diff --git a/.gitignore b/.gitignore index 8bdddb2..0146aaf 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ datasets/* !datasets/ds.py !datasets/Alkane/ !datasets/acyclic/ +!datasets/Acyclic/ !datasets/MAO/ !datasets/PAH/ !datasets/MUTAG/ @@ -14,6 +15,7 @@ datasets/* !datasets/NCI109/ !datasets/AIDS/ !datasets/monoterpenoides/ +!datasets/Monoterpenoides/ !datasets/Fingerprint/*.txt !datasets/Cuneiform/*.txt notebooks/results/* diff --git a/README.md b/README.md index b91452b..1f5aec6 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ A Python package for graph kernels, graph edit distances and graph pre-image pro ## Requirements -* python==3.6.9 +* python>=3.5 * numpy>=1.15.2 * scipy>=1.1.0 * matplotlib>=3.0.0 diff --git a/datasets/Acyclic/1,1-bis(ethylthio)ethane.ct b/datasets/Acyclic/1,1-bis(ethylthio)ethane.ct new file mode 100644 index 0000000..3635f4c --- /dev/null +++ b/datasets/Acyclic/1,1-bis(ethylthio)ethane.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 7 1 1 + 5 8 1 1 + 6 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/1,1-bis(isopropylthio)ethane.ct b/datasets/Acyclic/1,1-bis(isopropylthio)ethane.ct new file mode 100644 index 0000000..8138558 --- /dev/null +++ b/datasets/Acyclic/1,1-bis(isopropylthio)ethane.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 6 1 1 + 2 6 1 1 + 3 7 1 1 + 4 7 1 1 + 5 8 1 1 + 6 9 1 1 + 7 10 1 1 + 8 9 1 1 + 8 10 1 1 diff --git a/datasets/Acyclic/1,1-bis(methylthio)ethane.ct b/datasets/Acyclic/1,1-bis(methylthio)ethane.ct new file mode 100644 index 0000000..d11421d --- /dev/null +++ b/datasets/Acyclic/1,1-bis(methylthio)ethane.ct @@ -0,0 +1,13 @@ + + 6 5 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 5 1 1 + 4 6 1 1 diff --git a/datasets/Acyclic/1,1-diethoxyethane.ct b/datasets/Acyclic/1,1-diethoxyethane.ct new file mode 100644 index 0000000..b141f1c --- /dev/null +++ b/datasets/Acyclic/1,1-diethoxyethane.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 7 1 1 + 5 8 1 1 + 6 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/1,1-diethoxypentane.ct b/datasets/Acyclic/1,1-diethoxypentane.ct new file mode 100644 index 0000000..1a58848 --- /dev/null +++ b/datasets/Acyclic/1,1-diethoxypentane.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 7 1 1 + 5 10 1 1 + 6 11 1 1 + 7 8 1 1 + 8 9 1 1 + 9 10 1 1 + 9 11 1 1 diff --git a/datasets/Acyclic/1,1-diethoxypropane.ct b/datasets/Acyclic/1,1-diethoxypropane.ct new file mode 100644 index 0000000..abeafde --- /dev/null +++ b/datasets/Acyclic/1,1-diethoxypropane.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 7 1 1 + 5 8 1 1 + 6 9 1 1 + 7 8 1 1 + 7 9 1 1 diff --git a/datasets/Acyclic/1,1-diisopropoxyethane.ct b/datasets/Acyclic/1,1-diisopropoxyethane.ct new file mode 100644 index 0000000..d3e30f6 --- /dev/null +++ b/datasets/Acyclic/1,1-diisopropoxyethane.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 6 1 1 + 2 6 1 1 + 3 7 1 1 + 4 7 1 1 + 5 8 1 1 + 6 9 1 1 + 7 10 1 1 + 8 9 1 1 + 8 10 1 1 diff --git a/datasets/Acyclic/1,1-diisopropoxypropane.ct b/datasets/Acyclic/1,1-diisopropoxypropane.ct new file mode 100644 index 0000000..5c8b13d --- /dev/null +++ b/datasets/Acyclic/1,1-diisopropoxypropane.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 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b/datasets/Acyclic/1,1-dimethoxy-2-methylpropane.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 5 1 1 + 3 7 1 1 + 4 8 1 1 + 5 6 1 1 + 6 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/1,1-dimethoxybutane.ct b/datasets/Acyclic/1,1-dimethoxybutane.ct new file mode 100644 index 0000000..20d84b9 --- /dev/null +++ b/datasets/Acyclic/1,1-dimethoxybutane.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 7 1 1 + 3 8 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/1,1-dimethoxyethane.ct b/datasets/Acyclic/1,1-dimethoxyethane.ct new file mode 100644 index 0000000..3c1800d --- /dev/null +++ b/datasets/Acyclic/1,1-dimethoxyethane.ct @@ -0,0 +1,13 @@ + + 6 5 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 5 1 1 + 4 6 1 1 diff --git a/datasets/Acyclic/1,1-dimethoxyhexane.ct b/datasets/Acyclic/1,1-dimethoxyhexane.ct new file mode 100644 index 0000000..90f884b --- /dev/null +++ b/datasets/Acyclic/1,1-dimethoxyhexane.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 9 1 1 + 3 10 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 + 7 8 1 1 + 8 9 1 1 + 8 10 1 1 diff --git a/datasets/Acyclic/1,1-dipropoxyethane.ct b/datasets/Acyclic/1,1-dipropoxyethane.ct new file mode 100644 index 0000000..b0054a9 --- /dev/null +++ b/datasets/Acyclic/1,1-dipropoxyethane.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 5 1 1 + 3 8 1 1 + 4 6 1 1 + 5 7 1 1 + 6 9 1 1 + 7 10 1 1 + 8 9 1 1 + 8 10 1 1 diff --git a/datasets/Acyclic/1,1-dipropoxypropane.ct b/datasets/Acyclic/1,1-dipropoxypropane.ct new file mode 100644 index 0000000..328e1a8 --- /dev/null +++ b/datasets/Acyclic/1,1-dipropoxypropane.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 7 1 1 + 5 8 1 1 + 6 9 1 1 + 7 10 1 1 + 8 11 1 1 + 9 10 1 1 + 9 11 1 1 diff --git a/datasets/Acyclic/1,2-bis(ethylthio)ethane.ct b/datasets/Acyclic/1,2-bis(ethylthio)ethane.ct new file mode 100644 index 0000000..469a876 --- /dev/null +++ b/datasets/Acyclic/1,2-bis(ethylthio)ethane.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 7 1 1 + 4 8 1 1 + 5 6 1 1 + 5 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/1,2-bis(methylthio)ethane.ct b/datasets/Acyclic/1,2-bis(methylthio)ethane.ct new file mode 100644 index 0000000..956e4e9 --- /dev/null +++ b/datasets/Acyclic/1,2-bis(methylthio)ethane.ct @@ -0,0 +1,13 @@ + + 6 5 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 6 1 1 + 3 4 1 1 + 3 5 1 1 + 4 6 1 1 diff --git a/datasets/Acyclic/1,2-diethoxyethane.ct b/datasets/Acyclic/1,2-diethoxyethane.ct new file mode 100644 index 0000000..c7d9438 --- /dev/null +++ b/datasets/Acyclic/1,2-diethoxyethane.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 7 1 1 + 4 8 1 1 + 5 6 1 1 + 5 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/1,2-dimethoxyethane.ct b/datasets/Acyclic/1,2-dimethoxyethane.ct new file mode 100644 index 0000000..91086aa --- /dev/null +++ b/datasets/Acyclic/1,2-dimethoxyethane.ct @@ -0,0 +1,13 @@ + + 6 5 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 6 1 1 + 3 4 1 1 + 3 5 1 1 + 4 6 1 1 diff --git a/datasets/Acyclic/1,2-dimethoxypropane.ct b/datasets/Acyclic/1,2-dimethoxypropane.ct new file mode 100644 index 0000000..0b5eac4 --- /dev/null +++ b/datasets/Acyclic/1,2-dimethoxypropane.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 6 1 1 + 3 7 1 1 + 4 5 1 1 + 4 6 1 1 + 5 7 1 1 diff --git a/datasets/Acyclic/1,2-dimethylpropyl_ethyl_ether.ct b/datasets/Acyclic/1,2-dimethylpropyl_ethyl_ether.ct new file mode 100644 index 0000000..aa5d7e5 --- /dev/null +++ b/datasets/Acyclic/1,2-dimethylpropyl_ethyl_ether.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 6 1 1 + 3 6 1 1 + 4 7 1 1 + 5 8 1 1 + 6 7 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/1,2-dimethylpropyl_methyl_ether.ct b/datasets/Acyclic/1,2-dimethylpropyl_methyl_ether.ct new file mode 100644 index 0000000..a8f4e86 --- /dev/null +++ b/datasets/Acyclic/1,2-dimethylpropyl_methyl_ether.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 5 1 1 + 3 6 1 1 + 4 7 1 1 + 5 6 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/1,3-bis(ethylthio)propane.ct b/datasets/Acyclic/1,3-bis(ethylthio)propane.ct new file mode 100644 index 0000000..7e01527 --- /dev/null +++ b/datasets/Acyclic/1,3-bis(ethylthio)propane.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 8 1 1 + 4 9 1 1 + 5 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 diff --git a/datasets/Acyclic/1,3-diethoxypropane.ct b/datasets/Acyclic/1,3-diethoxypropane.ct new file mode 100644 index 0000000..1c16b97 --- /dev/null +++ b/datasets/Acyclic/1,3-diethoxypropane.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 8 1 1 + 4 9 1 1 + 5 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 diff --git a/datasets/Acyclic/1,3-diisopropoxypropane.ct b/datasets/Acyclic/1,3-diisopropoxypropane.ct new file mode 100644 index 0000000..1c4cd63 --- /dev/null +++ b/datasets/Acyclic/1,3-diisopropoxypropane.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 8 1 1 + 2 8 1 1 + 3 9 1 1 + 4 9 1 1 + 5 6 1 1 + 5 7 1 1 + 6 10 1 1 + 7 11 1 1 + 8 10 1 1 + 9 11 1 1 diff --git a/datasets/Acyclic/1,3-dimethoxybutane.ct b/datasets/Acyclic/1,3-dimethoxybutane.ct new file mode 100644 index 0000000..ed22474 --- /dev/null +++ b/datasets/Acyclic/1,3-dimethoxybutane.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 6 1 1 + 2 7 1 1 + 3 8 1 1 + 4 5 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/1,3-dimethoxypentane.ct b/datasets/Acyclic/1,3-dimethoxypentane.ct new file mode 100644 index 0000000..acffc40 --- /dev/null +++ b/datasets/Acyclic/1,3-dimethoxypentane.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 8 1 1 + 3 9 1 1 + 4 7 1 1 + 5 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 diff --git a/datasets/Acyclic/1,3-dimethoxypropane.ct b/datasets/Acyclic/1,3-dimethoxypropane.ct new file mode 100644 index 0000000..3d409c2 --- /dev/null +++ b/datasets/Acyclic/1,3-dimethoxypropane.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 6 1 1 + 2 7 1 1 + 3 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 7 1 1 diff --git a/datasets/Acyclic/1,3-dimethylpentyl_methyl_ether.ct b/datasets/Acyclic/1,3-dimethylpentyl_methyl_ether.ct new file mode 100644 index 0000000..76c9a68 --- /dev/null +++ b/datasets/Acyclic/1,3-dimethylpentyl_methyl_ether.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 7 1 1 + 3 8 1 1 + 4 9 1 1 + 5 7 1 1 + 6 7 1 1 + 6 8 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/1,3-dipropoxypropane.ct b/datasets/Acyclic/1,3-dipropoxypropane.ct new file mode 100644 index 0000000..a3571bf --- /dev/null +++ b/datasets/Acyclic/1,3-dipropoxypropane.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 6 1 1 + 4 7 1 1 + 5 8 1 1 + 5 9 1 1 + 6 10 1 1 + 7 11 1 1 + 8 10 1 1 + 9 11 1 1 diff --git a/datasets/Acyclic/1,4-diethoxybutane.ct b/datasets/Acyclic/1,4-diethoxybutane.ct new file mode 100644 index 0000000..54a6cf1 --- /dev/null +++ b/datasets/Acyclic/1,4-diethoxybutane.ct 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a/datasets/Acyclic/1,5-dimethylhexyl_methyl_ether.ct b/datasets/Acyclic/1,5-dimethylhexyl_methyl_ether.ct new file mode 100644 index 0000000..92ce5dd --- /dev/null +++ b/datasets/Acyclic/1,5-dimethylhexyl_methyl_ether.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 8 1 1 + 2 8 1 1 + 3 9 1 1 + 4 10 1 1 + 5 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/1-butyl-propane-2-ol.ct b/datasets/Acyclic/1-butyl-propane-2-ol.ct new file mode 100644 index 0000000..7a18d01 --- /dev/null +++ b/datasets/Acyclic/1-butyl-propane-2-ol.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 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b/datasets/Acyclic/2,2-bis(propylthio)propane.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 6 1 1 + 3 9 1 1 + 4 9 1 1 + 5 7 1 1 + 6 8 1 1 + 7 10 1 1 + 8 11 1 1 + 9 10 1 1 + 9 11 1 1 diff --git a/datasets/Acyclic/2,2-diethoxypropane.ct b/datasets/Acyclic/2,2-diethoxypropane.ct new file mode 100644 index 0000000..da40ef9 --- /dev/null +++ b/datasets/Acyclic/2,2-diethoxypropane.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 6 1 1 + 3 7 1 1 + 4 7 1 1 + 5 8 1 1 + 6 9 1 1 + 7 8 1 1 + 7 9 1 1 diff --git a/datasets/Acyclic/2,2-dimethoxypropane.ct b/datasets/Acyclic/2,2-dimethoxypropane.ct new file mode 100644 index 0000000..eae2ca4 --- /dev/null +++ b/datasets/Acyclic/2,2-dimethoxypropane.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 5 1 1 + 3 6 1 1 + 4 7 1 1 + 5 6 1 1 + 5 7 1 1 diff --git a/datasets/Acyclic/2,2-dimethylpropyl_ethyl_ether.ct b/datasets/Acyclic/2,2-dimethylpropyl_ethyl_ether.ct new file mode 100644 index 0000000..2d35aa3 --- /dev/null +++ b/datasets/Acyclic/2,2-dimethylpropyl_ethyl_ether.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 7 1 1 + 3 7 1 1 + 4 7 1 1 + 5 8 1 1 + 6 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/2,4-dimethoxy-2-methylpentane.ct b/datasets/Acyclic/2,4-dimethoxy-2-methylpentane.ct new file mode 100644 index 0000000..34be3c3 --- /dev/null +++ b/datasets/Acyclic/2,4-dimethoxy-2-methylpentane.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 7 1 1 + 2 8 1 1 + 3 8 1 1 + 4 9 1 1 + 5 10 1 1 + 6 7 1 1 + 6 8 1 1 + 7 9 1 1 + 8 10 1 1 diff --git a/datasets/Acyclic/2-ethoxy-2-methoxypropane.ct b/datasets/Acyclic/2-ethoxy-2-methoxypropane.ct new file mode 100644 index 0000000..6a85ca6 --- /dev/null +++ b/datasets/Acyclic/2-ethoxy-2-methoxypropane.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 6 1 1 + 3 6 1 1 + 4 7 1 1 + 5 8 1 1 + 6 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/2-ethylhexyl_methyl_ether.ct b/datasets/Acyclic/2-ethylhexyl_methyl_ether.ct new file mode 100644 index 0000000..c581b27 --- /dev/null +++ b/datasets/Acyclic/2-ethylhexyl_methyl_ether.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 5 1 1 + 3 10 1 1 + 4 6 1 1 + 5 9 1 1 + 6 7 1 1 + 7 9 1 1 + 8 9 1 1 + 8 10 1 1 diff --git a/datasets/Acyclic/3,5-dimethylhexyl_methyl_ether.ct b/datasets/Acyclic/3,5-dimethylhexyl_methyl_ether.ct new file mode 100644 index 0000000..96ce319 --- /dev/null +++ b/datasets/Acyclic/3,5-dimethylhexyl_methyl_ether.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 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+ 2 6 1 1 + 3 7 1 1 + 4 8 1 1 + 5 9 1 1 + 6 9 1 1 + 7 10 1 1 + 8 10 1 1 + 9 11 1 1 + 10 11 1 1 diff --git a/datasets/Acyclic/bis(1-methylbutyl)_ether.ct b/datasets/Acyclic/bis(1-methylbutyl)_ether.ct new file mode 100644 index 0000000..20c06d4 --- /dev/null +++ b/datasets/Acyclic/bis(1-methylbutyl)_ether.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 6 1 1 + 3 9 1 1 + 4 10 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 + 8 10 1 1 + 9 11 1 1 + 10 11 1 1 diff --git a/datasets/Acyclic/bis(butylthio)methane.ct b/datasets/Acyclic/bis(butylthio)methane.ct new file mode 100644 index 0000000..7380f35 --- /dev/null +++ b/datasets/Acyclic/bis(butylthio)methane.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 10 1 1 + 8 11 1 1 + 9 10 1 1 + 9 11 1 1 diff --git a/datasets/Acyclic/bis(ethylthio)methane.ct b/datasets/Acyclic/bis(ethylthio)methane.ct new file mode 100644 index 0000000..8fa4356 --- /dev/null +++ b/datasets/Acyclic/bis(ethylthio)methane.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 6 1 1 + 4 7 1 1 + 5 6 1 1 + 5 7 1 1 diff --git a/datasets/Acyclic/bis(methylthio)methane.ct b/datasets/Acyclic/bis(methylthio)methane.ct new file mode 100644 index 0000000..613daf5 --- /dev/null +++ b/datasets/Acyclic/bis(methylthio)methane.ct @@ -0,0 +1,11 @@ + + 5 4 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 5 1 1 + 3 4 1 1 + 3 5 1 1 diff --git a/datasets/Acyclic/butyl_ethyl_sulfide.ct b/datasets/Acyclic/butyl_ethyl_sulfide.ct new file mode 100644 index 0000000..7849626 --- /dev/null +++ b/datasets/Acyclic/butyl_ethyl_sulfide.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 7 1 1 + 5 6 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/butyl_isobutyl_ether.ct b/datasets/Acyclic/butyl_isobutyl_ether.ct new file mode 100644 index 0000000..37c9917 --- /dev/null +++ b/datasets/Acyclic/butyl_isobutyl_ether.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 8 1 1 + 3 8 1 1 + 4 5 1 1 + 5 6 1 1 + 6 9 1 1 + 7 8 1 1 + 7 9 1 1 diff --git a/datasets/Acyclic/butyl_isobutyl_sulfide.ct b/datasets/Acyclic/butyl_isobutyl_sulfide.ct new file mode 100644 index 0000000..eb146e5 --- /dev/null +++ b/datasets/Acyclic/butyl_isobutyl_sulfide.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 8 1 1 + 3 8 1 1 + 4 5 1 1 + 5 6 1 1 + 6 9 1 1 + 7 8 1 1 + 7 9 1 1 diff --git a/datasets/Acyclic/butyl_isopentyl_ether.ct b/datasets/Acyclic/butyl_isopentyl_ether.ct new file mode 100644 index 0000000..1c4eff8 --- /dev/null +++ b/datasets/Acyclic/butyl_isopentyl_ether.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 9 1 1 + 3 9 1 1 + 4 5 1 1 + 5 7 1 1 + 6 8 1 1 + 6 9 1 1 + 7 10 1 1 + 8 10 1 1 diff --git a/datasets/Acyclic/butyl_isopropyl_ether.ct b/datasets/Acyclic/butyl_isopropyl_ether.ct new file mode 100644 index 0000000..1d5cb24 --- /dev/null +++ b/datasets/Acyclic/butyl_isopropyl_ether.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 7 1 1 + 3 7 1 1 + 4 5 1 1 + 5 6 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/butyl_isopropyl_sulfide.ct b/datasets/Acyclic/butyl_isopropyl_sulfide.ct new file mode 100644 index 0000000..e8b13bc --- /dev/null +++ b/datasets/Acyclic/butyl_isopropyl_sulfide.ct 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0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 6 1 1 + 3 4 1 1 + 4 5 1 1 + 5 6 1 1 diff --git a/datasets/Acyclic/butyl_pentyl_ether.ct b/datasets/Acyclic/butyl_pentyl_ether.ct new file mode 100644 index 0000000..47a4957 --- /dev/null +++ b/datasets/Acyclic/butyl_pentyl_ether.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 + 8 10 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/butyl_propyl_ether.ct b/datasets/Acyclic/butyl_propyl_ether.ct new file mode 100644 index 0000000..eabcea6 --- /dev/null +++ b/datasets/Acyclic/butyl_propyl_ether.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/butyl_propyl_sulfide.ct b/datasets/Acyclic/butyl_propyl_sulfide.ct new file mode 100644 index 0000000..a3261b6 --- /dev/null +++ b/datasets/Acyclic/butyl_propyl_sulfide.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/butyl_sec-butyl_ether.ct b/datasets/Acyclic/butyl_sec-butyl_ether.ct new file mode 100644 index 0000000..416f7a2 --- /dev/null +++ b/datasets/Acyclic/butyl_sec-butyl_ether.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 5 1 1 + 3 8 1 1 + 4 6 1 1 + 5 8 1 1 + 6 7 1 1 + 7 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/butyl_sec-butyl_sulfide.ct b/datasets/Acyclic/butyl_sec-butyl_sulfide.ct new file mode 100644 index 0000000..ea74b02 --- /dev/null +++ b/datasets/Acyclic/butyl_sec-butyl_sulfide.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 5 1 1 + 3 8 1 1 + 4 6 1 1 + 5 8 1 1 + 6 7 1 1 + 7 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/butyl_tert-butyl_ether.ct b/datasets/Acyclic/butyl_tert-butyl_ether.ct new file mode 100644 index 0000000..8746059 --- /dev/null +++ b/datasets/Acyclic/butyl_tert-butyl_ether.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 8 1 1 + 3 8 1 1 + 4 8 1 1 + 5 6 1 1 + 6 7 1 1 + 7 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/ci00007a011.pdf b/datasets/Acyclic/ci00007a011.pdf new file mode 100644 index 0000000..c868670 --- /dev/null +++ b/datasets/Acyclic/ci00007a011.pdf @@ -0,0 +1,2718 @@ +%PDF-1.6 +% +1 0 obj +<>stream + + + + article doi: 10.1021/ci00007a011, Article metadata: Journal of Chemical Information and Computer Sciences_32_3_10.1021/ci00007a011_237_244PDFlib 7.0.3 (.NET/Win32); modified using iTextSharp 5.0.6 (c) 1T3XT BVBA +null +application/pdfCorrelations between chemical structure and normal boiling points of acyclic ethers, peroxides, acetals, and their sulfur analogsarticle doi: 10.1021/ci00007a011Article metadata: Journal of Chemical 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a/datasets/Acyclic/dataset_bps.ds b/datasets/Acyclic/dataset_bps.ds new file mode 100644 index 0000000..45c55c4 --- /dev/null +++ b/datasets/Acyclic/dataset_bps.ds @@ -0,0 +1,183 @@ +dimethyl_ether.ct -23.70 +dimethyl_peroxide.ct 14.00 +dimethyl_sulfide.ct 37.30 +dimethyl_disulfide.ct 109.70 +ethyl_methyl_ether.ct 10.80 +ethyl_methyl_peroxide.ct 39.00 +dimethoxymethane.ct 42.00 +ethyl_methyl_sulfide.ct 66.60 +ethyl_methyl_disulfide.ct 135.00 +bis(methylthio)methane.ct 148.50 +methyl_propyl_ether.ct 40.00 +diethyl_ether.ct 34.60 +isopropyl_methyl_ether.ct 32.00 +diethyl_peroxide.ct 63.00 +isopropyl_methyl_peroxide.ct 53.50 +1,1-dimethoxyethane.ct 64.40 +1,2-dimethoxyethane.ct 84.70 +methyl_propyl_sulfide.ct 95.50 +diethyl_sulfide.ct 92.00 +isopropyl_methyl_sulfide.ct 84.40 +diethyl_disulfide.ct 154.00 +1,1-bis(methylthio)ethane.ct 156.00 +ethylthiomethylthiomethane.ct 166.00 +1,2-bis(methylthio)ethane.ct 183.00 +butyl_methyl_ether.ct 70.30 +ethyl_propyl_ether.ct 63.60 +ethyl_isopropyl_ether.ct 52.50 +isobutyl_methyl_ether.ct 59.00 +sec-butyl_methyl_ether.ct 59.50 +tert-butyl_methyl_ether.ct 55.20 +diethoxymethane.ct 88.00 +2,2-dimethoxypropane.ct 83.00 +1,3-dimethoxypropane.ct 104.50 +1-ethoxy-2-methoxyethane.ct 102.00 +1,2-dimethoxypropane.ct 92.00 +ethyl_isopropyl_sulfide.ct 107.40 +butyl_methyl_sulfide.ct 123.20 +isobutyl_methyl_sulfide.ct 112.50 +ethyl_propyl_sulfide.ct 118.50 +tert-butyl_methyl_sulfide.ct 101.50 +ethyl_propyl_disulfide.ct 173.70 +ethyl_isopropyl_disulfide.ct 165.50 +bis(ethylthio)methane.ct 181.00 +methyl_pentyl_ether.ct 99.50 +ethyl_butyl_ether.ct 92.30 +dipropyl_ether.ct 90.10 +isopropyl_propyl_ether.ct 80.20 +ethyl_isobutyl_ether.ct 82.00 +isopentyl_methyl_ether.ct 91.20 +methyl_2-methylbutyl_ether.ct 91.50 +ethyl_sec-butyl_ether.ct 81.20 +methyl_1-methylbutyl_ether.ct 93.00 +diisopropyl_ether.ct 69.00 +methyl_tert-pentyl_ether.ct 86.30 +1,2-dimethylpropyl_methyl_ether.ct 82.00 +1,1-diethoxyethane.ct 103.00 +1,1-dimethoxy-2-methylpropane.ct 103.50 +2-ethoxy-2-methoxypropane.ct 96.00 +1,1-dimethoxybutane.ct 112.00 +1-methoxy-1-propoxyethane.ct 104.00 +1,4-dimethoxybutane.ct 132.50 +1,2-diethoxyethane.ct 123.50 +1,3-dimethoxybutane.ct 120.30 +methyl_pentyl_sulfide.ct 145.00 +butyl_ethyl_sulfide.ct 144.20 +dipropyl_sulfide.ct 142.80 +isopropyl_propyl_sulfide.ct 132.00 +ethyl_isobutyl_sulfide.ct 134.20 +isopentyl_methyl_sulfide.ct 137.00 +methyl_2-methylbutyl_sulfide.ct 139.00 +sec-butyl_ethyl_sulfide.ct 133.60 +tert-butyl_ethyl_sulfide.ct 120.40 +diisopropyl_sulfide.ct 120.00 +1-ethylpropyl_methyl_sulfide.ct 137.00 +dipropyl_disulfide.ct 195.80 +diisopropyl_disulfide.ct 177.20 +sec-butyl_ethyl_disulfide.ct 181.00 +isopropyl_propyl_disulfide.ct 185.90 +tert-butyl_ethyl_disulfide.ct 175.70 +1,1-bis(ethylthio)ethane.ct 186.00 +1,2-bis(ethylthio)ethane.ct 211.00 +hexyl_methyl_ether.ct 125.00 +ethyl_pentyl_ether.ct 118.00 +butyl_propyl_ether.ct 117.10 +butyl_isopropyl_ether.ct 107.00 +isobutyl_propyl_ether.ct 102.50 +ethyl_isopentyl_ether.ct 112.00 +tert-butyl_propyl_ether.ct 97.40 +2,2-dimethylpropyl_ethyl_ether.ct 91.50 +tert-butyl_isopropyl_ether.ct 87.60 +ethyl_1-methylbutyl_ether.ct 106.50 +ethyl_tert-pentyl_ether.ct 101.00 +1,2-dimethylpropyl_ethyl_ether.ct 99.30 +ethyl_1-ethylpropyl_ether.ct 90.00 +dipropoxymethane.ct 137.00 +2,2-diethoxypropane.ct 114.00 +1-ethoxy-1-propoxyethane.ct 126.00 +1,1-diethoxypropane.ct 124.00 +1,3-diethoxypropane.ct 140.50 +1,5-dimethoxypentane.ct 157.50 +1-ethoxy-4-methoxybutane.ct 146.00 +1,4-dimethoxypentane.ct 145.00 +1,3-dimethoxypentane.ct 141.00 +hexyl_methyl_sulfide.ct 171.00 +butyl_propyl_sulfide.ct 166.00 +isobutyl_propyl_sulfide.ct 155.00 +isobutyl_isopropyl_sulfide.ct 145.00 +ethyl_2-methylbutyl_sulfide.ct 159.00 +tert-butyl_propyl_sulfide.ct 138.00 +sec-butyl_isopropyl_sulfide.ct 142.00 +ethyl_isopentyl_sulfide.ct 159.00 +butyl_isopropyl_sulfide.ct 163.50 +1,3-bis(ethylthio)propane.ct 229.50 +dibutyl_ether.ct 142.00 +isopentyl_propyl_ether.ct 125.00 +butyl_isobutyl_ether.ct 132.00 +butyl_sec-butyl_ether.ct 130.50 +butyl_tert-butyl_ether.ct 125.00 +sec-butyl_isobutyl_ether.ct 122.00 +1,3-dimethylpentyl_methyl_ether.ct 121.00 +diisobutyl_ether.ct 122.20 +isobutyl_tert-butyl_ether.ct 112.00 +di-tert-butyl_ether.ct 106.00 +isopropyl_tert-pentyl_ether.ct 114.50 +heptyl_methyl_ether.ct 151.00 +1-ethylpropyl_propyl_ether.ct 128.50 +di-tert-butyl_peroxide.ct 109.50 +1,1-diisopropoxyethane.ct 126.00 +1,1-dipropoxyethane.ct 147.00 +1,1-dimethoxyhexane.ct 158.00 +2,4-dimethoxy-2-methylpentane.ct 147.00 +1,4-diethoxybutane.ct 165.00 +dibutylsulfide.ct 188.90 +diisobutyl_sulfide.ct 170.00 +butyl_isobutyl_sulfide.ct 178.00 +di-tert-butyl_sulfide.ct 148.50 +di-sec-butyl_sulfide.ct 165.00 +butyl_sec-butyl_sulfide.ct 177.00 +sec-butyl_isobutyl_sulfide.ct 167.00 +heptyl_methyl_sulfide.ct 195.00 +dibutyl_disulfide.ct 226.00 +diisobutyl_disulfide.ct 215.00 +di-tert-butyl_disulfide.ct 201.00 +1,1-bis(isopropylthio)ethane.ct 205.00 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.50 +ethyl_heptyl_ether.ct 165.50 +butyl_isopentyl_ether.ct 157.00 +tert-butyl_isopentyl_ether.ct 139.00 +butyl_pentyl_ether.ct 163.00 +1,5-dimethylhexyl_methyl_ether.ct 153.50 +isobutyl_isopentyl_ether.ct 139.00 +methyl_1-methylheptyl_ether.ct 162.00 +methyl_octyl_ether.ct 173.00 +2-ethylhexyl_methyl_ether.ct 159.50 +methyl_1,1,4-trimethylpentyl_ether.ct 159.50 +3,5-dimethylhexyl_methyl_ether.ct 155.50 +ethyl_1,1,3-trimethylbutyl_ether.ct 141.00 +tert-butyl_tert-pentyl_peroxide.ct 126.00 +1,1-dimethoxy-2,2-dimethylpentane.ct 164.00 +1,1-diethoxypentane.ct 163.00 +1,1-dipropoxypropane.ct 166.50 +1,1-diisopropoxypropane.ct 146.00 +1,3-dipropoxypropane.ct 165.00 +1,3-diisopropoxypropane.ct 159.00 +ethyl_heptyl_sulfide.ct 195.00 +methyl_octyl_sulfide.ct 218.00 +bis(butylthio)methane.ct 250.00 +2,2-bis(propylthio)propane.ct 235.00 +ethyl_octyl_ether.ct 186.50 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.50 +bis(1-ethylpropyl)_ether.ct 162.00 +bis(1-methylbutyl)_ether.ct 162.00 +diisopentyl_ether.ct 173.20 +dipentyl_ether.ct 186.80 +isopropyl_heptyl_ether.ct 173.00 +heptyl_propyl_ether.ct 187.00 +isopentyl_pentyl_ether.ct 174.00 +methyl_1-methyloctyl_ether.ct 188.50 +di-tert-pentyl_sulfide.ct 199.00 +dipentyl_sulfide.ct 228.00 +diisopentyl_sulfide.ct 215.00 +isobutyl_4-methylpentyl_sulfide.ct 216.00 +methyl_nonyl_sulfide.ct 240.00 diff --git a/datasets/Acyclic/dataset_bps_old.ds b/datasets/Acyclic/dataset_bps_old.ds new file mode 100644 index 0000000..85de530 --- /dev/null +++ b/datasets/Acyclic/dataset_bps_old.ds @@ -0,0 +1,185 @@ +dimethyl_ether.ct -23.70 +dimethyl_peroxide.ct 14.00 +dimethyl_sulfide.ct 37.30 +dimethyl_disulfide.ct 109.70 +ethyl_methyl_ether.ct 10.80 +ethyl_methyl_peroxide.ct 39.00 +dimethoxymethane.ct 42.00 +ethyl_methyl_sulfide.ct 66.60 +ethyl_methyl_disulfide.ct 135.00 +bis(methylthio)methane.ct 148.50 +methyl_propyl_ether.ct 40.00 +diethyl_ether.ct 34.60 +isopropyl_methyl_ether.ct 32.00 +diethyl_peroxide.ct 63.00 +isopropyl_methyl_peroxide.ct 53.50 +#ethoxymethoxyethane.ct 67.00 +1,1-dimethoxyethane.ct 64.40 +1,2-dimethoxyethane.ct 84.70 +methyl_propyl_sulfide.ct 95.50 +diethyl_sulfide.ct 92.00 +isopropyl_methyl_sulfide.ct 84.40 +diethyl_disulfide.ct 154.00 +1,1-bis(methylthio)ethane.ct 156.00 +ethylthiomethylthiomethane.ct 166.00 +1,2-bis(methylthio)ethane.ct 183.00 +butyl_methyl_ether.ct 70.30 +ethyl_propyl_ether.ct 63.60 +ethyl_isopropyl_ether.ct 52.50 +isobutyl_methyl_ether.ct 59.00 +sec-butyl_methyl_ether.ct 59.50 +tert-butyl_methyl_ether.ct 55.20 +diethoxymethane.ct 88.00 +2,2-dimethoxypropane.ct 83.00 +1,3-dimethoxypropane.ct 104.50 +1-ethoxy-2-methoxyethane.ct 102.00 +1,2-dimethoxypropane.ct 92.00 +ethyl_isopropyl_sulfide.ct 107.40 +butyl_methyl_sulfide.ct 123.20 +isobutyl_methyl_sulfide.ct 112.50 +ethyl_propyl_sulfide.ct 118.50 +tert-butyl_methyl_sulfide.ct 101.50 +ethyl_propyl_disulfide.ct 173.70 +ethyl_isopropyl_disulfide.ct 165.50 +bis(ethylthio)methane.ct 181.00 +methyl_pentyl_ether.ct 99.50 +ethyl_butyl_ether.ct 92.30 +dipropyl_ether.ct 90.10 +isopropyl_propyl_ether.ct 80.20 +ethyl_isobutyl_ether.ct 82.00 +isopentyl_methyl_ether.ct 91.20 +methyl_2-methylbutyl_ether.ct 91.50 +ethyl_sec-butyl_ether.ct 81.20 +methyl_1-methylbutyl_ether.ct 93.00 +diisopropyl_ether.ct 69.00 +methyl_tert-pentyl_ether.ct 86.30 +1,2-dimethylpropyl_methyl_ether.ct 82.00 +1,1-diethoxyethane.ct 103.00 +1,1-dimethoxy-2-methylpropane.ct 103.50 +2-ethoxy-2-methoxypropane.ct 96.00 +1,1-dimethoxybutane.ct 112.00 +1-methoxy-1-propoxyethane.ct 104.00 +1,4-dimethoxybutane.ct 132.50 +1,2-diethoxyethane.ct 123.50 +1,3-dimethoxybutane.ct 120.30 +methyl_pentyl_sulfide.ct 145.00 +butyl_ethyl_sulfide.ct 144.20 +dipropyl_sulfide.ct 142.80 +isopropyl_propyl_sulfide.ct 132.00 +ethyl_isobutyl_sulfide.ct 134.20 +isopentyl_methyl_sulfide.ct 137.00 +methyl_2-methylbutyl_sulfide.ct 139.00 +sec-butyl_ethyl_sulfide.ct 133.60 +tert-butyl_ethyl_sulfide.ct 120.40 +diisopropyl_sulfide.ct 120.00 +1-ethylpropyl_methyl_sulfide.ct 137.00 +dipropyl_disulfide.ct 195.80 +diisopropyl_disulfide.ct 177.20 +sec-butyl_ethyl_disulfide.ct 181.00 +isopropyl_propyl_disulfide.ct 185.90 +tert-butyl_ethyl_disulfide.ct 175.70 +1,1-bis(ethylthio)ethane.ct 186.00 +1,2-bis(ethylthio)ethane.ct 211.00 +hexyl_methyl_ether.ct 125.00 +ethyl_pentyl_ether.ct 118.00 +butyl_propyl_ether.ct 117.10 +butyl_isopropyl_ether.ct 107.00 +isobutyl_propyl_ether.ct 102.50 +ethyl_isopentyl_ether.ct 112.00 +tert-butyl_propyl_ether.ct 97.40 +2,2-dimethylpropyl_ethyl_ether.ct 91.50 +tert-butyl_isopropyl_ether.ct 87.60 +ethyl_1-methylbutyl_ether.ct 106.50 +ethyl_tert-pentyl_ether.ct 101.00 +1,2-dimethylpropyl_ethyl_ether.ct 99.30 +ethyl_1-ethylpropyl_ether.ct 90.00 +dipropoxymethane.ct 137.00 +2,2-diethoxypropane.ct 114.00 +1-ethoxy-1-propoxyethane.ct 126.00 +1,1-diethoxypropane.ct 124.00 +1,3-diethoxypropane.ct 140.50 +1,5-dimethoxypentane.ct 157.50 +1-ethoxy-4-methoxybutane.ct 146.00 +1,4-dimethoxypentane.ct 145.00 +1,3-dimethoxypentane.ct 141.00 +hexyl_methyl_sulfide.ct 171.00 +butyl_propyl_sulfide.ct 166.00 +isobutyl_propyl_sulfide.ct 155.00 +isobutyl_isopropyl_sulfide.ct 145.00 +ethyl_2-methylbutyl_sulfide.ct 159.00 +tert-butyl_propyl_sulfide.ct 138.00 +sec-butyl_isopropyl_sulfide.ct 142.00 +ethyl_isopentyl_sulfide.ct 159.00 +butyl_isopropyl_sulfide.ct 163.50 +1,3-bis(ethylthio)propane.ct 229.50 +dibutyl_ether.ct 142.00 +isopentyl_propyl_ether.ct 125.00 +butyl_isobutyl_ether.ct 132.00 +butyl_sec-butyl_ether.ct 130.50 +butyl_tert-butyl_ether.ct 125.00 +sec-butyl_isobutyl_ether.ct 122.00 +1,3-dimethylpentyl_methyl_ether.ct 121.00 +diisobutyl_ether.ct 122.20 +isobutyl_tert-butyl_ether.ct 112.00 +di-tert-butyl_ether.ct 106.00 +isopropyl_tert-pentyl_ether.ct 114.50 +heptyl_methyl_ether.ct 151.00 +1-ethylpropyl_propyl_ether.ct 128.50 +di-tert-butyl_peroxide.ct 109.50 +1,1-diisopropoxyethane.ct 126.00 +1,1-dipropoxyethane.ct 147.00 +1,1-dimethoxyhexane.ct 158.00 +2,4-dimethoxy-2-methylpentane.ct 147.00 +1,4-diethoxybutane.ct 165.00 +dibutylsulfide.ct 188.90 +diisobutyl_sulfide.ct 170.00 +butyl_isobutyl_sulfide.ct 178.00 +di-tert-butyl_sulfide.ct 148.50 +di-sec-butyl_sulfide.ct 165.00 +butyl_sec-butyl_sulfide.ct 177.00 +sec-butyl_isobutyl_sulfide.ct 167.00 +heptyl_methyl_sulfide.ct 195.00 +dibutyl_disulfide.ct 226.00 +diisobutyl_disulfide.ct 215.00 +di-tert-butyl_disulfide.ct 201.00 +1,1-bis(isopropylthio)ethane.ct 205.00 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.50 +ethyl_heptyl_ether.ct 165.50 +butyl_isopentyl_ether.ct 157.00 +tert-butyl_isopentyl_ether.ct 139.00 +butyl_pentyl_ether.ct 163.00 +1,5-dimethylhexyl_methyl_ether.ct 153.50 +isobutyl_isopentyl_ether.ct 139.00 +methyl_1-methylheptyl_ether.ct 162.00 +methyl_octyl_ether.ct 173.00 +2-ethylhexyl_methyl_ether.ct 159.50 +methyl_1,1,4-trimethylpentyl_ether.ct 159.50 +3,5-dimethylhexyl_methyl_ether.ct 155.50 +ethyl_1,1,3-trimethylbutyl_ether.ct 141.00 +tert-butyl_tert-pentyl_peroxide.ct 126.00 +1,1-dimethoxy-2,2-dimethylpentane.ct 164.00 +1,1-diethoxypentane.ct 163.00 +1,1-dipropoxypropane.ct 166.50 +1,1-diisopropoxypropane.ct 146.00 +1,3-dipropoxypropane.ct 165.00 +1,3-diisopropoxypropane.ct 159.00 +ethyl_heptyl_sulfide.ct 195.00 +methyl_octyl_sulfide.ct 218.00 +bis(butylthio)methane.ct 250.00 +2,2-bis(propylthio)propane.ct 235.00 +ethyl_octyl_ether.ct 186.50 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.50 +bis(1-ethylpropyl)_ether.ct 162.00 +bis(1-methylbutyl)_ether.ct 162.00 +#1-butyl-propane-2-ol.ct 170.20 +diisopentyl_ether.ct 173.20 +dipentyl_ether.ct 186.80 +isopropyl_heptyl_ether.ct 173.00 +heptyl_propyl_ether.ct 187.00 +isopentyl_pentyl_ether.ct 174.00 +methyl_1-methyloctyl_ether.ct 188.50 +di-tert-pentyl_sulfide.ct 199.00 +dipentyl_sulfide.ct 228.00 +diisopentyl_sulfide.ct 215.00 +isobutyl_4-methylpentyl_sulfide.ct 216.00 +methyl_nonyl_sulfide.ct 240.00 diff --git a/datasets/Acyclic/di-sec-butyl_sulfide.ct b/datasets/Acyclic/di-sec-butyl_sulfide.ct new file mode 100644 index 0000000..c89719e --- /dev/null +++ b/datasets/Acyclic/di-sec-butyl_sulfide.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 6 1 1 + 3 7 1 1 + 4 8 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/di-tert-butyl_disulfide.ct b/datasets/Acyclic/di-tert-butyl_disulfide.ct new file mode 100644 index 0000000..dc3551c --- /dev/null +++ b/datasets/Acyclic/di-tert-butyl_disulfide.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 7 1 1 + 2 7 1 1 + 3 7 1 1 + 4 8 1 1 + 5 8 1 1 + 6 8 1 1 + 7 9 1 1 + 8 10 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/di-tert-butyl_ether.ct b/datasets/Acyclic/di-tert-butyl_ether.ct new file mode 100644 index 0000000..ad79746 --- /dev/null +++ b/datasets/Acyclic/di-tert-butyl_ether.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 7 1 1 + 2 7 1 1 + 3 7 1 1 + 4 8 1 1 + 5 8 1 1 + 6 8 1 1 + 7 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/di-tert-butyl_peroxide.ct b/datasets/Acyclic/di-tert-butyl_peroxide.ct new file mode 100644 index 0000000..26ff92c --- /dev/null +++ b/datasets/Acyclic/di-tert-butyl_peroxide.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 7 1 1 + 2 7 1 1 + 3 7 1 1 + 4 8 1 1 + 5 8 1 1 + 6 8 1 1 + 7 9 1 1 + 8 10 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/di-tert-butyl_sulfide.ct b/datasets/Acyclic/di-tert-butyl_sulfide.ct new file mode 100644 index 0000000..cbf9ea0 --- /dev/null +++ b/datasets/Acyclic/di-tert-butyl_sulfide.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 7 1 1 + 2 7 1 1 + 3 7 1 1 + 4 8 1 1 + 5 8 1 1 + 6 8 1 1 + 7 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/di-tert-pentyl_sulfide.ct b/datasets/Acyclic/di-tert-pentyl_sulfide.ct new file mode 100644 index 0000000..a8b4763 --- /dev/null +++ b/datasets/Acyclic/di-tert-pentyl_sulfide.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 7 1 1 + 2 8 1 1 + 3 9 1 1 + 4 9 1 1 + 5 10 1 1 + 6 10 1 1 + 7 9 1 1 + 8 10 1 1 + 9 11 1 1 + 10 11 1 1 diff --git a/datasets/Acyclic/dibutyl_disulfide.ct b/datasets/Acyclic/dibutyl_disulfide.ct new file mode 100644 index 0000000..f4a9acb --- /dev/null +++ b/datasets/Acyclic/dibutyl_disulfide.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 + 8 10 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/dibutyl_ether.ct b/datasets/Acyclic/dibutyl_ether.ct new file mode 100644 index 0000000..4c65478 --- /dev/null +++ b/datasets/Acyclic/dibutyl_ether.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/dibutylsulfide.ct b/datasets/Acyclic/dibutylsulfide.ct new file mode 100644 index 0000000..0b1ce26 --- /dev/null +++ b/datasets/Acyclic/dibutylsulfide.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/diethoxymethane.ct b/datasets/Acyclic/diethoxymethane.ct new file mode 100644 index 0000000..6da12c1 --- /dev/null +++ b/datasets/Acyclic/diethoxymethane.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 6 1 1 + 4 7 1 1 + 5 6 1 1 + 5 7 1 1 diff --git a/datasets/Acyclic/diethyl_disulfide.ct b/datasets/Acyclic/diethyl_disulfide.ct new file mode 100644 index 0000000..15c4651 --- /dev/null +++ b/datasets/Acyclic/diethyl_disulfide.ct @@ -0,0 +1,13 @@ + + 6 5 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 6 1 1 diff --git a/datasets/Acyclic/diethyl_ether.ct b/datasets/Acyclic/diethyl_ether.ct new file mode 100644 index 0000000..e6e1421 --- /dev/null +++ b/datasets/Acyclic/diethyl_ether.ct @@ -0,0 +1,11 @@ + + 5 4 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 5 1 1 diff --git a/datasets/Acyclic/diethyl_peroxide.ct b/datasets/Acyclic/diethyl_peroxide.ct new file mode 100644 index 0000000..368b65b --- /dev/null +++ b/datasets/Acyclic/diethyl_peroxide.ct @@ -0,0 +1,13 @@ + + 6 5 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 6 1 1 diff --git a/datasets/Acyclic/diethyl_sulfide.ct b/datasets/Acyclic/diethyl_sulfide.ct new file mode 100644 index 0000000..fe170b1 --- /dev/null +++ b/datasets/Acyclic/diethyl_sulfide.ct @@ -0,0 +1,11 @@ + + 5 4 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 5 1 1 diff --git a/datasets/Acyclic/diisobutyl_disulfide.ct b/datasets/Acyclic/diisobutyl_disulfide.ct new file mode 100644 index 0000000..d202ca5 --- /dev/null +++ b/datasets/Acyclic/diisobutyl_disulfide.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 7 1 1 + 2 7 1 1 + 3 8 1 1 + 4 8 1 1 + 5 7 1 1 + 5 9 1 1 + 6 8 1 1 + 6 10 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/diisobutyl_ether.ct b/datasets/Acyclic/diisobutyl_ether.ct new file mode 100644 index 0000000..c4a28ab --- /dev/null +++ b/datasets/Acyclic/diisobutyl_ether.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 7 1 1 + 2 7 1 1 + 3 8 1 1 + 4 8 1 1 + 5 7 1 1 + 5 9 1 1 + 6 8 1 1 + 6 9 1 1 diff --git a/datasets/Acyclic/diisobutyl_sulfide.ct b/datasets/Acyclic/diisobutyl_sulfide.ct new file mode 100644 index 0000000..3a8fba8 --- /dev/null +++ b/datasets/Acyclic/diisobutyl_sulfide.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 7 1 1 + 2 7 1 1 + 3 8 1 1 + 4 8 1 1 + 5 7 1 1 + 5 9 1 1 + 6 8 1 1 + 6 9 1 1 diff --git a/datasets/Acyclic/diisopentyl_ether.ct b/datasets/Acyclic/diisopentyl_ether.ct new file mode 100644 index 0000000..04bc200 --- /dev/null +++ b/datasets/Acyclic/diisopentyl_ether.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 9 1 1 + 2 9 1 1 + 3 10 1 1 + 4 10 1 1 + 5 7 1 1 + 5 9 1 1 + 6 8 1 1 + 6 10 1 1 + 7 11 1 1 + 8 11 1 1 diff --git a/datasets/Acyclic/diisopentyl_sulfide.ct b/datasets/Acyclic/diisopentyl_sulfide.ct new file mode 100644 index 0000000..155ce40 --- /dev/null +++ b/datasets/Acyclic/diisopentyl_sulfide.ct @@ -0,0 +1,23 @@ +g + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 9 1 1 + 2 9 1 1 + 3 10 1 1 + 4 10 1 1 + 5 7 1 1 + 5 9 1 1 + 6 8 1 1 + 6 10 1 1 + 7 11 1 1 + 8 11 1 1 diff --git a/datasets/Acyclic/diisopropyl_disulfide.ct b/datasets/Acyclic/diisopropyl_disulfide.ct new file mode 100644 index 0000000..72204aa --- /dev/null +++ b/datasets/Acyclic/diisopropyl_disulfide.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 5 1 1 + 3 6 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/diisopropyl_ether.ct b/datasets/Acyclic/diisopropyl_ether.ct new file mode 100644 index 0000000..3d161e4 --- /dev/null +++ b/datasets/Acyclic/diisopropyl_ether.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 5 1 1 + 3 6 1 1 + 4 6 1 1 + 5 7 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/diisopropyl_sulfide.ct b/datasets/Acyclic/diisopropyl_sulfide.ct new file mode 100644 index 0000000..445ea0a --- /dev/null +++ b/datasets/Acyclic/diisopropyl_sulfide.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 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/dev/null +++ b/datasets/Acyclic/dimethyl_ether.ct @@ -0,0 +1,7 @@ + + 3 2 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 3 1 1 diff --git a/datasets/Acyclic/dimethyl_peroxide.ct b/datasets/Acyclic/dimethyl_peroxide.ct new file mode 100644 index 0000000..553b9a9 --- /dev/null +++ b/datasets/Acyclic/dimethyl_peroxide.ct @@ -0,0 +1,9 @@ + + 4 3 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 4 1 1 diff --git a/datasets/Acyclic/dimethyl_sulfide.ct b/datasets/Acyclic/dimethyl_sulfide.ct new file mode 100644 index 0000000..8a1d11f --- /dev/null +++ b/datasets/Acyclic/dimethyl_sulfide.ct @@ -0,0 +1,7 @@ + + 3 2 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 3 1 1 diff --git a/datasets/Acyclic/dipentyl_ether.ct b/datasets/Acyclic/dipentyl_ether.ct new file mode 100644 index 0000000..06552dd --- /dev/null +++ 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1 1 + 8 10 1 1 + 9 11 1 1 + 10 11 1 1 diff --git a/datasets/Acyclic/dipropoxymethane.ct b/datasets/Acyclic/dipropoxymethane.ct new file mode 100644 index 0000000..ed9f8a9 --- /dev/null +++ b/datasets/Acyclic/dipropoxymethane.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 6 1 1 + 5 8 1 1 + 6 9 1 1 + 7 8 1 1 + 7 9 1 1 diff --git a/datasets/Acyclic/dipropyl_disulfide.ct b/datasets/Acyclic/dipropyl_disulfide.ct new file mode 100644 index 0000000..fdee11e --- /dev/null +++ b/datasets/Acyclic/dipropyl_disulfide.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 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0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 5 1 1 + 3 7 1 1 + 4 7 1 1 + 5 8 1 1 + 6 7 1 1 + 6 8 1 1 diff --git a/datasets/Acyclic/ethyl_butyl_ether.ct b/datasets/Acyclic/ethyl_butyl_ether.ct new file mode 100644 index 0000000..06f8659 --- /dev/null +++ b/datasets/Acyclic/ethyl_butyl_ether.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 7 1 1 + 5 6 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/ethyl_heptyl_ether.ct b/datasets/Acyclic/ethyl_heptyl_ether.ct new file mode 100644 index 0000000..892ded1 --- /dev/null +++ b/datasets/Acyclic/ethyl_heptyl_ether.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 10 1 1 + 5 6 1 1 + 6 7 1 1 + 7 8 1 1 + 8 9 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/ethyl_heptyl_sulfide.ct b/datasets/Acyclic/ethyl_heptyl_sulfide.ct new file mode 100644 index 0000000..d664a89 --- /dev/null +++ b/datasets/Acyclic/ethyl_heptyl_sulfide.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 4 1 1 + 3 5 1 1 + 4 10 1 1 + 5 6 1 1 + 6 7 1 1 + 7 8 1 1 + 8 9 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/ethyl_isobutyl_ether.ct b/datasets/Acyclic/ethyl_isobutyl_ether.ct new file mode 100644 index 0000000..c7c40b4 --- /dev/null +++ b/datasets/Acyclic/ethyl_isobutyl_ether.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 6 1 1 + 3 6 1 1 + 4 7 1 1 + 5 6 1 1 + 5 7 1 1 diff --git a/datasets/Acyclic/ethyl_isobutyl_sulfide.ct b/datasets/Acyclic/ethyl_isobutyl_sulfide.ct new file mode 100644 index 0000000..8edf4bc --- /dev/null +++ b/datasets/Acyclic/ethyl_isobutyl_sulfide.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 6 1 1 + 3 6 1 1 + 4 7 1 1 + 5 6 1 1 + 5 7 1 1 diff --git a/datasets/Acyclic/ethyl_isopentyl_ether.ct b/datasets/Acyclic/ethyl_isopentyl_ether.ct new file mode 100644 index 0000000..6435e5c --- /dev/null +++ b/datasets/Acyclic/ethyl_isopentyl_ether.ct @@ -0,0 +1,17 @@ + + 8 7 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0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 8 1 1 + 2 8 1 1 + 3 9 1 1 + 4 9 1 1 + 5 10 1 1 + 6 7 1 1 + 6 8 1 1 + 7 9 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/methyl_1-methylbutyl_ether.ct b/datasets/Acyclic/methyl_1-methylbutyl_ether.ct new file mode 100644 index 0000000..81f9900 --- /dev/null +++ b/datasets/Acyclic/methyl_1-methylbutyl_ether.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 6 1 1 + 3 7 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/methyl_1-methylheptyl_ether.ct b/datasets/Acyclic/methyl_1-methylheptyl_ether.ct new file mode 100644 index 0000000..540c7fe --- /dev/null +++ b/datasets/Acyclic/methyl_1-methylheptyl_ether.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 9 1 1 + 3 10 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 + 7 8 1 1 + 8 9 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/methyl_1-methyloctyl_ether.ct b/datasets/Acyclic/methyl_1-methyloctyl_ether.ct new file mode 100644 index 0000000..bd1d4a6 --- /dev/null +++ b/datasets/Acyclic/methyl_1-methyloctyl_ether.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 10 1 1 + 3 11 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 + 7 8 1 1 + 8 9 1 1 + 9 10 1 1 + 10 11 1 1 diff --git a/datasets/Acyclic/methyl_2-methylbutyl_ether.ct b/datasets/Acyclic/methyl_2-methylbutyl_ether.ct new file mode 100644 index 0000000..ad22976 --- /dev/null +++ b/datasets/Acyclic/methyl_2-methylbutyl_ether.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 6 1 1 + 3 7 1 1 + 4 6 1 1 + 5 6 1 1 + 5 7 1 1 diff --git a/datasets/Acyclic/methyl_2-methylbutyl_sulfide.ct b/datasets/Acyclic/methyl_2-methylbutyl_sulfide.ct new file mode 100644 index 0000000..0b4c637 --- /dev/null +++ b/datasets/Acyclic/methyl_2-methylbutyl_sulfide.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 6 1 1 + 3 7 1 1 + 4 6 1 1 + 5 6 1 1 + 5 7 1 1 diff --git a/datasets/Acyclic/methyl_nonyl_sulfide.ct b/datasets/Acyclic/methyl_nonyl_sulfide.ct new file mode 100644 index 0000000..1d98167 --- /dev/null +++ b/datasets/Acyclic/methyl_nonyl_sulfide.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 11 1 1 + 3 4 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 + 7 8 1 1 + 8 9 1 1 + 9 10 1 1 + 10 11 1 1 diff --git a/datasets/Acyclic/methyl_octyl_ether.ct b/datasets/Acyclic/methyl_octyl_ether.ct new file mode 100644 index 0000000..887308b --- /dev/null +++ b/datasets/Acyclic/methyl_octyl_ether.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 10 1 1 + 3 4 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 + 7 8 1 1 + 8 9 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/methyl_octyl_sulfide.ct b/datasets/Acyclic/methyl_octyl_sulfide.ct new file mode 100644 index 0000000..bc0c443 --- /dev/null +++ b/datasets/Acyclic/methyl_octyl_sulfide.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 10 1 1 + 3 4 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 + 7 8 1 1 + 8 9 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/methyl_pentyl_ether.ct b/datasets/Acyclic/methyl_pentyl_ether.ct new file mode 100644 index 0000000..e4dbe02 --- /dev/null +++ b/datasets/Acyclic/methyl_pentyl_ether.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 7 1 1 + 3 4 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/methyl_pentyl_sulfide.ct b/datasets/Acyclic/methyl_pentyl_sulfide.ct new file mode 100644 index 0000000..e942c06 --- /dev/null +++ b/datasets/Acyclic/methyl_pentyl_sulfide.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 7 1 1 + 3 4 1 1 + 4 5 1 1 + 5 6 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/methyl_propyl_ether.ct b/datasets/Acyclic/methyl_propyl_ether.ct new file mode 100644 index 0000000..1435415 --- /dev/null +++ b/datasets/Acyclic/methyl_propyl_ether.ct @@ -0,0 +1,11 @@ + + 5 4 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 3 1 1 + 2 5 1 1 + 3 4 1 1 + 4 5 1 1 diff --git a/datasets/Acyclic/methyl_propyl_sulfide.ct b/datasets/Acyclic/methyl_propyl_sulfide.ct new file mode 100644 index 0000000..f261df2 --- /dev/null +++ b/datasets/Acyclic/methyl_propyl_sulfide.ct @@ -0,0 +1,11 @@ + + 5 4 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 3 1 1 + 2 5 1 1 + 3 4 1 1 + 4 5 1 1 diff --git a/datasets/Acyclic/methyl_tert-pentyl_ether.ct b/datasets/Acyclic/methyl_tert-pentyl_ether.ct new file mode 100644 index 0000000..092466b --- /dev/null +++ b/datasets/Acyclic/methyl_tert-pentyl_ether.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 6 1 1 + 3 6 1 1 + 4 7 1 1 + 5 6 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/readme.md b/datasets/Acyclic/readme.md new file mode 100644 index 0000000..5bc76d2 --- /dev/null +++ b/datasets/Acyclic/readme.md @@ -0,0 +1,6 @@ +A database of acyclic molecules with hetero atoms (acyclic ethers, peroxides, acetals and their sulfur analogues). The dataset is concerned with the determination of the boiling point using regression (see dataset.ds) In both results presented bellow GLK stands for graph Laplacian kernel. + +# references +[1] https://brunl01.users.greyc.fr/CHEMISTRY/index.html#Acyclic + +**Attention the file dataset_bps.ds is sent to me by Benoit. The original one has some problems and now is renamed dataset_bps_old.ds.** diff --git a/datasets/Acyclic/sec-butyl_ethyl_disulfide.ct b/datasets/Acyclic/sec-butyl_ethyl_disulfide.ct new file mode 100644 index 0000000..ea87f8a --- /dev/null +++ b/datasets/Acyclic/sec-butyl_ethyl_disulfide.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/sec-butyl_ethyl_sulfide.ct b/datasets/Acyclic/sec-butyl_ethyl_sulfide.ct new file mode 100644 index 0000000..e45c5c6 --- /dev/null +++ b/datasets/Acyclic/sec-butyl_ethyl_sulfide.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 6 1 1 + 5 7 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/sec-butyl_isobutyl_ether.ct b/datasets/Acyclic/sec-butyl_isobutyl_ether.ct new file mode 100644 index 0000000..e2d3caa --- /dev/null +++ b/datasets/Acyclic/sec-butyl_isobutyl_ether.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 7 1 1 + 3 7 1 1 + 4 8 1 1 + 5 8 1 1 + 6 7 1 1 + 6 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/sec-butyl_isobutyl_sulfide.ct b/datasets/Acyclic/sec-butyl_isobutyl_sulfide.ct new file mode 100644 index 0000000..7ca6ca7 --- /dev/null +++ b/datasets/Acyclic/sec-butyl_isobutyl_sulfide.ct @@ -0,0 +1,19 @@ + + 9 8 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 7 1 1 + 3 7 1 1 + 4 8 1 1 + 5 8 1 1 + 6 7 1 1 + 6 9 1 1 + 8 9 1 1 diff --git a/datasets/Acyclic/sec-butyl_isopropyl_sulfide.ct b/datasets/Acyclic/sec-butyl_isopropyl_sulfide.ct new file mode 100644 index 0000000..cae3480 --- /dev/null +++ b/datasets/Acyclic/sec-butyl_isopropyl_sulfide.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 6 1 1 + 3 6 1 1 + 4 7 1 1 + 5 7 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/sec-butyl_methyl_ether.ct b/datasets/Acyclic/sec-butyl_methyl_ether.ct new file mode 100644 index 0000000..90efb35 --- /dev/null +++ b/datasets/Acyclic/sec-butyl_methyl_ether.ct @@ -0,0 +1,13 @@ + + 6 5 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 4 1 1 + 2 5 1 1 + 3 6 1 1 + 4 5 1 1 + 5 6 1 1 diff --git a/datasets/Acyclic/tert-butyl_ethyl_disulfide.ct b/datasets/Acyclic/tert-butyl_ethyl_disulfide.ct new file mode 100644 index 0000000..5bf91b9 --- /dev/null +++ b/datasets/Acyclic/tert-butyl_ethyl_disulfide.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 6 1 1 + 3 6 1 1 + 4 6 1 1 + 5 7 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/tert-butyl_ethyl_sulfide.ct b/datasets/Acyclic/tert-butyl_ethyl_sulfide.ct new file mode 100644 index 0000000..6dfaa1d --- /dev/null +++ b/datasets/Acyclic/tert-butyl_ethyl_sulfide.ct @@ -0,0 +1,15 @@ + + 7 6 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 6 1 1 + 3 6 1 1 + 4 6 1 1 + 5 7 1 1 + 6 7 1 1 diff --git a/datasets/Acyclic/tert-butyl_isopentyl_ether.ct b/datasets/Acyclic/tert-butyl_isopentyl_ether.ct new file mode 100644 index 0000000..e25468c --- /dev/null +++ b/datasets/Acyclic/tert-butyl_isopentyl_ether.ct @@ -0,0 +1,21 @@ + + 10 9 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 8 1 1 + 2 8 1 1 + 3 9 1 1 + 4 9 1 1 + 5 9 1 1 + 6 7 1 1 + 6 8 1 1 + 7 10 1 1 + 9 10 1 1 diff --git a/datasets/Acyclic/tert-butyl_isopropyl_ether.ct b/datasets/Acyclic/tert-butyl_isopropyl_ether.ct new file mode 100644 index 0000000..3f62f44 --- /dev/null +++ b/datasets/Acyclic/tert-butyl_isopropyl_ether.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 6 1 1 + 2 6 1 1 + 3 7 1 1 + 4 7 1 1 + 5 7 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/tert-butyl_methyl_ether.ct b/datasets/Acyclic/tert-butyl_methyl_ether.ct new file mode 100644 index 0000000..8dd3c20 --- /dev/null +++ b/datasets/Acyclic/tert-butyl_methyl_ether.ct @@ -0,0 +1,13 @@ + + 6 5 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 5 1 1 + 3 5 1 1 + 4 6 1 1 + 5 6 1 1 diff --git a/datasets/Acyclic/tert-butyl_methyl_sulfide.ct b/datasets/Acyclic/tert-butyl_methyl_sulfide.ct new file mode 100644 index 0000000..c37cbe7 --- /dev/null +++ b/datasets/Acyclic/tert-butyl_methyl_sulfide.ct @@ -0,0 +1,13 @@ + + 6 5 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 5 1 1 + 3 5 1 1 + 4 6 1 1 + 5 6 1 1 diff --git a/datasets/Acyclic/tert-butyl_propyl_ether.ct b/datasets/Acyclic/tert-butyl_propyl_ether.ct new file mode 100644 index 0000000..5a1211e --- /dev/null +++ b/datasets/Acyclic/tert-butyl_propyl_ether.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 1 5 1 1 + 2 7 1 1 + 3 7 1 1 + 4 7 1 1 + 5 6 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/tert-butyl_propyl_sulfide.ct b/datasets/Acyclic/tert-butyl_propyl_sulfide.ct new file mode 100644 index 0000000..f36729f --- /dev/null +++ b/datasets/Acyclic/tert-butyl_propyl_sulfide.ct @@ -0,0 +1,17 @@ + + 8 7 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 S + 1 5 1 1 + 2 7 1 1 + 3 7 1 1 + 4 7 1 1 + 5 6 1 1 + 6 8 1 1 + 7 8 1 1 diff --git a/datasets/Acyclic/tert-butyl_tert-pentyl_peroxide.ct b/datasets/Acyclic/tert-butyl_tert-pentyl_peroxide.ct new file mode 100644 index 0000000..8b1f525 --- /dev/null +++ b/datasets/Acyclic/tert-butyl_tert-pentyl_peroxide.ct @@ -0,0 +1,23 @@ + + 11 10 + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 C + 0.0000 0.0000 0.0000 O + 0.0000 0.0000 0.0000 O + 1 7 1 1 + 2 8 1 1 + 3 8 1 1 + 4 8 1 1 + 5 9 1 1 + 6 9 1 1 + 7 9 1 1 + 8 10 1 1 + 9 11 1 1 + 10 11 1 1 diff --git a/datasets/Acyclic/testset_0.ds b/datasets/Acyclic/testset_0.ds new file mode 100644 index 0000000..66a795c --- /dev/null +++ b/datasets/Acyclic/testset_0.ds @@ -0,0 +1,19 @@ +dimethyl_ether.ct -23.7 +methyl_propyl_ether.ct 40 +diethyl_disulfide.ct 154 +diethoxymethane.ct 88 +ethyl_propyl_disulfide.ct 173.7 +ethyl_sec-butyl_ether.ct 81.2 +1,4-dimethoxybutane.ct 132.5 +sec-butyl_ethyl_sulfide.ct 133.6 +1,2-bis(ethylthio)ethane.ct 211 +ethyl_1-methylbutyl_ether.ct 106.5 +1-ethoxy-4-methoxybutane.ct 146 +ethyl_isopentyl_sulfide.ct 159 +diisobutyl_ether.ct 122.2 +2,4-dimethoxy-2-methylpentane.ct 147 +dibutyl_disulfide.ct 226 +isobutyl_isopentyl_ether.ct 139 +1,1-dipropoxypropane.ct 166.5 +bis(1-ethylpropyl)_ether.ct 162 +diisopentyl_sulfide.ct 215 diff --git a/datasets/Acyclic/testset_1.ds b/datasets/Acyclic/testset_1.ds new file mode 100644 index 0000000..0ca5f64 --- /dev/null +++ b/datasets/Acyclic/testset_1.ds @@ -0,0 +1,19 @@ +dimethyl_peroxide.ct 14 +diethyl_ether.ct 34.6 +1,1-bis(methylthio)ethane.ct 156 +2,2-dimethoxypropane.ct 83 +ethyl_isopropyl_disulfide.ct 165.5 +methyl_1-methylbutyl_ether.ct 93 +1,2-diethoxyethane.ct 123.5 +tert-butyl_ethyl_sulfide.ct 120.4 +hexyl_methyl_ether.ct 125 +ethyl_tert-pentyl_ether.ct 101 +1,4-dimethoxypentane.ct 145 +butyl_isopropyl_sulfide.ct 163.5 +isobutyl_tert-butyl_ether.ct 112 +1,4-diethoxybutane.ct 165 +diisobutyl_disulfide.ct 215 +methyl_1-methylheptyl_ether.ct 162 +1,1-diisopropoxypropane.ct 146 +bis(1-methylbutyl)_ether.ct 162 +isobutyl_4-methylpentyl_sulfide.ct 216 diff --git a/datasets/Acyclic/testset_2.ds b/datasets/Acyclic/testset_2.ds new file mode 100644 index 0000000..06bebd0 --- /dev/null +++ b/datasets/Acyclic/testset_2.ds @@ -0,0 +1,19 @@ +dimethyl_sulfide.ct 37.3 +isopropyl_methyl_ether.ct 32 +ethylthiomethylthiomethane.ct 166 +1,3-dimethoxypropane.ct 104.5 +bis(ethylthio)methane.ct 181 +diisopropyl_ether.ct 69 +1,3-dimethoxybutane.ct 120.3 +diisopropyl_sulfide.ct 120 +ethyl_pentyl_ether.ct 118 +1,2-dimethylpropyl_ethyl_ether.ct 99.3 +1,3-dimethoxypentane.ct 141 +1,3-bis(ethylthio)propane.ct 229.5 +di-tert-butyl_ether.ct 106 +dibutylsulfide.ct 188.9 +di-tert-butyl_disulfide.ct 201 +methyl_octyl_ether.ct 173 +1,3-dipropoxypropane.ct 165 +diisopentyl_ether.ct 173.2 +methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Acyclic/testset_3.ds b/datasets/Acyclic/testset_3.ds new file mode 100644 index 0000000..fb6a3a1 --- /dev/null +++ b/datasets/Acyclic/testset_3.ds @@ -0,0 +1,18 @@ +dimethyl_disulfide.ct 109.7 +diethyl_peroxide.ct 63 +1,2-bis(methylthio)ethane.ct 183 +1-ethoxy-2-methoxyethane.ct 102 +methyl_pentyl_ether.ct 99.5 +methyl_tert-pentyl_ether.ct 86.3 +methyl_pentyl_sulfide.ct 145 +1-ethylpropyl_methyl_sulfide.ct 137 +butyl_propyl_ether.ct 117.1 +ethyl_1-ethylpropyl_ether.ct 90 +hexyl_methyl_sulfide.ct 171 +dibutyl_ether.ct 142 +isopropyl_tert-pentyl_ether.ct 114.5 +diisobutyl_sulfide.ct 170 +1,1-bis(isopropylthio)ethane.ct 205 +2-ethylhexyl_methyl_ether.ct 159.5 +1,3-diisopropoxypropane.ct 159 +dipentyl_ether.ct 186.8 diff --git a/datasets/Acyclic/testset_4.ds b/datasets/Acyclic/testset_4.ds new file mode 100644 index 0000000..8fb5f78 --- /dev/null +++ b/datasets/Acyclic/testset_4.ds @@ -0,0 +1,18 @@ +ethyl_methyl_ether.ct 10.8 +isopropyl_methyl_peroxide.ct 53.5 +butyl_methyl_ether.ct 70.3 +1,2-dimethoxypropane.ct 92 +ethyl_butyl_ether.ct 92.3 +1,2-dimethylpropyl_methyl_ether.ct 82 +butyl_ethyl_sulfide.ct 144.2 +dipropyl_disulfide.ct 195.8 +butyl_isopropyl_ether.ct 107 +dipropoxymethane.ct 137 +butyl_propyl_sulfide.ct 166 +isopentyl_propyl_ether.ct 125 +heptyl_methyl_ether.ct 151 +butyl_isobutyl_sulfide.ct 178 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.5 +methyl_1,1,4-trimethylpentyl_ether.ct 159.5 +ethyl_heptyl_sulfide.ct 195 +isopropyl_heptyl_ether.ct 173 diff --git a/datasets/Acyclic/testset_5.ds b/datasets/Acyclic/testset_5.ds new file mode 100644 index 0000000..b3b22a6 --- /dev/null +++ b/datasets/Acyclic/testset_5.ds @@ -0,0 +1,18 @@ +ethyl_methyl_peroxide.ct 39 +1,1-dimethoxyethane.ct 64.4 +ethyl_propyl_ether.ct 63.6 +ethyl_isopropyl_sulfide.ct 107.4 +dipropyl_ether.ct 90.1 +1,1-diethoxyethane.ct 103 +dipropyl_sulfide.ct 142.8 +diisopropyl_disulfide.ct 177.2 +isobutyl_propyl_ether.ct 102.5 +2,2-diethoxypropane.ct 114 +isobutyl_propyl_sulfide.ct 155 +butyl_isobutyl_ether.ct 132 +1-ethylpropyl_propyl_ether.ct 128.5 +di-tert-butyl_sulfide.ct 148.5 +ethyl_heptyl_ether.ct 165.5 +3,5-dimethylhexyl_methyl_ether.ct 155.5 +methyl_octyl_sulfide.ct 218 +heptyl_propyl_ether.ct 187 diff --git a/datasets/Acyclic/testset_6.ds b/datasets/Acyclic/testset_6.ds new file mode 100644 index 0000000..27af996 --- /dev/null +++ b/datasets/Acyclic/testset_6.ds @@ -0,0 +1,18 @@ +dimethoxymethane.ct 42 +1,2-dimethoxyethane.ct 84.7 +ethyl_isopropyl_ether.ct 52.5 +butyl_methyl_sulfide.ct 123.2 +isopropyl_propyl_ether.ct 80.2 +1,1-dimethoxy-2-methylpropane.ct 103.5 +isopropyl_propyl_sulfide.ct 132 +sec-butyl_ethyl_disulfide.ct 181 +ethyl_isopentyl_ether.ct 112 +1-ethoxy-1-propoxyethane.ct 126 +isobutyl_isopropyl_sulfide.ct 145 +butyl_sec-butyl_ether.ct 130.5 +di-tert-butyl_peroxide.ct 109.5 +di-sec-butyl_sulfide.ct 165 +butyl_isopentyl_ether.ct 157 +ethyl_1,1,3-trimethylbutyl_ether.ct 141 +bis(butylthio)methane.ct 250 +isopentyl_pentyl_ether.ct 174 diff --git a/datasets/Acyclic/testset_7.ds b/datasets/Acyclic/testset_7.ds new file mode 100644 index 0000000..361e0fa --- /dev/null +++ b/datasets/Acyclic/testset_7.ds @@ -0,0 +1,18 @@ +ethyl_methyl_sulfide.ct 66.6 +methyl_propyl_sulfide.ct 95.5 +isobutyl_methyl_ether.ct 59 +isobutyl_methyl_sulfide.ct 112.5 +ethyl_isobutyl_ether.ct 82 +2-ethoxy-2-methoxypropane.ct 96 +ethyl_isobutyl_sulfide.ct 134.2 +isopropyl_propyl_disulfide.ct 185.9 +tert-butyl_propyl_ether.ct 97.4 +1,1-diethoxypropane.ct 124 +ethyl_2-methylbutyl_sulfide.ct 159 +butyl_tert-butyl_ether.ct 125 +1,1-diisopropoxyethane.ct 126 +butyl_sec-butyl_sulfide.ct 177 +tert-butyl_isopentyl_ether.ct 139 +tert-butyl_tert-pentyl_peroxide.ct 126 +2,2-bis(propylthio)propane.ct 235 +methyl_1-methyloctyl_ether.ct 188.5 diff --git a/datasets/Acyclic/testset_8.ds b/datasets/Acyclic/testset_8.ds new file mode 100644 index 0000000..2face29 --- /dev/null +++ b/datasets/Acyclic/testset_8.ds @@ -0,0 +1,18 @@ +ethyl_methyl_disulfide.ct 135 +diethyl_sulfide.ct 92 +sec-butyl_methyl_ether.ct 59.5 +ethyl_propyl_sulfide.ct 118.5 +isopentyl_methyl_ether.ct 91.2 +1,1-dimethoxybutane.ct 112 +isopentyl_methyl_sulfide.ct 137 +tert-butyl_ethyl_disulfide.ct 175.7 +2,2-dimethylpropyl_ethyl_ether.ct 91.5 +1,3-diethoxypropane.ct 140.5 +tert-butyl_propyl_sulfide.ct 138 +sec-butyl_isobutyl_ether.ct 122 +1,1-dipropoxyethane.ct 147 +sec-butyl_isobutyl_sulfide.ct 167 +butyl_pentyl_ether.ct 163 +1,1-dimethoxy-2,2-dimethylpentane.ct 164 +ethyl_octyl_ether.ct 186.5 +di-tert-pentyl_sulfide.ct 199 diff --git a/datasets/Acyclic/testset_9.ds b/datasets/Acyclic/testset_9.ds new file mode 100644 index 0000000..c427c66 --- /dev/null +++ b/datasets/Acyclic/testset_9.ds @@ -0,0 +1,18 @@ +bis(methylthio)methane.ct 148.5 +isopropyl_methyl_sulfide.ct 84.4 +tert-butyl_methyl_ether.ct 55.2 +tert-butyl_methyl_sulfide.ct 101.5 +methyl_2-methylbutyl_ether.ct 91.5 +1-methoxy-1-propoxyethane.ct 104 +methyl_2-methylbutyl_sulfide.ct 139 +1,1-bis(ethylthio)ethane.ct 186 +tert-butyl_isopropyl_ether.ct 87.6 +1,5-dimethoxypentane.ct 157.5 +sec-butyl_isopropyl_sulfide.ct 142 +1,3-dimethylpentyl_methyl_ether.ct 121 +1,1-dimethoxyhexane.ct 158 +heptyl_methyl_sulfide.ct 195 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +1,1-diethoxypentane.ct 163 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +dipentyl_sulfide.ct 228 diff --git a/datasets/Acyclic/trainset_0.ds b/datasets/Acyclic/trainset_0.ds new file mode 100644 index 0000000..1a62e74 --- /dev/null +++ b/datasets/Acyclic/trainset_0.ds @@ -0,0 +1,164 @@ +dimethyl_peroxide.ct 14 +dimethyl_sulfide.ct 37.3 +dimethyl_disulfide.ct 109.7 +ethyl_methyl_ether.ct 10.8 +ethyl_methyl_peroxide.ct 39 +dimethoxymethane.ct 42 +ethyl_methyl_sulfide.ct 66.6 +ethyl_methyl_disulfide.ct 135 +bis(methylthio)methane.ct 148.5 +diethyl_ether.ct 34.6 +isopropyl_methyl_ether.ct 32 +diethyl_peroxide.ct 63 +isopropyl_methyl_peroxide.ct 53.5 +1,1-dimethoxyethane.ct 64.4 +1,2-dimethoxyethane.ct 84.7 +methyl_propyl_sulfide.ct 95.5 +diethyl_sulfide.ct 92 +isopropyl_methyl_sulfide.ct 84.4 +1,1-bis(methylthio)ethane.ct 156 +ethylthiomethylthiomethane.ct 166 +1,2-bis(methylthio)ethane.ct 183 +butyl_methyl_ether.ct 70.3 +ethyl_propyl_ether.ct 63.6 +ethyl_isopropyl_ether.ct 52.5 +isobutyl_methyl_ether.ct 59 +sec-butyl_methyl_ether.ct 59.5 +tert-butyl_methyl_ether.ct 55.2 +2,2-dimethoxypropane.ct 83 +1,3-dimethoxypropane.ct 104.5 +1-ethoxy-2-methoxyethane.ct 102 +1,2-dimethoxypropane.ct 92 +ethyl_isopropyl_sulfide.ct 107.4 +butyl_methyl_sulfide.ct 123.2 +isobutyl_methyl_sulfide.ct 112.5 +ethyl_propyl_sulfide.ct 118.5 +tert-butyl_methyl_sulfide.ct 101.5 +ethyl_isopropyl_disulfide.ct 165.5 +bis(ethylthio)methane.ct 181 +methyl_pentyl_ether.ct 99.5 +ethyl_butyl_ether.ct 92.3 +dipropyl_ether.ct 90.1 +isopropyl_propyl_ether.ct 80.2 +ethyl_isobutyl_ether.ct 82 +isopentyl_methyl_ether.ct 91.2 +methyl_2-methylbutyl_ether.ct 91.5 +methyl_1-methylbutyl_ether.ct 93 +diisopropyl_ether.ct 69 +methyl_tert-pentyl_ether.ct 86.3 +1,2-dimethylpropyl_methyl_ether.ct 82 +1,1-diethoxyethane.ct 103 +1,1-dimethoxy-2-methylpropane.ct 103.5 +2-ethoxy-2-methoxypropane.ct 96 +1,1-dimethoxybutane.ct 112 +1-methoxy-1-propoxyethane.ct 104 +1,2-diethoxyethane.ct 123.5 +1,3-dimethoxybutane.ct 120.3 +methyl_pentyl_sulfide.ct 145 +butyl_ethyl_sulfide.ct 144.2 +dipropyl_sulfide.ct 142.8 +isopropyl_propyl_sulfide.ct 132 +ethyl_isobutyl_sulfide.ct 134.2 +isopentyl_methyl_sulfide.ct 137 +methyl_2-methylbutyl_sulfide.ct 139 +tert-butyl_ethyl_sulfide.ct 120.4 +diisopropyl_sulfide.ct 120 +1-ethylpropyl_methyl_sulfide.ct 137 +dipropyl_disulfide.ct 195.8 +diisopropyl_disulfide.ct 177.2 +sec-butyl_ethyl_disulfide.ct 181 +isopropyl_propyl_disulfide.ct 185.9 +tert-butyl_ethyl_disulfide.ct 175.7 +1,1-bis(ethylthio)ethane.ct 186 +hexyl_methyl_ether.ct 125 +ethyl_pentyl_ether.ct 118 +butyl_propyl_ether.ct 117.1 +butyl_isopropyl_ether.ct 107 +isobutyl_propyl_ether.ct 102.5 +ethyl_isopentyl_ether.ct 112 +tert-butyl_propyl_ether.ct 97.4 +2,2-dimethylpropyl_ethyl_ether.ct 91.5 +tert-butyl_isopropyl_ether.ct 87.6 +ethyl_tert-pentyl_ether.ct 101 +1,2-dimethylpropyl_ethyl_ether.ct 99.3 +ethyl_1-ethylpropyl_ether.ct 90 +dipropoxymethane.ct 137 +2,2-diethoxypropane.ct 114 +1-ethoxy-1-propoxyethane.ct 126 +1,1-diethoxypropane.ct 124 +1,3-diethoxypropane.ct 140.5 +1,5-dimethoxypentane.ct 157.5 +1,4-dimethoxypentane.ct 145 +1,3-dimethoxypentane.ct 141 +hexyl_methyl_sulfide.ct 171 +butyl_propyl_sulfide.ct 166 +isobutyl_propyl_sulfide.ct 155 +isobutyl_isopropyl_sulfide.ct 145 +ethyl_2-methylbutyl_sulfide.ct 159 +tert-butyl_propyl_sulfide.ct 138 +sec-butyl_isopropyl_sulfide.ct 142 +butyl_isopropyl_sulfide.ct 163.5 +1,3-bis(ethylthio)propane.ct 229.5 +dibutyl_ether.ct 142 +isopentyl_propyl_ether.ct 125 +butyl_isobutyl_ether.ct 132 +butyl_sec-butyl_ether.ct 130.5 +butyl_tert-butyl_ether.ct 125 +sec-butyl_isobutyl_ether.ct 122 +1,3-dimethylpentyl_methyl_ether.ct 121 +isobutyl_tert-butyl_ether.ct 112 +di-tert-butyl_ether.ct 106 +isopropyl_tert-pentyl_ether.ct 114.5 +heptyl_methyl_ether.ct 151 +1-ethylpropyl_propyl_ether.ct 128.5 +di-tert-butyl_peroxide.ct 109.5 +1,1-diisopropoxyethane.ct 126 +1,1-dipropoxyethane.ct 147 +1,1-dimethoxyhexane.ct 158 +1,4-diethoxybutane.ct 165 +dibutylsulfide.ct 188.9 +diisobutyl_sulfide.ct 170 +butyl_isobutyl_sulfide.ct 178 +di-tert-butyl_sulfide.ct 148.5 +di-sec-butyl_sulfide.ct 165 +butyl_sec-butyl_sulfide.ct 177 +sec-butyl_isobutyl_sulfide.ct 167 +heptyl_methyl_sulfide.ct 195 +diisobutyl_disulfide.ct 215 +di-tert-butyl_disulfide.ct 201 +1,1-bis(isopropylthio)ethane.ct 205 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.5 +ethyl_heptyl_ether.ct 165.5 +butyl_isopentyl_ether.ct 157 +tert-butyl_isopentyl_ether.ct 139 +butyl_pentyl_ether.ct 163 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +methyl_1-methylheptyl_ether.ct 162 +methyl_octyl_ether.ct 173 +2-ethylhexyl_methyl_ether.ct 159.5 +methyl_1,1,4-trimethylpentyl_ether.ct 159.5 +3,5-dimethylhexyl_methyl_ether.ct 155.5 +ethyl_1,1,3-trimethylbutyl_ether.ct 141 +tert-butyl_tert-pentyl_peroxide.ct 126 +1,1-dimethoxy-2,2-dimethylpentane.ct 164 +1,1-diethoxypentane.ct 163 +1,1-diisopropoxypropane.ct 146 +1,3-dipropoxypropane.ct 165 +1,3-diisopropoxypropane.ct 159 +ethyl_heptyl_sulfide.ct 195 +methyl_octyl_sulfide.ct 218 +bis(butylthio)methane.ct 250 +2,2-bis(propylthio)propane.ct 235 +ethyl_octyl_ether.ct 186.5 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +bis(1-methylbutyl)_ether.ct 162 +diisopentyl_ether.ct 173.2 +dipentyl_ether.ct 186.8 +isopropyl_heptyl_ether.ct 173 +heptyl_propyl_ether.ct 187 +isopentyl_pentyl_ether.ct 174 +methyl_1-methyloctyl_ether.ct 188.5 +di-tert-pentyl_sulfide.ct 199 +dipentyl_sulfide.ct 228 +isobutyl_4-methylpentyl_sulfide.ct 216 +methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Acyclic/trainset_1.ds b/datasets/Acyclic/trainset_1.ds new file mode 100644 index 0000000..5d86d48 --- /dev/null +++ b/datasets/Acyclic/trainset_1.ds @@ -0,0 +1,164 @@ +dimethyl_ether.ct -23.7 +dimethyl_sulfide.ct 37.3 +dimethyl_disulfide.ct 109.7 +ethyl_methyl_ether.ct 10.8 +ethyl_methyl_peroxide.ct 39 +dimethoxymethane.ct 42 +ethyl_methyl_sulfide.ct 66.6 +ethyl_methyl_disulfide.ct 135 +bis(methylthio)methane.ct 148.5 +methyl_propyl_ether.ct 40 +isopropyl_methyl_ether.ct 32 +diethyl_peroxide.ct 63 +isopropyl_methyl_peroxide.ct 53.5 +1,1-dimethoxyethane.ct 64.4 +1,2-dimethoxyethane.ct 84.7 +methyl_propyl_sulfide.ct 95.5 +diethyl_sulfide.ct 92 +isopropyl_methyl_sulfide.ct 84.4 +diethyl_disulfide.ct 154 +ethylthiomethylthiomethane.ct 166 +1,2-bis(methylthio)ethane.ct 183 +butyl_methyl_ether.ct 70.3 +ethyl_propyl_ether.ct 63.6 +ethyl_isopropyl_ether.ct 52.5 +isobutyl_methyl_ether.ct 59 +sec-butyl_methyl_ether.ct 59.5 +tert-butyl_methyl_ether.ct 55.2 +diethoxymethane.ct 88 +1,3-dimethoxypropane.ct 104.5 +1-ethoxy-2-methoxyethane.ct 102 +1,2-dimethoxypropane.ct 92 +ethyl_isopropyl_sulfide.ct 107.4 +butyl_methyl_sulfide.ct 123.2 +isobutyl_methyl_sulfide.ct 112.5 +ethyl_propyl_sulfide.ct 118.5 +tert-butyl_methyl_sulfide.ct 101.5 +ethyl_propyl_disulfide.ct 173.7 +bis(ethylthio)methane.ct 181 +methyl_pentyl_ether.ct 99.5 +ethyl_butyl_ether.ct 92.3 +dipropyl_ether.ct 90.1 +isopropyl_propyl_ether.ct 80.2 +ethyl_isobutyl_ether.ct 82 +isopentyl_methyl_ether.ct 91.2 +methyl_2-methylbutyl_ether.ct 91.5 +ethyl_sec-butyl_ether.ct 81.2 +diisopropyl_ether.ct 69 +methyl_tert-pentyl_ether.ct 86.3 +1,2-dimethylpropyl_methyl_ether.ct 82 +1,1-diethoxyethane.ct 103 +1,1-dimethoxy-2-methylpropane.ct 103.5 +2-ethoxy-2-methoxypropane.ct 96 +1,1-dimethoxybutane.ct 112 +1-methoxy-1-propoxyethane.ct 104 +1,4-dimethoxybutane.ct 132.5 +1,3-dimethoxybutane.ct 120.3 +methyl_pentyl_sulfide.ct 145 +butyl_ethyl_sulfide.ct 144.2 +dipropyl_sulfide.ct 142.8 +isopropyl_propyl_sulfide.ct 132 +ethyl_isobutyl_sulfide.ct 134.2 +isopentyl_methyl_sulfide.ct 137 +methyl_2-methylbutyl_sulfide.ct 139 +sec-butyl_ethyl_sulfide.ct 133.6 +diisopropyl_sulfide.ct 120 +1-ethylpropyl_methyl_sulfide.ct 137 +dipropyl_disulfide.ct 195.8 +diisopropyl_disulfide.ct 177.2 +sec-butyl_ethyl_disulfide.ct 181 +isopropyl_propyl_disulfide.ct 185.9 +tert-butyl_ethyl_disulfide.ct 175.7 +1,1-bis(ethylthio)ethane.ct 186 +1,2-bis(ethylthio)ethane.ct 211 +ethyl_pentyl_ether.ct 118 +butyl_propyl_ether.ct 117.1 +butyl_isopropyl_ether.ct 107 +isobutyl_propyl_ether.ct 102.5 +ethyl_isopentyl_ether.ct 112 +tert-butyl_propyl_ether.ct 97.4 +2,2-dimethylpropyl_ethyl_ether.ct 91.5 +tert-butyl_isopropyl_ether.ct 87.6 +ethyl_1-methylbutyl_ether.ct 106.5 +1,2-dimethylpropyl_ethyl_ether.ct 99.3 +ethyl_1-ethylpropyl_ether.ct 90 +dipropoxymethane.ct 137 +2,2-diethoxypropane.ct 114 +1-ethoxy-1-propoxyethane.ct 126 +1,1-diethoxypropane.ct 124 +1,3-diethoxypropane.ct 140.5 +1,5-dimethoxypentane.ct 157.5 +1-ethoxy-4-methoxybutane.ct 146 +1,3-dimethoxypentane.ct 141 +hexyl_methyl_sulfide.ct 171 +butyl_propyl_sulfide.ct 166 +isobutyl_propyl_sulfide.ct 155 +isobutyl_isopropyl_sulfide.ct 145 +ethyl_2-methylbutyl_sulfide.ct 159 +tert-butyl_propyl_sulfide.ct 138 +sec-butyl_isopropyl_sulfide.ct 142 +ethyl_isopentyl_sulfide.ct 159 +1,3-bis(ethylthio)propane.ct 229.5 +dibutyl_ether.ct 142 +isopentyl_propyl_ether.ct 125 +butyl_isobutyl_ether.ct 132 +butyl_sec-butyl_ether.ct 130.5 +butyl_tert-butyl_ether.ct 125 +sec-butyl_isobutyl_ether.ct 122 +1,3-dimethylpentyl_methyl_ether.ct 121 +diisobutyl_ether.ct 122.2 +di-tert-butyl_ether.ct 106 +isopropyl_tert-pentyl_ether.ct 114.5 +heptyl_methyl_ether.ct 151 +1-ethylpropyl_propyl_ether.ct 128.5 +di-tert-butyl_peroxide.ct 109.5 +1,1-diisopropoxyethane.ct 126 +1,1-dipropoxyethane.ct 147 +1,1-dimethoxyhexane.ct 158 +2,4-dimethoxy-2-methylpentane.ct 147 +dibutylsulfide.ct 188.9 +diisobutyl_sulfide.ct 170 +butyl_isobutyl_sulfide.ct 178 +di-tert-butyl_sulfide.ct 148.5 +di-sec-butyl_sulfide.ct 165 +butyl_sec-butyl_sulfide.ct 177 +sec-butyl_isobutyl_sulfide.ct 167 +heptyl_methyl_sulfide.ct 195 +dibutyl_disulfide.ct 226 +di-tert-butyl_disulfide.ct 201 +1,1-bis(isopropylthio)ethane.ct 205 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.5 +ethyl_heptyl_ether.ct 165.5 +butyl_isopentyl_ether.ct 157 +tert-butyl_isopentyl_ether.ct 139 +butyl_pentyl_ether.ct 163 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +isobutyl_isopentyl_ether.ct 139 +methyl_octyl_ether.ct 173 +2-ethylhexyl_methyl_ether.ct 159.5 +methyl_1,1,4-trimethylpentyl_ether.ct 159.5 +3,5-dimethylhexyl_methyl_ether.ct 155.5 +ethyl_1,1,3-trimethylbutyl_ether.ct 141 +tert-butyl_tert-pentyl_peroxide.ct 126 +1,1-dimethoxy-2,2-dimethylpentane.ct 164 +1,1-diethoxypentane.ct 163 +1,1-dipropoxypropane.ct 166.5 +1,3-dipropoxypropane.ct 165 +1,3-diisopropoxypropane.ct 159 +ethyl_heptyl_sulfide.ct 195 +methyl_octyl_sulfide.ct 218 +bis(butylthio)methane.ct 250 +2,2-bis(propylthio)propane.ct 235 +ethyl_octyl_ether.ct 186.5 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +bis(1-ethylpropyl)_ether.ct 162 +diisopentyl_ether.ct 173.2 +dipentyl_ether.ct 186.8 +isopropyl_heptyl_ether.ct 173 +heptyl_propyl_ether.ct 187 +isopentyl_pentyl_ether.ct 174 +methyl_1-methyloctyl_ether.ct 188.5 +di-tert-pentyl_sulfide.ct 199 +dipentyl_sulfide.ct 228 +diisopentyl_sulfide.ct 215 +methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Acyclic/trainset_2.ds b/datasets/Acyclic/trainset_2.ds new file mode 100644 index 0000000..c30337a --- /dev/null +++ b/datasets/Acyclic/trainset_2.ds @@ -0,0 +1,164 @@ +dimethyl_ether.ct -23.7 +dimethyl_peroxide.ct 14 +dimethyl_disulfide.ct 109.7 +ethyl_methyl_ether.ct 10.8 +ethyl_methyl_peroxide.ct 39 +dimethoxymethane.ct 42 +ethyl_methyl_sulfide.ct 66.6 +ethyl_methyl_disulfide.ct 135 +bis(methylthio)methane.ct 148.5 +methyl_propyl_ether.ct 40 +diethyl_ether.ct 34.6 +diethyl_peroxide.ct 63 +isopropyl_methyl_peroxide.ct 53.5 +1,1-dimethoxyethane.ct 64.4 +1,2-dimethoxyethane.ct 84.7 +methyl_propyl_sulfide.ct 95.5 +diethyl_sulfide.ct 92 +isopropyl_methyl_sulfide.ct 84.4 +diethyl_disulfide.ct 154 +1,1-bis(methylthio)ethane.ct 156 +1,2-bis(methylthio)ethane.ct 183 +butyl_methyl_ether.ct 70.3 +ethyl_propyl_ether.ct 63.6 +ethyl_isopropyl_ether.ct 52.5 +isobutyl_methyl_ether.ct 59 +sec-butyl_methyl_ether.ct 59.5 +tert-butyl_methyl_ether.ct 55.2 +diethoxymethane.ct 88 +2,2-dimethoxypropane.ct 83 +1-ethoxy-2-methoxyethane.ct 102 +1,2-dimethoxypropane.ct 92 +ethyl_isopropyl_sulfide.ct 107.4 +butyl_methyl_sulfide.ct 123.2 +isobutyl_methyl_sulfide.ct 112.5 +ethyl_propyl_sulfide.ct 118.5 +tert-butyl_methyl_sulfide.ct 101.5 +ethyl_propyl_disulfide.ct 173.7 +ethyl_isopropyl_disulfide.ct 165.5 +methyl_pentyl_ether.ct 99.5 +ethyl_butyl_ether.ct 92.3 +dipropyl_ether.ct 90.1 +isopropyl_propyl_ether.ct 80.2 +ethyl_isobutyl_ether.ct 82 +isopentyl_methyl_ether.ct 91.2 +methyl_2-methylbutyl_ether.ct 91.5 +ethyl_sec-butyl_ether.ct 81.2 +methyl_1-methylbutyl_ether.ct 93 +methyl_tert-pentyl_ether.ct 86.3 +1,2-dimethylpropyl_methyl_ether.ct 82 +1,1-diethoxyethane.ct 103 +1,1-dimethoxy-2-methylpropane.ct 103.5 +2-ethoxy-2-methoxypropane.ct 96 +1,1-dimethoxybutane.ct 112 +1-methoxy-1-propoxyethane.ct 104 +1,4-dimethoxybutane.ct 132.5 +1,2-diethoxyethane.ct 123.5 +methyl_pentyl_sulfide.ct 145 +butyl_ethyl_sulfide.ct 144.2 +dipropyl_sulfide.ct 142.8 +isopropyl_propyl_sulfide.ct 132 +ethyl_isobutyl_sulfide.ct 134.2 +isopentyl_methyl_sulfide.ct 137 +methyl_2-methylbutyl_sulfide.ct 139 +sec-butyl_ethyl_sulfide.ct 133.6 +tert-butyl_ethyl_sulfide.ct 120.4 +1-ethylpropyl_methyl_sulfide.ct 137 +dipropyl_disulfide.ct 195.8 +diisopropyl_disulfide.ct 177.2 +sec-butyl_ethyl_disulfide.ct 181 +isopropyl_propyl_disulfide.ct 185.9 +tert-butyl_ethyl_disulfide.ct 175.7 +1,1-bis(ethylthio)ethane.ct 186 +1,2-bis(ethylthio)ethane.ct 211 +hexyl_methyl_ether.ct 125 +butyl_propyl_ether.ct 117.1 +butyl_isopropyl_ether.ct 107 +isobutyl_propyl_ether.ct 102.5 +ethyl_isopentyl_ether.ct 112 +tert-butyl_propyl_ether.ct 97.4 +2,2-dimethylpropyl_ethyl_ether.ct 91.5 +tert-butyl_isopropyl_ether.ct 87.6 +ethyl_1-methylbutyl_ether.ct 106.5 +ethyl_tert-pentyl_ether.ct 101 +ethyl_1-ethylpropyl_ether.ct 90 +dipropoxymethane.ct 137 +2,2-diethoxypropane.ct 114 +1-ethoxy-1-propoxyethane.ct 126 +1,1-diethoxypropane.ct 124 +1,3-diethoxypropane.ct 140.5 +1,5-dimethoxypentane.ct 157.5 +1-ethoxy-4-methoxybutane.ct 146 +1,4-dimethoxypentane.ct 145 +hexyl_methyl_sulfide.ct 171 +butyl_propyl_sulfide.ct 166 +isobutyl_propyl_sulfide.ct 155 +isobutyl_isopropyl_sulfide.ct 145 +ethyl_2-methylbutyl_sulfide.ct 159 +tert-butyl_propyl_sulfide.ct 138 +sec-butyl_isopropyl_sulfide.ct 142 +ethyl_isopentyl_sulfide.ct 159 +butyl_isopropyl_sulfide.ct 163.5 +dibutyl_ether.ct 142 +isopentyl_propyl_ether.ct 125 +butyl_isobutyl_ether.ct 132 +butyl_sec-butyl_ether.ct 130.5 +butyl_tert-butyl_ether.ct 125 +sec-butyl_isobutyl_ether.ct 122 +1,3-dimethylpentyl_methyl_ether.ct 121 +diisobutyl_ether.ct 122.2 +isobutyl_tert-butyl_ether.ct 112 +isopropyl_tert-pentyl_ether.ct 114.5 +heptyl_methyl_ether.ct 151 +1-ethylpropyl_propyl_ether.ct 128.5 +di-tert-butyl_peroxide.ct 109.5 +1,1-diisopropoxyethane.ct 126 +1,1-dipropoxyethane.ct 147 +1,1-dimethoxyhexane.ct 158 +2,4-dimethoxy-2-methylpentane.ct 147 +1,4-diethoxybutane.ct 165 +diisobutyl_sulfide.ct 170 +butyl_isobutyl_sulfide.ct 178 +di-tert-butyl_sulfide.ct 148.5 +di-sec-butyl_sulfide.ct 165 +butyl_sec-butyl_sulfide.ct 177 +sec-butyl_isobutyl_sulfide.ct 167 +heptyl_methyl_sulfide.ct 195 +dibutyl_disulfide.ct 226 +diisobutyl_disulfide.ct 215 +1,1-bis(isopropylthio)ethane.ct 205 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.5 +ethyl_heptyl_ether.ct 165.5 +butyl_isopentyl_ether.ct 157 +tert-butyl_isopentyl_ether.ct 139 +butyl_pentyl_ether.ct 163 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +isobutyl_isopentyl_ether.ct 139 +methyl_1-methylheptyl_ether.ct 162 +2-ethylhexyl_methyl_ether.ct 159.5 +methyl_1,1,4-trimethylpentyl_ether.ct 159.5 +3,5-dimethylhexyl_methyl_ether.ct 155.5 +ethyl_1,1,3-trimethylbutyl_ether.ct 141 +tert-butyl_tert-pentyl_peroxide.ct 126 +1,1-dimethoxy-2,2-dimethylpentane.ct 164 +1,1-diethoxypentane.ct 163 +1,1-dipropoxypropane.ct 166.5 +1,1-diisopropoxypropane.ct 146 +1,3-diisopropoxypropane.ct 159 +ethyl_heptyl_sulfide.ct 195 +methyl_octyl_sulfide.ct 218 +bis(butylthio)methane.ct 250 +2,2-bis(propylthio)propane.ct 235 +ethyl_octyl_ether.ct 186.5 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +bis(1-ethylpropyl)_ether.ct 162 +bis(1-methylbutyl)_ether.ct 162 +dipentyl_ether.ct 186.8 +isopropyl_heptyl_ether.ct 173 +heptyl_propyl_ether.ct 187 +isopentyl_pentyl_ether.ct 174 +methyl_1-methyloctyl_ether.ct 188.5 +di-tert-pentyl_sulfide.ct 199 +dipentyl_sulfide.ct 228 +diisopentyl_sulfide.ct 215 +isobutyl_4-methylpentyl_sulfide.ct 216 diff --git a/datasets/Acyclic/trainset_3.ds b/datasets/Acyclic/trainset_3.ds new file mode 100644 index 0000000..bf754a2 --- /dev/null +++ b/datasets/Acyclic/trainset_3.ds @@ -0,0 +1,165 @@ +dimethyl_ether.ct -23.7 +dimethyl_peroxide.ct 14 +dimethyl_sulfide.ct 37.3 +ethyl_methyl_ether.ct 10.8 +ethyl_methyl_peroxide.ct 39 +dimethoxymethane.ct 42 +ethyl_methyl_sulfide.ct 66.6 +ethyl_methyl_disulfide.ct 135 +bis(methylthio)methane.ct 148.5 +methyl_propyl_ether.ct 40 +diethyl_ether.ct 34.6 +isopropyl_methyl_ether.ct 32 +isopropyl_methyl_peroxide.ct 53.5 +1,1-dimethoxyethane.ct 64.4 +1,2-dimethoxyethane.ct 84.7 +methyl_propyl_sulfide.ct 95.5 +diethyl_sulfide.ct 92 +isopropyl_methyl_sulfide.ct 84.4 +diethyl_disulfide.ct 154 +1,1-bis(methylthio)ethane.ct 156 +ethylthiomethylthiomethane.ct 166 +butyl_methyl_ether.ct 70.3 +ethyl_propyl_ether.ct 63.6 +ethyl_isopropyl_ether.ct 52.5 +isobutyl_methyl_ether.ct 59 +sec-butyl_methyl_ether.ct 59.5 +tert-butyl_methyl_ether.ct 55.2 +diethoxymethane.ct 88 +2,2-dimethoxypropane.ct 83 +1,3-dimethoxypropane.ct 104.5 +1,2-dimethoxypropane.ct 92 +ethyl_isopropyl_sulfide.ct 107.4 +butyl_methyl_sulfide.ct 123.2 +isobutyl_methyl_sulfide.ct 112.5 +ethyl_propyl_sulfide.ct 118.5 +tert-butyl_methyl_sulfide.ct 101.5 +ethyl_propyl_disulfide.ct 173.7 +ethyl_isopropyl_disulfide.ct 165.5 +bis(ethylthio)methane.ct 181 +ethyl_butyl_ether.ct 92.3 +dipropyl_ether.ct 90.1 +isopropyl_propyl_ether.ct 80.2 +ethyl_isobutyl_ether.ct 82 +isopentyl_methyl_ether.ct 91.2 +methyl_2-methylbutyl_ether.ct 91.5 +ethyl_sec-butyl_ether.ct 81.2 +methyl_1-methylbutyl_ether.ct 93 +diisopropyl_ether.ct 69 +1,2-dimethylpropyl_methyl_ether.ct 82 +1,1-diethoxyethane.ct 103 +1,1-dimethoxy-2-methylpropane.ct 103.5 +2-ethoxy-2-methoxypropane.ct 96 +1,1-dimethoxybutane.ct 112 +1-methoxy-1-propoxyethane.ct 104 +1,4-dimethoxybutane.ct 132.5 +1,2-diethoxyethane.ct 123.5 +1,3-dimethoxybutane.ct 120.3 +butyl_ethyl_sulfide.ct 144.2 +dipropyl_sulfide.ct 142.8 +isopropyl_propyl_sulfide.ct 132 +ethyl_isobutyl_sulfide.ct 134.2 +isopentyl_methyl_sulfide.ct 137 +methyl_2-methylbutyl_sulfide.ct 139 +sec-butyl_ethyl_sulfide.ct 133.6 +tert-butyl_ethyl_sulfide.ct 120.4 +diisopropyl_sulfide.ct 120 +dipropyl_disulfide.ct 195.8 +diisopropyl_disulfide.ct 177.2 +sec-butyl_ethyl_disulfide.ct 181 +isopropyl_propyl_disulfide.ct 185.9 +tert-butyl_ethyl_disulfide.ct 175.7 +1,1-bis(ethylthio)ethane.ct 186 +1,2-bis(ethylthio)ethane.ct 211 +hexyl_methyl_ether.ct 125 +ethyl_pentyl_ether.ct 118 +butyl_isopropyl_ether.ct 107 +isobutyl_propyl_ether.ct 102.5 +ethyl_isopentyl_ether.ct 112 +tert-butyl_propyl_ether.ct 97.4 +2,2-dimethylpropyl_ethyl_ether.ct 91.5 +tert-butyl_isopropyl_ether.ct 87.6 +ethyl_1-methylbutyl_ether.ct 106.5 +ethyl_tert-pentyl_ether.ct 101 +1,2-dimethylpropyl_ethyl_ether.ct 99.3 +dipropoxymethane.ct 137 +2,2-diethoxypropane.ct 114 +1-ethoxy-1-propoxyethane.ct 126 +1,1-diethoxypropane.ct 124 +1,3-diethoxypropane.ct 140.5 +1,5-dimethoxypentane.ct 157.5 +1-ethoxy-4-methoxybutane.ct 146 +1,4-dimethoxypentane.ct 145 +1,3-dimethoxypentane.ct 141 +butyl_propyl_sulfide.ct 166 +isobutyl_propyl_sulfide.ct 155 +isobutyl_isopropyl_sulfide.ct 145 +ethyl_2-methylbutyl_sulfide.ct 159 +tert-butyl_propyl_sulfide.ct 138 +sec-butyl_isopropyl_sulfide.ct 142 +ethyl_isopentyl_sulfide.ct 159 +butyl_isopropyl_sulfide.ct 163.5 +1,3-bis(ethylthio)propane.ct 229.5 +isopentyl_propyl_ether.ct 125 +butyl_isobutyl_ether.ct 132 +butyl_sec-butyl_ether.ct 130.5 +butyl_tert-butyl_ether.ct 125 +sec-butyl_isobutyl_ether.ct 122 +1,3-dimethylpentyl_methyl_ether.ct 121 +diisobutyl_ether.ct 122.2 +isobutyl_tert-butyl_ether.ct 112 +di-tert-butyl_ether.ct 106 +heptyl_methyl_ether.ct 151 +1-ethylpropyl_propyl_ether.ct 128.5 +di-tert-butyl_peroxide.ct 109.5 +1,1-diisopropoxyethane.ct 126 +1,1-dipropoxyethane.ct 147 +1,1-dimethoxyhexane.ct 158 +2,4-dimethoxy-2-methylpentane.ct 147 +1,4-diethoxybutane.ct 165 +dibutylsulfide.ct 188.9 +butyl_isobutyl_sulfide.ct 178 +di-tert-butyl_sulfide.ct 148.5 +di-sec-butyl_sulfide.ct 165 +butyl_sec-butyl_sulfide.ct 177 +sec-butyl_isobutyl_sulfide.ct 167 +heptyl_methyl_sulfide.ct 195 +dibutyl_disulfide.ct 226 +diisobutyl_disulfide.ct 215 +di-tert-butyl_disulfide.ct 201 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.5 +ethyl_heptyl_ether.ct 165.5 +butyl_isopentyl_ether.ct 157 +tert-butyl_isopentyl_ether.ct 139 +butyl_pentyl_ether.ct 163 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +isobutyl_isopentyl_ether.ct 139 +methyl_1-methylheptyl_ether.ct 162 +methyl_octyl_ether.ct 173 +methyl_1,1,4-trimethylpentyl_ether.ct 159.5 +3,5-dimethylhexyl_methyl_ether.ct 155.5 +ethyl_1,1,3-trimethylbutyl_ether.ct 141 +tert-butyl_tert-pentyl_peroxide.ct 126 +1,1-dimethoxy-2,2-dimethylpentane.ct 164 +1,1-diethoxypentane.ct 163 +1,1-dipropoxypropane.ct 166.5 +1,1-diisopropoxypropane.ct 146 +1,3-dipropoxypropane.ct 165 +ethyl_heptyl_sulfide.ct 195 +methyl_octyl_sulfide.ct 218 +bis(butylthio)methane.ct 250 +2,2-bis(propylthio)propane.ct 235 +ethyl_octyl_ether.ct 186.5 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +bis(1-ethylpropyl)_ether.ct 162 +bis(1-methylbutyl)_ether.ct 162 +diisopentyl_ether.ct 173.2 +isopropyl_heptyl_ether.ct 173 +heptyl_propyl_ether.ct 187 +isopentyl_pentyl_ether.ct 174 +methyl_1-methyloctyl_ether.ct 188.5 +di-tert-pentyl_sulfide.ct 199 +dipentyl_sulfide.ct 228 +diisopentyl_sulfide.ct 215 +isobutyl_4-methylpentyl_sulfide.ct 216 +methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Acyclic/trainset_4.ds b/datasets/Acyclic/trainset_4.ds new file mode 100644 index 0000000..c98c9c8 --- /dev/null +++ b/datasets/Acyclic/trainset_4.ds @@ -0,0 +1,165 @@ +dimethyl_ether.ct -23.7 +dimethyl_peroxide.ct 14 +dimethyl_sulfide.ct 37.3 +dimethyl_disulfide.ct 109.7 +ethyl_methyl_peroxide.ct 39 +dimethoxymethane.ct 42 +ethyl_methyl_sulfide.ct 66.6 +ethyl_methyl_disulfide.ct 135 +bis(methylthio)methane.ct 148.5 +methyl_propyl_ether.ct 40 +diethyl_ether.ct 34.6 +isopropyl_methyl_ether.ct 32 +diethyl_peroxide.ct 63 +1,1-dimethoxyethane.ct 64.4 +1,2-dimethoxyethane.ct 84.7 +methyl_propyl_sulfide.ct 95.5 +diethyl_sulfide.ct 92 +isopropyl_methyl_sulfide.ct 84.4 +diethyl_disulfide.ct 154 +1,1-bis(methylthio)ethane.ct 156 +ethylthiomethylthiomethane.ct 166 +1,2-bis(methylthio)ethane.ct 183 +ethyl_propyl_ether.ct 63.6 +ethyl_isopropyl_ether.ct 52.5 +isobutyl_methyl_ether.ct 59 +sec-butyl_methyl_ether.ct 59.5 +tert-butyl_methyl_ether.ct 55.2 +diethoxymethane.ct 88 +2,2-dimethoxypropane.ct 83 +1,3-dimethoxypropane.ct 104.5 +1-ethoxy-2-methoxyethane.ct 102 +ethyl_isopropyl_sulfide.ct 107.4 +butyl_methyl_sulfide.ct 123.2 +isobutyl_methyl_sulfide.ct 112.5 +ethyl_propyl_sulfide.ct 118.5 +tert-butyl_methyl_sulfide.ct 101.5 +ethyl_propyl_disulfide.ct 173.7 +ethyl_isopropyl_disulfide.ct 165.5 +bis(ethylthio)methane.ct 181 +methyl_pentyl_ether.ct 99.5 +dipropyl_ether.ct 90.1 +isopropyl_propyl_ether.ct 80.2 +ethyl_isobutyl_ether.ct 82 +isopentyl_methyl_ether.ct 91.2 +methyl_2-methylbutyl_ether.ct 91.5 +ethyl_sec-butyl_ether.ct 81.2 +methyl_1-methylbutyl_ether.ct 93 +diisopropyl_ether.ct 69 +methyl_tert-pentyl_ether.ct 86.3 +1,1-diethoxyethane.ct 103 +1,1-dimethoxy-2-methylpropane.ct 103.5 +2-ethoxy-2-methoxypropane.ct 96 +1,1-dimethoxybutane.ct 112 +1-methoxy-1-propoxyethane.ct 104 +1,4-dimethoxybutane.ct 132.5 +1,2-diethoxyethane.ct 123.5 +1,3-dimethoxybutane.ct 120.3 +methyl_pentyl_sulfide.ct 145 +dipropyl_sulfide.ct 142.8 +isopropyl_propyl_sulfide.ct 132 +ethyl_isobutyl_sulfide.ct 134.2 +isopentyl_methyl_sulfide.ct 137 +methyl_2-methylbutyl_sulfide.ct 139 +sec-butyl_ethyl_sulfide.ct 133.6 +tert-butyl_ethyl_sulfide.ct 120.4 +diisopropyl_sulfide.ct 120 +1-ethylpropyl_methyl_sulfide.ct 137 +diisopropyl_disulfide.ct 177.2 +sec-butyl_ethyl_disulfide.ct 181 +isopropyl_propyl_disulfide.ct 185.9 +tert-butyl_ethyl_disulfide.ct 175.7 +1,1-bis(ethylthio)ethane.ct 186 +1,2-bis(ethylthio)ethane.ct 211 +hexyl_methyl_ether.ct 125 +ethyl_pentyl_ether.ct 118 +butyl_propyl_ether.ct 117.1 +isobutyl_propyl_ether.ct 102.5 +ethyl_isopentyl_ether.ct 112 +tert-butyl_propyl_ether.ct 97.4 +2,2-dimethylpropyl_ethyl_ether.ct 91.5 +tert-butyl_isopropyl_ether.ct 87.6 +ethyl_1-methylbutyl_ether.ct 106.5 +ethyl_tert-pentyl_ether.ct 101 +1,2-dimethylpropyl_ethyl_ether.ct 99.3 +ethyl_1-ethylpropyl_ether.ct 90 +2,2-diethoxypropane.ct 114 +1-ethoxy-1-propoxyethane.ct 126 +1,1-diethoxypropane.ct 124 +1,3-diethoxypropane.ct 140.5 +1,5-dimethoxypentane.ct 157.5 +1-ethoxy-4-methoxybutane.ct 146 +1,4-dimethoxypentane.ct 145 +1,3-dimethoxypentane.ct 141 +hexyl_methyl_sulfide.ct 171 +isobutyl_propyl_sulfide.ct 155 +isobutyl_isopropyl_sulfide.ct 145 +ethyl_2-methylbutyl_sulfide.ct 159 +tert-butyl_propyl_sulfide.ct 138 +sec-butyl_isopropyl_sulfide.ct 142 +ethyl_isopentyl_sulfide.ct 159 +butyl_isopropyl_sulfide.ct 163.5 +1,3-bis(ethylthio)propane.ct 229.5 +dibutyl_ether.ct 142 +butyl_isobutyl_ether.ct 132 +butyl_sec-butyl_ether.ct 130.5 +butyl_tert-butyl_ether.ct 125 +sec-butyl_isobutyl_ether.ct 122 +1,3-dimethylpentyl_methyl_ether.ct 121 +diisobutyl_ether.ct 122.2 +isobutyl_tert-butyl_ether.ct 112 +di-tert-butyl_ether.ct 106 +isopropyl_tert-pentyl_ether.ct 114.5 +1-ethylpropyl_propyl_ether.ct 128.5 +di-tert-butyl_peroxide.ct 109.5 +1,1-diisopropoxyethane.ct 126 +1,1-dipropoxyethane.ct 147 +1,1-dimethoxyhexane.ct 158 +2,4-dimethoxy-2-methylpentane.ct 147 +1,4-diethoxybutane.ct 165 +dibutylsulfide.ct 188.9 +diisobutyl_sulfide.ct 170 +di-tert-butyl_sulfide.ct 148.5 +di-sec-butyl_sulfide.ct 165 +butyl_sec-butyl_sulfide.ct 177 +sec-butyl_isobutyl_sulfide.ct 167 +heptyl_methyl_sulfide.ct 195 +dibutyl_disulfide.ct 226 +diisobutyl_disulfide.ct 215 +di-tert-butyl_disulfide.ct 201 +1,1-bis(isopropylthio)ethane.ct 205 +ethyl_heptyl_ether.ct 165.5 +butyl_isopentyl_ether.ct 157 +tert-butyl_isopentyl_ether.ct 139 +butyl_pentyl_ether.ct 163 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +isobutyl_isopentyl_ether.ct 139 +methyl_1-methylheptyl_ether.ct 162 +methyl_octyl_ether.ct 173 +2-ethylhexyl_methyl_ether.ct 159.5 +3,5-dimethylhexyl_methyl_ether.ct 155.5 +ethyl_1,1,3-trimethylbutyl_ether.ct 141 +tert-butyl_tert-pentyl_peroxide.ct 126 +1,1-dimethoxy-2,2-dimethylpentane.ct 164 +1,1-diethoxypentane.ct 163 +1,1-dipropoxypropane.ct 166.5 +1,1-diisopropoxypropane.ct 146 +1,3-dipropoxypropane.ct 165 +1,3-diisopropoxypropane.ct 159 +methyl_octyl_sulfide.ct 218 +bis(butylthio)methane.ct 250 +2,2-bis(propylthio)propane.ct 235 +ethyl_octyl_ether.ct 186.5 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +bis(1-ethylpropyl)_ether.ct 162 +bis(1-methylbutyl)_ether.ct 162 +diisopentyl_ether.ct 173.2 +dipentyl_ether.ct 186.8 +heptyl_propyl_ether.ct 187 +isopentyl_pentyl_ether.ct 174 +methyl_1-methyloctyl_ether.ct 188.5 +di-tert-pentyl_sulfide.ct 199 +dipentyl_sulfide.ct 228 +diisopentyl_sulfide.ct 215 +isobutyl_4-methylpentyl_sulfide.ct 216 +methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Acyclic/trainset_5.ds b/datasets/Acyclic/trainset_5.ds new file mode 100644 index 0000000..74acc59 --- /dev/null +++ b/datasets/Acyclic/trainset_5.ds @@ -0,0 +1,165 @@ +dimethyl_ether.ct -23.7 +dimethyl_peroxide.ct 14 +dimethyl_sulfide.ct 37.3 +dimethyl_disulfide.ct 109.7 +ethyl_methyl_ether.ct 10.8 +dimethoxymethane.ct 42 +ethyl_methyl_sulfide.ct 66.6 +ethyl_methyl_disulfide.ct 135 +bis(methylthio)methane.ct 148.5 +methyl_propyl_ether.ct 40 +diethyl_ether.ct 34.6 +isopropyl_methyl_ether.ct 32 +diethyl_peroxide.ct 63 +isopropyl_methyl_peroxide.ct 53.5 +1,2-dimethoxyethane.ct 84.7 +methyl_propyl_sulfide.ct 95.5 +diethyl_sulfide.ct 92 +isopropyl_methyl_sulfide.ct 84.4 +diethyl_disulfide.ct 154 +1,1-bis(methylthio)ethane.ct 156 +ethylthiomethylthiomethane.ct 166 +1,2-bis(methylthio)ethane.ct 183 +butyl_methyl_ether.ct 70.3 +ethyl_isopropyl_ether.ct 52.5 +isobutyl_methyl_ether.ct 59 +sec-butyl_methyl_ether.ct 59.5 +tert-butyl_methyl_ether.ct 55.2 +diethoxymethane.ct 88 +2,2-dimethoxypropane.ct 83 +1,3-dimethoxypropane.ct 104.5 +1-ethoxy-2-methoxyethane.ct 102 +1,2-dimethoxypropane.ct 92 +butyl_methyl_sulfide.ct 123.2 +isobutyl_methyl_sulfide.ct 112.5 +ethyl_propyl_sulfide.ct 118.5 +tert-butyl_methyl_sulfide.ct 101.5 +ethyl_propyl_disulfide.ct 173.7 +ethyl_isopropyl_disulfide.ct 165.5 +bis(ethylthio)methane.ct 181 +methyl_pentyl_ether.ct 99.5 +ethyl_butyl_ether.ct 92.3 +isopropyl_propyl_ether.ct 80.2 +ethyl_isobutyl_ether.ct 82 +isopentyl_methyl_ether.ct 91.2 +methyl_2-methylbutyl_ether.ct 91.5 +ethyl_sec-butyl_ether.ct 81.2 +methyl_1-methylbutyl_ether.ct 93 +diisopropyl_ether.ct 69 +methyl_tert-pentyl_ether.ct 86.3 +1,2-dimethylpropyl_methyl_ether.ct 82 +1,1-dimethoxy-2-methylpropane.ct 103.5 +2-ethoxy-2-methoxypropane.ct 96 +1,1-dimethoxybutane.ct 112 +1-methoxy-1-propoxyethane.ct 104 +1,4-dimethoxybutane.ct 132.5 +1,2-diethoxyethane.ct 123.5 +1,3-dimethoxybutane.ct 120.3 +methyl_pentyl_sulfide.ct 145 +butyl_ethyl_sulfide.ct 144.2 +isopropyl_propyl_sulfide.ct 132 +ethyl_isobutyl_sulfide.ct 134.2 +isopentyl_methyl_sulfide.ct 137 +methyl_2-methylbutyl_sulfide.ct 139 +sec-butyl_ethyl_sulfide.ct 133.6 +tert-butyl_ethyl_sulfide.ct 120.4 +diisopropyl_sulfide.ct 120 +1-ethylpropyl_methyl_sulfide.ct 137 +dipropyl_disulfide.ct 195.8 +sec-butyl_ethyl_disulfide.ct 181 +isopropyl_propyl_disulfide.ct 185.9 +tert-butyl_ethyl_disulfide.ct 175.7 +1,1-bis(ethylthio)ethane.ct 186 +1,2-bis(ethylthio)ethane.ct 211 +hexyl_methyl_ether.ct 125 +ethyl_pentyl_ether.ct 118 +butyl_propyl_ether.ct 117.1 +butyl_isopropyl_ether.ct 107 +ethyl_isopentyl_ether.ct 112 +tert-butyl_propyl_ether.ct 97.4 +2,2-dimethylpropyl_ethyl_ether.ct 91.5 +tert-butyl_isopropyl_ether.ct 87.6 +ethyl_1-methylbutyl_ether.ct 106.5 +ethyl_tert-pentyl_ether.ct 101 +1,2-dimethylpropyl_ethyl_ether.ct 99.3 +ethyl_1-ethylpropyl_ether.ct 90 +dipropoxymethane.ct 137 +1-ethoxy-1-propoxyethane.ct 126 +1,1-diethoxypropane.ct 124 +1,3-diethoxypropane.ct 140.5 +1,5-dimethoxypentane.ct 157.5 +1-ethoxy-4-methoxybutane.ct 146 +1,4-dimethoxypentane.ct 145 +1,3-dimethoxypentane.ct 141 +hexyl_methyl_sulfide.ct 171 +butyl_propyl_sulfide.ct 166 +isobutyl_isopropyl_sulfide.ct 145 +ethyl_2-methylbutyl_sulfide.ct 159 +tert-butyl_propyl_sulfide.ct 138 +sec-butyl_isopropyl_sulfide.ct 142 +ethyl_isopentyl_sulfide.ct 159 +butyl_isopropyl_sulfide.ct 163.5 +1,3-bis(ethylthio)propane.ct 229.5 +dibutyl_ether.ct 142 +isopentyl_propyl_ether.ct 125 +butyl_sec-butyl_ether.ct 130.5 +butyl_tert-butyl_ether.ct 125 +sec-butyl_isobutyl_ether.ct 122 +1,3-dimethylpentyl_methyl_ether.ct 121 +diisobutyl_ether.ct 122.2 +isobutyl_tert-butyl_ether.ct 112 +di-tert-butyl_ether.ct 106 +isopropyl_tert-pentyl_ether.ct 114.5 +heptyl_methyl_ether.ct 151 +di-tert-butyl_peroxide.ct 109.5 +1,1-diisopropoxyethane.ct 126 +1,1-dipropoxyethane.ct 147 +1,1-dimethoxyhexane.ct 158 +2,4-dimethoxy-2-methylpentane.ct 147 +1,4-diethoxybutane.ct 165 +dibutylsulfide.ct 188.9 +diisobutyl_sulfide.ct 170 +butyl_isobutyl_sulfide.ct 178 +di-sec-butyl_sulfide.ct 165 +butyl_sec-butyl_sulfide.ct 177 +sec-butyl_isobutyl_sulfide.ct 167 +heptyl_methyl_sulfide.ct 195 +dibutyl_disulfide.ct 226 +diisobutyl_disulfide.ct 215 +di-tert-butyl_disulfide.ct 201 +1,1-bis(isopropylthio)ethane.ct 205 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.5 +butyl_isopentyl_ether.ct 157 +tert-butyl_isopentyl_ether.ct 139 +butyl_pentyl_ether.ct 163 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +isobutyl_isopentyl_ether.ct 139 +methyl_1-methylheptyl_ether.ct 162 +methyl_octyl_ether.ct 173 +2-ethylhexyl_methyl_ether.ct 159.5 +methyl_1,1,4-trimethylpentyl_ether.ct 159.5 +ethyl_1,1,3-trimethylbutyl_ether.ct 141 +tert-butyl_tert-pentyl_peroxide.ct 126 +1,1-dimethoxy-2,2-dimethylpentane.ct 164 +1,1-diethoxypentane.ct 163 +1,1-dipropoxypropane.ct 166.5 +1,1-diisopropoxypropane.ct 146 +1,3-dipropoxypropane.ct 165 +1,3-diisopropoxypropane.ct 159 +ethyl_heptyl_sulfide.ct 195 +bis(butylthio)methane.ct 250 +2,2-bis(propylthio)propane.ct 235 +ethyl_octyl_ether.ct 186.5 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +bis(1-ethylpropyl)_ether.ct 162 +bis(1-methylbutyl)_ether.ct 162 +diisopentyl_ether.ct 173.2 +dipentyl_ether.ct 186.8 +isopropyl_heptyl_ether.ct 173 +isopentyl_pentyl_ether.ct 174 +methyl_1-methyloctyl_ether.ct 188.5 +di-tert-pentyl_sulfide.ct 199 +dipentyl_sulfide.ct 228 +diisopentyl_sulfide.ct 215 +isobutyl_4-methylpentyl_sulfide.ct 216 +methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Acyclic/trainset_6.ds b/datasets/Acyclic/trainset_6.ds new file mode 100644 index 0000000..693b0c6 --- /dev/null +++ b/datasets/Acyclic/trainset_6.ds @@ -0,0 +1,165 @@ +dimethyl_ether.ct -23.7 +dimethyl_peroxide.ct 14 +dimethyl_sulfide.ct 37.3 +dimethyl_disulfide.ct 109.7 +ethyl_methyl_ether.ct 10.8 +ethyl_methyl_peroxide.ct 39 +ethyl_methyl_sulfide.ct 66.6 +ethyl_methyl_disulfide.ct 135 +bis(methylthio)methane.ct 148.5 +methyl_propyl_ether.ct 40 +diethyl_ether.ct 34.6 +isopropyl_methyl_ether.ct 32 +diethyl_peroxide.ct 63 +isopropyl_methyl_peroxide.ct 53.5 +1,1-dimethoxyethane.ct 64.4 +methyl_propyl_sulfide.ct 95.5 +diethyl_sulfide.ct 92 +isopropyl_methyl_sulfide.ct 84.4 +diethyl_disulfide.ct 154 +1,1-bis(methylthio)ethane.ct 156 +ethylthiomethylthiomethane.ct 166 +1,2-bis(methylthio)ethane.ct 183 +butyl_methyl_ether.ct 70.3 +ethyl_propyl_ether.ct 63.6 +isobutyl_methyl_ether.ct 59 +sec-butyl_methyl_ether.ct 59.5 +tert-butyl_methyl_ether.ct 55.2 +diethoxymethane.ct 88 +2,2-dimethoxypropane.ct 83 +1,3-dimethoxypropane.ct 104.5 +1-ethoxy-2-methoxyethane.ct 102 +1,2-dimethoxypropane.ct 92 +ethyl_isopropyl_sulfide.ct 107.4 +isobutyl_methyl_sulfide.ct 112.5 +ethyl_propyl_sulfide.ct 118.5 +tert-butyl_methyl_sulfide.ct 101.5 +ethyl_propyl_disulfide.ct 173.7 +ethyl_isopropyl_disulfide.ct 165.5 +bis(ethylthio)methane.ct 181 +methyl_pentyl_ether.ct 99.5 +ethyl_butyl_ether.ct 92.3 +dipropyl_ether.ct 90.1 +ethyl_isobutyl_ether.ct 82 +isopentyl_methyl_ether.ct 91.2 +methyl_2-methylbutyl_ether.ct 91.5 +ethyl_sec-butyl_ether.ct 81.2 +methyl_1-methylbutyl_ether.ct 93 +diisopropyl_ether.ct 69 +methyl_tert-pentyl_ether.ct 86.3 +1,2-dimethylpropyl_methyl_ether.ct 82 +1,1-diethoxyethane.ct 103 +2-ethoxy-2-methoxypropane.ct 96 +1,1-dimethoxybutane.ct 112 +1-methoxy-1-propoxyethane.ct 104 +1,4-dimethoxybutane.ct 132.5 +1,2-diethoxyethane.ct 123.5 +1,3-dimethoxybutane.ct 120.3 +methyl_pentyl_sulfide.ct 145 +butyl_ethyl_sulfide.ct 144.2 +dipropyl_sulfide.ct 142.8 +ethyl_isobutyl_sulfide.ct 134.2 +isopentyl_methyl_sulfide.ct 137 +methyl_2-methylbutyl_sulfide.ct 139 +sec-butyl_ethyl_sulfide.ct 133.6 +tert-butyl_ethyl_sulfide.ct 120.4 +diisopropyl_sulfide.ct 120 +1-ethylpropyl_methyl_sulfide.ct 137 +dipropyl_disulfide.ct 195.8 +diisopropyl_disulfide.ct 177.2 +isopropyl_propyl_disulfide.ct 185.9 +tert-butyl_ethyl_disulfide.ct 175.7 +1,1-bis(ethylthio)ethane.ct 186 +1,2-bis(ethylthio)ethane.ct 211 +hexyl_methyl_ether.ct 125 +ethyl_pentyl_ether.ct 118 +butyl_propyl_ether.ct 117.1 +butyl_isopropyl_ether.ct 107 +isobutyl_propyl_ether.ct 102.5 +tert-butyl_propyl_ether.ct 97.4 +2,2-dimethylpropyl_ethyl_ether.ct 91.5 +tert-butyl_isopropyl_ether.ct 87.6 +ethyl_1-methylbutyl_ether.ct 106.5 +ethyl_tert-pentyl_ether.ct 101 +1,2-dimethylpropyl_ethyl_ether.ct 99.3 +ethyl_1-ethylpropyl_ether.ct 90 +dipropoxymethane.ct 137 +2,2-diethoxypropane.ct 114 +1,1-diethoxypropane.ct 124 +1,3-diethoxypropane.ct 140.5 +1,5-dimethoxypentane.ct 157.5 +1-ethoxy-4-methoxybutane.ct 146 +1,4-dimethoxypentane.ct 145 +1,3-dimethoxypentane.ct 141 +hexyl_methyl_sulfide.ct 171 +butyl_propyl_sulfide.ct 166 +isobutyl_propyl_sulfide.ct 155 +ethyl_2-methylbutyl_sulfide.ct 159 +tert-butyl_propyl_sulfide.ct 138 +sec-butyl_isopropyl_sulfide.ct 142 +ethyl_isopentyl_sulfide.ct 159 +butyl_isopropyl_sulfide.ct 163.5 +1,3-bis(ethylthio)propane.ct 229.5 +dibutyl_ether.ct 142 +isopentyl_propyl_ether.ct 125 +butyl_isobutyl_ether.ct 132 +butyl_tert-butyl_ether.ct 125 +sec-butyl_isobutyl_ether.ct 122 +1,3-dimethylpentyl_methyl_ether.ct 121 +diisobutyl_ether.ct 122.2 +isobutyl_tert-butyl_ether.ct 112 +di-tert-butyl_ether.ct 106 +isopropyl_tert-pentyl_ether.ct 114.5 +heptyl_methyl_ether.ct 151 +1-ethylpropyl_propyl_ether.ct 128.5 +1,1-diisopropoxyethane.ct 126 +1,1-dipropoxyethane.ct 147 +1,1-dimethoxyhexane.ct 158 +2,4-dimethoxy-2-methylpentane.ct 147 +1,4-diethoxybutane.ct 165 +dibutylsulfide.ct 188.9 +diisobutyl_sulfide.ct 170 +butyl_isobutyl_sulfide.ct 178 +di-tert-butyl_sulfide.ct 148.5 +butyl_sec-butyl_sulfide.ct 177 +sec-butyl_isobutyl_sulfide.ct 167 +heptyl_methyl_sulfide.ct 195 +dibutyl_disulfide.ct 226 +diisobutyl_disulfide.ct 215 +di-tert-butyl_disulfide.ct 201 +1,1-bis(isopropylthio)ethane.ct 205 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.5 +ethyl_heptyl_ether.ct 165.5 +tert-butyl_isopentyl_ether.ct 139 +butyl_pentyl_ether.ct 163 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +isobutyl_isopentyl_ether.ct 139 +methyl_1-methylheptyl_ether.ct 162 +methyl_octyl_ether.ct 173 +2-ethylhexyl_methyl_ether.ct 159.5 +methyl_1,1,4-trimethylpentyl_ether.ct 159.5 +3,5-dimethylhexyl_methyl_ether.ct 155.5 +tert-butyl_tert-pentyl_peroxide.ct 126 +1,1-dimethoxy-2,2-dimethylpentane.ct 164 +1,1-diethoxypentane.ct 163 +1,1-dipropoxypropane.ct 166.5 +1,1-diisopropoxypropane.ct 146 +1,3-dipropoxypropane.ct 165 +1,3-diisopropoxypropane.ct 159 +ethyl_heptyl_sulfide.ct 195 +methyl_octyl_sulfide.ct 218 +2,2-bis(propylthio)propane.ct 235 +ethyl_octyl_ether.ct 186.5 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +bis(1-ethylpropyl)_ether.ct 162 +bis(1-methylbutyl)_ether.ct 162 +diisopentyl_ether.ct 173.2 +dipentyl_ether.ct 186.8 +isopropyl_heptyl_ether.ct 173 +heptyl_propyl_ether.ct 187 +methyl_1-methyloctyl_ether.ct 188.5 +di-tert-pentyl_sulfide.ct 199 +dipentyl_sulfide.ct 228 +diisopentyl_sulfide.ct 215 +isobutyl_4-methylpentyl_sulfide.ct 216 +methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Acyclic/trainset_7.ds b/datasets/Acyclic/trainset_7.ds new file mode 100644 index 0000000..c606226 --- /dev/null +++ b/datasets/Acyclic/trainset_7.ds @@ -0,0 +1,165 @@ +dimethyl_ether.ct -23.7 +dimethyl_peroxide.ct 14 +dimethyl_sulfide.ct 37.3 +dimethyl_disulfide.ct 109.7 +ethyl_methyl_ether.ct 10.8 +ethyl_methyl_peroxide.ct 39 +dimethoxymethane.ct 42 +ethyl_methyl_disulfide.ct 135 +bis(methylthio)methane.ct 148.5 +methyl_propyl_ether.ct 40 +diethyl_ether.ct 34.6 +isopropyl_methyl_ether.ct 32 +diethyl_peroxide.ct 63 +isopropyl_methyl_peroxide.ct 53.5 +1,1-dimethoxyethane.ct 64.4 +1,2-dimethoxyethane.ct 84.7 +diethyl_sulfide.ct 92 +isopropyl_methyl_sulfide.ct 84.4 +diethyl_disulfide.ct 154 +1,1-bis(methylthio)ethane.ct 156 +ethylthiomethylthiomethane.ct 166 +1,2-bis(methylthio)ethane.ct 183 +butyl_methyl_ether.ct 70.3 +ethyl_propyl_ether.ct 63.6 +ethyl_isopropyl_ether.ct 52.5 +sec-butyl_methyl_ether.ct 59.5 +tert-butyl_methyl_ether.ct 55.2 +diethoxymethane.ct 88 +2,2-dimethoxypropane.ct 83 +1,3-dimethoxypropane.ct 104.5 +1-ethoxy-2-methoxyethane.ct 102 +1,2-dimethoxypropane.ct 92 +ethyl_isopropyl_sulfide.ct 107.4 +butyl_methyl_sulfide.ct 123.2 +ethyl_propyl_sulfide.ct 118.5 +tert-butyl_methyl_sulfide.ct 101.5 +ethyl_propyl_disulfide.ct 173.7 +ethyl_isopropyl_disulfide.ct 165.5 +bis(ethylthio)methane.ct 181 +methyl_pentyl_ether.ct 99.5 +ethyl_butyl_ether.ct 92.3 +dipropyl_ether.ct 90.1 +isopropyl_propyl_ether.ct 80.2 +isopentyl_methyl_ether.ct 91.2 +methyl_2-methylbutyl_ether.ct 91.5 +ethyl_sec-butyl_ether.ct 81.2 +methyl_1-methylbutyl_ether.ct 93 +diisopropyl_ether.ct 69 +methyl_tert-pentyl_ether.ct 86.3 +1,2-dimethylpropyl_methyl_ether.ct 82 +1,1-diethoxyethane.ct 103 +1,1-dimethoxy-2-methylpropane.ct 103.5 +1,1-dimethoxybutane.ct 112 +1-methoxy-1-propoxyethane.ct 104 +1,4-dimethoxybutane.ct 132.5 +1,2-diethoxyethane.ct 123.5 +1,3-dimethoxybutane.ct 120.3 +methyl_pentyl_sulfide.ct 145 +butyl_ethyl_sulfide.ct 144.2 +dipropyl_sulfide.ct 142.8 +isopropyl_propyl_sulfide.ct 132 +isopentyl_methyl_sulfide.ct 137 +methyl_2-methylbutyl_sulfide.ct 139 +sec-butyl_ethyl_sulfide.ct 133.6 +tert-butyl_ethyl_sulfide.ct 120.4 +diisopropyl_sulfide.ct 120 +1-ethylpropyl_methyl_sulfide.ct 137 +dipropyl_disulfide.ct 195.8 +diisopropyl_disulfide.ct 177.2 +sec-butyl_ethyl_disulfide.ct 181 +tert-butyl_ethyl_disulfide.ct 175.7 +1,1-bis(ethylthio)ethane.ct 186 +1,2-bis(ethylthio)ethane.ct 211 +hexyl_methyl_ether.ct 125 +ethyl_pentyl_ether.ct 118 +butyl_propyl_ether.ct 117.1 +butyl_isopropyl_ether.ct 107 +isobutyl_propyl_ether.ct 102.5 +ethyl_isopentyl_ether.ct 112 +2,2-dimethylpropyl_ethyl_ether.ct 91.5 +tert-butyl_isopropyl_ether.ct 87.6 +ethyl_1-methylbutyl_ether.ct 106.5 +ethyl_tert-pentyl_ether.ct 101 +1,2-dimethylpropyl_ethyl_ether.ct 99.3 +ethyl_1-ethylpropyl_ether.ct 90 +dipropoxymethane.ct 137 +2,2-diethoxypropane.ct 114 +1-ethoxy-1-propoxyethane.ct 126 +1,3-diethoxypropane.ct 140.5 +1,5-dimethoxypentane.ct 157.5 +1-ethoxy-4-methoxybutane.ct 146 +1,4-dimethoxypentane.ct 145 +1,3-dimethoxypentane.ct 141 +hexyl_methyl_sulfide.ct 171 +butyl_propyl_sulfide.ct 166 +isobutyl_propyl_sulfide.ct 155 +isobutyl_isopropyl_sulfide.ct 145 +tert-butyl_propyl_sulfide.ct 138 +sec-butyl_isopropyl_sulfide.ct 142 +ethyl_isopentyl_sulfide.ct 159 +butyl_isopropyl_sulfide.ct 163.5 +1,3-bis(ethylthio)propane.ct 229.5 +dibutyl_ether.ct 142 +isopentyl_propyl_ether.ct 125 +butyl_isobutyl_ether.ct 132 +butyl_sec-butyl_ether.ct 130.5 +sec-butyl_isobutyl_ether.ct 122 +1,3-dimethylpentyl_methyl_ether.ct 121 +diisobutyl_ether.ct 122.2 +isobutyl_tert-butyl_ether.ct 112 +di-tert-butyl_ether.ct 106 +isopropyl_tert-pentyl_ether.ct 114.5 +heptyl_methyl_ether.ct 151 +1-ethylpropyl_propyl_ether.ct 128.5 +di-tert-butyl_peroxide.ct 109.5 +1,1-dipropoxyethane.ct 147 +1,1-dimethoxyhexane.ct 158 +2,4-dimethoxy-2-methylpentane.ct 147 +1,4-diethoxybutane.ct 165 +dibutylsulfide.ct 188.9 +diisobutyl_sulfide.ct 170 +butyl_isobutyl_sulfide.ct 178 +di-tert-butyl_sulfide.ct 148.5 +di-sec-butyl_sulfide.ct 165 +sec-butyl_isobutyl_sulfide.ct 167 +heptyl_methyl_sulfide.ct 195 +dibutyl_disulfide.ct 226 +diisobutyl_disulfide.ct 215 +di-tert-butyl_disulfide.ct 201 +1,1-bis(isopropylthio)ethane.ct 205 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.5 +ethyl_heptyl_ether.ct 165.5 +butyl_isopentyl_ether.ct 157 +butyl_pentyl_ether.ct 163 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +isobutyl_isopentyl_ether.ct 139 +methyl_1-methylheptyl_ether.ct 162 +methyl_octyl_ether.ct 173 +2-ethylhexyl_methyl_ether.ct 159.5 +methyl_1,1,4-trimethylpentyl_ether.ct 159.5 +3,5-dimethylhexyl_methyl_ether.ct 155.5 +ethyl_1,1,3-trimethylbutyl_ether.ct 141 +1,1-dimethoxy-2,2-dimethylpentane.ct 164 +1,1-diethoxypentane.ct 163 +1,1-dipropoxypropane.ct 166.5 +1,1-diisopropoxypropane.ct 146 +1,3-dipropoxypropane.ct 165 +1,3-diisopropoxypropane.ct 159 +ethyl_heptyl_sulfide.ct 195 +methyl_octyl_sulfide.ct 218 +bis(butylthio)methane.ct 250 +ethyl_octyl_ether.ct 186.5 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +bis(1-ethylpropyl)_ether.ct 162 +bis(1-methylbutyl)_ether.ct 162 +diisopentyl_ether.ct 173.2 +dipentyl_ether.ct 186.8 +isopropyl_heptyl_ether.ct 173 +heptyl_propyl_ether.ct 187 +isopentyl_pentyl_ether.ct 174 +di-tert-pentyl_sulfide.ct 199 +dipentyl_sulfide.ct 228 +diisopentyl_sulfide.ct 215 +isobutyl_4-methylpentyl_sulfide.ct 216 +methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Acyclic/trainset_8.ds b/datasets/Acyclic/trainset_8.ds new file mode 100644 index 0000000..f5196c3 --- /dev/null +++ b/datasets/Acyclic/trainset_8.ds @@ -0,0 +1,165 @@ +dimethyl_ether.ct -23.7 +dimethyl_peroxide.ct 14 +dimethyl_sulfide.ct 37.3 +dimethyl_disulfide.ct 109.7 +ethyl_methyl_ether.ct 10.8 +ethyl_methyl_peroxide.ct 39 +dimethoxymethane.ct 42 +ethyl_methyl_sulfide.ct 66.6 +bis(methylthio)methane.ct 148.5 +methyl_propyl_ether.ct 40 +diethyl_ether.ct 34.6 +isopropyl_methyl_ether.ct 32 +diethyl_peroxide.ct 63 +isopropyl_methyl_peroxide.ct 53.5 +1,1-dimethoxyethane.ct 64.4 +1,2-dimethoxyethane.ct 84.7 +methyl_propyl_sulfide.ct 95.5 +isopropyl_methyl_sulfide.ct 84.4 +diethyl_disulfide.ct 154 +1,1-bis(methylthio)ethane.ct 156 +ethylthiomethylthiomethane.ct 166 +1,2-bis(methylthio)ethane.ct 183 +butyl_methyl_ether.ct 70.3 +ethyl_propyl_ether.ct 63.6 +ethyl_isopropyl_ether.ct 52.5 +isobutyl_methyl_ether.ct 59 +tert-butyl_methyl_ether.ct 55.2 +diethoxymethane.ct 88 +2,2-dimethoxypropane.ct 83 +1,3-dimethoxypropane.ct 104.5 +1-ethoxy-2-methoxyethane.ct 102 +1,2-dimethoxypropane.ct 92 +ethyl_isopropyl_sulfide.ct 107.4 +butyl_methyl_sulfide.ct 123.2 +isobutyl_methyl_sulfide.ct 112.5 +tert-butyl_methyl_sulfide.ct 101.5 +ethyl_propyl_disulfide.ct 173.7 +ethyl_isopropyl_disulfide.ct 165.5 +bis(ethylthio)methane.ct 181 +methyl_pentyl_ether.ct 99.5 +ethyl_butyl_ether.ct 92.3 +dipropyl_ether.ct 90.1 +isopropyl_propyl_ether.ct 80.2 +ethyl_isobutyl_ether.ct 82 +methyl_2-methylbutyl_ether.ct 91.5 +ethyl_sec-butyl_ether.ct 81.2 +methyl_1-methylbutyl_ether.ct 93 +diisopropyl_ether.ct 69 +methyl_tert-pentyl_ether.ct 86.3 +1,2-dimethylpropyl_methyl_ether.ct 82 +1,1-diethoxyethane.ct 103 +1,1-dimethoxy-2-methylpropane.ct 103.5 +2-ethoxy-2-methoxypropane.ct 96 +1-methoxy-1-propoxyethane.ct 104 +1,4-dimethoxybutane.ct 132.5 +1,2-diethoxyethane.ct 123.5 +1,3-dimethoxybutane.ct 120.3 +methyl_pentyl_sulfide.ct 145 +butyl_ethyl_sulfide.ct 144.2 +dipropyl_sulfide.ct 142.8 +isopropyl_propyl_sulfide.ct 132 +ethyl_isobutyl_sulfide.ct 134.2 +methyl_2-methylbutyl_sulfide.ct 139 +sec-butyl_ethyl_sulfide.ct 133.6 +tert-butyl_ethyl_sulfide.ct 120.4 +diisopropyl_sulfide.ct 120 +1-ethylpropyl_methyl_sulfide.ct 137 +dipropyl_disulfide.ct 195.8 +diisopropyl_disulfide.ct 177.2 +sec-butyl_ethyl_disulfide.ct 181 +isopropyl_propyl_disulfide.ct 185.9 +1,1-bis(ethylthio)ethane.ct 186 +1,2-bis(ethylthio)ethane.ct 211 +hexyl_methyl_ether.ct 125 +ethyl_pentyl_ether.ct 118 +butyl_propyl_ether.ct 117.1 +butyl_isopropyl_ether.ct 107 +isobutyl_propyl_ether.ct 102.5 +ethyl_isopentyl_ether.ct 112 +tert-butyl_propyl_ether.ct 97.4 +tert-butyl_isopropyl_ether.ct 87.6 +ethyl_1-methylbutyl_ether.ct 106.5 +ethyl_tert-pentyl_ether.ct 101 +1,2-dimethylpropyl_ethyl_ether.ct 99.3 +ethyl_1-ethylpropyl_ether.ct 90 +dipropoxymethane.ct 137 +2,2-diethoxypropane.ct 114 +1-ethoxy-1-propoxyethane.ct 126 +1,1-diethoxypropane.ct 124 +1,5-dimethoxypentane.ct 157.5 +1-ethoxy-4-methoxybutane.ct 146 +1,4-dimethoxypentane.ct 145 +1,3-dimethoxypentane.ct 141 +hexyl_methyl_sulfide.ct 171 +butyl_propyl_sulfide.ct 166 +isobutyl_propyl_sulfide.ct 155 +isobutyl_isopropyl_sulfide.ct 145 +ethyl_2-methylbutyl_sulfide.ct 159 +sec-butyl_isopropyl_sulfide.ct 142 +ethyl_isopentyl_sulfide.ct 159 +butyl_isopropyl_sulfide.ct 163.5 +1,3-bis(ethylthio)propane.ct 229.5 +dibutyl_ether.ct 142 +isopentyl_propyl_ether.ct 125 +butyl_isobutyl_ether.ct 132 +butyl_sec-butyl_ether.ct 130.5 +butyl_tert-butyl_ether.ct 125 +1,3-dimethylpentyl_methyl_ether.ct 121 +diisobutyl_ether.ct 122.2 +isobutyl_tert-butyl_ether.ct 112 +di-tert-butyl_ether.ct 106 +isopropyl_tert-pentyl_ether.ct 114.5 +heptyl_methyl_ether.ct 151 +1-ethylpropyl_propyl_ether.ct 128.5 +di-tert-butyl_peroxide.ct 109.5 +1,1-diisopropoxyethane.ct 126 +1,1-dimethoxyhexane.ct 158 +2,4-dimethoxy-2-methylpentane.ct 147 +1,4-diethoxybutane.ct 165 +dibutylsulfide.ct 188.9 +diisobutyl_sulfide.ct 170 +butyl_isobutyl_sulfide.ct 178 +di-tert-butyl_sulfide.ct 148.5 +di-sec-butyl_sulfide.ct 165 +butyl_sec-butyl_sulfide.ct 177 +heptyl_methyl_sulfide.ct 195 +dibutyl_disulfide.ct 226 +diisobutyl_disulfide.ct 215 +di-tert-butyl_disulfide.ct 201 +1,1-bis(isopropylthio)ethane.ct 205 +1-ethyl-1,3-dimethylbutyl_methyl_ether.ct 151.5 +ethyl_heptyl_ether.ct 165.5 +butyl_isopentyl_ether.ct 157 +tert-butyl_isopentyl_ether.ct 139 +1,5-dimethylhexyl_methyl_ether.ct 153.5 +isobutyl_isopentyl_ether.ct 139 +methyl_1-methylheptyl_ether.ct 162 +methyl_octyl_ether.ct 173 +2-ethylhexyl_methyl_ether.ct 159.5 +methyl_1,1,4-trimethylpentyl_ether.ct 159.5 +3,5-dimethylhexyl_methyl_ether.ct 155.5 +ethyl_1,1,3-trimethylbutyl_ether.ct 141 +tert-butyl_tert-pentyl_peroxide.ct 126 +1,1-diethoxypentane.ct 163 +1,1-dipropoxypropane.ct 166.5 +1,1-diisopropoxypropane.ct 146 +1,3-dipropoxypropane.ct 165 +1,3-diisopropoxypropane.ct 159 +ethyl_heptyl_sulfide.ct 195 +methyl_octyl_sulfide.ct 218 +bis(butylthio)methane.ct 250 +2,2-bis(propylthio)propane.ct 235 +ethyl_1,1,3,3-tetramethylbutyl_ether.ct 156.5 +bis(1-ethylpropyl)_ether.ct 162 +bis(1-methylbutyl)_ether.ct 162 +diisopentyl_ether.ct 173.2 +dipentyl_ether.ct 186.8 +isopropyl_heptyl_ether.ct 173 +heptyl_propyl_ether.ct 187 +isopentyl_pentyl_ether.ct 174 +methyl_1-methyloctyl_ether.ct 188.5 +dipentyl_sulfide.ct 228 +diisopentyl_sulfide.ct 215 +isobutyl_4-methylpentyl_sulfide.ct 216 +methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Acyclic/trainset_9.ds b/datasets/Acyclic/trainset_9.ds new file mode 100644 index 0000000..d494de0 --- /dev/null +++ b/datasets/Acyclic/trainset_9.ds @@ -0,0 +1,165 @@ +dimethyl_ether.ct -23.7 +dimethyl_peroxide.ct 14 +dimethyl_sulfide.ct 37.3 +dimethyl_disulfide.ct 109.7 +ethyl_methyl_ether.ct 10.8 +ethyl_methyl_peroxide.ct 39 +dimethoxymethane.ct 42 +ethyl_methyl_sulfide.ct 66.6 +ethyl_methyl_disulfide.ct 135 +methyl_propyl_ether.ct 40 +diethyl_ether.ct 34.6 +isopropyl_methyl_ether.ct 32 +diethyl_peroxide.ct 63 +isopropyl_methyl_peroxide.ct 53.5 +1,1-dimethoxyethane.ct 64.4 +1,2-dimethoxyethane.ct 84.7 +methyl_propyl_sulfide.ct 95.5 +diethyl_sulfide.ct 92 +diethyl_disulfide.ct 154 +1,1-bis(methylthio)ethane.ct 156 +ethylthiomethylthiomethane.ct 166 +1,2-bis(methylthio)ethane.ct 183 +butyl_methyl_ether.ct 70.3 +ethyl_propyl_ether.ct 63.6 +ethyl_isopropyl_ether.ct 52.5 +isobutyl_methyl_ether.ct 59 +sec-butyl_methyl_ether.ct 59.5 +diethoxymethane.ct 88 +2,2-dimethoxypropane.ct 83 +1,3-dimethoxypropane.ct 104.5 +1-ethoxy-2-methoxyethane.ct 102 +1,2-dimethoxypropane.ct 92 +ethyl_isopropyl_sulfide.ct 107.4 +butyl_methyl_sulfide.ct 123.2 +isobutyl_methyl_sulfide.ct 112.5 +ethyl_propyl_sulfide.ct 118.5 +ethyl_propyl_disulfide.ct 173.7 +ethyl_isopropyl_disulfide.ct 165.5 +bis(ethylthio)methane.ct 181 +methyl_pentyl_ether.ct 99.5 +ethyl_butyl_ether.ct 92.3 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+methyl_nonyl_sulfide.ct 240 diff --git a/datasets/Monoterpenoides/1.ct b/datasets/Monoterpenoides/1.ct new file mode 100644 index 0000000..e9f0425 --- /dev/null +++ b/datasets/Monoterpenoides/1.ct @@ -0,0 +1,26 @@ + + 12 12 + -5.0223 0.0320 0.0000 O + -5.7368 0.4445 0.0000 C + -5.7368 1.2695 0.0000 C + -4.3079 1.2695 0.0000 C + -4.3079 0.4445 0.0000 C + -3.5934 0.0320 0.0000 C + -3.5934 0.8570 0.0000 C + -2.8789 0.4445 0.0000 C + -6.4512 0.0320 0.0000 C + -7.1657 0.4445 0.0000 C + -6.4513 -0.7930 0.0000 C + -7.1657 -1.2055 0.0000 O + 1 2 1 1 + 1 5 1 1 + 2 3 1 1 + 3 4 1 1 + 4 5 1 1 + 5 6 1 1 + 5 7 1 1 + 7 8 2 2 + 2 9 1 1 + 9 10 1 1 + 9 11 1 1 + 11 12 1 1 diff --git a/datasets/Monoterpenoides/1.gxl b/datasets/Monoterpenoides/1.gxl new file mode 100644 index 0000000..9e853c7 --- /dev/null +++ b/datasets/Monoterpenoides/1.gxl @@ -0,0 +1,79 @@ + + + + + + +8 + + +6 + + +6 + + +6 + + +6 + + +6 + + +6 + + +6 + + +6 + + +6 + + +6 + + +8 + + +1 + + +1 + + +1 + + +1 + + +1 + + +1 + + +1 + + +2 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a/datasets/Monoterpenoides/102.ct b/datasets/Monoterpenoides/102.ct new file mode 100644 index 0000000..25507fe --- /dev/null +++ b/datasets/Monoterpenoides/102.ct @@ -0,0 +1,24 @@ + + 11 11 + -6.8304 3.9723 0.0000 C + -7.5448 3.5598 0.0000 C + -7.5448 2.7348 0.0000 C + -6.8304 2.3223 0.0000 C + -6.1159 2.7348 0.0000 C + -6.1159 3.5598 0.0000 C + -6.8305 4.9206 0.0000 C + -8.2593 2.3223 0.0000 C + -8.2593 1.4973 0.0000 C + -8.9738 2.7348 0.0000 O + -8.9738 1.9098 0.0000 C + 1 2 1 1 + 2 3 1 1 + 3 4 1 1 + 4 5 2 2 + 5 6 1 1 + 1 6 2 2 + 1 7 1 1 + 3 8 1 1 + 8 9 1 1 + 8 10 1 1 + 8 11 1 1 diff --git a/datasets/Monoterpenoides/102.gxl b/datasets/Monoterpenoides/102.gxl new file mode 100644 index 0000000..87cf136 --- /dev/null +++ b/datasets/Monoterpenoides/102.gxl @@ -0,0 +1,73 @@ + + + + + + +6 + + +6 + + +6 + + +6 + + +6 + + +6 + + +6 + + +6 + + +6 + + +8 + + +6 + + +1 + + +1 + + +1 + + +2 + + +1 + + +2 + + +1 + + +1 + + +1 + + +1 + + +1 + + + diff --git a/datasets/Monoterpenoides/103.ct 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0000000..ac24c4b Binary files /dev/null and b/datasets/Monoterpenoides/monoterpenoides_gxl.tgz differ diff --git a/datasets/Monoterpenoides/trainset_9.ds b/datasets/Monoterpenoides/trainset_9.ds new file mode 100644 index 0000000..1dac86e --- /dev/null +++ b/datasets/Monoterpenoides/trainset_9.ds @@ -0,0 +1,73 @@ +11.gxl 1 +3.gxl 1 +46.gxl 1 +39.gxl 1 +33.gxl 1 +43.gxl 1 +48.gxl 1 +24.gxl 1 +19.gxl 1 +62.gxl 2 +68.gxl 2 +55.gxl 2 +73.gxl 2 +79.gxl 2 +87.gxl 2 +92.gxl 2 +56.gxl 2 +84.gxl 2 +97.gxl 3 +130.gxl 3 +127.gxl 3 +120.gxl 3 +115.gxl 3 +110.gxl 3 +105.gxl 3 +96.gxl 3 +129.gxl 3 +118.gxl 3 +133.gxl 4 +136.gxl 4 +137.gxl 4 +142.gxl 4 +143.gxl 4 +147.gxl 4 +140.gxl 4 +141.gxl 4 +146.gxl 4 +161.gxl 6 +162.gxl 6 +173.gxl 6 +175.gxl 6 +176.gxl 6 +184.gxl 6 +186.gxl 6 +187.gxl 6 +166.gxl 6 +194.gxl 7 +195.gxl 7 +197.gxl 7 +199.gxl 7 +201.gxl 7 +202.gxl 7 +205.gxl 7 +207.gxl 7 +208.gxl 7 +216.gxl 8 +224.gxl 8 +228.gxl 8 +237.gxl 8 +247.gxl 8 +262.gxl 8 +271.gxl 8 +275.gxl 8 +282.gxl 8 +289.gxl 9 +286.gxl 9 +287.gxl 9 +292.gxl 9 +293.gxl 9 +296.gxl 9 +298.gxl 9 +301.gxl 9 +302.gxl 9 \ No newline at end of file diff --git a/gklearn/ged/__init__.py b/gklearn/ged/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/gklearn/kernels/__init__.py b/gklearn/kernels/__init__.py index 73147ba..52df2b6 100644 --- a/gklearn/kernels/__init__.py +++ b/gklearn/kernels/__init__.py @@ -10,3 +10,6 @@ __date__ = "November 2018" from gklearn.kernels.graph_kernel import GraphKernel from gklearn.kernels.structural_sp import StructuralSP from gklearn.kernels.shortest_path import ShortestPath +from gklearn.kernels.path_up_to_h import PathUpToH +from gklearn.kernels.treelet import Treelet +from gklearn.kernels.weisfeiler_lehman import WeisfeilerLehman diff --git a/gklearn/kernels/path_up_to_h.py b/gklearn/kernels/path_up_to_h.py index 4b35463..b23687c 100644 --- a/gklearn/kernels/path_up_to_h.py +++ b/gklearn/kernels/path_up_to_h.py @@ -7,22 +7,23 @@ Created on Fri Apr 10 18:33:13 2020 @references: - [1] Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre - Baldi. Graph kernels for chemical informatics. Neural networks, - 18(8):1093–1110, 2005. + [1] Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre + Baldi. Graph kernels for chemical informatics. Neural networks, + 18(8):1093–1110, 2005. """ import sys -from itertools import product -# from functools import partial from multiprocessing import Pool from tqdm import tqdm import numpy as np +import networkx as nx +from collections import Counter +from functools import partial from gklearn.utils.parallel import parallel_gm, parallel_me -from gklearn.utils.utils import getSPGraph from gklearn.kernels import GraphKernel +from gklearn.utils import Trie -class PathUpToH(GraphKernel): +class PathUpToH(GraphKernel): # @todo: add function for k_func == None def __init__(self, **kwargs): GraphKernel.__init__(self) @@ -35,231 +36,557 @@ class PathUpToH(GraphKernel): def _compute_gm_series(self): - # get shortest path graph of each graph. - if self._verbose >= 2: - iterator = tqdm(self._graphs, desc='getting sp graphs', file=sys.stdout) - else: - iterator = self._graphs - self._graphs = [getSPGraph(g, edge_weight=self.__edge_weight) for g in iterator] - - # compute Gram matrix. - gram_matrix = np.zeros((len(self._graphs), len(self._graphs))) + self.__add_dummy_labels(self._graphs) from itertools import combinations_with_replacement - itr = combinations_with_replacement(range(0, len(self._graphs)), 2) + itr_kernel = combinations_with_replacement(range(0, len(self._graphs)), 2) if self._verbose >= 2: - iterator = tqdm(itr, desc='calculating kernels', file=sys.stdout) + iterator_ps = tqdm(range(0, len(self._graphs)), desc='getting paths', file=sys.stdout) + iterator_kernel = tqdm(itr_kernel, desc='calculating kernels', file=sys.stdout) else: - iterator = itr - for i, j in iterator: - kernel = self.__sp_do_(self._graphs[i], self._graphs[j]) - gram_matrix[i][j] = kernel - gram_matrix[j][i] = kernel + iterator_ps = range(0, len(self._graphs)) + iterator_kernel = itr_kernel + + gram_matrix = np.zeros((len(self._graphs), len(self._graphs))) + + if self.__compute_method == 'trie': + all_paths = [self.__find_all_path_as_trie(self._graphs[i]) for i in iterator_ps] + for i, j in iterator_kernel: + kernel = self.__kernel_do_trie(all_paths[i], all_paths[j]) + gram_matrix[i][j] = kernel + gram_matrix[j][i] = kernel + else: + all_paths = [self.__find_all_paths_until_length(self._graphs[i]) for i in iterator_ps] + for i, j in iterator_kernel: + kernel = self.__kernel_do_naive(all_paths[i], all_paths[j]) + gram_matrix[i][j] = kernel + gram_matrix[j][i] = kernel return gram_matrix def _compute_gm_imap_unordered(self): - # get shortest path graph of each graph. + self.__add_dummy_labels(self._graphs) + + # get all paths of all graphs before calculating kernels to save time, + # but this may cost a lot of memory for large datasets. pool = Pool(self._n_jobs) - get_sp_graphs_fun = self._wrapper_get_sp_graphs itr = zip(self._graphs, range(0, len(self._graphs))) if len(self._graphs) < 100 * self._n_jobs: chunksize = int(len(self._graphs) / self._n_jobs) + 1 else: chunksize = 100 + all_paths = [[] for _ in range(len(self._graphs))] + if self.__compute_method == 'trie' and self.__k_func is not None: + get_ps_fun = self._wrapper_find_all_path_as_trie + elif self.__compute_method != 'trie' and self.__k_func is not None: + get_ps_fun = partial(self._wrapper_find_all_paths_until_length, True) + else: + get_ps_fun = partial(self._wrapper_find_all_paths_until_length, False) if self._verbose >= 2: - iterator = tqdm(pool.imap_unordered(get_sp_graphs_fun, itr, chunksize), - desc='getting sp graphs', file=sys.stdout) + iterator = tqdm(pool.imap_unordered(get_ps_fun, itr, chunksize), + desc='getting paths', file=sys.stdout) else: - iterator = pool.imap_unordered(get_sp_graphs_fun, itr, chunksize) - for i, g in iterator: - self._graphs[i] = g + iterator = pool.imap_unordered(get_ps_fun, itr, chunksize) + for i, ps in iterator: + all_paths[i] = ps pool.close() pool.join() # compute Gram matrix. gram_matrix = np.zeros((len(self._graphs), len(self._graphs))) - - def init_worker(gs_toshare): - global G_gs - G_gs = gs_toshare - do_fun = self._wrapper_sp_do + + if self.__compute_method == 'trie' and self.__k_func is not None: + def init_worker(trie_toshare): + global G_trie + G_trie = trie_toshare + do_fun = self._wrapper_kernel_do_trie + elif self.__compute_method != 'trie' and self.__k_func is not None: + def init_worker(plist_toshare): + global G_plist + G_plist = plist_toshare + do_fun = self._wrapper_kernel_do_naive + else: + def init_worker(plist_toshare): + global G_plist + G_plist = plist_toshare + do_fun = self.__wrapper_kernel_do_kernelless # @todo: what is this? parallel_gm(do_fun, gram_matrix, self._graphs, init_worker=init_worker, - glbv=(self._graphs,), n_jobs=self._n_jobs, verbose=self._verbose) + glbv=(all_paths,), n_jobs=self._n_jobs, verbose=self._verbose) return gram_matrix def _compute_kernel_list_series(self, g1, g_list): - # get shortest path graphs of g1 and each graph in g_list. - g1 = getSPGraph(g1, edge_weight=self.__edge_weight) + self.__add_dummy_labels(g_list + [g1]) + if self._verbose >= 2: - iterator = tqdm(g_list, desc='getting sp graphs', file=sys.stdout) + iterator_ps = tqdm(g_list, desc='getting paths', file=sys.stdout) + iterator_kernel = tqdm(range(len(g_list)), desc='calculating kernels', file=sys.stdout) else: - iterator = g_list - g_list = [getSPGraph(g, edge_weight=self.__edge_weight) for g in iterator] - - # compute kernel list. + iterator_ps = g_list + iterator_kernel = range(len(g_list)) + kernel_list = [None] * len(g_list) - if self._verbose >= 2: - iterator = tqdm(range(len(g_list)), desc='calculating kernels', file=sys.stdout) + + if self.__compute_method == 'trie': + paths_g1 = self.__find_all_path_as_trie(g1) + paths_g_list = [self.__find_all_path_as_trie(g) for g in iterator_ps] + for i in iterator_kernel: + kernel = self.__kernel_do_trie(paths_g1, paths_g_list[i]) + kernel_list[i] = kernel else: - iterator = range(len(g_list)) - for i in iterator: - kernel = self.__sp_do(g1, g_list[i]) - kernel_list[i] = kernel + paths_g1 = self.__find_all_paths_until_length(g1) + paths_g_list = [self.__find_all_paths_until_length(g) for g in iterator_ps] + for i in iterator_kernel: + kernel = self.__kernel_do_naive(paths_g1, paths_g_list[i]) + kernel_list[i] = kernel return kernel_list def _compute_kernel_list_imap_unordered(self, g1, g_list): - # get shortest path graphs of g1 and each graph in g_list. - g1 = getSPGraph(g1, edge_weight=self.__edge_weight) + self.__add_dummy_labels(g_list + [g1]) + + # get all paths of all graphs before calculating kernels to save time, + # but this may cost a lot of memory for large datasets. pool = Pool(self._n_jobs) - get_sp_graphs_fun = self._wrapper_get_sp_graphs itr = zip(g_list, range(0, len(g_list))) if len(g_list) < 100 * self._n_jobs: chunksize = int(len(g_list) / self._n_jobs) + 1 else: chunksize = 100 + paths_g_list = [[] for _ in range(len(g_list))] + if self.__compute_method == 'trie' and self.__k_func is not None: + paths_g1 = self.__find_all_path_as_trie(g1) + get_ps_fun = self._wrapper_find_all_path_as_trie + elif self.__compute_method != 'trie' and self.__k_func is not None: + paths_g1 = self.__find_all_paths_until_length(g1) + get_ps_fun = partial(self._wrapper_find_all_paths_until_length, True) + else: + paths_g1 = self.__find_all_paths_until_length(g1) + get_ps_fun = partial(self._wrapper_find_all_paths_until_length, False) if self._verbose >= 2: - iterator = tqdm(pool.imap_unordered(get_sp_graphs_fun, itr, chunksize), - desc='getting sp graphs', file=sys.stdout) + iterator = tqdm(pool.imap_unordered(get_ps_fun, itr, chunksize), + desc='getting paths', file=sys.stdout) else: - iterator = pool.imap_unordered(get_sp_graphs_fun, itr, chunksize) - for i, g in iterator: - g_list[i] = g + iterator = pool.imap_unordered(get_ps_fun, itr, chunksize) + for i, ps in iterator: + paths_g_list[i] = ps pool.close() pool.join() - # compute Gram matrix. + # compute kernel list. kernel_list = [None] * len(g_list) - - def init_worker(g1_toshare, gl_toshare): - global G_g1, G_gl - G_g1 = g1_toshare - G_gl = gl_toshare + + def init_worker(p1_toshare, plist_toshare): + global G_p1, G_plist + G_p1 = p1_toshare + G_plist = plist_toshare do_fun = self._wrapper_kernel_list_do def func_assign(result, var_to_assign): var_to_assign[result[0]] = result[1] itr = range(len(g_list)) len_itr = len(g_list) parallel_me(do_fun, func_assign, kernel_list, itr, len_itr=len_itr, - init_worker=init_worker, glbv=(g1, g_list), method='imap_unordered', n_jobs=self._n_jobs, itr_desc='calculating kernels', verbose=self._verbose) - + init_worker=init_worker, glbv=(paths_g1, paths_g_list), method='imap_unordered', n_jobs=self._n_jobs, itr_desc='calculating kernels', verbose=self._verbose) + return kernel_list def _wrapper_kernel_list_do(self, itr): - return itr, self.__sp_do(G_g1, G_gl[itr]) + if self.__compute_method == 'trie' and self.__k_func is not None: + return itr, self.__kernel_do_trie(G_p1, G_plist[itr]) + elif self.__compute_method != 'trie' and self.__k_func is not None: + return itr, self.__kernel_do_naive(G_p1, G_plist[itr]) + else: + return itr, self.__kernel_do_kernelless(G_p1, G_plist[itr]) def _compute_single_kernel_series(self, g1, g2): - g1 = getSPGraph(g1, edge_weight=self.__edge_weight) - g2 = getSPGraph(g2, edge_weight=self.__edge_weight) - kernel = self.__sp_do(g1, g2) + self.__add_dummy_labels([g1] + [g2]) + if self.__compute_method == 'trie': + paths_g1 = self.__find_all_path_as_trie(g1) + paths_g2 = self.__find_all_path_as_trie(g2) + kernel = self.__kernel_do_trie(paths_g1, paths_g2) + else: + paths_g1 = self.__find_all_paths_until_length(g1) + paths_g2 = self.__find_all_paths_until_length(g2) + kernel = self.__kernel_do_naive(paths_g1, paths_g2) return kernel + + + def __kernel_do_trie(self, trie1, trie2): + """Calculate path graph kernels up to depth d between 2 graphs using trie. + + Parameters + ---------- + trie1, trie2 : list + Tries that contains all paths in 2 graphs. + k_func : function + A kernel function applied using different notions of fingerprint + similarity. + + Return + ------ + kernel : float + Path kernel up to h between 2 graphs. + """ + if self.__k_func == 'tanimoto': + # traverse all paths in graph1 and search them in graph2. Deep-first + # search is applied. + def traverseTrie1t(root, trie2, setlist, pcurrent=[]): + for key, node in root['children'].items(): + pcurrent.append(key) + if node['isEndOfWord']: + setlist[1] += 1 + count2 = trie2.searchWord(pcurrent) + if count2 != 0: + setlist[0] += 1 + if node['children'] != {}: + traverseTrie1t(node, trie2, setlist, pcurrent) + else: + del pcurrent[-1] + if pcurrent != []: + del pcurrent[-1] + + + # traverse all paths in graph2 and find out those that are not in + # graph1. Deep-first search is applied. + def traverseTrie2t(root, trie1, setlist, pcurrent=[]): + for key, node in root['children'].items(): + pcurrent.append(key) + if node['isEndOfWord']: + # print(node['count']) + count1 = trie1.searchWord(pcurrent) + if count1 == 0: + setlist[1] += 1 + if node['children'] != {}: + traverseTrie2t(node, trie1, setlist, pcurrent) + else: + del pcurrent[-1] + if pcurrent != []: + del pcurrent[-1] + + setlist = [0, 0] # intersection and union of path sets of g1, g2. + # print(trie1.root) + # print(trie2.root) + traverseTrie1t(trie1.root, trie2, setlist) + # print(setlist) + traverseTrie2t(trie2.root, trie1, setlist) + # print(setlist) + kernel = setlist[0] / setlist[1] + + elif self.__k_func == 'MinMax': # MinMax kernel + # traverse all paths in graph1 and search them in graph2. Deep-first + # search is applied. + def traverseTrie1m(root, trie2, sumlist, pcurrent=[]): + for key, node in root['children'].items(): + pcurrent.append(key) + if node['isEndOfWord']: +# print(node['count']) + count1 = node['count'] + count2 = trie2.searchWord(pcurrent) + sumlist[0] += min(count1, count2) + sumlist[1] += max(count1, count2) + if node['children'] != {}: + traverseTrie1m(node, trie2, sumlist, pcurrent) + else: + del pcurrent[-1] + if pcurrent != []: + del pcurrent[-1] + + # traverse all paths in graph2 and find out those that are not in + # graph1. Deep-first search is applied. + def traverseTrie2m(root, trie1, sumlist, pcurrent=[]): + for key, node in root['children'].items(): + pcurrent.append(key) + if node['isEndOfWord']: + # print(node['count']) + count1 = trie1.searchWord(pcurrent) + if count1 == 0: + sumlist[1] += node['count'] + if node['children'] != {}: + traverseTrie2m(node, trie1, sumlist, pcurrent) + else: + del pcurrent[-1] + if pcurrent != []: + del pcurrent[-1] + + sumlist = [0, 0] # sum of mins and sum of maxs +# print(trie1.root) +# print(trie2.root) + traverseTrie1m(trie1.root, trie2, sumlist) +# print(sumlist) + traverseTrie2m(trie2.root, trie1, sumlist) +# print(sumlist) + kernel = sumlist[0] / sumlist[1] + else: + raise Exception('The given "k_func" cannot be recognized. Possible choices include: "tanimoto", "MinMax".') + + return kernel + + + def _wrapper_kernel_do_trie(self, itr): + i = itr[0] + j = itr[1] + return i, j, self.__kernel_do_trie(G_trie[i], G_trie[j]) + + + def __kernel_do_naive(self, paths1, paths2): + """Calculate path graph kernels up to depth d between 2 graphs naively. + + Parameters + ---------- + paths_list : list of list + List of list of paths in all graphs, where for unlabeled graphs, each + path is represented by a list of nodes; while for labeled graphs, each + path is represented by a string consists of labels of nodes and/or + edges on that path. + k_func : function + A kernel function applied using different notions of fingerprint + similarity. + + Return + ------ + kernel : float + Path kernel up to h between 2 graphs. + """ + all_paths = list(set(paths1 + paths2)) + + if self.__k_func == 'tanimoto': + length_union = len(set(paths1 + paths2)) + kernel = (len(set(paths1)) + len(set(paths2)) - + length_union) / length_union + # vector1 = [(1 if path in paths1 else 0) for path in all_paths] + # vector2 = [(1 if path in paths2 else 0) for path in all_paths] + # kernel_uv = np.dot(vector1, vector2) + # kernel = kernel_uv / (len(set(paths1)) + len(set(paths2)) - kernel_uv) + + elif self.__k_func == 'MinMax': # MinMax kernel + path_count1 = Counter(paths1) + path_count2 = Counter(paths2) + vector1 = [(path_count1[key] if (key in path_count1.keys()) else 0) + for key in all_paths] + vector2 = [(path_count2[key] if (key in path_count2.keys()) else 0) + for key in all_paths] + kernel = np.sum(np.minimum(vector1, vector2)) / \ + np.sum(np.maximum(vector1, vector2)) + else: + raise Exception('The given "k_func" cannot be recognized. Possible choices include: "tanimoto", "MinMax".') + + return kernel + + + def _wrapper_kernel_do_naive(self, itr): + i = itr[0] + j = itr[1] + return i, j, self.__kernel_do_naive(G_plist[i], G_plist[j]) + + + def __find_all_path_as_trie(self, G): + # all_path = find_all_paths_until_length(G, length, ds_attrs, + # node_label=node_label, + # edge_label=edge_label) + # ptrie = Trie() + # for path in all_path: + # ptrie.insertWord(path) + # ptrie = Trie() + # path_l = [[n] for n in G.nodes] # paths of length l + # path_l_str = paths2labelseqs(path_l, G, ds_attrs, node_label, edge_label) + # for p in path_l_str: + # ptrie.insertWord(p) + # for l in range(1, length + 1): + # path_lplus1 = [] + # for path in path_l: + # for neighbor in G[path[-1]]: + # if neighbor not in path: + # tmp = path + [neighbor] + ## if tmp[::-1] not in path_lplus1: + # path_lplus1.append(tmp) + # path_l = path_lplus1[:] + # # consider labels + # path_l_str = paths2labelseqs(path_l, G, ds_attrs, node_label, edge_label) + # for p in path_l_str: + # ptrie.insertWord(p) + # + # print(time.time() - time1) + # print(ptrie.root) + # print() + + + # traverse all paths up to length h in a graph and construct a trie with + # them. Deep-first search is applied. Notice the reverse of each path is + # also stored to the trie. + def traverseGraph(root, ptrie, G, pcurrent=[]): + if len(pcurrent) < self.__depth + 1: + for neighbor in G[root]: + if neighbor not in pcurrent: + pcurrent.append(neighbor) + plstr = self.__paths2labelseqs([pcurrent], G) + ptrie.insertWord(plstr[0]) + traverseGraph(neighbor, ptrie, G, pcurrent) + del pcurrent[-1] + - def _wrapper_get_sp_graphs(self, itr_item): + ptrie = Trie() + path_l = [[n] for n in G.nodes] # paths of length l + path_l_str = self.__paths2labelseqs(path_l, G) + for p in path_l_str: + ptrie.insertWord(p) + for n in G.nodes: + traverseGraph(n, ptrie, G, pcurrent=[n]) + + + # def traverseGraph(root, all_paths, length, G, ds_attrs, node_label, edge_label, + # pcurrent=[]): + # if len(pcurrent) < length + 1: + # for neighbor in G[root]: + # if neighbor not in pcurrent: + # pcurrent.append(neighbor) + # plstr = paths2labelseqs([pcurrent], G, ds_attrs, + # node_label, edge_label) + # all_paths.append(pcurrent[:]) + # traverseGraph(neighbor, all_paths, length, G, ds_attrs, + # node_label, edge_label, pcurrent) + # del pcurrent[-1] + # + # + # path_l = [[n] for n in G.nodes] # paths of length l + # all_paths = path_l[:] + # path_l_str = paths2labelseqs(path_l, G, ds_attrs, node_label, edge_label) + ## for p in path_l_str: + ## ptrie.insertWord(p) + # for n in G.nodes: + # traverseGraph(n, all_paths, length, G, ds_attrs, node_label, edge_label, + # pcurrent=[n]) + + # print(ptrie.root) + return ptrie + + + def _wrapper_find_all_path_as_trie(self, itr_item): g = itr_item[0] i = itr_item[1] - return i, getSPGraph(g, edge_weight=self.__edge_weight) + return i, self.__find_all_path_as_trie(g) - def __sp_do(self, g1, g2): - - kernel = 0 + # @todo: (can be removed maybe) this method find paths repetively, it could be faster. + def __find_all_paths_until_length(self, G, tolabelseqs=True): + """Find all paths no longer than a certain maximum length in a graph. A + recursive depth first search is applied. + + Parameters + ---------- + G : NetworkX graphs + The graph in which paths are searched. + length : integer + The maximum length of paths. + ds_attrs: dict + Dataset attributes. + node_label : string + Node attribute used as label. The default node label is atom. + edge_label : string + Edge attribute used as label. The default edge label is bond_type. + + Return + ------ + path : list + List of paths retrieved, where for unlabeled graphs, each path is + represented by a list of nodes; while for labeled graphs, each path is + represented by a list of strings consists of labels of nodes and/or + edges on that path. + """ + # path_l = [tuple([n]) for n in G.nodes] # paths of length l + # all_paths = path_l[:] + # for l in range(1, self.__depth + 1): + # path_l_new = [] + # for path in path_l: + # for neighbor in G[path[-1]]: + # if len(path) < 2 or neighbor != path[-2]: + # tmp = path + (neighbor, ) + # if tuple(tmp[::-1]) not in path_l_new: + # path_l_new.append(tuple(tmp)) + + # all_paths += path_l_new + # path_l = path_l_new[:] + + path_l = [[n] for n in G.nodes] # paths of length l + all_paths = [p.copy() for p in path_l] + for l in range(1, self.__depth + 1): + path_lplus1 = [] + for path in path_l: + for neighbor in G[path[-1]]: + if neighbor not in path: + tmp = path + [neighbor] + # if tmp[::-1] not in path_lplus1: + path_lplus1.append(tmp) + + all_paths += path_lplus1 + path_l = [p.copy() for p in path_lplus1] + + # for i in range(0, self.__depth + 1): + # new_paths = find_all_paths(G, i) + # if new_paths == []: + # break + # all_paths.extend(new_paths) + + # consider labels + # print(paths2labelseqs(all_paths, G, ds_attrs, node_label, edge_label)) + # print() + return (self.__paths2labelseqs(all_paths, G) if tolabelseqs else all_paths) + + + def _wrapper_find_all_paths_until_length(self, tolabelseqs, itr_item): + g = itr_item[0] + i = itr_item[1] + return i, self.__find_all_paths_until_length(g, tolabelseqs=tolabelseqs) + - # compute shortest path matrices first, method borrowed from FCSP. - vk_dict = {} # shortest path matrices dict + def __paths2labelseqs(self, plist, G): if len(self.__node_labels) > 0: - # node symb and non-synb labeled - if len(self.__node_attrs) > 0: - kn = self.__node_kernels['mix'] - for n1, n2 in product( - g1.nodes(data=True), g2.nodes(data=True)): - n1_labels = [n1[1][nl] for nl in self.__node_labels] - n2_labels = [n2[1][nl] for nl in self.__node_labels] - n1_attrs = [n1[1][na] for na in self.__node_attrs] - n2_attrs = [n2[1][na] for na in self.__node_attrs] - vk_dict[(n1[0], n2[0])] = kn(n1_labels, n2_labels, n1_attrs, n2_attrs) - # node symb labeled + if len(self.__edge_labels) > 0: + path_strs = [] + for path in plist: + pths_tmp = [] + for idx, node in enumerate(path[:-1]): + pths_tmp.append(tuple(G.nodes[node][nl] for nl in self.__node_labels)) + pths_tmp.append(tuple(G[node][path[idx + 1]][el] for el in self.__edge_labels)) + pths_tmp.append(tuple(G.nodes[path[-1]][nl] for nl in self.__node_labels)) + path_strs.append(tuple(pths_tmp)) else: - kn = self.__node_kernels['symb'] - for n1 in g1.nodes(data=True): - for n2 in g2.nodes(data=True): - n1_labels = [n1[1][nl] for nl in self.__node_labels] - n2_labels = [n2[1][nl] for nl in self.__node_labels] - vk_dict[(n1[0], n2[0])] = kn(n1_labels, n2_labels) + path_strs = [] + for path in plist: + pths_tmp = [] + for node in path: + pths_tmp.append(tuple(G.nodes[node][nl] for nl in self.__node_labels)) + path_strs.append(tuple(pths_tmp)) + return path_strs else: - # node non-synb labeled - if len(self.__node_attrs) > 0: - kn = self.__node_kernels['nsymb'] - for n1 in g1.nodes(data=True): - for n2 in g2.nodes(data=True): - n1_attrs = [n1[1][na] for na in self.__node_attrs] - n2_attrs = [n2[1][na] for na in self.__node_attrs] - vk_dict[(n1[0], n2[0])] = kn(n1_attrs, n2_attrs) - # node unlabeled + if len(self.__edge_labels) > 0: + path_strs = [] + for path in plist: + if len(path) == 1: + path_strs.append(tuple()) + else: + pths_tmp = [] + for idx, node in enumerate(path[:-1]): + pths_tmp.append(tuple(G[node][path[idx + 1]][el] for el in self.__edge_labels)) + path_strs.append(tuple(pths_tmp)) + return path_strs else: - for e1, e2 in product( - g1.edges(data=True), g2.edges(data=True)): - if e1[2]['cost'] == e2[2]['cost']: - kernel += 1 - return kernel - - # compute graph kernels - if self.__ds_infos['directed']: - for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): - if e1[2]['cost'] == e2[2]['cost']: - nk11, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[(e1[1], e2[1])] - kn1 = nk11 * nk22 - kernel += kn1 - else: - for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): - if e1[2]['cost'] == e2[2]['cost']: - # each edge walk is counted twice, starting from both its extreme nodes. - nk11, nk12, nk21, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[( - e1[0], e2[1])], vk_dict[(e1[1], e2[0])], vk_dict[(e1[1], e2[1])] - kn1 = nk11 * nk22 - kn2 = nk12 * nk21 - kernel += kn1 + kn2 - - # # ---- exact implementation of the Fast Computation of Shortest Path Kernel (FCSP), reference [2], sadly it is slower than the current implementation - # # compute vertex kernels - # try: - # vk_mat = np.zeros((nx.number_of_nodes(g1), - # nx.number_of_nodes(g2))) - # g1nl = enumerate(g1.nodes(data=True)) - # g2nl = enumerate(g2.nodes(data=True)) - # for i1, n1 in g1nl: - # for i2, n2 in g2nl: - # vk_mat[i1][i2] = kn( - # n1[1][node_label], n2[1][node_label], - # [n1[1]['attributes']], [n2[1]['attributes']]) - - # range1 = range(0, len(edge_w_g[i])) - # range2 = range(0, len(edge_w_g[j])) - # for i1 in range1: - # x1 = edge_x_g[i][i1] - # y1 = edge_y_g[i][i1] - # w1 = edge_w_g[i][i1] - # for i2 in range2: - # x2 = edge_x_g[j][i2] - # y2 = edge_y_g[j][i2] - # w2 = edge_w_g[j][i2] - # ke = (w1 == w2) - # if ke > 0: - # kn1 = vk_mat[x1][x2] * vk_mat[y1][y2] - # kn2 = vk_mat[x1][y2] * vk_mat[y1][x2] - # kernel += kn1 + kn2 - - return kernel + return [tuple(['0' for node in path]) for path in plist] + # return [tuple([len(path)]) for path in all_paths] - def _wrapper_sp_do(self, itr): - i = itr[0] - j = itr[1] - return i, j, self.__sp_do(G_gs[i], G_gs[j]) + def __add_dummy_labels(self, Gn): + if self.__k_func is not None: + if len(self.__node_labels) == 0: + for G in Gn: + nx.set_node_attributes(G, '0', 'dummy') + self.__node_labels.append('dummy') + if len(self.__edge_labels) == 0: + for G in Gn: + nx.set_edge_attributes(G, '0', 'dummy') + self.__edge_labels.append('dummy') \ No newline at end of file diff --git a/gklearn/kernels/shortest_path.py b/gklearn/kernels/shortest_path.py index c11c2e5..1923b00 100644 --- a/gklearn/kernels/shortest_path.py +++ b/gklearn/kernels/shortest_path.py @@ -51,7 +51,7 @@ class ShortestPath(GraphKernel): else: iterator = itr for i, j in iterator: - kernel = self.__sp_do_(self._graphs[i], self._graphs[j]) + kernel = self.__sp_do(self._graphs[i], self._graphs[j]) gram_matrix[i][j] = kernel gram_matrix[j][i] = kernel diff --git a/gklearn/kernels/treelet.py b/gklearn/kernels/treelet.py new file mode 100644 index 0000000..134c683 --- /dev/null +++ b/gklearn/kernels/treelet.py @@ -0,0 +1,505 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Apr 13 18:02:46 2020 + +@author: ljia + +@references: + + [1] Gaüzère B, Brun L, Villemin D. Two new graphs kernels in + chemoinformatics. Pattern Recognition Letters. 2012 Nov 1;33(15):2038-47. +""" + +import sys +from multiprocessing import Pool +from tqdm import tqdm +import numpy as np +import networkx as nx +from collections import Counter +from itertools import chain +from gklearn.utils.parallel import parallel_gm, parallel_me +from gklearn.utils.utils import find_all_paths, get_mlti_dim_node_attrs +from gklearn.kernels import GraphKernel + + +class Treelet(GraphKernel): + + def __init__(self, **kwargs): + GraphKernel.__init__(self) + self.__node_labels = kwargs.get('node_labels', []) + self.__edge_labels = kwargs.get('edge_labels', []) + self.__sub_kernel = kwargs.get('sub_kernel', None) + self.__ds_infos = kwargs.get('ds_infos', {}) + if self.__sub_kernel is None: + raise Exception('Sub kernel not set.') + + + def _compute_gm_series(self): + self.__add_dummy_labels(self._graphs) + + # get all canonical keys of all graphs before calculating kernels to save + # time, but this may cost a lot of memory for large dataset. + canonkeys = [] + if self._verbose >= 2: + iterator = tqdm(self._graphs, desc='getting canonkeys', file=sys.stdout) + else: + iterator = self._graphs + for g in iterator: + canonkeys.append(self.__get_canonkeys(g)) + + # compute Gram matrix. + gram_matrix = np.zeros((len(self._graphs), len(self._graphs))) + + from itertools import combinations_with_replacement + itr = combinations_with_replacement(range(0, len(self._graphs)), 2) + if self._verbose >= 2: + iterator = tqdm(itr, desc='calculating kernels', file=sys.stdout) + else: + iterator = itr + for i, j in iterator: + kernel = self.__kernel_do(canonkeys[i], canonkeys[j]) + gram_matrix[i][j] = kernel + gram_matrix[j][i] = kernel # @todo: no directed graph considered? + + return gram_matrix + + + def _compute_gm_imap_unordered(self): + self.__add_dummy_labels(self._graphs) + + # get all canonical keys of all graphs before calculating kernels to save + # time, but this may cost a lot of memory for large dataset. + pool = Pool(self._n_jobs) + itr = zip(self._graphs, range(0, len(self._graphs))) + if len(self._graphs) < 100 * self._n_jobs: + chunksize = int(len(self._graphs) / self._n_jobs) + 1 + else: + chunksize = 100 + canonkeys = [[] for _ in range(len(self._graphs))] + get_fun = self._wrapper_get_canonkeys + if self._verbose >= 2: + iterator = tqdm(pool.imap_unordered(get_fun, itr, chunksize), + desc='getting canonkeys', file=sys.stdout) + else: + iterator = pool.imap_unordered(get_fun, itr, chunksize) + for i, ck in iterator: + canonkeys[i] = ck + pool.close() + pool.join() + + # compute Gram matrix. + gram_matrix = np.zeros((len(self._graphs), len(self._graphs))) + + def init_worker(canonkeys_toshare): + global G_canonkeys + G_canonkeys = canonkeys_toshare + do_fun = self._wrapper_kernel_do + parallel_gm(do_fun, gram_matrix, self._graphs, init_worker=init_worker, + glbv=(canonkeys,), n_jobs=self._n_jobs, verbose=self._verbose) + + return gram_matrix + + + def _compute_kernel_list_series(self, g1, g_list): + self.__add_dummy_labels(g_list + [g1]) + + # get all canonical keys of all graphs before calculating kernels to save + # time, but this may cost a lot of memory for large dataset. + canonkeys_1 = self.__get_canonkeys(g1) + canonkeys_list = [] + if self._verbose >= 2: + iterator = tqdm(g_list, desc='getting canonkeys', file=sys.stdout) + else: + iterator = g_list + for g in iterator: + canonkeys_list.append(self.__get_canonkeys(g)) + + # compute kernel list. + kernel_list = [None] * len(g_list) + if self._verbose >= 2: + iterator = tqdm(range(len(g_list)), desc='calculating kernels', file=sys.stdout) + else: + iterator = range(len(g_list)) + for i in iterator: + kernel = self.__kernel_do(canonkeys_1, canonkeys_list[i]) + kernel_list[i] = kernel + + return kernel_list + + + def _compute_kernel_list_imap_unordered(self, g1, g_list): + self.__add_dummy_labels(g_list + [g1]) + + # get all canonical keys of all graphs before calculating kernels to save + # time, but this may cost a lot of memory for large dataset. + canonkeys_1 = self.__get_canonkeys(g1) + canonkeys_list = [[] for _ in range(len(g_list))] + pool = Pool(self._n_jobs) + itr = zip(g_list, range(0, len(g_list))) + if len(g_list) < 100 * self._n_jobs: + chunksize = int(len(g_list) / self._n_jobs) + 1 + else: + chunksize = 100 + get_fun = self._wrapper_get_canonkeys + if self._verbose >= 2: + iterator = tqdm(pool.imap_unordered(get_fun, itr, chunksize), + desc='getting canonkeys', file=sys.stdout) + else: + iterator = pool.imap_unordered(get_fun, itr, chunksize) + for i, ck in iterator: + canonkeys_list[i] = ck + pool.close() + pool.join() + + # compute kernel list. + kernel_list = [None] * len(g_list) + + def init_worker(ck_1_toshare, ck_list_toshare): + global G_ck_1, G_ck_list + G_ck_1 = ck_1_toshare + G_ck_list = ck_list_toshare + do_fun = self._wrapper_kernel_list_do + def func_assign(result, var_to_assign): + var_to_assign[result[0]] = result[1] + itr = range(len(g_list)) + len_itr = len(g_list) + parallel_me(do_fun, func_assign, kernel_list, itr, len_itr=len_itr, + init_worker=init_worker, glbv=(canonkeys_1, canonkeys_list), method='imap_unordered', + n_jobs=self._n_jobs, itr_desc='calculating kernels', verbose=self._verbose) + + return kernel_list + + + def _wrapper_kernel_list_do(self, itr): + return itr, self.__kernel_do(G_ck_1, G_ck_list[itr]) + + + def _compute_single_kernel_series(self, g1, g2): + self.__add_dummy_labels([g1] + [g2]) + canonkeys_1 = self.__get_canonkeys(g1) + canonkeys_2 = self.__get_canonkeys(g2) + kernel = self.__kernel_do(canonkeys_1, canonkeys_2) + return kernel + + + def __kernel_do(self, canonkey1, canonkey2): + """Calculate treelet graph kernel between 2 graphs. + + Parameters + ---------- + canonkey1, canonkey2 : list + List of canonical keys in 2 graphs, where each key is represented by a string. + + Return + ------ + kernel : float + Treelet Kernel between 2 graphs. + """ + keys = set(canonkey1.keys()) & set(canonkey2.keys()) # find same canonical keys in both graphs + vector1 = np.array([(canonkey1[key] if (key in canonkey1.keys()) else 0) for key in keys]) + vector2 = np.array([(canonkey2[key] if (key in canonkey2.keys()) else 0) for key in keys]) + kernel = self.__sub_kernel(vector1, vector2) + return kernel + + + def _wrapper_kernel_do(self, itr): + i = itr[0] + j = itr[1] + return i, j, self.__kernel_do(G_canonkeys[i], G_canonkeys[j]) + + + def __get_canonkeys(self, G): + """Generate canonical keys of all treelets in a graph. + + Parameters + ---------- + G : NetworkX graphs + The graph in which keys are generated. + + Return + ------ + canonkey/canonkey_l : dict + For unlabeled graphs, canonkey is a dictionary which records amount of + every tree pattern. For labeled graphs, canonkey_l is one which keeps + track of amount of every treelet. + """ + patterns = {} # a dictionary which consists of lists of patterns for all graphlet. + canonkey = {} # canonical key, a dictionary which records amount of every tree pattern. + + ### structural analysis ### + ### In this section, a list of patterns is generated for each graphlet, + ### where every pattern is represented by nodes ordered by Morgan's + ### extended labeling. + # linear patterns + patterns['0'] = list(G.nodes()) + canonkey['0'] = nx.number_of_nodes(G) + for i in range(1, 6): # for i in range(1, 6): + patterns[str(i)] = find_all_paths(G, i, self.__ds_infos['directed']) + canonkey[str(i)] = len(patterns[str(i)]) + + # n-star patterns + patterns['3star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 3] + patterns['4star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 4] + patterns['5star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 5] + # n-star patterns + canonkey['6'] = len(patterns['3star']) + canonkey['8'] = len(patterns['4star']) + canonkey['d'] = len(patterns['5star']) + + # pattern 7 + patterns['7'] = [] # the 1st line of Table 1 in Ref [1] + for pattern in patterns['3star']: + for i in range(1, len(pattern)): # for each neighbor of node 0 + if G.degree(pattern[i]) >= 2: + pattern_t = pattern[:] + # set the node with degree >= 2 as the 4th node + pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i] + for neighborx in G[pattern[i]]: + if neighborx != pattern[0]: + new_pattern = pattern_t + [neighborx] + patterns['7'].append(new_pattern) + canonkey['7'] = len(patterns['7']) + + # pattern 11 + patterns['11'] = [] # the 4th line of Table 1 in Ref [1] + for pattern in patterns['4star']: + for i in range(1, len(pattern)): + if G.degree(pattern[i]) >= 2: + pattern_t = pattern[:] + pattern_t[i], pattern_t[4] = pattern_t[4], pattern_t[i] + for neighborx in G[pattern[i]]: + if neighborx != pattern[0]: + new_pattern = pattern_t + [neighborx] + patterns['11'].append(new_pattern) + canonkey['b'] = len(patterns['11']) + + # pattern 12 + patterns['12'] = [] # the 5th line of Table 1 in Ref [1] + rootlist = [] # a list of root nodes, whose extended labels are 3 + for pattern in patterns['3star']: + if pattern[0] not in rootlist: # prevent to count the same pattern twice from each of the two root nodes + rootlist.append(pattern[0]) + for i in range(1, len(pattern)): + if G.degree(pattern[i]) >= 3: + rootlist.append(pattern[i]) + pattern_t = pattern[:] + pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i] + for neighborx1 in G[pattern[i]]: + if neighborx1 != pattern[0]: + for neighborx2 in G[pattern[i]]: + if neighborx1 > neighborx2 and neighborx2 != pattern[0]: + new_pattern = pattern_t + [neighborx1] + [neighborx2] + # new_patterns = [ pattern + [neighborx1] + [neighborx2] for neighborx1 in G[pattern[i]] if neighborx1 != pattern[0] for neighborx2 in G[pattern[i]] if (neighborx1 > neighborx2 and neighborx2 != pattern[0]) ] + patterns['12'].append(new_pattern) + canonkey['c'] = int(len(patterns['12']) / 2) + + # pattern 9 + patterns['9'] = [] # the 2nd line of Table 1 in Ref [1] + for pattern in patterns['3star']: + for pairs in [ [neighbor1, neighbor2] for neighbor1 in G[pattern[0]] if G.degree(neighbor1) >= 2 \ + for neighbor2 in G[pattern[0]] if G.degree(neighbor2) >= 2 if neighbor1 > neighbor2]: + pattern_t = pattern[:] + # move nodes with extended labels 4 to specific position to correspond to their children + pattern_t[pattern_t.index(pairs[0])], pattern_t[2] = pattern_t[2], pattern_t[pattern_t.index(pairs[0])] + pattern_t[pattern_t.index(pairs[1])], pattern_t[3] = pattern_t[3], pattern_t[pattern_t.index(pairs[1])] + for neighborx1 in G[pairs[0]]: + if neighborx1 != pattern[0]: + for neighborx2 in G[pairs[1]]: + if neighborx2 != pattern[0]: + new_pattern = pattern_t + [neighborx1] + [neighborx2] + patterns['9'].append(new_pattern) + canonkey['9'] = len(patterns['9']) + + # pattern 10 + patterns['10'] = [] # the 3rd line of Table 1 in Ref [1] + for pattern in patterns['3star']: + for i in range(1, len(pattern)): + if G.degree(pattern[i]) >= 2: + for neighborx in G[pattern[i]]: + if neighborx != pattern[0] and G.degree(neighborx) >= 2: + pattern_t = pattern[:] + pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i] + new_patterns = [ pattern_t + [neighborx] + [neighborxx] for neighborxx in G[neighborx] if neighborxx != pattern[i] ] + patterns['10'].extend(new_patterns) + canonkey['a'] = len(patterns['10']) + + ### labeling information ### + ### In this section, a list of canonical keys is generated for every + ### pattern obtained in the structural analysis section above, which is a + ### string corresponding to a unique treelet. A dictionary is built to keep + ### track of the amount of every treelet. + if len(self.