List<String> valsForAtt = new ArrayList<String>();
for (int j = 0; j < instanceInfo.attribute(i).numValues(); j++) {
String origV = instanceInfo.attribute(i).value(j);
String replace =
m_ignoreCase ? m_renameMap.get(origV.toLowerCase()) : m_renameMap.get(origV);
if (replace != null && !valsForAtt.contains(replace)) {
valsForAtt.add(replace);
} else {
valsForAtt.add(origV);
}
}
Attribute newAtt = new Attribute(instanceInfo.attribute(i).name(), valsForAtt);
attributes.add(newAtt);
} else {
// ignore any selected attributes that are not nominal
Attribute att = (Attribute) instanceInfo.attribute(i).copy();
attributes.add(att);
}
} else {
Attribute att = (Attribute) instanceInfo.attribute(i).copy();
attributes.add(att);
}
}
Instances outputFormat = new Instances(instanceInfo.relationName(), attributes, 0);
outputFormat.setClassIndex(classIndex);
setOutputFormat(outputFormat);
return true;
}
/**
* Kind of a dummy search algorithm. Calls a Attribute evaluator to evaluate each attribute not
* included in the startSet and then sorts them to produce a ranked list of attributes.
*
* @param ASEval the attribute evaluator to guide the search
* @param data the training instances.
* @return an array (not necessarily ordered) of selected attribute indexes
* @throws Exception if the search can't be completed
*/
public int[] search(ASEvaluation ASEval, Instances data) throws Exception {
int i, j;
if (!(ASEval instanceof AttributeEvaluator)) {
throw new Exception(ASEval.getClass().getName() + " is not a" + "Attribute evaluator!");
}
m_numAttribs = data.numAttributes();
if (ASEval instanceof UnsupervisedAttributeEvaluator) {
m_hasClass = false;
} else {
m_classIndex = data.classIndex();
if (m_classIndex >= 0) {
m_hasClass = true;
} else {
m_hasClass = false;
}
}
// get the transformed data and check to see if the transformer
// preserves a class index
if (ASEval instanceof AttributeTransformer) {
data = ((AttributeTransformer) ASEval).transformedHeader();
if (m_classIndex >= 0 && data.classIndex() >= 0) {
m_classIndex = data.classIndex();
m_hasClass = true;
}
}
m_startRange.setUpper(m_numAttribs - 1);
if (!(getStartSet().equals(""))) {
m_starting = m_startRange.getSelection();
}
int sl = 0;
if (m_starting != null) {
sl = m_starting.length;
}
if ((m_starting != null) && (m_hasClass == true)) {
// see if the supplied list contains the class index
boolean ok = false;
for (i = 0; i < sl; i++) {
if (m_starting[i] == m_classIndex) {
ok = true;
break;
}
}
if (ok == false) {
sl++;
}
} else {
if (m_hasClass == true) {
sl++;
}
}
m_attributeList = new int[m_numAttribs - sl];
m_attributeMerit = new double[m_numAttribs - sl];
// add in those attributes not in the starting (omit list)
for (i = 0, j = 0; i < m_numAttribs; i++) {
if (!inStarting(i)) {
m_attributeList[j++] = i;
}
}
AttributeEvaluator ASEvaluator = (AttributeEvaluator) ASEval;
for (i = 0; i < m_attributeList.length; i++) {
m_attributeMerit[i] = ASEvaluator.evaluateAttribute(m_attributeList[i]);
}
double[][] tempRanked = rankedAttributes();
int[] rankedAttributes = new int[m_attributeList.length];
for (i = 0; i < m_attributeList.length; i++) {
rankedAttributes[i] = (int) tempRanked[i][0];
}
return rankedAttributes;
}
/**
* Searches the attribute subset space by linear forward selection
*
* @param ASEval the attribute evaluator to guide the search
* @param data the training instances.
