Improve coverage isdb #1144

Mertcan-Puncist · 2024-10-18T12:22:05Z

Description

this pull request focuses on enhancing the coverage test result for the isdb/metainference

1- regtest/isdb/rt-caliber-negres-zero

i coppyed this original test

rt-caliber

modified and name it

rt-caliber-negres-zero

to cover:

isdb/Caliber.cpp

covered isdb/Caliber.cpp lines|

 165           0 :     doregres_zero_=true;
 166           0 :     log.printf("  doing regression with zero intercept with stride: %d\n", nregres_zero_);

 225           4 :   if(doregres_zero_) {
 226           0 :     addComponent("scale");
 227           0 :     componentIsNotPeriodic("scale");
 228           0 :     valueScale=getPntrToComponent("scale");

2- regtest/isdb/rt-Metainference-averaging

i coppyed this original test

rt-jcouplings-mi

modified and name it

rt-Metainference-averaging

to cover:

isdb/Metainference.cpp

covered isdb/Metainference.cpp lines|

 410           0 :     average_weights_stride_ = averaging;
 411           0 :     optsigmamean_stride_    = averaging;

3- regtest/isdb/rt-coverage-EMMI-TEMP

i coppyed this original test

rt-emmi-gauss-mpi

modified and name it

rt-coverage-EMMI-TEMP

to cover:

isdb/EMMI.cpp

covered isdb/EMMI.cpp lines|

 513           0 :   else kbt_=plumed.getAtoms().getKbT();

4- regtest/isdb/rt-coverage-EMMI-anneal-fact

i coppyed this original test

rt-emmi-gauss-mpi

modified and name it

rt-coverage-EMMI-anneal-fact

to cover:

isdb/EMMI.cpp

covered isdb/EMMI.cpp lines|

 622           0 :     log.printf("  length of annealing cycle : %u\n",nanneal_);
 623           0 :     log.printf("  annealing factor : %f\n",kanneal_);

1596           0 :     anneal_ = get_annealing(step);
1597           0 :     getPntrToComponent("anneal")->set(anneal_);

5- regtest/isdb/rt-coverage-EMMI-ovstride

i coppyed this original test

rt-emmi-gauss-mpi

modified and name it

rt-coverage-EMMI-ovstride

to cover:

isdb/EMMI.cpp

covered isdb/EMMI.cpp lines|

 626           0 :     log.printf("  stride for writing model overlaps : %u\n",ovstride_);
 627           0 :     log.printf("  file for writing model overlaps : %s\n", ovfilename_.c_str());

6- regtest/isdb/rt-jcoupling-string-type

i coppyed this original test

rt-jcouplings-mi

modified and name it

rt-jcoupling-string-type

to cover:

isdb/Jcoupling.cpp

covered isdb/Jcoupling.cpp lines|

 173           0 :   } else if(string_type == "CUSTOM") {

7- regtest/isdb/rt-metainference-likelihood

i coppyed this original test

rt-noe-mi

modified and name it

rt-metainference-likelihood

to cover:

isdb/Metainference.cpp

covered isdb/Metainference.cpp lines|

 445           0 :     else if(stringa_like=="LOGN") gen_likelihood_ = LIKE_LOGN;

 726           0 :     else if(gen_likelihood_==LIKE_LOGN) log.printf(" and a log-normal likelihood\n");

8- regtest/isdb/rt-metainference-mc-chunksize

i coppyed this original test

rt-jcouplings-mi

modified and name it

rt-metainference-mc-chunksize

to cover:

isdb/Metainference.cpp

covered isdb/Metainference.cpp lines|

1185           0 :     if ((MCchunksize_ * i) >= sigma_.size()) {

1289           0 :       for (unsigned j=0; j<sigma_.size(); j++) {
1290           0 :         indices.push_back(j);

9- regtest/isdb/rt-metainference-optisigmamean-sem-max

i coppyed this original test

rt-jcouplings-mi

modified and name it

rt-metainference-optisigmamean-sem-max

to cover:

isdb/Metainference.cpp

covered isdb/Metainference.cpp lines|

 461           0 :   else if(stringa_optsigma=="SEM_MAX")  do_optsigmamean_=2;