__node_labels) > 0 or len(self.__edge_labels) > 0: + canonkey_l = {} # canonical key, a dictionary which keeps track of amount of every treelet. + + # linear patterns + canonkey_t = Counter(get_mlti_dim_node_attrs(G, self.__node_labels)) + for key in canonkey_t: + canonkey_l[('0', key)] = canonkey_t[key] + + for i in range(1, 6): # for i in range(1, 6): + treelet = [] + for pattern in patterns[str(i)]: + canonlist = [] + for idx, node in enumerate(pattern[:-1]): + canonlist.append(tuple(G.nodes[node][nl] for nl in self.__node_labels)) + canonlist.append(tuple(G[node][pattern[idx+1]][el] for el in self.__edge_labels)) + canonlist.append(tuple(G.nodes[pattern[-1]][nl] for nl in self.__node_labels)) + canonkey_t = canonlist if canonlist < canonlist[::-1] else canonlist[::-1] + treelet.append(tuple([str(i)] + canonkey_t)) + canonkey_l.update(Counter(treelet)) + + # n-star patterns + for i in range(3, 6): + treelet = [] + for pattern in patterns[str(i) + 'star']: + canonlist = [] + for leaf in pattern[1:]: + nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels) + elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels) + canonlist.append(tuple((nlabels, elabels))) + canonlist.sort() + canonlist = list(chain.from_iterable(canonlist)) + canonkey_t = tuple(['d' if i == 5 else str(i * 2)] + + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + + canonlist) + treelet.append(canonkey_t) + canonkey_l.update(Counter(treelet)) + + # pattern 7 + treelet = [] + for pattern in patterns['7']: + canonlist = [] + for leaf in pattern[1:3]: + nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels) + elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels) + canonlist.append(tuple((nlabels, elabels))) + canonlist.sort() + canonlist = list(chain.from_iterable(canonlist)) + canonkey_t = tuple(['7'] + + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + canonlist + + [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels)] + + [tuple(G[pattern[3]][pattern[0]][el] for el in self.__edge_labels)] + + [tuple(G.nodes[pattern[4]][nl] for nl in self.__node_labels)] + + [tuple(G[pattern[4]][pattern[3]][el] for el in self.__edge_labels)]) + treelet.append(canonkey_t) + canonkey_l.update(Counter(treelet)) + + # pattern 11 + treelet = [] + for pattern in patterns['11']: + canonlist = [] + for leaf in pattern[1:4]: + nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels) + elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels) + canonlist.append(tuple((nlabels, elabels))) + canonlist.sort() + canonlist = list(chain.from_iterable(canonlist)) + canonkey_t = tuple(['b'] + + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + canonlist + + [tuple(G.nodes[pattern[4]][nl] for nl in self.__node_labels)] + + [tuple(G[pattern[4]][pattern[0]][el] for el in self.__edge_labels)] + + [tuple(G.nodes[pattern[5]][nl] for nl in self.__node_labels)] + + [tuple(G[pattern[5]][pattern[4]][el] for el in self.__edge_labels)]) + treelet.append(canonkey_t) + canonkey_l.update(Counter(treelet)) + + # pattern 10 + treelet = [] + for pattern in patterns['10']: + canonkey4 = [tuple(G.nodes[pattern[5]][nl] for nl in self.__node_labels), + tuple(G[pattern[5]][pattern[4]][el] for el in self.__edge_labels)] + canonlist = [] + for leaf in pattern[1:3]: + nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels) + elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels) + canonlist.append(tuple((nlabels, elabels))) + canonlist.sort() + canonkey0 = list(chain.from_iterable(canonlist)) + canonkey_t = tuple(['a'] + + [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels)] + + [tuple(G.nodes[pattern[4]][nl] for nl in self.__node_labels)] + + [tuple(G[pattern[4]][pattern[3]][el] for el in self.__edge_labels)] + + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + + [tuple(G[pattern[0]][pattern[3]][el] for el in self.__edge_labels)] + + canonkey4 + canonkey0) + treelet.append(canonkey_t) + canonkey_l.update(Counter(treelet)) + + # pattern 12 + treelet = [] + for pattern in patterns['12']: + canonlist0 = [] + for leaf in pattern[1:3]: + nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels) + elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels) + canonlist0.append(tuple((nlabels, elabels))) + canonlist0.sort() + canonlist0 = list(chain.from_iterable(canonlist0)) + canonlist3 = [] + for leaf in pattern[4:6]: + nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels) + elabels = tuple(G[leaf][pattern[3]][el] for el in self.__edge_labels) + canonlist3.append(tuple((nlabels, elabels))) + canonlist3.sort() + canonlist3 = list(chain.from_iterable(canonlist3)) + + # 2 possible key can be generated from 2 nodes with extended label 3, + # select the one with lower lexicographic order. + canonkey_t1 = tuple(['c'] + + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + canonlist0 + + [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels)] + + [tuple(G[pattern[3]][pattern[0]][el] for el in self.__edge_labels)] + + canonlist3) + canonkey_t2 = tuple(['c'] + + [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels)] + canonlist3 + + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + + [tuple(G[pattern[0]][pattern[3]][el] for el in self.__edge_labels)] + + canonlist0) + treelet.append(canonkey_t1 if canonkey_t1 < canonkey_t2 else canonkey_t2) + canonkey_l.update(Counter(treelet)) + + # pattern 9 + treelet = [] + for pattern in patterns['9']: + canonkey2 = [tuple(G.nodes[pattern[4]][nl] for nl in self.__node_labels), + tuple(G[pattern[4]][pattern[2]][el] for el in self.__edge_labels)] + canonkey3 = [tuple(G.nodes[pattern[5]][nl] for nl in self.__node_labels), + tuple(G[pattern[5]][pattern[3]][el] for el in self.__edge_labels)] + prekey2 = [tuple(G.nodes[pattern[2]][nl] for nl in self.__node_labels), + tuple(G[pattern[2]][pattern[0]][el] for el in self.__edge_labels)] + prekey3 = [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels), + tuple(G[pattern[3]][pattern[0]][el] for el in self.__edge_labels)] + if prekey2 + canonkey2 < prekey3 + canonkey3: + canonkey_t = [tuple(G.nodes[pattern[1]][nl] for nl in self.__node_labels)] \ + + [tuple(G[pattern[1]][pattern[0]][el] for el in self.__edge_labels)] \ + + prekey2 + prekey3 + canonkey2 + canonkey3 + else: + canonkey_t = [tuple(G.nodes[pattern[1]][nl] for nl in self.__node_labels)] \ + + [tuple(G[pattern[1]][pattern[0]][el] for el in self.__edge_labels)] \ + + prekey3 + prekey2 + canonkey3 + canonkey2 + treelet.append(tuple(['9'] + + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + + canonkey_t)) + canonkey_l.update(Counter(treelet)) + + return canonkey_l + + return canonkey + + + def _wrapper_get_canonkeys(self, itr_item): + g = itr_item[0] + i = itr_item[1] + return i, self.__get_canonkeys(g) + + + def __add_dummy_labels(self, Gn): + if len(self.__node_labels) == 0: + for G in Gn: + nx.set_node_attributes(G, '0', 'dummy') + self.__node_labels.append('dummy') + if len(self.__edge_labels) == 0: + for G in Gn: + nx.set_edge_attributes(G, '0', 'dummy') + self.__edge_labels.append('dummy') \ No newline at end of file diff --git a/gklearn/kernels/treeletKernel.py b/gklearn/kernels/treeletKernel.py index 946f20f..d4447b1 100644 --- a/gklearn/kernels/treeletKernel.py +++ b/gklearn/kernels/treeletKernel.py @@ -3,8 +3,8 @@ @references: - [1] Gaüzère B, Brun L, Villemin D. Two new graphs kernels in - chemoinformatics. Pattern Recognition Letters. 2012 Nov 1;33(15):2038-47. + [1] Gaüzère B, Brun L, Villemin D. Two new graphs kernels in + chemoinformatics. Pattern Recognition Letters. 2012 Nov 1;33(15):2038-47. """ import sys @@ -22,467 +22,467 @@ from gklearn.utils.graphdataset import get_dataset_attributes from gklearn.utils.parallel import parallel_gm def treeletkernel(*args, - sub_kernel, - node_label='atom', - edge_label='bond_type', - parallel='imap_unordered', - n_jobs=None, - verbose=True): - """Calculate treelet graph kernels between graphs. - - Parameters - ---------- - Gn : List of NetworkX graph - List of graphs between which the kernels are calculated. - - G1, G2 : NetworkX graphs - Two graphs between which the kernel is calculated. - - sub_kernel : function - The sub-kernel between 2 real number vectors. Each vector counts the - numbers of isomorphic treelets in a graph. - - node_label : string - Node attribute used as label. The default node label is atom. - - edge_label : string - Edge attribute used as label. The default edge label is bond_type. - - parallel : string/None - Which paralleliztion method is applied to compute the kernel. The - Following choices are available: - - 'imap_unordered': use Python's multiprocessing.Pool.imap_unordered - method. - - None: no parallelization is applied. - - n_jobs : int - Number of jobs for parallelization. The default is to use all - computational cores. This argument is only valid when one of the - parallelization method is applied. - - Return - ------ - Kmatrix : Numpy matrix - Kernel matrix, each element of which is the treelet kernel between 2 praphs. - """ - # pre-process - Gn = args[0] if len(args) == 1 else [args[0], args[1]] - Gn = [g.copy() for g in Gn] - Kmatrix = np.zeros((len(Gn), len(Gn))) - ds_attrs = get_dataset_attributes(Gn, - attr_names=['node_labeled', 'edge_labeled', 'is_directed'], - node_label=node_label, edge_label=edge_label) - labeled = False - if ds_attrs['node_labeled'] or ds_attrs['edge_labeled']: - labeled = True - if not ds_attrs['node_labeled']: - for G in Gn: - nx.set_node_attributes(G, '0', 'atom') - if not ds_attrs['edge_labeled']: - for G in Gn: - nx.set_edge_attributes(G, '0', 'bond_type') - - start_time = time.time() - - # ---- use pool.imap_unordered to parallel and track progress. ---- - if parallel == 'imap_unordered': - # get all canonical keys of all graphs before calculating kernels to save - # time, but this may cost a lot of memory for large dataset. - pool = Pool(n_jobs) - itr = zip(Gn, range(0, len(Gn))) - if len(Gn) < 100 * n_jobs: - chunksize = int(len(Gn) / n_jobs) + 1 - else: - chunksize = 100 - canonkeys = [[] for _ in range(len(Gn))] - get_partial = partial(wrapper_get_canonkeys, node_label, edge_label, - labeled, ds_attrs['is_directed']) - if verbose: - iterator = tqdm(pool.imap_unordered(get_partial, itr, chunksize), - desc='getting canonkeys', file=sys.stdout) - else: - iterator = pool.imap_unordered(get_partial, itr, chunksize) - for i, ck in iterator: - canonkeys[i] = ck - pool.close() - pool.join() - - # compute kernels. - def init_worker(canonkeys_toshare): - global G_canonkeys - G_canonkeys = canonkeys_toshare - do_partial = partial(wrapper_treeletkernel_do, sub_kernel) - parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker, - glbv=(canonkeys,), n_jobs=n_jobs, verbose=verbose) - - # ---- do not use parallelization. ---- - elif parallel == None: - # get all canonical keys of all graphs before calculating kernels to save - # time, but this may cost a lot of memory for large dataset. - canonkeys = [] - for g in (tqdm(Gn, desc='getting canonkeys', file=sys.stdout) if verbose else Gn): - canonkeys.append(get_canonkeys(g, node_label, edge_label, labeled, - ds_attrs['is_directed'])) - - # compute kernels. - from itertools import combinations_with_replacement - itr = combinations_with_replacement(range(0, len(Gn)), 2) - for i, j in (tqdm(itr, desc='getting canonkeys', file=sys.stdout) if verbose else itr): - Kmatrix[i][j] = _treeletkernel_do(canonkeys[i], canonkeys[j], sub_kernel) - Kmatrix[j][i] = Kmatrix[i][j] # @todo: no directed graph considered? - - else: - raise Exception('No proper parallelization method designated.') - - - run_time = time.time() - start_time - if verbose: - print("\n --- treelet kernel matrix of size %d built in %s seconds ---" - % (len(Gn), run_time)) - - return Kmatrix, run_time + sub_kernel, + node_label='atom', + edge_label='bond_type', + parallel='imap_unordered', + n_jobs=None, + verbose=True): + """Calculate treelet graph kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + + G1, G2 : NetworkX graphs + Two graphs between which the kernel is calculated. + + sub_kernel : function + The sub-kernel between 2 real number vectors. Each vector counts the + numbers of isomorphic treelets in a graph. + + node_label : string + Node attribute used as label. The default node label is atom. + + edge_label : string + Edge attribute used as label. The default edge label is bond_type. + + parallel : string/None + Which paralleliztion method is applied to compute the kernel. The + Following choices are available: + + 'imap_unordered': use Python's multiprocessing.Pool.imap_unordered + method. + + None: no parallelization is applied. + + n_jobs : int + Number of jobs for parallelization. The default is to use all + computational cores. This argument is only valid when one of the + parallelization method is applied. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the treelet kernel between 2 praphs. + """ + # pre-process + Gn = args[0] if len(args) == 1 else [args[0], args[1]] + Gn = [g.copy() for g in Gn] + Kmatrix = np.zeros((len(Gn), len(Gn))) + ds_attrs = get_dataset_attributes(Gn, + attr_names=['node_labeled', 'edge_labeled', 'is_directed'], + node_label=node_label, edge_label=edge_label) + labeled = False + if ds_attrs['node_labeled'] or ds_attrs['edge_labeled']: + labeled = True + if not ds_attrs['node_labeled']: + for G in Gn: + nx.set_node_attributes(G, '0', 'atom') + if not ds_attrs['edge_labeled']: + for G in Gn: + nx.set_edge_attributes(G, '0', 'bond_type') + + start_time = time.time() + + # ---- use pool.imap_unordered to parallel and track progress. ---- + if parallel == 'imap_unordered': + # get all canonical keys of all graphs before calculating kernels to save + # time, but this may cost a lot of memory for large dataset. + pool = Pool(n_jobs) + itr = zip(Gn, range(0, len(Gn))) + if len(Gn) < 100 * n_jobs: + chunksize = int(len(Gn) / n_jobs) + 1 + else: + chunksize = 100 + canonkeys = [[] for _ in range(len(Gn))] + get_partial = partial(wrapper_get_canonkeys, node_label, edge_label, + labeled, ds_attrs['is_directed']) + if verbose: + iterator = tqdm(pool.imap_unordered(get_partial, itr, chunksize), + desc='getting canonkeys', file=sys.stdout) + else: + iterator = pool.imap_unordered(get_partial, itr, chunksize) + for i, ck in iterator: + canonkeys[i] = ck + pool.close() + pool.join() + + # compute kernels. + def init_worker(canonkeys_toshare): + global G_canonkeys + G_canonkeys = canonkeys_toshare + do_partial = partial(wrapper_treeletkernel_do, sub_kernel) + parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker, + glbv=(canonkeys,), n_jobs=n_jobs, verbose=verbose) + + # ---- do not use parallelization. ---- + elif parallel == None: + # get all canonical keys of all graphs before calculating kernels to save + # time, but this may cost a lot of memory for large dataset. + canonkeys = [] + for g in (tqdm(Gn, desc='getting canonkeys', file=sys.stdout) if verbose else Gn): + canonkeys.append(get_canonkeys(g, node_label, edge_label, labeled, + ds_attrs['is_directed'])) + + # compute kernels. + from itertools import combinations_with_replacement + itr = combinations_with_replacement(range(0, len(Gn)), 2) + for i, j in (tqdm(itr, desc='getting canonkeys', file=sys.stdout) if verbose else itr): + Kmatrix[i][j] = _treeletkernel_do(canonkeys[i], canonkeys[j], sub_kernel) + Kmatrix[j][i] = Kmatrix[i][j] # @todo: no directed graph considered? + + else: + raise Exception('No proper parallelization method designated.') + + + run_time = time.time() - start_time + if verbose: + print("\n --- treelet kernel matrix of size %d built in %s seconds ---" + % (len(Gn), run_time)) + + return Kmatrix, run_time def _treeletkernel_do(canonkey1, canonkey2, sub_kernel): - """Calculate treelet graph kernel between 2 graphs. - - Parameters - ---------- - canonkey1, canonkey2 : list - List of canonical keys in 2 graphs, where each key is represented by a string. - - Return - ------ - kernel : float - Treelet Kernel between 2 graphs. - """ - keys = set(canonkey1.keys()) & set(canonkey2.keys()) # find same canonical keys in both graphs - vector1 = np.array([(canonkey1[key] if (key in canonkey1.keys()) else 0) for key in keys]) - vector2 = np.array([(canonkey2[key] if (key in canonkey2.keys()) else 0) for key in keys]) - kernel = sub_kernel(vector1, vector2) - return kernel + """Calculate treelet graph kernel between 2 graphs. + + Parameters + ---------- + canonkey1, canonkey2 : list + List of canonical keys in 2 graphs, where each key is represented by a string. + + Return + ------ + kernel : float + Treelet Kernel between 2 graphs. + """ + keys = set(canonkey1.keys()) & set(canonkey2.keys()) # find same canonical keys in both graphs + vector1 = np.array([(canonkey1[key] if (key in canonkey1.keys()) else 0) for key in keys]) + vector2 = np.array([(canonkey2[key] if (key in canonkey2.keys()) else 0) for key in keys]) + kernel = sub_kernel(vector1, vector2) + return kernel def wrapper_treeletkernel_do(sub_kernel, itr): - i = itr[0] - j = itr[1] - return i, j, _treeletkernel_do(G_canonkeys[i], G_canonkeys[j], sub_kernel) + i = itr[0] + j = itr[1] + return i, j, _treeletkernel_do(G_canonkeys[i], G_canonkeys[j], sub_kernel) def get_canonkeys(G, node_label, edge_label, labeled, is_directed): - """Generate canonical keys of all treelets in a graph. - - Parameters - ---------- - G : NetworkX graphs - The graph in which keys are generated. - node_label : string - node attribute used as label. The default node label is atom. - edge_label : string - edge attribute used as label. The default edge label is bond_type. - labeled : boolean - Whether the graphs are labeled. The default is True. - - Return - ------ - canonkey/canonkey_l : dict - For unlabeled graphs, canonkey is a dictionary which records amount of - every tree pattern. For labeled graphs, canonkey_l is one which keeps - track of amount of every treelet. - """ - patterns = {} # a dictionary which consists of lists of patterns for all graphlet. - canonkey = {} # canonical key, a dictionary which records amount of every tree pattern. - - ### structural analysis ### - ### In this section, a list of patterns is generated for each graphlet, - ### where every pattern is represented by nodes ordered by Morgan's - ### extended labeling. - # linear patterns - patterns['0'] = G.nodes() - canonkey['0'] = nx.number_of_nodes(G) - for i in range(1, 6): # for i in range(1, 6): - patterns[str(i)] = find_all_paths(G, i, is_directed) - canonkey[str(i)] = len(patterns[str(i)]) - - # n-star patterns - patterns['3star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 3] - patterns['4star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 4] - patterns['5star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 5] - # n-star patterns - canonkey['6'] = len(patterns['3star']) - canonkey['8'] = len(patterns['4star']) - canonkey['d'] = len(patterns['5star']) - - # pattern 7 - patterns['7'] = [] # the 1st line of Table 1 in Ref [1] - for pattern in patterns['3star']: - for i in range(1, len(pattern)): # for each neighbor of node 0 - if G.degree(pattern[i]) >= 2: - pattern_t = pattern[:] - # set the node with degree >= 2 as the 4th node - pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i] - for neighborx in G[pattern[i]]: - if neighborx != pattern[0]: - new_pattern = pattern_t + [neighborx] - patterns['7'].append(new_pattern) - canonkey['7'] = len(patterns['7']) - - # pattern 11 - patterns['11'] = [] # the 4th line of Table 1 in Ref [1] - for pattern in patterns['4star']: - for i in range(1, len(pattern)): - if G.degree(pattern[i]) >= 2: - pattern_t = pattern[:] - pattern_t[i], pattern_t[4] = pattern_t[4], pattern_t[i] - for neighborx in G[pattern[i]]: - if neighborx != pattern[0]: - new_pattern = pattern_t + [ neighborx ] - patterns['11'].append(new_pattern) - canonkey['b'] = len(patterns['11']) - - # pattern 12 - patterns['12'] = [] # the 5th line of Table 1 in Ref [1] - rootlist = [] # a list of root nodes, whose extended labels are 3 - for pattern in patterns['3star']: - if pattern[0] not in rootlist: # prevent to count the same pattern twice from each of the two root nodes - rootlist.append(pattern[0]) - for i in range(1, len(pattern)): - if G.degree(pattern[i]) >= 3: - rootlist.append(pattern[i]) - pattern_t = pattern[:] - pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i] - for neighborx1 in G[pattern[i]]: - if neighborx1 != pattern[0]: - for neighborx2 in G[pattern[i]]: - if neighborx1 > neighborx2 and neighborx2 != pattern[0]: - new_pattern = pattern_t + [neighborx1] + [neighborx2] -# new_patterns = [ pattern + [neighborx1] + [neighborx2] for neighborx1 in G[pattern[i]] if neighborx1 != pattern[0] for neighborx2 in G[pattern[i]] if (neighborx1 > neighborx2 and neighborx2 != pattern[0]) ] - patterns['12'].append(new_pattern) - canonkey['c'] = int(len(patterns['12']) / 2) - - # pattern 9 - patterns['9'] = [] # the 2nd line of Table 1 in Ref [1] - for pattern in patterns['3star']: - for pairs in [ [neighbor1, neighbor2] for neighbor1 in G[pattern[0]] if G.degree(neighbor1) >= 2 \ - for neighbor2 in G[pattern[0]] if G.degree(neighbor2) >= 2 if neighbor1 > neighbor2 ]: - pattern_t = pattern[:] - # move nodes with extended labels 4 to specific position to correspond to their children - pattern_t[pattern_t.index(pairs[0])], pattern_t[2] = pattern_t[2], pattern_t[pattern_t.index(pairs[0])] - pattern_t[pattern_t.index(pairs[1])], pattern_t[3] = pattern_t[3], pattern_t[pattern_t.index(pairs[1])] - for neighborx1 in G[pairs[0]]: - if neighborx1 != pattern[0]: - for neighborx2 in G[pairs[1]]: - if neighborx2 != pattern[0]: - new_pattern = pattern_t + [neighborx1] + [neighborx2] - patterns['9'].append(new_pattern) - canonkey['9'] = len(patterns['9']) - - # pattern 10 - patterns['10'] = [] # the 3rd line of Table 1 in Ref [1] - for pattern in patterns['3star']: - for i in range(1, len(pattern)): - if G.degree(pattern[i]) >= 2: - for neighborx in G[pattern[i]]: - if neighborx != pattern[0] and G.degree(neighborx) >= 2: - pattern_t = pattern[:] - pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i] - new_patterns = [ pattern_t + [neighborx] + [neighborxx] for neighborxx in G[neighborx] if neighborxx != pattern[i] ] - patterns['10'].extend(new_patterns) - canonkey['a'] = len(patterns['10']) - - ### labeling information ### - ### In this section, a list of canonical keys is generated for every - ### pattern obtained in the structural analysis section above, which is a - ### string corresponding to a unique treelet. A dictionary is built to keep - ### track of the amount of every treelet. - if labeled == True: - canonkey_l = {} # canonical key, a dictionary which keeps track of amount of every treelet. - - # linear patterns - canonkey_t = Counter(list(nx.get_node_attributes(G, node_label).values())) - for key in canonkey_t: - canonkey_l[('0', key)] = canonkey_t[key] - - for i in range(1, 6): # for i in range(1, 6): - treelet = [] - for pattern in patterns[str(i)]: - canonlist = list(chain.from_iterable((G.nodes[node][node_label], \ - G[node][pattern[idx+1]][edge_label]) for idx, node in enumerate(pattern[:-1]))) - canonlist.append(G.nodes[pattern[-1]][node_label]) - canonkey_t = canonlist if canonlist < canonlist[::-1] else canonlist[::-1] - treelet.append(tuple([str(i)] + canonkey_t)) - canonkey_l.update(Counter(treelet)) - - # n-star patterns - for i in range(3, 6): - treelet = [] - for pattern in patterns[str(i) + 'star']: - canonlist = [tuple((G.nodes[leaf][node_label], - G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:]] - canonlist.sort() - canonlist = list(chain.from_iterable(canonlist)) - canonkey_t = tuple(['d' if i == 5 else str(i * 2)] + - [G.nodes[pattern[0]][node_label]] + canonlist) - treelet.append(canonkey_t) - canonkey_l.update(Counter(treelet)) - - # pattern 7 - treelet = [] - for pattern in patterns['7']: - canonlist = [tuple((G.nodes[leaf][node_label], - G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:3]] - canonlist.sort() - canonlist = list(chain.from_iterable(canonlist)) - canonkey_t = tuple(['7'] + [G.nodes[pattern[0]][node_label]] + canonlist - + [G.nodes[pattern[3]][node_label]] - + [G[pattern[3]][pattern[0]][edge_label]] - + [G.nodes[pattern[4]][node_label]] - + [G[pattern[4]][pattern[3]][edge_label]]) - treelet.append(canonkey_t) - canonkey_l.update(Counter(treelet)) - - # pattern 11 - treelet = [] - for pattern in patterns['11']: - canonlist = [tuple((G.nodes[leaf][node_label], - G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:4]] - canonlist.sort() - canonlist = list(chain.from_iterable(canonlist)) - canonkey_t = tuple(['b'] + [G.nodes[pattern[0]][node_label]] + canonlist - + [G.nodes[pattern[4]][node_label]] - + [G[pattern[4]][pattern[0]][edge_label]] - + [G.nodes[pattern[5]][node_label]] - + [G[pattern[5]][pattern[4]][edge_label]]) - treelet.append(canonkey_t) - canonkey_l.update(Counter(treelet)) - - # pattern 10 - treelet = [] - for pattern in patterns['10']: - canonkey4 = [G.nodes[pattern[5]][node_label], G[pattern[5]][pattern[4]][edge_label]] - canonlist = [tuple((G.nodes[leaf][node_label], - G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:3]] - canonlist.sort() - canonkey0 = list(chain.from_iterable(canonlist)) - canonkey_t = tuple(['a'] + [G.nodes[pattern[3]][node_label]] - + [G.nodes[pattern[4]][node_label]] - + [G[pattern[4]][pattern[3]][edge_label]] - + [G.nodes[pattern[0]][node_label]] - + [G[pattern[0]][pattern[3]][edge_label]] - + canonkey4 + canonkey0) - treelet.append(canonkey_t) - canonkey_l.update(Counter(treelet)) - - # pattern 12 - treelet = [] - for pattern in patterns['12']: - canonlist0 = [tuple((G.nodes[leaf][node_label], - G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:3]] - canonlist0.sort() - canonlist0 = list(chain.from_iterable(canonlist0)) - canonlist3 = [tuple((G.nodes[leaf][node_label], - G[leaf][pattern[3]][edge_label])) for leaf in pattern[4:6]] - canonlist3.sort() - canonlist3 = list(chain.from_iterable(canonlist3)) - - # 2 possible key can be generated from 2 nodes with extended label 3, - # select the one with lower lexicographic order. - canonkey_t1 = tuple(['c'] + [G.nodes[pattern[0]][node_label]] + canonlist0 - + [G.nodes[pattern[3]][node_label]] - + [G[pattern[3]][pattern[0]][edge_label]] - + canonlist3) - canonkey_t2 = tuple(['c'] + [G.nodes[pattern[3]][node_label]] + canonlist3 - + [G.nodes[pattern[0]][node_label]] - + [G[pattern[0]][pattern[3]][edge_label]] - + canonlist0) - treelet.append(canonkey_t1 if canonkey_t1 < canonkey_t2 else canonkey_t2) - canonkey_l.update(Counter(treelet)) - - # pattern 9 - treelet = [] - for pattern in patterns['9']: - canonkey2 = [G.nodes[pattern[4]][node_label], G[pattern[4]][pattern[2]][edge_label]] - canonkey3 = [G.nodes[pattern[5]][node_label], G[pattern[5]][pattern[3]][edge_label]] - prekey2 = [G.nodes[pattern[2]][node_label], G[pattern[2]][pattern[0]][edge_label]] - prekey3 = [G.nodes[pattern[3]][node_label], G[pattern[3]][pattern[0]][edge_label]] - if prekey2 + canonkey2 < prekey3 + canonkey3: - canonkey_t = [G.nodes[pattern[1]][node_label]] \ - + [G[pattern[1]][pattern[0]][edge_label]] \ - + prekey2 + prekey3 + canonkey2 + canonkey3 - else: - canonkey_t = [G.nodes[pattern[1]][node_label]] \ - + [G[pattern[1]][pattern[0]][edge_label]] \ - + prekey3 + prekey2 + canonkey3 + canonkey2 - treelet.append(tuple(['9'] + [G.nodes[pattern[0]][node_label]] + canonkey_t)) - canonkey_l.update(Counter(treelet)) - - return canonkey_l - - return canonkey + """Generate canonical keys of all treelets in a graph. + + Parameters + ---------- + G : NetworkX graphs + The graph in which keys are generated. + node_label : string + node attribute used as label. The default node label is atom. + edge_label : string + edge attribute used as label. The default edge label is bond_type. + labeled : boolean + Whether the graphs are labeled. The default is True. + + Return + ------ + canonkey/canonkey_l : dict + For unlabeled graphs, canonkey is a dictionary which records amount of + every tree pattern. For labeled graphs, canonkey_l is one which keeps + track of amount of every treelet. + """ + patterns = {} # a dictionary which consists of lists of patterns for all graphlet. + canonkey = {} # canonical key, a dictionary which records amount of every tree pattern. + + ### structural analysis ### + ### In this section, a list of patterns is generated for each graphlet, + ### where every pattern is represented by nodes ordered by Morgan's + ### extended labeling. + # linear patterns + patterns['0'] = G.nodes() + canonkey['0'] = nx.number_of_nodes(G) + for i in range(1, 6): # for i in range(1, 6): + patterns[str(i)] = find_all_paths(G, i, is_directed) + canonkey[str(i)] = len(patterns[str(i)]) + + # n-star patterns + patterns['3star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 3] + patterns['4star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 4] + patterns['5star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 5] + # n-star patterns + canonkey['6'] = len(patterns['3star']) + canonkey['8'] = len(patterns['4star']) + canonkey['d'] = len(patterns['5star']) + + # pattern 7 + patterns['7'] = [] # the 1st line of Table 1 in Ref [1] + for pattern in patterns['3star']: + for i in range(1, len(pattern)): # for each neighbor of node 0 + if G.degree(pattern[i]) >= 2: + pattern_t = pattern[:] + # set the node with degree >= 2 as the 4th node + pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i] + for neighborx in G[pattern[i]]: + if neighborx != pattern[0]: + new_pattern = pattern_t + [neighborx] + patterns['7'].append(new_pattern) + canonkey['7'] = len(patterns['7']) + + # pattern 11 + patterns['11'] = [] # the 4th line of Table 1 in Ref [1] + for pattern in patterns['4star']: + for i in range(1, len(pattern)): + if G.degree(pattern[i]) >= 2: + pattern_t = pattern[:] + pattern_t[i], pattern_t[4] = pattern_t[4], pattern_t[i] + for neighborx in G[pattern[i]]: + if neighborx != pattern[0]: + new_pattern = pattern_t + [ neighborx ] + patterns['11'].append(new_pattern) + canonkey['b'] = len(patterns['11']) + + # pattern 12 + patterns['12'] = [] # the 5th line of Table 1 in Ref [1] + rootlist = [] # a list of root nodes, whose extended labels are 3 + for pattern in patterns['3star']: + if pattern[0] not in rootlist: # prevent to count the same pattern twice from each of the two root nodes + rootlist.append(pattern[0]) + for i in range(1, len(pattern)): + if G.degree(pattern[i]) >= 3: + rootlist.append(pattern[i]) + pattern_t = pattern[:] + pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i] + for neighborx1 in G[pattern[i]]: + if neighborx1 != pattern[0]: + for neighborx2 in G[pattern[i]]: + if neighborx1 > neighborx2 and neighborx2 != pattern[0]: + new_pattern = pattern_t + [neighborx1] + [neighborx2] +# new_patterns = [ pattern + [neighborx1] + [neighborx2] for neighborx1 in G[pattern[i]] if neighborx1 != pattern[0] for neighborx2 in G[pattern[i]] if (neighborx1 > neighborx2 and neighborx2 != pattern[0]) ] + patterns['12'].append(new_pattern) + canonkey['c'] = int(len(patterns['12']) / 2) + + # pattern 9 + patterns['9'] = [] # the 2nd line of Table 1 in Ref [1] + for pattern in patterns['3star']: + for pairs in [ [neighbor1, neighbor2] for neighbor1 in G[pattern[0]] if G.degree(neighbor1) >= 2 \ + for neighbor2 in G[pattern[0]] if G.degree(neighbor2) >= 2 if neighbor1 > neighbor2 ]: + pattern_t = pattern[:] + # move nodes with extended labels 4 to specific position to correspond to their children + pattern_t[pattern_t.index(pairs[0])], pattern_t[2] = pattern_t[2], pattern_t[pattern_t.index(pairs[0])] + pattern_t[pattern_t.index(pairs[1])], pattern_t[3] = pattern_t[3], pattern_t[pattern_t.index(pairs[1])] + for neighborx1 in G[pairs[0]]: + if neighborx1 != pattern[0]: + for neighborx2 in G[pairs[1]]: + if neighborx2 != pattern[0]: + new_pattern = pattern_t + [neighborx1] + [neighborx2] + patterns['9'].append(new_pattern) + canonkey['9'] = len(patterns['9']) + + # pattern 10 + patterns['10'] = [] # the 3rd line of Table 1 in Ref [1] + for pattern in patterns['3star']: + for i in range(1, len(pattern)): + if G.degree(pattern[i]) >= 2: + for neighborx in G[pattern[i]]: + if neighborx != pattern[0] and G.degree(neighborx) >= 2: + pattern_t = pattern[:] + pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i] + new_patterns = [ pattern_t + [neighborx] + [neighborxx] for neighborxx in G[neighborx] if neighborxx != pattern[i] ] + patterns['10'].extend(new_patterns) + canonkey['a'] = len(patterns['10']) + + ### labeling information ### + ### In this section, a list of canonical keys is generated for every + ### pattern obtained in the structural analysis section above, which is a + ### string corresponding to a unique treelet. A dictionary is built to keep + ### track of the amount of every treelet. + if labeled == True: + canonkey_l = {} # canonical key, a dictionary which keeps track of amount of every treelet. + + # linear patterns + canonkey_t = Counter(list(nx.get_node_attributes(G, node_label).values())) + for key in canonkey_t: + canonkey_l[('0', key)] = canonkey_t[key] + + for i in range(1, 6): # for i in range(1, 6): + treelet = [] + for pattern in patterns[str(i)]: + canonlist = list(chain.from_iterable((G.nodes[node][node_label], \ + G[node][pattern[idx+1]][edge_label]) for idx, node in enumerate(pattern[:-1]))) + canonlist.append(G.nodes[pattern[-1]][node_label]) + canonkey_t = canonlist if canonlist < canonlist[::-1] else canonlist[::-1] + treelet.append(tuple([str(i)] + canonkey_t)) + canonkey_l.update(Counter(treelet)) + + # n-star patterns + for i in range(3, 6): + treelet = [] + for pattern in patterns[str(i) + 'star']: + canonlist = [tuple((G.nodes[leaf][node_label], + G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:]] + canonlist.sort() + canonlist = list(chain.from_iterable(canonlist)) + canonkey_t = tuple(['d' if i == 5 else str(i * 2)] + + [G.nodes[pattern[0]][node_label]] + canonlist) + treelet.append(canonkey_t) + canonkey_l.update(Counter(treelet)) + + # pattern 7 + treelet = [] + for pattern in patterns['7']: + canonlist = [tuple((G.nodes[leaf][node_label], + G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:3]] + canonlist.sort() + canonlist = list(chain.from_iterable(canonlist)) + canonkey_t = tuple(['7'] + [G.nodes[pattern[0]][node_label]] + canonlist + + [G.nodes[pattern[3]][node_label]] + + [G[pattern[3]][pattern[0]][edge_label]] + + [G.nodes[pattern[4]][node_label]] + + [G[pattern[4]][pattern[3]][edge_label]]) + treelet.append(canonkey_t) + canonkey_l.update(Counter(treelet)) + + # pattern 11 + treelet = [] + for pattern in patterns['11']: + canonlist = [tuple((G.nodes[leaf][node_label], + G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:4]] + canonlist.sort() + canonlist = list(chain.from_iterable(canonlist)) + canonkey_t = tuple(['b'] + [G.nodes[pattern[0]][node_label]] + canonlist + + [G.nodes[pattern[4]][node_label]] + + [G[pattern[4]][pattern[0]][edge_label]] + + [G.nodes[pattern[5]][node_label]] + + [G[pattern[5]][pattern[4]][edge_label]]) + treelet.append(canonkey_t) + canonkey_l.update(Counter(treelet)) + + # pattern 10 + treelet = [] + for pattern in patterns['10']: + canonkey4 = [G.nodes[pattern[5]][node_label], G[pattern[5]][pattern[4]][edge_label]] + canonlist = [tuple((G.nodes[leaf][node_label], + G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:3]] + canonlist.sort() + canonkey0 = list(chain.from_iterable(canonlist)) + canonkey_t = tuple(['a'] + [G.nodes[pattern[3]][node_label]] + + [G.nodes[pattern[4]][node_label]] + + [G[pattern[4]][pattern[3]][edge_label]] + + [G.nodes[pattern[0]][node_label]] + + [G[pattern[0]][pattern[3]][edge_label]] + + canonkey4 + canonkey0) + treelet.append(canonkey_t) + canonkey_l.update(Counter(treelet)) + + # pattern 12 + treelet = [] + for pattern in patterns['12']: + canonlist0 = [tuple((G.nodes[leaf][node_label], + G[leaf][pattern[0]][edge_label])) for leaf in pattern[1:3]] + canonlist0.sort() + canonlist0 = list(chain.from_iterable(canonlist0)) + canonlist3 = [tuple((G.nodes[leaf][node_label], + G[leaf][pattern[3]][edge_label])) for leaf in pattern[4:6]] + canonlist3.sort() + canonlist3 = list(chain.from_iterable(canonlist3)) + + # 2 possible key can be generated from 2 nodes with extended label 3, + # select the one with lower lexicographic order. + canonkey_t1 = tuple(['c'] + [G.nodes[pattern[0]][node_label]] + canonlist0 + + [G.nodes[pattern[3]][node_label]] + + [G[pattern[3]][pattern[0]][edge_label]] + + canonlist3) + canonkey_t2 = tuple(['c'] + [G.nodes[pattern[3]][node_label]] + canonlist3 + + [G.nodes[pattern[0]][node_label]] + + [G[pattern[0]][pattern[3]][edge_label]] + + canonlist0) + treelet.append(canonkey_t1 if canonkey_t1 < canonkey_t2 else canonkey_t2) + canonkey_l.update(Counter(treelet)) + + # pattern 9 + treelet = [] + for pattern in patterns['9']: + canonkey2 = [G.nodes[pattern[4]][node_label], G[pattern[4]][pattern[2]][edge_label]] + canonkey3 = [G.nodes[pattern[5]][node_label], G[pattern[5]][pattern[3]][edge_label]] + prekey2 = [G.nodes[pattern[2]][node_label], G[pattern[2]][pattern[0]][edge_label]] + prekey3 = [G.nodes[pattern[3]][node_label], G[pattern[3]][pattern[0]][edge_label]] + if prekey2 + canonkey2 < prekey3 + canonkey3: + canonkey_t = [G.nodes[pattern[1]][node_label]] \ + + [G[pattern[1]][pattern[0]][edge_label]] \ + + prekey2 + prekey3 + canonkey2 + canonkey3 + else: + canonkey_t = [G.nodes[pattern[1]][node_label]] \ + + [G[pattern[1]][pattern[0]][edge_label]] \ + + prekey3 + prekey2 + canonkey3 + canonkey2 + treelet.append(tuple(['9'] + [G.nodes[pattern[0]][node_label]] + canonkey_t)) + canonkey_l.update(Counter(treelet)) + + return canonkey_l + + return canonkey def wrapper_get_canonkeys(node_label, edge_label, labeled, is_directed, itr_item): - g = itr_item[0] - i = itr_item[1] - return i, get_canonkeys(g, node_label, edge_label, labeled, is_directed) - + g = itr_item[0] + i = itr_item[1] + return i, get_canonkeys(g, node_label, edge_label, labeled, is_directed) + def find_paths(G, source_node, length): - """Find all paths with a certain length those start from a source node. - A recursive depth first search is applied. - - Parameters - ---------- - G : NetworkX graphs - The graph in which paths are searched. - source_node : integer - The number of the node from where all paths start. - length : integer - The length of paths. - - Return - ------ - path : list of list - List of paths retrieved, where each path is represented by a list of nodes. - """ - if length == 0: - return [[source_node]] - path = [[source_node] + path for neighbor in G[source_node] \ - for path in find_paths(G, neighbor, length - 1) if source_node not in path] - return path + """Find all paths with a certain length those start from a source node. + A recursive depth first search is applied. + + Parameters + ---------- + G : NetworkX graphs + The graph in which paths are searched. + source_node : integer + The number of the node from where all paths start. + length : integer + The length of paths. + + Return + ------ + path : list of list + List of paths retrieved, where each path is represented by a list of nodes. + """ + if length == 0: + return [[source_node]] + path = [[source_node] + path for neighbor in G[source_node] \ + for path in find_paths(G, neighbor, length - 1) if source_node not in path] + return path def find_all_paths(G, length, is_directed): - """Find all paths with a certain length in a graph. A recursive depth first - search is applied. - - Parameters - ---------- - G : NetworkX graphs - The graph in which paths are searched. - length : integer - The length of paths. - - Return - ------ - path : list of list - List of paths retrieved, where each path is represented by a list of nodes. - """ - all_paths = [] - for node in G: - all_paths.extend(find_paths(G, node, length)) - - if not is_directed: - # For each path, two presentations are retrieved from its two extremities. - # Remove one of them. - all_paths_r = [path[::-1] for path in all_paths] - for idx, path in enumerate(all_paths[:-1]): - for path2 in all_paths_r[idx+1::]: - if path == path2: - all_paths[idx] = [] - break - all_paths = list(filter(lambda a: a != [], all_paths)) - - return all_paths + """Find all paths with a certain length in a graph. A recursive depth first + search is applied. + + Parameters + ---------- + G : NetworkX graphs + The graph in which paths are searched. + length : integer + The length of paths. + + Return + ------ + path : list of list + List of paths retrieved, where each path is represented by a list of nodes. + """ + all_paths = [] + for node in G: + all_paths.extend(find_paths(G, node, length)) + + if not is_directed: + # For each path, two presentations are retrieved from its two extremities. + # Remove one of them. + all_paths_r = [path[::-1] for path in all_paths] + for idx, path in enumerate(all_paths[:-1]): + for path2 in all_paths_r[idx+1::]: + if path == path2: + all_paths[idx] = [] + break + all_paths = list(filter(lambda a: a != [], all_paths)) + + return all_paths diff --git a/gklearn/kernels/weisfeilerLehmanKernel.py b/gklearn/kernels/weisfeilerLehmanKernel.py index cb8c592..ecbcf49 100644 --- a/gklearn/kernels/weisfeilerLehmanKernel.py +++ b/gklearn/kernels/weisfeilerLehmanKernel.py @@ -3,9 +3,9 @@ @references: - [1] Shervashidze N, Schweitzer P, Leeuwen EJ, Mehlhorn K, Borgwardt KM. - Weisfeiler-lehman graph kernels. Journal of Machine Learning Research. - 2011;12(Sep):2539-61. + [1] Shervashidze N, Schweitzer P, Leeuwen EJ, Mehlhorn K, Borgwardt KM. + Weisfeiler-lehman graph kernels. Journal of Machine Learning Research. + 2011;12(Sep):2539-61. """ import sys @@ -24,546 +24,546 @@ from gklearn.utils.parallel import parallel_gm # @todo: support edge kernel, sp kernel, user-defined kernel. def weisfeilerlehmankernel(*args, - node_label='atom', - edge_label='bond_type', - height=0, - base_kernel='subtree', - parallel=None, - n_jobs=None, - verbose=True): - """Calculate Weisfeiler-Lehman kernels between graphs. - - Parameters - ---------- - Gn : List of NetworkX graph - List of graphs between which the kernels are calculated. - - G1, G2 : NetworkX graphs - Two graphs between which the kernel is calculated. - - node_label : string - Node attribute used as label. The default node label is atom. - - edge_label : string - Edge attribute used as label. The default edge label is bond_type. - - height : int - Subtree height. - - base_kernel : string - Base kernel used in each iteration of WL kernel. Only default 'subtree' - kernel can be applied for now. - - parallel : None - Which paralleliztion method is applied to compute the kernel. No - parallelization can be applied for now. - - n_jobs : int - Number of jobs for parallelization. The default is to use all - computational cores. This argument is only valid when one of the - parallelization method is applied and can be ignored for now. - - Return - ------ - Kmatrix : Numpy matrix - Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. - - Notes - ----- - This function now supports WL subtree kernel only. - """ -# The default base -# kernel is subtree kernel. For user-defined kernel, base_kernel is the -# name of the base kernel function used in each iteration of WL kernel. -# This function returns a Numpy matrix, each element of which is the -# user-defined Weisfeiler-Lehman kernel between 2 praphs. - # pre-process - base_kernel = base_kernel.lower() - Gn = args[0] if len(args) == 1 else [args[0], args[1]] # arrange all graphs in a list - Gn = [g.copy() for g in Gn] - ds_attrs = get_dataset_attributes(Gn, attr_names=['node_labeled'], - node_label=node_label) - if not ds_attrs['node_labeled']: - for G in Gn: - nx.set_node_attributes(G, '0', 'atom') - - start_time = time.time() - - # for WL subtree kernel - if base_kernel == 'subtree': - Kmatrix = _wl_kernel_do(Gn, node_label, edge_label, height, parallel, n_jobs, verbose) - - # for WL shortest path kernel - elif base_kernel == 'sp': - Kmatrix = _wl_spkernel_do(Gn, node_label, edge_label, height) - - # for WL edge kernel - elif base_kernel == 'edge': - Kmatrix = _wl_edgekernel_do(Gn, node_label, edge_label, height) - - # for user defined base kernel - else: - Kmatrix = _wl_userkernel_do(Gn, node_label, edge_label, height, base_kernel) - - run_time = time.time() - start_time - if verbose: - print("\n --- Weisfeiler-Lehman %s kernel matrix of size %d built in %s seconds ---" - % (base_kernel, len(args[0]), run_time)) - - return Kmatrix, run_time + node_label='atom', + edge_label='bond_type', + height=0, + base_kernel='subtree', + parallel=None, + n_jobs=None, + verbose=True): + """Calculate Weisfeiler-Lehman kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + + G1, G2 : NetworkX graphs + Two graphs between which the kernel is calculated. + + node_label : string + Node attribute used as label. The default node label is atom. + + edge_label : string + Edge attribute used as label. The default edge label is bond_type. + + height : int + Subtree height. + + base_kernel : string + Base kernel used in each iteration of WL kernel. Only default 'subtree' + kernel can be applied for now. + + parallel : None + Which paralleliztion method is applied to compute the kernel. No + parallelization can be applied for now. + + n_jobs : int + Number of jobs for parallelization. The default is to use all + computational cores. This argument is only valid when one of the + parallelization method is applied and can be ignored for now. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + + Notes + ----- + This function now supports WL subtree kernel only. + """ +# The default base +# kernel is subtree kernel. For user-defined kernel, base_kernel is the +# name of the base kernel function used in each iteration of WL kernel. +# This function returns a Numpy matrix, each element of which is the +# user-defined Weisfeiler-Lehman kernel between 2 praphs. + # pre-process + base_kernel = base_kernel.lower() + Gn = args[0] if len(args) == 1 else [args[0], args[1]] # arrange all graphs in a list + Gn = [g.copy() for g in Gn] + ds_attrs = get_dataset_attributes(Gn, attr_names=['node_labeled'], + node_label=node_label) + if not ds_attrs['node_labeled']: + for G in Gn: + nx.set_node_attributes(G, '0', 'atom') + + start_time = time.time() + + # for WL subtree kernel + if base_kernel == 'subtree': + Kmatrix = _wl_kernel_do(Gn, node_label, edge_label, height, parallel, n_jobs, verbose) + + # for WL shortest path kernel + elif base_kernel == 'sp': + Kmatrix = _wl_spkernel_do(Gn, node_label, edge_label, height) + + # for WL edge kernel + elif base_kernel == 'edge': + Kmatrix = _wl_edgekernel_do(Gn, node_label, edge_label, height) + + # for user defined base kernel + else: + Kmatrix = _wl_userkernel_do(Gn, node_label, edge_label, height, base_kernel) + + run_time = time.time() - start_time + if verbose: + print("\n --- Weisfeiler-Lehman %s kernel matrix of size %d built in %s seconds ---" + % (base_kernel, len(args[0]), run_time)) + + return Kmatrix, run_time def _wl_kernel_do(Gn, node_label, edge_label, height, parallel, n_jobs, verbose): - """Calculate Weisfeiler-Lehman kernels between graphs. - - Parameters - ---------- - Gn : List of NetworkX graph - List of graphs between which the kernels are calculated. - node_label : string - node attribute used as label. - edge_label : string - edge attribute used as label. - height : int - wl height. - - Return - ------ - Kmatrix : Numpy matrix - Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. - """ - height = int(height) - Kmatrix = np.zeros((len(Gn), len(Gn))) - - # initial for height = 0 - all_num_of_each_label = [] # number of occurence of each label in each graph in this iteration - - # for each graph - for G in Gn: - # get the set of original labels - labels_ori = list(nx.get_node_attributes(G, node_label).values()) - # number of occurence of each label in G - all_num_of_each_label.append(dict(Counter(labels_ori))) - - # calculate subtree kernel with the 0th iteration and add it to the final kernel - compute_kernel_matrix(Kmatrix, all_num_of_each_label, Gn, parallel, n_jobs, False) - - # iterate each height - for h in range(1, height + 1): - all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration - num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs -# all_labels_ori = set() # all unique orignal labels in all graphs in this iteration - all_num_of_each_label = [] # number of occurence of each label in G - -# # for each graph -# # ---- use pool.imap_unordered to parallel and track progress. ---- -# pool = Pool(n_jobs) -# itr = zip(Gn, range(0, len(Gn))) -# if len(Gn) < 100 * n_jobs: -# chunksize = int(len(Gn) / n_jobs) + 1 -# else: -# chunksize = 100 -# all_multisets_list = [[] for _ in range(len(Gn))] -## set_unique_list = [[] for _ in range(len(Gn))] -# get_partial = partial(wrapper_wl_iteration, node_label) -## if verbose: -## iterator = tqdm(pool.imap_unordered(get_partial, itr, chunksize), -## desc='wl iteration', file=sys.stdout) -## else: -# iterator = pool.imap_unordered(get_partial, itr, chunksize) -# for i, all_multisets in iterator: -# all_multisets_list[i] = all_multisets -## set_unique_list[i] = set_unique -## all_set_unique = all_set_unique | set(set_unique) -# pool.close() -# pool.join() - -# all_set_unique = set() -# for uset in all_multisets_list: -# all_set_unique = all_set_unique | set(uset) -# -# all_set_unique = list(all_set_unique) -## # a dictionary mapping original labels to new ones. -## set_compressed = {} -## for idx, uset in enumerate(all_set_unique): -## set_compressed.update({uset: idx}) -# -# for ig, G in enumerate(Gn): + """Calculate Weisfeiler-Lehman kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + wl height. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + height = int(height) + Kmatrix = np.zeros((len(Gn), len(Gn))) + + # initial for height = 0 + all_num_of_each_label = [] # number of occurence of each label in each graph in this iteration + + # for each graph + for G in Gn: + # get the set of original labels + labels_ori = list(nx.get_node_attributes(G, node_label).values()) + # number of occurence of each label in G + all_num_of_each_label.append(dict(Counter(labels_ori))) + + # calculate subtree kernel with the 0th iteration and add it to the final kernel + compute_kernel_matrix(Kmatrix, all_num_of_each_label, Gn, parallel, n_jobs, False) + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs +# all_labels_ori = set() # all unique orignal labels in all graphs in this iteration + all_num_of_each_label = [] # number of occurence of each label in G + +# # for each graph +# # ---- use pool.imap_unordered to parallel and track progress. ---- +# pool = Pool(n_jobs) +# itr = zip(Gn, range(0, len(Gn))) +# if len(Gn) < 100 * n_jobs: +# chunksize = int(len(Gn) / n_jobs) + 1 +# else: +# chunksize = 100 +# all_multisets_list = [[] for _ in range(len(Gn))] +## set_unique_list = [[] for _ in range(len(Gn))] +# get_partial = partial(wrapper_wl_iteration, node_label) +## if verbose: +## iterator = tqdm(pool.imap_unordered(get_partial, itr, chunksize), +## desc='wl iteration', file=sys.stdout) +## else: +# iterator = pool.imap_unordered(get_partial, itr, chunksize) +# for i, all_multisets in iterator: +# all_multisets_list[i] = all_multisets +## set_unique_list[i] = set_unique +## all_set_unique = all_set_unique | set(set_unique) +# pool.close() +# pool.join() + +# all_set_unique = set() +# for uset in all_multisets_list: +# all_set_unique = all_set_unique | set(uset) +# +# all_set_unique = list(all_set_unique) +## # a dictionary mapping original labels to new ones. +## set_compressed = {} +## for idx, uset in enumerate(all_set_unique): +## set_compressed.update({uset: idx}) +# +# for ig, G in enumerate(Gn): # -## # a dictionary mapping original labels to new ones. -## set_compressed = {} -## # if a label occured before, assign its former compressed label, -## # else assign the number of labels occured + 1 as the compressed label. -## for value in set_unique_list[i]: -## if uset in all_set_unique: -## set_compressed.update({uset: all_set_compressed[value]}) -## else: -## set_compressed.update({value: str(num_of_labels_occured + 1)}) -## num_of_labels_occured += 1 -# -## all_set_compressed.update(set_compressed) -# -# # relabel nodes -# for idx, node in enumerate(G.nodes()): -# G.nodes[node][node_label] = all_set_unique.index(all_multisets_list[ig][idx]) -# -# # get the set of compressed labels -# labels_comp = list(nx.get_node_attributes(G, node_label).values()) -## all_labels_ori.update(labels_comp) -# all_num_of_each_label[ig] = dict(Counter(labels_comp)) - - - - -# all_set_unique = list(all_set_unique) - - - # @todo: parallel this part. - for idx, G in enumerate(Gn): - - all_multisets = [] - for node, attrs in G.nodes(data=True): - # Multiset-label determination. - multiset = [G.nodes[neighbors][node_label] for neighbors in G[node]] - # sorting each multiset - multiset.sort() - multiset = [attrs[node_label]] + multiset # add the prefix - all_multisets.append(tuple(multiset)) - - # label compression - set_unique = list(set(all_multisets)) # set of unique multiset labels - # a dictionary mapping original labels to new ones. - set_compressed = {} - # if a label occured before, assign its former compressed label, - # else assign the number of labels occured + 1 as the compressed label. - for value in set_unique: - if value in all_set_compressed.keys(): - set_compressed.update({value: all_set_compressed[value]}) - else: - set_compressed.update({value: str(num_of_labels_occured + 1)}) - num_of_labels_occured += 1 - - all_set_compressed.update(set_compressed) - - # relabel nodes - for idx, node in enumerate(G.nodes()): - G.nodes[node][node_label] = set_compressed[all_multisets[idx]] - - # get the set of compressed labels - labels_comp = list(nx.get_node_attributes(G, node_label).values()) -# all_labels_ori.update(labels_comp) - all_num_of_each_label.append(dict(Counter(labels_comp))) - - # calculate subtree kernel with h iterations and add it to the final kernel - compute_kernel_matrix(Kmatrix, all_num_of_each_label, Gn, parallel, n_jobs, False) - - return Kmatrix +## # a dictionary mapping original labels to new ones. +## set_compressed = {} +## # if a label occured before, assign its former compressed label, +## # else assign the number of labels occured + 1 as the compressed label. +## for value in set_unique_list[i]: +## if uset in all_set_unique: +## set_compressed.update({uset: all_set_compressed[value]}) +## else: +## set_compressed.update({value: str(num_of_labels_occured + 1)}) +## num_of_labels_occured += 1 +# +## all_set_compressed.update(set_compressed) +# +# # relabel nodes +# for idx, node in enumerate(G.nodes()): +# G.nodes[node][node_label] = all_set_unique.index(all_multisets_list[ig][idx]) +# +# # get the set of compressed labels +# labels_comp = list(nx.get_node_attributes(G, node_label).values()) +## all_labels_ori.update(labels_comp) +# all_num_of_each_label[ig] = dict(Counter(labels_comp)) + + + + +# all_set_unique = list(all_set_unique) + + + # @todo: parallel this part. + for idx, G in enumerate(Gn): + + all_multisets = [] + for node, attrs in G.nodes(data=True): + # Multiset-label determination. + multiset = [G.nodes[neighbors][node_label] for neighbors in G[node]] + # sorting each multiset + multiset.sort() + multiset = [attrs[node_label]] + multiset # add the prefix + all_multisets.append(tuple(multiset)) + + # label compression + set_unique = list(set(all_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, + # else assign the number of labels occured + 1 as the compressed label. + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({value: all_set_compressed[value]}) + else: + set_compressed.update({value: str(num_of_labels_occured + 1)}) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for idx, node in enumerate(G.nodes()): + G.nodes[node][node_label] = set_compressed[all_multisets[idx]] + + # get the set of compressed labels + labels_comp = list(nx.get_node_attributes(G, node_label).values()) +# all_labels_ori.update(labels_comp) + all_num_of_each_label.append(dict(Counter(labels_comp))) + + # calculate subtree kernel with h iterations and add it to the final kernel + compute_kernel_matrix(Kmatrix, all_num_of_each_label, Gn, parallel, n_jobs, False) + + return Kmatrix def wl_iteration(G, node_label): - all_multisets = [] - for node, attrs in G.nodes(data=True): - # Multiset-label determination. - multiset = [G.nodes[neighbors][node_label] for neighbors in G[node]] - # sorting each multiset - multiset.sort() - multiset = [attrs[node_label]] + multiset # add the prefix - all_multisets.append(tuple(multiset)) -# # label compression -# set_unique = list(set(all_multisets)) # set of unique multiset labels - return all_multisets - -# # a dictionary mapping original labels to new ones. -# set_compressed = {} -# # if a label occured before, assign its former compressed label, -# # else assign the number of labels occured + 1 as the compressed label. -# for value in set_unique: -# if value in all_set_compressed.keys(): -# set_compressed.update({value: all_set_compressed[value]}) -# else: -# set_compressed.update({value: str(num_of_labels_occured + 1)}) -# num_of_labels_occured += 1 + all_multisets = [] + for node, attrs in G.nodes(data=True): + # Multiset-label determination. + multiset = [G.nodes[neighbors][node_label] for neighbors in G[node]] + # sorting each multiset + multiset.sort() + multiset = [attrs[node_label]] + multiset # add the prefix + all_multisets.append(tuple(multiset)) +# # label compression +# set_unique = list(set(all_multisets)) # set of unique multiset labels + return all_multisets + +# # a dictionary mapping original labels to new ones. +# set_compressed = {} +# # if a label occured before, assign its former compressed label, +# # else assign the number of labels occured + 1 as the compressed label. +# for value in set_unique: +# if value in all_set_compressed.keys(): +# set_compressed.update({value: all_set_compressed[value]}) +# else: +# set_compressed.update({value: str(num_of_labels_occured + 1)}) +# num_of_labels_occured += 1 # -# all_set_compressed.update(set_compressed) +# all_set_compressed.update(set_compressed) # -# # relabel nodes -# for idx, node in enumerate(G.nodes()): -# G.nodes[node][node_label] = set_compressed[all_multisets[idx]] +# # relabel nodes +# for idx, node in enumerate(G.nodes()): +# G.nodes[node][node_label] = set_compressed[all_multisets[idx]] # -# # get the set of compressed labels -# labels_comp = list(nx.get_node_attributes(G, node_label).values()) -# all_labels_ori.update(labels_comp) -# all_num_of_each_label.append(dict(Counter(labels_comp))) -# return +# # get the set of compressed labels +# labels_comp = list(nx.get_node_attributes(G, node_label).values()) +# all_labels_ori.update(labels_comp) +# all_num_of_each_label.append(dict(Counter(labels_comp))) +# return def wrapper_wl_iteration(node_label, itr_item): - g = itr_item[0] - i = itr_item[1] - all_multisets = wl_iteration(g, node_label) - return i, all_multisets + g = itr_item[0] + i = itr_item[1] + all_multisets = wl_iteration(g, node_label) + return i, all_multisets def compute_kernel_matrix(Kmatrix, all_num_of_each_label, Gn, parallel, n_jobs, verbose): - """Compute kernel matrix using the base kernel. - """ - if parallel == 'imap_unordered': - # compute kernels. - def init_worker(alllabels_toshare): - global G_alllabels - G_alllabels = alllabels_toshare - do_partial = partial(wrapper_compute_subtree_kernel, Kmatrix) - parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker, - glbv=(all_num_of_each_label,), n_jobs=n_jobs, verbose=verbose) - elif parallel == None: - for i in range(len(Kmatrix)): - for j in range(i, len(Kmatrix)): - Kmatrix[i][j] = compute_subtree_kernel(all_num_of_each_label[i], - all_num_of_each_label[j], Kmatrix[i][j]) - Kmatrix[j][i] = Kmatrix[i][j] + """Compute kernel matrix using the base kernel. + """ + if parallel == 'imap_unordered': + # compute kernels. + def init_worker(alllabels_toshare): + global G_alllabels + G_alllabels = alllabels_toshare + do_partial = partial(wrapper_compute_subtree_kernel, Kmatrix) + parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker, + glbv=(all_num_of_each_label,), n_jobs=n_jobs, verbose=verbose) + elif parallel == None: + for i in range(len(Kmatrix)): + for j in range(i, len(Kmatrix)): + Kmatrix[i][j] = compute_subtree_kernel(all_num_of_each_label[i], + all_num_of_each_label[j], Kmatrix[i][j]) + Kmatrix[j][i] = Kmatrix[i][j] def compute_subtree_kernel(num_of_each_label1, num_of_each_label2, kernel): - """Compute the subtree kernel. - """ - labels = set(list(num_of_each_label1.keys()) + list(num_of_each_label2.keys())) - vector1 = np.array([(num_of_each_label1[label] - if (label in num_of_each_label1.keys()) else 0) - for label in labels]) - vector2 = np.array([(num_of_each_label2[label] - if (label in num_of_each_label2.keys()) else 0) - for label in labels]) - kernel += np.dot(vector1, vector2) - return kernel + """Compute the subtree kernel. + """ + labels = set(list(num_of_each_label1.keys()) + list(num_of_each_label2.keys())) + vector1 = np.array([(num_of_each_label1[label] + if (label in num_of_each_label1.keys()) else 0) + for label in labels]) + vector2 = np.array([(num_of_each_label2[label] + if (label in num_of_each_label2.keys()) else 0) + for label in labels]) + kernel += np.dot(vector1, vector2) + return kernel def wrapper_compute_subtree_kernel(Kmatrix, itr): - i = itr[0] - j = itr[1] - return i, j, compute_subtree_kernel(G_alllabels[i], G_alllabels[j], Kmatrix[i][j]) - + i = itr[0] + j = itr[1] + return i, j, compute_subtree_kernel(G_alllabels[i], G_alllabels[j], Kmatrix[i][j]) + def _wl_spkernel_do(Gn, node_label, edge_label, height): - """Calculate Weisfeiler-Lehman shortest path kernels between graphs. - - Parameters - ---------- - Gn : List of NetworkX graph - List of graphs between which the kernels are calculated. - node_label : string - node attribute used as label. - edge_label : string - edge attribute used as label. - height : int - subtree height. - - Return - ------ - Kmatrix : Numpy matrix - Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. - """ - pass - from gklearn.utils.utils import getSPGraph - - # init. - height = int(height) - Kmatrix = np.zeros((len(Gn), len(Gn))) # init kernel - - Gn = [ getSPGraph(G, edge_weight = edge_label) for G in Gn ] # get shortest path graphs of Gn - - # initial for height = 0 - for i in range(0, len(Gn)): - for j in range(i, len(Gn)): - for e1 in Gn[i].edges(data = True): - for e2 in Gn[j].edges(data = True): - if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): - Kmatrix[i][j] += 1 - Kmatrix[j][i] = Kmatrix[i][j] - - # iterate each height - for h in range(1, height + 1): - all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration - num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs - for G in Gn: # for each graph - set_multisets = [] - for node in G.nodes(data = True): - # Multiset-label determination. - multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] - # sorting each multiset - multiset.sort() - multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix - set_multisets.append(multiset) - - # label compression - set_unique = list(set(set_multisets)) # set of unique multiset labels - # a dictionary mapping original labels to new ones. - set_compressed = {} - # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label - for value in set_unique: - if value in all_set_compressed.keys(): - set_compressed.update({ value : all_set_compressed[value] }) - else: - set_compressed.update({ value : str(num_of_labels_occured + 1) }) - num_of_labels_occured += 1 - - all_set_compressed.update(set_compressed) - - # relabel nodes - for node in G.nodes(data = True): - node[1][node_label] = set_compressed[set_multisets[node[0]]] - - # calculate subtree kernel with h iterations and add it to the final kernel - for i in range(0, len(Gn)): - for j in range(i, len(Gn)): - for e1 in Gn[i].edges(data = True): - for e2 in Gn[j].edges(data = True): - if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): - Kmatrix[i][j] += 1 - Kmatrix[j][i] = Kmatrix[i][j] - - return Kmatrix + """Calculate Weisfeiler-Lehman shortest path kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + subtree height. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + pass + from gklearn.utils.utils import getSPGraph + + # init. + height = int(height) + Kmatrix = np.zeros((len(Gn), len(Gn))) # init kernel + + Gn = [ getSPGraph(G, edge_weight = edge_label) for G in Gn ] # get shortest path graphs of Gn + + # initial for height = 0 + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + Kmatrix[i][j] += 1 + Kmatrix[j][i] = Kmatrix[i][j] + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + for G in Gn: # for each graph + set_multisets = [] + for node in G.nodes(data = True): + # Multiset-label determination. + multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] + # sorting each multiset + multiset.sort() + multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix + set_multisets.append(multiset) + + # label compression + set_unique = list(set(set_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({ value : all_set_compressed[value] }) + else: + set_compressed.update({ value : str(num_of_labels_occured + 1) }) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for node in G.nodes(data = True): + node[1][node_label] = set_compressed[set_multisets[node[0]]] + + # calculate subtree kernel with h iterations and add it to the final kernel + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + Kmatrix[i][j] += 1 + Kmatrix[j][i] = Kmatrix[i][j] + + return Kmatrix def _wl_edgekernel_do(Gn, node_label, edge_label, height): - """Calculate Weisfeiler-Lehman edge kernels between graphs. - - Parameters - ---------- - Gn : List of NetworkX graph - List of graphs between which the kernels are calculated. - node_label : string - node attribute used as label. - edge_label : string - edge attribute used as label. - height : int - subtree height. - - Return - ------ - Kmatrix : Numpy matrix - Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. - """ - pass - # init. - height = int(height) - Kmatrix = np.zeros((len(Gn), len(Gn))) # init kernel + """Calculate Weisfeiler-Lehman edge kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + subtree height. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + pass + # init. + height = int(height) + Kmatrix = np.zeros((len(Gn), len(Gn))) # init kernel - # initial for height = 0 - for i in range(0, len(Gn)): - for j in range(i, len(Gn)): - for e1 in Gn[i].edges(data = True): - for e2 in Gn[j].edges(data = True): - if e1[2][edge_label] == e2[2][edge_label] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): - Kmatrix[i][j] += 1 - Kmatrix[j][i] = Kmatrix[i][j] - - # iterate each height - for h in range(1, height + 1): - all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration - num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs - for G in Gn: # for each graph - set_multisets = [] - for node in G.nodes(data = True): - # Multiset-label determination. - multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] - # sorting each multiset - multiset.sort() - multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix - set_multisets.append(multiset) - - # label compression - set_unique = list(set(set_multisets)) # set of unique multiset labels - # a dictionary mapping original labels to new ones. - set_compressed = {} - # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label - for value in set_unique: - if value in all_set_compressed.keys(): - set_compressed.update({ value : all_set_compressed[value] }) - else: - set_compressed.update({ value : str(num_of_labels_occured + 1) }) - num_of_labels_occured += 1 - - all_set_compressed.update(set_compressed) - - # relabel nodes - for node in G.nodes(data = True): - node[1][node_label] = set_compressed[set_multisets[node[0]]] - - # calculate subtree kernel with h iterations and add it to the final kernel - for i in range(0, len(Gn)): - for j in range(i, len(Gn)): - for e1 in Gn[i].edges(data = True): - for e2 in Gn[j].edges(data = True): - if e1[2][edge_label] == e2[2][edge_label] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): - Kmatrix[i][j] += 1 - Kmatrix[j][i] = Kmatrix[i][j] - - return Kmatrix + # initial for height = 0 + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2][edge_label] == e2[2][edge_label] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + Kmatrix[i][j] += 1 + Kmatrix[j][i] = Kmatrix[i][j] + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + for G in Gn: # for each graph + set_multisets = [] + for node in G.nodes(data = True): + # Multiset-label determination. + multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] + # sorting each multiset + multiset.sort() + multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix + set_multisets.append(multiset) + + # label compression + set_unique = list(set(set_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({ value : all_set_compressed[value] }) + else: + set_compressed.update({ value : str(num_of_labels_occured + 1) }) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for node in G.nodes(data = True): + node[1][node_label] = set_compressed[set_multisets[node[0]]] + + # calculate subtree kernel with h iterations and add it to the final kernel + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2][edge_label] == e2[2][edge_label] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + Kmatrix[i][j] += 1 + Kmatrix[j][i] = Kmatrix[i][j] + + return Kmatrix def _wl_userkernel_do(Gn, node_label, edge_label, height, base_kernel): - """Calculate Weisfeiler-Lehman kernels based on user-defined kernel between graphs. - - Parameters - ---------- - Gn : List of NetworkX graph - List of graphs between which the kernels are calculated. - node_label : string - node attribute used as label. - edge_label : string - edge attribute used as label. - height : int - subtree height. - base_kernel : string - Name of the base kernel function used in each iteration of WL kernel. This function returns a Numpy matrix, each element of which is the user-defined Weisfeiler-Lehman kernel between 2 praphs. - - Return - ------ - Kmatrix : Numpy matrix - Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. - """ - pass - # init. - height = int(height) - Kmatrix = np.zeros((len(Gn), len(Gn))) # init kernel + """Calculate Weisfeiler-Lehman kernels based on user-defined kernel between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + subtree height. + base_kernel : string + Name of the base kernel function used in each iteration of WL kernel. This function returns a Numpy matrix, each element of which is the user-defined Weisfeiler-Lehman kernel between 2 praphs. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + pass + # init. + height = int(height) + Kmatrix = np.zeros((len(Gn), len(Gn))) # init kernel - # initial for height = 0 - Kmatrix = base_kernel(Gn, node_label, edge_label) - - # iterate each height - for h in range(1, height + 1): - all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration - num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs - for G in Gn: # for each graph - set_multisets = [] - for node in G.nodes(data = True): - # Multiset-label determination. - multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] - # sorting each multiset - multiset.sort() - multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix - set_multisets.append(multiset) - - # label compression - set_unique = list(set(set_multisets)) # set of unique multiset labels - # a dictionary mapping original labels to new ones. - set_compressed = {} - # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label - for value in set_unique: - if value in all_set_compressed.keys(): - set_compressed.update({ value : all_set_compressed[value] }) - else: - set_compressed.update({ value : str(num_of_labels_occured + 1) }) - num_of_labels_occured += 1 - - all_set_compressed.update(set_compressed) - - # relabel nodes - for node in G.nodes(data = True): - node[1][node_label] = set_compressed[set_multisets[node[0]]] - - # calculate kernel with h iterations and add it to the final kernel - Kmatrix += base_kernel(Gn, node_label, edge_label) - - return Kmatrix + # initial for height = 0 + Kmatrix = base_kernel(Gn, node_label, edge_label) + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + for G in Gn: # for each graph + set_multisets = [] + for node in G.nodes(data = True): + # Multiset-label determination. + multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] + # sorting each multiset + multiset.sort() + multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix + set_multisets.append(multiset) + + # label compression + set_unique = list(set(set_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({ value : all_set_compressed[value] }) + else: + set_compressed.update({ value : str(num_of_labels_occured + 1) }) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for node in G.nodes(data = True): + node[1][node_label] = set_compressed[set_multisets[node[0]]] + + # calculate kernel with h iterations and add it to the final kernel + Kmatrix += base_kernel(Gn, node_label, edge_label) + + return Kmatrix diff --git a/gklearn/kernels/weisfeiler_lehman.py b/gklearn/kernels/weisfeiler_lehman.py new file mode 100644 index 0000000..4ecb13f --- /dev/null +++ b/gklearn/kernels/weisfeiler_lehman.py @@ -0,0 +1,465 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Apr 14 15:16:34 2020 + +@author: ljia + +@references: + + [1] Shervashidze N, Schweitzer P, Leeuwen EJ, Mehlhorn K, Borgwardt KM. + Weisfeiler-lehman graph kernels. Journal of Machine Learning Research. + 2011;12(Sep):2539-61. +""" + +import numpy as np +import networkx as nx +from collections import Counter +from functools import partial +from gklearn.utils.parallel import parallel_gm +from gklearn.kernels import GraphKernel + + +class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge user kernel. + + def __init__(self, **kwargs): + GraphKernel.__init__(self) + self.__node_labels = kwargs.get('node_labels', []) + self.__edge_labels = kwargs.get('edge_labels', []) + self.__height = int(kwargs.get('height', 0)) + self.__base_kernel = kwargs.get('base_kernel', 'subtree') + self.__ds_infos = kwargs.get('ds_infos', {}) + + + def _compute_gm_series(self): + self.__add_dummy_node_labels(self._graphs) + + # for WL subtree kernel + if self.__base_kernel == 'subtree': + gram_matrix = self.__subtree_kernel_do(self._graphs) + + # for WL shortest path kernel + elif self.__base_kernel == 'sp': + gram_matrix = self.__sp_kernel_do(self._graphs) + + # for WL edge kernel + elif self.__base_kernel == 'edge': + gram_matrix = self.__edge_kernel_do(self._graphs) + + # for user defined base kernel + else: + gram_matrix = self.