* @return an array (not necessarily ordered) of selected attribute indexes
* @exception Exception if the search can't be completed
*/
public int[] search(ASEvaluation ASEval, Instances data) throws Exception {
m_totalEvals = 0;
if (!(ASEval instanceof SubsetEvaluator)) {
throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!");
}
if (ASEval instanceof UnsupervisedSubsetEvaluator) {
m_hasClass = false;
} else {
m_hasClass = true;
m_classIndex = data.classIndex();
}
((ASEvaluation) ASEval).buildEvaluator(data);
m_numAttribs = data.numAttributes();
if (m_numUsedAttributes > m_numAttribs) {
System.out.println(
"Decreasing number of top-ranked attributes to total number of attributes: "
+ data.numAttributes());
m_numUsedAttributes = m_numAttribs;
}
BitSet start_group = new BitSet(m_numAttribs);
m_startRange.setUpper(m_numAttribs - 1);
if (!(getStartSet().equals(""))) {
m_starting = m_startRange.getSelection();
}
// If a starting subset has been supplied, then initialise the bitset
if (m_starting != null) {
for (int i = 0; i < m_starting.length; i++) {
if ((m_starting[i]) != m_classIndex) {
start_group.set(m_starting[i]);
}
}
}
LFSMethods LFS = new LFSMethods();
int[] ranking;
if (m_performRanking) {
ranking = LFS.rankAttributes(data, (SubsetEvaluator) ASEval, m_verbose);
} else {
ranking = new int[m_numAttribs];
for (int i = 0; i < ranking.length; i++) {
ranking[i] = i;
}
}
if (m_forwardSearchMethod == SEARCH_METHOD_FORWARD) {
LFS.forwardSearch(
m_cacheSize,
start_group,
ranking,
m_numUsedAttributes,
m_linearSelectionType == TYPE_FIXED_WIDTH,
m_maxStale,
-1,
data,
(SubsetEvaluator) ASEval,
m_verbose);
} else if (m_forwardSearchMethod == SEARCH_METHOD_FLOATING) {
LFS.floatingForwardSearch(
m_cacheSize,
start_group,
ranking,
m_numUsedAttributes,
m_linearSelectionType == TYPE_FIXED_WIDTH,
m_maxStale,
data,
(SubsetEvaluator) ASEval,
m_verbose);
}
m_totalEvals = LFS.getNumEvalsTotal();
m_bestMerit = LFS.getBestMerit();
return attributeList(LFS.getBestGroup());
}
/**
* Determines the output format based on the input format and returns this. In case the output
* format cannot be returned immediately, i.e., hasImmediateOutputFormat() returns false, then
* this method will called from batchFinished() after the call of preprocess(Instances), in which,
* e.g., statistics for the actual processing step can be gathered.
*
* @param inputFormat the input format to base the output format on
* @return the output format
* @throws Exception in case the determination goes wrong
* @see #hasImmediateOutputFormat()
* @see #batchFinished()
*/
protected Instances determineOutputFormat(Instances inputFormat) throws Exception {
FastVector atts;
FastVector values;
Instances result;
int i;
// attributes must be numeric
m_Attributes.setUpper(inputFormat.numAttributes() - 1);
m_AttributeIndices = m_Attributes.getSelection();
for (i = 0; i < m_AttributeIndices.length; i++) {
// ignore class
if (m_AttributeIndices[i] == inputFormat.classIndex()) {
m_AttributeIndices[i] = NON_NUMERIC;
continue;
}
// not numeric -> ignore it
if (!inputFormat.attribute(m_AttributeIndices[i]).isNumeric())
m_AttributeIndices[i] = NON_NUMERIC;
}
// get old attributes
atts = new FastVector();
for (i = 0; i < inputFormat.numAttributes(); i++) atts.addElement(inputFormat.attribute(i));
if (!getDetectionPerAttribute()) {
m_OutlierAttributePosition = new int[1];
m_OutlierAttributePosition[0] = atts.size();
// add 2 new attributes
values = new FastVector();
values.addElement("no");
values.addElement("yes");
atts.addElement(new Attribute("Outlier", values));
values = new FastVector();
values.addElement("no");
values.addElement("yes");
atts.addElement(new Attribute("ExtremeValue", values));
} else {
m_OutlierAttributePosition = new int[m_AttributeIndices.length];
for (i = 0; i < m_AttributeIndices.length; i++) {
if (m_AttributeIndices[i] == NON_NUMERIC) continue;
m_OutlierAttributePosition[i] = atts.size();
// add new attributes
values = new FastVector();
values.addElement("no");
values.addElement("yes");
atts.addElement(
new Attribute(
inputFormat.attribute(m_AttributeIndices[i]).name() + "_Outlier", values));
values = new FastVector();
values.addElement("no");
values.addElement("yes");
atts.addElement(
new Attribute(
inputFormat.attribute(m_AttributeIndices[i]).name() + "_ExtremeValue", values));
if (getOutputOffsetMultiplier())
atts.addElement(