1581         178 :   if(do_optsigmamean_>0) {
1582             :     // remove first entry of the history std::vector
1583           0 :     if(sigma_mean2_last_[iselect][0].size()==optsigmamean_stride_&&optsigmamean_stride_>0)
1584           0 :       for(unsigned i=0; i<narg; ++i) sigma_mean2_last_[iselect][i].erase(sigma_mean2_last_[iselect][i].begin());
1585             :     /* this is the current estimate of sigma mean for each argument
1586             :        there is one of this per argument in any case  because it is
1587             :        the maximum among these to be used in case of GAUSS/OUTLIER */
1588           0 :     std::vector<double> sigma_mean2_now(narg,0);
1589           0 :     if(master) {
1590           0 :       for(unsigned i=0; i<narg; ++i) sigma_mean2_now[i] = weight*(getArgument(i)-mean[i])*(getArgument(i)-mean[i]);
1591           0 :       if(nrep_>1) multi_sim_comm.Sum(&sigma_mean2_now[0], narg);
1592             :     }
1593           0 :     comm.Sum(&sigma_mean2_now[0], narg);
1594           0 :     for(unsigned i=0; i<narg; ++i) sigma_mean2_now[i] *= 1.0/(neff-1.)/norm;
1595             : 
1596             :     // add sigma_mean2 to history
1597           0 :     if(optsigmamean_stride_>0) {
1598           0 :       for(unsigned i=0; i<narg; ++i) sigma_mean2_last_[iselect][i].push_back(sigma_mean2_now[i]);
1599             :     } else {
1600           0 :       for(unsigned i=0; i<narg; ++i) if(sigma_mean2_now[i] > sigma_mean2_last_[iselect][i][0]) sigma_mean2_last_[iselect][i][0] = sigma_mean2_now[i];
1601             :     }
1602             : 
1603           0 :     if(noise_type_==MGAUSS||noise_type_==MOUTLIERS||noise_type_==GENERIC) {
1604           0 :       for(unsigned i=0; i<narg; ++i) {
1605             :         /* set to the maximum in history std::vector */
1606           0 :         sigma_mean2_tmp[i] = *max_element(sigma_mean2_last_[iselect][i].begin(), sigma_mean2_last_[iselect][i].end());
1607             :         /* the standard error of the mean */
1608           0 :         valueSigmaMean[i]->set(std::sqrt(sigma_mean2_tmp[i]));
1609           0 :         if(noise_type_==GENERIC) {
1610           0 :           sigma_min_[i] = std::sqrt(sigma_mean2_tmp[i]);
1611           0 :           if(sigma_[i] < sigma_min_[i]) sigma_[i] = sigma_min_[i];
1612             :         }
1613             :       }
1614           0 :     } else if(noise_type_==GAUSS||noise_type_==OUTLIERS) {
1615             :       // find maximum for each data point
1616             :       std::vector <double> max_values;
1617           0 :       for(unsigned i=0; i<narg; ++i) max_values.push_back(*max_element(sigma_mean2_last_[iselect][i].begin(), sigma_mean2_last_[iselect][i].end()));
1618             :       // find maximum across data points
1619           0 :       const double max_now = *max_element(max_values.begin(), max_values.end());
1620             :       // set new value
1621           0 :       sigma_mean2_tmp[0] = max_now;
1622           0 :       valueSigmaMean[0]->set(std::sqrt(sigma_mean2_tmp[0]));
1623             :     }
1624             :     // endif sigma mean optimization
1625             :     // start sigma max optimization
1626           0 :     if(do_optsigmamean_>1&&!sigmamax_opt_done_) {
1627           0 :       for(unsigned i=0; i<sigma_max_.size(); i++) {
1628           0 :         if(sigma_max_est_[i]<sigma_mean2_tmp[i]&&optimized_step_>optsigmamean_stride_) sigma_max_est_[i]=sigma_mean2_tmp[i];
1629             :         // ready to set once and for all the value of sigma_max
1630           0 :         if(optimized_step_==N_optimized_step_) {
1631           0 :           sigmamax_opt_done_=true;
1632           0 :           for(unsigned i=0; i<sigma_max_.size(); i++) {
1633           0 :             sigma_max_[i]=std::sqrt(sigma_max_est_[i]*dnrep);
1634           0 :             Dsigma_[i] = 0.05*(sigma_max_[i] - sigma_min_[i]);
1635           0 :             if(sigma_[i]>sigma_max_[i]) sigma_[i]=sigma_max_[i];
1636             :           }
1637             :         }
1638             :       }
1639           0 :       optimized_step_++;

10- regtest/isdb/rt-metainference-optisigmamean-sem

i coppyed this original test

rt-jcouplings-mi

modified and name it

rt-metainference-optisigmamean-sem

to cover:

isdb/Metainference.cpp

covered isdb/Metainference.cpp lines|

 460           0 :   else if(stringa_optsigma=="SEM")  do_optsigmamean_=1;