__user_kernel_do(self._graphs) + + return gram_matrix + + + def _compute_gm_imap_unordered(self): + if self._verbose >= 2: + raise Warning('Only a part of the computation is parallelized due to the structure of this kernel.') + return self._compute_gm_series() + + + def _compute_kernel_list_series(self, g1, g_list): # @todo: this should be better. + self.__add_dummy_node_labels(g_list + [g1]) + + # for WL subtree kernel + if self.__base_kernel == 'subtree': + gram_matrix = self.__subtree_kernel_do(g_list + [g1]) + + # for WL shortest path kernel + elif self.__base_kernel == 'sp': + gram_matrix = self.__sp_kernel_do(g_list + [g1]) + + # for WL edge kernel + elif self.__base_kernel == 'edge': + gram_matrix = self.__edge_kernel_do(g_list + [g1]) + + # for user defined base kernel + else: + gram_matrix = self.__user_kernel_do(g_list + [g1]) + + return list(gram_matrix[-1][0:-1]) + + + def _compute_kernel_list_imap_unordered(self, g1, g_list): + if self._verbose >= 2: + raise Warning('Only a part of the computation is parallelized due to the structure of this kernel.') + return self._compute_gm_imap_unordered() + + + def _wrapper_kernel_list_do(self, itr): + pass + + + def _compute_single_kernel_series(self, g1, g2): # @todo: this should be better. + self.__add_dummy_node_labels([g1] + [g2]) + + # for WL subtree kernel + if self.__base_kernel == 'subtree': + gram_matrix = self.__subtree_kernel_do([g1] + [g2]) + + # for WL shortest path kernel + elif self.__base_kernel == 'sp': + gram_matrix = self.__sp_kernel_do([g1] + [g2]) + + # for WL edge kernel + elif self.__base_kernel == 'edge': + gram_matrix = self.__edge_kernel_do([g1] + [g2]) + + # for user defined base kernel + else: + gram_matrix = self.__user_kernel_do([g1] + [g2]) + + return gram_matrix[0][1] + + + def __subtree_kernel_do(self, Gn): + """Calculate Weisfeiler-Lehman kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + + Return + ------ + gram_matrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + gram_matrix = np.zeros((len(Gn), len(Gn))) + + # initial for height = 0 + all_num_of_each_label = [] # number of occurence of each label in each graph in this iteration + + # for each graph + for G in Gn: + # set all labels into a tuple. + for nd, attrs in G.nodes(data=True): # @todo: there may be a better way. + G.nodes[nd]['label_tuple'] = tuple(attrs[name] for name in self.__node_labels) + # get the set of original labels + labels_ori = list(nx.get_node_attributes(G, 'label_tuple').values()) + # number of occurence of each label in G + all_num_of_each_label.append(dict(Counter(labels_ori))) + + # calculate subtree kernel with the 0th iteration and add it to the final kernel. + self.__compute_gram_matrix(gram_matrix, all_num_of_each_label, Gn) + + # iterate each height + for h in range(1, self.__height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + # all_labels_ori = set() # all unique orignal labels in all graphs in this iteration + all_num_of_each_label = [] # number of occurence of each label in G + + # @todo: parallel this part. + for idx, G in enumerate(Gn): + + all_multisets = [] + for node, attrs in G.nodes(data=True): + # Multiset-label determination. + multiset = [G.nodes[neighbors]['label_tuple'] for neighbors in G[node]] + # sorting each multiset + multiset.sort() + multiset = [attrs['label_tuple']] + multiset # add the prefix + all_multisets.append(tuple(multiset)) + + # label compression + set_unique = list(set(all_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, + # else assign the number of labels occured + 1 as the compressed label. + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({value: all_set_compressed[value]}) + else: + set_compressed.update({value: str(num_of_labels_occured + 1)}) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for idx, node in enumerate(G.nodes()): + G.nodes[node]['label_tuple'] = set_compressed[all_multisets[idx]] + + # get the set of compressed labels + labels_comp = list(nx.get_node_attributes(G, 'label_tuple').values()) + # all_labels_ori.update(labels_comp) + all_num_of_each_label.append(dict(Counter(labels_comp))) + + # calculate subtree kernel with h iterations and add it to the final kernel + self.__compute_gram_matrix(gram_matrix, all_num_of_each_label, Gn) + + return gram_matrix + + + def __compute_gram_matrix(self, gram_matrix, all_num_of_each_label, Gn): + """Compute Gram matrix using the base kernel. + """ + if self._parallel == 'imap_unordered': + # compute kernels. + def init_worker(alllabels_toshare): + global G_alllabels + G_alllabels = alllabels_toshare + do_partial = partial(self._wrapper_compute_subtree_kernel, gram_matrix) + parallel_gm(do_partial, gram_matrix, Gn, init_worker=init_worker, + glbv=(all_num_of_each_label,), n_jobs=self._n_jobs, verbose=self._verbose) + elif self._parallel is None: + for i in range(len(gram_matrix)): + for j in range(i, len(gram_matrix)): + gram_matrix[i][j] = self.__compute_subtree_kernel(all_num_of_each_label[i], + all_num_of_each_label[j], gram_matrix[i][j]) + gram_matrix[j][i] = gram_matrix[i][j] + + + def __compute_subtree_kernel(self, num_of_each_label1, num_of_each_label2, kernel): + """Compute the subtree kernel. + """ + labels = set(list(num_of_each_label1.keys()) + list(num_of_each_label2.keys())) + vector1 = np.array([(num_of_each_label1[label] + if (label in num_of_each_label1.keys()) else 0) + for label in labels]) + vector2 = np.array([(num_of_each_label2[label] + if (label in num_of_each_label2.keys()) else 0) + for label in labels]) + kernel += np.dot(vector1, vector2) + return kernel + + + def _wrapper_compute_subtree_kernel(self, gram_matrix, itr): + i = itr[0] + j = itr[1] + return i, j, self.__compute_subtree_kernel(G_alllabels[i], G_alllabels[j], gram_matrix[i][j]) + + + def _wl_spkernel_do(Gn, node_label, edge_label, height): + """Calculate Weisfeiler-Lehman shortest path kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + subtree height. + + Return + ------ + gram_matrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + pass + from gklearn.utils.utils import getSPGraph + + # init. + height = int(height) + gram_matrix = np.zeros((len(Gn), len(Gn))) # init kernel + + Gn = [ getSPGraph(G, edge_weight = edge_label) for G in Gn ] # get shortest path graphs of Gn + + # initial for height = 0 + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + gram_matrix[i][j] += 1 + gram_matrix[j][i] = gram_matrix[i][j] + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + for G in Gn: # for each graph + set_multisets = [] + for node in G.nodes(data = True): + # Multiset-label determination. + multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] + # sorting each multiset + multiset.sort() + multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix + set_multisets.append(multiset) + + # label compression + set_unique = list(set(set_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({ value : all_set_compressed[value] }) + else: + set_compressed.update({ value : str(num_of_labels_occured + 1) }) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for node in G.nodes(data = True): + node[1][node_label] = set_compressed[set_multisets[node[0]]] + + # calculate subtree kernel with h iterations and add it to the final kernel + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + gram_matrix[i][j] += 1 + gram_matrix[j][i] = gram_matrix[i][j] + + return gram_matrix + + + + def _wl_edgekernel_do(Gn, node_label, edge_label, height): + """Calculate Weisfeiler-Lehman edge kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + subtree height. + + Return + ------ + gram_matrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + pass + # init. + height = int(height) + gram_matrix = np.zeros((len(Gn), len(Gn))) # init kernel + + # initial for height = 0 + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2][edge_label] == e2[2][edge_label] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + gram_matrix[i][j] += 1 + gram_matrix[j][i] = gram_matrix[i][j] + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + for G in Gn: # for each graph + set_multisets = [] + for node in G.nodes(data = True): + # Multiset-label determination. + multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] + # sorting each multiset + multiset.sort() + multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix + set_multisets.append(multiset) + + # label compression + set_unique = list(set(set_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({ value : all_set_compressed[value] }) + else: + set_compressed.update({ value : str(num_of_labels_occured + 1) }) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for node in G.nodes(data = True): + node[1][node_label] = set_compressed[set_multisets[node[0]]] + + # calculate subtree kernel with h iterations and add it to the final kernel + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2][edge_label] == e2[2][edge_label] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + gram_matrix[i][j] += 1 + gram_matrix[j][i] = gram_matrix[i][j] + + return gram_matrix + + + def _wl_userkernel_do(Gn, node_label, edge_label, height, base_kernel): + """Calculate Weisfeiler-Lehman kernels based on user-defined kernel between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + subtree height. + base_kernel : string + Name of the base kernel function used in each iteration of WL kernel. This function returns a Numpy matrix, each element of which is the user-defined Weisfeiler-Lehman kernel between 2 praphs. + + Return + ------ + gram_matrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + pass + # init. + height = int(height) + gram_matrix = np.zeros((len(Gn), len(Gn))) # init kernel + + # initial for height = 0 + gram_matrix = base_kernel(Gn, node_label, edge_label) + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + for G in Gn: # for each graph + set_multisets = [] + for node in G.nodes(data = True): + # Multiset-label determination. + multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] + # sorting each multiset + multiset.sort() + multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix + set_multisets.append(multiset) + + # label compression + set_unique = list(set(set_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({ value : all_set_compressed[value] }) + else: + set_compressed.update({ value : str(num_of_labels_occured + 1) }) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for node in G.nodes(data = True): + node[1][node_label] = set_compressed[set_multisets[node[0]]] + + # calculate kernel with h iterations and add it to the final kernel + gram_matrix += base_kernel(Gn, node_label, edge_label) + + return gram_matrix + + + def __add_dummy_node_labels(self, Gn): + if len(self.__node_labels) == 0: + for G in Gn: + nx.set_node_attributes(G, '0', 'dummy') + self.__node_labels.append('dummy') \ No newline at end of file diff --git a/gklearn/preimage/experiments/xp_median_preimage.py b/gklearn/preimage/experiments/xp_median_preimage.py index 7ae20ba..903faaa 100644 --- a/gklearn/preimage/experiments/xp_median_preimage.py +++ b/gklearn/preimage/experiments/xp_median_preimage.py @@ -13,7 +13,7 @@ from gklearn.utils import compute_gram_matrices_by_class def xp_median_preimage_9_1(): - """xp 9_1: MAO, sspkernel, using CONSTANT. + """xp 9_1: MAO, StructuralSP, using CONSTANT, symbolic only. """ # set parameters. ds_name = 'MAO' # @@ -29,7 +29,7 @@ def xp_median_preimage_9_1(): 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - kernel_options = {'name': 'structuralspkernel', + kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, @@ -53,7 +53,7 @@ def xp_median_preimage_9_1(): 'verbose': 2, 'refine': False} save_results = True - dir_save='../results/xp_median_preimage/' + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # edge_required = False # @@ -69,31 +69,895 @@ def xp_median_preimage_9_1(): print() # generate preimages. - for fit_method in ['k-graphs', 'expert', 'random', 'random', 'random']: + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_9_2(): + """xp 9_2: MAO, PathUpToH, using CONSTANT, symbolic only. + """ + # set parameters. + ds_name = 'MAO' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + kernel_options = {'name': 'PathUpToH', + 'depth': 9, # + 'k_func': 'MinMax', # + 'compute_method': 'trie', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_9_3(): + """xp 9_3: MAO, Treelet, using CONSTANT. + """ + from gklearn.utils.kernels import polynomialkernel + # set parameters. + ds_name = 'MAO' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + pkernel = functools.partial(polynomialkernel, d=4, c=1e+7) + kernel_options = {'name': 'Treelet', # + 'sub_kernel': pkernel, + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_9_4(): + """xp 9_4: MAO, WeisfeilerLehman, using CONSTANT. + """ + # set parameters. + ds_name = 'MAO' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + kernel_options = {'name': 'WeisfeilerLehman', + 'height': 6, + 'base_kernel': 'subtree', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_8_1(): + """xp 8_1: Monoterpenoides, StructuralSP, using CONSTANT. + """ + # set parameters. + ds_name = 'Monoterpenoides' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [3, 3, 1, 3, 3, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + kernel_options = {'name': 'StructuralSP', + 'edge_weight': None, + 'node_kernels': sub_kernels, + 'edge_kernels': sub_kernels, + 'compute_method': 'naive', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_8_2(): + """xp 8_2: Monoterpenoides, PathUpToH, using CONSTANT. + """ + # set parameters. + ds_name = 'Monoterpenoides' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + kernel_options = {'name': 'PathUpToH', + 'depth': 7, # + 'k_func': 'MinMax', # + 'compute_method': 'trie', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_8_3(): + """xp 8_3: Monoterpenoides, Treelet, using CONSTANT. + """ + from gklearn.utils.kernels import polynomialkernel + # set parameters. + ds_name = 'Monoterpenoides' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + pkernel = functools.partial(polynomialkernel, d=2, c=1e+5) + kernel_options = {'name': 'Treelet', + 'sub_kernel': pkernel, + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_8_4(): + """xp 8_4: Monoterpenoides, WeisfeilerLehman, using CONSTANT. + """ + # set parameters. + ds_name = 'Monoterpenoides' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + kernel_options = {'name': 'WeisfeilerLehman', + 'height': 4, + 'base_kernel': 'subtree', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_7_1(): + """xp 7_1: MUTAG, StructuralSP, using CONSTANT. + """ + # set parameters. + ds_name = 'MUTAG' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + kernel_options = {'name': 'StructuralSP', + 'edge_weight': None, + 'node_kernels': sub_kernels, + 'edge_kernels': sub_kernels, + 'compute_method': 'naive', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_7_2(): + """xp 7_2: MUTAG, PathUpToH, using CONSTANT. + """ + # set parameters. + ds_name = 'MUTAG' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + kernel_options = {'name': 'PathUpToH', + 'depth': 2, # + 'k_func': 'MinMax', # + 'compute_method': 'trie', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_7_3(): + """xp 7_3: MUTAG, Treelet, using CONSTANT. + """ + from gklearn.utils.kernels import polynomialkernel + # set parameters. + ds_name = 'MUTAG' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + pkernel = functools.partial(polynomialkernel, d=3, c=1e+8) + kernel_options = {'name': 'Treelet', + 'sub_kernel': pkernel, + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_7_4(): + """xp 7_4: MUTAG, WeisfeilerLehman, using CONSTANT. + """ + # set parameters. + ds_name = 'MUTAG' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, # + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + kernel_options = {'name': 'WeisfeilerLehman', + 'height': 1, + 'base_kernel': 'subtree', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'CONSTANT', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_6_1(): + """xp 6_1: COIL-RAG, StructuralSP, using NON_SYMBOLIC. + """ + # set parameters. + ds_name = 'COIL-RAG' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [3, 3, 1, 3, 3, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + kernel_options = {'name': 'StructuralSP', + 'edge_weight': None, + 'node_kernels': sub_kernels, + 'edge_kernels': sub_kernels, + 'compute_method': 'naive', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'NON_SYMBOLIC', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + + +def xp_median_preimage_6_2(): + """xp 6_2: COIL-RAG, ShortestPath, using NON_SYMBOLIC. + """ + # set parameters. + ds_name = 'COIL-RAG' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [3, 3, 1, 3, 3, 1], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + kernel_options = {'name': 'ShortestPath', + 'edge_weight': None, + 'node_kernels': sub_kernels, + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'NON_SYMBOLIC', # + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = True # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + +def xp_median_preimage_5_1(): + """xp 5_1: FRANKENSTEIN, StructuralSP, using NON_SYMBOLIC. + """ + # set parameters. + ds_name = 'FRANKENSTEIN' # + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [3, 3, 1, 3, 3, 0], # + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + kernel_options = {'name': 'StructuralSP', + 'edge_weight': None, + 'node_kernels': sub_kernels, + 'edge_kernels': sub_kernels, + 'compute_method': 'naive', + 'parallel': 'imap_unordered', + # 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'NON_SYMBOLIC', + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = False # + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() + + # generate preimages. + for fit_method in ['k-graphs'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + -def xp_median_preimage_8_1(): - """xp 8_1: Monoterpenoides, sspkernel, using CONSTANT. +def xp_median_preimage_4_1(): + """xp 4_1: COLORS-3, StructuralSP, using NON_SYMBOLIC. """ # set parameters. - ds_name = 'Monoterpenoides' # + ds_name = 'COLORS-3' # mpg_options = {'fit_method': 'k-graphs', - 'init_ecc': [3, 3, 1, 3, 3, 1], # + 'init_ecc': [3, 3, 1, 3, 3, 0], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, - 'max_itrs': 100, # + 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - kernel_options = {'name': 'structuralspkernel', + kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, @@ -106,7 +970,7 @@ def xp_median_preimage_8_1(): ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 - 'edit_cost': 'CONSTANT', # + 'edit_cost': 'NON_SYMBOLIC', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), @@ -117,7 +981,7 @@ def xp_median_preimage_8_1(): 'verbose': 2, 'refine': False} save_results = True - dir_save='../results/xp_median_preimage/' + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' irrelevant_labels = None # edge_required = False # @@ -133,35 +997,33 @@ def xp_median_preimage_8_1(): print() # generate preimages. - for fit_method in ['k-graphs', 'expert', 'random', 'random', 'random']: + for fit_method in ['k-graphs'] + ['random'] * 5: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) - -def xp_median_preimage_7_1(): - """xp 7_1: MUTAG, sspkernel, using CONSTANT. + +def xp_median_preimage_3_2(): + """xp 3_2: Fingerprint, ShortestPath, using LETTER2, only node attrs. """ # set parameters. - ds_name = 'MUTAG' # + ds_name = 'Fingerprint' # mpg_options = {'fit_method': 'k-graphs', - 'init_ecc': [4, 4, 2, 1, 1, 1], # + 'init_ecc': [0.525, 0.525, 0.001, 0.125, 0.125], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, - 'max_itrs': 100, # + 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - kernel_options = {'name': 'structuralspkernel', + kernel_options = {'name': 'ShortestPath', 'edge_weight': None, 'node_kernels': sub_kernels, - 'edge_kernels': sub_kernels, - 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), @@ -170,7 +1032,7 @@ def xp_median_preimage_7_1(): ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 - 'edit_cost': 'CONSTANT', # + 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), @@ -181,9 +1043,9 @@ def xp_median_preimage_7_1(): 'verbose': 2, 'refine': False} save_results = True - dir_save='../results/xp_median_preimage/' - irrelevant_labels = None # - edge_required = False # + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = {'edge_attrs': ['orient', 'angle']} # + edge_required = True # # print settings. print('parameters:') @@ -197,20 +1059,20 @@ def xp_median_preimage_7_1(): print() # generate preimages. - for fit_method in ['k-graphs', 'expert', 'random', 'random', 'random']: + for fit_method in ['k-graphs'] + ['random'] * 5: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) -def xp_median_preimage_6_1(): - """xp 6_1: COIL-RAG, sspkernel, using NON_SYMBOLIC. +def xp_median_preimage_3_1(): + """xp 3_1: Fingerprint, StructuralSP, using LETTER2, only node attrs. """ # set parameters. - ds_name = 'COIL-RAG' # + ds_name = 'Fingerprint' # mpg_options = {'fit_method': 'k-graphs', - 'init_ecc': [3, 3, 1, 3, 3, 1], # + 'init_ecc': [0.525, 0.525, 0.001, 0.125, 0.125], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, @@ -221,7 +1083,7 @@ def xp_median_preimage_6_1(): 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - kernel_options = {'name': 'structuralspkernel', + kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, @@ -234,7 +1096,7 @@ def xp_median_preimage_6_1(): ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 - 'edit_cost': 'NON_SYMBOLIC', # + 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), @@ -245,8 +1107,8 @@ def xp_median_preimage_6_1(): 'verbose': 2, 'refine': False} save_results = True - dir_save='../results/xp_median_preimage/' - irrelevant_labels = None # + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = {'edge_attrs': ['orient', 'angle']} # edge_required = False # # print settings. @@ -261,20 +1123,20 @@ def xp_median_preimage_6_1(): print() # generate preimages. - for fit_method in ['k-graphs', 'random', 'random', 'random']: + for fit_method in ['k-graphs'] + ['random'] * 5: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + - -def xp_median_preimage_5_1(): - """xp 5_1: FRANKENSTEIN, sspkernel, using NON_SYMBOLIC. +def xp_median_preimage_2_1(): + """xp 2_1: COIL-DEL, StructuralSP, using LETTER2, only node attrs. """ # set parameters. - ds_name = 'FRANKENSTEIN' # + ds_name = 'COIL-DEL' # mpg_options = {'fit_method': 'k-graphs', - 'init_ecc': [3, 3, 1, 3, 3, 0], # + 'init_ecc': [3, 3, 1, 3, 3], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, @@ -285,7 +1147,7 @@ def xp_median_preimage_5_1(): 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - kernel_options = {'name': 'structuralspkernel', + kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, @@ -298,7 +1160,7 @@ def xp_median_preimage_5_1(): ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 - 'edit_cost': 'NON_SYMBOLIC', + 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), @@ -309,9 +1171,8 @@ def xp_median_preimage_5_1(): 'verbose': 2, 'refine': False} save_results = True - dir_save='../results/xp_median_preimage/' - irrelevant_labels = None # - edge_required = False # + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = {'edge_labels': ['valence']} # print settings. print('parameters:') @@ -324,21 +1185,25 @@ def xp_median_preimage_5_1(): print('irrelevant_labels:', irrelevant_labels) print() +# # compute gram matrices for each class a priori. +# print('Compute gram matrices for each class a priori.') +# compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, dir_save=dir_save, irrelevant_labels=irrelevant_labels) + # generate preimages. - for fit_method in ['k-graphs', 'random', 'random', 'random']: + for fit_method in ['k-graphs'] + ['random'] * 5: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method - generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) - + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels) -def xp_median_preimage_4_1(): - """xp 4_1: COLORS-3, sspkernel, using NON_SYMBOLIC. + +def xp_median_preimage_1_1(): + """xp 1_1: Letter-high, StructuralSP. """ # set parameters. - ds_name = 'COLORS-3' # + ds_name = 'Letter-high' mpg_options = {'fit_method': 'k-graphs', - 'init_ecc': [3, 3, 1, 3, 3, 0], # + 'init_ecc': [0.675, 0.675, 0.75, 0.425, 0.425], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, @@ -349,20 +1214,78 @@ def xp_median_preimage_4_1(): 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - kernel_options = {'name': 'structuralspkernel', + kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', - 'parallel': 'imap_unordered', - # 'parallel': None, + 'parallel': 'imap_unordered', +# 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 - 'edit_cost': 'NON_SYMBOLIC', + 'edit_cost': 'LETTER2', + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save) + + +def xp_median_preimage_1_2(): + """xp 1_2: Letter-high, ShortestPath. + """ + # set parameters. + ds_name = 'Letter-high' + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [0.675, 0.675, 0.75, 0.425, 0.425], + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + kernel_options = {'name': 'ShortestPath', + 'edge_weight': None, + 'node_kernels': sub_kernels, + 'parallel': 'imap_unordered', +# 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 + 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), @@ -373,9 +1296,9 @@ def xp_median_preimage_4_1(): 'verbose': 2, 'refine': False} save_results = True - dir_save='../results/xp_median_preimage/' + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' irrelevant_labels = None # - edge_required = False # + edge_required = True # # print settings. print('parameters:') @@ -389,20 +1312,20 @@ def xp_median_preimage_4_1(): print() # generate preimages. - for fit_method in ['k-graphs', 'random', 'random', 'random']: + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + - -def xp_median_preimage_3_1(): - """xp 3_1: Fingerprint, sspkernel, using LETTER2, only node attrs. +def xp_median_preimage_10_1(): + """xp 10_1: Letter-med, StructuralSP. """ # set parameters. - ds_name = 'Fingerprint' # + ds_name = 'Letter-med' mpg_options = {'fit_method': 'k-graphs', - 'init_ecc': [0.525, 0.525, 0.001, 0.125, 0.125], # + 'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, @@ -413,13 +1336,13 @@ def xp_median_preimage_3_1(): 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - kernel_options = {'name': 'structuralspkernel', + kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', - 'parallel': 'imap_unordered', - # 'parallel': None, + 'parallel': 'imap_unordered', +# 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} @@ -437,9 +1360,7 @@ def xp_median_preimage_3_1(): 'verbose': 2, 'refine': False} save_results = True - dir_save='../results/xp_median_preimage/' - irrelevant_labels = {'edge_attrs': ['orient', 'angle']} # - edge_required = False # + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' # print settings. print('parameters:') @@ -449,24 +1370,22 @@ def xp_median_preimage_3_1(): print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) - print('irrelevant_labels:', irrelevant_labels) - print() # generate preimages. - for fit_method in ['k-graphs', 'random', 'random', 'random']: + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method - generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save) - -def xp_median_preimage_2_1(): - """xp 2_1: COIL-DEL, sspkernel, using LETTER2, only node attrs. + +def xp_median_preimage_10_2(): + """xp 10_2: Letter-med, ShortestPath. """ # set parameters. - ds_name = 'COIL-DEL' # + ds_name = 'Letter-med' mpg_options = {'fit_method': 'k-graphs', - 'init_ecc': [3, 3, 1, 3, 3], + 'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, @@ -477,13 +1396,11 @@ def xp_median_preimage_2_1(): 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - kernel_options = {'name': 'structuralspkernel', + kernel_options = {'name': 'ShortestPath', 'edge_weight': None, 'node_kernels': sub_kernels, - 'edge_kernels': sub_kernels, - 'compute_method': 'naive', - 'parallel': 'imap_unordered', - # 'parallel': None, + 'parallel': 'imap_unordered', +# 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} @@ -501,8 +1418,9 @@ def xp_median_preimage_2_1(): 'verbose': 2, 'refine': False} save_results = True - dir_save='../results/xp_median_preimage/' - irrelevant_labels = {'edge_labels': ['valence']} + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = True # # print settings. print('parameters:') @@ -515,25 +1433,21 @@ def xp_median_preimage_2_1(): print('irrelevant_labels:', irrelevant_labels) print() -# # compute gram matrices for each class a priori. -# print('Compute gram matrices for each class a priori.') -# compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, dir_save=dir_save, irrelevant_labels=irrelevant_labels) - # generate preimages. - for fit_method in ['k-graphs', 'random', 'random', 'random']: + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method - generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels) + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) -def xp_median_preimage_1_1(): - """xp 1_1: Letter-high, sspkernel. +def xp_median_preimage_11_1(): + """xp 11_1: Letter-low, StructuralSP. """ # set parameters. - ds_name = 'Letter-high' + ds_name = 'Letter-low' mpg_options = {'fit_method': 'k-graphs', - 'init_ecc': [3, 3, 1, 3, 3], + 'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, @@ -544,7 +1458,7 @@ def xp_median_preimage_1_1(): 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - kernel_options = {'name': 'structuralspkernel', + kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, @@ -556,7 +1470,65 @@ def xp_median_preimage_1_1(): 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' - 'initial_solutions': 1, # 1 + 'initial_solutions': 10, # 1 + 'edit_cost': 'LETTER2', + 'attr_distance': 'euclidean', + 'ratio_runs_from_initial_solutions': 1, + 'threads': multiprocessing.cpu_count(), + 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} + mge_options = {'init_type': 'MEDOID', + 'random_inits': 10, + 'time_limit': 600, + 'verbose': 2, + 'refine': False} + save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + + # print settings. + print('parameters:') + print('dataset name:', ds_name) + print('mpg_options:', mpg_options) + print('kernel_options:', kernel_options) + print('ged_options:', ged_options) + print('mge_options:', mge_options) + print('save_results:', save_results) + + # generate preimages. + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: + print('\n-------------------------------------') + print('fit method:', fit_method, '\n') + mpg_options['fit_method'] = fit_method + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save) + + +def xp_median_preimage_11_2(): + """xp 11_2: Letter-low, ShortestPath. + """ + # set parameters. + ds_name = 'Letter-low' + mpg_options = {'fit_method': 'k-graphs', + 'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075], + 'ds_name': ds_name, + 'parallel': True, # False + 'time_limit_in_sec': 0, + 'max_itrs': 100, + 'max_itrs_without_update': 3, + 'epsilon_residual': 0.01, + 'epsilon_ec': 0.1, + 'verbose': 2} + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + kernel_options = {'name': 'ShortestPath', + 'edge_weight': None, + 'node_kernels': sub_kernels, + 'parallel': 'imap_unordered', +# 'parallel': None, + 'n_jobs': multiprocessing.cpu_count(), + 'normalize': True, + 'verbose': 2} + ged_options = {'method': 'IPFP', + 'initialization_method': 'RANDOM', # 'NODE' + 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, @@ -568,6 +1540,9 @@ def xp_median_preimage_1_1(): 'verbose': 2, 'refine': False} save_results = True + dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/' + irrelevant_labels = None # + edge_required = True # # print settings. print('parameters:') @@ -577,40 +1552,90 @@ def xp_median_preimage_1_1(): print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) + print('irrelevant_labels:', irrelevant_labels) + print() # generate preimages. - for fit_method in ['k-graphs', 'expert', 'random', 'random', 'random']: + for fit_method in ['k-graphs', 'expert'] + ['random'] * 5: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method - generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save='../results/xp_median_preimage/') + generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) if __name__ == "__main__": - #### xp 1_1: Letter-high, sspkernel. + #### xp 1_1: Letter-high, StructuralSP. # xp_median_preimage_1_1() + + #### xp 1_2: Letter-high, ShortestPath. +# xp_median_preimage_1_2() + + #### xp 10_1: Letter-med, StructuralSP. + # xp_median_preimage_10_1() + + #### xp 10_2: Letter-med, ShortestPath. + # xp_median_preimage_10_2() + + #### xp 11_1: Letter-low, StructuralSP. + # xp_median_preimage_11_1() + + #### xp 11_2: Letter-low, ShortestPath. +# xp_median_preimage_11_2() - #### xp 2_1: COIL-DEL, sspkernel, using LETTER2, only node attrs. + #### xp 2_1: COIL-DEL, StructuralSP, using LETTER2, only node attrs. # xp_median_preimage_2_1() - #### xp 3_1: Fingerprint, sspkernel, using LETTER2, only node attrs. + #### xp 3_1: Fingerprint, StructuralSP, using LETTER2, only node attrs. # xp_median_preimage_3_1() - #### xp 4_1: COLORS-3, sspkernel, using NON_SYMBOLIC. + #### xp 3_2: Fingerprint, ShortestPath, using LETTER2, only node attrs. +# xp_median_preimage_3_2() + + #### xp 4_1: COLORS-3, StructuralSP, using NON_SYMBOLIC. # xp_median_preimage_4_1() - #### xp 5_1: FRANKENSTEIN, sspkernel, using NON_SYMBOLIC. + #### xp 5_1: FRANKENSTEIN, StructuralSP, using NON_SYMBOLIC. # xp_median_preimage_5_1() - #### xp 6_1: COIL-RAG, sspkernel, using NON_SYMBOLIC. + #### xp 6_1: COIL-RAG, StructuralSP, using NON_SYMBOLIC. # xp_median_preimage_6_1() - #### xp 7_1: MUTAG, sspkernel, using CONSTANT. + #### xp 6_2: COIL-RAG, ShortestPath, using NON_SYMBOLIC. +# xp_median_preimage_6_2() + + #### xp 7_1: MUTAG, StructuralSP, using CONSTANT. # xp_median_preimage_7_1() + + #### xp 7_2: MUTAG, PathUpToH, using CONSTANT. + # xp_median_preimage_7_2() + + #### xp 7_3: MUTAG, Treelet, using CONSTANT. + # xp_median_preimage_7_3() + + #### xp 7_4: MUTAG, WeisfeilerLehman, using CONSTANT. +# xp_median_preimage_7_4() - #### xp 8_1: Monoterpenoides, sspkernel, using CONSTANT. + #### xp 8_1: Monoterpenoides, StructuralSP, using CONSTANT. # xp_median_preimage_8_1() - #### xp 9_1: MAO, sspkernel, using CONSTANT. - xp_median_preimage_9_1() \ No newline at end of file + #### xp 8_2: Monoterpenoides, PathUpToH, using CONSTANT. + # xp_median_preimage_8_2() + + #### xp 8_3: Monoterpenoides, Treelet, using CONSTANT. +# xp_median_preimage_8_3() + + #### xp 8_4: Monoterpenoides, WeisfeilerLehman, using CONSTANT. +# xp_median_preimage_8_4() + + #### xp 9_1: MAO, StructuralSP, using CONSTANT, symbolic only. +# xp_median_preimage_9_1() + + #### xp 9_2: MAO, PathUpToH, using CONSTANT, symbolic only. + # xp_median_preimage_9_2() + + #### xp 9_3: MAO, Treelet, using CONSTANT. +# xp_median_preimage_9_3() + + #### xp 9_4: MAO, WeisfeilerLehman, using CONSTANT. + xp_median_preimage_9_4() \ No newline at end of file diff --git a/gklearn/preimage/find_best_k.py b/gklearn/preimage/find_best_k.py deleted file mode 100644 index df38d32..0000000 --- a/gklearn/preimage/find_best_k.py +++ /dev/null @@ -1,170 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Jan 9 11:54:32 2020 - -@author: ljia -""" -import numpy as np -import random -import csv - -from gklearn.utils.graphfiles import loadDataset -from gklearn.preimage.test_k_closest_graphs import median_on_k_closest_graphs - -def find_best_k(): - ds = {'name': 'monoterpenoides', - 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:50] - gkernel = 'treeletkernel' - node_label = 'atom' - edge_label = 'bond_type' - ds_name = 'mono' - dir_output = 'results/test_find_best_k/' - - repeats = 50 - k_list = range(2, 11) - fit_method = 'k-graphs' - # fitted on the whole dataset - treelet - mono - edit_costs = [0.1268873773592978, 0.004084633224249829, 0.0897581955378986, 0.15328856114451297, 0.3109956881625734, 0.0] - - # create result files. - fn_output_detail = 'results_detail.' + fit_method + '.csv' - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'fit method', 'k', - 'repeat', 'median set', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM']) - f_detail.close() - fn_output_summary = 'results_summary.csv' - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'fit method', 'k', - 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM', '# SOD SM -> GM', '# dis_k SM -> GM', - '# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM', - 'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM', - 'repeats better dis_k gi -> GM']) - f_summary.close() - - random.seed(1) - rdn_seed_list = random.sample(range(0, repeats * 100), repeats) - - for k in k_list: - print('\n--------- k =', k, '----------') - - sod_sm_list = [] - sod_gm_list = [] - dis_k_sm_list = [] - dis_k_gm_list = [] - dis_k_gi_min_list = [] - nb_sod_sm2gm = [0, 0, 0] - nb_dis_k_sm2gm = [0, 0, 0] - nb_dis_k_gi2sm = [0, 0, 0] - nb_dis_k_gi2gm = [0, 0, 0] - repeats_better_sod_sm2gm = [] - repeats_better_dis_k_sm2gm = [] - repeats_better_dis_k_gi2sm = [] - repeats_better_dis_k_gi2gm = [] - - - for repeat in range(repeats): - print('\nrepeat =', repeat) - random.seed(rdn_seed_list[repeat]) - median_set_idx = random.sample(range(0, len(Gn)), k) - print('median set: ', median_set_idx) - - sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \ - = median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k, - fit_method='k-graphs', - edit_costs=edit_costs, - group_min=median_set_idx, - parallel=False) - - # write result detail. - sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm)) - dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm)) - dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min)) - dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min)) - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow([ds_name, gkernel, fit_method, k, repeat, - median_set_idx, sod_sm, sod_gm, dis_k_sm, dis_k_gm, - dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm, - dis_k_gi2gm]) - f_detail.close() - - # compute result summary. - sod_sm_list.append(sod_sm) - sod_gm_list.append(sod_gm) - dis_k_sm_list.append(dis_k_sm) - dis_k_gm_list.append(dis_k_gm) - dis_k_gi_min_list.append(dis_k_gi_min) - # # SOD SM -> GM - if sod_sm > sod_gm: - nb_sod_sm2gm[0] += 1 - repeats_better_sod_sm2gm.append(repeat) - elif sod_sm == sod_gm: - nb_sod_sm2gm[1] += 1 - elif sod_sm < sod_gm: - nb_sod_sm2gm[2] += 1 - # # dis_k SM -> GM - if dis_k_sm > dis_k_gm: - nb_dis_k_sm2gm[0] += 1 - repeats_better_dis_k_sm2gm.append(repeat) - elif dis_k_sm == dis_k_gm: - nb_dis_k_sm2gm[1] += 1 - elif dis_k_sm < dis_k_gm: - nb_dis_k_sm2gm[2] += 1 - # # dis_k gi -> SM - if dis_k_gi_min > dis_k_sm: - nb_dis_k_gi2sm[0] += 1 - repeats_better_dis_k_gi2sm.append(repeat) - elif dis_k_gi_min == dis_k_sm: - nb_dis_k_gi2sm[1] += 1 - elif dis_k_gi_min < dis_k_sm: - nb_dis_k_gi2sm[2] += 1 - # # dis_k gi -> GM - if dis_k_gi_min > dis_k_gm: - nb_dis_k_gi2gm[0] += 1 - repeats_better_dis_k_gi2gm.append(repeat) - elif dis_k_gi_min == dis_k_gm: - nb_dis_k_gi2gm[1] += 1 - elif dis_k_gi_min < dis_k_gm: - nb_dis_k_gi2gm[2] += 1 - - # write result summary. - sod_sm_mean = np.mean(sod_sm_list) - sod_gm_mean = np.mean(sod_gm_list) - dis_k_sm_mean = np.mean(dis_k_sm_list) - dis_k_gm_mean = np.mean(dis_k_gm_list) - dis_k_gi_min_mean = np.mean(dis_k_gi_min_list) - sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean)) - dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean)) - dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean)) - dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean)) - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, - sod_sm_mean, sod_gm_mean, dis_k_sm_mean, dis_k_gm_mean, - dis_k_gi_min_mean, sod_sm2gm_mean, dis_k_sm2gm_mean, - dis_k_gi2sm_mean, dis_k_gi2gm_mean, nb_sod_sm2gm, - nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm, - repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm, - repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm]) - f_summary.close() - - print('\ncomplete.') - return - - -def getRelations(sign): - if sign == -1: - return 'better' - elif sign == 0: - return 'same' - elif sign == 1: - return 'worse' - - -if __name__ == '__main__': - find_best_k() \ No newline at end of file diff --git a/gklearn/preimage/fitDistance.py b/gklearn/preimage/fitDistance.py deleted file mode 100644 index 234f7fc..0000000 --- a/gklearn/preimage/fitDistance.py +++ /dev/null @@ -1,430 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Oct 16 14:20:06 2019 - -@author: ljia -""" -import numpy as np -from tqdm import tqdm -from itertools import combinations_with_replacement, combinations -import multiprocessing -from multiprocessing import Pool -from functools import partial -import time -import random -import sys - -from scipy import optimize -from scipy.optimize import minimize -import cvxpy as cp - -from gklearn.preimage.ged import GED, get_nb_edit_operations, get_nb_edit_operations_letter, get_nb_edit_operations_nonsymbolic -from gklearn.preimage.utils import kernel_distance_matrix - -def fit_GED_to_kernel_distance(Gn, node_label, edge_label, gkernel, itr_max, - params_ged={'lib': 'gedlibpy', 'cost': 'CONSTANT', - 'method': 'IPFP', 'stabilizer': None}, - init_costs=[3, 3, 1, 3, 3, 1], - dataset='monoterpenoides', Kmatrix=None, - parallel=True): -# dataset = dataset.lower() - - # c_vi, c_vr, c_vs, c_ei, c_er, c_es or parts of them. -# random.seed(1) -# cost_rdm = random.sample(range(1, 10), 6) -# init_costs = cost_rdm + [0] -# init_costs = cost_rdm -# init_costs = [3, 3, 1, 3, 3, 1] -# init_costs = [i * 0.01 for i in cost_rdm] + [0] -# init_costs = [0.2, 0.2, 0.2, 0.2, 0.2, 0] -# init_costs = [0, 0, 0.9544, 0.026, 0.0196, 0] -# init_costs = [0.008429912251810438, 0.025461055985319694, 0.2047320869225948, 0.004148727085832133, 0.0, 0] -# idx_cost_nonzeros = [i for i, item in enumerate(edit_costs) if item != 0] - - # compute distances in feature space. - dis_k_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, - Kmatrix=Kmatrix, gkernel=gkernel) - dis_k_vec = [] - for i in range(len(dis_k_mat)): -# for j in range(i, len(dis_k_mat)): - for j in range(i + 1, len(dis_k_mat)): - dis_k_vec.append(dis_k_mat[i, j]) - dis_k_vec = np.array(dis_k_vec) - - # init ged. - print('\ninitial:') - time0 = time.time() - params_ged['dataset'] = dataset - params_ged['edit_cost_constant'] = init_costs - ged_vec_init, ged_mat, n_edit_operations = compute_geds(Gn, params_ged, - parallel=parallel) - residual_list = [np.sqrt(np.sum(np.square(np.array(ged_vec_init) - dis_k_vec)))] - time_list = [time.time() - time0] - edit_cost_list = [init_costs] - nb_cost_mat = np.array(n_edit_operations) - nb_cost_mat_list = [nb_cost_mat] - print('edit_costs:', init_costs) - print('residual_list:', residual_list) - - for itr in range(itr_max): - print('\niteration', itr) - time0 = time.time() - # "fit" geds to distances in feature space by tuning edit costs using the - # Least Squares Method. - np.savez('results/xp_fit_method/fit_data_debug' + str(itr) + '.gm', - nb_cost_mat=nb_cost_mat, dis_k_vec=dis_k_vec, - n_edit_operations=n_edit_operations, ged_vec_init=ged_vec_init, - ged_mat=ged_mat) - edit_costs_new, residual = update_costs(nb_cost_mat, dis_k_vec, - dataset=dataset, cost=params_ged['cost']) - for i in range(len(edit_costs_new)): - if -1e-9 <= edit_costs_new[i] <= 1e-9: - edit_costs_new[i] = 0 - if edit_costs_new[i] < 0: - raise ValueError('The edit cost is negative.') -# for i in range(len(edit_costs_new)): -# if edit_costs_new[i] < 0: -# edit_costs_new[i] = 0 - - # compute new GEDs and numbers of edit operations. - params_ged['edit_cost_constant'] = edit_costs_new # np.array([edit_costs_new[0], edit_costs_new[1], 0.75]) - ged_vec, ged_mat, n_edit_operations = compute_geds(Gn, params_ged, - parallel=parallel) - residual_list.append(np.sqrt(np.sum(np.square(np.array(ged_vec) - dis_k_vec)))) - time_list.append(time.time() - time0) - edit_cost_list.append(edit_costs_new) - nb_cost_mat = np.array(n_edit_operations) - nb_cost_mat_list.append(nb_cost_mat) - print('edit_costs:', edit_costs_new) - print('residual_list:', residual_list) - - return edit_costs_new, residual_list, edit_cost_list, dis_k_mat, ged_mat, \ - time_list, nb_cost_mat_list - - -def compute_geds(Gn, params_ged, parallel=False): - edit_cost_name = params_ged['cost'] - if edit_cost_name == 'LETTER' or edit_cost_name == 'LETTER2': - get_nb_eo = get_nb_edit_operations_letter - elif edit_cost_name == 'NON_SYMBOLIC': - get_nb_eo = get_nb_edit_operations_nonsymbolic - else: - get_nb_eo = get_nb_edit_operations - ged_mat = np.zeros((len(Gn), len(Gn))) - if parallel: -# print('parallel') -# len_itr = int(len(Gn) * (len(Gn) + 1) / 2) - len_itr = int(len(Gn) * (len(Gn) - 1) / 2) - ged_vec = [0 for i in range(len_itr)] - n_edit_operations = [0 for i in range(len_itr)] -# itr = combinations_with_replacement(range(0, len(Gn)), 2) - itr = combinations(range(0, len(Gn)), 2) - n_jobs = multiprocessing.cpu_count() - if len_itr < 100 * n_jobs: - chunksize = int(len_itr / n_jobs) + 1 - else: - chunksize = 100 - def init_worker(gn_toshare): - global G_gn - G_gn = gn_toshare - do_partial = partial(_wrapper_compute_ged_parallel, params_ged, get_nb_eo) - pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(Gn,)) - iterator = tqdm(pool.imap_unordered(do_partial, itr, chunksize), - desc='computing GEDs', file=sys.stdout) -# iterator = pool.imap_unordered(do_partial, itr, chunksize) - for i, j, dis, n_eo_tmp in iterator: - idx_itr = int(len(Gn) * i + j - (i + 1) * (i + 2) / 2) - ged_vec[idx_itr] = dis - ged_mat[i][j] = dis - ged_mat[j][i] = dis - n_edit_operations[idx_itr] = n_eo_tmp -# print('\n-------------------------------------------') -# print(i, j, idx_itr, dis) - pool.close() - pool.join() - - else: - ged_vec = [] - n_edit_operations = [] - for i in tqdm(range(len(Gn)), desc='computing GEDs', file=sys.stdout): -# for i in range(len(Gn)): - for j in range(i + 1, len(Gn)): - dis, pi_forward, pi_backward = GED(Gn[i], Gn[j], **params_ged) - ged_vec.append(dis) - ged_mat[i][j] = dis - ged_mat[j][i] = dis - n_eo_tmp = get_nb_eo(Gn[i], Gn[j], pi_forward, pi_backward) - n_edit_operations.append(n_eo_tmp) - - return ged_vec, ged_mat, n_edit_operations - - -def _wrapper_compute_ged_parallel(params_ged, get_nb_eo, itr): - i = itr[0] - j = itr[1] - dis, n_eo_tmp = _compute_ged_parallel(G_gn[i], G_gn[j], params_ged, get_nb_eo) - return i, j, dis, n_eo_tmp - - -def _compute_ged_parallel(g1, g2, params_ged, get_nb_eo): - dis, pi_forward, pi_backward = GED(g1, g2, **params_ged) - n_eo_tmp = get_nb_eo(g1, g2, pi_forward, pi_backward) # [0,0,0,0,0,0] - return dis, n_eo_tmp - - -def update_costs(nb_cost_mat, dis_k_vec, dataset='monoterpenoides', - cost='CONSTANT', rw_constraints='inequality'): -# if dataset == 'Letter-high': - if cost == 'LETTER': - pass -# # method 1: set alpha automatically, just tune c_vir and c_eir by -# # LMS using cvxpy. -# alpha = 0.5 -# coeff = 100 # np.max(alpha * nb_cost_mat[:,4] / dis_k_vec) -## if np.count_nonzero(nb_cost_mat[:,4]) == 0: -## alpha = 0.75 -## else: -## alpha = np.min([dis_k_vec / c_vs for c_vs in nb_cost_mat[:,4] if c_vs != 0]) -## alpha = alpha * 0.99 -# param_vir = alpha * (nb_cost_mat[:,0] + nb_cost_mat[:,1]) -# param_eir = (1 - alpha) * (nb_cost_mat[:,4] + nb_cost_mat[:,5]) -# nb_cost_mat_new = np.column_stack((param_vir, param_eir)) -# dis_new = coeff * dis_k_vec - alpha * nb_cost_mat[:,3] -# -# x = cp.Variable(nb_cost_mat_new.shape[1]) -# cost = cp.sum_squares(nb_cost_mat_new * x - dis_new) -# constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]] -# prob = cp.Problem(cp.Minimize(cost), constraints) -# prob.solve() -# edit_costs_new = x.value -# edit_costs_new = np.array([edit_costs_new[0], edit_costs_new[1], alpha]) -# residual = np.sqrt(prob.value) - -# # method 2: tune c_vir, c_eir and alpha by nonlinear programming by -# # scipy.optimize.minimize. -# w0 = nb_cost_mat[:,0] + nb_cost_mat[:,1] -# w1 = nb_cost_mat[:,4] + nb_cost_mat[:,5] -# w2 = nb_cost_mat[:,3] -# w3 = dis_k_vec -# func_min = lambda x: np.sum((w0 * x[0] * x[3] + w1 * x[1] * (1 - x[2]) \ -# + w2 * x[2] - w3 * x[3]) ** 2) -# bounds = ((0, None), (0., None), (0.5, 0.5), (0, None)) -# res = minimize(func_min, [0.9, 1.7, 0.75, 10], bounds=bounds) -# edit_costs_new = res.x[0:3] -# residual = res.fun - - # method 3: tune c_vir, c_eir and alpha by nonlinear programming using cvxpy. - - -# # method 4: tune c_vir, c_eir and alpha by QP function -# # scipy.optimize.least_squares. An initial guess is required. -# w0 = nb_cost_mat[:,0] + nb_cost_mat[:,1] -# w1 = nb_cost_mat[:,4] + nb_cost_mat[:,5] -# w2 = nb_cost_mat[:,3] -# w3 = dis_k_vec -# func = lambda x: (w0 * x[0] * x[3] + w1 * x[1] * (1 - x[2]) \ -# + w2 * x[2] - w3 * x[3]) ** 2 -# res = optimize.root(func, [0.9, 1.7, 0.75, 100]) -# edit_costs_new = res.x -# residual = None - elif cost == 'LETTER2': -# # 1. if c_vi != c_vr, c_ei != c_er. -# nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] -# x = cp.Variable(nb_cost_mat_new.shape[1]) -# cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) -## # 1.1 no constraints. -## constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]] -# # 1.2 c_vs <= c_vi + c_vr. -# constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], -# np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] -## # 2. if c_vi == c_vr, c_ei == c_er. -## nb_cost_mat_new = nb_cost_mat[:,[0,3,4]] -## nb_cost_mat_new[:,0] += nb_cost_mat[:,1] -## nb_cost_mat_new[:,2] += nb_cost_mat[:,5] -## x = cp.Variable(nb_cost_mat_new.shape[1]) -## cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) -## # 2.1 no constraints. -## constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]] -### # 2.2 c_vs <= c_vi + c_vr. -### constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], -### np.array([2.0, -1.0, 0.0]).T@x >= 0.0] -# -# prob = cp.Problem(cp.Minimize(cost_fun), constraints) -# prob.solve() -# edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]] -# edit_costs_new = np.array(edit_costs_new) -# residual = np.sqrt(prob.value) - if rw_constraints == 'inequality': - # c_vs <= c_vi + c_vr. - nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] - x = cp.Variable(nb_cost_mat_new.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) - constraints = [x >= [0.001 for i in range(nb_cost_mat_new.shape[1])], - np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - try: - prob.solve(verbose=True) - except MemoryError as error0: - print('\nUsing solver "OSQP" caused a memory error.') - print('the original error message is\n', error0) - print('solver status: ', prob.status) - print('trying solver "CVXOPT" instead...\n') - try: - prob.solve(solver=cp.CVXOPT, verbose=True) - except Exception as error1: - print('\nAn error occured when using solver "CVXOPT".') - print('the original error message is\n', error1) - print('solver status: ', prob.status) - print('trying solver "MOSEK" instead. Notice this solver is commercial and a lisence is required.\n') - prob.solve(solver=cp.MOSEK, verbose=True) - else: - print('solver status: ', prob.status) - else: - print('solver status: ', prob.status) - print() - edit_costs_new = x.value - residual = np.sqrt(prob.value) - elif rw_constraints == '2constraints': - # c_vs <= c_vi + c_vr and c_vi == c_vr, c_ei == c_er. - nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] - x = cp.Variable(nb_cost_mat_new.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) - constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], - np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0, - np.array([1.0, -1.0, 0.0, 0.0, 0.0]).T@x == 0.0, - np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - prob.solve() - edit_costs_new = x.value - residual = np.sqrt(prob.value) - elif rw_constraints == 'no-constraint': - # no constraint. - nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] - x = cp.Variable(nb_cost_mat_new.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) - constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - prob.solve() - edit_costs_new = x.value - residual = np.sqrt(prob.value) -# elif method == 'inequality_modified': -# # c_vs <= c_vi + c_vr. -# nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] -# x = cp.Variable(nb_cost_mat_new.shape[1]) -# cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) -# constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], -# np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] -# prob = cp.Problem(cp.Minimize(cost_fun), constraints) -# prob.solve() -# # use same costs for insertion and removal rather than the fitted costs. -# edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]] -# edit_costs_new = np.array(edit_costs_new) -# residual = np.sqrt(prob.value) - elif cost == 'NON_SYMBOLIC': - is_n_attr = np.count_nonzero(nb_cost_mat[:,2]) - is_e_attr = np.count_nonzero(nb_cost_mat[:,5]) - - if dataset == 'SYNTHETICnew': -# nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] - nb_cost_mat_new = nb_cost_mat[:,[2,3,4]] - x = cp.Variable(nb_cost_mat_new.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) -# constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])], -# np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0] -# constraints = [x >= [0.0001 for i in range(nb_cost_mat_new.shape[1])]] - constraints = [x >= [0.0001 for i in range(nb_cost_mat_new.shape[1])], - np.array([0.0, 1.0, -1.0]).T@x == 0.0] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - prob.solve() -# print(x.value) - edit_costs_new = np.concatenate((np.array([0.0, 0.0]), x.value, - np.array([0.0]))) - residual = np.sqrt(prob.value) - - elif rw_constraints == 'inequality': - # c_vs <= c_vi + c_vr. - if is_n_attr and is_e_attr: - nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]] - x = cp.Variable(nb_cost_mat_new.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) - constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], - np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, - np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - prob.solve() - edit_costs_new = x.value - residual = np.sqrt(prob.value) - elif is_n_attr and not is_e_attr: - nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]] - x = cp.Variable(nb_cost_mat_new.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) - constraints = [x >= [0.001 for i in range(nb_cost_mat_new.shape[1])], - np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - prob.solve() - print(x.value) - edit_costs_new = np.concatenate((x.value, np.array([0.0]))) - residual = np.sqrt(prob.value) - elif not is_n_attr and is_e_attr: - nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]] - x = cp.Variable(nb_cost_mat_new.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) - constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])], - np.array([0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - prob.solve() - edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:])) - residual = np.sqrt(prob.value) - else: - nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]] - x = cp.Variable(nb_cost_mat_new.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) - constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - prob.solve() - edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), - x.value[2:], np.array([0.0]))) - residual = np.sqrt(prob.value) - else: -# # method 1: simple least square method. -# edit_costs_new, residual, _, _ = np.linalg.lstsq(nb_cost_mat, dis_k_vec, -# rcond=None) - -# # method 2: least square method with x_i >= 0. -# edit_costs_new, residual = optimize.nnls(nb_cost_mat, dis_k_vec) - - # method 3: solve as a quadratic program with constraints. -# P = np.dot(nb_cost_mat.T, nb_cost_mat) -# q_T = -2 * np.dot(dis_k_vec.T, nb_cost_mat) -# G = -1 * np.identity(nb_cost_mat.shape[1]) -# h = np.array([0 for i in range(nb_cost_mat.shape[1])]) -# A = np.array([1 for i in range(nb_cost_mat.shape[1])]) -# b = 1 -# x = cp.Variable(nb_cost_mat.shape[1]) -# prob = cp.Problem(cp.Minimize(cp.quad_form(x, P) + q_T@x), -# [G@x <= h]) -# prob.solve() -# edit_costs_new = x.value -# residual = prob.value - np.dot(dis_k_vec.T, dis_k_vec) - -# G = -1 * np.identity(nb_cost_mat.shape[1]) -# h = np.array([0 for i in range(nb_cost_mat.shape[1])]) - x = cp.Variable(nb_cost_mat.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec) - constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])], - # np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] - np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, - np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - prob.solve() - edit_costs_new = x.value - residual = np.sqrt(prob.value) - - # method 4: - - return edit_costs_new, residual - - -if __name__ == '__main__': - print('check test_fitDistance.py') \ No newline at end of file diff --git a/gklearn/preimage/ged.py b/gklearn/preimage/ged.py deleted file mode 100644 index a66baaf..0000000 --- a/gklearn/preimage/ged.py +++ /dev/null @@ -1,467 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Oct 17 18:44:59 2019 - -@author: ljia -""" -import numpy as np -import networkx as nx -from tqdm import tqdm -import sys -import multiprocessing -from multiprocessing import Pool -from functools import partial - -#from gedlibpy_linlin import librariesImport, gedlibpy -from gklearn.gedlib import librariesImport, gedlibpy - -def GED(g1, g2, dataset='monoterpenoides', lib='gedlibpy', cost='CHEM_1', method='IPFP', - edit_cost_constant=[], algo_options='', stabilizer='min', repeat=50): - """ - Compute GED for 2 graphs. - """ - -# dataset = dataset.lower() - - if lib == 'gedlibpy': - gedlibpy.restart_env() - gedlibpy.add_nx_graph(convertGraph(g1, cost), "") - gedlibpy.add_nx_graph(convertGraph(g2, cost), "") - - listID = gedlibpy.get_all_graph_ids() - gedlibpy.set_edit_cost(cost, edit_cost_constant=edit_cost_constant) - gedlibpy.init() - gedlibpy.set_method(method, algo_options) - gedlibpy.init_method() - - g = listID[0] - h = listID[1] - if stabilizer is None: - gedlibpy.run_method(g, h) - pi_forward = gedlibpy.get_forward_map(g, h) - pi_backward = gedlibpy.get_backward_map(g, h) - upper = gedlibpy.get_upper_bound(g, h) - lower = gedlibpy.get_lower_bound(g, h) - elif stabilizer == 'mean': - # @todo: to be finished... - upper_list = [np.inf] * repeat - for itr in range(repeat): - gedlibpy.run_method(g, h) - upper_list[itr] = gedlibpy.get_upper_bound(g, h) - pi_forward = gedlibpy.get_forward_map(g, h) - pi_backward = gedlibpy.get_backward_map(g, h) - lower = gedlibpy.get_lower_bound(g, h) - upper = np.mean(upper_list) - elif stabilizer == 'median': - if repeat % 2 == 0: - repeat += 1 - upper_list = [np.inf] * repeat - pi_forward_list = [0] * repeat - pi_backward_list = [0] * repeat - for itr in range(repeat): - gedlibpy.run_method(g, h) - upper_list[itr] = gedlibpy.get_upper_bound(g, h) - pi_forward_list[itr] = gedlibpy.get_forward_map(g, h) - pi_backward_list[itr] = gedlibpy.get_backward_map(g, h) - lower = gedlibpy.get_lower_bound(g, h) - upper = np.median(upper_list) - idx_median = upper_list.index(upper) - pi_forward = pi_forward_list[idx_median] - pi_backward = pi_backward_list[idx_median] - elif stabilizer == 'min': - upper = np.inf - for itr in range(repeat): - gedlibpy.run_method(g, h) - upper_tmp = gedlibpy.get_upper_bound(g, h) - if upper_tmp < upper: - upper = upper_tmp - pi_forward = gedlibpy.get_forward_map(g, h) - pi_backward = gedlibpy.get_backward_map(g, h) - lower = gedlibpy.get_lower_bound(g, h) - if upper == 0: - break - elif stabilizer == 'max': - upper = 0 - for itr in range(repeat): - gedlibpy.run_method(g, h) - upper_tmp = gedlibpy.get_upper_bound(g, h) - if upper_tmp > upper: - upper = upper_tmp - pi_forward = gedlibpy.get_forward_map(g, h) - pi_backward = gedlibpy.get_backward_map(g, h) - lower = gedlibpy.get_lower_bound(g, h) - elif stabilizer == 'gaussian': - pass - - dis = upper - - elif lib == 'gedlib-bash': - import time - import random - import os - from gklearn.utils.graphfiles import saveDataset - - tmp_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/' - if not os.path.exists(tmp_dir): - os.makedirs(tmp_dir) - fn_collection = tmp_dir + 'collection.' + str(time.time()) + str(random.randint(0, 1e9)) - xparams = {'method': 'gedlib', 'graph_dir': fn_collection} - saveDataset([g1, g2], ['dummy', 'dummy'], gformat='gxl', group='xml', - filename=fn_collection, xparams=xparams) - - command = 'GEDLIB_HOME=\'/media/ljia/DATA/research-repo/codes/others/gedlib/gedlib2\'\n' - command += 'LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$GEDLIB_HOME/lib\n' - command += 'export LD_LIBRARY_PATH\n' - command += 'cd \'' + os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/bin\'\n' - command += './ged_for_python_bash monoterpenoides ' + fn_collection \ - + ' \'' + algo_options + '\' ' - for ec in edit_cost_constant: - command += str(ec) + ' ' -# output = os.system(command) - stream = os.popen(command) - output = stream.readlines() -# print(output) - - dis = float(output[0].strip()) - runtime = float(output[1].strip()) - size_forward = int(output[2].strip()) - pi_forward = [int(item.strip()) for item in output[3:3+size_forward]] - pi_backward = [int(item.strip()) for item in output[3+size_forward:]] - -# print(dis) -# print(runtime) -# print(size_forward) -# print(pi_forward) -# print(pi_backward) - - - # make the map label correct (label remove map as np.inf) - nodes1 = [n for n in g1.nodes()] - nodes2 = [n for n in g2.nodes()] - nb1 = nx.number_of_nodes(g1) - nb2 = nx.number_of_nodes(g2) - pi_forward = [nodes2[pi] if pi < nb2 else np.inf for pi in pi_forward] - pi_backward = [nodes1[pi] if pi < nb1 else np.inf for pi in pi_backward] -# print(pi_forward) - - - return dis, pi_forward, pi_backward - - -def convertGraph(G, cost): - """Convert a graph to the proper NetworkX format that can be - recognized by library gedlibpy. - """ - G_new = nx.Graph() - if cost == 'LETTER' or cost == 'LETTER2': - for nd, attrs in G.nodes(data=True): - G_new.add_node(str(nd), x=str(attrs['attributes'][0]), - y=str(attrs['attributes'][1])) - for nd1, nd2, attrs in G.edges(data=True): - G_new.add_edge(str(nd1), str(nd2)) - elif cost == 'NON_SYMBOLIC': - for nd, attrs in G.nodes(data=True): - G_new.add_node(str(nd)) - for a_name in G.graph['node_attrs']: - G_new.nodes[str(nd)][a_name] = str(attrs[a_name]) - for nd1, nd2, attrs in G.edges(data=True): - G_new.add_edge(str(nd1), str(nd2)) - for a_name in G.graph['edge_attrs']: - G_new.edges[str(nd1), str(nd2)][a_name] = str(attrs[a_name]) - else: - for nd, attrs in G.nodes(data=True): - G_new.add_node(str(nd), chem=attrs['atom']) - for nd1, nd2, attrs in G.edges(data=True): - G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type']) -# G_new.add_edge(str(nd1), str(nd2)) - - return G_new - - -def GED_n(Gn, lib='gedlibpy', cost='CHEM_1', method='IPFP', - edit_cost_constant=[], stabilizer='min', repeat=50): - """ - Compute GEDs for a group of graphs. - """ - if lib == 'gedlibpy': - def convertGraph(G): - """Convert a graph to the proper NetworkX format that can be - recognized by library gedlibpy. - """ - G_new = nx.Graph() - for nd, attrs in G.nodes(data=True): - G_new.add_node(str(nd), chem=attrs['atom']) - for nd1, nd2, attrs in G.edges(data=True): -# G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type']) - G_new.add_edge(str(nd1), str(nd2)) - - return G_new - - gedlibpy.restart_env() - gedlibpy.add_nx_graph(convertGraph(g1), "") - gedlibpy.add_nx_graph(convertGraph(g2), "") - - listID = gedlibpy.get_all_graph_ids() - gedlibpy.set_edit_cost(cost, edit_cost_constant=edit_cost_constant) - gedlibpy.init() - gedlibpy.set_method(method, "") - gedlibpy.init_method() - - g = listID[0] - h = listID[1] - if stabilizer is None: - gedlibpy.run_method(g, h) - pi_forward = gedlibpy.get_forward_map(g, h) - pi_backward = gedlibpy.get_backward_map(g, h) - upper = gedlibpy.get_upper_bound(g, h) - lower = gedlibpy.get_lower_bound(g, h) - elif stabilizer == 'min': - upper = np.inf - for itr in range(repeat): - gedlibpy.run_method(g, h) - upper_tmp = gedlibpy.get_upper_bound(g, h) - if upper_tmp < upper: - upper = upper_tmp - pi_forward = gedlibpy.get_forward_map(g, h) - pi_backward = gedlibpy.get_backward_map(g, h) - lower = gedlibpy.get_lower_bound(g, h) - if upper == 0: - break - - dis = upper - - # make the map label correct (label remove map as np.inf) - nodes1 = [n for n in g1.nodes()] - nodes2 = [n for n in g2.nodes()] - nb1 = nx.number_of_nodes(g1) - nb2 = nx.number_of_nodes(g2) - pi_forward = [nodes2[pi] if pi < nb2 else np.inf for pi in pi_forward] - pi_backward = [nodes1[pi] if pi < nb1 else np.inf for pi in pi_backward] - - return dis, pi_forward, pi_backward - - -def ged_median(Gn, Gn_median, verbose=False, params_ged={'lib': 'gedlibpy', - 'cost': 'CHEM_1', 'method': 'IPFP', 'edit_cost_constant': [], - 'algo_options': '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1', - 'stabilizer': None}, parallel=False): - if parallel: - len_itr = int(len(Gn)) - pi_forward_list = [[] for i in range(len_itr)] - dis_list = [0 for i in range(len_itr)] - - itr = range(0, len_itr) - n_jobs = multiprocessing.cpu_count() - if len_itr < 100 * n_jobs: - chunksize = int(len_itr / n_jobs) + 1 - else: - chunksize = 100 - def init_worker(gn_toshare, gn_median_toshare): - global G_gn, G_gn_median - G_gn = gn_toshare - G_gn_median = gn_median_toshare - do_partial = partial(_compute_ged_median, params_ged) - pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(Gn, Gn_median)) - if verbose: - iterator = tqdm(pool.imap_unordered(do_partial, itr, chunksize), - desc='computing GEDs', file=sys.stdout) - else: - iterator = pool.imap_unordered(do_partial, itr, chunksize) - for i, dis_sum, pi_forward in iterator: - pi_forward_list[i] = pi_forward - dis_list[i] = dis_sum -# print('\n-------------------------------------------') -# print(i, j, idx_itr, dis) - pool.close() - pool.join() - - else: - dis_list = [] - pi_forward_list = [] - for idx, G in tqdm(enumerate(Gn), desc='computing median distances', - file=sys.stdout) if verbose else enumerate(Gn): - dis_sum = 0 - pi_forward_list.append([]) - for G_p in Gn_median: - dis_tmp, pi_tmp_forward, pi_tmp_backward = GED(G, G_p, - **params_ged) - pi_forward_list[idx].append(pi_tmp_forward) - dis_sum += dis_tmp - dis_list.append(dis_sum) - - return dis_list, pi_forward_list - - -def _compute_ged_median(params_ged, itr): -# print(itr) - dis_sum = 0 - pi_forward = [] - for G_p in G_gn_median: - dis_tmp, pi_tmp_forward, pi_tmp_backward = GED(G_gn[itr], G_p, - **params_ged) - pi_forward.append(pi_tmp_forward) - dis_sum += dis_tmp - - return itr, dis_sum, pi_forward - - -def get_nb_edit_operations(g1, g2, forward_map, backward_map): - """Compute the number of each edit operations. - """ - n_vi = 0 - n_vr = 0 - n_vs = 0 - n_ei = 0 - n_er = 0 - n_es = 0 - - nodes1 = [n for n in g1.nodes()] - for i, map_i in enumerate(forward_map): - if map_i == np.inf: - n_vr += 1 - elif g1.node[nodes1[i]]['atom'] != g2.node[map_i]['atom']: - n_vs += 1 - for map_i in backward_map: - if map_i == np.inf: - n_vi += 1 - -# idx_nodes1 = range(0, len(node1)) - - edges1 = [e for e in g1.edges()] - nb_edges2_cnted = 0 - for n1, n2 in edges1: - idx1 = nodes1.index(n1) - idx2 = nodes1.index(n2) - # one of the nodes is removed, thus the edge is removed. - if forward_map[idx1] == np.inf or forward_map[idx2] == np.inf: - n_er += 1 - # corresponding edge is in g2. - elif (forward_map[idx1], forward_map[idx2]) in g2.edges(): - nb_edges2_cnted += 1 - # edge labels are different. - if g2.edges[((forward_map[idx1], forward_map[idx2]))]['bond_type'] \ - != g1.edges[(n1, n2)]['bond_type']: - n_es += 1 - elif (forward_map[idx2], forward_map[idx1]) in g2.edges(): - nb_edges2_cnted += 1 - # edge labels are different. - if g2.edges[((forward_map[idx2], forward_map[idx1]))]['bond_type'] \ - != g1.edges[(n1, n2)]['bond_type']: - n_es += 1 - # corresponding nodes are in g2, however the edge is removed. - else: - n_er += 1 - n_ei = nx.number_of_edges(g2) - nb_edges2_cnted - - return n_vi, n_vr, n_vs, n_ei, n_er, n_es - - -def get_nb_edit_operations_letter(g1, g2, forward_map, backward_map): - """Compute the number of each edit operations. - """ - n_vi = 0 - n_vr = 0 - n_vs = 0 - sod_vs = 0 - n_ei = 0 - n_er = 0 - - nodes1 = [n for n in g1.nodes()] - for i, map_i in enumerate(forward_map): - if map_i == np.inf: - n_vr += 1 - else: - n_vs += 1 - diff_x = float(g1.nodes[nodes1[i]]['x']) - float(g2.nodes[map_i]['x']) - diff_y = float(g1.nodes[nodes1[i]]['y']) - float(g2.nodes[map_i]['y']) - sod_vs += np.sqrt(np.square(diff_x) + np.square(diff_y)) - for map_i in backward_map: - if map_i == np.inf: - n_vi += 1 - -# idx_nodes1 = range(0, len(node1)) - - edges1 = [e for e in g1.edges()] - nb_edges2_cnted = 0 - for n1, n2 in edges1: - idx1 = nodes1.index(n1) - idx2 = nodes1.index(n2) - # one of the nodes is removed, thus the edge is removed. - if forward_map[idx1] == np.inf or forward_map[idx2] == np.inf: - n_er += 1 - # corresponding edge is in g2. Edge label is not considered. - elif (forward_map[idx1], forward_map[idx2]) in g2.edges() or \ - (forward_map[idx2], forward_map[idx1]) in g2.edges(): - nb_edges2_cnted += 1 - # corresponding nodes are in g2, however the edge is removed. - else: - n_er += 1 - n_ei = nx.number_of_edges(g2) - nb_edges2_cnted - - return n_vi, n_vr, n_vs, sod_vs, n_ei, n_er - - -def get_nb_edit_operations_nonsymbolic(g1, g2, forward_map, backward_map): - """Compute the number of each edit operations. - """ - n_vi = 0 - n_vr = 0 - n_vs = 0 - sod_vs = 0 - n_ei = 0 - n_er = 0 - n_es = 0 - sod_es = 0 - - nodes1 = [n for n in g1.nodes()] - for i, map_i in enumerate(forward_map): - if map_i == np.inf: - n_vr += 1 - else: - n_vs += 1 - sum_squares = 0 - for a_name in g1.graph['node_attrs']: - diff = float(g1.nodes[nodes1[i]][a_name]) - float(g2.nodes[map_i][a_name]) - sum_squares += np.square(diff) - sod_vs += np.sqrt(sum_squares) - for map_i in backward_map: - if map_i == np.inf: - n_vi += 1 - -# idx_nodes1 = range(0, len(node1)) - - edges1 = [e for e in g1.edges()] - for n1, n2 in edges1: - idx1 = nodes1.index(n1) - idx2 = nodes1.index(n2) - n1_g2 = forward_map[idx1] - n2_g2 = forward_map[idx2] - # one of the nodes is removed, thus the edge is removed. - if n1_g2 == np.inf or n2_g2 == np.inf: - n_er += 1 - # corresponding edge is in g2. - elif (n1_g2, n2_g2) in g2.edges(): - n_es += 1 - sum_squares = 0 - for a_name in g1.graph['edge_attrs']: - diff = float(g1.edges[n1, n2][a_name]) - float(g2.nodes[n1_g2, n2_g2][a_name]) - sum_squares += np.square(diff) - sod_es += np.sqrt(sum_squares) - elif (n2_g2, n1_g2) in g2.edges(): - n_es += 1 - sum_squares = 0 - for a_name in g1.graph['edge_attrs']: - diff = float(g1.edges[n2, n1][a_name]) - float(g2.nodes[n2_g2, n1_g2][a_name]) - sum_squares += np.square(diff) - sod_es += np.sqrt(sum_squares) - # corresponding nodes are in g2, however the edge is removed. - else: - n_er += 1 - n_ei = nx.number_of_edges(g2) - n_es - - return n_vi, n_vr, sod_vs, n_ei, n_er, sod_es - - -if __name__ == '__main__': - print('check test_ged.py') \ No newline at end of file diff --git a/gklearn/preimage/iam.py b/gklearn/preimage/iam.py deleted file mode 100644 index f3e2165..0000000 --- a/gklearn/preimage/iam.py +++ /dev/null @@ -1,775 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Apr 26 11:49:12 2019 - -Iterative alternate minimizations using GED. -@author: ljia -""" -import numpy as np -import random -import networkx as nx -from tqdm import tqdm - -from gklearn.utils.graphdataset import get_dataset_attributes -from gklearn.utils.utils import graph_isIdentical, get_node_labels, get_edge_labels -from gklearn.preimage.ged import GED, ged_median - - -def iam_upgraded(Gn_median, Gn_candidate, c_ei=3, c_er=3, c_es=1, ite_max=50, - epsilon=0.001, node_label='atom', edge_label='bond_type', - connected=False, removeNodes=True, allBestInit=False, allBestNodes=False, - allBestEdges=False, allBestOutput=False, - params_ged={'lib': 'gedlibpy', 'cost': 'CHEM_1', 'method': 'IPFP', - 'edit_cost_constant': [], 'stabilizer': None, - 'algo_options': '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1'}): - """See my name, then you know what I do. - """ -# Gn_median = Gn_median[0:10] -# Gn_median = [nx.convert_node_labels_to_integers(g) for g in Gn_median] - node_ir = np.inf # corresponding to the node remove and insertion. - label_r = 'thanksdanny' # the label for node remove. # @todo: make this label unrepeatable. - ds_attrs = get_dataset_attributes(Gn_median + Gn_candidate, - attr_names=['edge_labeled', 'node_attr_dim', 'edge_attr_dim'], - edge_label=edge_label) - node_label_set = get_node_labels(Gn_median, node_label) - edge_label_set = get_edge_labels(Gn_median, edge_label) - - - def generate_graph(G, pi_p_forward): - G_new_list = [G.copy()] # all "best" graphs generated in this iteration. -# nx.draw_networkx(G) -# import matplotlib.pyplot as plt -# plt.show() -# print(pi_p_forward) - - # update vertex labels. - # pre-compute h_i0 for each label. -# for label in get_node_labels(Gn, node_label): -# print(label) -# for nd in G.nodes(data=True): -# pass - if not ds_attrs['node_attr_dim']: # labels are symbolic - for ndi, (nd, _) in enumerate(G.nodes(data=True)): - h_i0_list = [] - label_list = [] - for label in node_label_set: - h_i0 = 0 - for idx, g in enumerate(Gn_median): - pi_i = pi_p_forward[idx][ndi] - if pi_i != node_ir and g.nodes[pi_i][node_label] == label: - h_i0 += 1 - h_i0_list.append(h_i0) - label_list.append(label) - # case when the node is to be removed. - if removeNodes: - h_i0_remove = 0 # @todo: maybe this can be added to the node_label_set above. - for idx, g in enumerate(Gn_median): - pi_i = pi_p_forward[idx][ndi] - if pi_i == node_ir: - h_i0_remove += 1 - h_i0_list.append(h_i0_remove) - label_list.append(label_r) - # get the best labels. - idx_max = np.argwhere(h_i0_list == np.max(h_i0_list)).flatten().tolist() - if allBestNodes: # choose all best graphs. - nlabel_best = [label_list[idx] for idx in idx_max] - # generate "best" graphs with regard to "best" node labels. - G_new_list_nd = [] - for g in G_new_list: # @todo: seems it can be simplified. The G_new_list will only contain 1 graph for now. - for nl in nlabel_best: - g_tmp = g.copy() - if nl == label_r: - g_tmp.remove_node(nd) - else: - g_tmp.nodes[nd][node_label] = nl - G_new_list_nd.append(g_tmp) - # nx.draw_networkx(g_tmp) - # import matplotlib.pyplot as plt - # plt.show() - # print(g_tmp.nodes(data=True)) - # print(g_tmp.edges(data=True)) - G_new_list = [ggg.copy() for ggg in G_new_list_nd] - else: - # choose one of the best randomly. - idx_rdm = random.randint(0, len(idx_max) - 1) - best_label = label_list[idx_max[idx_rdm]] - h_i0_max = h_i0_list[idx_max[idx_rdm]] - - g_new = G_new_list[0] - if best_label == label_r: - g_new.remove_node(nd) - else: - g_new.nodes[nd][node_label] = best_label - G_new_list = [g_new] - else: # labels are non-symbolic - for ndi, (nd, _) in enumerate(G.nodes(data=True)): - Si_norm = 0 - phi_i_bar = np.array([0.0 for _ in range(ds_attrs['node_attr_dim'])]) - for idx, g in enumerate(Gn_median): - pi_i = pi_p_forward[idx][ndi] - if g.has_node(pi_i): #@todo: what if no g has node? phi_i_bar = 0? - Si_norm += 1 - phi_i_bar += np.array([float(itm) for itm in g.nodes[pi_i]['attributes']]) - phi_i_bar /= Si_norm - G_new_list[0].nodes[nd]['attributes'] = phi_i_bar - -# for g in G_new_list: -# import matplotlib.pyplot as plt -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - - # update edge labels and adjacency matrix. - if ds_attrs['edge_labeled']: - G_new_list_edge = [] - for g_new in G_new_list: - nd_list = [n for n in g_new.nodes()] - g_tmp_list = [g_new.copy()] - for nd1i in range(nx.number_of_nodes(g_new)): - nd1 = nd_list[nd1i]# @todo: not just edges, but all pairs of nodes - for nd2i in range(nd1i + 1, nx.number_of_nodes(g_new)): - nd2 = nd_list[nd2i] -# for nd1, nd2, _ in g_new.edges(data=True): - h_ij0_list = [] - label_list = [] - for label in edge_label_set: - h_ij0 = 0 - for idx, g in enumerate(Gn_median): - pi_i = pi_p_forward[idx][nd1i] - pi_j = pi_p_forward[idx][nd2i] - h_ij0_p = (g.has_node(pi_i) and g.has_node(pi_j) and - g.has_edge(pi_i, pi_j) and - g.edges[pi_i, pi_j][edge_label] == label) - h_ij0 += h_ij0_p - h_ij0_list.append(h_ij0) - label_list.append(label) - - # get the best labels. - idx_max = np.argwhere(h_ij0_list == np.max(h_ij0_list)).flatten().tolist() - if allBestEdges: # choose all best graphs. - elabel_best = [label_list[idx] for idx in idx_max] - h_ij0_max = [h_ij0_list[idx] for idx in idx_max] - # generate "best" graphs with regard to "best" node labels. - G_new_list_ed = [] - for g_tmp in g_tmp_list: # @todo: seems it can be simplified. The G_new_list will only contain 1 graph for now. - for idxl, el in enumerate(elabel_best): - g_tmp_copy = g_tmp.copy() - # check whether a_ij is 0 or 1. - sij_norm = 0 - for idx, g in enumerate(Gn_median): - pi_i = pi_p_forward[idx][nd1i] - pi_j = pi_p_forward[idx][nd2i] - if g.has_node(pi_i) and g.has_node(pi_j) and \ - g.has_edge(pi_i, pi_j): - sij_norm += 1 - if h_ij0_max[idxl] > len(Gn_median) * c_er / c_es + \ - sij_norm * (1 - (c_er + c_ei) / c_es): - if not g_tmp_copy.has_edge(nd1, nd2): - g_tmp_copy.add_edge(nd1, nd2) - g_tmp_copy.edges[nd1, nd2][edge_label] = elabel_best[idxl] - else: - if g_tmp_copy.has_edge(nd1, nd2): - g_tmp_copy.remove_edge(nd1, nd2) - G_new_list_ed.append(g_tmp_copy) - g_tmp_list = [ggg.copy() for ggg in G_new_list_ed] - else: # choose one of the best randomly. - idx_rdm = random.randint(0, len(idx_max) - 1) - best_label = label_list[idx_max[idx_rdm]] - h_ij0_max = h_ij0_list[idx_max[idx_rdm]] - - # check whether a_ij is 0 or 1. - sij_norm = 0 - for idx, g in enumerate(Gn_median): - pi_i = pi_p_forward[idx][nd1i] - pi_j = pi_p_forward[idx][nd2i] - if g.has_node(pi_i) and g.has_node(pi_j) and g.has_edge(pi_i, pi_j): - sij_norm += 1 - if h_ij0_max > len(Gn_median) * c_er / c_es + sij_norm * (1 - (c_er + c_ei) / c_es): - if not g_new.has_edge(nd1, nd2): - g_new.add_edge(nd1, nd2) - g_new.edges[nd1, nd2][edge_label] = best_label - else: -# elif h_ij0_max < len(Gn_median) * c_er / c_es + sij_norm * (1 - (c_er + c_ei) / c_es): - if g_new.has_edge(nd1, nd2): - g_new.remove_edge(nd1, nd2) - g_tmp_list = [g_new] - G_new_list_edge += g_tmp_list - G_new_list = [ggg.copy() for ggg in G_new_list_edge] - - - else: # if edges are unlabeled - # @todo: is this even right? G or g_tmp? check if the new one is right - # @todo: works only for undirected graphs. - - for g_tmp in G_new_list: - nd_list = [n for n in g_tmp.nodes()] - for nd1i in range(nx.number_of_nodes(g_tmp)): - nd1 = nd_list[nd1i] - for nd2i in range(nd1i + 1, nx.number_of_nodes(g_tmp)): - nd2 = nd_list[nd2i] - sij_norm = 0 - for idx, g in enumerate(Gn_median): - pi_i = pi_p_forward[idx][nd1i] - pi_j = pi_p_forward[idx][nd2i] - if g.has_node(pi_i) and g.has_node(pi_j) and g.has_edge(pi_i, pi_j): - sij_norm += 1 - if sij_norm > len(Gn_median) * c_er / (c_er + c_ei): - # @todo: should we consider if nd1 and nd2 in g_tmp? - # or just add the edge anyway? - if g_tmp.has_node(nd1) and g_tmp.has_node(nd2) \ - and not g_tmp.has_edge(nd1, nd2): - g_tmp.add_edge(nd1, nd2) - else: # @todo: which to use? -# elif sij_norm < len(Gn_median) * c_er / (c_er + c_ei): - if g_tmp.has_edge(nd1, nd2): - g_tmp.remove_edge(nd1, nd2) - # do not change anything when equal. - -# for i, g in enumerate(G_new_list): -# import matplotlib.pyplot as plt -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -## plt.savefig("results/gk_iam/simple_two/xx" + str(i) + ".png", format="PNG") -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - -# # find the best graph generated in this iteration and update pi_p. - # @todo: should we update all graphs generated or just the best ones? - dis_list, pi_forward_list = ged_median(G_new_list, Gn_median, - params_ged=params_ged) - # @todo: should we remove the identical and connectivity check? - # Don't know which is faster. - if ds_attrs['node_attr_dim'] == 0 and ds_attrs['edge_attr_dim'] == 0: - G_new_list, idx_list = remove_duplicates(G_new_list) - pi_forward_list = [pi_forward_list[idx] for idx in idx_list] - dis_list = [dis_list[idx] for idx in idx_list] -# if connected == True: -# G_new_list, idx_list = remove_disconnected(G_new_list) -# pi_forward_list = [pi_forward_list[idx] for idx in idx_list] -# idx_min_list = np.argwhere(dis_list == np.min(dis_list)).flatten().tolist() -# dis_min = dis_list[idx_min_tmp_list[0]] -# pi_forward_list = [pi_forward_list[idx] for idx in idx_min_list] -# G_new_list = [G_new_list[idx] for idx in idx_min_list] - -# for g in G_new_list: -# import matplotlib.pyplot as plt -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - - return G_new_list, pi_forward_list, dis_list - - - def best_median_graphs(Gn_candidate, pi_all_forward, dis_all): - idx_min_list = np.argwhere(dis_all == np.min(dis_all)).flatten().tolist() - dis_min = dis_all[idx_min_list[0]] - pi_forward_min_list = [pi_all_forward[idx] for idx in idx_min_list] - G_min_list = [Gn_candidate[idx] for idx in idx_min_list] - return G_min_list, pi_forward_min_list, dis_min - - - def iteration_proc(G, pi_p_forward, cur_sod): - G_list = [G] - pi_forward_list = [pi_p_forward] - old_sod = cur_sod * 2 - sod_list = [cur_sod] - dis_list = [cur_sod] - # iterations. - itr = 0 - # @todo: what if difference == 0? -# while itr < ite_max and (np.abs(old_sod - cur_sod) > epsilon or -# np.abs(old_sod - cur_sod) == 0): - while itr < ite_max and np.abs(old_sod - cur_sod) > epsilon: -# while itr < ite_max: -# for itr in range(0, 5): # the convergence condition? - print('itr_iam is', itr) - G_new_list = [] - pi_forward_new_list = [] - dis_new_list = [] - for idx, g in enumerate(G_list): -# label_set = get_node_labels(Gn_median + [g], node_label) - G_tmp_list, pi_forward_tmp_list, dis_tmp_list = generate_graph( - g, pi_forward_list[idx]) - G_new_list += G_tmp_list - pi_forward_new_list += pi_forward_tmp_list - dis_new_list += dis_tmp_list - # @todo: need to remove duplicates here? - G_list = [ggg.copy() for ggg in G_new_list] - pi_forward_list = [pitem.copy() for pitem in pi_forward_new_list] - dis_list = dis_new_list[:] - - old_sod = cur_sod - cur_sod = np.min(dis_list) - sod_list.append(cur_sod) - - itr += 1 - - # @todo: do we return all graphs or the best ones? - # get the best ones of the generated graphs. - G_list, pi_forward_list, dis_min = best_median_graphs( - G_list, pi_forward_list, dis_list) - - if ds_attrs['node_attr_dim'] == 0 and ds_attrs['edge_attr_dim'] == 0: - G_list, idx_list = remove_duplicates(G_list) - pi_forward_list = [pi_forward_list[idx] for idx in idx_list] -# dis_list = [dis_list[idx] for idx in idx_list] - -# import matplotlib.pyplot as plt -# for g in G_list: -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - - print('\nsods:', sod_list, '\n') - - return G_list, pi_forward_list, dis_min, sod_list - - - def remove_duplicates(Gn): - """Remove duplicate graphs from list. - """ - Gn_new = [] - idx_list = [] - for idx, g in enumerate(Gn): - dupl = False - for g_new in Gn_new: - if graph_isIdentical(g_new, g): - dupl = True - break - if not dupl: - Gn_new.append(g) - idx_list.append(idx) - return Gn_new, idx_list - - - def remove_disconnected(Gn): - """Remove disconnected graphs from list. - """ - Gn_new = [] - idx_list = [] - for idx, g in enumerate(Gn): - if nx.is_connected(g): - Gn_new.append(g) - idx_list.append(idx) - return Gn_new, idx_list - - - ########################################################################### - - # phase 1: initilize. - # compute set-median. - dis_min = np.inf - dis_list, pi_forward_all = ged_median(Gn_candidate, Gn_median, - params_ged=params_ged, parallel=True) - print('finish computing GEDs.') - # find all smallest distances. - if allBestInit: # try all best init graphs. - idx_min_list = range(len(dis_list)) - dis_min = dis_list - else: - idx_min_list = np.argwhere(dis_list == np.min(dis_list)).flatten().tolist() - dis_min = [dis_list[idx_min_list[0]]] * len(idx_min_list) - idx_min_rdm = random.randint(0, len(idx_min_list) - 1) - idx_min_list = [idx_min_list[idx_min_rdm]] - sod_set_median = np.min(dis_min) - - - # phase 2: iteration. - G_list = [] - dis_list = [] - pi_forward_list = [] - G_set_median_list = [] -# sod_list = [] - for idx_tmp, idx_min in enumerate(idx_min_list): -# print('idx_min is', idx_min) - G = Gn_candidate[idx_min].copy() - G_set_median_list.append(G.copy()) - # list of edit operations. - pi_p_forward = pi_forward_all[idx_min] -# pi_p_backward = pi_all_backward[idx_min] - Gi_list, pi_i_forward_list, dis_i_min, sod_list = iteration_proc(G, - pi_p_forward, dis_min[idx_tmp]) - G_list += Gi_list - dis_list += [dis_i_min] * len(Gi_list) - pi_forward_list += pi_i_forward_list - - - if ds_attrs['node_attr_dim'] == 0 and ds_attrs['edge_attr_dim'] == 0: - G_list, idx_list = remove_duplicates(G_list) - dis_list = [dis_list[idx] for idx in idx_list] - pi_forward_list = [pi_forward_list[idx] for idx in idx_list] - if connected == True: - G_list_con, idx_list = remove_disconnected(G_list) - # if there is no connected graphs at all, then remain the disconnected ones. - if len(G_list_con) > 0: # @todo: ?????????????????????????? - G_list = G_list_con - dis_list = [dis_list[idx] for idx in idx_list] - pi_forward_list = [pi_forward_list[idx] for idx in idx_list] - -# import matplotlib.pyplot as plt -# for g in G_list: -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - - # get the best median graphs - G_gen_median_list, pi_forward_min_list, sod_gen_median = best_median_graphs( - G_list, pi_forward_list, dis_list) -# for g in G_gen_median_list: -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - - if not allBestOutput: - # randomly choose one graph. - idx_rdm = random.randint(0, len(G_gen_median_list) - 1) - G_gen_median_list = [G_gen_median_list[idx_rdm]] - - return G_gen_median_list, sod_gen_median, sod_list, G_set_median_list, sod_set_median - - -def iam_bash(Gn_names, edit_cost_constant, cost='CONSTANT', initial_solutions=1, - dataset='monoterpenoides', - graph_dir=''): - """Compute the iam by c++ implementation (gedlib) through bash. - """ - import os - import time - - def createCollectionFile(Gn_names, y, filename): - """Create collection file. - """ - dirname_ds = os.path.dirname(filename) - if dirname_ds != '': - dirname_ds += '/' - if not os.path.exists(dirname_ds) : - os.makedirs(dirname_ds) - - with open(filename + '.xml', 'w') as fgroup: - fgroup.write("") - fgroup.write("\n") - fgroup.write("\n") - for idx, fname in enumerate(Gn_names): - fgroup.write("\n\t") - fgroup.write("\n") - fgroup.close() - - tmp_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/' - fn_collection = tmp_dir + 'collection.' + str(time.time()) + str(random.randint(0, 1e9)) - createCollectionFile(Gn_names, ['dummy'] * len(Gn_names), fn_collection) -# fn_collection = tmp_dir + 'collection_for_debug' -# graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/gxl' - -# if dataset == 'Letter-high' or dataset == 'Fingerprint': -# dataset = 'letter' - command = 'GEDLIB_HOME=\'/media/ljia/DATA/research-repo/codes/Linlin/gedlib\'\n' - command += 'LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$GEDLIB_HOME/lib\n' - command += 'export LD_LIBRARY_PATH\n' - command += 'cd \'' + os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/bin\'\n' - command += './iam_for_python_bash ' + dataset + ' ' + fn_collection \ - + ' \'' + graph_dir + '\' ' + ' ' + cost + ' ' + str(initial_solutions) + ' ' - if edit_cost_constant is None: - command += 'None' - else: - for ec in edit_cost_constant: - command += str(ec) + ' ' -# output = os.system(command) - stream = os.popen(command) - - output = stream.readlines() -# print(output) - sod_sm = float(output[0].strip()) - sod_gm = float(output[1].strip()) - - fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl' - fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl' - - return sod_sm, sod_gm, fname_sm, fname_gm - - - -############################################################################### -# Old implementations. - -def iam(Gn, c_ei=3, c_er=3, c_es=1, node_label='atom', edge_label='bond_type', - connected=True): - """See my name, then you know what I do. - """ -# Gn = Gn[0:10] - Gn = [nx.convert_node_labels_to_integers(g) for g in Gn] - - # phase 1: initilize. - # compute set-median. - dis_min = np.inf - pi_p = [] - pi_all = [] - for idx1, G_p in enumerate(Gn): - dist_sum = 0 - pi_all.append([]) - for idx2, G_p_prime in enumerate(Gn): - dist_tmp, pi_tmp, _ = GED(G_p, G_p_prime) - pi_all[idx1].append(pi_tmp) - dist_sum += dist_tmp - if dist_sum < dis_min: - dis_min = dist_sum - G = G_p.copy() - idx_min = idx1 - # list of edit operations. - pi_p = pi_all[idx_min] - - # phase 2: iteration. - ds_attrs = get_dataset_attributes(Gn, attr_names=['edge_labeled', 'node_attr_dim'], - edge_label=edge_label) - for itr in range(0, 10): # @todo: the convergence condition? - G_new = G.copy() - # update vertex labels. - # pre-compute h_i0 for each label. -# for label in get_node_labels(Gn, node_label): -# print(label) -# for nd in G.nodes(data=True): -# pass - if not ds_attrs['node_attr_dim']: # labels are symbolic - for nd, _ in G.nodes(data=True): - h_i0_list = [] - label_list = [] - for label in get_node_labels(Gn, node_label): - h_i0 = 0 - for idx, g in enumerate(Gn): - pi_i = pi_p[idx][nd] - if g.has_node(pi_i) and g.nodes[pi_i][node_label] == label: - h_i0 += 1 - h_i0_list.append(h_i0) - label_list.append(label) - # choose one of the best randomly. - idx_max = np.argwhere(h_i0_list == np.max(h_i0_list)).flatten().tolist() - idx_rdm = random.randint(0, len(idx_max) - 1) - G_new.nodes[nd][node_label] = label_list[idx_max[idx_rdm]] - else: # labels are non-symbolic - for nd, _ in G.nodes(data=True): - Si_norm = 0 - phi_i_bar = np.array([0.0 for _ in range(ds_attrs['node_attr_dim'])]) - for idx, g in enumerate(Gn): - pi_i = pi_p[idx][nd] - if g.has_node(pi_i): #@todo: what if no g has node? phi_i_bar = 0? - Si_norm += 1 - phi_i_bar += np.array([float(itm) for itm in g.nodes[pi_i]['attributes']]) - phi_i_bar /= Si_norm - G_new.nodes[nd]['attributes'] = phi_i_bar - - # update edge labels and adjacency matrix. - if ds_attrs['edge_labeled']: - for nd1, nd2, _ in G.edges(data=True): - h_ij0_list = [] - label_list = [] - for label in get_edge_labels(Gn, edge_label): - h_ij0 = 0 - for idx, g in enumerate(Gn): - pi_i = pi_p[idx][nd1] - pi_j = pi_p[idx][nd2] - h_ij0_p = (g.has_node(pi_i) and g.has_node(pi_j) and - g.has_edge(pi_i, pi_j) and - g.edges[pi_i, pi_j][edge_label] == label) - h_ij0 += h_ij0_p - h_ij0_list.append(h_ij0) - label_list.append(label) - # choose one of the best randomly. - idx_max = np.argwhere(h_ij0_list == np.max(h_ij0_list)).flatten().tolist() - h_ij0_max = h_ij0_list[idx_max[0]] - idx_rdm = random.randint(0, len(idx_max) - 1) - best_label = label_list[idx_max[idx_rdm]] - - # check whether a_ij is 0 or 1. - sij_norm = 0 - for idx, g in enumerate(Gn): - pi_i = pi_p[idx][nd1] - pi_j = pi_p[idx][nd2] - if g.has_node(pi_i) and g.has_node(pi_j) and g.has_edge(pi_i, pi_j): - sij_norm += 1 - if h_ij0_max > len(Gn) * c_er / c_es + sij_norm * (1 - (c_er + c_ei) / c_es): - if not G_new.has_edge(nd1, nd2): - G_new.add_edge(nd1, nd2) - G_new.edges[nd1, nd2][edge_label] = best_label - else: - if G_new.has_edge(nd1, nd2): - G_new.remove_edge(nd1, nd2) - else: # if edges are unlabeled - for nd1, nd2, _ in G.edges(data=True): - sij_norm = 0 - for idx, g in enumerate(Gn): - pi_i = pi_p[idx][nd1] - pi_j = pi_p[idx][nd2] - if g.has_node(pi_i) and g.has_node(pi_j) and g.has_edge(pi_i, pi_j): - sij_norm += 1 - if sij_norm > len(Gn) * c_er / (c_er + c_ei): - if not G_new.has_edge(nd1, nd2): - G_new.add_edge(nd1, nd2) - else: - if G_new.has_edge(nd1, nd2): - G_new.remove_edge(nd1, nd2) - - G = G_new.copy() - - # update pi_p - pi_p = [] - for idx1, G_p in enumerate(Gn): - dist_tmp, pi_tmp, _ = GED(G, G_p) - pi_p.append(pi_tmp) - - return G - -# --------------------------- These are tests --------------------------------# - -def test_iam_with_more_graphs_as_init(Gn, G_candidate, c_ei=3, c_er=3, c_es=1, - node_label='atom', edge_label='bond_type'): - """See my name, then you know what I do. - """ -# Gn = Gn[0:10] - Gn = [nx.convert_node_labels_to_integers(g) for g in Gn] - - # phase 1: initilize. - # compute set-median. - dis_min = np.inf -# pi_p = [] - pi_all_forward = [] - pi_all_backward = [] - for idx1, G_p in tqdm(enumerate(G_candidate), desc='computing GEDs', file=sys.stdout): - dist_sum = 0 - pi_all_forward.append([]) - pi_all_backward.append([]) - for idx2, G_p_prime in enumerate(Gn): - dist_tmp, pi_tmp_forward, pi_tmp_backward = GED(G_p, G_p_prime) - pi_all_forward[idx1].append(pi_tmp_forward) - pi_all_backward[idx1].append(pi_tmp_backward) - dist_sum += dist_tmp - if dist_sum <= dis_min: - dis_min = dist_sum - G = G_p.copy() - idx_min = idx1 - # list of edit operations. - pi_p_forward = pi_all_forward[idx_min] - pi_p_backward = pi_all_backward[idx_min] - - # phase 2: iteration. - ds_attrs = get_dataset_attributes(Gn + [G], attr_names=['edge_labeled', 'node_attr_dim'], - edge_label=edge_label) - label_set = get_node_labels(Gn + [G], node_label) - for itr in range(0, 10): # @todo: the convergence condition? - G_new = G.copy() - # update vertex labels. - # pre-compute h_i0 for each label. -# for label in get_node_labels(Gn, node_label): -# print(label) -# for nd in G.nodes(data=True): -# pass - if not ds_attrs['node_attr_dim']: # labels are symbolic - for nd in G.nodes(): - h_i0_list = [] - label_list = [] - for label in label_set: - h_i0 = 0 - for idx, g in enumerate(Gn): - pi_i = pi_p_forward[idx][nd] - if g.has_node(pi_i) and g.nodes[pi_i][node_label] == label: - h_i0 += 1 - h_i0_list.append(h_i0) - label_list.append(label) - # choose one of the best randomly. - idx_max = np.argwhere(h_i0_list == np.max(h_i0_list)).flatten().tolist() - idx_rdm = random.randint(0, len(idx_max) - 1) - G_new.nodes[nd][node_label] = label_list[idx_max[idx_rdm]] - else: # labels are non-symbolic - for nd in G.nodes(): - Si_norm = 0 - phi_i_bar = np.array([0.0 for _ in range(ds_attrs['node_attr_dim'])]) - for idx, g in enumerate(Gn): - pi_i = pi_p_forward[idx][nd] - if g.has_node(pi_i): #@todo: what if no g has node? phi_i_bar = 0? - Si_norm += 1 - phi_i_bar += np.array([float(itm) for itm in g.nodes[pi_i]['attributes']]) - phi_i_bar /= Si_norm - G_new.nodes[nd]['attributes'] = phi_i_bar - - # update edge labels and adjacency matrix. - if ds_attrs['edge_labeled']: - for nd1, nd2, _ in G.edges(data=True): - h_ij0_list = [] - label_list = [] - for label in get_edge_labels(Gn, edge_label): - h_ij0 = 0 - for idx, g in enumerate(Gn): - pi_i = pi_p_forward[idx][nd1] - pi_j = pi_p_forward[idx][nd2] - h_ij0_p = (g.has_node(pi_i) and g.has_node(pi_j) and - g.has_edge(pi_i, pi_j) and - g.edges[pi_i, pi_j][edge_label] == label) - h_ij0 += h_ij0_p - h_ij0_list.append(h_ij0) - label_list.append(label) - # choose one of the best randomly. - idx_max = np.argwhere(h_ij0_list == np.max(h_ij0_list)).flatten().tolist() - h_ij0_max = h_ij0_list[idx_max[0]] - idx_rdm = random.randint(0, len(idx_max) - 1) - best_label = label_list[idx_max[idx_rdm]] - - # check whether a_ij is 0 or 1. - sij_norm = 0 - for idx, g in enumerate(Gn): - pi_i = pi_p_forward[idx][nd1] - pi_j = pi_p_forward[idx][nd2] - if g.has_node(pi_i) and g.has_node(pi_j) and g.has_edge(pi_i, pi_j): - sij_norm += 1 - if h_ij0_max > len(Gn) * c_er / c_es + sij_norm * (1 - (c_er + c_ei) / c_es): - if not G_new.has_edge(nd1, nd2): - G_new.add_edge(nd1, nd2) - G_new.edges[nd1, nd2][edge_label] = best_label - else: - if G_new.has_edge(nd1, nd2): - G_new.remove_edge(nd1, nd2) - else: # if edges are unlabeled - # @todo: works only for undirected graphs. - for nd1 in range(nx.number_of_nodes(G)): - for nd2 in range(nd1 + 1, nx.number_of_nodes(G)): - sij_norm = 0 - for idx, g in enumerate(Gn): - pi_i = pi_p_forward[idx][nd1] - pi_j = pi_p_forward[idx][nd2] - if g.has_node(pi_i) and g.has_node(pi_j) and g.has_edge(pi_i, pi_j): - sij_norm += 1 - if sij_norm > len(Gn) * c_er / (c_er + c_ei): - if not G_new.has_edge(nd1, nd2): - G_new.add_edge(nd1, nd2) - elif sij_norm < len(Gn) * c_er / (c_er + c_ei): - if G_new.has_edge(nd1, nd2): - G_new.remove_edge(nd1, nd2) - # do not change anything when equal. - - G = G_new.copy() - - # update pi_p - pi_p_forward = [] - for G_p in Gn: - dist_tmp, pi_tmp_forward, pi_tmp_backward = GED(G, G_p) - pi_p_forward.append(pi_tmp_forward) - - return G - - -############################################################################### - -if __name__ == '__main__': - from gklearn.utils.graphfiles import loadDataset - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat', - 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}} # node/edge symb -# ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt', -# 'extra_params': {}} # node nsymb -# ds = {'name': 'Acyclic', 'dataset': '../datasets/monoterpenoides/trainset_9.ds', -# 'extra_params': {}} - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) - - iam(Gn) \ No newline at end of file diff --git a/gklearn/preimage/knn.py b/gklearn/preimage/knn.py deleted file mode 100644 index c179287..0000000 --- a/gklearn/preimage/knn.py +++ /dev/null @@ -1,114 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Jan 10 13:22:04 2020 - -@author: ljia -""" -import numpy as np -#import matplotlib.pyplot as plt -from tqdm import tqdm -import random -#import csv -from shutil import copyfile -import os - -from gklearn.preimage.iam import iam_bash -from gklearn.utils.graphfiles import loadDataset, loadGXL -from gklearn.preimage.ged import GED -from gklearn.preimage.utils import get_same_item_indices - -def test_knn(): - ds = {'name': 'monoterpenoides', - 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:50] -# gkernel = 'treeletkernel' -# node_label = 'atom' -# edge_label = 'bond_type' -# ds_name = 'mono' - dir_output = 'results/knn/' - graph_dir = os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/' - - k_nn = 1 - percent = 0.1 - repeats = 50 - edit_cost_constant = [3, 3, 1, 3, 3, 1] - - # get indices by classes. - y_idx = get_same_item_indices(y_all) - sod_sm_list_list - for repeat in range(0, repeats): - print('\n---------------------------------') - print('repeat =', repeat) - accuracy_sm_list = [] - accuracy_gm_list = [] - sod_sm_list = [] - sod_gm_list = [] - - random.seed(repeat) - set_median_list = [] - gen_median_list = [] - train_y_set = [] - for y, values in y_idx.items(): - print('\ny =', y) - size_median_set = int(len(values) * percent) - median_set_idx = random.sample(values, size_median_set) - print('median set: ', median_set_idx) - - # compute set median and gen median using IAM (C++ through bash). - # Gn_median = [Gn[idx] for idx in median_set_idx] - group_fnames = [Gn[g].graph['filename'] for g in median_set_idx] - sod_sm, sod_gm, fname_sm, fname_gm = iam_bash(group_fnames, edit_cost_constant, - graph_dir=graph_dir) - print('sod_sm, sod_gm:', sod_sm, sod_gm) - sod_sm_list.append(sod_sm) - sod_gm_list.append(sod_gm) - fname_sm_new = dir_output + 'medians/set_median.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' - copyfile(fname_sm, fname_sm_new) - fname_gm_new = dir_output + 'medians/gen_median.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' - copyfile(fname_gm, fname_gm_new) - set_median_list.append(loadGXL(fname_sm_new)) - gen_median_list.append(loadGXL(fname_gm_new)) - train_y_set.append(int(y)) - - print(sod_sm, sod_gm) - - # do 1-nn. - test_y_set = [int(y) for y in y_all] - accuracy_sm = knn(set_median_list, train_y_set, Gn, test_y_set, k=k_nn, distance='ged') - accuracy_gm = knn(set_median_list, train_y_set, Gn, test_y_set, k=k_nn, distance='ged') - accuracy_sm_list.append(accuracy_sm) - accuracy_gm_list.append(accuracy_gm) - print('current accuracy sm and gm:', accuracy_sm, accuracy_gm) - - # output - accuracy_sm_mean = np.mean(accuracy_sm_list) - accuracy_gm_mean = np.mean(accuracy_gm_list) - print('\ntotal average accuracy sm and gm:', accuracy_sm_mean, accuracy_gm_mean) - - -def knn(train_set, train_y_set, test_set, test_y_set, k=1, distance='ged'): - if k == 1 and distance == 'ged': - algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' - params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', - 'algo_options': algo_options, 'stabilizer': None} - accuracy = 0 - for idx_test, g_test in tqdm(enumerate(test_set), desc='computing 1-nn', - file=sys.stdout): - dis = np.inf - for idx_train, g_train in enumerate(train_set): - dis_cur, _, _ = GED(g_test, g_train, **params_ged) - if dis_cur < dis: - dis = dis_cur - test_y_cur = train_y_set[idx_train] - if test_y_cur == test_y_set[idx_test]: - accuracy += 1 - accuracy = accuracy / len(test_set) - - return accuracy - - - -if __name__ == '__main__': - test_knn() \ No newline at end of file diff --git a/gklearn/preimage/libs.py b/gklearn/preimage/libs.py deleted file mode 100644 index 76005c6..0000000 --- a/gklearn/preimage/libs.py +++ /dev/null @@ -1,6 +0,0 @@ -import sys -import pathlib - -# insert gedlibpy library. -sys.path.insert(0, "../../../") -from gedlibpy import librariesImport, gedlibpy diff --git a/gklearn/preimage/median.py b/gklearn/preimage/median.py deleted file mode 100644 index 1c5bb0f..0000000 --- a/gklearn/preimage/median.py +++ /dev/null @@ -1,218 +0,0 @@ -import sys -sys.path.insert(0, "../") -#import pathlib -import numpy as np -import networkx as nx -import time - -from gedlibpy import librariesImport, gedlibpy -#import script -sys.path.insert(0, "/home/bgauzere/dev/optim-graphes/") -import gklearn -from gklearn.utils.graphfiles import loadDataset - -def replace_graph_in_env(script, graph, old_id, label='median'): - """ - Replace a graph in script - - If old_id is -1, add a new graph to the environnemt - - """ - if(old_id > -1): - script.PyClearGraph(old_id) - new_id = script.PyAddGraph(label) - for i in graph.nodes(): - script.PyAddNode(new_id,str(i),graph.node[i]) # !! strings are required bt gedlib - for e in graph.edges: - script.PyAddEdge(new_id, str(e[0]),str(e[1]), {}) - script.PyInitEnv() - script.PySetMethod("IPFP", "") - script.PyInitMethod() - - return new_id - -#Dessin median courrant -def draw_Letter_graph(graph, savepath=''): - import numpy as np - import networkx as nx - import matplotlib.pyplot as plt - plt.figure() - pos = {} - for n in graph.nodes: - pos[n] = np.array([float(graph.node[n]['attributes'][0]), - float(graph.node[n]['attributes'][1])]) - nx.draw_networkx(graph, pos) - if savepath != '': - plt.savefig(savepath + str(time.time()) + '.eps', format='eps', dpi=300) - plt.show() - plt.clf() - -#compute new mappings -def update_mappings(script,median_id,listID): - med_distances = {} - med_mappings = {} - sod = 0 - for i in range(0,len(listID)): - script.PyRunMethod(median_id,listID[i]) - med_distances[i] = script.PyGetUpperBound(median_id,listID[i]) - med_mappings[i] = script.PyGetForwardMap(median_id,listID[i]) - sod += med_distances[i] - return med_distances, med_mappings, sod - -def calcul_Sij(all_mappings, all_graphs,i,j): - s_ij = 0 - for k in range(0,len(all_mappings)): - cur_graph = all_graphs[k] - cur_mapping = all_mappings[k] - size_graph = cur_graph.order() - if ((cur_mapping[i] < size_graph) and - (cur_mapping[j] < size_graph) and - (cur_graph.has_edge(cur_mapping[i], cur_mapping[j]) == True)): - s_ij += 1 - - return s_ij - -# def update_median_nodes_L1(median,listIdSet,median_id,dataset, mappings): -# from scipy.stats.mstats import gmean - -# for i in median.nodes(): -# for k in listIdSet: -# vectors = [] #np.zeros((len(listIdSet),2)) -# if(k != median_id): -# phi_i = mappings[k][i] -# if(phi_i < dataset[k].order()): -# vectors.append([float(dataset[k].node[phi_i]['x']),float(dataset[k].node[phi_i]['y'])]) - -# new_labels = gmean(vectors) -# median.node[i]['x'] = str(new_labels[0]) -# median.node[i]['y'] = str(new_labels[1]) -# return median - -def update_median_nodes(median,dataset,mappings): - #update node attributes - for i in median.nodes(): - nb_sub=0 - mean_label = {'x' : 0, 'y' : 0} - for k in range(0,len(mappings)): - phi_i = mappings[k][i] - if ( phi_i < dataset[k].order() ): - nb_sub += 1 - mean_label['x'] += 0.75*float(dataset[k].node[phi_i]['x']) - mean_label['y'] += 0.75*float(dataset[k].node[phi_i]['y']) - median.node[i]['x'] = str((1/0.75)*(mean_label['x']/nb_sub)) - median.node[i]['y'] = str((1/0.75)*(mean_label['y']/nb_sub)) - return median - -def update_median_edges(dataset, mappings, median, cei=0.425,cer=0.425): -#for letter high, ceir = 1.7, alpha = 0.75 - size_dataset = len(dataset) - ratio_cei_cer = cer/(cei + cer) - threshold = size_dataset*ratio_cei_cer - order_graph_median = median.order() - for i in range(0,order_graph_median): - for j in range(i+1,order_graph_median): - s_ij = calcul_Sij(mappings,dataset,i,j) - if(s_ij > threshold): - median.add_edge(i,j) - else: - if(median.has_edge(i,j)): - median.remove_edge(i,j) - return median - - - -def compute_median(script, listID, dataset,verbose=False): - """Compute a graph median of a dataset according to an environment - - Parameters - - script : An gedlib initialized environnement - listID (list): a list of ID in script: encodes the dataset - dataset (list): corresponding graphs in networkX format. We assume that graph - listID[i] corresponds to dataset[i] - - Returns: - A networkX graph, which is the median, with corresponding sod - """ - print(len(listID)) - median_set_index, median_set_sod = compute_median_set(script, listID) - print(median_set_index) - print(median_set_sod) - sods = [] - #Ajout median dans environnement - set_median = dataset[median_set_index].copy() - median = dataset[median_set_index].copy() - cur_med_id = replace_graph_in_env(script,median,-1) - med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID) - sods.append(cur_sod) - if(verbose): - print(cur_sod) - ite_max = 50 - old_sod = cur_sod * 2 - ite = 0 - epsilon = 0.001 - - best_median - while((ite < ite_max) and (np.abs(old_sod - cur_sod) > epsilon )): - median = update_median_nodes(median,dataset, med_mappings) - median = update_median_edges(dataset,med_mappings,median) - - cur_med_id = replace_graph_in_env(script,median,cur_med_id) - med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID) - - - sods.append(cur_sod) - if(verbose): - print(cur_sod) - ite += 1 - return median, cur_sod, sods, set_median - - draw_Letter_graph(median) - - -def compute_median_set(script,listID): - 'Returns the id in listID corresponding to median set' - #Calcul median set - N=len(listID) - map_id_to_index = {} - map_index_to_id = {} - for i in range(0,len(listID)): - map_id_to_index[listID[i]] = i - map_index_to_id[i] = listID[i] - - distances = np.zeros((N,N)) - for i in listID: - for j in listID: - script.PyRunMethod(i,j) - distances[map_id_to_index[i],map_id_to_index[j]] = script.PyGetUpperBound(i,j) - - median_set_index = np.argmin(np.sum(distances,0)) - sod = np.min(np.sum(distances,0)) - - return median_set_index, sod - -if __name__ == "__main__": - #Chargement du dataset - script.PyLoadGXLGraph('/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/', '/home/bgauzere/dev/gedlib/data/collections/Letter_Z.xml') - script.PySetEditCost("LETTER") - script.PyInitEnv() - script.PySetMethod("IPFP", "") - script.PyInitMethod() - - dataset,my_y = gklearn.utils.graphfiles.loadDataset("/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/Letter_Z.cxl") - - listID = script.PyGetAllGraphIds() - median, sod = compute_median(script,listID,dataset,verbose=True) - - print(sod) - draw_Letter_graph(median) - - -#if __name__ == '__main__': -# # test draw_Letter_graph -# ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt', -# 'extra_params': {}} # node nsymb -# Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# print(y_all) -# for g in Gn: -# draw_Letter_graph(g) \ No newline at end of file diff --git a/gklearn/preimage/median_benoit.py b/gklearn/preimage/median_benoit.py deleted file mode 100644 index 6712196..0000000 --- a/gklearn/preimage/median_benoit.py +++ /dev/null @@ -1,201 +0,0 @@ -import sys -import pathlib -import numpy as np -import networkx as nx - -import librariesImport -import script -sys.path.insert(0, "/home/bgauzere/dev/optim-graphes/") -import gklearn - -def replace_graph_in_env(script, graph, old_id, label='median'): - """ - Replace a graph in script - - If old_id is -1, add a new graph to the environnemt - - """ - if(old_id > -1): - script.PyClearGraph(old_id) - new_id = script.PyAddGraph(label) - for i in graph.nodes(): - script.PyAddNode(new_id,str(i),graph.node[i]) # !! strings are required bt gedlib - for e in graph.edges: - script.PyAddEdge(new_id, str(e[0]),str(e[1]), {}) - script.PyInitEnv() - script.PySetMethod("IPFP", "") - script.PyInitMethod() - - return new_id - -#Dessin median courrant -def draw_Letter_graph(graph): - import numpy as np - import networkx as nx - import matplotlib.pyplot as plt - plt.figure() - pos = {} - for n in graph.nodes: - pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])]) - nx.draw_networkx(graph,pos) - plt.show() - -#compute new mappings -def update_mappings(script,median_id,listID): - med_distances = {} - med_mappings = {} - sod = 0 - for i in range(0,len(listID)): - script.PyRunMethod(median_id,listID[i]) - med_distances[i] = script.PyGetUpperBound(median_id,listID[i]) - med_mappings[i] = script.PyGetForwardMap(median_id,listID[i]) - sod += med_distances[i] - return med_distances, med_mappings, sod - -def calcul_Sij(all_mappings, all_graphs,i,j): - s_ij = 0 - for k in range(0,len(all_mappings)): - cur_graph = all_graphs[k] - cur_mapping = all_mappings[k] - size_graph = cur_graph.order() - if ((cur_mapping[i] < size_graph) and - (cur_mapping[j] < size_graph) and - (cur_graph.has_edge(cur_mapping[i], cur_mapping[j]) == True)): - s_ij += 1 - - return s_ij - -# def update_median_nodes_L1(median,listIdSet,median_id,dataset, mappings): -# from scipy.stats.mstats import gmean - -# for i in median.nodes(): -# for k in listIdSet: -# vectors = [] #np.zeros((len(listIdSet),2)) -# if(k != median_id): -# phi_i = mappings[k][i] -# if(phi_i < dataset[k].order()): -# vectors.append([float(dataset[k].node[phi_i]['x']),float(dataset[k].node[phi_i]['y'])]) - -# new_labels = gmean(vectors) -# median.node[i]['x'] = str(new_labels[0]) -# median.node[i]['y'] = str(new_labels[1]) -# return median - -def update_median_nodes(median,dataset,mappings): - #update node attributes - for i in median.nodes(): - nb_sub=0 - mean_label = {'x' : 0, 'y' : 0} - for k in range(0,len(mappings)): - phi_i = mappings[k][i] - if ( phi_i < dataset[k].order() ): - nb_sub += 1 - mean_label['x'] += 0.75*float(dataset[k].node[phi_i]['x']) - mean_label['y'] += 0.75*float(dataset[k].node[phi_i]['y']) - median.node[i]['x'] = str((1/0.75)*(mean_label['x']/nb_sub)) - median.node[i]['y'] = str((1/0.75)*(mean_label['y']/nb_sub)) - return median - -def update_median_edges(dataset, mappings, median, cei=0.425,cer=0.425): -#for letter high, ceir = 1.7, alpha = 0.75 - size_dataset = len(dataset) - ratio_cei_cer = cer/(cei + cer) - threshold = size_dataset*ratio_cei_cer - order_graph_median = median.order() - for i in range(0,order_graph_median): - for j in range(i+1,order_graph_median): - s_ij = calcul_Sij(mappings,dataset,i,j) - if(s_ij > threshold): - median.add_edge(i,j) - else: - if(median.has_edge(i,j)): - median.remove_edge(i,j) - return median - - - -def compute_median(script, listID, dataset,verbose=False): - """Compute a graph median of a dataset according to an environment - - Parameters - - script : An gedlib initialized environnement - listID (list): a list of ID in script: encodes the dataset - dataset (list): corresponding graphs in networkX format. We assume that graph - listID[i] corresponds to dataset[i] - - Returns: - A networkX graph, which is the median, with corresponding sod - """ - print(len(listID)) - median_set_index, median_set_sod = compute_median_set(script, listID) - print(median_set_index) - print(median_set_sod) - sods = [] - #Ajout median dans environnement - set_median = dataset[median_set_index].copy() - median = dataset[median_set_index].copy() - cur_med_id = replace_graph_in_env(script,median,-1) - med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID) - sods.append(cur_sod) - if(verbose): - print(cur_sod) - ite_max = 50 - old_sod = cur_sod * 2 - ite = 0 - epsilon = 0.001 - - best_median - while((ite < ite_max) and (np.abs(old_sod - cur_sod) > epsilon )): - median = update_median_nodes(median,dataset, med_mappings) - median = update_median_edges(dataset,med_mappings,median) - - cur_med_id = replace_graph_in_env(script,median,cur_med_id) - med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID) - - - sods.append(cur_sod) - if(verbose): - print(cur_sod) - ite += 1 - return median, cur_sod, sods, set_median - - draw_Letter_graph(median) - - -def compute_median_set(script,listID): - 'Returns the id in listID corresponding to median set' - #Calcul median set - N=len(listID) - map_id_to_index = {} - map_index_to_id = {} - for i in range(0,len(listID)): - map_id_to_index[listID[i]] = i - map_index_to_id[i] = listID[i] - - distances = np.zeros((N,N)) - for i in listID: - for j in listID: - script.PyRunMethod(i,j) - distances[map_id_to_index[i],map_id_to_index[j]] = script.PyGetUpperBound(i,j) - - median_set_index = np.argmin(np.sum(distances,0)) - sod = np.min(np.sum(distances,0)) - - return median_set_index, sod - -if __name__ == "__main__": - #Chargement du dataset - script.PyLoadGXLGraph('/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/', '/home/bgauzere/dev/gedlib/data/collections/Letter_Z.xml') - script.PySetEditCost("LETTER") - script.PyInitEnv() - script.PySetMethod("IPFP", "") - script.PyInitMethod() - - dataset,my_y = gklearn.utils.graphfiles.loadDataset("/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/Letter_Z.cxl") - - listID = script.PyGetAllGraphIds() - median, sod = compute_median(script,listID,dataset,verbose=True) - - print(sod) - draw_Letter_graph(median) diff --git a/gklearn/preimage/median_linlin.py b/gklearn/preimage/median_linlin.py deleted file mode 100644 index 6139558..0000000 --- a/gklearn/preimage/median_linlin.py +++ /dev/null @@ -1,215 +0,0 @@ -import sys -import pathlib -import numpy as np -import networkx as nx - -from gedlibpy import librariesImport, gedlibpy -sys.path.insert(0, "/home/bgauzere/dev/optim-graphes/") -import gklearn - -def replace_graph_in_env(script, graph, old_id, label='median'): - """ - Replace a graph in script - - If old_id is -1, add a new graph to the environnemt - - """ - if(old_id > -1): - script.PyClearGraph(old_id) - new_id = script.PyAddGraph(label) - for i in graph.nodes(): - script.PyAddNode(new_id,str(i),graph.node[i]) # !! strings are required bt gedlib - for e in graph.edges: - script.PyAddEdge(new_id, str(e[0]),str(e[1]), {}) - script.PyInitEnv() - script.PySetMethod("IPFP", "") - script.PyInitMethod() - - return new_id - -#Dessin median courrant -def draw_Letter_graph(graph): - import numpy as np - import networkx as nx - import matplotlib.pyplot as plt - plt.figure() - pos = {} - for n in graph.nodes: - pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])]) - nx.draw_networkx(graph,pos) - plt.show() - -#compute new mappings -def update_mappings(script,median_id,listID): - med_distances = {} - med_mappings = {} - sod = 0 - for i in range(0,len(listID)): - script.PyRunMethod(median_id,listID[i]) - med_distances[i] = script.PyGetUpperBound(median_id,listID[i]) - med_mappings[i] = script.PyGetForwardMap(median_id,listID[i]) - sod += med_distances[i] - return med_distances, med_mappings, sod - -def calcul_Sij(all_mappings, all_graphs,i,j): - s_ij = 0 - for k in range(0,len(all_mappings)): - cur_graph = all_graphs[k] - cur_mapping = all_mappings[k] - size_graph = cur_graph.order() - if ((cur_mapping[i] < size_graph) and - (cur_mapping[j] < size_graph) and - (cur_graph.has_edge(cur_mapping[i], cur_mapping[j]) == True)): - s_ij += 1 - - return s_ij - -# def update_median_nodes_L1(median,listIdSet,median_id,dataset, mappings): -# from scipy.stats.mstats import gmean - -# for i in median.nodes(): -# for k in listIdSet: -# vectors = [] #np.zeros((len(listIdSet),2)) -# if(k != median_id): -# phi_i = mappings[k][i] -# if(phi_i < dataset[k].order()): -# vectors.append([float(dataset[k].node[phi_i]['x']),float(dataset[k].node[phi_i]['y'])]) - -# new_labels = gmean(vectors) -# median.node[i]['x'] = str(new_labels[0]) -# median.node[i]['y'] = str(new_labels[1]) -# return median - -def update_median_nodes(median,dataset,mappings): - #update node attributes - for i in median.nodes(): - nb_sub=0 - mean_label = {'x' : 0, 'y' : 0} - for k in range(0,len(mappings)): - phi_i = mappings[k][i] - if ( phi_i < dataset[k].order() ): - nb_sub += 1 - mean_label['x'] += 0.75*float(dataset[k].node[phi_i]['x']) - mean_label['y'] += 0.75*float(dataset[k].node[phi_i]['y']) - median.node[i]['x'] = str((1/0.75)*(mean_label['x']/nb_sub)) - median.node[i]['y'] = str((1/0.75)*(mean_label['y']/nb_sub)) - return median - -def update_median_edges(dataset, mappings, median, cei=0.425,cer=0.425): -#for letter high, ceir = 1.7, alpha = 0.75 - size_dataset = len(dataset) - ratio_cei_cer = cer/(cei + cer) - threshold = size_dataset*ratio_cei_cer - order_graph_median = median.order() - for i in range(0,order_graph_median): - for j in range(i+1,order_graph_median): - s_ij = calcul_Sij(mappings,dataset,i,j) - if(s_ij > threshold): - median.add_edge(i,j) - else: - if(median.has_edge(i,j)): - median.remove_edge(i,j) - return median - - - -def compute_median(script, listID, dataset,verbose=False): - """Compute a graph median of a dataset according to an environment - - Parameters - - script : An gedlib initialized environnement - listID (list): a list of ID in script: encodes the dataset - dataset (list): corresponding graphs in networkX format. We assume that graph - listID[i] corresponds to dataset[i] - - Returns: - A networkX graph, which is the median, with corresponding sod - """ - print(len(listID)) - median_set_index, median_set_sod = compute_median_set(script, listID) - print(median_set_index) - print(median_set_sod) - sods = [] - #Ajout median dans environnement - set_median = dataset[median_set_index].copy() - median = dataset[median_set_index].copy() - cur_med_id = replace_graph_in_env(script,median,-1) - med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID) - sods.append(cur_sod) - if(verbose): - print(cur_sod) - ite_max = 50 - old_sod = cur_sod * 2 - ite = 0 - epsilon = 0.001 - - best_median - while((ite < ite_max) and (np.abs(old_sod - cur_sod) > epsilon )): - median = update_median_nodes(median,dataset, med_mappings) - median = update_median_edges(dataset,med_mappings,median) - - cur_med_id = replace_graph_in_env(script,median,cur_med_id) - med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID) - - - sods.append(cur_sod) - if(verbose): - print(cur_sod) - ite += 1 - return median, cur_sod, sods, set_median - - draw_Letter_graph(median) - - -def compute_median_set(script,listID): - 'Returns the id in listID corresponding to median set' - #Calcul median set - N=len(listID) - map_id_to_index = {} - map_index_to_id = {} - for i in range(0,len(listID)): - map_id_to_index[listID[i]] = i - map_index_to_id[i] = listID[i] - - distances = np.zeros((N,N)) - for i in listID: - for j in listID: - script.PyRunMethod(i,j) - distances[map_id_to_index[i],map_id_to_index[j]] = script.PyGetUpperBound(i,j) - - median_set_index = np.argmin(np.sum(distances,0)) - sod = np.min(np.sum(distances,0)) - - return median_set_index, sod - -def _convertGraph(G): - """Convert a graph to the proper NetworkX format that can be - recognized by library gedlibpy. - """ - G_new = nx.Graph() - for nd, attrs in G.nodes(data=True): - G_new.add_node(str(nd), chem=attrs['atom']) -# G_new.add_node(str(nd), x=str(attrs['attributes'][0]), -# y=str(attrs['attributes'][1])) - for nd1, nd2, attrs in G.edges(data=True): - G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type']) -# G_new.add_edge(str(nd1), str(nd2)) - - return G_new - -if __name__ == "__main__": - #Chargement du dataset - gedlibpy.PyLoadGXLGraph('/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/', '/home/bgauzere/dev/gedlib/data/collections/Letter_Z.xml') - gedlibpy.PySetEditCost("LETTER") - gedlibpy.PyInitEnv() - gedlibpy.PySetMethod("IPFP", "") - gedlibpy.PyInitMethod() - - dataset,my_y = gklearn.utils.graphfiles.loadDataset("/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/Letter_Z.cxl") - - listID = gedlibpy.PyGetAllGraphIds() - median, sod = compute_median(gedlibpy,listID,dataset,verbose=True) - - print(sod) - draw_Letter_graph(median) diff --git a/gklearn/preimage/median_preimage_generator.py b/gklearn/preimage/median_preimage_generator.py index 6878701..f342465 100644 --- a/gklearn/preimage/median_preimage_generator.py +++ b/gklearn/preimage/median_preimage_generator.py @@ -725,7 +725,13 @@ class MedianPreimageGenerator(PreimageGenerator): def __set_graph_kernel_by_name(self): - if self.kernel_options['name'] == 'structuralspkernel': + if self._kernel_options['name'] == 'ShortestPath': + from gklearn.kernels import ShortestPath + self._graph_kernel = ShortestPath(node_labels=self._dataset.node_labels, + node_attrs=self._dataset.node_attrs, + ds_infos=self._dataset.get_dataset_infos(keys=['directed']), + **self._kernel_options) + elif self._kernel_options['name'] == 'StructuralSP': from gklearn.kernels import StructuralSP self._graph_kernel = StructuralSP(node_labels=self._dataset.node_labels, edge_labels=self._dataset.edge_labels, @@ -733,6 +739,26 @@ class MedianPreimageGenerator(PreimageGenerator): edge_attrs=self._dataset.edge_attrs, ds_infos=self._dataset.get_dataset_infos(keys=['directed']), **self._kernel_options) + elif self._kernel_options['name'] == 'PathUpToH': + from gklearn.kernels import PathUpToH + self._graph_kernel = PathUpToH(node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + ds_infos=self._dataset.get_dataset_infos(keys=['directed']), + **self._kernel_options) + elif self._kernel_options['name'] == 'Treelet': + from gklearn.kernels import Treelet + self._graph_kernel = Treelet(node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + ds_infos=self._dataset.get_dataset_infos(keys=['directed']), + **self._kernel_options) + elif self._kernel_options['name'] == 'WeisfeilerLehman': + from gklearn.kernels import WeisfeilerLehman + self._graph_kernel = WeisfeilerLehman(node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + ds_infos=self._dataset.get_dataset_infos(keys=['directed']), + **self._kernel_options) + else: + raise Exception('The graph kernel given is not defined. Possible choices include: "StructuralSP", "ShortestPath", "PathUpToH", "Treelet", "WeisfeilerLehman".') # def __clean_graph(self, G, node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]): diff --git a/gklearn/preimage/pathfrequency.py b/gklearn/preimage/pathfrequency.py deleted file mode 100644 index 3bca1bc..0000000 --- a/gklearn/preimage/pathfrequency.py +++ /dev/null @@ -1,201 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Mar 20 10:12:15 2019 - -inferring a graph grom path frequency. -@author: ljia -""" -#import numpy as np -import networkx as nx -from scipy.spatial.distance import hamming -import itertools - -def SISF(K, v): - if output: - return output - else: - return 'no solution' - - -def SISF_M(K, v): - return output - - -def GIPF_tree(v_obj, K=1, alphabet=[0, 1]): - if K == 1: - n_graph = v_obj[0] + v_obj[1] - D_T, father_idx = getDynamicTable(n_graph, alphabet) - - # get the vector the closest to v_obj. - if v_obj not in D_T: - print('no exact solution') - dis_lim = 1 / len(v_obj) # the possible shortest distance. - dis_min = 1.0 # minimum proportional distance - v_min = v_obj - for vc in D_T: - if vc[0] + vc[1] == n_graph: -# print(vc) - dis = hamming(vc, v_obj) - if dis < dis_min: - dis_min = dis - v_min = vc - if dis_min <= dis_lim: - break - v_obj = v_min - - # obtain required graph by traceback procedure. - return getObjectGraph(v_obj, D_T, father_idx, alphabet), v_obj - -def GIPF_M(K, v): - return G - - -def getDynamicTable(n_graph, alphabet=[0, 1]): - # init. When only one node exists. - D_T = {(1, 0, 0, 0, 0, 0): 1, (0, 1, 0, 0, 0, 0): 1, (0, 0, 1, 0, 0, 0): 0, - (0, 0, 0, 1, 0, 0): 0, (0, 0, 0, 0, 1, 0): 0, (0, 0, 0, 0, 0, 1): 0,} - D_T = [(1, 0, 0, 0, 0, 0), (0, 1, 0, 0, 0, 0)] - father_idx = [-1, -1] # index of each vector's father - # add possible vectors. - for idx, v in enumerate(D_T): - if v[0] + v[1] < n_graph: - D_T.append((v[0] + 1, v[1], v[2] + 2, v[3], v[4], v[5])) - D_T.append((v[0] + 1, v[1], v[2], v[3] + 1, v[4] + 1, v[5])) - D_T.append((v[0], v[1] + 1, v[2], v[3] + 1, v[4] + 1, v[5])) - D_T.append((v[0], v[1] + 1, v[2], v[3], v[4], v[5] + 2)) - father_idx += [idx, idx, idx, idx] - -# D_T = itertools.chain([(1, 0, 0, 0, 0, 0)], [(0, 1, 0, 0, 0, 0)]) -# father_idx = itertools.chain([-1], [-1]) # index of each vector's father -# # add possible vectors. -# for idx, v in enumerate(D_T): -# if v[0] + v[1] < n_graph: -# D_T = itertools.chain(D_T, [(v[0] + 1, v[1], v[2] + 2, v[3], v[4], v[5])]) -# D_T = itertools.chain(D_T, [(v[0] + 1, v[1], v[2], v[3] + 1, v[4] + 1, v[5])]) -# D_T = itertools.chain(D_T, [(v[0], v[1] + 1, v[2], v[3] + 1, v[4] + 1, v[5])]) -# D_T = itertools.chain(D_T, [(v[0], v[1] + 1, v[2], v[3], v[4], v[5] + 2)]) -# father_idx = itertools.chain(father_idx, [idx, idx, idx, idx]) - return D_T, father_idx - - -def getObjectGraph(v_obj, D_T, father_idx, alphabet=[0, 1]): - g_obj = nx.Graph() - - # do vector traceback. - v_tb = [list(v_obj)] # traceback vectors. - v_tb_idx = [D_T.index(v_obj)] # indices of traceback vectors. - while v_tb_idx[-1] > 1: - idx_pre = father_idx[v_tb_idx[-1]] - v_tb_idx.append(idx_pre) - v_tb.append(list(D_T[idx_pre])) - v_tb = v_tb[::-1] # reverse -# v_tb_idx = v_tb_idx[::-1] - - # construct tree. - v_c = v_tb[0] # current vector. - if v_c[0] == 1: - g_obj.add_node(0, node_label=alphabet[0]) - else: - g_obj.add_node(0, node_label=alphabet[1]) - for vct in v_tb[1:]: - if vct[0] - v_c[0] == 1: - if vct[2] - v_c[2] == 2: # transfer 1 - label1 = alphabet[0] - label2 = alphabet[0] - else: # transfer 2 - label1 = alphabet[1] - label2 = alphabet[0] - else: - if vct[3] - v_c[3] == 1: # transfer 3 - label1 = alphabet[0] - label2 = alphabet[1] - else: # transfer 4 - label1 = alphabet[1] - label2 = alphabet[1] - for nd, attr in g_obj.nodes(data=True): - if attr['node_label'] == label1: - nb_node = nx.number_of_nodes(g_obj) - g_obj.add_node(nb_node, node_label=label2) - g_obj.add_edge(nd, nb_node) - break - v_c = vct - return g_obj - - -import random -def hierarchy_pos(G, root=None, width=1., vert_gap = 0.2, vert_loc = 0, xcenter = 0.5): - - ''' - From Joel's answer at https://stackoverflow.com/a/29597209/2966723. - Licensed under Creative Commons Attribution-Share Alike - - If the graph is a tree this will return the positions to plot this in a - hierarchical layout. - - G: the graph (must be a tree) - - root: the root node of current branch - - if the tree is directed and this is not given, - the root will be found and used - - if the tree is directed and this is given, then - the positions will be just for the descendants of this node. - - if the tree is undirected and not given, - then a random choice will be used. - - width: horizontal space allocated for this branch - avoids overlap with other branches - - vert_gap: gap between levels of hierarchy - - vert_loc: vertical location of root - - xcenter: horizontal location of root - ''' - if not nx.is_tree(G): - raise TypeError('cannot use hierarchy_pos on a graph that is not a tree') - - if root is None: - if isinstance(G, nx.DiGraph): - root = next(iter(nx.topological_sort(G))) #allows back compatibility with nx version 1.11 - else: - root = random.choice(list(G.nodes)) - - def _hierarchy_pos(G, root, width=1., vert_gap = 0.2, vert_loc = 0, xcenter = 0.5, pos = None, parent = None): - ''' - see hierarchy_pos docstring for most arguments - - pos: a dict saying where all nodes go if they have been assigned - parent: parent of this branch. - only affects it if non-directed - - ''' - - if pos is None: - pos = {root:(xcenter,vert_loc)} - else: - pos[root] = (xcenter, vert_loc) - children = list(G.neighbors(root)) - if not isinstance(G, nx.DiGraph) and parent is not None: - children.remove(parent) - if len(children)!=0: - dx = width/len(children) - nextx = xcenter - width/2 - dx/2 - for child in children: - nextx += dx - pos = _hierarchy_pos(G,child, width = dx, vert_gap = vert_gap, - vert_loc = vert_loc-vert_gap, xcenter=nextx, - pos=pos, parent = root) - return pos - - - return _hierarchy_pos(G, root, width, vert_gap, vert_loc, xcenter) - - -if __name__ == '__main__': - v_obj = (6, 4, 10, 3, 3, 2) -# v_obj = (6, 5, 10, 3, 3, 2) - tree_obj, v_obj = GIPF_tree(v_obj) - print('One closest vector is', v_obj) - # plot - pos = hierarchy_pos(tree_obj, 0) - node_labels = nx.get_node_attributes(tree_obj, 'node_label') - nx.draw(tree_obj, pos=pos, labels=node_labels, with_labels=True) \ No newline at end of file diff --git a/gklearn/preimage/preimage_iam.py b/gklearn/preimage/preimage_iam.py deleted file mode 100644 index bf79d0e..0000000 --- a/gklearn/preimage/preimage_iam.py +++ /dev/null @@ -1,705 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Apr 30 17:07:43 2019 - -A graph pre-image method combining iterative pre-image method in reference [1] -and the iterative alternate minimizations (IAM) in reference [2]. -@author: ljia -@references: - [1] Gökhan H Bakir, Alexander Zien, and Koji Tsuda. Learning to and graph - pre-images. In Joint Pattern Re ognition Symposium , pages 253-261. Springer, 2004. - [2] Generalized median graph via iterative alternate minimization. -""" -import sys -import numpy as np -from tqdm import tqdm -import networkx as nx -import matplotlib.pyplot as plt -import random - -from iam import iam_upgraded -from utils import dis_gstar, compute_kernel - - -def preimage_iam(Gn_init, Gn_median, alpha, idx_gi, Kmatrix, k, r_max, - gkernel, epsilon=0.001, InitIAMWithAllDk=False, - params_iam={'c_ei': 1, 'c_er': 1, 'c_es': 1, - 'ite_max': 50, 'epsilon': 0.001, - 'removeNodes': True, 'connected': False}, - params_ged={'lib': 'gedlibpy', 'cost': 'CHEM_1', 'method': 'IPFP', - 'edit_cost_constant': [], 'stabilizer': 'min', - 'repeat': 50}): - """This function constructs graph pre-image by the iterative pre-image - framework in reference [1], algorithm 1, where the step of generating new - graphs randomly is replaced by the IAM algorithm in reference [2]. - - notes - ----- - Every time a set of n better graphs is acquired, their distances in kernel space are - compared with the k nearest ones, and the k nearest distances from the k+n - distances will be used as the new ones. - """ - # compute k nearest neighbors of phi in DN. - dis_all = [] # distance between g_star and each graph. - term3 = 0 - for i1, a1 in enumerate(alpha): - for i2, a2 in enumerate(alpha): - term3 += a1 * a2 * Kmatrix[idx_gi[i1], idx_gi[i2]] - for ig, g in tqdm(enumerate(Gn_init), desc='computing distances', file=sys.stdout): - dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix, term3=term3) - dis_all.append(dtemp) - - # sort - sort_idx = np.argsort(dis_all) - dis_k = [dis_all[idis] for idis in sort_idx[0:k]] # the k shortest distances - nb_best = len(np.argwhere(dis_k == dis_k[0]).flatten().tolist()) - ghat_list = [Gn_init[idx].copy() for idx in sort_idx[0:nb_best]] # the nearest neighbors of phi in DN - if dis_k[0] == 0: # the exact pre-image. - print('The exact pre-image is found from the input dataset.') - return 0, ghat_list, 0, 0 - dhat = dis_k[0] # the nearest distance -# for g in ghat_list: -# draw_Letter_graph(g) -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - Gk = [Gn_init[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors -# for gi in Gk: -# nx.draw(gi, labels=nx.get_node_attributes(gi, 'atom'), with_labels=True) -## nx.draw_networkx(gi) -# plt.show() -## draw_Letter_graph(g) -# print(gi.nodes(data=True)) -# print(gi.edges(data=True)) - -# i = 1 - r = 0 - itr_total = 0 - dis_of_each_itr = [dhat] - found = False - nb_updated = 0 - nb_updated_k = 0 - while r < r_max:# and not found: # @todo: if not found?# and np.abs(old_dis - cur_dis) > epsilon: - print('\n-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-') - print('Current preimage iteration =', r) - print('Total preimage iteration =', itr_total, '\n') - found = False - - Gn_nearest_median = [g.copy() for g in Gk] - if InitIAMWithAllDk: # each graph in D_k is used to initialize IAM. - ghat_new_list = [] - for g_tmp in Gk: - Gn_nearest_init = [g_tmp.copy()] - ghat_new_list_tmp, _, _ = iam_upgraded(Gn_nearest_median, - Gn_nearest_init, params_ged=params_ged, **params_iam) - ghat_new_list += ghat_new_list_tmp - else: # only the best graph in D_k is used to initialize IAM. - Gn_nearest_init = [g.copy() for g in Gk] - ghat_new_list, _, _ = iam_upgraded(Gn_nearest_median, Gn_nearest_init, - params_ged=params_ged, **params_iam) - -# for g in g_tmp_list: -# nx.draw_networkx(g) -# plt.show() -# draw_Letter_graph(g) -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - - # compute distance between \psi and the new generated graphs. - knew = compute_kernel(ghat_new_list + Gn_median, gkernel, False) - dhat_new_list = [] - for idx, g_tmp in enumerate(ghat_new_list): - # @todo: the term3 below could use the one at the beginning of the function. - dhat_new_list.append(dis_gstar(idx, range(len(ghat_new_list), - len(ghat_new_list) + len(Gn_median) + 1), - alpha, knew, withterm3=False)) - - for idx_g, ghat_new in enumerate(ghat_new_list): - dhat_new = dhat_new_list[idx_g] - - # if the new distance is smaller than the max of D_k. - if dhat_new < dis_k[-1] and np.abs(dhat_new - dis_k[-1]) >= epsilon: - # check if the new distance is the same as one in D_k. - is_duplicate = False - for dis_tmp in dis_k[1:-1]: - if np.abs(dhat_new - dis_tmp) < epsilon: - is_duplicate = True - print('IAM: duplicate k nearest graph generated.') - break - if not is_duplicate: - if np.abs(dhat_new - dhat) < epsilon: - print('IAM: I am equal!') -# dhat = dhat_new -# ghat_list = [ghat_new.copy()] - else: - print('IAM: we got better k nearest neighbors!') - nb_updated_k += 1 - print('the k nearest neighbors are updated', - nb_updated_k, 'times.') - - dis_k = [dhat_new] + dis_k[0:k-1] # add the new nearest distance. - Gk = [ghat_new.copy()] + Gk[0:k-1] # add the corresponding graph. - sort_idx = np.argsort(dis_k) - dis_k = [dis_k[idx] for idx in sort_idx[0:k]] # the new k nearest distances. - Gk = [Gk[idx] for idx in sort_idx[0:k]] - if dhat_new < dhat: - print('IAM: I have smaller distance!') - print(str(dhat) + '->' + str(dhat_new)) - dhat = dhat_new - ghat_list = [Gk[0].copy()] - r = 0 - nb_updated += 1 - - print('the graph is updated', nb_updated, 'times.') - nx.draw(Gk[0], labels=nx.get_node_attributes(Gk[0], 'atom'), - with_labels=True) - ## plt.savefig("results/gk_iam/simple_two/xx" + str(i) + ".png", format="PNG") - plt.show() - - found = True - if not found: - r += 1 - - dis_of_each_itr.append(dhat) - itr_total += 1 - print('\nthe k shortest distances are', dis_k) - print('the shortest distances for previous iterations are', dis_of_each_itr) - - print('\n\nthe graph is updated', nb_updated, 'times.') - print('\nthe k nearest neighbors are updated', nb_updated_k, 'times.') - print('distances in kernel space:', dis_of_each_itr, '\n') - - return dhat, ghat_list, dis_of_each_itr[-1], nb_updated, nb_updated_k - - - - -def preimage_iam_random_mix(Gn_init, Gn_median, alpha, idx_gi, Kmatrix, k, r_max, - l_max, gkernel, epsilon=0.001, - InitIAMWithAllDk=False, InitRandomWithAllDk=True, - params_iam={'c_ei': 1, 'c_er': 1, 'c_es': 1, - 'ite_max': 50, 'epsilon': 0.001, - 'removeNodes': True, 'connected': False}, - params_ged={'lib': 'gedlibpy', 'cost': 'CHEM_1', - 'method': 'IPFP', 'edit_cost_constant': [], - 'stabilizer': 'min', 'repeat': 50}): - """This function constructs graph pre-image by the iterative pre-image - framework in reference [1], algorithm 1, where new graphs are generated - randomly and by the IAM algorithm in reference [2]. - - notes - ----- - Every time a set of n better graphs is acquired, their distances in kernel space are - compared with the k nearest ones, and the k nearest distances from the k+n - distances will be used as the new ones. - """ - Gn_init = [nx.convert_node_labels_to_integers(g) for g in Gn_init] - # compute k nearest neighbors of phi in DN. - dis_all = [] # distance between g_star and each graph. - term3 = 0 - for i1, a1 in enumerate(alpha): - for i2, a2 in enumerate(alpha): - term3 += a1 * a2 * Kmatrix[idx_gi[i1], idx_gi[i2]] - for ig, g in tqdm(enumerate(Gn_init), desc='computing distances', file=sys.stdout): - dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix, term3=term3) - dis_all.append(dtemp) - - # sort - sort_idx = np.argsort(dis_all) - dis_k = [dis_all[idis] for idis in sort_idx[0:k]] # the k shortest distances - nb_best = len(np.argwhere(dis_k == dis_k[0]).flatten().tolist()) - ghat_list = [Gn_init[idx].copy() for idx in sort_idx[0:nb_best]] # the nearest neighbors of psi in DN - if dis_k[0] == 0: # the exact pre-image. - print('The exact pre-image is found from the input dataset.') - return 0, ghat_list, 0, 0 - dhat = dis_k[0] # the nearest distance -# for g in ghat_list: -# draw_Letter_graph(g) -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - Gk = [Gn_init[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors -# for gi in Gk: -# nx.draw(gi, labels=nx.get_node_attributes(gi, 'atom'), with_labels=True) -## nx.draw_networkx(gi) -# plt.show() -## draw_Letter_graph(g) -# print(gi.nodes(data=True)) -# print(gi.edges(data=True)) - - r = 0 - itr_total = 0 - dis_of_each_itr = [dhat] - nb_updated_iam = 0 - nb_updated_k_iam = 0 - nb_updated_random = 0 - nb_updated_k_random = 0 -# is_iam_duplicate = False - while r < r_max: # and not found: # @todo: if not found?# and np.abs(old_dis - cur_dis) > epsilon: - print('\n-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-') - print('Current preimage iteration =', r) - print('Total preimage iteration =', itr_total, '\n') - found_iam = False - - Gn_nearest_median = [g.copy() for g in Gk] - if InitIAMWithAllDk: # each graph in D_k is used to initialize IAM. - ghat_new_list = [] - for g_tmp in Gk: - Gn_nearest_init = [g_tmp.copy()] - ghat_new_list_tmp, _ = iam_upgraded(Gn_nearest_median, - Gn_nearest_init, params_ged=params_ged, **params_iam) - ghat_new_list += ghat_new_list_tmp - else: # only the best graph in D_k is used to initialize IAM. - Gn_nearest_init = [g.copy() for g in Gk] - ghat_new_list, _ = iam_upgraded(Gn_nearest_median, Gn_nearest_init, - params_ged=params_ged, **params_iam) - -# for g in g_tmp_list: -# nx.draw_networkx(g) -# plt.show() -# draw_Letter_graph(g) -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - - # compute distance between \psi and the new generated graphs. - knew = compute_kernel(ghat_new_list + Gn_median, gkernel, False) - dhat_new_list = [] - - for idx, g_tmp in enumerate(ghat_new_list): - # @todo: the term3 below could use the one at the beginning of the function. - dhat_new_list.append(dis_gstar(idx, range(len(ghat_new_list), - len(ghat_new_list) + len(Gn_median) + 1), - alpha, knew, withterm3=False)) - - # find the new k nearest graphs. - for idx_g, ghat_new in enumerate(ghat_new_list): - dhat_new = dhat_new_list[idx_g] - - # if the new distance is smaller than the max of D_k. - if dhat_new < dis_k[-1] and np.abs(dhat_new - dis_k[-1]) >= epsilon: - # check if the new distance is the same as one in D_k. - is_duplicate = False - for dis_tmp in dis_k[1:-1]: - if np.abs(dhat_new - dis_tmp) < epsilon: - is_duplicate = True - print('IAM: duplicate k nearest graph generated.') - break - if not is_duplicate: - if np.abs(dhat_new - dhat) < epsilon: - print('IAM: I am equal!') -# dhat = dhat_new -# ghat_list = [ghat_new.copy()] - else: - print('IAM: we got better k nearest neighbors!') - nb_updated_k_iam += 1 - print('the k nearest neighbors are updated', - nb_updated_k_iam, 'times.') - - dis_k = [dhat_new] + dis_k[0:k-1] # add the new nearest distance. - Gk = [ghat_new.copy()] + Gk[0:k-1] # add the corresponding graph. - sort_idx = np.argsort(dis_k) - dis_k = [dis_k[idx] for idx in sort_idx[0:k]] # the new k nearest distances. - Gk = [Gk[idx] for idx in sort_idx[0:k]] - if dhat_new < dhat: - print('IAM: I have smaller distance!') - print(str(dhat) + '->' + str(dhat_new)) - dhat = dhat_new - ghat_list = [Gk[0].copy()] - r = 0 - nb_updated_iam += 1 - - print('the graph is updated by IAM', nb_updated_iam, - 'times.') - nx.draw(Gk[0], labels=nx.get_node_attributes(Gk[0], 'atom'), - with_labels=True) - ## plt.savefig("results/gk_iam/simple_two/xx" + str(i) + ".png", format="PNG") - plt.show() - - found_iam = True - - # when new distance is not smaller than the max of D_k, use random generation. - if not found_iam: - print('Distance not better, switching to random generation now.') - print(str(dhat) + '->' + str(dhat_new)) - - if InitRandomWithAllDk: # use all k nearest graphs as the initials. - init_list = [g_init.copy() for g_init in Gk] - else: # use just the nearest graph as the initial. - init_list = [Gk[0].copy()] - - # number of edges to be changed. - if len(init_list) == 1: - # @todo what if the log is negetive? how to choose alpha (scalar)? seems fdgs is always 1. - # fdgs = dhat_new - fdgs = nb_updated_random + 1 - if fdgs < 1: - fdgs = 1 - fdgs = int(np.ceil(np.log(fdgs))) - if fdgs < 1: - fdgs += 1 - # fdgs = nb_updated_random + 1 # @todo: - fdgs_list = [fdgs] - else: - # @todo what if the log is negetive? how to choose alpha (scalar)? - fdgs_list = np.array(dis_k[:]) - if np.min(fdgs_list) < 1: - fdgs_list /= dis_k[0] - fdgs_list = [int(item) for item in np.ceil(np.log(fdgs_list))] - if np.min(fdgs_list) < 1: - fdgs_list = np.array(fdgs_list) + 1 - - l = 0 - found_random = False - while l < l_max and not found_random: - for idx_g, g_tmp in enumerate(init_list): - # add and delete edges. - ghat_new = nx.convert_node_labels_to_integers(g_tmp.copy()) - # @todo: should we use just half of the adjacency matrix for undirected graphs? - nb_vpairs = nx.number_of_nodes(ghat_new) * (nx.number_of_nodes(ghat_new) - 1) - np.random.seed() - # which edges to change. - # @todo: what if fdgs is bigger than nb_vpairs? - idx_change = random.sample(range(nb_vpairs), fdgs_list[idx_g] if - fdgs_list[idx_g] < nb_vpairs else nb_vpairs) -# idx_change = np.random.randint(0, nx.number_of_nodes(gs) * -# (nx.number_of_nodes(gs) - 1), fdgs) - for item in idx_change: - node1 = int(item / (nx.number_of_nodes(ghat_new) - 1)) - node2 = (item - node1 * (nx.number_of_nodes(ghat_new) - 1)) - if node2 >= node1: # skip the self pair. - node2 += 1 - # @todo: is the randomness correct? - if not ghat_new.has_edge(node1, node2): - ghat_new.add_edge(node1, node2) - # nx.draw_networkx(gs) - # plt.show() - # nx.draw_networkx(ghat_new) - # plt.show() - else: - ghat_new.remove_edge(node1, node2) - # nx.draw_networkx(gs) - # plt.show() - # nx.draw_networkx(ghat_new) - # plt.show() - # nx.draw_networkx(ghat_new) - # plt.show() - - # compute distance between \psi and the new generated graph. - knew = compute_kernel([ghat_new] + Gn_median, gkernel, verbose=False) - dhat_new = dis_gstar(0, range(1, len(Gn_median) + 1), - alpha, knew, withterm3=False) - # @todo: the new distance is smaller or also equal? - if dhat_new < dis_k[-1] and np.abs(dhat_new - dis_k[-1]) >= epsilon: - # check if the new distance is the same as one in D_k. - is_duplicate = False - for dis_tmp in dis_k[1:-1]: - if np.abs(dhat_new - dis_tmp) < epsilon: - is_duplicate = True - print('Random: duplicate k nearest graph generated.') - break - if not is_duplicate: - if np.abs(dhat_new - dhat) < epsilon: - print('Random: I am equal!') - # dhat = dhat_new - # ghat_list = [ghat_new.copy()] - else: - print('Random: we got better k nearest neighbors!') - print('l =', str(l)) - nb_updated_k_random += 1 - print('the k nearest neighbors are updated by random generation', - nb_updated_k_random, 'times.') - - dis_k = [dhat_new] + dis_k # add the new nearest distances. - Gk = [ghat_new.copy()] + Gk # add the corresponding graphs. - sort_idx = np.argsort(dis_k) - dis_k = [dis_k[idx] for idx in sort_idx[0:k]] # the new k nearest distances. - Gk = [Gk[idx] for idx in sort_idx[0:k]] - if dhat_new < dhat: - print('\nRandom: I am smaller!') - print('l =', str(l)) - print(dhat, '->', dhat_new) - dhat = dhat_new - ghat_list = [ghat_new.copy()] - r = 0 - nb_updated_random += 1 - - print('the graph is updated by random generation', - nb_updated_random, 'times.') - - nx.draw(ghat_new, labels=nx.get_node_attributes(ghat_new, 'atom'), - with_labels=True) - ## plt.savefig("results/gk_iam/simple_two/xx" + str(i) + ".png", format="PNG") - plt.show() - found_random = True - break - l += 1 - if not found_random: # l == l_max: - r += 1 - - dis_of_each_itr.append(dhat) - itr_total += 1 - print('\nthe k shortest distances are', dis_k) - print('the shortest distances for previous iterations are', dis_of_each_itr) - - print('\n\nthe graph is updated by IAM', nb_updated_iam, 'times, and by random generation', - nb_updated_random, 'times.') - print('\nthe k nearest neighbors are updated by IAM', nb_updated_k_iam, - 'times, and by random generation', nb_updated_k_random, 'times.') - print('distances in kernel space:', dis_of_each_itr, '\n') - - return dhat, ghat_list, dis_of_each_itr[-1], \ - nb_updated_iam, nb_updated_random, nb_updated_k_iam, nb_updated_k_random - - -############################################################################### -# Old implementations. - -#def gk_iam(Gn, alpha): -# """This function constructs graph pre-image by the iterative pre-image -# framework in reference [1], algorithm 1, where the step of generating new -# graphs randomly is replaced by the IAM algorithm in reference [2]. -# -# notes -# ----- -# Every time a better graph is acquired, the older one is replaced by it. -# """ -# pass -# # compute k nearest neighbors of phi in DN. -# dis_list = [] # distance between g_star and each graph. -# for ig, g in tqdm(enumerate(Gn), desc='computing distances', file=sys.stdout): -# dtemp = k_list[ig] - 2 * (alpha * k_g1_list[ig] + (1 - alpha) * -# k_g2_list[ig]) + (alpha * alpha * k_list[idx1] + alpha * -# (1 - alpha) * k_g2_list[idx1] + (1 - alpha) * alpha * -# k_g1_list[idx2] + (1 - alpha) * (1 - alpha) * k_list[idx2]) -# dis_list.append(dtemp) -# -# # sort -# sort_idx = np.argsort(dis_list) -# dis_gs = [dis_list[idis] for idis in sort_idx[0:k]] -# g0hat = Gn[sort_idx[0]] # the nearest neighbor of phi in DN -# if dis_gs[0] == 0: # the exact pre-image. -# print('The exact pre-image is found from the input dataset.') -# return 0, g0hat -# dhat = dis_gs[0] # the nearest distance -# Gk = [Gn[ig] for ig in sort_idx[0:k]] # the k nearest neighbors -# gihat_list = [] -# -## i = 1 -# r = 1 -# while r < r_max: -# print('r =', r) -## found = False -# Gs_nearest = Gk + gihat_list -# g_tmp = iam(Gs_nearest) -# -# # compute distance between \psi and the new generated graph. -# knew = marginalizedkernel([g_tmp, g1, g2], node_label='atom', edge_label=None, -# p_quit=lmbda, n_iteration=20, remove_totters=False, -# n_jobs=multiprocessing.cpu_count(), verbose=False) -# dnew = knew[0][0, 0] - 2 * (alpha * knew[0][0, 1] + (1 - alpha) * -# knew[0][0, 2]) + (alpha * alpha * k_list[idx1] + alpha * -# (1 - alpha) * k_g2_list[idx1] + (1 - alpha) * alpha * -# k_g1_list[idx2] + (1 - alpha) * (1 - alpha) * k_list[idx2]) -# if dnew <= dhat: # the new distance is smaller -# print('I am smaller!') -# dhat = dnew -# g_new = g_tmp.copy() # found better graph. -# gihat_list = [g_new] -# dis_gs.append(dhat) -# r = 0 -# else: -# r += 1 -# -# ghat = ([g0hat] if len(gihat_list) == 0 else gihat_list) -# -# return dhat, ghat - - -#def gk_iam_nearest(Gn, alpha, idx_gi, Kmatrix, k, r_max): -# """This function constructs graph pre-image by the iterative pre-image -# framework in reference [1], algorithm 1, where the step of generating new -# graphs randomly is replaced by the IAM algorithm in reference [2]. -# -# notes -# ----- -# Every time a better graph is acquired, its distance in kernel space is -# compared with the k nearest ones, and the k nearest distances from the k+1 -# distances will be used as the new ones. -# """ -# # compute k nearest neighbors of phi in DN. -# dis_list = [] # distance between g_star and each graph. -# for ig, g in tqdm(enumerate(Gn), desc='computing distances', file=sys.stdout): -# dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix) -## dtemp = k_list[ig] - 2 * (alpha * k_g1_list[ig] + (1 - alpha) * -## k_g2_list[ig]) + (alpha * alpha * k_list[0] + alpha * -## (1 - alpha) * k_g2_list[0] + (1 - alpha) * alpha * -## k_g1_list[6] + (1 - alpha) * (1 - alpha) * k_list[6]) -# dis_list.append(dtemp) -# -# # sort -# sort_idx = np.argsort(dis_list) -# dis_gs = [dis_list[idis] for idis in sort_idx[0:k]] # the k shortest distances -# g0hat = Gn[sort_idx[0]] # the nearest neighbor of phi in DN -# if dis_gs[0] == 0: # the exact pre-image. -# print('The exact pre-image is found from the input dataset.') -# return 0, g0hat -# dhat = dis_gs[0] # the nearest distance -# ghat = g0hat.copy() -# Gk = [Gn[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors -# for gi in Gk: -# nx.draw_networkx(gi) -# plt.show() -# print(gi.nodes(data=True)) -# print(gi.edges(data=True)) -# Gs_nearest = Gk.copy() -## gihat_list = [] -# -## i = 1 -# r = 1 -# while r < r_max: -# print('r =', r) -## found = False -## Gs_nearest = Gk + gihat_list -## g_tmp = iam(Gs_nearest) -# g_tmp = test_iam_with_more_graphs_as_init(Gs_nearest, Gs_nearest, c_ei=1, c_er=1, c_es=1) -# nx.draw_networkx(g_tmp) -# plt.show() -# print(g_tmp.nodes(data=True)) -# print(g_tmp.edges(data=True)) -# -# # compute distance between \psi and the new generated graph. -# gi_list = [Gn[i] for i in idx_gi] -# knew = compute_kernel([g_tmp] + gi_list, 'untilhpathkernel', False) -# dnew = dis_gstar(0, range(1, len(gi_list) + 1), alpha, knew) -# -## dnew = knew[0, 0] - 2 * (alpha[0] * knew[0, 1] + alpha[1] * -## knew[0, 2]) + (alpha[0] * alpha[0] * k_list[0] + alpha[0] * -## alpha[1] * k_g2_list[0] + alpha[1] * alpha[0] * -## k_g1_list[1] + alpha[1] * alpha[1] * k_list[1]) -# if dnew <= dhat and g_tmp != ghat: # the new distance is smaller -# print('I am smaller!') -# print(str(dhat) + '->' + str(dnew)) -## nx.draw_networkx(ghat) -## plt.show() -## print('->') -## nx.draw_networkx(g_tmp) -## plt.show() -# -# dhat = dnew -# g_new = g_tmp.copy() # found better graph. -# ghat = g_tmp.copy() -# dis_gs.append(dhat) # add