Applies to all experiments:

• stratified 5-fold cross validation over training/test splits;
• mean performance scores over the folds.

https://github.com/Wikidata/soweego/issues/285

• run: May 3 2019;
• output folder: soweego-2.eqiad.wmflabs:/srv/dev/20190503/;
• head commit: d0d390e622f2782a49a1bd0ebfc64478ed34aa0c;
• command: python -m soweego linker evaluate slp ${Dataset} ${Entity} optimizer=${Optimizer}.

Optimizer	Precision	Recall	F-score
sgd	.782	.945	.856
rmsprop	.801	.930	.860
nadam	.805	.925	.861
adamax	.795	.938	.861
adam	.800	.929	.860
adagrad	.802	.927	.859
adadelta	.799	.934	.861

Optimizer	Precision	Recall	F-score
sgd	.815	.985	.892
rmsprop	.816	.985	.893
nadam	.816	.986	.893
adamax	.817	.985	.893
adam	.816	.985	.893
adagrad	.816	.986	.893
adadelta	.815	.986	.892

Optimizer	Precision	Recall	F-score
sgd	.918	.954	.936
rmsprop	.895	.954	.923
nadam	.908	.954	.930
adamax	.907	.955	.930
adam	.909	.953	.931
adagrad	.867	.950	.907
adadelta	.902	.954	.927

Optimizer	Precision	Recall	F-score
sgd	.912	.927	.920
rmsprop	.913	.929	.921
nadam	.913	.929	.921
adamax	.913	.928	.921
adam	.913	.928	.921
adagrad	.873	.860	.866
adadelta	.913	.928	.921

Optimizer	Precision	Recall	F-score
sgd	.917	.942	.929
rmsprop	.916	.938	.927
nadam	.916	.938	.927
adamax	.916	.940	.928
adam	.916	.938	.927
adagrad	.852	.684	.756
adadelta	.916	.939	.928

Optimizer	Precision	Recall	F-score
sgd	.929	.943	.936
rmsprop	.927	.940	.934
nadam	.930	.940	.935
adamax	.930	.941	.935
adam	.930	.940	.935
adagrad	.872	.923	.896
adadelta	.931	.941	.936

Optimizer	Precision	Recall	F-score
sgd	.952	.869	.909
rmsprop	.949	.875	.911
nadam	.949	.877	.911
adamax	.952	.871	.910
adam	.951	.875	.911
adagrad	.932	.886	.909
adadelta	.952	.874	.911

Optimizer	Precision	Recall	F-score
sgd	.942	.957	.949
rmsprop	.941	.958	.949
nadam	.941	.958	.949
adamax	.941	.958	.949
adam	.941	.958	.949
adagrad	.946	.953	.950
adadelta	.941	.958	.950

• All optimizers seem to do a similar job;
• no specific impact on the performance.

https://github.com/Wikidata/soweego/issues/176

• run: May 7 2019;
• output folder: soweego-2.eqiad.wmflabs:/srv/dev/20190507/;
• head commit: ddd5d719793ea217267413a52d1d2e5b90c341a7;
• command: python -m soweego linker evaluate ${Algorithm} ${Dataset} ${Entity}.

Algorithm	Precision	Recall	F-score
nb max	.787	.955	.863
nb avg	.789	.941	.859
lsvm max	.780	.960	.861
lsvm avg	.785	.946	.858
svm max	.777	.963	.860
svm avg	.777	.963	.860
slp max	.784	.954	.861
slp avg	.776	.956	.857
mlp max	.822	.925	.870

Algorithm	Precision	Recall	F-score
nb max	.831	.975	.897
nb avg	.836	.958	.893
lsvm max	.818	.985	.894
lsvm avg	.814	.986	.892
svm max	.815	.985	.892
svm avg	.815	.985	.892
slp max	.821	.983	.895
slp avg	.815	.985	.892
mlp max	.852	.963	.904

Algorithm	Precision	Recall	F-score
nb max	.896	.971	.932
nb avg	.897	.971	.932
lsvm max	.919	.943	.931
lsvm avg	.919	.942	.930
svm max	.911	.950	.930
svm avg	.908	.958	.932
slp max	.917	.953	.935
slp avg	.867	.953	.908
mlp max	.913	.964	.938

Algorithm	Precision	Recall	F-score
nb max	.889	.962	.924
nb avg	.891	.960	.924
lsvm max	.917	.938	.927
lsvm avg	.917	.937	.927
svm max	.904	.944	.924
svm avg	.908	.942	.924
slp max	.924	.929	.926
slp avg	.922	.914	.918
mlp max	.912	.951	.931

Algorithm	Precision	Recall	F-score
nb max	.870	.971	.918
nb avg	.871	.970	.918
lsvm max	.920	.940	.930
lsvm avg	.920	.938	.929
svm max	.923	.927	.925
svm avg	.923	.926	.925
slp max	.914	.940	.927
slp avg	.862	.914	.883
mlp max	.911	.956	.933