1581         178 :   if(do_optsigmamean_>0) {
1582             :     // remove first entry of the history std::vector
1583           0 :     if(sigma_mean2_last_[iselect][0].size()==optsigmamean_stride_&&optsigmamean_stride_>0)
1584           0 :       for(unsigned i=0; i<narg; ++i) sigma_mean2_last_[iselect][i].erase(sigma_mean2_last_[iselect][i].begin());
1585             :     /* this is the current estimate of sigma mean for each argument
1586             :        there is one of this per argument in any case  because it is
1587             :        the maximum among these to be used in case of GAUSS/OUTLIER */
1588           0 :     std::vector<double> sigma_mean2_now(narg,0);
1589           0 :     if(master) {
1590           0 :       for(unsigned i=0; i<narg; ++i) sigma_mean2_now[i] = weight*(getArgument(i)-mean[i])*(getArgument(i)-mean[i]);
1591           0 :       if(nrep_>1) multi_sim_comm.Sum(&sigma_mean2_now[0], narg);
1592             :     }
1593           0 :     comm.Sum(&sigma_mean2_now[0], narg);
1594           0 :     for(unsigned i=0; i<narg; ++i) sigma_mean2_now[i] *= 1.0/(neff-1.)/norm;
1595             : 
1596             :     // add sigma_mean2 to history
1597           0 :     if(optsigmamean_stride_>0) {
1598           0 :       for(unsigned i=0; i<narg; ++i) sigma_mean2_last_[iselect][i].push_back(sigma_mean2_now[i]);
1599             :     } else {
1600           0 :       for(unsigned i=0; i<narg; ++i) if(sigma_mean2_now[i] > sigma_mean2_last_[iselect][i][0]) sigma_mean2_last_[iselect][i][0] = sigma_mean2_now[i];
1601             :     }
1602             : 
1603           0 :     if(noise_type_==MGAUSS||noise_type_==MOUTLIERS||noise_type_==GENERIC) {
1604           0 :       for(unsigned i=0; i<narg; ++i) {
1605             :         /* set to the maximum in history std::vector */
1606           0 :         sigma_mean2_tmp[i] = *max_element(sigma_mean2_last_[iselect][i].begin(), sigma_mean2_last_[iselect][i].end());
1607             :         /* the standard error of the mean */
1608           0 :         valueSigmaMean[i]->set(std::sqrt(sigma_mean2_tmp[i]));
1609           0 :         if(noise_type_==GENERIC) {
1610           0 :           sigma_min_[i] = std::sqrt(sigma_mean2_tmp[i]);
1611           0 :           if(sigma_[i] < sigma_min_[i]) sigma_[i] = sigma_min_[i];
1612             :         }
1613             :       }
1614           0 :     } else if(noise_type_==GAUSS||noise_type_==OUTLIERS) {
1615             :       // find maximum for each data point
1616             :       std::vector <double> max_values;
1617           0 :       for(unsigned i=0; i<narg; ++i) max_values.push_back(*max_element(sigma_mean2_last_[iselect][i].begin(), sigma_mean2_last_[iselect][i].end()));
1618             :       // find maximum across data points
1619           0 :       const double max_now = *max_element(max_values.begin(), max_values.end());
1620             :       // set new value
1621           0 :       sigma_mean2_tmp[0] = max_now;
1622           0 :       valueSigmaMean[0]->set(std::sqrt(sigma_mean2_tmp[0]));
1623             :     }
1624             :     // endif sigma mean optimization
1625             :     // start sigma max optimization
1626           0 :     if(do_optsigmamean_>1&&!sigmamax_opt_done_) {
1627           0 :       for(unsigned i=0; i<sigma_max_.size(); i++) {
1628           0 :         if(sigma_max_est_[i]<sigma_mean2_tmp[i]&&optimized_step_>optsigmamean_stride_) sigma_max_est_[i]=sigma_mean2_tmp[i];
1629             :         // ready to set once and for all the value of sigma_max
1630           0 :         if(optimized_step_==N_optimized_step_) {
1631           0 :           sigmamax_opt_done_=true;
1632           0 :           for(unsigned i=0; i<sigma_max_.size(); i++) {
1633           0 :             sigma_max_[i]=std::sqrt(sigma_max_est_[i]*dnrep);
1634           0 :             Dsigma_[i] = 0.05*(sigma_max_[i] - sigma_min_[i]);
1635           0 :             if(sigma_[i]>sigma_max_[i]) sigma_[i]=sigma_max_[i];
1636             :           }
1637             :         }
1638             :       }
1639           0 :       optimized_step_++;

Note about test 9 and 10 :

