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Commit c014eaee authored by Franck Dary's avatar Franck Dary
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Changed score computation during training

parent c0935734
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trainer/include/TrainInfos.hpp
+ 3
− 12
View file @ c014eaee
......@@ -9,6 +9,7 @@
#include <string>
#include <vector>
#include "ProgramParameters.hpp"
#include "Config.hpp"
class TrainInfos
{
......@@ -23,10 +24,8 @@ class TrainInfos
std::map< std::string, std::vector<float> > devScoresPerClassifierPerEpoch;
std::map< std::string, std::vector<bool> > mustSavePerClassifierPerEpoch;
std::map< std::string, std::pair<int,int> > trainCounter;
std::map<std::string, float> trainLossCounter;
std::map<std::string, float> devLossCounter;
std::map< std::string, std::pair<int,int> > devCounter;
std::map<std::string, bool> topologyPrinted;
......@@ -34,8 +33,6 @@ class TrainInfos
void readFromFilename();
void saveToFilename();
float computeTrainScore(const std::string & classifier);
float computeDevScore(const std::string & classifier);
void addTrainScore(const std::string & classifier, float score);
void addDevScore(const std::string & classifier, float score);
......@@ -44,14 +41,8 @@ class TrainInfos
TrainInfos();
void addTrainLoss(const std::string & classifier, float loss);
void addDevLoss(const std::string & classifier, float loss);
void addTrainExample(const std::string & classifier, float loss);
void addDevExample(const std::string & classifier);
void addDevExample(const std::string & classifier, float loss);
void addTrainSuccess(const std::string & classifier);
void addDevSuccess(const std::string & classifier);
void resetCounters();
void computeTrainScores();
void computeDevScores();
void computeTrainScores(Config & c);
void computeDevScores(Config & c);
void computeMustSaves();
int getEpoch();
bool isTopologyPrinted(const std::string & classifier);
......
trainer/src/TrainInfos.cpp
+ 23
− 59
View file @ c014eaee
......@@ -9,10 +9,8 @@ TrainInfos::TrainInfos()
lastSaved = 0;
if (fileExists(filename))
{
readFromFilename();
}
}
void TrainInfos::readFromFilename()
{
......@@ -127,7 +125,7 @@ void TrainInfos::saveToFilename()
void TrainInfos::addTrainLoss(const std::string & classifier, float loss)
{
trainLossesPerClassifierPerEpoch[classifier].emplace_back(loss);
trainLossesPerClassifierPerEpoch[classifier].back() += loss;
}
void TrainInfos::addDevLoss(const std::string & classifier, float loss)
......@@ -145,68 +143,30 @@ void TrainInfos::addDevScore(const std::string & classifier, float score)
devScoresPerClassifierPerEpoch[classifier].emplace_back(score);
}
float TrainInfos::computeTrainScore(const std::string & classifier)
void TrainInfos::computeTrainScores(Config & c)
{
return 100.0*trainCounter[classifier].first / trainCounter[classifier].second;
}
float TrainInfos::computeDevScore(const std::string & classifier)
for (auto & it : topologyPrinted)
{
return 100.0*devCounter[classifier].first / devCounter[classifier].second;
}
void TrainInfos::addTrainExample(const std::string & classifier, float loss)
if (it.first == "Parser")
{
trainCounter[classifier].second++;
trainLossCounter[classifier] += loss;
float govScore = c.getTape("GOV").getScore();
float labelScore = c.getTape("LABEL").getScore();
float score = (govScore + labelScore) / 2;
addTrainScore(it.first, score);
}
void TrainInfos::addDevExample(const std::string & classifier)
{
devCounter[classifier].second++;
}
void TrainInfos::addDevExample(const std::string & classifier, float loss)
{
devCounter[classifier].second++;
devLossCounter[classifier] += loss;
}
void TrainInfos::addTrainSuccess(const std::string & classifier)
void TrainInfos::computeDevScores(Config & c)
{
trainCounter[classifier].first++;
}
void TrainInfos::addDevSuccess(const std::string & classifier)
for (auto & it : topologyPrinted)
{
devCounter[classifier].first++;
}
void TrainInfos::resetCounters()
{
trainCounter.clear();
devCounter.clear();
}
void TrainInfos::computeTrainScores()
{
for (auto & it : trainCounter)
{
addTrainScore(it.first, computeTrainScore(it.first));
addTrainLoss(it.first, trainLossCounter[it.first]);
trainLossCounter[it.first] = 0.0;
}
}
void TrainInfos::computeDevScores()
if (it.first == "Parser")
{
for (auto & it : devCounter)
{
addDevScore(it.first, computeDevScore(it.first));
if (devLossCounter.count(it.first))
{
addDevLoss(it.first, devLossCounter[it.first]);
devLossCounter[it.first] = 0.0;
float govScore = c.getTape("GOV").getScore();
float labelScore = c.getTape("LABEL").getScore();
float score = (govScore + labelScore) / 2;
addDevScore(it.first, score);
}
}
}
......@@ -223,14 +183,18 @@ bool TrainInfos::isTopologyPrinted(const std::string & classifier)
void TrainInfos::setTopologyPrinted(const std::string & classifier)
{
topologyPrinted[classifier] = true;
trainLossesPerClassifierPerEpoch[classifier].emplace_back(0.0);
if (ProgramParameters::devLoss)
devLossesPerClassifierPerEpoch[classifier].emplace_back(0.0);
}
void TrainInfos::nextEpoch()
{
lastEpoch++;
saveToFilename();
for (auto & it : topologyPrinted)
trainLossesPerClassifierPerEpoch[it.first].emplace_back(0.0);
}
void TrainInfos::computeMustSaves()
......