the new nearest distance. -# Gs_nearest.append(g_new) # add the corresponding graph. -# sort_idx = np.argsort(dis_gs) -# dis_gs = [dis_gs[idx] for idx in sort_idx[0:k]] # the new k nearest distances. -# Gs_nearest = [Gs_nearest[idx] for idx in sort_idx[0:k]] -# r = 0 -# else: -# r += 1 -# -# return dhat, ghat - - -#def gk_iam_nearest_multi(Gn, alpha, idx_gi, Kmatrix, k, r_max): -# """This function constructs graph pre-image by the iterative pre-image -# framework in reference [1], algorithm 1, where the step of generating new -# graphs randomly is replaced by the IAM algorithm in reference [2]. -# -# notes -# ----- -# Every time a set of n better graphs is acquired, their distances in kernel space are -# compared with the k nearest ones, and the k nearest distances from the k+n -# distances will be used as the new ones. -# """ -# Gn_median = [Gn[idx].copy() for idx in idx_gi] -# # compute k nearest neighbors of phi in DN. -# dis_list = [] # distance between g_star and each graph. -# for ig, g in tqdm(enumerate(Gn), desc='computing distances', file=sys.stdout): -# dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix) -## dtemp = k_list[ig] - 2 * (alpha * k_g1_list[ig] + (1 - alpha) * -## k_g2_list[ig]) + (alpha * alpha * k_list[0] + alpha * -## (1 - alpha) * k_g2_list[0] + (1 - alpha) * alpha * -## k_g1_list[6] + (1 - alpha) * (1 - alpha) * k_list[6]) -# dis_list.append(dtemp) -# -# # sort -# sort_idx = np.argsort(dis_list) -# dis_gs = [dis_list[idis] for idis in sort_idx[0:k]] # the k shortest distances -# nb_best = len(np.argwhere(dis_gs == dis_gs[0]).flatten().tolist()) -# g0hat_list = [Gn[idx] for idx in sort_idx[0:nb_best]] # the nearest neighbors of phi in DN -# if dis_gs[0] == 0: # the exact pre-image. -# print('The exact pre-image is found from the input dataset.') -# return 0, g0hat_list -# dhat = dis_gs[0] # the nearest distance -# ghat_list = [g.copy() for g in g0hat_list] -# for g in ghat_list: -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) -# Gk = [Gn[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors -# for gi in Gk: -# nx.draw_networkx(gi) -# plt.show() -# print(gi.nodes(data=True)) -# print(gi.edges(data=True)) -# Gs_nearest = Gk.copy() -## gihat_list = [] -# -## i = 1 -# r = 1 -# while r < r_max: -# print('r =', r) -## found = False -## Gs_nearest = Gk + gihat_list -## g_tmp = iam(Gs_nearest) -# g_tmp_list = test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations( -# Gn_median, Gs_nearest, c_ei=1, c_er=1, c_es=1) -# for g in g_tmp_list: -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) -# -# # compute distance between \psi and the new generated graphs. -# gi_list = [Gn[i] for i in idx_gi] -# knew = compute_kernel(g_tmp_list + gi_list, 'marginalizedkernel', False) -# dnew_list = [] -# for idx, g_tmp in enumerate(g_tmp_list): -# dnew_list.append(dis_gstar(idx, range(len(g_tmp_list), -# len(g_tmp_list) + len(gi_list) + 1), alpha, knew)) -# -## dnew = knew[0, 0] - 2 * (alpha[0] * knew[0, 1] + alpha[1] * -## knew[0, 2]) + (alpha[0] * alpha[0] * k_list[0] + alpha[0] * -## alpha[1] * k_g2_list[0] + alpha[1] * alpha[0] * -## k_g1_list[1] + alpha[1] * alpha[1] * k_list[1]) -# -# # find the new k nearest graphs. -# dis_gs = dnew_list + dis_gs # add the new nearest distances. -# Gs_nearest = [g.copy() for g in g_tmp_list] + Gs_nearest # add the corresponding graphs. -# sort_idx = np.argsort(dis_gs) -# if len([i for i in sort_idx[0:k] if i < len(dnew_list)]) > 0: -# print('We got better k nearest neighbors! Hurray!') -# dis_gs = [dis_gs[idx] for idx in sort_idx[0:k]] # the new k nearest distances. -# print(dis_gs[-1]) -# Gs_nearest = [Gs_nearest[idx] for idx in sort_idx[0:k]] -# nb_best = len(np.argwhere(dis_gs == dis_gs[0]).flatten().tolist()) -# if len([i for i in sort_idx[0:nb_best] if i < len(dnew_list)]) > 0: -# print('I have smaller or equal distance!') -# dhat = dis_gs[0] -# print(str(dhat) + '->' + str(dhat)) -# idx_best_list = np.argwhere(dnew_list == dhat).flatten().tolist() -# ghat_list = [g_tmp_list[idx].copy() for idx in idx_best_list] -# for g in ghat_list: -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) -# r = 0 -# else: -# r += 1 -# -# return dhat, ghat_list \ No newline at end of file diff --git a/gklearn/preimage/preimage_random.py b/gklearn/preimage/preimage_random.py deleted file mode 100644 index e5f74cd..0000000 --- a/gklearn/preimage/preimage_random.py +++ /dev/null @@ -1,309 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Mar 6 16:03:11 2019 - -pre-image -@author: ljia -""" - -import sys -import numpy as np -import random -from tqdm import tqdm -import networkx as nx -import matplotlib.pyplot as plt - -from gklearn.preimage.utils import compute_kernel, dis_gstar - - -def preimage_random(Gn_init, Gn_median, alpha, idx_gi, Kmatrix, k, r_max, l, gkernel): - Gn_init = [nx.convert_node_labels_to_integers(g) for g in Gn_init] - - # compute k nearest neighbors of phi in DN. - dis_list = [] # distance between g_star and each graph. - term3 = 0 - for i1, a1 in enumerate(alpha): - for i2, a2 in enumerate(alpha): - term3 += a1 * a2 * Kmatrix[idx_gi[i1], idx_gi[i2]] - for ig, g in tqdm(enumerate(Gn_init), desc='computing distances', file=sys.stdout): - dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix, term3=term3) - dis_list.append(dtemp) -# print(np.max(dis_list)) -# print(np.min(dis_list)) -# print(np.min([item for item in dis_list if item != 0])) -# print(np.mean(dis_list)) - - # sort - sort_idx = np.argsort(dis_list) - dis_gs = [dis_list[idis] for idis in sort_idx[0:k]] # the k shortest distances - nb_best = len(np.argwhere(dis_gs == dis_gs[0]).flatten().tolist()) - g0hat_list = [Gn_init[idx] for idx in sort_idx[0:nb_best]] # the nearest neighbors of phi in DN - if dis_gs[0] == 0: # the exact pre-image. - print('The exact pre-image is found from the input dataset.') - return 0, g0hat_list[0], 0 - dhat = dis_gs[0] # the nearest distance -# ghat_list = [g.copy() for g in g0hat_list] -# for g in ghat_list: -# draw_Letter_graph(g) -# nx.draw_networkx(g) -# plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - Gk = [Gn_init[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors -# for gi in Gk: -## nx.draw_networkx(gi) -## plt.show() -# draw_Letter_graph(g) -# print(gi.nodes(data=True)) -# print(gi.edges(data=True)) - Gs_nearest = [g.copy() for g in Gk] - gihat_list = [] - dihat_list = [] - -# i = 1 - r = 0 -# sod_list = [dhat] -# found = False - dis_of_each_itr = [dhat] - nb_updated = 0 - g_best = [] - while r < r_max: - print('\nr =', r) - print('itr for gk =', nb_updated, '\n') - found = False - dis_bests = dis_gs + dihat_list - # @todo what if the log is negetive? how to choose alpha (scalar)? - fdgs_list = np.array(dis_bests) - if np.min(fdgs_list) < 1: - fdgs_list /= np.min(dis_bests) - fdgs_list = [int(item) for item in np.ceil(np.log(fdgs_list))] - if np.min(fdgs_list) < 1: - fdgs_list = np.array(fdgs_list) + 1 - - for ig, gs in enumerate(Gs_nearest + gihat_list): -# nx.draw_networkx(gs) -# plt.show() - for trail in range(0, l): -# for trail in tqdm(range(0, l), desc='l loops', file=sys.stdout): - # add and delete edges. - gtemp = gs.copy() - np.random.seed() - # which edges to change. - # @todo: should we use just half of the adjacency matrix for undirected graphs? - nb_vpairs = nx.number_of_nodes(gs) * (nx.number_of_nodes(gs) - 1) - # @todo: what if fdgs is bigger than nb_vpairs? - idx_change = random.sample(range(nb_vpairs), fdgs_list[ig] if - fdgs_list[ig] < nb_vpairs else nb_vpairs) -# idx_change = np.random.randint(0, nx.number_of_nodes(gs) * -# (nx.number_of_nodes(gs) - 1), fdgs) - for item in idx_change: - node1 = int(item / (nx.number_of_nodes(gs) - 1)) - node2 = (item - node1 * (nx.number_of_nodes(gs) - 1)) - if node2 >= node1: # skip the self pair. - node2 += 1 - # @todo: is the randomness correct? - if not gtemp.has_edge(node1, node2): - gtemp.add_edge(node1, node2) -# nx.draw_networkx(gs) -# plt.show() -# nx.draw_networkx(gtemp) -# plt.show() - else: - gtemp.remove_edge(node1, node2) -# nx.draw_networkx(gs) -# plt.show() -# nx.draw_networkx(gtemp) -# plt.show() -# nx.draw_networkx(gtemp) -# plt.show() - - # compute distance between \psi and the new generated graph. -# knew = marginalizedkernel([gtemp, g1, g2], node_label='atom', edge_label=None, -# p_quit=lmbda, n_iteration=20, remove_totters=False, -# n_jobs=multiprocessing.cpu_count(), verbose=False) - knew = compute_kernel([gtemp] + Gn_median, gkernel, verbose=False) - dnew = dis_gstar(0, range(1, len(Gn_median) + 1), alpha, knew, - withterm3=False) - if dnew <= dhat: # @todo: the new distance is smaller or also equal? - if dnew < dhat: - print('\nI am smaller!') - print('ig =', str(ig), ', l =', str(trail)) - print(dhat, '->', dnew) - nb_updated += 1 - elif dnew == dhat: - print('I am equal!') -# nx.draw_networkx(gtemp) -# plt.show() -# print(gtemp.nodes(data=True)) -# print(gtemp.edges(data=True)) - dhat = dnew - gnew = gtemp.copy() - found = True # found better graph. - if found: - r = 0 - gihat_list = [gnew] - dihat_list = [dhat] - else: - r += 1 - - dis_of_each_itr.append(dhat) - print('the shortest distances for previous iterations are', dis_of_each_itr) -# dis_best.append(dhat) - g_best = (g0hat_list[0] if len(gihat_list) == 0 else gihat_list[0]) - print('distances in kernel space:', dis_of_each_itr, '\n') - - return dhat, g_best, nb_updated -# return 0, 0, 0 - - -if __name__ == '__main__': - from gklearn.utils.graphfiles import loadDataset - -# ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', -# 'extra_params': {}} # node/edge symb - ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt', - 'extra_params': {}} # node nsymb -# ds = {'name': 'Acyclic', 'dataset': '../datasets/monoterpenoides/trainset_9.ds', -# 'extra_params': {}} -# ds = {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', -# 'extra_params': {}} # node symb - - DN, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) - #DN = DN[0:10] - - lmbda = 0.03 # termination probalility - r_max = 3 # 10 # iteration limit. - l = 500 - alpha_range = np.linspace(0.5, 0.5, 1) - #alpha_range = np.linspace(0.1, 0.9, 9) - k = 10 # 5 # k nearest neighbors - - # randomly select two molecules - #np.random.seed(1) - #idx1, idx2 = np.random.randint(0, len(DN), 2) - #g1 = DN[idx1] - #g2 = DN[idx2] - idx1 = 0 - idx2 = 6 - g1 = DN[idx1] - g2 = DN[idx2] - - # compute - k_list = [] # kernel between each graph and itself. - k_g1_list = [] # kernel between each graph and g1 - k_g2_list = [] # kernel between each graph and g2 - for ig, g in tqdm(enumerate(DN), desc='computing self kernels', file=sys.stdout): - # ktemp = marginalizedkernel([g, g1, g2], node_label='atom', edge_label=None, - # p_quit=lmbda, n_iteration=20, remove_totters=False, - # n_jobs=multiprocessing.cpu_count(), verbose=False) - ktemp = compute_kernel([g, g1, g2], 'untilhpathkernel', verbose=False) - k_list.append(ktemp[0, 0]) - k_g1_list.append(ktemp[0, 1]) - k_g2_list.append(ktemp[0, 2]) - - g_best = [] - dis_best = [] - # for each alpha - for alpha in alpha_range: - print('alpha =', alpha) - # compute k nearest neighbors of phi in DN. - dis_list = [] # distance between g_star and each graph. - for ig, g in tqdm(enumerate(DN), desc='computing distances', file=sys.stdout): - dtemp = k_list[ig] - 2 * (alpha * k_g1_list[ig] + (1 - alpha) * - k_g2_list[ig]) + (alpha * alpha * k_list[idx1] + alpha * - (1 - alpha) * k_g2_list[idx1] + (1 - alpha) * alpha * - k_g1_list[idx2] + (1 - alpha) * (1 - alpha) * k_list[idx2]) - dis_list.append(np.sqrt(dtemp)) - - # sort - sort_idx = np.argsort(dis_list) - dis_gs = [dis_list[idis] for idis in sort_idx[0:k]] - g0hat = DN[sort_idx[0]] # the nearest neighbor of phi in DN - if dis_gs[0] == 0: # the exact pre-image. - print('The exact pre-image is found from the input dataset.') - g_pimg = g0hat - break - dhat = dis_gs[0] # the nearest distance - Dk = [DN[ig] for ig in sort_idx[0:k]] # the k nearest neighbors - gihat_list = [] - - i = 1 - r = 1 - while r < r_max: - print('r =', r) - found = False - for ig, gs in enumerate(Dk + gihat_list): - # nx.draw_networkx(gs) - # plt.show() - # @todo what if the log is negetive? - fdgs = int(np.abs(np.ceil(np.log(alpha * dis_gs[ig])))) - for trail in tqdm(range(0, l), desc='l loop', file=sys.stdout): - # add and delete edges. - gtemp = gs.copy() - np.random.seed() - # which edges to change. - # @todo: should we use just half of the adjacency matrix for undirected graphs? - nb_vpairs = nx.number_of_nodes(gs) * (nx.number_of_nodes(gs) - 1) - # @todo: what if fdgs is bigger than nb_vpairs? - idx_change = random.sample(range(nb_vpairs), fdgs if fdgs < nb_vpairs else nb_vpairs) - # idx_change = np.random.randint(0, nx.number_of_nodes(gs) * - # (nx.number_of_nodes(gs) - 1), fdgs) - for item in idx_change: - node1 = int(item / (nx.number_of_nodes(gs) - 1)) - node2 = (item - node1 * (nx.number_of_nodes(gs) - 1)) - if node2 >= node1: # skip the self pair. - node2 += 1 - # @todo: is the randomness correct? - if not gtemp.has_edge(node1, node2): - # @todo: how to update the bond_type? 0 or 1? - gtemp.add_edges_from([(node1, node2, {'bond_type': 1})]) - # nx.draw_networkx(gs) - # plt.show() - # nx.draw_networkx(gtemp) - # plt.show() - else: - gtemp.remove_edge(node1, node2) - # nx.draw_networkx(gs) - # plt.show() - # nx.draw_networkx(gtemp) - # plt.show() - # nx.draw_networkx(gtemp) - # plt.show() - - # compute distance between phi and the new generated graph. - # knew = marginalizedkernel([gtemp, g1, g2], node_label='atom', edge_label=None, - # p_quit=lmbda, n_iteration=20, remove_totters=False, - # n_jobs=multiprocessing.cpu_count(), verbose=False) - knew = compute_kernel([gtemp, g1, g2], 'untilhpathkernel', verbose=False) - dnew = np.sqrt(knew[0, 0] - 2 * (alpha * knew[0, 1] + (1 - alpha) * - knew[0, 2]) + (alpha * alpha * k_list[idx1] + alpha * - (1 - alpha) * k_g2_list[idx1] + (1 - alpha) * alpha * - k_g1_list[idx2] + (1 - alpha) * (1 - alpha) * k_list[idx2])) - if dnew < dhat: # @todo: the new distance is smaller or also equal? - print('I am smaller!') - print(dhat, '->', dnew) - nx.draw_networkx(gtemp) - plt.show() - print(gtemp.nodes(data=True)) - print(gtemp.edges(data=True)) - dhat = dnew - gnew = gtemp.copy() - found = True # found better graph. - r = 0 - elif dnew == dhat: - print('I am equal!') - if found: - gihat_list = [gnew] - dis_gs.append(dhat) - else: - r += 1 - dis_best.append(dhat) - g_best += ([g0hat] if len(gihat_list) == 0 else gihat_list) - - - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_best[idx]) - print('the corresponding pre-image is') - nx.draw_networkx(g_best[idx]) - plt.show() \ No newline at end of file diff --git a/gklearn/preimage/python_code.py b/gklearn/preimage/python_code.py deleted file mode 100644 index 3772526..0000000 --- a/gklearn/preimage/python_code.py +++ /dev/null @@ -1,122 +0,0 @@ - elif opt_name == 'random-inits': - try: - num_random_inits_ = std::stoul(opt_val) - desired_num_random_inits_ = num_random_inits_ - - except: - raise Error('Invalid argument "' + opt_val + '" for option random-inits. Usage: options = "[--random-inits ]"') - - if num_random_inits_ <= 0: - raise Error('Invalid argument "' + opt_val + '" for option random-inits. Usage: options = "[--random-inits ]"') - - } - elif opt_name == 'randomness': - if opt_val == 'PSEUDO': - use_real_randomness_ = False - - elif opt_val == 'REAL': - use_real_randomness_ = True - - else: - raise Error('Invalid argument "' + opt_val + '" for option randomness. Usage: options = "[--randomness REAL|PSEUDO] [...]"') - - } - elif opt_name == 'stdout': - if opt_val == '0': - print_to_stdout_ = 0 - - elif opt_val == '1': - print_to_stdout_ = 1 - - elif opt_val == '2': - print_to_stdout_ = 2 - - else: - raise Error('Invalid argument "' + opt_val + '" for option stdout. Usage: options = "[--stdout 0|1|2] [...]"') - - } - elif opt_name == 'refine': - if opt_val == 'TRUE': - refine_ = True - - elif opt_val == 'FALSE': - refine_ = False - - else: - raise Error('Invalid argument "' + opt_val + '" for option refine. Usage: options = "[--refine TRUE|FALSE] [...]"') - - } - elif opt_name == 'time-limit': - try: - time_limit_in_sec_ = std::stod(opt_val) - - except: - raise Error('Invalid argument "' + opt_val + '" for option time-limit. Usage: options = "[--time-limit ] [...]') - - } - elif opt_name == 'max-itrs': - try: - max_itrs_ = std::stoi(opt_val) - - except: - raise Error('Invalid argument "' + opt_val + '" for option max-itrs. Usage: options = "[--max-itrs ] [...]') - - } - elif opt_name == 'max-itrs-without-update': - try: - max_itrs_without_update_ = std::stoi(opt_val) - - except: - raise Error('Invalid argument "' + opt_val + '" for option max-itrs-without-update. Usage: options = "[--max-itrs-without-update ] [...]') - - } - elif opt_name == 'seed': - try: - seed_ = std::stoul(opt_val) - - except: - raise Error('Invalid argument "' + opt_val + '" for option seed. Usage: options = "[--seed ] [...]') - - } - elif opt_name == 'epsilon': - try: - epsilon_ = std::stod(opt_val) - - except: - raise Error('Invalid argument "' + opt_val + '" for option epsilon. Usage: options = "[--epsilon ] [...]') - - if epsilon_ <= 0: - raise Error('Invalid argument "' + opt_val + '" for option epsilon. Usage: options = "[--epsilon ] [...]') - - } - elif opt_name == 'inits-increase-order': - try: - num_inits_increase_order_ = std::stoul(opt_val) - - except: - raise Error('Invalid argument "' + opt_val + '" for option inits-increase-order. Usage: options = "[--inits-increase-order ]"') - - if num_inits_increase_order_ <= 0: - raise Error('Invalid argument "' + opt_val + '" for option inits-increase-order. Usage: options = "[--inits-increase-order ]"') - - } - elif opt_name == 'init-type-increase-order': - init_type_increase_order_ = opt_val - if opt_val != 'CLUSTERS' and opt_val != 'K-MEANS++': - raise Exception(std::string('Invalid argument ') + opt_val + ' for option init-type-increase-order. Usage: options = "[--init-type-increase-order CLUSTERS|K-MEANS++] [...]"') - - } - elif opt_name == 'max-itrs-increase-order': - try: - max_itrs_increase_order_ = std::stoi(opt_val) - - except: - raise Error('Invalid argument "' + opt_val + '" for option max-itrs-increase-order. Usage: options = "[--max-itrs-increase-order ] [...]') - - } - else: - std::string valid_options('[--init-type ] [--random-inits ] [--randomness ] [--seed ] [--stdout ] ') - valid_options += '[--time-limit ] [--max-itrs ] [--epsilon ] ' - valid_options += '[--inits-increase-order ] [--init-type-increase-order ] [--max-itrs-increase-order ]' - raise Error(std::string('Invalid option "') + opt_name + '". Usage: options = "' + valid_options + '"') - diff --git a/gklearn/preimage/test.py b/gklearn/preimage/test.py deleted file mode 100644 index 4110a6f..0000000 --- a/gklearn/preimage/test.py +++ /dev/null @@ -1,83 +0,0 @@ -#export LD_LIBRARY_PATH=.:/export/home/lambertn/Documents/gedlibpy/lib/fann/:/export/home/lambertn/Documents/gedlibpy/lib/libsvm.3.22:/export/home/lambertn/Documents/gedlibpy/lib/nomad - -#Pour que "import script" trouve les librairies qu'a besoin GedLib -#Equivalent à définir la variable d'environnement LD_LIBRARY_PATH sur un bash -import gedlibpy.librariesImport -from gedlibpy import gedlibpy -import networkx as nx - - -def init() : - print("List of Edit Cost Options : ") - for i in gedlibpy.list_of_edit_cost_options : - print (i) - print("") - - print("List of Method Options : ") - for j in gedlibpy.list_of_method_options : - print (j) - print("") - - print("List of Init Options : ") - for k in gedlibpy.list_of_init_options : - print (k) - print("") - -def test(): - - gedlibpy.load_GXL_graphs('include/gedlib-master/data/datasets/Mutagenicity/data/', 'collections/MUTA_10.xml') - listID = gedlibpy.get_all_graph_ids() - gedlibpy.set_edit_cost("CHEM_1") - gedlibpy.init() - gedlibpy.set_method("IPFP", "") - gedlibpy.init_method() - g = listID[0] - h = listID[1] - gedlibpy.run_method(g, h) - print("Node Map : ", gedlibpy.get_node_map(g,h)) - print("Forward map : " , gedlibpy.get_forward_map(g, h), ", Backward map : ", gedlibpy.get_backward_map(g, h)) - print("Assignment Matrix : ") - print(gedlibpy.get_assignment_matrix(g, h)) - print ("Upper Bound = " + str(gedlibpy.get_upper_bound(g,h)) + ", Lower Bound = " + str(gedlibpy.get_lower_bound(g, h)) + ", Runtime = " + str(gedlibpy.get_runtime(g, h))) - - -def convertGraph(G): - G_new = nx.Graph() - for nd, attrs in G.nodes(data=True): - G_new.add_node(str(nd), chem=attrs['atom']) - for nd1, nd2, attrs in G.edges(data=True): - G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type']) - - return G_new - - -def testNxGrapĥ(): - from gklearn.utils.graphfiles import loadDataset - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) - - gedlibpy.restart_env() - for graph in Gn: - g_new = convertGraph(graph) - gedlibpy.add_nx_graph(g_new, "") - - listID = gedlibpy.get_all_graph_ids() - gedlibpy.set_edit_cost("CHEM_1") - gedlibpy.init() - gedlibpy.set_method("IPFP", "") - gedlibpy.init_method() - - print(listID) - g = listID[0] - h = listID[1] - - gedlibpy.run_method(g, h) - - print("Node Map : ", gedlibpy.get_node_map(g, h)) - print("Forward map : " , gedlibpy.get_forward_map(g, h), ", Backward map : ", gedlibpy.get_backward_map(g, h)) - print ("Upper Bound = " + str(gedlibpy.get_upper_bound(g, h)) + ", Lower Bound = " + str(gedlibpy.get_lower_bound(g, h)) + ", Runtime = " + str(gedlibpy.get_runtime(g, h))) - -#test() -init() -#testNxGrapĥ() diff --git a/gklearn/preimage/test_fitDistance.py b/gklearn/preimage/test_fitDistance.py deleted file mode 100644 index 2945a24..0000000 --- a/gklearn/preimage/test_fitDistance.py +++ /dev/null @@ -1,648 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Oct 24 11:50:56 2019 - -@author: ljia -""" -from matplotlib import pyplot as plt -import numpy as np -from tqdm import tqdm - -from gklearn.utils.graphfiles import loadDataset -from gklearn.preimage.utils import remove_edges -from gklearn.preimage.fitDistance import fit_GED_to_kernel_distance -from gklearn.preimage.utils import normalize_distance_matrix - - -def test_update_costs(): - from preimage.fitDistance import update_costs - import cvxpy as cp - - ds = np.load('results/xp_fit_method/fit_data_debug4.gm.npz') - nb_cost_mat = ds['nb_cost_mat'] - dis_k_vec = ds['dis_k_vec'] - n_edit_operations = ds['n_edit_operations'] - ged_vec_init = ds['ged_vec_init'] - ged_mat = ds['ged_mat'] - - nb_cost_mat_new = nb_cost_mat[:,[2,3,4]] - x = cp.Variable(nb_cost_mat_new.shape[1]) - cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec) -# constraints = [x >= [0.000 for i in range(nb_cost_mat_new.shape[1])], -# np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0] -# constraints = [x >= [0.000 for i in range(nb_cost_mat_new.shape[1])], -# np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0, -# np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0] - constraints = [x >= [0.00 for i in range(nb_cost_mat_new.shape[1])], - np.array([0.0, 1.0, -1.0]).T@x == 0.0] -# constraints = [x >= [0.00000 for i in range(nb_cost_mat_new.shape[1])]] - prob = cp.Problem(cp.Minimize(cost_fun), constraints) - prob.solve() - print(x.value) - edit_costs_new = np.concatenate((x.value, np.array([0.0]))) - residual = np.sqrt(prob.value) - - -def median_paper_clcpc_python_best(): - """c_vs <= c_vi + c_vr, c_es <= c_ei + c_er with ged computation with - python invoking the c++ code by bash command (with updated library). - """ -# ds = {'name': 'monoterpenoides', -# 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb -# _, y_all = loadDataset(ds['dataset']) - gkernel = 'untilhpathkernel' - node_label = 'atom' - edge_label = 'bond_type' - itr_max = 6 - algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' - params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', - 'algo_options': algo_options, 'stabilizer': None} - - y_all = ['3', '1', '4', '6', '7', '8', '9', '2'] - repeats = 50 - collection_path = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/' - graph_dir = collection_path + 'gxl/' - - fn_edit_costs_output = 'results/median_paper/edit_costs_output.python_init40.k10.txt' - - for y in y_all: - for repeat in range(repeats): - edit_costs_output_file = open(fn_edit_costs_output, 'a') - collection_file = collection_path + 'monoterpenoides_' + y + '_' + str(repeat) + '.xml' - Gn, _ = loadDataset(collection_file, extra_params=graph_dir) - edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \ - nb_cost_mat_list = fit_GED_to_kernel_distance(Gn, node_label, edge_label, - gkernel, itr_max, params_ged=params_ged, - parallel=True) - total_time = np.sum(time_list) -# print('\nedit_costs:', edit_costs) -# print('\nresidual_list:', residual_list) -# print('\nedit_cost_list:', edit_cost_list) -# print('\ndistance matrix in kernel space:', dis_k_mat) -# print('\nged matrix:', ged_mat) -# print('\ntotal time:', total_time) -# print('\nnb_cost_mat:', nb_cost_mat_list[-1]) - np.savez('results/median_paper/fit_distance.clcpc.python_init40.monot.elabeled.uhpkernel.y' - + y + '.repeat' + str(repeat) + '.k10..gm', - edit_costs=edit_costs, - residual_list=residual_list, edit_cost_list=edit_cost_list, - dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, - total_time=total_time, nb_cost_mat_list=nb_cost_mat_list) - - for ec in edit_costs: - edit_costs_output_file.write(str(ec) + ' ') - edit_costs_output_file.write('\n') - edit_costs_output_file.close() - - -# # normalized distance matrices. -# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.monot.elabeled.uhpkernel.gm.npz') -# edit_costs = gmfile['edit_costs'] -# residual_list = gmfile['residual_list'] -# edit_cost_list = gmfile['edit_cost_list'] -# dis_k_mat = gmfile['dis_k_mat'] -# ged_mat = gmfile['ged_mat'] -# total_time = gmfile['total_time'] -# nb_cost_mat_list = gmfile['nb_cost_mat_list'] - - nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat) - print(nb_consistent, nb_inconsistent, ratio_consistent) - -# norm_dis_k_mat = normalize_distance_matrix(dis_k_mat) -# plt.imshow(norm_dis_k_mat) -# plt.colorbar() -# plt.savefig('results/median_paper/norm_dis_k_mat.clcpc.python_best.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300) -# plt.savefig('results/median_paper/norm_dis_k_mat.clcpc.python_best.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.png', format='png') -# # plt.show() -# plt.clf() -# -# norm_ged_mat = normalize_distance_matrix(ged_mat) -# plt.imshow(norm_ged_mat) -# plt.colorbar() -# plt.savefig('results/median_paper/norm_ged_mat.clcpc.python_best.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300) -# plt.savefig('results/median_paper/norm_ged_mat.clcpc.python_best.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.png', format='png') -# # plt.show() -# plt.clf() -# -# norm_diff = norm_ged_mat - norm_dis_k_mat -# plt.imshow(norm_diff) -# plt.colorbar() -# plt.savefig('results/median_paper/diff_mat_norm_ged_dis_k.clcpc.python_best.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300) -# plt.savefig('results/median_paper/diff_mat_norm_ged_dis_k.clcpc.python_best.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.png', format='png') -# # plt.show() -# plt.clf() -# # draw_count_bar(norm_diff) - - -def median_paper_clcpc_python_bash_cpp(): - """c_vs <= c_vi + c_vr, c_es <= c_ei + c_er with ged computation with - python invoking the c++ code by bash command (with updated library). - """ -# ds = {'name': 'monoterpenoides', -# 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb -# _, y_all = loadDataset(ds['dataset']) - gkernel = 'untilhpathkernel' - node_label = 'atom' - edge_label = 'bond_type' - itr_max = 20 - algo_options = '--threads 6 --initial-solutions 10 --ratio-runs-from-initial-solutions .5' - params_ged = {'lib': 'gedlib-bash', 'cost': 'CONSTANT', 'method': 'IPFP', - 'algo_options': algo_options} - - y_all = ['3', '1', '4', '6', '7', '8', '9', '2'] - repeats = 50 - collection_path = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/' - graph_dir = collection_path + 'gxl/' - - fn_edit_costs_output = 'results/median_paper/edit_costs_output.txt' - - for y in y_all: - for repeat in range(repeats): - edit_costs_output_file = open(fn_edit_costs_output, 'a') - collection_file = collection_path + 'monoterpenoides_' + y + '_' + str(repeat) + '.xml' - Gn, _ = loadDataset(collection_file, extra_params=graph_dir) - edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \ - nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label, - gkernel, itr_max, params_ged=params_ged, - parallel=False) - total_time = np.sum(time_list) -# print('\nedit_costs:', edit_costs) -# print('\nresidual_list:', residual_list) -# print('\nedit_cost_list:', edit_cost_list) -# print('\ndistance matrix in kernel space:', dis_k_mat) -# print('\nged matrix:', ged_mat) -# print('\ntotal time:', total_time) -# print('\nnb_cost_mat:', nb_cost_mat_list[-1]) - np.savez('results/median_paper/fit_distance.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y' - + y + '.repeat' + str(repeat) + '.gm', - edit_costs=edit_costs, - residual_list=residual_list, edit_cost_list=edit_cost_list, - dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, - total_time=total_time, nb_cost_mat_list=nb_cost_mat_list, - coef_dk=coef_dk) - - for ec in edit_costs: - edit_costs_output_file.write(str(ec) + ' ') - edit_costs_output_file.write('\n') - edit_costs_output_file.close() - - -# # normalized distance matrices. -# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.monot.elabeled.uhpkernel.gm.npz') -# edit_costs = gmfile['edit_costs'] -# residual_list = gmfile['residual_list'] -# edit_cost_list = gmfile['edit_cost_list'] -# dis_k_mat = gmfile['dis_k_mat'] -# ged_mat = gmfile['ged_mat'] -# total_time = gmfile['total_time'] -# nb_cost_mat_list = gmfile['nb_cost_mat_list'] -# coef_dk = gmfile['coef_dk'] - - nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat) - print(nb_consistent, nb_inconsistent, ratio_consistent) - -# norm_dis_k_mat = normalize_distance_matrix(dis_k_mat) -# plt.imshow(norm_dis_k_mat) -# plt.colorbar() -# plt.savefig('results/median_paper/norm_dis_k_mat.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300) -# plt.savefig('results/median_paper/norm_dis_k_mat.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.png', format='png') -# # plt.show() -# plt.clf() -# -# norm_ged_mat = normalize_distance_matrix(ged_mat) -# plt.imshow(norm_ged_mat) -# plt.colorbar() -# plt.savefig('results/median_paper/norm_ged_mat.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300) -# plt.savefig('results/median_paper/norm_ged_mat.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.png', format='png') -# # plt.show() -# plt.clf() -# -# norm_diff = norm_ged_mat - norm_dis_k_mat -# plt.imshow(norm_diff) -# plt.colorbar() -# plt.savefig('results/median_paper/diff_mat_norm_ged_dis_k.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300) -# plt.savefig('results/median_paper/diff_mat_norm_ged_dis_k.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y' -# + y + '.repeat' + str(repeat) + '.png', format='png') -# # plt.show() -# plt.clf() -# # draw_count_bar(norm_diff) - - - - - -def test_cs_leq_ci_plus_cr_python_bash_cpp(): - """c_vs <= c_vi + c_vr, c_es <= c_ei + c_er with ged computation with - python invoking the c++ code by bash command (with updated library). - """ - ds = {'name': 'monoterpenoides', - 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:10] - gkernel = 'untilhpathkernel' - node_label = 'atom' - edge_label = 'bond_type' - itr_max = 10 - algo_options = '--threads 6 --initial-solutions 10 --ratio-runs-from-initial-solutions .5' - params_ged = {'lib': 'gedlib-bash', 'cost': 'CONSTANT', 'method': 'IPFP', - 'algo_options': algo_options} - edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \ - nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label, - gkernel, itr_max, params_ged=params_ged, - parallel=False) - total_time = np.sum(time_list) - print('\nedit_costs:', edit_costs) - print('\nresidual_list:', residual_list) - print('\nedit_cost_list:', edit_cost_list) - print('\ndistance matrix in kernel space:', dis_k_mat) - print('\nged matrix:', ged_mat) - print('\ntotal time:', total_time) - print('\nnb_cost_mat:', nb_cost_mat_list[-1]) - np.savez('results/fit_distance.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel.gm', - edit_costs=edit_costs, - residual_list=residual_list, edit_cost_list=edit_cost_list, - dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, - total_time=total_time, nb_cost_mat_list=nb_cost_mat_list, - coef_dk=coef_dk) - -# ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', -# 'extra_params': {}} # node/edge symb -# Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -## Gn = Gn[0:10] -## remove_edges(Gn) -# gkernel = 'untilhpathkernel' -# node_label = 'atom' -# edge_label = 'bond_type' -# itr_max = 10 -# edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \ -# nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label, -# gkernel, itr_max) -# total_time = np.sum(time_list) -# print('\nedit_costs:', edit_costs) -# print('\nresidual_list:', residual_list) -# print('\nedit_cost_list:', edit_cost_list) -# print('\ndistance matrix in kernel space:', dis_k_mat) -# print('\nged matrix:', ged_mat) -# print('\ntotal time:', total_time) -# print('\nnb_cost_mat:', nb_cost_mat_list[-1]) -# np.savez('results/fit_distance.cs_leq_ci_plus_cr.mutag.elabeled.uhpkernel.gm', -# edit_costs=edit_costs, -# residual_list=residual_list, edit_cost_list=edit_cost_list, -# dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, -# total_time=total_time, nb_cost_mat_list=nb_cost_mat_list, coef_dk) - - -# # normalized distance matrices. -# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.monot.elabeled.uhpkernel.gm.npz') -# edit_costs = gmfile['edit_costs'] -# residual_list = gmfile['residual_list'] -# edit_cost_list = gmfile['edit_cost_list'] -# dis_k_mat = gmfile['dis_k_mat'] -# ged_mat = gmfile['ged_mat'] -# total_time = gmfile['total_time'] -# nb_cost_mat_list = gmfile['nb_cost_mat_list'] -# coef_dk = gmfile['coef_dk'] - - nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat) - print(nb_consistent, nb_inconsistent, ratio_consistent) - -# dis_k_sub = pairwise_substitution(dis_k_mat) -# ged_sub = pairwise_substitution(ged_mat) -# np.savez('results/sub_dis_mat.cs_leq_ci_plus_cr.gm', -# dis_k_sub=dis_k_sub, ged_sub=ged_sub) - - - norm_dis_k_mat = normalize_distance_matrix(dis_k_mat) - plt.imshow(norm_dis_k_mat) - plt.colorbar() - plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel' - + '.eps', format='eps', dpi=300) - plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel' - + '.png', format='png') -# plt.show() - plt.clf() - - norm_ged_mat = normalize_distance_matrix(ged_mat) - plt.imshow(norm_ged_mat) - plt.colorbar() - plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel' - + '.eps', format='eps', dpi=300) - plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel' - + '.png', format='png') -# plt.show() - plt.clf() - - norm_diff = norm_ged_mat - norm_dis_k_mat - plt.imshow(norm_diff) - plt.colorbar() - plt.savefig('results/diff_mat_norm_ged_dis_k.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel' - + '.eps', format='eps', dpi=300) - plt.savefig('results/diff_mat_norm_ged_dis_k.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel' - + '.png', format='png') -# plt.show() - plt.clf() -# draw_count_bar(norm_diff) - - -def test_anycosts(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:10] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - itr_max = 10 - edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \ - nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, gkernel, itr_max) - total_time = np.sum(time_list) - print('\nedit_costs:', edit_costs) - print('\nresidual_list:', residual_list) - print('\nedit_cost_list:', edit_cost_list) - print('\ndistance matrix in kernel space:', dis_k_mat) - print('\nged matrix:', ged_mat) - print('\ntotal time:', total_time) - print('\nnb_cost_mat:', nb_cost_mat_list[-1]) - np.savez('results/fit_distance.any_costs.gm', edit_costs=edit_costs, - residual_list=residual_list, edit_cost_list=edit_cost_list, - dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, - total_time=total_time, nb_cost_mat_list=nb_cost_mat_list) - -# # normalized distance matrices. -# gmfile = np.load('results/fit_distance.any_costs.gm.npz') -# edit_costs = gmfile['edit_costs'] -# residual_list = gmfile['residual_list'] -# edit_cost_list = gmfile['edit_cost_list'] -# dis_k_mat = gmfile['dis_k_mat'] -# ged_mat = gmfile['ged_mat'] -# total_time = gmfile['total_time'] -## nb_cost_mat_list = gmfile['nb_cost_mat_list'] - - norm_dis_k_mat = normalize_distance_matrix(dis_k_mat) - plt.imshow(norm_dis_k_mat) - plt.colorbar() - plt.savefig('results/norm_dis_k_mat.any_costs' + '.eps', format='eps', dpi=300) -# plt.savefig('results/norm_dis_k_mat.any_costs' + '.png', format='png') -# plt.show() - plt.clf() - - norm_ged_mat = normalize_distance_matrix(ged_mat) - plt.imshow(norm_ged_mat) - plt.colorbar() - plt.savefig('results/norm_ged_mat.any_costs' + '.eps', format='eps', dpi=300) -# plt.savefig('results/norm_ged_mat.any_costs' + '.png', format='png') -# plt.show() - plt.clf() - - norm_diff = norm_ged_mat - norm_dis_k_mat - plt.imshow(norm_diff) - plt.colorbar() - plt.savefig('results/diff_mat_norm_ged_dis_k.any_costs' + '.eps', format='eps', dpi=300) -# plt.savefig('results/diff_mat_norm_ged_dis_k.any_costs' + '.png', format='png') -# plt.show() - plt.clf() -# draw_count_bar(norm_diff) - - -def test_cs_leq_ci_plus_cr(): - """c_vs <= c_vi + c_vr, c_es <= c_ei + c_er - """ - ds = {'name': 'monoterpenoides', - 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:10] - gkernel = 'untilhpathkernel' - node_label = 'atom' - edge_label = 'bond_type' - itr_max = 10 - edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \ - nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label, - gkernel, itr_max, - fitkernel='gaussian') - total_time = np.sum(time_list) - print('\nedit_costs:', edit_costs) - print('\nresidual_list:', residual_list) - print('\nedit_cost_list:', edit_cost_list) - print('\ndistance matrix in kernel space:', dis_k_mat) - print('\nged matrix:', ged_mat) - print('\ntotal time:', total_time) - print('\nnb_cost_mat:', nb_cost_mat_list[-1]) - np.savez('results/fit_distance.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel.gm', - edit_costs=edit_costs, - residual_list=residual_list, edit_cost_list=edit_cost_list, - dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, - total_time=total_time, nb_cost_mat_list=nb_cost_mat_list, - coef_dk=coef_dk) - -# ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', -# 'extra_params': {}} # node/edge symb -# Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -## Gn = Gn[0:10] -## remove_edges(Gn) -# gkernel = 'untilhpathkernel' -# node_label = 'atom' -# edge_label = 'bond_type' -# itr_max = 10 -# edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \ -# nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label, -# gkernel, itr_max) -# total_time = np.sum(time_list) -# print('\nedit_costs:', edit_costs) -# print('\nresidual_list:', residual_list) -# print('\nedit_cost_list:', edit_cost_list) -# print('\ndistance matrix in kernel space:', dis_k_mat) -# print('\nged matrix:', ged_mat) -# print('\ntotal time:', total_time) -# print('\nnb_cost_mat:', nb_cost_mat_list[-1]) -# np.savez('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.mutag.elabeled.uhpkernel.gm', -# edit_costs=edit_costs, -# residual_list=residual_list, edit_cost_list=edit_cost_list, -# dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, -# total_time=total_time, nb_cost_mat_list=nb_cost_mat_list, coef_dk) - - -# # normalized distance matrices. -# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.monot.elabeled.uhpkernel.gm.npz') -# edit_costs = gmfile['edit_costs'] -# residual_list = gmfile['residual_list'] -# edit_cost_list = gmfile['edit_cost_list'] -# dis_k_mat = gmfile['dis_k_mat'] -# ged_mat = gmfile['ged_mat'] -# total_time = gmfile['total_time'] -# nb_cost_mat_list = gmfile['nb_cost_mat_list'] -# coef_dk = gmfile['coef_dk'] - - nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat) - print(nb_consistent, nb_inconsistent, ratio_consistent) - -# dis_k_sub = pairwise_substitution(dis_k_mat) -# ged_sub = pairwise_substitution(ged_mat) -# np.savez('results/sub_dis_mat.cs_leq_ci_plus_cr.cost_leq_1en2.gm', -# dis_k_sub=dis_k_sub, ged_sub=ged_sub) - - - norm_dis_k_mat = normalize_distance_matrix(dis_k_mat) - plt.imshow(norm_dis_k_mat) - plt.colorbar() - plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel' - + '.eps', format='eps', dpi=300) - plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel' - + '.png', format='png') -# plt.show() - plt.clf() - - norm_ged_mat = normalize_distance_matrix(ged_mat) - plt.imshow(norm_ged_mat) - plt.colorbar() - plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel' - + '.eps', format='eps', dpi=300) - plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel' - + '.png', format='png') -# plt.show() - plt.clf() - - norm_diff = norm_ged_mat - norm_dis_k_mat - plt.imshow(norm_diff) - plt.colorbar() - plt.savefig('results/diff_mat_norm_ged_dis_k.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel' - + '.eps', format='eps', dpi=300) - plt.savefig('results/diff_mat_norm_ged_dis_k.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel' - + '.png', format='png') -# plt.show() - plt.clf() -# draw_count_bar(norm_diff) - - -def test_unfitted(): - """unfitted. - """ - from fitDistance import compute_geds - from utils import kernel_distance_matrix - ds = {'name': 'monoterpenoides', - 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:10] - gkernel = 'untilhpathkernel' - node_label = 'atom' - edge_label = 'bond_type' - - -# ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', -# 'extra_params': {}} # node/edge symb -# Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -## Gn = Gn[0:10] -## remove_edges(Gn) -# gkernel = 'marginalizedkernel' - - dis_k_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, gkernel=gkernel) - ged_all, ged_mat, n_edit_operations = compute_geds(Gn, [3, 3, 1, 3, 3, 1], - [0, 1, 2, 3, 4, 5], parallel=True) - print('\ndistance matrix in kernel space:', dis_k_mat) - print('\nged matrix:', ged_mat) -# np.savez('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.gm', edit_costs=edit_costs, -# residual_list=residual_list, edit_cost_list=edit_cost_list, -# dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, -# total_time=total_time, nb_cost_mat_list=nb_cost_mat_list) - - # normalized distance matrices. -# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en3.gm.npz') -# edit_costs = gmfile['edit_costs'] -# residual_list = gmfile['residual_list'] -# edit_cost_list = gmfile['edit_cost_list'] -# dis_k_mat = gmfile['dis_k_mat'] -# ged_mat = gmfile['ged_mat'] -# total_time = gmfile['total_time'] -# nb_cost_mat_list = gmfile['nb_cost_mat_list'] - - nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat) - print(nb_consistent, nb_inconsistent, ratio_consistent) - - norm_dis_k_mat = normalize_distance_matrix(dis_k_mat) - plt.imshow(norm_dis_k_mat) - plt.colorbar() - plt.savefig('results/norm_dis_k_mat.unfitted.MUTAG' + '.eps', format='eps', dpi=300) - plt.savefig('results/norm_dis_k_mat.unfitted.MUTAG' + '.png', format='png') -# plt.show() - plt.clf() - - norm_ged_mat = normalize_distance_matrix(ged_mat) - plt.imshow(norm_ged_mat) - plt.colorbar() - plt.savefig('results/norm_ged_mat.unfitted.MUTAG' + '.eps', format='eps', dpi=300) - plt.savefig('results/norm_ged_mat.unfitted.MUTAG' + '.png', format='png') -# plt.show() - plt.clf() - - norm_diff = norm_ged_mat - norm_dis_k_mat - plt.imshow(norm_diff) - plt.colorbar() - plt.savefig('results/diff_mat_norm_ged_dis_k.unfitted.MUTAG' + '.eps', format='eps', dpi=300) - plt.savefig('results/diff_mat_norm_ged_dis_k.unfitted.MUTAG' + '.png', format='png') -# plt.show() - plt.clf() - draw_count_bar(norm_diff) - - -def pairwise_substitution_consistence(mat1, mat2): - """ - """ - nb_consistent = 0 - nb_inconsistent = 0 - # the matrix is considered symmetric. - upper_tri1 = mat1[np.triu_indices_from(mat1)] - upper_tri2 = mat2[np.tril_indices_from(mat2)] - for i in tqdm(range(len(upper_tri1)), desc='computing consistence', file=sys.stdout): - for j in range(i, len(upper_tri1)): - if np.sign(upper_tri1[i] - upper_tri1[j]) == np.sign(upper_tri2[i] - upper_tri2[j]): - nb_consistent += 1 - else: - nb_inconsistent += 1 - return nb_consistent, nb_inconsistent, nb_consistent / (nb_consistent + nb_inconsistent) - - -def pairwise_substitution(mat): - # the matrix is considered symmetric. - upper_tri = mat[np.triu_indices_from(mat)] - sub_list = [] - for i in tqdm(range(len(upper_tri)), desc='computing', file=sys.stdout): - for j in range(i, len(upper_tri)): - sub_list.append(upper_tri[i] - upper_tri[j]) - return sub_list - - -def draw_count_bar(norm_diff): - import pandas - from collections import Counter, OrderedDict - norm_diff_cnt = norm_diff.flatten() - norm_diff_cnt = norm_diff_cnt * 10 - norm_diff_cnt = np.floor(norm_diff_cnt) - norm_diff_cnt = Counter(norm_diff_cnt) - norm_diff_cnt = OrderedDict(sorted(norm_diff_cnt.items())) - df = pandas.DataFrame.from_dict(norm_diff_cnt, orient='index') - df.plot(kind='bar') - - -if __name__ == '__main__': -# test_anycosts() -# test_cs_leq_ci_plus_cr() -# test_unfitted() - -# test_cs_leq_ci_plus_cr_python_bash_cpp() -# median_paper_clcpc_python_bash_cpp() -# median_paper_clcpc_python_best() - -# x = np.array([[1,2,3],[4,5,6],[7,8,9]]) -# xx = pairwise_substitution(x) - - test_update_costs() \ No newline at end of file diff --git a/gklearn/preimage/test_ged.py b/gklearn/preimage/test_ged.py deleted file mode 100644 index 74e18a0..0000000 --- a/gklearn/preimage/test_ged.py +++ /dev/null @@ -1,520 +0,0 @@ -#export LD_LIBRARY_PATH=.:/export/home/lambertn/Documents/gedlibpy/lib/fann/:/export/home/lambertn/Documents/gedlibpy/lib/libsvm.3.22:/export/home/lambertn/Documents/gedlibpy/lib/nomad - -#Pour que "import script" trouve les librairies qu'a besoin GedLib -#Equivalent à définir la variable d'environnement LD_LIBRARY_PATH sur un bash -#import gedlibpy_linlin.librariesImport -#from gedlibpy_linlin import gedlibpy -from libs import * -import networkx as nx -import numpy as np -from tqdm import tqdm -import sys - - -def test_NON_SYMBOLIC_cost(): - """Test edit cost LETTER2. - """ - from gklearn.preimage.ged import GED, get_nb_edit_operations_nonsymbolic, get_nb_edit_operations_letter - from gklearn.preimage.test_k_closest_graphs import reform_attributes - from gklearn.utils.graphfiles import loadDataset - - dataset = '../../datasets/Letter-high/Letter-high_A.txt' - Gn, y_all = loadDataset(dataset) - - g1 = Gn[200] - g2 = Gn[1780] - reform_attributes(g1) - reform_attributes(g2) - - c_vi = 0.675 - c_vr = 0.675 - c_vs = 0.75 - c_ei = 0.425 - c_er = 0.425 - c_es = 0 - - edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es] - dis, pi_forward, pi_backward = GED(g1, g2, lib='gedlibpy', - cost='NON_SYMBOLIC', method='IPFP', edit_cost_constant=edit_cost_constant, - algo_options='', stabilizer=None) - n_vi, n_vr, sod_vs, n_ei, n_er, sod_es = get_nb_edit_operations_nonsymbolic(g1, g2, - pi_forward, pi_backward) - - print('# of operations:', n_vi, n_vr, sod_vs, n_ei, n_er, sod_es) - print('c_vi, c_vr, c_vs, c_ei, c_er:', c_vi, c_vr, c_vs, c_ei, c_er, c_es) - cost_computed = c_vi * n_vi + c_vr * n_vr + c_vs * sod_vs \ - + c_ei * n_ei + c_er * n_er + c_es * sod_es - print('dis (cost computed by GED):', dis) - print('cost computed by # of operations and edit cost constants:', cost_computed) - - -def test_LETTER2_cost(): - """Test edit cost LETTER2. - """ - from gklearn.preimage.ged import GED, get_nb_edit_operations_letter - from gklearn.preimage.test_k_closest_graphs import reform_attributes - from gklearn.utils.graphfiles import loadDataset - - ds = {'dataset': 'cpp_ext/data/collections/Letter.xml', - 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir']) - - g1 = Gn[200] - g2 = Gn[1780] - reform_attributes(g1) - reform_attributes(g2) - - c_vi = 0.675 - c_vr = 0.675 - c_vs = 0.75 - c_ei = 0.425 - c_er = 0.425 - - edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er] - dis, pi_forward, pi_backward = GED(g1, g2, dataset='letter', lib='gedlibpy', - cost='LETTER2', method='IPFP', edit_cost_constant=edit_cost_constant, - algo_options='', stabilizer=None) - n_vi, n_vr, n_vs, sod_vs, n_ei, n_er = get_nb_edit_operations_letter(g1, g2, - pi_forward, pi_backward) - - print('# of operations:', n_vi, n_vr, n_vs, sod_vs, n_ei, n_er) - print('c_vi, c_vr, c_vs, c_ei, c_er:', c_vi, c_vr, c_vs, c_ei, c_er) - cost_computed = c_vi * n_vi + c_vr * n_vr + c_vs * sod_vs \ - + c_ei * n_ei + c_er * n_er - print('dis (cost computed by GED):', dis) - print('cost computed by # of operations and edit cost constants:', cost_computed) - - - -def test_get_nb_edit_operations_letter(): - """Test whether function preimage.ged.get_nb_edit_operations_letter returns - correct numbers of edit operations. The distance/cost computed by GED - should be the same as the cost computed by number of operations and edit - cost constants. - """ - from gklearn.preimage.ged import GED, get_nb_edit_operations_letter - from gklearn.preimage.test_k_closest_graphs import reform_attributes - from gklearn.utils.graphfiles import loadDataset - - ds = {'dataset': 'cpp_ext/data/collections/Letter.xml', - 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir']) - - g1 = Gn[200] - g2 = Gn[1780] - reform_attributes(g1) - reform_attributes(g2) - - c_vir = 0.9 - c_eir = 1.7 - alpha = 0.75 - - edit_cost_constant = [c_vir, c_eir, alpha] - dis, pi_forward, pi_backward = GED(g1, g2, dataset='letter', lib='gedlibpy', - cost='LETTER', method='IPFP', edit_cost_constant=edit_cost_constant, - algo_options='', stabilizer=None) - n_vi, n_vr, n_vs, c_vs, n_ei, n_er = get_nb_edit_operations_letter(g1, g2, - pi_forward, pi_backward) - - print('# of operations and costs:', n_vi, n_vr, n_vs, c_vs, n_ei, n_er) - print('c_vir, c_eir, alpha:', c_vir, c_eir, alpha) - cost_computed = alpha * c_vir * (n_vi + n_vr) \ - + alpha * c_vs \ - + (1 - alpha) * c_eir * (n_ei + n_er) - print('dis (cost computed by GED):', dis) - print('cost computed by # of operations and edit cost constants:', cost_computed) - - -def test_get_nb_edit_operations(): - """Test whether function preimage.ged.get_nb_edit_operations returns correct - numbers of edit operations. The distance/cost computed by GED should be the - same as the cost computed by number of operations and edit cost constants. - """ - from gklearn.preimage.ged import GED, get_nb_edit_operations - from gklearn.utils.graphfiles import loadDataset - import os - - ds = {'dataset': '../../datasets/monoterpenoides/dataset_10+.ds', - 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset']) - - g1 = Gn[20] - g2 = Gn[108] - - c_vi = 3 - c_vr = 3 - c_vs = 1 - c_ei = 3 - c_er = 3 - c_es = 1 - - edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es] - dis, pi_forward, pi_backward = GED(g1, g2, dataset='monoterpenoides', lib='gedlibpy', - cost='CONSTANT', method='IPFP', edit_cost_constant=edit_cost_constant, - algo_options='', stabilizer=None) - n_vi, n_vr, n_vs, n_ei, n_er, n_es = get_nb_edit_operations(g1, g2, - pi_forward, pi_backward) - - print('# of operations and costs:', n_vi, n_vr, n_vs, n_ei, n_er, n_es) - print('edit costs:', c_vi, c_vr, c_vs, c_ei, c_er, c_es) - cost_computed = n_vi * c_vi + n_vr * c_vr + n_vs * c_vs \ - + n_ei * c_ei + n_er * c_er + n_es * c_es - print('dis (cost computed by GED):', dis) - print('cost computed by # of operations and edit cost constants:', cost_computed) - - -def test_ged_python_bash_cpp(): - """Test ged computation with python invoking the c++ code by bash command (with updated library). - """ - from gklearn.utils.graphfiles import loadDataset - from gklearn.preimage.ged import GED - - data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/' -# collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml' - collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/monoterpenoides_3_20.xml' - graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/' - - Gn, y = loadDataset(collection_file, extra_params=graph_dir) - - algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' - - for repeat in range(0, 3): - # Generate the result file. - ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_bash_' + str(repeat) + '_init40.3_20.txt' -# runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_min_' + str(repeat) + '.txt' - - ged_file = open(ged_filename, 'a') -# runtime_file = open(runtime_filename, 'a') - - ged_mat = np.empty((len(Gn), len(Gn))) -# runtime_mat = np.empty((len(Gn), len(Gn))) - - for i in tqdm(range(len(Gn)), desc='computing GEDs', file=sys.stdout): - for j in range(len(Gn)): - print(i, j) - g1 = Gn[i] - g2 = Gn[j] - upper_bound, _, _ = GED(g1, g2, lib='gedlib-bash', cost='CONSTANT', - method='IPFP', - edit_cost_constant=[3.0, 3.0, 1.0, 3.0, 3.0, 1.0], - algo_options=algo_options) -# runtime = gedlibpy.get_runtime(g1, g2) - ged_mat[i][j] = upper_bound -# runtime_mat[i][j] = runtime - - # Write to files. - ged_file.write(str(int(upper_bound)) + ' ') -# runtime_file.write(str(runtime) + ' ') - - ged_file.write('\n') -# runtime_file.write('\n') - - ged_file.close() -# runtime_file.close() - - print('ged_mat') - print(ged_mat) -# print('runtime_mat:') -# print(runtime_mat) - - return - - - -def test_ged_best_settings_updated(): - """Test ged computation with best settings the same as in the C++ code (with updated library). - """ - - data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/' - collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml' -# collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/monoterpenoides_3_20.xml' - - graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/' - - algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' - - for repeat in range(0, 3): - # Generate the result file. - ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_updated_' + str(repeat) + '_init40.txt' - runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_updated_' + str(repeat) + '_init40.txt' - - gedlibpy.restart_env() - gedlibpy.load_GXL_graphs(graph_dir, collection_file) - listID = gedlibpy.get_all_graph_ids() - gedlibpy.set_edit_cost('CONSTANT', [3.0, 3.0, 1.0, 3.0, 3.0, 1.0]) - gedlibpy.init() - gedlibpy.set_method("IPFP", algo_options) - gedlibpy.init_method() - - ged_mat = np.empty((len(listID), len(listID))) - runtime_mat = np.empty((len(listID), len(listID))) - - for i in tqdm(range(len(listID)), desc='computing GEDs', file=sys.stdout): - ged_file = open(ged_filename, 'a') - runtime_file = open(runtime_filename, 'a') - - for j in range(len(listID)): - g1 = listID[i] - g2 = listID[j] - gedlibpy.run_method(g1, g2) - upper_bound = gedlibpy.get_upper_bound(g1, g2) - runtime = gedlibpy.get_runtime(g1, g2) - ged_mat[i][j] = upper_bound - runtime_mat[i][j] = runtime - - # Write to files. - ged_file.write(str(int(upper_bound)) + ' ') - runtime_file.write(str(runtime) + ' ') - - ged_file.write('\n') - runtime_file.write('\n') - - ged_file.close() - runtime_file.close() - - print('ged_mat') - print(ged_mat) - print('runtime_mat:') - print(runtime_mat) - - return - - -def test_ged_best_settings(): - """Test ged computation with best settings the same as in the C++ code. - """ - - data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/' - collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml' - graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/' - - algo_options = '--threads 6 --initial-solutions 10 --ratio-runs-from-initial-solutions .5' - - for repeat in range(0, 3): - # Generate the result file. - ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_best_settings_' + str(repeat) + '.txt' - runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_best_settings_' + str(repeat) + '.txt' - - ged_file = open(ged_filename, 'a') - runtime_file = open(runtime_filename, 'a') - - gedlibpy.restart_env() - gedlibpy.load_GXL_graphs(graph_dir, collection_file) - listID = gedlibpy.get_all_graph_ids() - gedlibpy.set_edit_cost('CONSTANT', [3.0, 3.0, 1.0, 3.0, 3.0, 1.0]) - gedlibpy.init() - gedlibpy.set_method("IPFP", algo_options) - gedlibpy.init_method() - - ged_mat = np.empty((len(listID), len(listID))) - runtime_mat = np.empty((len(listID), len(listID))) - - for i in tqdm(range(len(listID)), desc='computing GEDs', file=sys.stdout): - for j in range(len(listID)): - g1 = listID[i] - g2 = listID[j] - gedlibpy.run_method(g1, g2) - upper_bound = gedlibpy.get_upper_bound(g1, g2) - runtime = gedlibpy.get_runtime(g1, g2) - ged_mat[i][j] = upper_bound - runtime_mat[i][j] = runtime - - # Write to files. - ged_file.write(str(int(upper_bound)) + ' ') - runtime_file.write(str(runtime) + ' ') - - ged_file.write('\n') - runtime_file.write('\n') - - ged_file.close() - runtime_file.close() - - print('ged_mat') - print(ged_mat) - print('runtime_mat:') - print(runtime_mat) - - return - - - -def test_ged_default(): - """Test ged computation with default settings. - """ - - data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/' - collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml' - graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/' - - for repeat in range(3): - # Generate the result file. - ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_default_' + str(repeat) + '.txt' - runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_default_' + str(repeat) + '.txt' - - ged_file = open(ged_filename, 'a') - runtime_file = open(runtime_filename, 'a') - - gedlibpy.restart_env() - gedlibpy.load_GXL_graphs(graph_dir, collection_file) - listID = gedlibpy.get_all_graph_ids() - gedlibpy.set_edit_cost('CONSTANT', [3.0, 3.0, 1.0, 3.0, 3.0, 1.0]) - gedlibpy.init() - gedlibpy.set_method("IPFP", "") - gedlibpy.init_method() - - ged_mat = np.empty((len(listID), len(listID))) - runtime_mat = np.empty((len(listID), len(listID))) - - for i in tqdm(range(len(listID)), desc='computing GEDs', file=sys.stdout): - for j in range(len(listID)): - g1 = listID[i] - g2 = listID[j] - gedlibpy.run_method(g1, g2) - upper_bound = gedlibpy.get_upper_bound(g1, g2) - runtime = gedlibpy.get_runtime(g1, g2) - ged_mat[i][j] = upper_bound - runtime_mat[i][j] = runtime - - # Write to files. - ged_file.write(str(int(upper_bound)) + ' ') - runtime_file.write(str(runtime) + ' ') - - ged_file.write('\n') - runtime_file.write('\n') - - ged_file.close() - runtime_file.close() - - print('ged_mat') - print(ged_mat) - print('runtime_mat:') - print(runtime_mat) - - return - - -def test_ged_min(): - """Test ged computation with the "min" stabilizer. - """ - from gklearn.utils.graphfiles import loadDataset - from gklearn.preimage.ged import GED - - data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/' - collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml' - graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/' - - Gn, y = loadDataset(collection_file, extra_params=graph_dir) - -# algo_options = '--threads 6 --initial-solutions 10 --ratio-runs-from-initial-solutions .5' - - for repeat in range(0, 3): - # Generate the result file. - ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_min_' + str(repeat) + '.txt' -# runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_min_' + str(repeat) + '.txt' - - ged_file = open(ged_filename, 'a') -# runtime_file = open(runtime_filename, 'a') - - ged_mat = np.empty((len(Gn), len(Gn))) -# runtime_mat = np.empty((len(Gn), len(Gn))) - - for i in tqdm(range(len(Gn)), desc='computing GEDs', file=sys.stdout): - for j in range(len(Gn)): - g1 = Gn[i] - g2 = Gn[j] - upper_bound, _, _ = GED(g1, g2, lib='gedlibpy', cost='CONSTANT', - method='IPFP', - edit_cost_constant=[3.0, 3.0, 1.0, 3.0, 3.0, 1.0], - stabilizer='min', repeat=10) -# runtime = gedlibpy.get_runtime(g1, g2) - ged_mat[i][j] = upper_bound -# runtime_mat[i][j] = runtime - - # Write to files. - ged_file.write(str(int(upper_bound)) + ' ') -# runtime_file.write(str(runtime) + ' ') - - ged_file.write('\n') -# runtime_file.write('\n') - - ged_file.close() -# runtime_file.close() - - print('ged_mat') - print(ged_mat) -# print('runtime_mat:') -# print(runtime_mat) - - return - - -def init() : - print("List of Edit Cost Options : ") - for i in gedlibpy.list_of_edit_cost_options : - print (i) - print("") - - print("List of Method Options : ") - for j in gedlibpy.list_of_method_options : - print (j) - print("") - - print("List of Init Options : ") - for k in gedlibpy.list_of_init_options : - print (k) - print("") - - - - -def convertGraph(G): - G_new = nx.Graph() - for nd, attrs in G.nodes(data=True): - G_new.add_node(str(nd), chem=attrs['atom']) - for nd1, nd2, attrs in G.edges(data=True): - G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type']) - - return G_new - - -def testNxGrapĥ(): - from gklearn.utils.graphfiles import loadDataset - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) - - gedlibpy.restart_env() - for graph in Gn: - g_new = convertGraph(graph) - gedlibpy.add_nx_graph(g_new, "") - - listID = gedlibpy.get_all_graph_ids() - gedlibpy.set_edit_cost("CHEM_1") - gedlibpy.init() - gedlibpy.set_method("IPFP", "") - gedlibpy.init_method() - - print(listID) - g = listID[0] - h = listID[1] - - gedlibpy.run_method(g, h) - - print("Node Map : ", gedlibpy.get_node_map(g, h)) - print("Forward map : " , gedlibpy.get_forward_map(g, h), ", Backward map : ", gedlibpy.get_backward_map(g, h)) - print ("Upper Bound = " + str(gedlibpy.get_upper_bound(g, h)) + ", Lower Bound = " + str(gedlibpy.get_lower_bound(g, h)) + ", Runtime = " + str(gedlibpy.get_runtime(g, h))) - -if __name__ == '__main__': -# test_ged_default() -# test_ged_min() -# test_ged_best_settings() -# test_ged_best_settings_updated() -# test_ged_python_bash_cpp() -# test_get_nb_edit_operations() -# test_get_nb_edit_operations_letter() -# test_LETTER2_cost() - test_NON_SYMBOLIC_cost() - - - #init() - #testNxGrapĥ() diff --git a/gklearn/preimage/test_iam.py b/gklearn/preimage/test_iam.py deleted file mode 100644 index 5897f50..0000000 --- a/gklearn/preimage/test_iam.py +++ /dev/null @@ -1,964 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Sep 5 15:59:00 2019 - -@author: ljia -""" - -import numpy as np -import networkx as nx -import matplotlib.pyplot as plt -import time -import random -#from tqdm import tqdm - -from gklearn.utils.graphfiles import loadDataset -#from gklearn.utils.logger2file import * -from gklearn.preimage.iam import iam_upgraded -from gklearn.preimage.utils import remove_edges, compute_kernel, get_same_item_indices, dis_gstar -#from gklearn.preimage.ged import ged_median - - -def test_iam_monoterpenoides_with_init40(): - gkernel = 'untilhpathkernel' - node_label = 'atom' - edge_label = 'bond_type' - # unfitted edit costs. - c_vi = 3 - c_vr = 3 - c_vs = 1 - c_ei = 3 - c_er = 3 - c_es = 1 - ite_max_iam = 50 - epsilon_iam = 0.0001 - removeNodes = False - connected_iam = False - # parameters for IAM function -# ged_cost = 'CONSTANT' - ged_cost = 'CONSTANT' - ged_method = 'IPFP' - edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es] - ged_stabilizer = None -# ged_repeat = 50 - algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' - params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method, - 'edit_cost_constant': edit_cost_constant, - 'algo_options': algo_options, - 'stabilizer': ged_stabilizer} - - - collection_path = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/' - graph_dir = collection_path + 'gxl/' - y_all = ['3', '1', '4', '6', '7', '8', '9', '2'] - repeats = 50 - - # classify graphs according to classes. - time_list = [] - dis_ks_min_list = [] - dis_ks_set_median_list = [] - sod_gs_list = [] - g_best = [] - sod_set_median_list = [] - sod_list_list = [] - for y in y_all: - print('\n-------------------------------------------------------') - print('class of y:', y) - - time_list.append([]) - dis_ks_min_list.append([]) - dis_ks_set_median_list.append([]) - sod_gs_list.append([]) - g_best.append([]) - sod_set_median_list.append([]) - - for repeat in range(repeats): - # load median set. - collection_file = collection_path + 'monoterpenoides_' + y + '_' + str(repeat) + '.xml' - Gn_median, _ = loadDataset(collection_file, extra_params=graph_dir) - Gn_candidate = [g.copy() for g in Gn_median] - - time0 = time.time() - G_gen_median_list, sod_gen_median, sod_list, G_set_median_list, sod_set_median \ - = iam_upgraded(Gn_median, - Gn_candidate, c_ei=c_ei, c_er=c_er, c_es=c_es, ite_max=ite_max_iam, - epsilon=epsilon_iam, node_label=node_label, edge_label=edge_label, - connected=connected_iam, removeNodes=removeNodes, - params_ged=params_ged) - time_total = time.time() - time0 - print('\ntime: ', time_total) - time_list[-1].append(time_total) - g_best[-1].append(G_gen_median_list[0]) - sod_set_median_list[-1].append(sod_set_median) - print('\nsmallest sod of the set median:', sod_set_median) - sod_gs_list[-1].append(sod_gen_median) - print('\nsmallest sod in graph space:', sod_gen_median) - sod_list_list.append(sod_list) - -# # show the best graph and save it to file. -# print('one of the possible corresponding pre-images is') -# nx.draw(G_gen_median_list[0], labels=nx.get_node_attributes(G_gen_median_list[0], 'atom'), -# with_labels=True) -## plt.show() -# # plt.savefig('results/iam/mutag_median.fit_costs2.001.nb' + str(nb_median) + -## plt.savefig('results/iam/paper_compare/monoter_y' + str(y_class) + -## '_repeat' + str(repeat) + '_' + str(time.time()) + -## '.png', format="PNG") -# plt.clf() -# # print(G_gen_median_list[0].nodes(data=True)) -# # print(G_gen_median_list[0].edges(data=True)) - - print('\nsods of the set median for this class:', sod_set_median_list[-1]) - print('\nsods in graph space for this class:', sod_gs_list[-1]) -# print('\ndistance in kernel space of set median for this class:', -# dis_ks_set_median_list[-1]) -# print('\nsmallest distances in kernel space for this class:', -# dis_ks_min_list[-1]) - print('\ntimes for this class:', time_list[-1]) - - sod_set_median_list[-1] = np.mean(sod_set_median_list[-1]) - sod_gs_list[-1] = np.mean(sod_gs_list[-1]) -# dis_ks_set_median_list[-1] = np.mean(dis_ks_set_median_list[-1]) -# dis_ks_min_list[-1] = np.mean(dis_ks_min_list[-1]) - time_list[-1] = np.mean(time_list[-1]) - - print() - print('\nmean sods of the set median for each class:', sod_set_median_list) - print('\nmean sods in graph space for each class:', sod_gs_list) -# print('\ndistances in kernel space of set median for each class:', -# dis_ks_set_median_list) -# print('\nmean smallest distances in kernel space for each class:', -# dis_ks_min_list) - print('\nmean times for each class:', time_list) - - print('\nmean sods of the set median of all:', np.mean(sod_set_median_list)) - print('\nmean sods in graph space of all:', np.mean(sod_gs_list)) -# print('\nmean distances in kernel space of set median of all:', -# np.mean(dis_ks_set_median_list)) -# print('\nmean smallest distances in kernel space of all:', -# np.mean(dis_ks_min_list)) - print('\nmean times of all:', np.mean(time_list)) - - - - -def test_iam_monoterpenoides(): - ds = {'name': 'monoterpenoides', - 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:50] - gkernel = 'untilhpathkernel' - node_label = 'atom' - edge_label = 'bond_type' - - # parameters for GED function from the IAM paper. - # fitted edit costs (Gaussian). - c_vi = 0.03620133402089074 - c_vr = 0.0417574590207099 - c_vs = 0.009992282328587499 - c_ei = 0.08293120042342755 - c_er = 0.09512220476358019 - c_es = 0.09222529696841467 -# # fitted edit costs (linear combinations). -# c_vi = 0.1749684054238749 -# c_vr = 0.0734054228711457 -# c_vs = 0.05017781726016715 -# c_ei = 0.1869431164806936 -# c_er = 0.32055856948274 -# c_es = 0.2569469379247611 -# # unfitted edit costs. -# c_vi = 3 -# c_vr = 3 -# c_vs = 1 -# c_ei = 3 -# c_er = 3 -# c_es = 1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = False - connected_iam = False - # parameters for IAM function -# ged_cost = 'CONSTANT' - ged_cost = 'CONSTANT' - ged_method = 'IPFP' - edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es] -# edit_cost_constant = [] - ged_stabilizer = 'min' - ged_repeat = 50 - params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method, - 'edit_cost_constant': edit_cost_constant, - 'stabilizer': ged_stabilizer, 'repeat': ged_repeat} - - # classify graphs according to letters. - time_list = [] - dis_ks_min_list = [] - dis_ks_set_median_list = [] - sod_gs_list = [] - g_best = [] - sod_set_median_list = [] - sod_list_list = [] - idx_dict = get_same_item_indices(y_all) - for y_class in idx_dict: - print('\n-------------------------------------------------------') - print('class of y:', y_class) - Gn_class = [Gn[i].copy() for i in idx_dict[y_class]] - - time_list.append([]) - dis_ks_min_list.append([]) - dis_ks_set_median_list.append([]) - sod_gs_list.append([]) - g_best.append([]) - sod_set_median_list.append([]) - - for repeat in range(50): - idx_rdm = random.sample(range(len(Gn_class)), 10) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn_class[idx].copy() for idx in idx_rdm] - Gn_candidate = [g.copy() for g in Gn_median] - - alpha_range = [1 / len(Gn_median)] * len(Gn_median) - time0 = time.time() - G_gen_median_list, sod_gen_median, sod_list, G_set_median_list, sod_set_median \ - = iam_upgraded(Gn_median, - Gn_candidate, c_ei=c_ei, c_er=c_er, c_es=c_es, ite_max=ite_max_iam, - epsilon=epsilon_iam, connected=connected_iam, removeNodes=removeNodes, - params_ged=params_ged) - time_total = time.time() - time0 - print('\ntime: ', time_total) - time_list[-1].append(time_total) - g_best[-1].append(G_gen_median_list[0]) - sod_set_median_list[-1].append(sod_set_median) - print('\nsmallest sod of the set median:', sod_set_median) - sod_gs_list[-1].append(sod_gen_median) - print('\nsmallest sod in graph space:', sod_gen_median) - sod_list_list.append(sod_list) - - # show the best graph and save it to file. - print('one of the possible corresponding pre-images is') - nx.draw(G_gen_median_list[0], labels=nx.get_node_attributes(G_gen_median_list[0], 'atom'), - with_labels=True) -# plt.show() - # plt.savefig('results/iam/mutag_median.fit_costs2.001.nb' + str(nb_median) + -# plt.savefig('results/iam/paper_compare/monoter_y' + str(y_class) + -# '_repeat' + str(repeat) + '_' + str(time.time()) + -# '.png', format="PNG") - plt.clf() - # print(G_gen_median_list[0].nodes(data=True)) - # print(G_gen_median_list[0].edges(data=True)) - - - # compute distance between \psi and the set median graph. - knew_set_median = compute_kernel(G_set_median_list + Gn_median, - gkernel, node_label, edge_label, False) - dhat_new_set_median_list = [] - for idx, g_tmp in enumerate(G_set_median_list): - # @todo: the term3 below could use the one at the beginning of the function. - dhat_new_set_median_list.append(dis_gstar(idx, range(len(G_set_median_list), - len(G_set_median_list) + len(Gn_median) + 1), - alpha_range, knew_set_median, withterm3=False)) - - print('\ndistance in kernel space of set median: ', dhat_new_set_median_list[0]) - dis_ks_set_median_list[-1].append(dhat_new_set_median_list[0]) - - - # compute distance between \psi and the new generated graphs. - knew = compute_kernel(G_gen_median_list + Gn_median, gkernel, node_label, - edge_label, False) - dhat_new_list = [] - for idx, g_tmp in enumerate(G_gen_median_list): - # @todo: the term3 below could use the one at the beginning of the function. - dhat_new_list.append(dis_gstar(idx, range(len(G_gen_median_list), - len(G_gen_median_list) + len(Gn_median) + 1), - alpha_range, knew, withterm3=False)) - - print('\nsmallest distance in kernel space: ', dhat_new_list[0]) - dis_ks_min_list[-1].append(dhat_new_list[0]) - - - print('\nsods of the set median for this class:', sod_set_median_list[-1]) - print('\nsods in graph space for this class:', sod_gs_list[-1]) - print('\ndistance in kernel space of set median for this class:', - dis_ks_set_median_list[-1]) - print('\nsmallest distances in kernel space for this class:', - dis_ks_min_list[-1]) - print('\ntimes for this class:', time_list[-1]) - - sod_set_median_list[-1] = np.mean(sod_set_median_list[-1]) - sod_gs_list[-1] = np.mean(sod_gs_list[-1]) - dis_ks_set_median_list[-1] = np.mean(dis_ks_set_median_list[-1]) - dis_ks_min_list[-1] = np.mean(dis_ks_min_list[-1]) - time_list[-1] = np.mean(time_list[-1]) - - print() - print('\nmean sods of the set median for each class:', sod_set_median_list) - print('\nmean sods in graph space for each class:', sod_gs_list) - print('\ndistances in kernel space of set median for each class:', - dis_ks_set_median_list) - print('\nmean smallest distances in kernel space for each class:', - dis_ks_min_list) - print('\nmean times for each class:', time_list) - - print('\nmean sods of the set median of all:', np.mean(sod_set_median_list)) - print('\nmean sods in graph space of all:', np.mean(sod_gs_list)) - print('\nmean distances in kernel space of set median of all:', - np.mean(dis_ks_set_median_list)) - print('\nmean smallest distances in kernel space of all:', - np.mean(dis_ks_min_list)) - print('\nmean times of all:', np.mean(time_list)) - - nb_better_sods = 0 - nb_worse_sods = 0 - nb_same_sods = 0 - for sods in sod_list_list: - if sods[0] > sods[-1]: - nb_better_sods += 1 - elif sods[0] < sods[-1]: - nb_worse_sods += 1 - else: - nb_same_sods += 1 - print('\n In', str(len(sod_list_list)), 'sod lists,', str(nb_better_sods), - 'are getting better,', str(nb_worse_sods), 'are getting worse,', - str(nb_same_sods), 'are not changed; ', str(nb_better_sods / len(sod_list_list)), - 'sods are improved.') - - -def test_iam_mutag(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - gkernel = 'untilhpathkernel' - node_label = 'atom' - edge_label = 'bond_type' - - # parameters for GED function from the IAM paper. - # fitted edit costs. - c_vi = 0.03523843108436513 - c_vr = 0.03347339739350128 - c_vs = 0.06871290673612238 - c_ei = 0.08591999846720685 - c_er = 0.07962086440894103 - c_es = 0.08596855855478233 - # unfitted edit costs. -# c_vi = 3 -# c_vr = 3 -# c_vs = 1 -# c_ei = 3 -# c_er = 3 -# c_es = 1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = False - connected_iam = False - # parameters for IAM function -# ged_cost = 'CONSTANT' - ged_cost = 'CONSTANT' - ged_method = 'IPFP' - edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es] -# edit_cost_constant = [] - ged_stabilizer = 'min' - ged_repeat = 50 - params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method, - 'edit_cost_constant': edit_cost_constant, - 'stabilizer': ged_stabilizer, 'repeat': ged_repeat} - - # classify graphs according to letters. - time_list = [] - dis_ks_min_list = [] - dis_ks_set_median_list = [] - sod_gs_list = [] - g_best = [] - sod_set_median_list = [] - sod_list_list = [] - idx_dict = get_same_item_indices(y_all) - for y_class in idx_dict: - print('\n-------------------------------------------------------') - print('class of y:', y_class) - Gn_class = [Gn[i].copy() for i in idx_dict[y_class]] - - time_list.append([]) - dis_ks_min_list.append([]) - dis_ks_set_median_list.append([]) - sod_gs_list.append([]) - g_best.append([]) - sod_set_median_list.append([]) - - for repeat in range(50): - idx_rdm = random.sample(range(len(Gn_class)), 10) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn_class[idx].copy() for