These two tests covers the same lines between 1581-1639

11-regtest/isdb/rt-metainference-regres-zero

i coppyed this original test

rt-jcouplings-mi

modified and name it

rt-metainference-regres-zero

to cover:

isdb/Metainference.cpp

covered isdb/Metainference.cpp lines|

 547           0 :     doregres_zero_=true;
 548             :     // check if already sampling scale and offset
 549           0 :     if(doscale_)  error("REGRES_ZERO and SCALEDATA are mutually exclusive");
 550           0 :     if(dooffset_) error("REGRES_ZERO and ADDOFFSET are mutually exclusive");

 790           0 :     log.printf("  doing regression with zero intercept with stride: %d\n", nregres_zero_);

12- regtest/isdb/rt-metainferencebase-averaging

i coppyed this original test:

rt-jcouplings-mi

modified and name it:

rt-metainferencebase-averaging

to cover:

isdb/MetainferenceBase.cpp

covered isdb/MetainferenceBase.cpp lines|

 168           0 :     decay_w_ = 1./static_cast<double> (averaging);
 169           0 :     optsigmamean_stride_ = averaging;

1365          84 :     if(optsigmamean_stride_>0) {
1366           0 :       for(unsigned i=0; i<narg; ++i) sigma_mean2_last_[iselect][i].push_back(sigma_mean2_now[i]);

13- regtest/isdb/rt-metainferencebase-likelihood

i coppyed this original test:

rt-noe-mi

modified and name it:

rt-metainferencebase-likelihood

to cover:

isdb/MetainferenceBase.cpp

covered isdb/MetainferenceBase.cpp lines|

 185           0 :     else if(stringa_like=="LOGN") gen_likelihood_ = LIKE_LOGN;

 587           0 :     else if(gen_likelihood_==LIKE_LOGN) log.printf(" and a log-normal likelihood\n");

14- regtest/isdb/rt-metainferencebase-regres-zero

i coppyed this original test:

rt-jcouplings-mi

modified and name it:

rt-metainferencebase-regres-zero

to cover:

isdb/MetainferenceBase.cpp

covered isdb/MetainferenceBase.cpp lines|

 274           0 :     doregres_zero_=true;

 650          31 :   if(doregres_zero_)
 651           0 :     log.printf("  doing regression with zero intercept with stride: %d\n", nregres_zero_);

15- regtest/isdb/rt-metainferencebase-sem-max

i coppyed this original test:

rt-jcouplings-mi

modified and name it:

rt-metainferencebase-sem-max

to cover:

isdb/MetainferenceBase.cpp

covered isdb/MetainferenceBase.cpp lines|

 200           0 :   else if(stringa_optsigma=="SEM_MAX")  do_optsigmamean_=2;

Target release

I would like my code to appear in release v2.9

Type of contribution

changes to code or doc authored by PLUMED developers, or additions of code in the core or within the default modules
changes to a module not authored by you
new module contribution or edit of a module authored by you

Copyright

I agree to transfer the copyright of the code I have written to the PLUMED developers or to the author of the code I am modifying.

the module I added or modified contains a COPYRIGHT file with the correct license information. Code should be released under an open source license. I also used the command cd src && ./header.sh mymodulename in order to make sure the headers of the module are correct.

Tests

I added a new regtest or modified an existing regtest to validate my changes.
I verified that all regtests are passed successfully on GitHub Actions.

Iximiel · 2024-10-21T08:33:31Z

Before going down in particular (I think someone like @carlocamilloni or @GiovanniBussi should be better than me at commenting on the scientific side of the tests)

I took your comment and ran diff on all the couples of directories you listed.

Output in spoiler

Using diffstat to produce a readable report:

diff rt-caliber rt-caliber-negres-zero | diffstat

 allforce.0.reference |62712 +++++++++++++++++++++++++--------------------------
 plumed.dat           |    1 
 2 files changed, 31357 insertions(+), 31356 deletions(-)

diff rt-jcouplings-mi rt-Metainference-averaging | diffstat

 plumed.dat |    1 +
 report.txt |only
 tmp        |only
 3 files changed, 1 insertion(+)

diff rt-emmi-gauss-mpi rt-coverage-EMMI-TEMP | diffstat

 COLVAR.reference |    2 
 deriva.reference |  412 +++++++++++++++++++++++++++----------------------------
 plumed.dat       |    2 
 3 files changed, 208 insertions(+), 208 deletions(-)

diff rt-emmi-gauss-mpi rt-coverage-EMMI-anneal-fact | diffstat

 plumed.dat |    2 ++
 1 file changed, 2 insertions(+)

diff rt-emmi-gauss-mpi rt-coverage-EMMI-ovstride | diffstat

 plumed.dat |    2 ++
 1 file changed, 2 insertions(+)