Algorithm	Precision	Recall	F-score
nb max	.904	.975	.938
nb avg	.910	.961	.935
lsvm max	.936	.949	.943
lsvm avg	.936	.948	.942
svm max	.932	.954	.943
svm avg	.932	.954	.943
slp max	.938	.946	.942
slp avg	.903	.955	.928
mlp max	.930	.963	.946

Algorithm	Precision	Recall	F-score
nb max	.821	.987	.896
nb avg	.822	.985	.896
lsvm max	.944	.879	.910
lsvm avg	.943	.888	.914
svm max	.930	.891	.910
svm avg	.939	.893	.915
slp max	.953	.865	.907
slp avg	.930	.885	.907
mlp max	.906	.918	.911

Algorithm	Precision	Recall	F-score
nb max	.955	.936	.946
nb avg	.955	.936	.946
lsvm max	.941	.963	.952
lsvm avg	.941	.962	.952
svm max	.951	.938	.944
svm avg	.950	.938	.944
slp max	.942	.957	.949
slp avg	.943	.956	.949
mlp max	.939	.970	.954

Max Levenshtein has the following impact:

• NB is always improved or left untouched;
• LSVM is always improved, left untouched for IMDb director, but worsens for MusicBrainz band;
• SVM is often left untouched, but worsens for IMDb director and MusicBrainz band;
• SLP is always improved with the highest impact, left untouched for MusicBrainz;
• conclusion: max Levenshtein should replace the average one.

https://github.com/Wikidata/soweego/issues/174

• run: May 8 2019;
• output folder: soweego-2.eqiad.wmflabs:/srv/dev/20190508/;
• head commit: 0c5137fc4fe446abdb6df6dbde277b7aa15881c5;
• command: python -m soweego linker evaluate ${Algorithm} ${Dataset} ${Entity}.

Algorithm	Precision	Recall	F-score
nb +sk	.788	.942	.859
nb	.789	.941	.859
lsvm +sk	.785	.946	.858
lsvm	.785	.946	.858
svm +sk	.778	.963	.861
svm	.777	.963	.860
slp +sk	.783	.947	.857
slp	.776	.956	.857
mlp +sk	.848	.913	.879

Algorithm	Precision	Recall	F-score
nb +sk	.836	.958	.893
nb	.836	.958	.893
lsvm +sk	.816	.985	.892
lsvm	.814	.986	.892
svm +sk	.815	.985	.892
svm	.815	.985	.892
slp +sk	.820	.978	.892
slp	.815	.985	.892
mlp +sk	.868	.948	.906

Algorithm	Precision	Recall	F-score
nb +sk	.897	.971	.932
nb	.897	.971	.932
lsvm +sk	.923	.949	.935
lsvm	.919	.942	.930
svm +sk	.914	.950	.931
svm	.908	.958	.932
slp +sk	.918	.955	.936
slp	.867	.953	.908
mlp +sk	.918	.964	.941

Algorithm	Precision	Recall	F-score
nb +sk	.891	.961	.924
nb	.891	.960	.924
lsvm +sk	.922	.941	.931
lsvm	.917	.937	.927
svm +sk	.910	.949	.929
svm	.908	.942	.924
slp +sk	.922	.934	.928
slp	.922	.914	.918
mlp +sk	.914	.958	.935

Algorithm	Precision	Recall	F-score
nb +sk	.871	.970	.918
nb	.871	.970	.918
lsvm +sk	.921	.943	.932
lsvm	.920	.938	.929
svm +sk	.923	.927	.925
svm	.923	.926	.925
slp +sk	.916	.942	.929
slp	.862	.914	.883
mlp +sk	.912	.959	.935

Algorithm	Precision	Recall	F-score
nb +sk	.910	.961	.935
nb	.910	.961	.935
lsvm +sk	.938	.953	.945
lsvm	.936	.948	.942
svm +sk	.933	.957	.945
svm	.932	.954	.943
slp +sk	.939	.948	.943
slp	.903	.955	.928
mlp +sk	.931	.968	.949

Algorithm	Precision	Recall	F-score
nb +sk	.821	.985	.896
nb	.822	.985	.896
lsvm +sk	.940	.895	.917
lsvm	.943	.888	.914
svm +sk	.937	.899	.918
svm	.939	.893	.915
slp +sk	.952	.873	.911
slp	.930	.885	.907
mlp +sk	.937	.904	.920

Algorithm	Precision	Recall	F-score
nb +sk	.955	.936	.946
nb	.955	.936	.946
lsvm +sk	.938	.965	.951
lsvm	.941	.962	.952
svm +sk	.951	.938	.944
svm	.950	.938	.944
slp +sk	.941	.958	.950
slp	.943	.956	.949
mlp +sk	.939	.972	.955

The string kernel feature:

• has the most positive impact on SLP;
• slightly improves performance in most cases, but sligthly worsens:
  • precision in 1 case, i.e., NB for MusicBrainz band;
  • recall in 3 cases, i.e., SLP for Discogs band, LSVM & SLP for Discogs musician;
  • f-score in 2 cases, i.e., SVM for IMDb director, LSVM for MusicBrainz musician.
• conclusion: the string kernel feature should be added.