idx in idx_rdm] - Gn_candidate = [g.copy() for g in Gn_median] - - alpha_range = [1 / len(Gn_median)] * len(Gn_median) - time0 = time.time() - G_gen_median_list, sod_gen_median, sod_list, G_set_median_list, sod_set_median \ - = iam_upgraded(Gn_median, - Gn_candidate, c_ei=c_ei, c_er=c_er, c_es=c_es, ite_max=ite_max_iam, - epsilon=epsilon_iam, connected=connected_iam, removeNodes=removeNodes, - params_ged=params_ged) - time_total = time.time() - time0 - print('\ntime: ', time_total) - time_list[-1].append(time_total) - g_best[-1].append(G_gen_median_list[0]) - sod_set_median_list[-1].append(sod_set_median) - print('\nsmallest sod of the set median:', sod_set_median) - sod_gs_list[-1].append(sod_gen_median) - print('\nsmallest sod in graph space:', sod_gen_median) - sod_list_list.append(sod_list) - - # show the best graph and save it to file. - print('one of the possible corresponding pre-images is') - nx.draw(G_gen_median_list[0], labels=nx.get_node_attributes(G_gen_median_list[0], 'atom'), - with_labels=True) -# plt.show() - # plt.savefig('results/iam/mutag_median.fit_costs2.001.nb' + str(nb_median) + -# plt.savefig('results/iam/paper_compare/mutag_y' + str(y_class) + -# '_repeat' + str(repeat) + '_' + str(time.time()) + -# '.png', format="PNG") - plt.clf() - # print(G_gen_median_list[0].nodes(data=True)) - # print(G_gen_median_list[0].edges(data=True)) - - - # compute distance between \psi and the set median graph. - knew_set_median = compute_kernel(G_set_median_list + Gn_median, - gkernel, node_label, edge_label, False) - dhat_new_set_median_list = [] - for idx, g_tmp in enumerate(G_set_median_list): - # @todo: the term3 below could use the one at the beginning of the function. - dhat_new_set_median_list.append(dis_gstar(idx, range(len(G_set_median_list), - len(G_set_median_list) + len(Gn_median) + 1), - alpha_range, knew_set_median, withterm3=False)) - - print('\ndistance in kernel space of set median: ', dhat_new_set_median_list[0]) - dis_ks_set_median_list[-1].append(dhat_new_set_median_list[0]) - - - # compute distance between \psi and the new generated graphs. - knew = compute_kernel(G_gen_median_list + Gn_median, gkernel, node_label, - edge_label, False) - dhat_new_list = [] - for idx, g_tmp in enumerate(G_gen_median_list): - # @todo: the term3 below could use the one at the beginning of the function. - dhat_new_list.append(dis_gstar(idx, range(len(G_gen_median_list), - len(G_gen_median_list) + len(Gn_median) + 1), - alpha_range, knew, withterm3=False)) - - print('\nsmallest distance in kernel space: ', dhat_new_list[0]) - dis_ks_min_list[-1].append(dhat_new_list[0]) - - - print('\nsods of the set median for this class:', sod_set_median_list[-1]) - print('\nsods in graph space for this class:', sod_gs_list[-1]) - print('\ndistance in kernel space of set median for this class:', - dis_ks_set_median_list[-1]) - print('\nsmallest distances in kernel space for this class:', - dis_ks_min_list[-1]) - print('\ntimes for this class:', time_list[-1]) - - sod_set_median_list[-1] = np.mean(sod_set_median_list[-1]) - sod_gs_list[-1] = np.mean(sod_gs_list[-1]) - dis_ks_set_median_list[-1] = np.mean(dis_ks_set_median_list[-1]) - dis_ks_min_list[-1] = np.mean(dis_ks_min_list[-1]) - time_list[-1] = np.mean(time_list[-1]) - - print() - print('\nmean sods of the set median for each class:', sod_set_median_list) - print('\nmean sods in graph space for each class:', sod_gs_list) - print('\ndistances in kernel space of set median for each class:', - dis_ks_set_median_list) - print('\nmean smallest distances in kernel space for each class:', - dis_ks_min_list) - print('\nmean times for each class:', time_list) - - print('\nmean sods of the set median of all:', np.mean(sod_set_median_list)) - print('\nmean sods in graph space of all:', np.mean(sod_gs_list)) - print('\nmean distances in kernel space of set median of all:', - np.mean(dis_ks_set_median_list)) - print('\nmean smallest distances in kernel space of all:', - np.mean(dis_ks_min_list)) - print('\nmean times of all:', np.mean(time_list)) - - nb_better_sods = 0 - nb_worse_sods = 0 - nb_same_sods = 0 - for sods in sod_list_list: - if sods[0] > sods[-1]: - nb_better_sods += 1 - elif sods[0] < sods[-1]: - nb_worse_sods += 1 - else: - nb_same_sods += 1 - print('\n In', str(len(sod_list_list)), 'sod lists,', str(nb_better_sods), - 'are getting better,', str(nb_worse_sods), 'are getting worse,', - str(nb_same_sods), 'are not changed; ', str(nb_better_sods / len(sod_list_list)), - 'sods are improved.') - - -############################################################################### -# tests on different numbers of median-sets. - -def test_iam_median_nb(): - - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility -# # parameters for GED function -# c_vi = 0.037 -# c_vr = 0.038 -# c_vs = 0.075 -# c_ei = 0.001 -# c_er = 0.001 -# c_es = 0.0 -# ite_max_iam = 50 -# epsilon_iam = 0.001 -# removeNodes = False -# connected_iam = False -# # parameters for IAM function -# ged_cost = 'CONSTANT' -# ged_method = 'IPFP' -# edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es] -# ged_stabilizer = 'min' -# ged_repeat = 50 -# params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method, -# 'edit_cost_constant': edit_cost_constant, -# 'stabilizer': ged_stabilizer, 'repeat': ged_repeat} - - # parameters for GED function - c_vi = 4 - c_vr = 4 - c_vs = 2 - c_ei = 1 - c_er = 1 - c_es = 1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = False - connected_iam = False - # parameters for IAM function - ged_cost = 'CHEM_1' - ged_method = 'IPFP' - edit_cost_constant = [] - ged_stabilizer = 'min' - ged_repeat = 50 - params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method, - 'edit_cost_constant': edit_cost_constant, - 'stabilizer': ged_stabilizer, 'repeat': ged_repeat} - - # find out all the graphs classified to positive group 1. - idx_dict = get_same_item_indices(y_all) - Gn = [Gn[i] for i in idx_dict[1]] - - # number of graphs; we what to compute the median of these graphs. -# nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100] - nb_median_range = [len(Gn)] - -# # compute Gram matrix. -# time0 = time.time() -# km = compute_kernel(Gn, gkernel, True) -# time_km = time.time() - time0 -# # write Gram matrix to file. -# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km) - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] -# sod_gs_min_list = [] -# nb_updated_list = [] -# nb_updated_k_list = [] - g_best = [] - for nb_median in nb_median_range: - print('\n-------------------------------------------------------') - print('number of median graphs =', nb_median) - random.seed(1) - idx_rdm = random.sample(range(len(Gn)), nb_median) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn[idx].copy() for idx in idx_rdm] - Gn_candidate = [g.copy() for g in Gn] - -# for g in Gn_median: -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -## plt.savefig("results/preimage_mix/mutag.png", format="PNG") -# plt.show() -# plt.clf() - - ################################################################### -# gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz') -# km_tmp = gmfile['gm'] -# time_km = gmfile['gmtime'] -# # modify mixed gram matrix. -# km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median)) -# for i in range(len(Gn)): -# for j in range(i, len(Gn)): -# km[i, j] = km_tmp[i, j] -# km[j, i] = km[i, j] -# for i in range(len(Gn)): -# for j, idx in enumerate(idx_rdm): -# km[i, len(Gn) + j] = km[i, idx] -# km[len(Gn) + j, i] = km[i, idx] -# for i, idx1 in enumerate(idx_rdm): -# for j, idx2 in enumerate(idx_rdm): -# km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2] - - ################################################################### - alpha_range = [1 / nb_median] * nb_median - time0 = time.time() - ghat_new_list, sod_min = iam_upgraded(Gn_median, Gn_candidate, - c_ei=c_ei, c_er=c_er, c_es=c_es, ite_max=ite_max_iam, - epsilon=epsilon_iam, connected=connected_iam, removeNodes=removeNodes, - params_ged=params_ged) - - time_total = time.time() - time0 - print('\ntime: ', time_total) - time_list.append(time_total) - - # compute distance between \psi and the new generated graphs. - knew = compute_kernel(ghat_new_list + Gn_median, gkernel, False) - dhat_new_list = [] - for idx, g_tmp in enumerate(ghat_new_list): - # @todo: the term3 below could use the one at the beginning of the function. - dhat_new_list.append(dis_gstar(idx, range(len(ghat_new_list), - len(ghat_new_list) + len(Gn_median) + 1), - alpha_range, knew, withterm3=False)) - - print('\nsmallest distance in kernel space: ', dhat_new_list[0]) - dis_ks_min_list.append(dhat_new_list[0]) - g_best.append(ghat_new_list[0]) - - # show the best graph and save it to file. -# print('the shortest distance is', dhat) - print('one of the possible corresponding pre-images is') - nx.draw(ghat_new_list[0], labels=nx.get_node_attributes(ghat_new_list[0], 'atom'), - with_labels=True) - plt.show() -# plt.savefig('results/iam/mutag_median.fit_costs2.001.nb' + str(nb_median) + - plt.savefig('results/iam/mutag_median_unfit2.nb' + str(nb_median) + - '.png', format="PNG") - plt.clf() -# print(ghat_list[0].nodes(data=True)) -# print(ghat_list[0].edges(data=True)) - - sod_gs_list.append(sod_min) -# sod_gs_min_list.append(np.min(sod_min)) - print('\nsmallest sod in graph space: ', sod_min) - - print('\nsods in graph space: ', sod_gs_list) -# print('\nsmallest sod in graph space for each set of median graphs: ', sod_gs_min_list) - print('\nsmallest distance in kernel space for each set of median graphs: ', - dis_ks_min_list) -# print('\nnumber of updates of the best graph for each set of median graphs by IAM: ', -# nb_updated_list) -# print('\nnumber of updates of k nearest graphs for each set of median graphs by IAM: ', -# nb_updated_k_list) - print('\ntimes:', time_list) - - -def test_iam_letter_h(): - from median import draw_Letter_graph - ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt', - 'extra_params': {}} # node nsymb -# ds = {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt', -# 'extra_params': {}} # node nsymb -# Gn = Gn[0:50] - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) - gkernel = 'structuralspkernel' - - # parameters for GED function from the IAM paper. - c_vi = 3 - c_vr = 3 - c_vs = 1 - c_ei = 3 - c_er = 3 - c_es = 1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = False - connected_iam = False - # parameters for IAM function -# ged_cost = 'CONSTANT' - ged_cost = 'LETTER' - ged_method = 'IPFP' -# edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es] - edit_cost_constant = [] - ged_stabilizer = 'min' - ged_repeat = 50 - params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method, - 'edit_cost_constant': edit_cost_constant, - 'stabilizer': ged_stabilizer, 'repeat': ged_repeat} - - # classify graphs according to letters. - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - g_best = [] - sod_set_median_list = [] - idx_dict = get_same_item_indices(y_all) - for letter in idx_dict: - print('\n-------------------------------------------------------') - print('letter', letter) - Gn_let = [Gn[i].copy() for i in idx_dict[letter]] - - time_list.append([]) - dis_ks_min_list.append([]) - sod_gs_list.append([]) - g_best.append([]) - sod_set_median_list.append([]) - - for repeat in range(50): - idx_rdm = random.sample(range(len(Gn_let)), 50) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn_let[idx].copy() for idx in idx_rdm] - Gn_candidate = [g.copy() for g in Gn_median] - - alpha_range = [1 / len(Gn_median)] * len(Gn_median) - time0 = time.time() - ghat_new_list, sod_min, sod_set_median = iam_upgraded(Gn_median, - Gn_candidate, c_ei=c_ei, c_er=c_er, c_es=c_es, ite_max=ite_max_iam, - epsilon=epsilon_iam, connected=connected_iam, removeNodes=removeNodes, - params_ged=params_ged) - time_total = time.time() - time0 - print('\ntime: ', time_total) - time_list[-1].append(time_total) - g_best[-1].append(ghat_new_list[0]) - sod_set_median_list[-1].append(sod_set_median) - print('\nsmallest sod of the set median:', sod_set_median) - sod_gs_list[-1].append(sod_min) - print('\nsmallest sod in graph space:', sod_min) - - # show the best graph and save it to file. - print('one of the possible corresponding pre-images is') - draw_Letter_graph(ghat_new_list[0], savepath='results/iam/paper_compare/') - - # compute distance between \psi and the new generated graphs. - knew = compute_kernel(ghat_new_list + Gn_median, gkernel, False) - dhat_new_list = [] - for idx, g_tmp in enumerate(ghat_new_list): - # @todo: the term3 below could use the one at the beginning of the function. - dhat_new_list.append(dis_gstar(idx, range(len(ghat_new_list), - len(ghat_new_list) + len(Gn_median) + 1), - alpha_range, knew, withterm3=False)) - - print('\nsmallest distance in kernel space: ', dhat_new_list[0]) - dis_ks_min_list[-1].append(dhat_new_list[0]) - - print('\nsods of the set median for this letter:', sod_set_median_list[-1]) - print('\nsods in graph space for this letter:', sod_gs_list[-1]) - print('\nsmallest distances in kernel space for this letter:', - dis_ks_min_list[-1]) - print('\ntimes for this letter:', time_list[-1]) - - sod_set_median_list[-1] = np.mean(sod_set_median_list[-1]) - sod_gs_list[-1] = np.mean(sod_gs_list[-1]) - dis_ks_min_list[-1] = np.mean(dis_ks_min_list[-1]) - time_list[-1] = np.mean(time_list[-1]) - - print('\nmean sods of the set median for each letter:', sod_set_median_list) - print('\nmean sods in graph space for each letter:', sod_gs_list) - print('\nmean smallest distances in kernel space for each letter:', - dis_ks_min_list) - print('\nmean times for each letter:', time_list) - - print('\nmean sods of the set median of all:', np.mean(sod_set_median_list)) - print('\nmean sods in graph space of all:', np.mean(sod_gs_list)) - print('\nmean smallest distances in kernel space of all:', - np.mean(dis_ks_min_list)) - print('\nmean times of all:', np.mean(time_list)) - - - - - - - - - -def test_iam_fitdistance(): - - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] -# remove_edges(Gn) - gkernel = 'marginalizedkernel' - node_label = 'atom' - edge_label = 'bond_type' - -# lmbda = 0.03 # termination probalility -# # parameters for GED function -# c_vi = 0.037 -# c_vr = 0.038 -# c_vs = 0.075 -# c_ei = 0.001 -# c_er = 0.001 -# c_es = 0.0 -# ite_max_iam = 50 -# epsilon_iam = 0.001 -# removeNodes = False -# connected_iam = False -# # parameters for IAM function -# ged_cost = 'CONSTANT' -# ged_method = 'IPFP' -# edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es] -# ged_stabilizer = 'min' -# ged_repeat = 50 -# params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method, -# 'edit_cost_constant': edit_cost_constant, -# 'stabilizer': ged_stabilizer, 'repeat': ged_repeat} - - # parameters for GED function - c_vi = 4 - c_vr = 4 - c_vs = 2 - c_ei = 1 - c_er = 1 - c_es = 1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = False - connected_iam = False - # parameters for IAM function - ged_cost = 'CHEM_1' - ged_method = 'IPFP' - edit_cost_constant = [] - ged_stabilizer = 'min' - ged_repeat = 50 - params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method, - 'edit_cost_constant': edit_cost_constant, - 'stabilizer': ged_stabilizer, 'repeat': ged_repeat} - - # find out all the graphs classified to positive group 1. - idx_dict = get_same_item_indices(y_all) - Gn = [Gn[i] for i in idx_dict[1]] - - # number of graphs; we what to compute the median of these graphs. -# nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100] - nb_median_range = [10] - -# # compute Gram matrix. -# time0 = time.time() -# km = compute_kernel(Gn, gkernel, True) -# time_km = time.time() - time0 -# # write Gram matrix to file. -# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km) - - time_list = [] - dis_ks_min_list = [] - dis_ks_gen_median_list = [] - sod_gs_list = [] -# sod_gs_min_list = [] -# nb_updated_list = [] -# nb_updated_k_list = [] - g_best = [] - for nb_median in nb_median_range: - print('\n-------------------------------------------------------') - print('number of median graphs =', nb_median) - random.seed(1) - idx_rdm = random.sample(range(len(Gn)), nb_median) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn[idx].copy() for idx in idx_rdm] - Gn_candidate = [g.copy() for g in Gn_median] - -# for g in Gn_median: -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -## plt.savefig("results/preimage_mix/mutag.png", format="PNG") -# plt.show() -# plt.clf() - - ################################################################### -# gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz') -# km_tmp = gmfile['gm'] -# time_km = gmfile['gmtime'] -# # modify mixed gram matrix. -# km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median)) -# for i in range(len(Gn)): -# for j in range(i, len(Gn)): -# km[i, j] = km_tmp[i, j] -# km[j, i] = km[i, j] -# for i in range(len(Gn)): -# for j, idx in enumerate(idx_rdm): -# km[i, len(Gn) + j] = km[i, idx] -# km[len(Gn) + j, i] = km[i, idx] -# for i, idx1 in enumerate(idx_rdm): -# for j, idx2 in enumerate(idx_rdm): -# km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2] - - ################################################################### - alpha_range = [1 / nb_median] * nb_median - time0 = time.time() - G_gen_median_list, sod_gen_median, sod_list, G_set_median_list, sod_set_median \ - = iam_upgraded(Gn_median, Gn_candidate, - c_ei=c_ei, c_er=c_er, c_es=c_es, ite_max=ite_max_iam, - epsilon=epsilon_iam, connected=connected_iam, removeNodes=removeNodes, - params_ged=params_ged) - - time_total = time.time() - time0 - print('\ntime: ', time_total) - time_list.append(time_total) - - # compute distance between \psi and the new generated graphs. - knew = compute_kernel(G_gen_median_list + Gn_median, gkernel, node_label, - edge_label, False) - dhat_new_list = [] - for idx, g_tmp in enumerate(G_gen_median_list): - # @todo: the term3 below could use the one at the beginning of the function. - dhat_new_list.append(dis_gstar(idx, range(len(G_gen_median_list), - len(G_gen_median_list) + len(Gn_median) + 1), - alpha_range, knew, withterm3=False)) - - print('\nsmallest distance in kernel space: ', dhat_new_list[0]) - dis_ks_min_list.append(dhat_new_list[0]) - g_best.append(G_gen_median_list[0]) - - # show the best graph and save it to file. -# print('the shortest distance is', dhat) - print('one of the possible corresponding pre-images is') - nx.draw(G_gen_median_list[0], labels=nx.get_node_attributes(G_gen_median_list[0], 'atom'), - with_labels=True) - plt.show() -# plt.savefig('results/iam/mutag_median.fit_costs2.001.nb' + str(nb_median) + -# plt.savefig('results/iam/mutag_median_unfit2.nb' + str(nb_median) + -# '.png', format="PNG") - plt.clf() -# print(ghat_list[0].nodes(data=True)) -# print(ghat_list[0].edges(data=True)) - - sod_gs_list.append(sod_gen_median) -# sod_gs_min_list.append(np.min(sod_gen_median)) - print('\nsmallest sod in graph space: ', sod_gen_median) - print('\nsmallest sod of set median in graph space: ', sod_set_median) - - print('\nsods in graph space: ', sod_gs_list) -# print('\nsmallest sod in graph space for each set of median graphs: ', sod_gs_min_list) - print('\nsmallest distance in kernel space for each set of median graphs: ', - dis_ks_min_list) -# print('\nnumber of updates of the best graph for each set of median graphs by IAM: ', -# nb_updated_list) -# print('\nnumber of updates of k nearest graphs for each set of median graphs by IAM: ', -# nb_updated_k_list) - print('\ntimes:', time_list) - - - - - -############################################################################### - - -if __name__ == '__main__': -############################################################################### -# tests on different numbers of median-sets. -# test_iam_median_nb() -# test_iam_letter_h() -# test_iam_monoterpenoides() -# test_iam_mutag() - -# test_iam_fitdistance() -# print("test log") - - test_iam_monoterpenoides_with_init40() diff --git a/gklearn/preimage/test_k_closest_graphs.py b/gklearn/preimage/test_k_closest_graphs.py deleted file mode 100644 index 56971c7..0000000 --- a/gklearn/preimage/test_k_closest_graphs.py +++ /dev/null @@ -1,462 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Mon Dec 16 11:53:54 2019 - -@author: ljia -""" -import numpy as np -import math -import networkx as nx -import matplotlib.pyplot as plt -import time -import random -from tqdm import tqdm -from itertools import combinations, islice -import multiprocessing -from multiprocessing import Pool -from functools import partial - -from gklearn.utils.graphfiles import loadDataset, loadGXL -#from gklearn.utils.logger2file import * -from gklearn.preimage.iam import iam_upgraded, iam_bash -from gklearn.preimage.utils import compute_kernel, dis_gstar, kernel_distance_matrix -from gklearn.preimage.fitDistance import fit_GED_to_kernel_distance -#from gklearn.preimage.ged import ged_median - - -def fit_edit_cost_constants(fit_method, edit_cost_name, - edit_cost_constants=None, initial_solutions=1, - Gn_median=None, node_label=None, edge_label=None, - gkernel=None, dataset=None, init_ecc=None, - Gn=None, Kmatrix_median=None): - """fit edit cost constants. - """ - if fit_method == 'random': # random - if edit_cost_name == 'LETTER': - edit_cost_constants = random.sample(range(1, 10), 3) - edit_cost_constants = [item * 0.1 for item in edit_cost_constants] - elif edit_cost_name == 'LETTER2': - random.seed(time.time()) - edit_cost_constants = random.sample(range(1, 10), 5) -# edit_cost_constants = [item * 0.1 for item in edit_cost_constants] - elif edit_cost_name == 'NON_SYMBOLIC': - edit_cost_constants = random.sample(range(1, 10), 6) - if Gn_median[0].graph['node_attrs'] == []: - edit_cost_constants[2] = 0 - if Gn_median[0].graph['edge_attrs'] == []: - edit_cost_constants[5] = 0 - else: - edit_cost_constants = random.sample(range(1, 10), 6) - print('edit cost constants used:', edit_cost_constants) - elif fit_method == 'expert': # expert - if init_ecc is None: - if edit_cost_name == 'LETTER': - edit_cost_constants = [0.9, 1.7, 0.75] - elif edit_cost_name == 'LETTER2': - edit_cost_constants = [0.675, 0.675, 0.75, 0.425, 0.425] - else: - edit_cost_constants = [3, 3, 1, 3, 3, 1] - else: - edit_cost_constants = init_ecc - elif fit_method == 'k-graphs': - itr_max = 6 - if init_ecc is None: - if edit_cost_name == 'LETTER': - init_costs = [0.9, 1.7, 0.75] - elif edit_cost_name == 'LETTER2': - init_costs = [0.675, 0.675, 0.75, 0.425, 0.425] - elif edit_cost_name == 'NON_SYMBOLIC': - init_costs = [0, 0, 1, 1, 1, 0] - if Gn_median[0].graph['node_attrs'] == []: - init_costs[2] = 0 - if Gn_median[0].graph['edge_attrs'] == []: - init_costs[5] = 0 - else: - init_costs = [3, 3, 1, 3, 3, 1] - else: - init_costs = init_ecc - algo_options = '--threads 1 --initial-solutions ' \ - + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1' - params_ged = {'lib': 'gedlibpy', 'cost': edit_cost_name, 'method': 'IPFP', - 'algo_options': algo_options, 'stabilizer': None} - # fit on k-graph subset - edit_cost_constants, _, _, _, _, _, _ = fit_GED_to_kernel_distance(Gn_median, - node_label, edge_label, gkernel, itr_max, params_ged=params_ged, - init_costs=init_costs, dataset=dataset, Kmatrix=Kmatrix_median, - parallel=True) - elif fit_method == 'whole-dataset': - itr_max = 6 - if init_ecc is None: - if edit_cost_name == 'LETTER': - init_costs = [0.9, 1.7, 0.75] - elif edit_cost_name == 'LETTER2': - init_costs = [0.675, 0.675, 0.75, 0.425, 0.425] - else: - init_costs = [3, 3, 1, 3, 3, 1] - else: - init_costs = init_ecc - algo_options = '--threads 1 --initial-solutions ' \ - + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1' - params_ged = {'lib': 'gedlibpy', 'cost': edit_cost_name, 'method': 'IPFP', - 'algo_options': algo_options, 'stabilizer': None} - # fit on all subset - edit_cost_constants, _, _, _, _, _, _ = fit_GED_to_kernel_distance(Gn, - node_label, edge_label, gkernel, itr_max, params_ged=params_ged, - init_costs=init_costs, dataset=dataset, parallel=True) - elif fit_method == 'precomputed': - pass - - return edit_cost_constants - - -def compute_distances_to_true_median(Gn_median, fname_sm, fname_gm, - gkernel, edit_cost_name, - Kmatrix_median=None): - # reform graphs. - set_median = loadGXL(fname_sm) - gen_median = loadGXL(fname_gm) -# print(gen_median.nodes(data=True)) -# print(gen_median.edges(data=True)) - if edit_cost_name == 'LETTER' or edit_cost_name == 'LETTER2' or edit_cost_name == 'NON_SYMBOLIC': -# dataset == 'Fingerprint': -# for g in Gn_median: -# reform_attributes(g) - reform_attributes(set_median, Gn_median[0].graph['node_attrs'], - Gn_median[0].graph['edge_attrs']) - reform_attributes(gen_median, Gn_median[0].graph['node_attrs'], - Gn_median[0].graph['edge_attrs']) - - if edit_cost_name == 'LETTER' or edit_cost_name == 'LETTER2' or edit_cost_name == 'NON_SYMBOLIC': - node_label = None - edge_label = None - else: - node_label = 'chem' - edge_label = 'valence' - - # compute Gram matrix for median set. - if Kmatrix_median is None: - Kmatrix_median = compute_kernel(Gn_median, gkernel, node_label, edge_label, False) - - # compute distance in kernel space for set median. - kernel_sm = [] - for G_median in Gn_median: - km_tmp = compute_kernel([set_median, G_median], gkernel, node_label, edge_label, False) - kernel_sm.append(km_tmp[0, 1]) - Kmatrix_sm = np.concatenate((np.array([kernel_sm]), np.copy(Kmatrix_median)), axis=0) - Kmatrix_sm = np.concatenate((np.array([[km_tmp[0, 0]] + kernel_sm]).T, Kmatrix_sm), axis=1) -# Kmatrix_sm = compute_kernel([set_median] + Gn_median, gkernel, -# node_label, edge_label, False) - dis_k_sm = dis_gstar(0, range(1, 1+len(Gn_median)), - [1 / len(Gn_median)] * len(Gn_median), Kmatrix_sm, withterm3=False) -# print(gen_median.nodes(data=True)) -# print(gen_median.edges(data=True)) -# print(set_median.nodes(data=True)) -# print(set_median.edges(data=True)) - - # compute distance in kernel space for generalized median. - kernel_gm = [] - for G_median in Gn_median: - km_tmp = compute_kernel([gen_median, G_median], gkernel, node_label, edge_label, False) - kernel_gm.append(km_tmp[0, 1]) - Kmatrix_gm = np.concatenate((np.array([kernel_gm]), np.copy(Kmatrix_median)), axis=0) - Kmatrix_gm = np.concatenate((np.array([[km_tmp[0, 0]] + kernel_gm]).T, Kmatrix_gm), axis=1) -# Kmatrix_gm = compute_kernel([gen_median] + Gn_median, gkernel, -# node_label, edge_label, False) - dis_k_gm = dis_gstar(0, range(1, 1+len(Gn_median)), - [1 / len(Gn_median)] * len(Gn_median), Kmatrix_gm, withterm3=False) - - # compute distance in kernel space for each graph in median set. - dis_k_gi = [] - for idx in range(len(Gn_median)): - dis_k_gi.append(dis_gstar(idx+1, range(1, 1+len(Gn_median)), - [1 / len(Gn_median)] * len(Gn_median), Kmatrix_gm, withterm3=False)) - - print('dis_k_sm:', dis_k_sm) - print('dis_k_gm:', dis_k_gm) - print('dis_k_gi:', dis_k_gi) - idx_dis_k_gi_min = np.argmin(dis_k_gi) - dis_k_gi_min = dis_k_gi[idx_dis_k_gi_min] - print('min dis_k_gi:', dis_k_gi_min) - - return dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min - - -def median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k, fit_method, - graph_dir=None, initial_solutions=1, - edit_cost_constants=None, group_min=None, - dataset=None, edit_cost_name=None, init_ecc=None, - Kmatrix=None, parallel=True): -# dataset = dataset.lower() - -# # compute distances in kernel space. -# dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, -# Kmatrix=None, gkernel=gkernel) -# # ged. -# gmfile = np.load('results/test_k_closest_graphs/ged_mat.fit_on_whole_dataset.with_medians.gm.npz') -# ged_mat = gmfile['ged_mat'] -# dis_mat = ged_mat[0:len(Gn), 0:len(Gn)] - -# # choose k closest graphs -# time0 = time.time() -# sod_ks_min, group_min = get_closest_k_graphs(dis_mat, k, parallel) -# time_spent = time.time() - time0 -# print('closest graphs:', sod_ks_min, group_min) -# print('time spent:', time_spent) -# group_min = (12, 13, 22, 29) # closest w.r.t path kernel -# group_min = (77, 85, 160, 171) # closest w.r.t ged -# group_min = (0,1,2,3,4,5,6,7,8,9,10,11) # closest w.r.t treelet kernel - Gn_median = [Gn[g].copy() for g in group_min] - if Kmatrix is not None: - Kmatrix_median = np.copy(Kmatrix[group_min,:]) - Kmatrix_median = Kmatrix_median[:,group_min] - else: - Kmatrix_median = None - - - # 1. fit edit cost constants. - time0 = time.time() - edit_cost_constants = fit_edit_cost_constants(fit_method, edit_cost_name, - edit_cost_constants=edit_cost_constants, initial_solutions=initial_solutions, - Gn_median=Gn_median, node_label=node_label, edge_label=edge_label, - gkernel=gkernel, dataset=dataset, init_ecc=init_ecc, - Gn=Gn, Kmatrix_median=Kmatrix_median) - time_fitting = time.time() - time0 - - - # 2. compute set median and gen median using IAM (C++ through bash). - print('\nstart computing set median and gen median using IAM (C++ through bash)...\n') - group_fnames = [Gn[g].graph['filename'] for g in group_min] - time0 = time.time() - sod_sm, sod_gm, fname_sm, fname_gm = iam_bash(group_fnames, edit_cost_constants, - cost=edit_cost_name, initial_solutions=initial_solutions, - graph_dir=graph_dir, dataset=dataset) - time_generating = time.time() - time0 - print('\nmedians computed.\n') - - - # 3. compute distances to the true median. - print('\nstart computing distances to true median....\n') - Gn_median = [Gn[g].copy() for g in group_min] - dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min = \ - compute_distances_to_true_median(Gn_median, fname_sm, fname_gm, - gkernel, edit_cost_name, - Kmatrix_median=Kmatrix_median) - idx_dis_k_gi_min = group_min[idx_dis_k_gi_min] - print('index min dis_k_gi:', idx_dis_k_gi_min) - print('sod_sm:', sod_sm) - print('sod_gm:', sod_gm) - - # collect return values. - return (sod_sm, sod_gm), \ - (dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min), \ - (time_fitting, time_generating) - - -def reform_attributes(G, na_names=[], ea_names=[]): - if not na_names == []: - for node in G.nodes: - G.nodes[node]['attributes'] = [G.node[node][a_name] for a_name in na_names] - if not ea_names == []: - for edge in G.edges: - G.edges[edge]['attributes'] = [G.edge[edge][a_name] for a_name in ea_names] - - -def get_closest_k_graphs(dis_mat, k, parallel): - k_graph_groups = combinations(range(0, len(dis_mat)), k) - sod_ks_min = np.inf - if parallel: - len_combination = get_combination_length(len(dis_mat), k) - len_itr_max = int(len_combination if len_combination < 1e7 else 1e7) -# pos_cur = 0 - graph_groups_slices = split_iterable(k_graph_groups, len_itr_max, len_combination) - for graph_groups_cur in graph_groups_slices: -# while True: -# graph_groups_cur = islice(k_graph_groups, pos_cur, pos_cur + len_itr_max) - graph_groups_cur_list = list(graph_groups_cur) - print('current position:', graph_groups_cur_list[0]) - len_itr_cur = len(graph_groups_cur_list) -# if len_itr_cur < len_itr_max: -# break - - itr = zip(graph_groups_cur_list, range(0, len_itr_cur)) - sod_k_list = np.empty(len_itr_cur) - graphs_list = [None] * len_itr_cur - n_jobs = multiprocessing.cpu_count() - chunksize = int(len_itr_max / n_jobs + 1) - n_jobs = multiprocessing.cpu_count() - def init_worker(dis_mat_toshare): - global G_dis_mat - G_dis_mat = dis_mat_toshare - pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(dis_mat,)) -# iterator = tqdm(pool.imap_unordered(_get_closest_k_graphs_parallel, -# itr, chunksize), -# desc='Choosing k closest graphs', file=sys.stdout) - iterator = pool.imap_unordered(_get_closest_k_graphs_parallel, itr, chunksize) - for graphs, i, sod_ks in iterator: - sod_k_list[i] = sod_ks - graphs_list[i] = graphs - pool.close() - pool.join() - - arg_min = np.argmin(sod_k_list) - sod_ks_cur = sod_k_list[arg_min] - group_cur = graphs_list[arg_min] - if sod_ks_cur < sod_ks_min: - sod_ks_min = sod_ks_cur - group_min = group_cur - print('get closer graphs:', sod_ks_min, group_min) - else: - for items in tqdm(k_graph_groups, desc='Choosing k closest graphs', file=sys.stdout): - # if items[0] != itmp: - # itmp = items[0] - # print(items) - k_graph_pairs = combinations(items, 2) - sod_ks = 0 - for i1, i2 in k_graph_pairs: - sod_ks += dis_mat[i1, i2] - if sod_ks < sod_ks_min: - sod_ks_min = sod_ks - group_min = items - print('get closer graphs:', sod_ks_min, group_min) - - return sod_ks_min, group_min - - -def _get_closest_k_graphs_parallel(itr): - k_graph_pairs = combinations(itr[0], 2) - sod_ks = 0 - for i1, i2 in k_graph_pairs: - sod_ks += G_dis_mat[i1, i2] - - return itr[0], itr[1], sod_ks - - -def split_iterable(iterable, n, len_iter): - it = iter(iterable) - for i in range(0, len_iter, n): - piece = islice(it, n) - yield piece - - -def get_combination_length(n, k): - len_combination = 1 - for i in range(n, n - k, -1): - len_combination *= i - return int(len_combination / math.factorial(k)) - - -############################################################################### - -def test_k_closest_graphs(): - ds = {'name': 'monoterpenoides', - 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:50] -# gkernel = 'untilhpathkernel' -# gkernel = 'weisfeilerlehmankernel' - gkernel = 'treeletkernel' - node_label = 'atom' - edge_label = 'bond_type' - - k = 5 - edit_costs = [0.16229209837639536, 0.06612870523413916, 0.04030113378793905, 0.20723547009415202, 0.3338607220394598, 0.27054392518077297] - -# sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \ -# = median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k, -# 'precomputed', edit_costs=edit_costs, -## 'k-graphs', -# parallel=False) -# -# sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \ -# = median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k, -# 'expert', parallel=False) - - sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \ - = median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k, - 'expert', parallel=False) - return - - -def test_k_closest_graphs_with_cv(): - gkernel = 'untilhpathkernel' - node_label = 'atom' - edge_label = 'bond_type' - - k = 4 - - y_all = ['3', '1', '4', '6', '7', '8', '9', '2'] - repeats = 50 - collection_path = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/' - graph_dir = collection_path + 'gxl/' - - sod_sm_list = [] - sod_gm_list = [] - dis_k_sm_list = [] - dis_k_gm_list = [] - dis_k_gi_min_list = [] - for y in y_all: - print('\n-------------------------------------------------------') - print('class of y:', y) - - sod_sm_list.append([]) - sod_gm_list.append([]) - dis_k_sm_list.append([]) - dis_k_gm_list.append([]) - dis_k_gi_min_list.append([]) - - for repeat in range(repeats): - print('\nrepeat ', repeat) - collection_file = collection_path + 'monoterpenoides_' + y + '_' + str(repeat) + '.xml' - Gn, _ = loadDataset(collection_file, extra_params=graph_dir) - sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \ - = median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, - k, 'whole-dataset', graph_dir=graph_dir, - parallel=False) - - sod_sm_list[-1].append(sod_sm) - sod_gm_list[-1].append(sod_gm) - dis_k_sm_list[-1].append(dis_k_sm) - dis_k_gm_list[-1].append(dis_k_gm) - dis_k_gi_min_list[-1].append(dis_k_gi_min) - - print('\nsods of the set median for this class:', sod_sm_list[-1]) - print('\nsods of the gen median for this class:', sod_gm_list[-1]) - print('\ndistances in kernel space of set median for this class:', - dis_k_sm_list[-1]) - print('\ndistances in kernel space of gen median for this class:', - dis_k_gm_list[-1]) - print('\ndistances in kernel space of min graph for this class:', - dis_k_gi_min_list[-1]) - - sod_sm_list[-1] = np.mean(sod_sm_list[-1]) - sod_gm_list[-1] = np.mean(sod_gm_list[-1]) - dis_k_sm_list[-1] = np.mean(dis_k_sm_list[-1]) - dis_k_gm_list[-1] = np.mean(dis_k_gm_list[-1]) - dis_k_gi_min_list[-1] = np.mean(dis_k_gi_min_list[-1]) - - print() - print('\nmean sods of the set median for each class:', sod_sm_list) - print('\nmean sods of the gen median for each class:', sod_gm_list) - print('\nmean distance in kernel space of set median for each class:', - dis_k_sm_list) - print('\nmean distances in kernel space of gen median for each class:', - dis_k_gm_list) - print('\nmean distances in kernel space of min graph for each class:', - dis_k_gi_min_list) - - print('\nmean sods of the set median of all:', np.mean(sod_sm_list)) - print('\nmean sods of the gen median of all:', np.mean(sod_gm_list)) - print('\nmean distances in kernel space of set median of all:', - np.mean(dis_k_sm_list)) - print('\nmean distances in kernel space of gen median of all:', - np.mean(dis_k_gm_list)) - print('\nmean distances in kernel space of min graph of all:', - np.mean(dis_k_gi_min_list)) - - return - - -if __name__ == '__main__': - test_k_closest_graphs() -# test_k_closest_graphs_with_cv() \ No newline at end of file diff --git a/gklearn/preimage/test_median_preimage_generator.py b/gklearn/preimage/test_median_preimage_generator.py deleted file mode 100644 index 2f458af..0000000 --- a/gklearn/preimage/test_median_preimage_generator.py +++ /dev/null @@ -1,69 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Mar 27 17:30:55 2020 - -@author: ljia -""" -import multiprocessing -import functools -from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct -from gklearn.preimage import MedianPreimageGenerator -from gklearn.utils import Dataset - - -def test_median_preimage_generator(): - - # 1. set parameters. - print('1. setting parameters...') - ds_name = 'Letter-high' - mpg = MedianPreimageGenerator() - mpg_options = {'fit_method': 'k-graphs', - 'init_ecc': [3, 3, 1, 3, 3], - 'ds_name': 'Letter-high', - 'parallel': True, - 'time_limit_in_sec': 0, - 'max_itrs': 100, - 'max_itrs_without_update': 3, - 'epsilon_ratio': 0.01, - 'verbose': 2} - mpg.set_options(**mpg_options) - mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) - sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} - mpg.kernel_options = {'name': 'structuralspkernel', - 'edge_weight': None, - 'node_kernels': sub_kernels, - 'edge_kernels': sub_kernels, - 'compute_method': 'naive', - 'parallel': 'imap_unordered', -# 'parallel': None, - 'n_jobs': multiprocessing.cpu_count(), - 'normalize': True, - 'verbose': 2} - mpg.ged_options = {'method': 'IPFP', - 'initial_solutions': 40, - 'edit_cost': 'LETTER2', - 'attr_distance': 'euclidean', - 'ratio_runs_from_initial_solutions': 1, - 'threads': multiprocessing.cpu_count(), - 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} - mpg.mge_options = {'init_type': 'MEDOID', - 'random_inits': 10, - 'time_limit': 600, - 'verbose': 2, - 'refine': False} - - - # 2. get dataset. - print('2. getting dataset...') - mpg.dataset = Dataset() - mpg.dataset.load_predefined_dataset(ds_name) - mpg.dataset.cut_graphs(range(0, 10)) - - # 3. compute median preimage. - print('3. computing median preimage...') - mpg.run() - - -if __name__ == '__main__': - test_median_preimage_generator() \ No newline at end of file diff --git a/gklearn/preimage/test_others.py b/gklearn/preimage/test_others.py deleted file mode 100644 index a277a17..0000000 --- a/gklearn/preimage/test_others.py +++ /dev/null @@ -1,686 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Jul 4 12:20:16 2019 - -@author: ljia -""" -import numpy as np -import networkx as nx -import matplotlib.pyplot as plt -import time -from tqdm import tqdm - -from gklearn.utils.graphfiles import loadDataset -from gklearn.preimage.median import draw_Letter_graph -from gklearn.preimage.ged import GED, ged_median -from gklearn.preimage.utils import get_same_item_indices, compute_kernel, gram2distances, \ - dis_gstar, remove_edges - - -# --------------------------- These are tests --------------------------------# - -def test_who_is_the_closest_in_kernel_space(Gn): - idx_gi = [0, 6] - g1 = Gn[idx_gi[0]] - g2 = Gn[idx_gi[1]] - # create the "median" graph. - gnew = g2.copy() - gnew.remove_node(0) - nx.draw_networkx(gnew) - plt.show() - print(gnew.nodes(data=True)) - Gn = [gnew] + Gn - - # compute gram matrix - Kmatrix = compute_kernel(Gn, 'untilhpathkernel', True) - # the distance matrix - dmatrix = gram2distances(Kmatrix) - print(np.sort(dmatrix[idx_gi[0] + 1])) - print(np.argsort(dmatrix[idx_gi[0] + 1])) - print(np.sort(dmatrix[idx_gi[1] + 1])) - print(np.argsort(dmatrix[idx_gi[1] + 1])) - # for all g in Gn, compute (d(g1, g) + d(g2, g)) / 2 - dis_median = [(dmatrix[i, idx_gi[0] + 1] + dmatrix[i, idx_gi[1] + 1]) / 2 for i in range(len(Gn))] - print(np.sort(dis_median)) - print(np.argsort(dis_median)) - return - - -def test_who_is_the_closest_in_GED_space(Gn): - idx_gi = [0, 6] - g1 = Gn[idx_gi[0]] - g2 = Gn[idx_gi[1]] - # create the "median" graph. - gnew = g2.copy() - gnew.remove_node(0) - nx.draw_networkx(gnew) - plt.show() - print(gnew.nodes(data=True)) - Gn = [gnew] + Gn - - # compute GEDs - ged_matrix = np.zeros((len(Gn), len(Gn))) - for i1 in tqdm(range(len(Gn)), desc='computing GEDs', file=sys.stdout): - for i2 in range(len(Gn)): - dis, _, _ = GED(Gn[i1], Gn[i2], lib='gedlib') - ged_matrix[i1, i2] = dis - print(np.sort(ged_matrix[idx_gi[0] + 1])) - print(np.argsort(ged_matrix[idx_gi[0] + 1])) - print(np.sort(ged_matrix[idx_gi[1] + 1])) - print(np.argsort(ged_matrix[idx_gi[1] + 1])) - # for all g in Gn, compute (GED(g1, g) + GED(g2, g)) / 2 - dis_median = [(ged_matrix[i, idx_gi[0] + 1] + ged_matrix[i, idx_gi[1] + 1]) / 2 for i in range(len(Gn))] - print(np.sort(dis_median)) - print(np.argsort(dis_median)) - return - - -def test_will_IAM_give_the_median_graph_we_wanted(Gn): - idx_gi = [0, 6] - g1 = Gn[idx_gi[0]].copy() - g2 = Gn[idx_gi[1]].copy() -# del Gn[idx_gi[0]] -# del Gn[idx_gi[1] - 1] - g_median = test_iam_with_more_graphs_as_init([g1, g2], [g1, g2], c_ei=1, c_er=1, c_es=1) -# g_median = test_iam_with_more_graphs_as_init(Gn, Gn, c_ei=1, c_er=1, c_es=1) - nx.draw_networkx(g_median) - plt.show() - print(g_median.nodes(data=True)) - print(g_median.edges(data=True)) - - -def test_new_IAM_allGraph_deleteNodes(Gn): - idx_gi = [0, 6] -# g1 = Gn[idx_gi[0]].copy() -# g2 = Gn[idx_gi[1]].copy() - -# g1 = nx.Graph(name='haha') -# g1.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'O'}), (2, {'atom': 'C'})]) -# g1.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'})]) -# g2 = nx.Graph(name='hahaha') -# g2.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'O'}), (2, {'atom': 'C'}), -# (3, {'atom': 'O'}), (4, {'atom': 'C'})]) -# g2.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}), -# (2, 3, {'bond_type': '1'}), (3, 4, {'bond_type': '1'})]) - - g1 = nx.Graph(name='haha') - g1.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'C'}), (2, {'atom': 'C'}), - (3, {'atom': 'S'}), (4, {'atom': 'S'})]) - g1.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}), - (2, 3, {'bond_type': '1'}), (2, 4, {'bond_type': '1'})]) - g2 = nx.Graph(name='hahaha') - g2.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'C'}), (2, {'atom': 'C'}), - (3, {'atom': 'O'}), (4, {'atom': 'O'})]) - g2.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}), - (2, 3, {'bond_type': '1'}), (2, 4, {'bond_type': '1'})]) - -# g2 = g1.copy() -# g2.add_nodes_from([(3, {'atom': 'O'})]) -# g2.add_nodes_from([(4, {'atom': 'C'})]) -# g2.add_edges_from([(1, 3, {'bond_type': '1'})]) -# g2.add_edges_from([(3, 4, {'bond_type': '1'})]) - -# del Gn[idx_gi[0]] -# del Gn[idx_gi[1] - 1] - - nx.draw_networkx(g1) - plt.show() - print(g1.nodes(data=True)) - print(g1.edges(data=True)) - nx.draw_networkx(g2) - plt.show() - print(g2.nodes(data=True)) - print(g2.edges(data=True)) - - g_median = test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations([g1, g2], [g1, g2], c_ei=1, c_er=1, c_es=1) -# g_median = test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations(Gn, Gn, c_ei=1, c_er=1, c_es=1) - nx.draw_networkx(g_median) - plt.show() - print(g_median.nodes(data=True)) - print(g_median.edges(data=True)) - - -def test_the_simple_two(Gn, gkernel): - from gk_iam import gk_iam_nearest_multi - lmbda = 0.03 # termination probalility - r_max = 10 # recursions - l = 500 - alpha_range = np.linspace(0.5, 0.5, 1) - k = 2 # k nearest neighbors - - # randomly select two molecules - np.random.seed(1) - idx_gi = [0, 6] # np.random.randint(0, len(Gn), 2) - g1 = Gn[idx_gi[0]] - g2 = Gn[idx_gi[1]] - Gn_mix = [g.copy() for g in Gn] - Gn_mix.append(g1.copy()) - Gn_mix.append(g2.copy()) - -# g_tmp = iam([g1, g2]) -# nx.draw_networkx(g_tmp) -# plt.show() - - # compute -# k_list = [] # kernel between each graph and itself. -# k_g1_list = [] # kernel between each graph and g1 -# k_g2_list = [] # kernel between each graph and g2 -# for ig, g in tqdm(enumerate(Gn), desc='computing self kernels', file=sys.stdout): -# ktemp = compute_kernel([g, g1, g2], 'marginalizedkernel', False) -# k_list.append(ktemp[0][0, 0]) -# k_g1_list.append(ktemp[0][0, 1]) -# k_g2_list.append(ktemp[0][0, 2]) - - km = compute_kernel(Gn_mix, gkernel, True) -# k_list = np.diag(km) # kernel between each graph and itself. -# k_g1_list = km[idx_gi[0]] # kernel between each graph and g1 -# k_g2_list = km[idx_gi[1]] # kernel between each graph and g2 - - g_best = [] - dis_best = [] - # for each alpha - for alpha in alpha_range: - print('alpha =', alpha) - dhat, ghat_list = gk_iam_nearest_multi(Gn, [g1, g2], [alpha, 1 - alpha], - range(len(Gn), len(Gn) + 2), km, - k, r_max,gkernel) - dis_best.append(dhat) - g_best.append(ghat_list) - - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_best[idx]) - print('the corresponding pre-images are') - for g in g_best[idx]: - nx.draw_networkx(g) - plt.show() - print(g.nodes(data=True)) - print(g.edges(data=True)) - - -def test_remove_bests(Gn, gkernel): - from gk_iam import gk_iam_nearest_multi - lmbda = 0.03 # termination probalility - r_max = 10 # recursions - l = 500 - alpha_range = np.linspace(0.5, 0.5, 1) - k = 20 # k nearest neighbors - - # randomly select two molecules - np.random.seed(1) - idx_gi = [0, 6] # np.random.randint(0, len(Gn), 2) - g1 = Gn[idx_gi[0]] - g2 = Gn[idx_gi[1]] - # remove the best 2 graphs. - del Gn[idx_gi[0]] - del Gn[idx_gi[1] - 1] -# del Gn[8] - - Gn_mix = [g.copy() for g in Gn] - Gn_mix.append(g1.copy()) - Gn_mix.append(g2.copy()) - - - # compute - km = compute_kernel(Gn_mix, gkernel, True) - g_best = [] - dis_best = [] - # for each alpha - for alpha in alpha_range: - print('alpha =', alpha) - dhat, ghat_list = gk_iam_nearest_multi(Gn, [g1, g2], [alpha, 1 - alpha], - range(len(Gn), len(Gn) + 2), km, - k, r_max, gkernel) - dis_best.append(dhat) - g_best.append(ghat_list) - - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_best[idx]) - print('the corresponding pre-images are') - for g in g_best[idx]: - draw_Letter_graph(g) -# nx.draw_networkx(g) -# plt.show() - print(g.nodes(data=True)) - print(g.edges(data=True)) - - -############################################################################### -# Tests on dataset Letter-H. - -def test_gkiam_letter_h(): - from gk_iam import gk_iam_nearest_multi - ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt', - 'extra_params': {}} # node nsymb -# ds = {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt', -# 'extra_params': {}} # node nsymb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) - gkernel = 'structuralspkernel' - - lmbda = 0.03 # termination probalility - r_max = 3 # recursions -# alpha_range = np.linspace(0.5, 0.5, 1) - k = 10 # k nearest neighbors - - # classify graphs according to letters. - idx_dict = get_same_item_indices(y_all) - time_list = [] - sod_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list = [] - for letter in idx_dict: - print('\n-------------------------------------------------------\n') - Gn_let = [Gn[i].copy() for i in idx_dict[letter]] - Gn_mix = Gn_let + [g.copy() for g in Gn_let] - - alpha_range = np.linspace(1 / len(Gn_let), 1 / len(Gn_let), 1) - - # compute - time0 = time.time() - km = compute_kernel(Gn_mix, gkernel, True) - g_best = [] - dis_best = [] - # for each alpha - for alpha in alpha_range: - print('alpha =', alpha) - dhat, ghat_list, sod_ks, nb_updated = gk_iam_nearest_multi(Gn_let, - Gn_let, [alpha] * len(Gn_let), range(len(Gn_let), len(Gn_mix)), - km, k, r_max, gkernel, c_ei=1.7, c_er=1.7, c_es=1.7, - ged_cost='LETTER', ged_method='IPFP', saveGXL='gedlib-letter') - dis_best.append(dhat) - g_best.append(ghat_list) - time_list.append(time.time() - time0) - - # show best graphs and save them to file. - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_best[idx]) - print('the corresponding pre-images are') - for g in g_best[idx]: - draw_Letter_graph(g, savepath='results/gk_iam/') -# nx.draw_networkx(g) -# plt.show() - print(g.nodes(data=True)) - print(g.edges(data=True)) - - # compute the corresponding sod in graph space. (alpha range not considered.) - sod_tmp, _ = ged_median(g_best[0], Gn_let, ged_cost='LETTER', - ged_method='IPFP', saveGXL='gedlib-letter') - sod_gs_list.append(sod_tmp) - sod_gs_min_list.append(np.min(sod_tmp)) - sod_ks_min_list.append(sod_ks) - nb_updated_list.append(nb_updated) - - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each letter: ', sod_gs_min_list) - print('\nsmallest sod in kernel space for each letter: ', sod_ks_min_list) - print('\nnumber of updates for each letter: ', nb_updated_list) - print('\ntimes:', time_list) - -#def compute_letter_median_by_average(Gn): -# return g_median - - -def test_iam_letter_h(): - from iam import test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations - ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt', - 'extra_params': {}} # node nsymb -# ds = {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt', -# 'extra_params': {}} # node nsymb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) - - lmbda = 0.03 # termination probalility -# alpha_range = np.linspace(0.5, 0.5, 1) - - # classify graphs according to letters. - idx_dict = get_same_item_indices(y_all) - time_list = [] - sod_list = [] - sod_min_list = [] - for letter in idx_dict: - Gn_let = [Gn[i].copy() for i in idx_dict[letter]] - - alpha_range = np.linspace(1 / len(Gn_let), 1 / len(Gn_let), 1) - - # compute - g_best = [] - dis_best = [] - time0 = time.time() - # for each alpha - for alpha in alpha_range: - print('alpha =', alpha) - ghat_list, dhat = test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations( - Gn_let, Gn_let, c_ei=1.7, c_er=1.7, c_es=1.7, - ged_cost='LETTER', ged_method='IPFP', saveGXL='gedlib-letter') - dis_best.append(dhat) - g_best.append(ghat_list) - time_list.append(time.time() - time0) - - # show best graphs and save them to file. - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_best[idx]) - print('the corresponding pre-images are') - for g in g_best[idx]: - draw_Letter_graph(g, savepath='results/iam/') -# nx.draw_networkx(g) -# plt.show() - print(g.nodes(data=True)) - print(g.edges(data=True)) - - # compute the corresponding sod in kernel space. (alpha range not considered.) - gkernel = 'structuralspkernel' - sod_tmp = [] - Gn_mix = g_best[0] + Gn_let - km = compute_kernel(Gn_mix, gkernel, True) - for ig, g in tqdm(enumerate(g_best[0]), desc='computing kernel sod', file=sys.stdout): - dtemp = dis_gstar(ig, range(len(g_best[0]), len(Gn_mix)), - [alpha_range[0]] * len(Gn_let), km, withterm3=False) - sod_tmp.append(dtemp) - sod_list.append(sod_tmp) - sod_min_list.append(np.min(sod_tmp)) - - - print('\nsods in kernel space: ', sod_list) - print('\nsmallest sod in kernel space for each letter: ', sod_min_list) - print('\ntimes:', time_list) - - -def test_random_preimage_letter_h(): - from preimage_random import preimage_random - ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt', - 'extra_params': {}} # node nsymb -# ds = {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt', -# 'extra_params': {}} # node nsymb - # ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', -# 'extra_params': {}} # node/edge symb -# ds = {'name': 'Acyclic', 'dataset': '../datasets/monoterpenoides/trainset_9.ds', -# 'extra_params': {}} -# ds = {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', -# 'extra_params': {}} # node symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) - gkernel = 'structuralspkernel' - -# lmbda = 0.03 # termination probalility - r_max = 3 # 10 # recursions - l = 500 -# alpha_range = np.linspace(0.5, 0.5, 1) - #alpha_range = np.linspace(0.1, 0.9, 9) - k = 10 # 5 # k nearest neighbors - - # classify graphs according to letters. - idx_dict = get_same_item_indices(y_all) - time_list = [] - sod_list = [] - sod_min_list = [] - for letter in idx_dict: - print('\n-------------------------------------------------------\n') - Gn_let = [Gn[i].copy() for i in idx_dict[letter]] - Gn_mix = Gn_let + [g.copy() for g in Gn_let] - - alpha_range = np.linspace(1 / len(Gn_let), 1 / len(Gn_let), 1) - - # compute - time0 = time.time() - km = compute_kernel(Gn_mix, gkernel, True) - g_best = [] - dis_best = [] - # for each alpha - for alpha in alpha_range: - print('alpha =', alpha) - dhat, ghat_list = preimage_random(Gn_let, Gn_let, [alpha] * len(Gn_let), - range(len(Gn_let), len(Gn_mix)), km, - k, r_max, gkernel, c_ei=1.7, - c_er=1.7, c_es=1.7) - dis_best.append(dhat) - g_best.append(ghat_list) - time_list.append(time.time() - time0) - - # show best graphs and save them to file. - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_best[idx]) - print('the corresponding pre-images are') - for g in g_best[idx]: - draw_Letter_graph(g, savepath='results/gk_iam/') -# nx.draw_networkx(g) -# plt.show() - print(g.nodes(data=True)) - print(g.edges(data=True)) - - # compute the corresponding sod in graph space. (alpha range not considered.) - sod_tmp, _ = ged_median(g_best[0], Gn_let) - sod_list.append(sod_tmp) - sod_min_list.append(np.min(sod_tmp)) - - - print('\nsods in graph space: ', sod_list) - print('\nsmallest sod in graph space for each letter: ', sod_min_list) - print('\ntimes:', time_list) - - - - - - - -def test_gkiam_mutag(): - from gk_iam import gk_iam_nearest_multi - ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt', - 'extra_params': {}} # node nsymb -# ds = {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt', -# 'extra_params': {}} # node nsymb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) - gkernel = 'structuralspkernel' - - lmbda = 0.03 # termination probalility - r_max = 3 # recursions -# alpha_range = np.linspace(0.5, 0.5, 1) - k = 20 # k nearest neighbors - - # classify graphs according to letters. - idx_dict = get_same_item_indices(y_all) - time_list = [] - sod_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list = [] - for letter in idx_dict: - print('\n-------------------------------------------------------\n') - Gn_let = [Gn[i].copy() for i in idx_dict[letter]] - Gn_mix = Gn_let + [g.copy() for g in Gn_let] - - alpha_range = np.linspace(1 / len(Gn_let), 1 / len(Gn_let), 1) - - # compute - time0 = time.time() - km = compute_kernel(Gn_mix, gkernel, True) - g_best = [] - dis_best = [] - # for each alpha - for alpha in alpha_range: - print('alpha =', alpha) - dhat, ghat_list, sod_ks, nb_updated = gk_iam_nearest_multi(Gn_let, Gn_let, [alpha] * len(Gn_let), - range(len(Gn_let), len(Gn_mix)), km, - k, r_max, gkernel, c_ei=1.7, - c_er=1.7, c_es=1.7) - dis_best.append(dhat) - g_best.append(ghat_list) - time_list.append(time.time() - time0) - - # show best graphs and save them to file. - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_best[idx]) - print('the corresponding pre-images are') - for g in g_best[idx]: - draw_Letter_graph(g, savepath='results/gk_iam/') -# nx.draw_networkx(g) -# plt.show() - print(g.nodes(data=True)) - print(g.edges(data=True)) - - # compute the corresponding sod in graph space. (alpha range not considered.) - sod_tmp, _ = ged_median(g_best[0], Gn_let) - sod_gs_list.append(sod_tmp) - sod_gs_min_list.append(np.min(sod_tmp)) - sod_ks_min_list.append(sod_ks) - nb_updated_list.append(nb_updated) - - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each letter: ', sod_gs_min_list) - print('\nsmallest sod in kernel space for each letter: ', sod_ks_min_list) - print('\nnumber of updates for each letter: ', nb_updated_list) - print('\ntimes:', time_list) - - -############################################################################### -# Re-test. - -def retest_the_simple_two(): - from gk_iam import gk_iam_nearest_multi - - # The two simple graphs. -# g1 = nx.Graph(name='haha') -# g1.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'O'}), (2, {'atom': 'C'})]) -# g1.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'})]) -# g2 = nx.Graph(name='hahaha') -# g2.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'O'}), (2, {'atom': 'C'}), -# (3, {'atom': 'O'}), (4, {'atom': 'C'})]) -# g2.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}), -# (2, 3, {'bond_type': '1'}), (3, 4, {'bond_type': '1'})]) - - g1 = nx.Graph(name='haha') - g1.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'C'}), (2, {'atom': 'C'}), - (3, {'atom': 'S'}), (4, {'atom': 'S'})]) - g1.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}), - (2, 3, {'bond_type': '1'}), (2, 4, {'bond_type': '1'})]) - g2 = nx.Graph(name='hahaha') - g2.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'C'}), (2, {'atom': 'C'}), - (3, {'atom': 'O'}), (4, {'atom': 'O'})]) - g2.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}), - (2, 3, {'bond_type': '1'}), (2, 4, {'bond_type': '1'})]) - -# # randomly select two molecules -# np.random.seed(1) -# idx_gi = [0, 6] # np.random.randint(0, len(Gn), 2) -# g1 = Gn[idx_gi[0]] -# g2 = Gn[idx_gi[1]] -# Gn_mix = [g.copy() for g in Gn] -# Gn_mix.append(g1.copy()) -# Gn_mix.append(g2.copy()) - - Gn = [g1.copy(), g2.copy()] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 10 # recursions -# l = 500 - alpha_range = np.linspace(0.5, 0.5, 1) - k = 2 # k nearest neighbors - epsilon = 1e-6 - ged_cost='CHEM_1' - ged_method='IPFP' - saveGXL='gedlib' - c_ei=1 - c_er=1 - c_es=1 - - Gn_mix = Gn + [g1.copy(), g2.copy()] - - # compute - time0 = time.time() - km = compute_kernel(Gn_mix, gkernel, True) - time_km = time.time() - time0 - - time_list = [] - sod_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list = [] - g_best = [] - # for each alpha - for alpha in alpha_range: - print('\n-------------------------------------------------------\n') - print('alpha =', alpha) - time0 = time.time() - dhat, ghat_list, sod_ks, nb_updated = gk_iam_nearest_multi(Gn, [g1, g2], - [alpha, 1 - alpha], range(len(Gn), len(Gn) + 2), km, k, r_max, - gkernel, c_ei=c_ei, c_er=c_er, c_es=c_es, epsilon=epsilon, - ged_cost=ged_cost, ged_method=ged_method, saveGXL=saveGXL) - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list.append(time_total) - sod_ks_min_list.append(dhat) - g_best.append(ghat_list) - nb_updated_list.append(nb_updated) - - # show best graphs and save them to file. - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', sod_ks_min_list[idx]) - print('one of the possible corresponding pre-images is') - nx.draw(g_best[idx][0], labels=nx.get_node_attributes(g_best[idx][0], 'atom'), - with_labels=True) - plt.savefig('results/gk_iam/mutag_alpha' + str(item) + '.png', format="PNG") - plt.show() - print(g_best[idx][0].nodes(data=True)) - print(g_best[idx][0].edges(data=True)) - -# for g in g_best[idx]: -# draw_Letter_graph(g, savepath='results/gk_iam/') -## nx.draw_networkx(g) -## plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - - # compute the corresponding sod in graph space. - for idx, item in enumerate(alpha_range): - sod_tmp, _ = ged_median(g_best[0], [g1, g2], ged_cost=ged_cost, - ged_method=ged_method, saveGXL=saveGXL) - sod_gs_list.append(sod_tmp) - sod_gs_min_list.append(np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each alpha: ', sod_gs_min_list) - print('\nsmallest sod in kernel space for each alpha: ', sod_ks_min_list) - print('\nnumber of updates for each alpha: ', nb_updated_list) - print('\ntimes:', time_list) - - - -if __name__ == '__main__': -# ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', -# 'extra_params': {}} # node/edge symb -# ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt', -# 'extra_params': {}} # node nsymb -# ds = {'name': 'Acyclic', 'dataset': '../datasets/monoterpenoides/trainset_9.ds', -# 'extra_params': {}} -# ds = {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', -# 'extra_params': {}} # node symb -# Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:20] - -# import networkx.algorithms.isomorphism as iso -# G1 = nx.MultiDiGraph() -# G2 = nx.MultiDiGraph() -# G1.add_nodes_from([1,2,3], fill='red') -# G2.add_nodes_from([10,20,30,40], fill='red') -# nx.add_path(G1, [1,2,3,4], weight=3, linewidth=2.5) -# nx.add_path(G2, [10,20,30,40], weight=3) -# nm = iso.categorical_node_match('fill', 'red') -# print(nx.is_isomorphic(G1, G2, node_match=nm)) -# -# test_new_IAM_allGraph_deleteNodes(Gn) -# test_will_IAM_give_the_median_graph_we_wanted(Gn) -# test_who_is_the_closest_in_GED_space(Gn) -# test_who_is_the_closest_in_kernel_space(Gn) - -# test_the_simple_two(Gn, 'untilhpathkernel') -# test_remove_bests(Gn, 'untilhpathkernel') -# test_gkiam_letter_h() -# test_iam_letter_h() -# test_random_preimage_letter_h - -############################################################################### -# retests. - retest_the_simple_two() \ No newline at end of file diff --git a/gklearn/preimage/test_preimage_iam.py b/gklearn/preimage/test_preimage_iam.py deleted file mode 100644 index 9b05dd9..0000000 --- a/gklearn/preimage/test_preimage_iam.py +++ /dev/null @@ -1,620 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Sep 5 15:59:00 2019 - -@author: ljia -""" - -import numpy as np -import networkx as nx -import matplotlib.pyplot as plt -import time -import random -#from tqdm import tqdm - -from gklearn.utils.graphfiles import loadDataset -from gklearn.preimage.utils import remove_edges, compute_kernel, get_same_item_indices -from gklearn.preimage.ged import ged_median - -from gklearn.preimage.preimage_iam import preimage_iam - - -############################################################################### -# tests on different values on grid of median-sets and k. - -def test_preimage_iam_grid_k_median_nb(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 5 # iteration limit for pre-image. -# alpha_range = np.linspace(0.5, 0.5, 1) -# k = 5 # k nearest neighbors - epsilon = 1e-6 - InitIAMWithAllDk = True - # parameters for GED function - ged_cost='CHEM_1' - ged_method='IPFP' - saveGXL='gedlib' - # parameters for IAM function - c_ei=1 - c_er=1 - c_es=1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = True - connected_iam = False - - # number of graphs; we what to compute the median of these graphs. - nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100] - # number of nearest neighbors. - k_range = [5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100] - - # find out all the graphs classified to positive group 1. - idx_dict = get_same_item_indices(y_all) - Gn = [Gn[i] for i in idx_dict[1]] - -# # compute Gram matrix. -# time0 = time.time() -# km = compute_kernel(Gn, gkernel, True) -# time_km = time.time() - time0 -# # write Gram matrix to file. -# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km) - - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list = [] - nb_updated_k_list = [] - g_best = [] - for idx_nb, nb_median in enumerate(nb_median_range): - print('\n-------------------------------------------------------') - print('number of median graphs =', nb_median) - random.seed(1) - idx_rdm = random.sample(range(len(Gn)), nb_median) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn[idx].copy() for idx in idx_rdm] - -# for g in Gn_median: -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -## plt.savefig("results/preimage_mix/mutag.png", format="PNG") -# plt.show() -# plt.clf() - - ################################################################### - gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz') - km_tmp = gmfile['gm'] - time_km = gmfile['gmtime'] - # modify mixed gram matrix. - km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median)) - for i in range(len(Gn)): - for j in range(i, len(Gn)): - km[i, j] = km_tmp[i, j] - km[j, i] = km[i, j] - for i in range(len(Gn)): - for j, idx in enumerate(idx_rdm): - km[i, len(Gn) + j] = km[i, idx] - km[len(Gn) + j, i] = km[i, idx] - for i, idx1 in enumerate(idx_rdm): - for j, idx2 in enumerate(idx_rdm): - km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2] - - ################################################################### - alpha_range = [1 / nb_median] * nb_median - - time_list.append([]) - dis_ks_min_list.append([]) - sod_gs_list.append([]) - sod_gs_min_list.append([]) - nb_updated_list.append([]) - nb_updated_k_list.append([]) - g_best.append([]) - - for k in k_range: - print('\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n') - print('k =', k) - time0 = time.time() - dhat, ghat_list, dis_of_each_itr, nb_updated, nb_updated_k = \ - preimage_iam(Gn, Gn_median, - alpha_range, range(len(Gn), len(Gn) + nb_median), km, k, r_max, - gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk, - params_iam={'c_ei': c_ei, 'c_er': c_er, 'c_es': c_es, - 'ite_max': ite_max_iam, 'epsilon': epsilon_iam, - 'removeNodes': removeNodes, 'connected': connected_iam}, - params_ged={'ged_cost': ged_cost, 'ged_method': ged_method, - 'saveGXL': saveGXL}) - - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list[idx_nb].append(time_total) - print('\nsmallest distance in kernel space: ', dhat) - dis_ks_min_list[idx_nb].append(dhat) - g_best[idx_nb].append(ghat_list) - print('\nnumber of updates of the best graph by IAM: ', nb_updated) - nb_updated_list[idx_nb].append(nb_updated) - print('\nnumber of updates of k nearest graphs by IAM: ', nb_updated_k) - nb_updated_k_list[idx_nb].append(nb_updated_k) - - # show the best graph and save it to file. - print('the shortest distance is', dhat) - print('one of the possible corresponding pre-images is') - nx.draw(ghat_list[0], labels=nx.get_node_attributes(ghat_list[0], 'atom'), - with_labels=True) - plt.savefig('results/preimage_iam/mutag_median_nb' + str(nb_median) + - '_k' + str(k) + '.png', format="PNG") - # plt.show() - plt.clf() - # print(ghat_list[0].nodes(data=True)) - # print(ghat_list[0].edges(data=True)) - - # compute the corresponding sod in graph space. - sod_tmp, _ = ged_median([ghat_list[0]], Gn_median, ged_cost=ged_cost, - ged_method=ged_method, saveGXL=saveGXL) - sod_gs_list[idx_nb].append(sod_tmp) - sod_gs_min_list[idx_nb].append(np.min(sod_tmp)) - print('\nsmallest sod in graph space: ', np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each set of median graphs and k: ', - sod_gs_min_list) - print('\nsmallest distance in kernel space for each set of median graphs and k: ', - dis_ks_min_list) - print('\nnumber of updates of the best graph for each set of median graphs and k by IAM: ', - nb_updated_list) - print('\nnumber of updates of k nearest graphs for each set of median graphs and k by IAM: ', - nb_updated_k_list) - print('\ntimes:', time_list) - - - - - - -############################################################################### -# tests on different numbers of median-sets. - -def test_preimage_iam_median_nb(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 3 # iteration limit for pre-image. -# alpha_range = np.linspace(0.5, 0.5, 1) - k = 5 # k nearest neighbors - epsilon = 1e-6 - InitIAMWithAllDk = True - # parameters for IAM function -# c_vi = 0.037 -# c_vr = 0.038 -# c_vs = 0.075 -# c_ei = 0.001 -# c_er = 0.001 -# c_es = 0.0 - c_vi = 4 - c_vr = 4 - c_vs = 2 - c_ei = 1 - c_er = 1 - c_es = 1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = True - connected_iam = False - # parameters for GED function -# ged_cost='CHEM_1' - ged_cost = 'CONSTANT' - ged_method = 'IPFP' - edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es] - ged_stabilizer = 'min' - ged_repeat = 50 - params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method, - 'edit_cost_constant': edit_cost_constant, - 'stabilizer': ged_stabilizer, 'repeat': ged_repeat} - - # number of graphs; we what to compute the median of these graphs. -# nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100] - nb_median_range = [2] - - # find out all the graphs classified to positive group 1. - idx_dict = get_same_item_indices(y_all) - Gn = [Gn[i] for i in idx_dict[1]] - -# # compute Gram matrix. -# time0 = time.time() -# km = compute_kernel(Gn, gkernel, True) -# time_km = time.time() - time0 -# # write Gram matrix to file. -# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km) - - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list = [] - nb_updated_k_list = [] - g_best = [] - for nb_median in nb_median_range: - print('\n-------------------------------------------------------') - print('number of median graphs =', nb_median) - random.seed(1) - idx_rdm = random.sample(range(len(Gn)), nb_median) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn[idx].copy() for idx in idx_rdm] - -# for g in Gn_median: -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -## plt.savefig("results/preimage_mix/mutag.png", format="PNG") -# plt.show() -# plt.clf() - - ################################################################### - gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz') - km_tmp = gmfile['gm'] - time_km = gmfile['gmtime'] - # modify mixed gram matrix. - km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median)) - for i in range(len(Gn)): - for j in range(i, len(Gn)): - km[i, j] = km_tmp[i, j] - km[j, i] = km[i, j] - for i in range(len(Gn)): - for j, idx in enumerate(idx_rdm): - km[i, len(Gn) + j] = km[i, idx] - km[len(Gn) + j, i] = km[i, idx] - for i, idx1 in enumerate(idx_rdm): - for j, idx2 in enumerate(idx_rdm): - km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2] - - ################################################################### - alpha_range = [1 / nb_median] * nb_median - time0 = time.time() - dhat, ghat_list, dis_of_each_itr, nb_updated, nb_updated_k = \ - preimage_iam(Gn, Gn_median, - alpha_range, range(len(Gn), len(Gn) + nb_median), km, k, r_max, - gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk, - params_iam={'c_ei': c_ei, 'c_er': c_er, 'c_es': c_es, - 'ite_max': ite_max_iam, 'epsilon': epsilon_iam, - 'removeNodes': removeNodes, 'connected': connected_iam}, - params_ged=params_ged) - - time_total = time.time() - time0 + time_km - print('\ntime: ', time_total) - time_list.append(time_total) - print('\nsmallest distance in kernel space: ', dhat) - dis_ks_min_list.append(dhat) - g_best.append(ghat_list) - print('\nnumber of updates of the best graph: ', nb_updated) - nb_updated_list.append(nb_updated) - print('\nnumber of updates of k nearest graphs: ', nb_updated_k) - nb_updated_k_list.append(nb_updated_k) - - # show the best graph and save it to file. - print('the shortest distance is', dhat) - print('one of the possible corresponding pre-images is') - nx.draw(ghat_list[0], labels=nx.get_node_attributes(ghat_list[0], 'atom'), - with_labels=True) - plt.show() -# plt.savefig('results/preimage_iam/mutag_median_cs.001_nb' + str(nb_median) + -# '.png', format="PNG") - plt.clf() -# print(ghat_list[0].nodes(data=True)) -# print(ghat_list[0].edges(data=True)) - - # compute the corresponding sod in graph space. - sod_tmp, _ = ged_median([ghat_list[0]], Gn_median, params_ged=params_ged) - sod_gs_list.append(sod_tmp) - sod_gs_min_list.append(np.min(sod_tmp)) - print('\nsmallest sod in graph space: ', np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each set of median graphs: ', sod_gs_min_list) - print('\nsmallest distance in kernel space for each set of median graphs: ', - dis_ks_min_list) - print('\nnumber of updates of the best graph for each set of median graphs by IAM: ', - nb_updated_list) - print('\nnumber of updates of k nearest graphs for each set of median graphs by IAM: ', - nb_updated_k_list) - print('\ntimes:', time_list) - - - - - - -############################################################################### -# test on the combination of the two randomly chosen graphs. (the same as in the -# random pre-image paper.) - -def test_gkiam_2combination_all_pairs(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 10 # iteration limit for pre-image. - alpha_range = np.linspace(0.5, 0.5, 1) - k = 5 # k nearest neighbors - epsilon = 1e-6 - InitIAMWithAllDk = False - # parameters for GED function - ged_cost='CHEM_1' - ged_method='IPFP' - saveGXL='gedlib' - # parameters for IAM function - c_ei=1 - c_er=1 - c_es=1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = True - connected_iam = False - - nb_update_mat = np.full((len(Gn), len(Gn)), np.inf) - # test on each pair of graphs. -# for idx1 in range(len(Gn) - 1, -1, -1): -# for idx2 in range(idx1, -1, -1): - for idx1 in range(187, 188): - for idx2 in range(167, 168): - g1 = Gn[idx1].copy() - g2 = Gn[idx2].copy() - # Gn[10] = [] - # Gn[10] = [] - - nx.draw(g1, labels=nx.get_node_attributes(g1, 'atom'), with_labels=True) - plt.savefig("results/gk_iam/all_pairs/mutag187.png", format="PNG") - plt.show() - plt.clf() - nx.draw(g2, labels=nx.get_node_attributes(g2, 'atom'), with_labels=True) - plt.savefig("results/gk_iam/all_pairs/mutag167.png", format="PNG") - plt.show() - plt.clf() - - ################################################################### -# Gn_mix = [g.copy() for g in Gn] -# Gn_mix.append(g1.copy()) -# Gn_mix.append(g2.copy()) -# -# # compute -# time0 = time.time() -# km = compute_kernel(Gn_mix, gkernel, True) -# time_km = time.time() - time0 -# -# # write Gram matrix to file and read it. -# np.savez('results/gram_matrix_uhpath_itr7_pq0.8.gm', gm=km, gmtime=time_km) - - ################################################################### - gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03.gm.npz') - km = gmfile['gm'] - time_km = gmfile['gmtime'] - # modify mixed gram matrix. - for i in range(len(Gn)): - km[i, len(Gn)] = km[i, idx1] - km[i, len(Gn) + 1] = km[i, idx2] - km[len(Gn), i] = km[i, idx1] - km[len(Gn) + 1, i] = km[i, idx2] - km[len(Gn), len(Gn)] = km[idx1, idx1] - km[len(Gn), len(Gn) + 1] = km[idx1, idx2] - km[len(Gn) + 1, len(Gn)] = km[idx2, idx1] - km[len(Gn) + 1, len(Gn) + 1] = km[idx2, idx2] - - ################################################################### -# # use only the two graphs in median set as candidates. -# Gn = [g1.copy(), g2.copy()] -# Gn_mix = Gn + [g1.copy(), g2.copy()] -# # compute -# time0 = time.time() -# km = compute_kernel(Gn_mix, gkernel, True) -# time_km = time.time() - time0 - - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list = [] - nb_updated_k_list = [] - g_best = [] - # for each alpha - for alpha in alpha_range: - print('\n-------------------------------------------------------\n') - print('alpha =', alpha) - time0 = time.time() - dhat, ghat_list, sod_ks, nb_updated, nb_updated_k = \ - preimage_iam(Gn, [g1, g2], - [alpha, 1 - alpha], range(len(Gn), len(Gn) + 2), km, k, r_max, - gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk, - params_iam={'c_ei': c_ei, 'c_er': c_er, 'c_es': c_es, - 'ite_max': ite_max_iam, 'epsilon': epsilon_iam, - 'removeNodes': removeNodes, 'connected': connected_iam}, - params_ged={'ged_cost': ged_cost, 'ged_method': ged_method, - 'saveGXL': saveGXL}) - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list.append(time_total) - dis_ks_min_list.append(dhat) - g_best.append(ghat_list) - nb_updated_list.append(nb_updated) - nb_updated_k_list.append(nb_updated_k) - - # show best graphs and save them to file. - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_ks_min_list[idx]) - print('one of the possible corresponding pre-images is') - nx.draw(g_best[idx][0], labels=nx.get_node_attributes(g_best[idx][0], 'atom'), - with_labels=True) - plt.savefig('results/gk_iam/mutag' + str(idx1) + '_' + str(idx2) - + '_alpha' + str(item) + '.png', format="PNG") -# plt.show() - plt.clf() -# print(g_best[idx][0].nodes(data=True)) -# print(g_best[idx][0].edges(data=True)) - - # for g in g_best[idx]: - # draw_Letter_graph(g, savepath='results/gk_iam/') - ## nx.draw_networkx(g) - ## plt.show() - # print(g.nodes(data=True)) - # print(g.edges(data=True)) - - # compute the corresponding sod in graph space. - for idx, item in enumerate(alpha_range): - sod_tmp, _ = ged_median([g_best[0]], [g1, g2], ged_cost=ged_cost, - ged_method=ged_method, saveGXL=saveGXL) - sod_gs_list.append(sod_tmp) - sod_gs_min_list.append(np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each alpha: ', sod_gs_min_list) - print('\nsmallest distance in kernel space for each alpha: ', dis_ks_min_list) - print('\nnumber of updates of the best graph for each alpha: ', - nb_updated_list) - print('\nnumber of updates of the k nearest graphs for each alpha: ', - nb_updated_k_list) - print('\ntimes:', time_list) - nb_update_mat[idx1, idx2] = nb_updated_list[0] - - str_fw = 'graphs %d and %d: %d.