diff rt-jcouplings-mi rt-jcoupling-string-type | diffstat

 atom_forces.reference |  276 +++++++++++++++++++++++++-------------------------
 miorig.reference      |   12 +-
 plumed.dat            |    2 
 3 files changed, 145 insertions(+), 145 deletions(-)

diff rt-noe-mi rt-metainference-likelihood | diffstat

 COLVAR.reference |   24 ++++++++++++------------
 plumed.dat       |    1 +
 2 files changed, 13 insertions(+), 12 deletions(-)

diff rt-jcouplings-mi rt-metainference-mc-chunksize | diffstat

 plumed.dat |    3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff rt-jcouplings-mi rt-metainference-optisigmamean-sem-max | diffstat

 plumed.dat |    1 +
 1 file changed, 1 insertion(+)

diff rt-jcouplings-mi rt-metainference-optisigmamean-sem | diffstat

 plumed.dat |    1 +
 1 file changed, 1 insertion(+)

diff rt-jcouplings-mi rt-metainference-regres-zero | diffstat

 MISTATUSmij.reference |    2 
 atom_forces.reference |  274 +++++++++++++++++++++++++-------------------------
 miorig.reference      |   14 +-
 plumed.dat            |    3 
 4 files changed, 147 insertions(+), 146 deletions(-)

diff rt-jcouplings-mi rt-metainferencebase-averaging | diffstat

 plumed.dat |    4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff rt-noe-mi rt-metainferencebase-likelihood | diffstat

 COLVAR.reference |   24 ++++++++++++------------
 plumed.dat       |    1 +
 2 files changed, 13 insertions(+), 12 deletions(-)

diff rt-jcouplings-mi rt-metainferencebase-regres-zero | diffstat

 plumed.dat |    2 ++
 1 file changed, 2 insertions(+)

diff rt-jcouplings-mi rt-metainferencebase-sem-max | diffstat

 plumed.dat |    2 ++
 1 file changed, 2 insertions(+)

in rt-Metainference-averaging you accidentally pushed tmp and the report.txt

For some tests there is no difference between the original one and the modified one:

rt-emmi-gauss-mpi rt-coverage-EMMI-anneal-fact
rt-emmi-gauss-mpi rt-coverage-EMMI-ovstride
rt-jcouplings-mi rt-metainference-mc-chunksize
rt-jcouplings-mi rt-metainference-optisigmamean-sem-max
rt-jcouplings-mi rt-metainference-optisigmamean-sem
rt-jcouplings-mi rt-metainferencebase-averaging
rt-jcouplings-mi rt-metainferencebase-regres-zero
rt-jcouplings-mi rt-metainferencebase-sem-max
rt-jcouplings-mi rt-Metainference-averaging

This means that the test is passing through those lines (since the coverage is improving) but there is no proof of that happening in the output, we should either rethink the new tests or add some extra output references.

= added 7 commits October 15, 2024 10:22

improved coverage test result of isdb/metainference

d8995e2

adding new tests to cover new lines for isdb module

f22e5fd

moved tests from other branch to this branch (correct procedure)

c134baf

removed test that doesnt work

2fec94d

removed test that doesnt do anythink

d871737

removed test that no need

102fca1

removed test that has no effect on test

240a495

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Improve coverage isdb #1144

Improve coverage isdb #1144

Mertcan-Puncist commented Oct 18, 2024

Iximiel commented Oct 21, 2024

Improve coverage isdb #1144

Are you sure you want to change the base?

Improve coverage isdb #1144

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Mertcan-Puncist commented Oct 18, 2024

Description

1- regtest/isdb/rt-caliber-negres-zero

2- regtest/isdb/rt-Metainference-averaging

3- regtest/isdb/rt-coverage-EMMI-TEMP

4- regtest/isdb/rt-coverage-EMMI-anneal-fact

5- regtest/isdb/rt-coverage-EMMI-ovstride

6- regtest/isdb/rt-jcoupling-string-type

7- regtest/isdb/rt-metainference-likelihood

8- regtest/isdb/rt-metainference-mc-chunksize

9- regtest/isdb/rt-metainference-optisigmamean-sem-max

10- regtest/isdb/rt-metainference-optisigmamean-sem

11-regtest/isdb/rt-metainference-regres-zero

12- regtest/isdb/rt-metainferencebase-averaging

13- regtest/isdb/rt-metainferencebase-likelihood

14- regtest/isdb/rt-metainferencebase-regres-zero

15- regtest/isdb/rt-metainferencebase-sem-max

Target release

Type of contribution

Copyright

Tests

Iximiel commented Oct 21, 2024