CommandParameter potupersplit("otupersplit", "Multiple", "log2-squareroot", "log2", "", "", "","",false,false); parameters.push_back(potupersplit);
CommandParameter psplitcriteria("splitcriteria", "Multiple", "gainratio-infogain", "gainratio", "", "", "","",false,false); parameters.push_back(psplitcriteria);
CommandParameter pnumtrees("numtrees", "Number", "", "100", "", "", "","",false,false); parameters.push_back(pnumtrees);
+
+ // parameters related to pruning
+ CommandParameter pdopruning("prune", "Boolean", "", "T", "", "", "", "", false, false); parameters.push_back(pdopruning);
+ CommandParameter ppruneaggrns("pruneaggressiveness", "Number", "", "0.9", "", "", "", "", false, false); parameters.push_back(ppruneaggrns);
+ CommandParameter pdiscardhetrees("discarderrortrees", "Boolean", "", "T", "", "", "", "", false, false); parameters.push_back(pdiscardhetrees);
+ CommandParameter phetdiscardthreshold("errorthreshold", "Number", "", "0.4", "", "", "", "", false, false); parameters.push_back(phetdiscardthreshold);
+ CommandParameter psdthreshold("stdthreshold", "Number", "", "0.0", "", "", "", "", false, false); parameters.push_back(psdthreshold);
+ // pruning params end
CommandParameter pgroups("groups", "String", "", "", "", "", "","",false,false); parameters.push_back(pgroups);
CommandParameter plabel("label", "String", "", "", "", "", "","",false,false); parameters.push_back(plabel);
exit(1);
}
}
+
//**********************************************************************************************************************
ClassifySharedCommand::ClassifySharedCommand(string option) {
try {
for (it = parameters.begin(); it != parameters.end(); it++) {
if (validParameter.isValidParameter(it->first, myArray, it->second) != true) { abort = true; }
}
-
vector<string> tempOutNames;
outputTypes["summary"] = tempOutNames;
}
}
-
//check for parameters
//get shared file, it is required
sharedfile = validParameter.validFile(parameters, "shared", true);
outputDir = m->hasPath(sharedfile); //if user entered a file with a path then preserve it
}
-
// NEW CODE for OTU per split selection criteria
- otupersplit = validParameter.validFile(parameters, "otupersplit", false);
- if (otupersplit == "not found") { otupersplit = "log2"; }
- if ((otupersplit == "squareroot") || (otupersplit == "log2")) {
- optimumFeatureSubsetSelectionCriteria = otupersplit;
- }else { m->mothurOut("Not a valid OTU per split selection method. Valid OTU per split selection methods are 'log2' and 'squareroot'."); m->mothurOutEndLine(); abort = true; }
-
- // splitcriteria
- splitcriteria = validParameter.validFile(parameters, "splitcriteria", false);
- if (splitcriteria == "not found") { splitcriteria = "gainratio"; }
- if ((splitcriteria == "gainratio") || (splitcriteria == "infogain")) {
- treeSplitCriterion = splitcriteria;
- }else { m->mothurOut("Not a valid tree splitting criterio. Valid tree splitting criteria are 'gainratio' and 'infogain'."); m->mothurOutEndLine(); abort = true; }
-
-
- string temp = validParameter.validFile(parameters, "numtrees", false); if (temp == "not found"){ temp = "100"; }
- m->mothurConvert(temp, numDecisionTrees);
-
+ string temp = validParameter.validFile(parameters, "splitcriteria", false);
+ if (temp == "not found") { temp = "gainratio"; }
+ if ((temp == "gainratio") || (temp == "infogain")) {
+ treeSplitCriterion = temp;
+ } else { m->mothurOut("Not a valid tree splitting criterio. Valid tree splitting criteria are 'gainratio' and 'infogain'.");
+ m->mothurOutEndLine();
+ abort = true;
+ }
+
+ temp = validParameter.validFile(parameters, "numtrees", false); if (temp == "not found"){ temp = "100"; }
+ m->mothurConvert(temp, numDecisionTrees);