trainer/src/Trainer.cpp
+ 5
− 22
View file @ c014eaee
......@@ -111,20 +111,11 @@ void Trainer::computeScoreOnDev()
}
}
bool pActionIsZeroCost = tm.getCurrentClassifier()->getActionCost(*devConfig, pAction) == 0;
if (ProgramParameters::devLoss)
{
float loss = tm.getCurrentClassifier()->getLoss(*devConfig, tm.getCurrentClassifier()->getActionIndex(oAction));
TI.addDevExample(tm.getCurrentClassifier()->name, loss);
TI.addDevLoss(tm.getCurrentClassifier()->name, loss);
}
else
{
TI.addDevExample(tm.getCurrentClassifier()->name);
}
if (((!ProgramParameters::devEvalOnGold) && pActionIsZeroCost) || (pAction == oAction))
TI.addDevSuccess(tm.getCurrentClassifier()->name);
std::string actionName;
......@@ -189,7 +180,7 @@ void Trainer::computeScoreOnDev()
if (ProgramParameters::debug)
fprintf(stderr, "Dev Config is final\n");
TI.computeDevScores();
TI.computeDevScores(*devConfig);
if (ProgramParameters::debug)
fprintf(stderr, "End of %s\n", __func__);
......@@ -202,8 +193,6 @@ void Trainer::resetAndShuffle()
if(ProgramParameters::shuffleExamples)
trainConfig.shuffle(ProgramParameters::sequenceDelimiterTape, ProgramParameters::sequenceDelimiter);
TI.resetCounters();
}
void Trainer::doStepNoTrain()
......@@ -302,9 +291,7 @@ void Trainer::doStepTrain()
if (!ProgramParameters::featureExtraction)
loss = tm.getCurrentClassifier()->trainOnExample(trainConfig, tm.getCurrentClassifier()->getActionIndex(oAction));
TI.addTrainExample(tm.getCurrentClassifier()->name, loss);
if (pActionIsZeroCost)
TI.addTrainSuccess(tm.getCurrentClassifier()->name);
TI.addTrainLoss(tm.getCurrentClassifier()->name, loss);
int k = ProgramParameters::dynamicEpoch;
......@@ -400,17 +387,13 @@ void Trainer::doStepTrain()
if (newCost >= lastCost)
{
loss = tm.getCurrentClassifier()->trainOnExample(pendingFD[tm.getCurrentClassifier()->name], tm.getCurrentClassifier()->getActionIndex("EPSILON"));
if (pActionIsZeroCost)
TI.addTrainSuccess(tm.getCurrentClassifier()->name);
}
else
{
loss = tm.getCurrentClassifier()->trainOnExample(pendingFD[tm.getCurrentClassifier()->name], tm.getCurrentClassifier()->getActionIndex(trainConfig.getCurrentStateHistory().top()));
TI.addTrainSuccess(tm.getCurrentClassifier()->name);
}
TI.addTrainExample(tm.getCurrentClassifier()->name, loss);
TI.addTrainLoss(tm.getCurrentClassifier()->name, loss);
}
if (ProgramParameters::debug)
......@@ -509,7 +492,7 @@ void Trainer::train()
void Trainer::printScoresAndSave(FILE * output)
{
TI.computeTrainScores();
TI.computeTrainScores(trainConfig);
computeScoreOnDev();
TI.computeMustSaves();
......
transition_machine/include/Config.hpp
+ 4
− 0
View file @ c014eaee
......@@ -130,6 +130,10 @@ class Config
///
/// @param index the index to mask
void maskIndex(int index);
/// @brief Compare hyp and ref to give a matching score.
///
/// @return The score as a percentage.
float getScore();
};
private :
......
transition_machine/src/Config.cpp
+ 13
− 0
View file @ c014eaee
......@@ -625,3 +625,16 @@ std::vector< std::pair<std::string, int> > & Config::getActionsHistory(std::stri
return actionsHistory[classifier+"_"+std::to_string(head)];
}
float Config::Tape::getScore()
{
float res = 0.0;
for (int i = 0; i < refSize(); i++)
{
if (getRef(i-head) == getHyp(i-head))
res += 1;
}
return 100.0*res / refSize();
}
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