new Attribute(inputFormat.attribute(m_AttributeIndices[i]).name() + "_Offset"));
}
}
// generate header
result = new Instances(inputFormat.relationName(), atts, 0);
result.setClassIndex(inputFormat.classIndex());
return result;
}
private void readHeader() throws IOException {
m_rowCount = 1;
m_incrementalReader = null;
m_current = new ArrayList<Object>();
openTempFiles();
m_rowBuffer = new ArrayList<String>();
String firstRow = m_sourceReader.readLine();
if (firstRow == null) {
throw new IOException("No data in the file!");
}
if (m_noHeaderRow) {
m_rowBuffer.add(firstRow);
}
ArrayList<Attribute> attribNames = new ArrayList<Attribute>();
// now tokenize to determine attribute names (or create att names if
// no header row
StringReader sr = new StringReader(firstRow + "\n");
// System.out.print(firstRow + "\n");
m_st = new StreamTokenizer(sr);
initTokenizer(m_st);
m_st.ordinaryChar(m_FieldSeparator.charAt(0));
int attNum = 1;
StreamTokenizerUtils.getFirstToken(m_st);
if (m_st.ttype == StreamTokenizer.TT_EOF) {
StreamTokenizerUtils.errms(m_st, "premature end of file");
}
boolean first = true;
boolean wasSep;
while (m_st.ttype != StreamTokenizer.TT_EOL && m_st.ttype != StreamTokenizer.TT_EOF) {
// Get next token
if (!first) {
StreamTokenizerUtils.getToken(m_st);
}
if (m_st.ttype == m_FieldSeparator.charAt(0) || m_st.ttype == StreamTokenizer.TT_EOL) {
wasSep = true;
} else {
wasSep = false;
String attName = null;
if (m_noHeaderRow) {
attName = "att" + attNum;
attNum++;
} else {
attName = m_st.sval;
}
attribNames.add(new Attribute(attName, (java.util.List<String>) null));
}
if (!wasSep) {
StreamTokenizerUtils.getToken(m_st);
}
first = false;
}
String relationName;
if (m_sourceFile != null) {
relationName = (m_sourceFile.getName()).replaceAll("\\.[cC][sS][vV]$", "");
} else {
relationName = "stream";
}
m_structure = new Instances(relationName, attribNames, 0);
m_NominalAttributes.setUpper(m_structure.numAttributes() - 1);
m_StringAttributes.setUpper(m_structure.numAttributes() - 1);
m_dateAttributes.setUpper(m_structure.numAttributes() - 1);
m_numericAttributes.setUpper(m_structure.numAttributes() - 1);
m_nominalVals = new HashMap<Integer, LinkedHashSet<String>>();
m_types = new TYPE[m_structure.numAttributes()];
for (int i = 0; i < m_structure.numAttributes(); i++) {
if (m_NominalAttributes.isInRange(i)) {
m_types[i] = TYPE.NOMINAL;
LinkedHashSet<String> ts = new LinkedHashSet<String>();
m_nominalVals.put(i, ts);
} else if (m_StringAttributes.isInRange(i)) {
m_types[i] = TYPE.STRING;
} else if (m_dateAttributes.isInRange(i)) {
m_types[i] = TYPE.DATE;
} else if (m_numericAttributes.isInRange(i)) {
m_types[i] = TYPE.NUMERIC;
} else {
m_types[i] = TYPE.UNDETERMINED;
}
}
if (m_nominalLabelSpecs.size() > 0) {
for (String spec : m_nominalLabelSpecs) {
String[] attsAndLabels = spec.split(":");
if (attsAndLabels.length == 2) {
String[] labels = attsAndLabels[1].split(",");
try {
// try as a range string first
Range tempR = new Range();
tempR.setRanges(attsAndLabels[0].trim());
tempR.setUpper(m_structure.numAttributes() - 1);
int[] rangeIndexes = tempR.getSelection();
for (int i = 0; i < rangeIndexes.length; i++) {
m_types[rangeIndexes[i]] = TYPE.NOMINAL;
LinkedHashSet<String> ts = new LinkedHashSet<String>();
for (String lab : labels) {
ts.add(lab);
}
m_nominalVals.put(rangeIndexes[i], ts);
}
} catch (IllegalArgumentException e) {
// one or more named attributes?
String[] attNames = attsAndLabels[0].split(",");
for (String attN : attNames) {
Attribute a = m_structure.attribute(attN.trim());
if (a != null) {
int attIndex = a.index();
m_types[attIndex] = TYPE.NOMINAL;
LinkedHashSet<String> ts = new LinkedHashSet<String>();
for (String lab : labels) {
ts.add(lab);
}
m_nominalVals.put(attIndex, ts);
}
}
}
}
}
}
// Prevents the first row from getting lost in the
// case where there is no header row and we're
// running in batch mode
if (m_noHeaderRow && getRetrieval() == BATCH) {
StreamTokenizer tempT = new StreamTokenizer(new StringReader(firstRow));
initTokenizer(tempT);
tempT.ordinaryChar(m_FieldSeparator.charAt(0));
String checked = getInstance(tempT);
dumpRow(checked);
}
m_st = new StreamTokenizer(m_sourceReader);
initTokenizer(m_st);
m_st.ordinaryChar(m_FieldSeparator.charAt(0));
// try and determine a more accurate structure from the first batch
readData(false || getRetrieval() == BATCH);
makeStructure();
}