+
+ // parameters for pruning
+ temp = validParameter.validFile(parameters, "prune", false);
+ if (temp == "not found") { temp = "f"; }
+ doPruning = m->isTrue(temp);
+
+ temp = validParameter.validFile(parameters, "pruneaggressiveness", false);
+ if (temp == "not found") { temp = "0.9"; }
+ m->mothurConvert(temp, pruneAggressiveness);
+
+ temp = validParameter.validFile(parameters, "discarderrortrees", false);
+ if (temp == "not found") { temp = "f"; }
+ discardHighErrorTrees = m->isTrue(temp);
+
+ temp = validParameter.validFile(parameters, "errorthreshold", false);
+ if (temp == "not found") { temp = "0.4"; }
+ m->mothurConvert(temp, highErrorTreeDiscardThreshold);
+
+ temp = validParameter.validFile(parameters, "otupersplit", false);
+ if (temp == "not found") { temp = "log2"; }
+ if ((temp == "squareroot") || (temp == "log2")) {
+ optimumFeatureSubsetSelectionCriteria = temp;
+ } else { m->mothurOut("Not a valid OTU per split selection method. Valid OTU per split selection methods are 'log2' and 'squareroot'.");
+ m->mothurOutEndLine();
+ abort = true;
+ }
+
+ temp = validParameter.validFile(parameters, "stdthreshold", false);
+ if (temp == "not found") { temp = "0.0"; }
+ m->mothurConvert(temp, featureStandardDeviationThreshold);
+
+ // end of pruning params
+
//Groups must be checked later to make sure they are valid. SharedUtilities has functions of check the validity, just make to so m->setGroups() after the checks. If you are using these with a shared file no need to check the SharedRAbundVector class will call SharedUtilites for you, kinda nice, huh?
string groups = validParameter.validFile(parameters, "groups", false);
if (groups == "not found") { groups = ""; }
for (int i = 0; i < lookup.size(); i++) { delete lookup[i]; }
lookup = input.getSharedRAbundVectors(lastLabel);
m->mothurOut(lookup[0]->getLabel()); m->mothurOutEndLine();
-
processSharedAndDesignData(lookup);
processedLabels.insert(lookup[0]->getLabel());
vector< vector<int> > dataSet(numRows, vector<int>(numColumns, 0));
+ vector<string> names;
+
for (int i = 0; i < lookup.size(); i++) {
string sharedGroupName = lookup[i]->getGroup();
+ names.push_back(sharedGroupName);
string treatmentName = designMap.getGroup(sharedGroupName);
int j = 0;
dataSet[i][j] = treatmentToIntMap[treatmentName];
}
- RandomForest randomForest(dataSet, numDecisionTrees, treeSplitCriterion);
+ RandomForest randomForest(dataSet, numDecisionTrees, treeSplitCriterion, doPruning, pruneAggressiveness, discardHighErrorTrees, highErrorTreeDiscardThreshold, optimumFeatureSubsetSelectionCriteria, featureStandardDeviationThreshold);
+
randomForest.populateDecisionTrees();
randomForest.calcForrestErrorRate();
+ randomForest.printConfusionMatrix(intToTreatmentMap);
map<string, string> variables;
- variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(sharedfile));
+ variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(sharedfile)) + "RF.";
variables["[distance]"] = lookup[0]->getLabel();
string filename = getOutputFileName("summary", variables);
outputNames.push_back(filename); outputTypes["summary"].push_back(filename);
-
randomForest.calcForrestVariableImportance(filename);
+ //
+ map<string, string> variable;
+ variable["[filename]"] = outputDir + m->getRootName(m->getSimpleName(sharedfile)) + "misclassifications.";
+ variable["[distance]"] = lookup[0]->getLabel();
+ string mc_filename = getOutputFileName("summary", variable);
+ outputNames.push_back(mc_filename); outputTypes["summary"].push_back(mc_filename);
+ randomForest.getMissclassifications(mc_filename, intToTreatmentMap, names);
+ //
+
m->mothurOutEndLine();
}
catch(exception& e) {
projects / mothur.git / blobdiff