\n' % (idx1, idx2, nb_updated_list[0]) - with open('results/gk_iam/all_pairs/nb_updates.txt', 'r+') as file: - content = file.read() - file.seek(0, 0) - file.write(str_fw + content) - - - -def test_gkiam_2combination(): - from gk_iam import gk_iam_nearest_multi - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 10 # iteration limit for pre-image. - alpha_range = np.linspace(0.5, 0.5, 1) - k = 20 # k nearest neighbors - epsilon = 1e-6 - ged_cost='CHEM_1' - ged_method='IPFP' - saveGXL='gedlib' - c_ei=1 - c_er=1 - c_es=1 - - # randomly select two molecules - np.random.seed(1) - idx_gi = [10, 11] # np.random.randint(0, len(Gn), 2) - g1 = Gn[idx_gi[0]].copy() - g2 = Gn[idx_gi[1]].copy() -# Gn[10] = [] -# Gn[10] = [] - -# nx.draw(g1, labels=nx.get_node_attributes(g1, 'atom'), with_labels=True) -# plt.savefig("results/random_preimage/mutag10.png", format="PNG") -# plt.show() -# nx.draw(g2, labels=nx.get_node_attributes(g2, 'atom'), with_labels=True) -# plt.savefig("results/random_preimage/mutag11.png", format="PNG") -# plt.show() - - Gn_mix = [g.copy() for g in Gn] - Gn_mix.append(g1.copy()) - Gn_mix.append(g2.copy()) - - # compute -# time0 = time.time() -# km = compute_kernel(Gn_mix, gkernel, True) -# time_km = time.time() - time0 - - # write Gram matrix to file and read it. -# np.savez('results/gram_matrix.gm', gm=km, gmtime=time_km) - gmfile = np.load('results/gram_matrix.gm.npz') - km = gmfile['gm'] - time_km = gmfile['gmtime'] - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list = [] - g_best = [] - # for each alpha - for alpha in alpha_range: - print('\n-------------------------------------------------------\n') - print('alpha =', alpha) - time0 = time.time() - dhat, ghat_list, sod_ks, nb_updated = gk_iam_nearest_multi(Gn, [g1, g2], - [alpha, 1 - alpha], range(len(Gn), len(Gn) + 2), km, k, r_max, - gkernel, c_ei=c_ei, c_er=c_er, c_es=c_es, epsilon=epsilon, - ged_cost=ged_cost, ged_method=ged_method, saveGXL=saveGXL) - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list.append(time_total) - dis_ks_min_list.append(dhat) - g_best.append(ghat_list) - nb_updated_list.append(nb_updated) - - # show best graphs and save them to file. - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_ks_min_list[idx]) - print('one of the possible corresponding pre-images is') - nx.draw(g_best[idx][0], labels=nx.get_node_attributes(g_best[idx][0], 'atom'), - with_labels=True) - plt.savefig('results/gk_iam/mutag_alpha' + str(item) + '.png', format="PNG") - plt.show() - print(g_best[idx][0].nodes(data=True)) - print(g_best[idx][0].edges(data=True)) - -# for g in g_best[idx]: -# draw_Letter_graph(g, savepath='results/gk_iam/') -## nx.draw_networkx(g) -## plt.show() -# print(g.nodes(data=True)) -# print(g.edges(data=True)) - - # compute the corresponding sod in graph space. - for idx, item in enumerate(alpha_range): - sod_tmp, _ = ged_median([g_best[0]], [g1, g2], ged_cost=ged_cost, - ged_method=ged_method, saveGXL=saveGXL) - sod_gs_list.append(sod_tmp) - sod_gs_min_list.append(np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each alpha: ', sod_gs_min_list) - print('\nsmallest distance in kernel space for each alpha: ', dis_ks_min_list) - print('\nnumber of updates for each alpha: ', nb_updated_list) - print('\ntimes:', time_list) - - -############################################################################### - - -if __name__ == '__main__': -############################################################################### -# test on the combination of the two randomly chosen graphs. (the same as in the -# random pre-image paper.) -# test_gkiam_2combination() -# test_gkiam_2combination_all_pairs() - -############################################################################### -# tests on different numbers of median-sets. - test_preimage_iam_median_nb() - -############################################################################### -# tests on different values on grid of median-sets and k. -# test_preimage_iam_grid_k_median_nb() \ No newline at end of file diff --git a/gklearn/preimage/test_preimage_mix.py b/gklearn/preimage/test_preimage_mix.py deleted file mode 100644 index 888de86..0000000 --- a/gklearn/preimage/test_preimage_mix.py +++ /dev/null @@ -1,539 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Sep 5 15:59:00 2019 - -@author: ljia -""" - -import numpy as np -import networkx as nx -import matplotlib.pyplot as plt -import time -import random -#from tqdm import tqdm - -from gklearn.utils.graphfiles import loadDataset -from gklearn.preimage.ged import ged_median -from gklearn.preimage.utils import compute_kernel, get_same_item_indices, remove_edges -from gklearn.preimage.preimage_iam import preimage_iam_random_mix - -############################################################################### -# tests on different values on grid of median-sets and k. - -def test_preimage_mix_grid_k_median_nb(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 5 # iteration limit for pre-image. - l_max = 500 # update limit for random generation -# alpha_range = np.linspace(0.5, 0.5, 1) -# k = 5 # k nearest neighbors - epsilon = 1e-6 - InitIAMWithAllDk = True - InitRandomWithAllDk = True - # parameters for GED function - ged_cost='CHEM_1' - ged_method='IPFP' - saveGXL='gedlib' - # parameters for IAM function - c_ei=1 - c_er=1 - c_es=1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = True - connected_iam = False - - # number of graphs; we what to compute the median of these graphs. - nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100] - # number of nearest neighbors. - k_range = [5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100] - - # find out all the graphs classified to positive group 1. - idx_dict = get_same_item_indices(y_all) - Gn = [Gn[i] for i in idx_dict[1]] - -# # compute Gram matrix. -# time0 = time.time() -# km = compute_kernel(Gn, gkernel, True) -# time_km = time.time() - time0 -# # write Gram matrix to file. -# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km) - - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list_iam = [] - nb_updated_list_random = [] - nb_updated_k_list_iam = [] - nb_updated_k_list_random = [] - g_best = [] - for idx_nb, nb_median in enumerate(nb_median_range): - print('\n-------------------------------------------------------') - print('number of median graphs =', nb_median) - random.seed(1) - idx_rdm = random.sample(range(len(Gn)), nb_median) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn[idx].copy() for idx in idx_rdm] - -# for g in Gn_median: -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -## plt.savefig("results/preimage_mix/mutag.png", format="PNG") -# plt.show() -# plt.clf() - - ################################################################### - gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz') - km_tmp = gmfile['gm'] - time_km = gmfile['gmtime'] - # modify mixed gram matrix. - km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median)) - for i in range(len(Gn)): - for j in range(i, len(Gn)): - km[i, j] = km_tmp[i, j] - km[j, i] = km[i, j] - for i in range(len(Gn)): - for j, idx in enumerate(idx_rdm): - km[i, len(Gn) + j] = km[i, idx] - km[len(Gn) + j, i] = km[i, idx] - for i, idx1 in enumerate(idx_rdm): - for j, idx2 in enumerate(idx_rdm): - km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2] - - ################################################################### - alpha_range = [1 / nb_median] * nb_median - - time_list.append([]) - dis_ks_min_list.append([]) - sod_gs_list.append([]) - sod_gs_min_list.append([]) - nb_updated_list_iam.append([]) - nb_updated_list_random.append([]) - nb_updated_k_list_iam.append([]) - nb_updated_k_list_random.append([]) - g_best.append([]) - - for k in k_range: - print('\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n') - print('k =', k) - time0 = time.time() - dhat, ghat_list, dis_of_each_itr, nb_updated_iam, nb_updated_random, \ - nb_updated_k_iam, nb_updated_k_random = \ - preimage_iam_random_mix(Gn, Gn_median, - alpha_range, range(len(Gn), len(Gn) + nb_median), km, k, r_max, - l_max, gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk, - InitRandomWithAllDk=InitRandomWithAllDk, - params_iam={'c_ei': c_ei, 'c_er': c_er, 'c_es': c_es, - 'ite_max': ite_max_iam, 'epsilon': epsilon_iam, - 'removeNodes': removeNodes, 'connected': connected_iam}, - params_ged={'ged_cost': ged_cost, 'ged_method': ged_method, - 'saveGXL': saveGXL}) - - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list[idx_nb].append(time_total) - print('\nsmallest distance in kernel space: ', dhat) - dis_ks_min_list[idx_nb].append(dhat) - g_best[idx_nb].append(ghat_list) - print('\nnumber of updates of the best graph by IAM: ', nb_updated_iam) - nb_updated_list_iam[idx_nb].append(nb_updated_iam) - print('\nnumber of updates of the best graph by random generation: ', - nb_updated_random) - nb_updated_list_random[idx_nb].append(nb_updated_random) - print('\nnumber of updates of k nearest graphs by IAM: ', nb_updated_k_iam) - nb_updated_k_list_iam[idx_nb].append(nb_updated_k_iam) - print('\nnumber of updates of k nearest graphs by random generation: ', - nb_updated_k_random) - nb_updated_k_list_random[idx_nb].append(nb_updated_k_random) - - # show the best graph and save it to file. - print('the shortest distance is', dhat) - print('one of the possible corresponding pre-images is') - nx.draw(ghat_list[0], labels=nx.get_node_attributes(ghat_list[0], 'atom'), - with_labels=True) - plt.savefig('results/preimage_mix/mutag_median_nb' + str(nb_median) + - '_k' + str(k) + '.png', format="PNG") - # plt.show() - plt.clf() - # print(ghat_list[0].nodes(data=True)) - # print(ghat_list[0].edges(data=True)) - - # compute the corresponding sod in graph space. - sod_tmp, _ = ged_median([ghat_list[0]], Gn_median, ged_cost=ged_cost, - ged_method=ged_method, saveGXL=saveGXL) - sod_gs_list[idx_nb].append(sod_tmp) - sod_gs_min_list[idx_nb].append(np.min(sod_tmp)) - print('\nsmallest sod in graph space: ', np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each set of median graphs and k: ', - sod_gs_min_list) - print('\nsmallest distance in kernel space for each set of median graphs and k: ', - dis_ks_min_list) - print('\nnumber of updates of the best graph for each set of median graphs and k by IAM: ', - nb_updated_list_iam) - print('\nnumber of updates of the best graph for each set of median graphs and k by random generation: ', - nb_updated_list_random) - print('\nnumber of updates of k nearest graphs for each set of median graphs and k by IAM: ', - nb_updated_k_list_iam) - print('\nnumber of updates of k nearest graphs for each set of median graphs and k by random generation: ', - nb_updated_k_list_random) - print('\ntimes:', time_list) - - - - -############################################################################### -# tests on different numbers of median-sets. - -def test_preimage_mix_median_nb(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 5 # iteration limit for pre-image. - l_max = 500 # update limit for random generation -# alpha_range = np.linspace(0.5, 0.5, 1) - k = 5 # k nearest neighbors - epsilon = 1e-6 - InitIAMWithAllDk = True - InitRandomWithAllDk = True - # parameters for GED function - ged_cost='CHEM_1' - ged_method='IPFP' - saveGXL='gedlib' - # parameters for IAM function - c_ei=1 - c_er=1 - c_es=1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = True - connected_iam = False - - # number of graphs; we what to compute the median of these graphs. - nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100] - - # find out all the graphs classified to positive group 1. - idx_dict = get_same_item_indices(y_all) - Gn = [Gn[i] for i in idx_dict[1]] - -# # compute Gram matrix. -# time0 = time.time() -# km = compute_kernel(Gn, gkernel, True) -# time_km = time.time() - time0 -# # write Gram matrix to file. -# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km) - - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list_iam = [] - nb_updated_list_random = [] - nb_updated_k_list_iam = [] - nb_updated_k_list_random = [] - g_best = [] - for nb_median in nb_median_range: - print('\n-------------------------------------------------------') - print('number of median graphs =', nb_median) - random.seed(1) - idx_rdm = random.sample(range(len(Gn)), nb_median) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn[idx].copy() for idx in idx_rdm] - -# for g in Gn_median: -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -## plt.savefig("results/preimage_mix/mutag.png", format="PNG") -# plt.show() -# plt.clf() - - ################################################################### - gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz') - km_tmp = gmfile['gm'] - time_km = gmfile['gmtime'] - # modify mixed gram matrix. - km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median)) - for i in range(len(Gn)): - for j in range(i, len(Gn)): - km[i, j] = km_tmp[i, j] - km[j, i] = km[i, j] - for i in range(len(Gn)): - for j, idx in enumerate(idx_rdm): - km[i, len(Gn) + j] = km[i, idx] - km[len(Gn) + j, i] = km[i, idx] - for i, idx1 in enumerate(idx_rdm): - for j, idx2 in enumerate(idx_rdm): - km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2] - - ################################################################### - alpha_range = [1 / nb_median] * nb_median - time0 = time.time() - dhat, ghat_list, dis_of_each_itr, nb_updated_iam, nb_updated_random, \ - nb_updated_k_iam, nb_updated_k_random = \ - preimage_iam_random_mix(Gn, Gn_median, - alpha_range, range(len(Gn), len(Gn) + nb_median), km, k, r_max, - l_max, gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk, - InitRandomWithAllDk=InitRandomWithAllDk, - params_iam={'c_ei': c_ei, 'c_er': c_er, 'c_es': c_es, - 'ite_max': ite_max_iam, 'epsilon': epsilon_iam, - 'removeNodes': removeNodes, 'connected': connected_iam}, - params_ged={'ged_cost': ged_cost, 'ged_method': ged_method, - 'saveGXL': saveGXL}) - - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list.append(time_total) - print('\nsmallest distance in kernel space: ', dhat) - dis_ks_min_list.append(dhat) - g_best.append(ghat_list) - print('\nnumber of updates of the best graph by IAM: ', nb_updated_iam) - nb_updated_list_iam.append(nb_updated_iam) - print('\nnumber of updates of the best graph by random generation: ', - nb_updated_random) - nb_updated_list_random.append(nb_updated_random) - print('\nnumber of updates of k nearest graphs by IAM: ', nb_updated_k_iam) - nb_updated_k_list_iam.append(nb_updated_k_iam) - print('\nnumber of updates of k nearest graphs by random generation: ', - nb_updated_k_random) - nb_updated_k_list_random.append(nb_updated_k_random) - - # show the best graph and save it to file. - print('the shortest distance is', dhat) - print('one of the possible corresponding pre-images is') - nx.draw(ghat_list[0], labels=nx.get_node_attributes(ghat_list[0], 'atom'), - with_labels=True) - plt.savefig('results/preimage_mix/mutag_median_nb' + str(nb_median) + - '.png', format="PNG") -# plt.show() - plt.clf() -# print(ghat_list[0].nodes(data=True)) -# print(ghat_list[0].edges(data=True)) - - # compute the corresponding sod in graph space. - sod_tmp, _ = ged_median([ghat_list[0]], Gn_median, ged_cost=ged_cost, - ged_method=ged_method, saveGXL=saveGXL) - sod_gs_list.append(sod_tmp) - sod_gs_min_list.append(np.min(sod_tmp)) - print('\nsmallest sod in graph space: ', np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each set of median graphs: ', sod_gs_min_list) - print('\nsmallest distance in kernel space for each set of median graphs: ', - dis_ks_min_list) - print('\nnumber of updates of the best graph for each set of median graphs by IAM: ', - nb_updated_list_iam) - print('\nnumber of updates of the best graph for each set of median graphs by random generation: ', - nb_updated_list_random) - print('\nnumber of updates of k nearest graphs for each set of median graphs by IAM: ', - nb_updated_k_list_iam) - print('\nnumber of updates of k nearest graphs for each set of median graphs by random generation: ', - nb_updated_k_list_random) - print('\ntimes:', time_list) - - - -############################################################################### -# test on the combination of the two randomly chosen graphs. (the same as in the -# random pre-image paper.) - -def test_preimage_mix_2combination_all_pairs(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 10 # iteration limit for pre-image. - l_max = 500 # update limit for random generation - alpha_range = np.linspace(0.5, 0.5, 1) - k = 5 # k nearest neighbors - epsilon = 1e-6 - InitIAMWithAllDk = True - InitRandomWithAllDk = True - # parameters for GED function - ged_cost='CHEM_1' - ged_method='IPFP' - saveGXL='gedlib' - # parameters for IAM function - c_ei=1 - c_er=1 - c_es=1 - ite_max_iam = 50 - epsilon_iam = 0.001 - removeNodes = True - connected_iam = False - - nb_update_mat_iam = np.full((len(Gn), len(Gn)), np.inf) - nb_update_mat_random = np.full((len(Gn), len(Gn)), np.inf) - # test on each pair of graphs. -# for idx1 in range(len(Gn) - 1, -1, -1): -# for idx2 in range(idx1, -1, -1): - for idx1 in range(187, 188): - for idx2 in range(167, 168): - g1 = Gn[idx1].copy() - g2 = Gn[idx2].copy() - # Gn[10] = [] - # Gn[10] = [] - - nx.draw(g1, labels=nx.get_node_attributes(g1, 'atom'), with_labels=True) - plt.savefig("results/preimage_mix/mutag187.png", format="PNG") - plt.show() - plt.clf() - nx.draw(g2, labels=nx.get_node_attributes(g2, 'atom'), with_labels=True) - plt.savefig("results/preimage_mix/mutag167.png", format="PNG") - plt.show() - plt.clf() - - ################################################################### -# Gn_mix = [g.copy() for g in Gn] -# Gn_mix.append(g1.copy()) -# Gn_mix.append(g2.copy()) -# -# # compute -# time0 = time.time() -# km = compute_kernel(Gn_mix, gkernel, True) -# time_km = time.time() - time0 -# -# # write Gram matrix to file and read it. -# np.savez('results/gram_matrix_uhpath_itr7_pq0.8.gm', gm=km, gmtime=time_km) - - ################################################################### - gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03.gm.npz') - km = gmfile['gm'] - time_km = gmfile['gmtime'] - # modify mixed gram matrix. - for i in range(len(Gn)): - km[i, len(Gn)] = km[i, idx1] - km[i, len(Gn) + 1] = km[i, idx2] - km[len(Gn), i] = km[i, idx1] - km[len(Gn) + 1, i] = km[i, idx2] - km[len(Gn), len(Gn)] = km[idx1, idx1] - km[len(Gn), len(Gn) + 1] = km[idx1, idx2] - km[len(Gn) + 1, len(Gn)] = km[idx2, idx1] - km[len(Gn) + 1, len(Gn) + 1] = km[idx2, idx2] - - ################################################################### -# # use only the two graphs in median set as candidates. -# Gn = [g1.copy(), g2.copy()] -# Gn_mix = Gn + [g1.copy(), g2.copy()] -# # compute -# time0 = time.time() -# km = compute_kernel(Gn_mix, gkernel, True) -# time_km = time.time() - time0 - - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list_iam = [] - nb_updated_list_random = [] - nb_updated_k_list_iam = [] - nb_updated_k_list_random = [] - g_best = [] - # for each alpha - for alpha in alpha_range: - print('\n-------------------------------------------------------\n') - print('alpha =', alpha) - time0 = time.time() - dhat, ghat_list, dis_of_each_itr, nb_updated_iam, nb_updated_random, \ - nb_updated_k_iam, nb_updated_k_random = \ - preimage_iam_random_mix(Gn, [g1, g2], - [alpha, 1 - alpha], range(len(Gn), len(Gn) + 2), km, k, r_max, - l_max, gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk, - InitRandomWithAllDk=InitRandomWithAllDk, - params_iam={'c_ei': c_ei, 'c_er': c_er, 'c_es': c_es, - 'ite_max': ite_max_iam, 'epsilon': epsilon_iam, - 'removeNodes': removeNodes, 'connected': connected_iam}, - params_ged={'ged_cost': ged_cost, 'ged_method': ged_method, - 'saveGXL': saveGXL}) - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list.append(time_total) - dis_ks_min_list.append(dhat) - g_best.append(ghat_list) - nb_updated_list_iam.append(nb_updated_iam) - nb_updated_list_random.append(nb_updated_random) - nb_updated_k_list_iam.append(nb_updated_k_iam) - nb_updated_k_list_random.append(nb_updated_k_random) - - # show best graphs and save them to file. - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_ks_min_list[idx]) - print('one of the possible corresponding pre-images is') - nx.draw(g_best[idx][0], labels=nx.get_node_attributes(g_best[idx][0], 'atom'), - with_labels=True) - plt.savefig('results/preimage_mix/mutag' + str(idx1) + '_' + str(idx2) - + '_alpha' + str(item) + '.png', format="PNG") -# plt.show() - plt.clf() -# print(g_best[idx][0].nodes(data=True)) -# print(g_best[idx][0].edges(data=True)) - - # for g in g_best[idx]: - # draw_Letter_graph(g, savepath='results/gk_iam/') - ## nx.draw_networkx(g) - ## plt.show() - # print(g.nodes(data=True)) - # print(g.edges(data=True)) - - # compute the corresponding sod in graph space. - for idx, item in enumerate(alpha_range): - sod_tmp, _ = ged_median([g_best[0]], [g1, g2], ged_cost=ged_cost, - ged_method=ged_method, saveGXL=saveGXL) - sod_gs_list.append(sod_tmp) - sod_gs_min_list.append(np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each alpha: ', sod_gs_min_list) - print('\nsmallest distance in kernel space for each alpha: ', dis_ks_min_list) - print('\nnumber of updates of the best graph for each alpha by IAM: ', nb_updated_list_iam) - print('\nnumber of updates of the best graph for each alpha by random generation: ', - nb_updated_list_random) - print('\nnumber of updates of k nearest graphs for each alpha by IAM: ', - nb_updated_k_list_iam) - print('\nnumber of updates of k nearest graphs for each alpha by random generation: ', - nb_updated_k_list_random) - print('\ntimes:', time_list) - nb_update_mat_iam[idx1, idx2] = nb_updated_list_iam[0] - nb_update_mat_random[idx1, idx2] = nb_updated_list_random[0] - - str_fw = 'graphs %d and %d: %d times by IAM, %d times by random generation.\n' \ - % (idx1, idx2, nb_updated_list_iam[0], nb_updated_list_random[0]) - with open('results/preimage_mix/nb_updates.txt', 'r+') as file: - content = file.read() - file.seek(0, 0) - file.write(str_fw + content) - -############################################################################### - - -if __name__ == '__main__': -############################################################################### -# test on the combination of the two randomly chosen graphs. (the same as in the -# random pre-image paper.) -# test_preimage_mix_2combination_all_pairs() - -############################################################################### -# tests on different numbers of median-sets. -# test_preimage_mix_median_nb() - -############################################################################### -# tests on different values on grid of median-sets and k. - test_preimage_mix_grid_k_median_nb() \ No newline at end of file diff --git a/gklearn/preimage/test_preimage_random.py b/gklearn/preimage/test_preimage_random.py deleted file mode 100644 index bb77d2f..0000000 --- a/gklearn/preimage/test_preimage_random.py +++ /dev/null @@ -1,398 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Sep 5 15:59:00 2019 - -@author: ljia -""" - -import numpy as np -import networkx as nx -import matplotlib.pyplot as plt -import time -import random -#from tqdm import tqdm - -from gklearn.utils.graphfiles import loadDataset -from gklearn.preimage.preimage_random import preimage_random -from gklearn.preimage.ged import ged_median -from gklearn.preimage.utils import compute_kernel, get_same_item_indices, remove_edges - - -############################################################################### -# tests on different values on grid of median-sets and k. - -def test_preimage_random_grid_k_median_nb(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 5 # iteration limit for pre-image. - l = 500 # update limit for random generation -# alpha_range = np.linspace(0.5, 0.5, 1) -# k = 5 # k nearest neighbors - # parameters for GED function - ged_cost='CHEM_1' - ged_method='IPFP' - saveGXL='gedlib' - - # number of graphs; we what to compute the median of these graphs. - nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100] - # number of nearest neighbors. - k_range = [5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100] - - # find out all the graphs classified to positive group 1. - idx_dict = get_same_item_indices(y_all) - Gn = [Gn[i] for i in idx_dict[1]] - -# # compute Gram matrix. -# time0 = time.time() -# km = compute_kernel(Gn, gkernel, True) -# time_km = time.time() - time0 -# # write Gram matrix to file. -# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km) - - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list = [] - g_best = [] - for idx_nb, nb_median in enumerate(nb_median_range): - print('\n-------------------------------------------------------') - print('number of median graphs =', nb_median) - random.seed(1) - idx_rdm = random.sample(range(len(Gn)), nb_median) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn[idx].copy() for idx in idx_rdm] - -# for g in Gn_median: -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -## plt.savefig("results/preimage_mix/mutag.png", format="PNG") -# plt.show() -# plt.clf() - - ################################################################### - gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz') - km_tmp = gmfile['gm'] - time_km = gmfile['gmtime'] - # modify mixed gram matrix. - km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median)) - for i in range(len(Gn)): - for j in range(i, len(Gn)): - km[i, j] = km_tmp[i, j] - km[j, i] = km[i, j] - for i in range(len(Gn)): - for j, idx in enumerate(idx_rdm): - km[i, len(Gn) + j] = km[i, idx] - km[len(Gn) + j, i] = km[i, idx] - for i, idx1 in enumerate(idx_rdm): - for j, idx2 in enumerate(idx_rdm): - km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2] - - ################################################################### - alpha_range = [1 / nb_median] * nb_median - - time_list.append([]) - dis_ks_min_list.append([]) - sod_gs_list.append([]) - sod_gs_min_list.append([]) - nb_updated_list.append([]) - g_best.append([]) - - for k in k_range: - print('\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n') - print('k =', k) - time0 = time.time() - dhat, ghat, nb_updated = preimage_random(Gn, Gn_median, alpha_range, - range(len(Gn), len(Gn) + nb_median), km, k, r_max, l, gkernel) - - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list[idx_nb].append(time_total) - print('\nsmallest distance in kernel space: ', dhat) - dis_ks_min_list[idx_nb].append(dhat) - g_best[idx_nb].append(ghat) - print('\nnumber of updates of the best graph: ', nb_updated) - nb_updated_list[idx_nb].append(nb_updated) - - # show the best graph and save it to file. - print('the shortest distance is', dhat) - print('one of the possible corresponding pre-images is') - nx.draw(ghat, labels=nx.get_node_attributes(ghat, 'atom'), - with_labels=True) - plt.savefig('results/preimage_random/mutag_median_nb' + str(nb_median) + - '_k' + str(k) + '.png', format="PNG") - # plt.show() - plt.clf() - # print(ghat_list[0].nodes(data=True)) - # print(ghat_list[0].edges(data=True)) - - # compute the corresponding sod in graph space. - sod_tmp, _ = ged_median([ghat], Gn_median, ged_cost=ged_cost, - ged_method=ged_method, saveGXL=saveGXL) - sod_gs_list[idx_nb].append(sod_tmp) - sod_gs_min_list[idx_nb].append(np.min(sod_tmp)) - print('\nsmallest sod in graph space: ', np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each set of median graphs and k: ', - sod_gs_min_list) - print('\nsmallest distance in kernel space for each set of median graphs and k: ', - dis_ks_min_list) - print('\nnumber of updates of the best graph for each set of median graphs and k by IAM: ', - nb_updated_list) - print('\ntimes:', time_list) - - - - -############################################################################### -# tests on different numbers of median-sets. - -def test_preimage_random_median_nb(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:50] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - - lmbda = 0.03 # termination probalility - r_max = 5 # iteration limit for pre-image. - l = 500 # update limit for random generation -# alpha_range = np.linspace(0.5, 0.5, 1) - k = 5 # k nearest neighbors - # parameters for GED function - ged_cost='CHEM_1' - ged_method='IPFP' - saveGXL='gedlib' - - # number of graphs; we what to compute the median of these graphs. - nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100] - - # find out all the graphs classified to positive group 1. - idx_dict = get_same_item_indices(y_all) - Gn = [Gn[i] for i in idx_dict[1]] - -# # compute Gram matrix. -# time0 = time.time() -# km = compute_kernel(Gn, gkernel, True) -# time_km = time.time() - time0 -# # write Gram matrix to file. -# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km) - - - time_list = [] - dis_ks_min_list = [] - sod_gs_list = [] - sod_gs_min_list = [] - nb_updated_list = [] - g_best = [] - for nb_median in nb_median_range: - print('\n-------------------------------------------------------') - print('number of median graphs =', nb_median) - random.seed(1) - idx_rdm = random.sample(range(len(Gn)), nb_median) - print('graphs chosen:', idx_rdm) - Gn_median = [Gn[idx].copy() for idx in idx_rdm] - -# for g in Gn_median: -# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True) -## plt.savefig("results/preimage_mix/mutag.png", format="PNG") -# plt.show() -# plt.clf() - - ################################################################### - gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz') - km_tmp = gmfile['gm'] - time_km = gmfile['gmtime'] - # modify mixed gram matrix. - km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median)) - for i in range(len(Gn)): - for j in range(i, len(Gn)): - km[i, j] = km_tmp[i, j] - km[j, i] = km[i, j] - for i in range(len(Gn)): - for j, idx in enumerate(idx_rdm): - km[i, len(Gn) + j] = km[i, idx] - km[len(Gn) + j, i] = km[i, idx] - for i, idx1 in enumerate(idx_rdm): - for j, idx2 in enumerate(idx_rdm): - km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2] - - ################################################################### - alpha_range = [1 / nb_median] * nb_median - time0 = time.time() - dhat, ghat, nb_updated = preimage_random(Gn, Gn_median, alpha_range, - range(len(Gn), len(Gn) + nb_median), km, k, r_max, l, gkernel) - - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list.append(time_total) - print('\nsmallest distance in kernel space: ', dhat) - dis_ks_min_list.append(dhat) - g_best.append(ghat) - print('\nnumber of updates of the best graph: ', nb_updated) - nb_updated_list.append(nb_updated) - - # show the best graph and save it to file. - print('the shortest distance is', dhat) - print('one of the possible corresponding pre-images is') - nx.draw(ghat, labels=nx.get_node_attributes(ghat, 'atom'), - with_labels=True) - plt.savefig('results/preimage_random/mutag_median_nb' + str(nb_median) + - '.png', format="PNG") -# plt.show() - plt.clf() -# print(ghat_list[0].nodes(data=True)) -# print(ghat_list[0].edges(data=True)) - - # compute the corresponding sod in graph space. - sod_tmp, _ = ged_median([ghat], Gn_median, ged_cost=ged_cost, - ged_method=ged_method, saveGXL=saveGXL) - sod_gs_list.append(sod_tmp) - sod_gs_min_list.append(np.min(sod_tmp)) - print('\nsmallest sod in graph space: ', np.min(sod_tmp)) - - print('\nsods in graph space: ', sod_gs_list) - print('\nsmallest sod in graph space for each set of median graphs: ', sod_gs_min_list) - print('\nsmallest distance in kernel space for each set of median graphs: ', - dis_ks_min_list) - print('\nnumber of updates of the best graph for each set of median graphs: ', - nb_updated_list) - print('\ntimes:', time_list) - - - -############################################################################### -# test on the combination of the two randomly chosen graphs. (the same as in the -# random pre-image paper.) - -def test_random_preimage_2combination(): - ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt', - 'extra_params': {}} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params']) -# Gn = Gn[0:12] - remove_edges(Gn) - gkernel = 'marginalizedkernel' - -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, gkernel=gkernel) -# print(dis_max, dis_min, dis_mean) - - lmbda = 0.03 # termination probalility - r_max = 10 # iteration limit for pre-image. - l = 500 - alpha_range = np.linspace(0, 1, 11) - k = 5 # k nearest neighbors - - # randomly select two molecules - np.random.seed(1) - idx_gi = [187, 167] # np.random.randint(0, len(Gn), 2) - g1 = Gn[idx_gi[0]].copy() - g2 = Gn[idx_gi[1]].copy() - -# nx.draw(g1, labels=nx.get_node_attributes(g1, 'atom'), with_labels=True) -# plt.savefig("results/random_preimage/mutag10.png", format="PNG") -# plt.show() -# nx.draw(g2, labels=nx.get_node_attributes(g2, 'atom'), with_labels=True) -# plt.savefig("results/random_preimage/mutag11.png", format="PNG") -# plt.show() - - ###################################################################### -# Gn_mix = [g.copy() for g in Gn] -# Gn_mix.append(g1.copy()) -# Gn_mix.append(g2.copy()) -# -## g_tmp = iam([g1, g2]) -## nx.draw_networkx(g_tmp) -## plt.show() -# -# # compute -# time0 = time.time() -# km = compute_kernel(Gn_mix, gkernel, True) -# time_km = time.time() - time0 - - ################################################################### - idx1 = idx_gi[0] - idx2 = idx_gi[1] - gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03.gm.npz') - km = gmfile['gm'] - time_km = gmfile['gmtime'] - # modify mixed gram matrix. - for i in range(len(Gn)): - km[i, len(Gn)] = km[i, idx1] - km[i, len(Gn) + 1] = km[i, idx2] - km[len(Gn), i] = km[i, idx1] - km[len(Gn) + 1, i] = km[i, idx2] - km[len(Gn), len(Gn)] = km[idx1, idx1] - km[len(Gn), len(Gn) + 1] = km[idx1, idx2] - km[len(Gn) + 1, len(Gn)] = km[idx2, idx1] - km[len(Gn) + 1, len(Gn) + 1] = km[idx2, idx2] - - ################################################################### - - time_list = [] - nb_updated_list = [] - g_best = [] - dis_ks_min_list = [] - # for each alpha - for alpha in alpha_range: - print('\n-------------------------------------------------------\n') - print('alpha =', alpha) - time0 = time.time() - dhat, ghat, nb_updated = preimage_random(Gn, [g1, g2], [alpha, 1 - alpha], - range(len(Gn), len(Gn) + 2), km, - k, r_max, l, gkernel) - time_total = time.time() - time0 + time_km - print('time: ', time_total) - time_list.append(time_total) - dis_ks_min_list.append(dhat) - g_best.append(ghat) - nb_updated_list.append(nb_updated) - - # show best graphs and save them to file. - for idx, item in enumerate(alpha_range): - print('when alpha is', item, 'the shortest distance is', dis_ks_min_list[idx]) - print('one of the possible corresponding pre-images is') - nx.draw(g_best[idx], labels=nx.get_node_attributes(g_best[idx], 'atom'), - with_labels=True) - plt.show() - plt.savefig('results/random_preimage/mutag_alpha' + str(item) + '.png', format="PNG") - plt.clf() - print(g_best[idx].nodes(data=True)) - print(g_best[idx].edges(data=True)) - -# # compute the corresponding sod in graph space. (alpha range not considered.) -# sod_tmp, _ = median_distance(g_best[0], Gn_let) -# sod_gs_list.append(sod_tmp) -# sod_gs_min_list.append(np.min(sod_tmp)) -# sod_ks_min_list.append(sod_ks) -# nb_updated_list.append(nb_updated) - -# print('\nsmallest sod in graph space for each alpha: ', sod_gs_min_list) - print('\nsmallest distance in kernel space for each alpha: ', dis_ks_min_list) - print('\nnumber of updates for each alpha: ', nb_updated_list) - print('\ntimes:', time_list) - -############################################################################### - - -if __name__ == '__main__': -############################################################################### -# test on the combination of the two randomly chosen graphs. (the same as in the -# random pre-image paper.) -# test_random_preimage_2combination() - -############################################################################### -# tests all algorithms on different numbers of median-sets. - test_preimage_random_median_nb() - -############################################################################### -# tests all algorithms on different values on grid of median-sets and k. -# test_preimage_random_grid_k_median_nb() \ No newline at end of file diff --git a/gklearn/preimage/utils.py b/gklearn/preimage/utils.py index 63c8b9e..823ecc5 100644 --- a/gklearn/preimage/utils.py +++ b/gklearn/preimage/utils.py @@ -22,6 +22,7 @@ from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel from gklearn.utils import Dataset import csv import networkx as nx +import os def generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=True, save_medians=True, plot_medians=True, load_gm='auto', dir_save='', irrelevant_labels=None, edge_required=False): @@ -215,6 +216,8 @@ def generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged # save median graphs. if save_medians: + if not os.path.exists(dir_save + 'medians/'): + os.makedirs(dir_save + 'medians/') print('Saving median graphs to files...') fn_pre_sm = dir_save + 'medians/set_median.' + mpg_options['fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) saveGXL(mpg.set_median, fn_pre_sm + '.gxl', method='default', @@ -286,6 +289,8 @@ def generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged def __init_output_file(ds_name, gkernel, fit_method, dir_output): + if not os.path.exists(dir_output): + os.makedirs(dir_output) # fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.csv' f_detail = open(dir_output + fn_output_detail, 'a') diff --git a/gklearn/preimage/xp_fit_method.py b/gklearn/preimage/xp_fit_method.py deleted file mode 100644 index ead2786..0000000 --- a/gklearn/preimage/xp_fit_method.py +++ /dev/null @@ -1,935 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jan 14 15:39:29 2020 - -@author: ljia -""" -import numpy as np -import random -import csv -from shutil import copyfile -import networkx as nx -import matplotlib.pyplot as plt -import os -import time - -from gklearn.utils.graphfiles import loadDataset, loadGXL, saveGXL -from gklearn.preimage.test_k_closest_graphs import median_on_k_closest_graphs, reform_attributes -from gklearn.preimage.utils import get_same_item_indices, kernel_distance_matrix, compute_kernel -from gklearn.preimage.find_best_k import getRelations - - -def get_dataset(ds_name): - if ds_name == 'Letter-high': # node non-symb - dataset = 'cpp_ext/data/collections/Letter.xml' - graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/' - Gn, y_all = loadDataset(dataset, extra_params=graph_dir) - for G in Gn: - reform_attributes(G, na_names=['x', 'y']) - G.graph['node_labels'] = [] - G.graph['edge_labels'] = [] - G.graph['node_attrs'] = ['x', 'y'] - G.graph['edge_attrs'] = [] - elif ds_name == 'Letter-med': # node non-symb - dataset = 'cpp_ext/data/collections/Letter.xml' - graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/MED/' - Gn, y_all = loadDataset(dataset, extra_params=graph_dir) - for G in Gn: - reform_attributes(G, na_names=['x', 'y']) - G.graph['node_labels'] = [] - G.graph['edge_labels'] = [] - G.graph['node_attrs'] = ['x', 'y'] - G.graph['edge_attrs'] = [] - elif ds_name == 'Letter-low': # node non-symb - dataset = 'cpp_ext/data/collections/Letter.xml' - graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/LOW/' - Gn, y_all = loadDataset(dataset, extra_params=graph_dir) - for G in Gn: - reform_attributes(G, na_names=['x', 'y']) - G.graph['node_labels'] = [] - G.graph['edge_labels'] = [] - G.graph['node_attrs'] = ['x', 'y'] - G.graph['edge_attrs'] = [] - elif ds_name == 'Fingerprint': -# dataset = 'cpp_ext/data/collections/Fingerprint.xml' -# graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/Fingerprint/node_attrs/' -# Gn, y_all = loadDataset(dataset, extra_params=graph_dir) -# for G in Gn: -# reform_attributes(G) - dataset = '../../datasets/Fingerprint/Fingerprint_A.txt' - graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/Fingerprint/node_attrs/' - Gn, y_all = loadDataset(dataset) - elif ds_name == 'SYNTHETIC': - pass - elif ds_name == 'SYNTHETICnew': - dataset = '../../datasets/SYNTHETICnew/SYNTHETICnew_A.txt' - graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/SYNTHETICnew' -# dataset = '../../datasets/Letter-high/Letter-high_A.txt' -# graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/' - Gn, y_all = loadDataset(dataset) - elif ds_name == 'Synthie': - pass - elif ds_name == 'COIL-DEL': - dataset = '../../datasets/COIL-DEL/COIL-DEL_A.txt' - graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/COIL-DEL/' - Gn, y_all = loadDataset(dataset) - elif ds_name == 'COIL-RAG': - pass - elif ds_name == 'COLORS-3': - pass - elif ds_name == 'FRANKENSTEIN': - pass - - return Gn, y_all, graph_dir - - -def init_output_file(ds_name, gkernel, fit_method, dir_output): -# fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' - fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.csv' - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'edit cost', - 'GED method', 'attr distance', 'fit method', 'k', - 'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM', 'fitting time', 'generating time', 'total time', - 'median set']) - f_detail.close() - -# fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' - fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.csv' - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'edit cost', - 'GED method', 'attr distance', 'fit method', 'k', - 'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM', 'fitting time', 'generating time', 'total time', - '# SOD SM -> GM', '# dis_k SM -> GM', - '# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM', - 'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM', - 'repeats better dis_k gi -> GM']) - f_summary.close() - - return fn_output_detail, fn_output_summary - - -def xp_fit_method_for_non_symbolic(parameters, save_results=True, initial_solutions=1, - Gn_data=None, k_dis_data=None, Kmatrix=None, - is_separate=False): - - # 1. set parameters. - print('1. setting parameters...') - ds_name = parameters['ds_name'] - gkernel = parameters['gkernel'] - edit_cost_name = parameters['edit_cost_name'] - ged_method = parameters['ged_method'] - attr_distance = parameters['attr_distance'] - fit_method = parameters['fit_method'] - init_ecc = parameters['init_ecc'] - - node_label = None - edge_label = None - dir_output = 'results/xp_fit_method/' - - - # 2. get dataset. - print('2. getting dataset...') - if Gn_data is None: - Gn, y_all, graph_dir = get_dataset(ds_name) - else: - Gn = Gn_data[0] - y_all = Gn_data[1] - graph_dir = Gn_data[2] - - - # 3. compute kernel distance matrix. - print('3. computing kernel distance matrix...') - if k_dis_data is None: - dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, - None, Kmatrix=Kmatrix, gkernel=gkernel) - else: -# dis_mat = k_dis_data[0] -# dis_max = k_dis_data[1] -# dis_min = k_dis_data[2] -# dis_mean = k_dis_data[3] -# print('pair distances - dis_max, dis_min, dis_mean:', dis_max, dis_min, dis_mean) - pass - - - if save_results: - # create result files. - print('creating output files...') - fn_output_detail, fn_output_summary = init_output_file(ds_name, gkernel, - fit_method, dir_output) - - - # start repeats. - repeats = 1 -# k_list = range(2, 11) - k_list = [0] - # get indices by classes. - y_idx = get_same_item_indices(y_all) - random.seed(1) - rdn_seed_list = random.sample(range(0, repeats * 100), repeats) - - for k in k_list: -# print('\n--------- k =', k, '----------') - - sod_sm_mean_list = [] - sod_gm_mean_list = [] - dis_k_sm_mean_list = [] - dis_k_gm_mean_list = [] - dis_k_gi_min_mean_list = [] - time_fitting_mean_list = [] - time_generating_mean_list = [] - time_total_mean_list = [] - - # 3. start generating and computing over targets. - print('4. starting generating and computing over targets......') - for i, (y, values) in enumerate(y_idx.items()): -# y = 'I' -# values = y_idx[y] -# values = values[0:10] - print('\ny =', y) -# if y.strip() == 'A': -# continue - - k = len(values) - print('\n--------- k =', k, '----------') - - if k < 2: - print('\nk = ', k, ', skip.\n') - continue - - sod_sm_list = [] - sod_gm_list = [] - dis_k_sm_list = [] - dis_k_gm_list = [] - dis_k_gi_min_list = [] - time_fitting_list = [] - time_generating_list = [] - time_total_list = [] - nb_sod_sm2gm = [0, 0, 0] - nb_dis_k_sm2gm = [0, 0, 0] - nb_dis_k_gi2sm = [0, 0, 0] - nb_dis_k_gi2gm = [0, 0, 0] - repeats_better_sod_sm2gm = [] - repeats_better_dis_k_sm2gm = [] - repeats_better_dis_k_gi2sm = [] - repeats_better_dis_k_gi2gm = [] - - # get Gram matrix for this part of data. - if Kmatrix is not None: - if is_separate: - Kmatrix_sub = Kmatrix[i].copy() - else: - Kmatrix_sub = Kmatrix[values,:] - Kmatrix_sub = Kmatrix_sub[:,values] - else: - Kmatrix_sub = None - - for repeat in range(repeats): - print('\nrepeat =', repeat) - random.seed(rdn_seed_list[repeat]) - median_set_idx_idx = random.sample(range(0, len(values)), k) - median_set_idx = [values[idx] for idx in median_set_idx_idx] - print('median set: ', median_set_idx) - Gn_median = [Gn[g] for g in values] -# from notebooks.utils.plot_all_graphs import draw_Fingerprint_graph -# for Gn in Gn_median: -# draw_Fingerprint_graph(Gn, save=None) - - # GENERATING & COMPUTING!! - res_sods, res_dis_ks, res_times = median_on_k_closest_graphs(Gn_median, - node_label, edge_label, - gkernel, k, fit_method=fit_method, graph_dir=graph_dir, - edit_cost_constants=None, group_min=median_set_idx_idx, - dataset=ds_name, initial_solutions=initial_solutions, - edit_cost_name=edit_cost_name, init_ecc=init_ecc, - Kmatrix=Kmatrix_sub, parallel=False) - sod_sm = res_sods[0] - sod_gm = res_sods[1] - dis_k_sm = res_dis_ks[0] - dis_k_gm = res_dis_ks[1] - dis_k_gi = res_dis_ks[2] - dis_k_gi_min = res_dis_ks[3] - idx_dis_k_gi_min = res_dis_ks[4] - time_fitting = res_times[0] - time_generating = res_times[1] - - # write result detail. - sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm)) - dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm)) - dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min)) - dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min)) - if save_results: - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow([ds_name, gkernel, - edit_cost_name, ged_method, attr_distance, - fit_method, k, y, repeat, - sod_sm, sod_gm, dis_k_sm, dis_k_gm, - dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm, - dis_k_gi2gm, time_fitting, time_generating, - time_fitting + time_generating, median_set_idx]) - f_detail.close() - - # compute result summary. - sod_sm_list.append(sod_sm) - sod_gm_list.append(sod_gm) - dis_k_sm_list.append(dis_k_sm) - dis_k_gm_list.append(dis_k_gm) - dis_k_gi_min_list.append(dis_k_gi_min) - time_fitting_list.append(time_fitting) - time_generating_list.append(time_generating) - time_total_list.append(time_fitting + time_generating) - # # SOD SM -> GM - if sod_sm > sod_gm: - nb_sod_sm2gm[0] += 1 - repeats_better_sod_sm2gm.append(repeat) - elif sod_sm == sod_gm: - nb_sod_sm2gm[1] += 1 - elif sod_sm < sod_gm: - nb_sod_sm2gm[2] += 1 - # # dis_k SM -> GM - if dis_k_sm > dis_k_gm: - nb_dis_k_sm2gm[0] += 1 - repeats_better_dis_k_sm2gm.append(repeat) - elif dis_k_sm == dis_k_gm: - nb_dis_k_sm2gm[1] += 1 - elif dis_k_sm < dis_k_gm: - nb_dis_k_sm2gm[2] += 1 - # # dis_k gi -> SM - if dis_k_gi_min > dis_k_sm: - nb_dis_k_gi2sm[0] += 1 - repeats_better_dis_k_gi2sm.append(repeat) - elif dis_k_gi_min == dis_k_sm: - nb_dis_k_gi2sm[1] += 1 - elif dis_k_gi_min < dis_k_sm: - nb_dis_k_gi2sm[2] += 1 - # # dis_k gi -> GM - if dis_k_gi_min > dis_k_gm: - nb_dis_k_gi2gm[0] += 1 - repeats_better_dis_k_gi2gm.append(repeat) - elif dis_k_gi_min == dis_k_gm: - nb_dis_k_gi2gm[1] += 1 - elif dis_k_gi_min < dis_k_gm: - nb_dis_k_gi2gm[2] += 1 - - # save median graphs. - fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl' - fn_pre_sm_new = dir_output + 'medians/set_median.' + fit_method \ - + '.k' + str(int(k)) + '.y' + str(y) + '.repeat' + str(repeat) - copyfile(fname_sm, fn_pre_sm_new + '.gxl') - fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl' - fn_pre_gm_new = dir_output + 'medians/gen_median.' + fit_method \ - + '.k' + str(int(k)) + '.y' + str(y) + '.repeat' + str(repeat) - copyfile(fname_gm, fn_pre_gm_new + '.gxl') - G_best_kernel = Gn_median[idx_dis_k_gi_min].copy() -# reform_attributes(G_best_kernel) - fn_pre_g_best_kernel = dir_output + 'medians/g_best_kernel.' + fit_method \ - + '.k' + str(int(k)) + '.y' + str(y) + '.repeat' + str(repeat) - saveGXL(G_best_kernel, fn_pre_g_best_kernel + '.gxl', method='default') - - # plot median graphs. - if ds_name == 'Letter-high' or ds_name == 'Letter-med' or ds_name == 'Letter-low': - set_median = loadGXL(fn_pre_sm_new + '.gxl') - gen_median = loadGXL(fn_pre_gm_new + '.gxl') - draw_Letter_graph(set_median, fn_pre_sm_new) - draw_Letter_graph(gen_median, fn_pre_gm_new) - draw_Letter_graph(G_best_kernel, fn_pre_g_best_kernel) - - # write result summary for each letter. - sod_sm_mean_list.append(np.mean(sod_sm_list)) - sod_gm_mean_list.append(np.mean(sod_gm_list)) - dis_k_sm_mean_list.append(np.mean(dis_k_sm_list)) - dis_k_gm_mean_list.append(np.mean(dis_k_gm_list)) - dis_k_gi_min_mean_list.append(np.mean(dis_k_gi_min_list)) - time_fitting_mean_list.append(np.mean(time_fitting_list)) - time_generating_mean_list.append(np.mean(time_generating_list)) - time_total_mean_list.append(np.mean(time_total_list)) - sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean_list[-1] - sod_sm_mean_list[-1])) - dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_sm_mean_list[-1])) - dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean_list[-1] - dis_k_gi_min_mean_list[-1])) - dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_gi_min_mean_list[-1])) - if save_results: - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow([ds_name, gkernel, - edit_cost_name, ged_method, attr_distance, - fit_method, k, y, - sod_sm_mean_list[-1], sod_gm_mean_list[-1], - dis_k_sm_mean_list[-1], dis_k_gm_mean_list[-1], - dis_k_gi_min_mean_list[-1], sod_sm2gm_mean, dis_k_sm2gm_mean, - dis_k_gi2sm_mean, dis_k_gi2gm_mean, - time_fitting_mean_list[-1], time_generating_mean_list[-1], - time_total_mean_list[-1], nb_sod_sm2gm, - nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm, - repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm, - repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm]) - f_summary.close() - - - # write result summary for each letter. - sod_sm_mean = np.mean(sod_sm_mean_list) - sod_gm_mean = np.mean(sod_gm_mean_list) - dis_k_sm_mean = np.mean(dis_k_sm_mean_list) - dis_k_gm_mean = np.mean(dis_k_gm_mean_list) - dis_k_gi_min_mean = np.mean(dis_k_gi_min_list) - time_fitting_mean = np.mean(time_fitting_list) - time_generating_mean = np.mean(time_generating_list) - time_total_mean = np.mean(time_total_list) - sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean)) - dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean)) - dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean)) - dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean)) - if save_results: - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow([ds_name, gkernel, - edit_cost_name, ged_method, attr_distance, - fit_method, k, 'all', - sod_sm_mean, sod_gm_mean, dis_k_sm_mean, dis_k_gm_mean, - dis_k_gi_min_mean, sod_sm2gm_mean, dis_k_sm2gm_mean, - dis_k_gi2sm_mean, dis_k_gi2gm_mean, - time_fitting_mean, time_generating_mean, time_total_mean]) - f_summary.close() - - print('\ncomplete.') - - -#Dessin median courrant -def draw_Letter_graph(graph, file_prefix): - plt.figure() - pos = {} - for n in graph.nodes: - pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])]) - nx.draw_networkx(graph, pos) - plt.savefig(file_prefix + '.eps', format='eps', dpi=300) -# plt.show() - plt.clf() - - -def compute_gm_for_each_class(Gn, y_all, gkernel, parallel='imap_unordered', is_separate=True): - - if is_separate: - print('the Gram matrix is computed for each class.') - y_idx = get_same_item_indices(y_all) - Kmatrix = [] - run_time = [] - k_dis_data = [] - for i, (y, values) in enumerate(y_idx.items()): - print('The ', str(i), ' class:') - Gn_i = [Gn[val] for val in values] - time0 = time.time() - Kmatrix.append(compute_kernel(Gn_i, gkernel, None, None, True, parallel=parallel)) - run_time.append(time.time() - time0) - k_dis_data.append(kernel_distance_matrix(Gn_i, None, None, - Kmatrix=Kmatrix[i], gkernel=gkernel, verbose=True)) - np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', - Kmatrix=Kmatrix, run_time=run_time, is_separate=is_separate) - dis_max = np.max([item[1] for item in k_dis_data]) - dis_min = np.min([item[2] for item in k_dis_data]) - dis_mean = np.mean([item[3] for item in k_dis_data]) - print('pair distances - dis_max, dis_min, dis_mean:', dis_max, dis_min, - dis_mean) - - else: - time0 = time.time() - Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel=parallel) - run_time = time.time() - time0 - np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', - Kmatrix=Kmatrix, run_time=run_time, is_separate=is_separate) - k_dis_data = kernel_distance_matrix(Gn, None, None, - Kmatrix=Kmatrix, gkernel=gkernel, verbose=True) - print('the Gram matrix is computed for the whole dataset.') - print('pair distances - dis_max, dis_min, dis_mean:', k_dis_data[1], - k_dis_data[2], k_dis_data[3]) - - print('\nTime to compute Gram matrix for the whole dataset: ', run_time) -# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean] - return Kmatrix, run_time, k_dis_data - - -if __name__ == "__main__": -# #### xp 1: Letter-high, spkernel. -# # load dataset. -# print('getting dataset and computing kernel distance matrix first...') -# ds_name = 'Letter-high' -# gkernel = 'spkernel' -# Gn, y_all, graph_dir = get_dataset(ds_name) -# # remove graphs without edges. -# Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_edges(G) != 0] -# idx = [G[0] for G in Gn] -# Gn = [G[1] for G in Gn] -# y_all = [y_all[i] for i in idx] -## Gn = Gn[0:50] -## y_all = y_all[0:50] -# # compute pair distances. -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=None, gkernel=gkernel, verbose=True) -## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 -# # fitting and computing. -# fit_methods = ['random', 'expert', 'k-graphs'] -# for fit_method in fit_methods: -# print('\n-------------------------------------') -# print('fit method:', fit_method) -# parameters = {'ds_name': ds_name, -# 'gkernel': gkernel, -# 'edit_cost_name': 'LETTER2', -# 'ged_method': 'mIPFP', -# 'attr_distance': 'euclidean', -# 'fit_method': fit_method} -# xp_fit_method_for_non_symbolic(parameters, save_results=True, -# initial_solutions=40, -# Gn_data = [Gn, y_all, graph_dir], -# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean]) - - -# #### xp 2: Letter-high, sspkernel. -# # load dataset. -# print('getting dataset and computing kernel distance matrix first...') -# ds_name = 'Letter-high' -# gkernel = 'structuralspkernel' -# Gn, y_all, graph_dir = get_dataset(ds_name) -## Gn = Gn[0:50] -## y_all = y_all[0:50] -# # compute pair distances. -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=None, gkernel=gkernel, verbose=True) -## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 -# # fitting and computing. -# fit_methods = ['random', 'expert', 'k-graphs'] -# for fit_method in fit_methods: -# print('\n-------------------------------------') -# print('fit method:', fit_method) -# parameters = {'ds_name': ds_name, -# 'gkernel': gkernel, -# 'edit_cost_name': 'LETTER2', -# 'ged_method': 'mIPFP', -# 'attr_distance': 'euclidean', -# 'fit_method': fit_method} -# print('parameters: ', parameters) -# xp_fit_method_for_non_symbolic(parameters, save_results=True, -# initial_solutions=40, -# Gn_data = [Gn, y_all, graph_dir], -# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean]) - - -# #### xp 3: SYNTHETICnew, sspkernel, using NON_SYMBOLIC. -# gmfile = np.load('results/xp_fit_method/Kmatrix.SYNTHETICnew.structuralspkernel.gm.npz') -# Kmatrix = gmfile['Kmatrix'] -# run_time = gmfile['run_time'] -# # normalization -# Kmatrix_diag = Kmatrix.diagonal().copy() -# for i in range(len(Kmatrix)): -# for j in range(i, len(Kmatrix)): -# Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j]) -# Kmatrix[j][i] = Kmatrix[i][j] -## np.savez('results/xp_fit_method/Kmatrix.SYNTHETICnew.spkernel.gm', -## Kmatrix=Kmatrix, run_time=run_time) -# # load dataset. -# print('getting dataset and computing kernel distance matrix first...') -# ds_name = 'SYNTHETICnew' -# gkernel = 'structuralspkernel' -# Gn, y_all, graph_dir = get_dataset(ds_name) -# # remove graphs without nodes and edges. -# Gn = [(idx, G) for idx, G in enumerate(Gn) if (nx.number_of_nodes(G) != 0 -# and nx.number_of_edges(G) != 0)] -# idx = [G[0] for G in Gn] -# Gn = [G[1] for G in Gn] -# y_all = [y_all[i] for i in idx] -## Gn = Gn[0:10] -## y_all = y_all[0:10] -# for G in Gn: -# G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl' -# # compute pair distances. -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True) -## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 -# # fitting and computing. -# fit_methods = ['k-graphs', 'random', 'random', 'random'] -# for fit_method in fit_methods: -# print('\n-------------------------------------') -# print('fit method:', fit_method) -# parameters = {'ds_name': ds_name, -# 'gkernel': gkernel, -# 'edit_cost_name': 'NON_SYMBOLIC', -# 'ged_method': 'mIPFP', -# 'attr_distance': 'euclidean', -# 'fit_method': fit_method} -# xp_fit_method_for_non_symbolic(parameters, save_results=True, -# initial_solutions=1, -# Gn_data = [Gn, y_all, graph_dir], -# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean], -# Kmatrix=Kmatrix) - - -# ### xp 4: SYNTHETICnew, spkernel, using NON_SYMBOLIC. -# gmfile = np.load('results/xp_fit_method/Kmatrix.SYNTHETICnew.spkernel.gm.npz') -# Kmatrix = gmfile['Kmatrix'] -# # normalization -# Kmatrix_diag = Kmatrix.diagonal().copy() -# for i in range(len(Kmatrix)): -# for j in range(i, len(Kmatrix)): -# Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j]) -# Kmatrix[j][i] = Kmatrix[i][j] -# run_time = 21821.35 -# np.savez('results/xp_fit_method/Kmatrix.SYNTHETICnew.spkernel.gm', -# Kmatrix=Kmatrix, run_time=run_time) -# -# # load dataset. -# print('getting dataset and computing kernel distance matrix first...') -# ds_name = 'SYNTHETICnew' -# gkernel = 'spkernel' -# Gn, y_all, graph_dir = get_dataset(ds_name) -## # remove graphs without nodes and edges. -## Gn = [(idx, G) for idx, G in enumerate(Gn) if (nx.number_of_node(G) != 0 -## and nx.number_of_edges(G) != 0)] -## idx = [G[0] for G in Gn] -## Gn = [G[1] for G in Gn] -## y_all = [y_all[i] for i in idx] -## Gn = Gn[0:5] -## y_all = y_all[0:5] -# for G in Gn: -# G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl' -# -# # compute/read Gram matrix and pair distances. -## Kmatrix = compute_kernel(Gn, gkernel, None, None, True) -## np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', -## Kmatrix=Kmatrix) -# gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz') -# Kmatrix = gmfile['Kmatrix'] -# run_time = gmfile['run_time'] -## Kmatrix = Kmatrix[[0,1,2,3,4],:] -## Kmatrix = Kmatrix[:,[0,1,2,3,4]] -# print('\nTime to compute Gram matrix for the whole dataset: ', run_time) -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True) -## Kmatrix = np.zeros((len(Gn), len(Gn))) -## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 -# -# # fitting and computing. -# fit_methods = ['k-graphs', 'random', 'random', 'random'] -# for fit_method in fit_methods: -# print('\n-------------------------------------') -# print('fit method:', fit_method) -# parameters = {'ds_name': ds_name, -# 'gkernel': gkernel, -# 'edit_cost_name': 'NON_SYMBOLIC', -# 'ged_method': 'mIPFP', -# 'attr_distance': 'euclidean', -# 'fit_method': fit_method} -# xp_fit_method_for_non_symbolic(parameters, save_results=True, -# initial_solutions=1, -# Gn_data=[Gn, y_all, graph_dir], -# k_dis_data=[dis_mat, dis_max, dis_min, dis_mean], -# Kmatrix=Kmatrix) - - -# #### xp 5: Fingerprint, sspkernel, using LETTER2, only node attrs. -# # load dataset. -# print('getting dataset and computing kernel distance matrix first...') -# ds_name = 'Fingerprint' -# gkernel = 'structuralspkernel' -# Gn, y_all, graph_dir = get_dataset(ds_name) -# # remove graphs without nodes and edges. -# Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_nodes(G) != 0] -## and nx.number_of_edges(G) != 0)] -# idx = [G[0] for G in Gn] -# Gn = [G[1] for G in Gn] -# y_all = [y_all[i] for i in idx] -# y_idx = get_same_item_indices(y_all) -# # remove unused labels. -# for G in Gn: -# G.graph['edge_attrs'] = [] -# for edge in G.edges: -# del G.edges[edge]['attributes'] -# del G.edges[edge]['orient'] -# del G.edges[edge]['angle'] -## Gn = Gn[805:815] -## y_all = y_all[805:815] -# for G in Gn: -# G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl' -# -# # compute/read Gram matrix and pair distances. -## Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered') -## np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', -## Kmatrix=Kmatrix) -# gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz') -# Kmatrix = gmfile['Kmatrix'] -## run_time = gmfile['run_time'] -## Kmatrix = Kmatrix[[0,1,2,3,4],:] -## Kmatrix = Kmatrix[:,[0,1,2,3,4]] -## print('\nTime to compute Gram matrix for the whole dataset: ', run_time) -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True) -## Kmatrix = np.zeros((len(Gn), len(Gn))) -## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 -# -# # fitting and computing. -# fit_methods = ['k-graphs', 'random', 'random', 'random'] -# for fit_method in fit_methods: -# print('\n-------------------------------------') -# print('fit method:', fit_method) -# parameters = {'ds_name': ds_name, -# 'gkernel': gkernel, -# 'edit_cost_name': 'LETTER2', -# 'ged_method': 'mIPFP', -# 'attr_distance': 'euclidean', -# 'fit_method': fit_method, -# 'init_ecc': [1,1,1,1,1]} # [0.525, 0.525, 0.001, 0.125, 0.125]} -# xp_fit_method_for_non_symbolic(parameters, save_results=True, -# initial_solutions=40, -# Gn_data = [Gn, y_all, graph_dir], -# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean], -# Kmatrix=Kmatrix) - - -# #### xp 6: Letter-med, sspkernel. -# # load dataset. -# print('getting dataset and computing kernel distance matrix first...') -# ds_name = 'Letter-med' -# gkernel = 'structuralspkernel' -# Gn, y_all, graph_dir = get_dataset(ds_name) -## Gn = Gn[0:50] -## y_all = y_all[0:50] -# -# # compute/read Gram matrix and pair distances. -# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered') -# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', -# Kmatrix=Kmatrix) -## gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz') -## Kmatrix = gmfile['Kmatrix'] -## run_time = gmfile['run_time'] -## Kmatrix = Kmatrix[[0,1,2,3,4],:] -## Kmatrix = Kmatrix[:,[0,1,2,3,4]] -## print('\nTime to compute Gram matrix for the whole dataset: ', run_time) -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True) -## Kmatrix = np.zeros((len(Gn), len(Gn))) -## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 -# -# # fitting and computing. -# fit_methods = ['k-graphs', 'expert', 'random', 'random', 'random'] -# for fit_method in fit_methods: -# print('\n-------------------------------------') -# print('fit method:', fit_method) -# parameters = {'ds_name': ds_name, -# 'gkernel': gkernel, -# 'edit_cost_name': 'LETTER2', -# 'ged_method': 'mIPFP', -# 'attr_distance': 'euclidean', -# 'fit_method': fit_method, -# 'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475]} -# print('parameters: ', parameters) -# xp_fit_method_for_non_symbolic(parameters, save_results=True, -# initial_solutions=40, -# Gn_data = [Gn, y_all, graph_dir], -# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean], -# Kmatrix=Kmatrix) - - -# #### xp 7: Letter-low, sspkernel. -# # load dataset. -# print('getting dataset and computing kernel distance matrix first...') -# ds_name = 'Letter-low' -# gkernel = 'structuralspkernel' -# Gn, y_all, graph_dir = get_dataset(ds_name) -## Gn = Gn[0:50] -## y_all = y_all[0:50] -# -# # compute/read Gram matrix and pair distances. -# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered') -# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', -# Kmatrix=Kmatrix) -## gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz') -## Kmatrix = gmfile['Kmatrix'] -## run_time = gmfile['run_time'] -## Kmatrix = Kmatrix[[0,1,2,3,4],:] -## Kmatrix = Kmatrix[:,[0,1,2,3,4]] -## print('\nTime to compute Gram matrix for the whole dataset: ', run_time) -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True) -## Kmatrix = np.zeros((len(Gn), len(Gn))) -## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 -# -# # fitting and computing. -# fit_methods = ['k-graphs', 'expert', 'random', 'random', 'random'] -# for fit_method in fit_methods: -# print('\n-------------------------------------') -# print('fit method:', fit_method) -# parameters = {'ds_name': ds_name, -# 'gkernel': gkernel, -# 'edit_cost_name': 'LETTER2', -# 'ged_method': 'mIPFP', -# 'attr_distance': 'euclidean', -# 'fit_method': fit_method, -# 'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075]} -# print('parameters: ', parameters) -# xp_fit_method_for_non_symbolic(parameters, save_results=True, -# initial_solutions=40, -# Gn_data = [Gn, y_all, graph_dir], -# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean], -# Kmatrix=Kmatrix) - - -# #### xp 8: Letter-med, spkernel. -# # load dataset. -# print('getting dataset and computing kernel distance matrix first...') -# ds_name = 'Letter-med' -# gkernel = 'spkernel' -# Gn, y_all, graph_dir = get_dataset(ds_name) -# # remove graphs without nodes and edges. -# Gn = [(idx, G) for idx, G in enumerate(Gn) if (nx.number_of_nodes(G) != 0 -# and nx.number_of_edges(G) != 0)] -# idx = [G[0] for G in Gn] -# Gn = [G[1] for G in Gn] -# y_all = [y_all[i] for i in idx] -## Gn = Gn[0:50] -## y_all = y_all[0:50] -# -# # compute/read Gram matrix and pair distances. -# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered') -# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', -# Kmatrix=Kmatrix) -## gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz') -## Kmatrix = gmfile['Kmatrix'] -## run_time = gmfile['run_time'] -## Kmatrix = Kmatrix[[0,1,2,3,4],:] -## Kmatrix = Kmatrix[:,[0,1,2,3,4]] -## print('\nTime to compute Gram matrix for the whole dataset: ', run_time) -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True) -## Kmatrix = np.zeros((len(Gn), len(Gn))) -## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 -# -# # fitting and computing. -# fit_methods = ['k-graphs', 'expert', 'random', 'random', 'random'] -# for fit_method in fit_methods: -# print('\n-------------------------------------') -# print('fit method:', fit_method) -# parameters = {'ds_name': ds_name, -# 'gkernel': gkernel, -# 'edit_cost_name': 'LETTER2', -# 'ged_method': 'mIPFP', -# 'attr_distance': 'euclidean', -# 'fit_method': fit_method, -# 'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475]} -# print('parameters: ', parameters) -# xp_fit_method_for_non_symbolic(parameters, save_results=True, -# initial_solutions=40, -# Gn_data = [Gn, y_all, graph_dir], -# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean], -# Kmatrix=Kmatrix) - - -# #### xp 9: Letter-low, spkernel. -# # load dataset. -# print('getting dataset and computing kernel distance matrix first...') -# ds_name = 'Letter-low' -# gkernel = 'spkernel' -# Gn, y_all, graph_dir = get_dataset(ds_name) -# # remove graphs without nodes and edges. -# Gn = [(idx, G) for idx, G in enumerate(Gn) if (nx.number_of_nodes(G) != 0 -# and nx.number_of_edges(G) != 0)] -# idx = [G[0] for G in Gn] -# Gn = [G[1] for G in Gn] -# y_all = [y_all[i] for i in idx] -## Gn = Gn[0:50] -## y_all = y_all[0:50] -# -# # compute/read Gram matrix and pair distances. -# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered') -# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', -# Kmatrix=Kmatrix) -## gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz') -## Kmatrix = gmfile['Kmatrix'] -## run_time = gmfile['run_time'] -## Kmatrix = Kmatrix[[0,1,2,3,4],:] -## Kmatrix = Kmatrix[:,[0,1,2,3,4]] -## print('\nTime to compute Gram matrix for the whole dataset: ', run_time) -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True) -## Kmatrix = np.zeros((len(Gn), len(Gn))) -## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 -# -# # fitting and computing. -# fit_methods = ['k-graphs', 'expert', 'random', 'random', 'random'] -# for fit_method in fit_methods: -# print('\n-------------------------------------') -# print('fit method:', fit_method) -# parameters = {'ds_name': ds_name, -# 'gkernel': gkernel, -# 'edit_cost_name': 'LETTER2', -# 'ged_method': 'mIPFP', -# 'attr_distance': 'euclidean', -# 'fit_method': fit_method, -# 'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075]} -# print('parameters: ', parameters) -# xp_fit_method_for_non_symbolic(parameters, save_results=True, -# initial_solutions=40, -# Gn_data = [Gn, y_all, graph_dir], -# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean], -# Kmatrix=Kmatrix) - - - #### xp 5: COIL-DEL, sspkernel, using LETTER2, only node attrs. - # load dataset. - print('getting dataset and computing kernel distance matrix first...') - ds_name = 'COIL-DEL' - gkernel = 'structuralspkernel' - Gn, y_all, graph_dir = get_dataset(ds_name) - # remove graphs without nodes and edges. - Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_nodes(G) != 0] -# and nx.number_of_edges(G) != 0)] - idx = [G[0] for G in Gn] - Gn = [G[1] for G in Gn] - y_all = [y_all[i] for i in idx] - # remove unused labels. - for G in Gn: - G.graph['edge_labels'] = [] - for edge in G.edges: - del G.edges[edge]['bond_type'] - del G.edges[edge]['valence'] -# Gn = Gn[805:815] -# y_all = y_all[805:815] - for G in Gn: - G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl' - - # compute/read Gram matrix and pair distances. - is_separate = True - Kmatrix, run_time, k_dis_data = compute_gm_for_each_class(Gn, - y_all, - gkernel, - parallel='imap_unordered', - is_separate=is_separate) -# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered') -# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm', -# Kmatrix=Kmatrix) -# gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz') -# Kmatrix = gmfile['Kmatrix'] -# run_time = gmfile['run_time'] -# Kmatrix = Kmatrix[[0,1,2,3,4],:] -# Kmatrix = Kmatrix[:,[0,1,2,3,4]] -# print('\nTime to compute Gram matrix for the whole dataset: ', run_time) -# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, -# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True) -# Kmatrix = np.zeros((len(Gn), len(Gn))) -# dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0 - - # fitting and computing. - fit_methods = ['k-graphs', 'random', 'random', 'random'] - for fit_method in fit_methods: - print('\n-------------------------------------') - print('fit method:', fit_method) - parameters = {'ds_name': ds_name, - 'gkernel': gkernel, - 'edit_cost_name': 'LETTER2', - 'ged_method': 'mIPFP', - 'attr_distance': 'euclidean', - 'fit_method': fit_method, - 'init_ecc': [3,3,1,3,3]} # [0.525, 0.525, 0.001, 0.125, 0.125]} - xp_fit_method_for_non_symbolic(parameters, save_results=True, - initial_solutions=40, - Gn_data=[Gn, y_all, graph_dir], - k_dis_data=k_dis_data, - Kmatrix=Kmatrix, - is_separate=is_separate) \ No newline at end of file diff --git a/gklearn/preimage/xp_letter_h.py b/gklearn/preimage/xp_letter_h.py deleted file mode 100644 index 1e16fcf..0000000 --- a/gklearn/preimage/xp_letter_h.py +++ /dev/null @@ -1,476 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jan 14 15:39:29 2020 - -@author: ljia -""" -import numpy as np -import random -import csv -from shutil import copyfile -import networkx as nx -import matplotlib.pyplot as plt - -from gklearn.utils.graphfiles import loadDataset, loadGXL, saveGXL -from gklearn.preimage.test_k_closest_graphs import median_on_k_closest_graphs, reform_attributes -from gklearn.preimage.utils import get_same_item_indices, kernel_distance_matrix -from gklearn.preimage.find_best_k import getRelations - - -def xp_letter_h_LETTER2_cost(): - ds = {'dataset': 'cpp_ext/data/collections/Letter.xml', - 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir']) - - dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, Kmatrix=None, gkernel='structuralspkernel') - for G in Gn: - reform_attributes(G) -# ds = {'name': 'Letter-high', -# 'dataset': '../datasets/Letter-high/Letter-high_A.txt'} # node/edge symb -# Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:50] - gkernel = 'structuralspkernel' - node_label = None - edge_label = None - ds_name = 'letter-h' - dir_output = 'results/xp_letter_h/' - save_results = True - cost = 'LETTER2' - - repeats = 1 -# k_list = range(2, 11) - k_list = [150] - fit_method = 'k-graphs' - # get indices by classes. - y_idx = get_same_item_indices(y_all) - - if save_results: - # create result files. - fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'fit method', 'k', - 'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM', 'median set']) - f_detail.close() - fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'fit method', 'k', - 'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM', '# SOD SM -> GM', '# dis_k SM -> GM', - '# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM', - 'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM', - 'repeats better dis_k gi -> GM']) - f_summary.close() - - random.seed(1) - rdn_seed_list = random.sample(range(0, repeats * 100), repeats) - - for k in k_list: - print('\n--------- k =', k, '----------') - - sod_sm_mean_list = [] - sod_gm_mean_list = [] - dis_k_sm_mean_list = [] - dis_k_gm_mean_list = [] - dis_k_gi_min_mean_list = [] -# nb_sod_sm2gm = [0, 0, 0] -# nb_dis_k_sm2gm = [0, 0, 0] -# nb_dis_k_gi2sm = [0, 0, 0] -# nb_dis_k_gi2gm = [0, 0, 0] -# repeats_better_sod_sm2gm = [] -# repeats_better_dis_k_sm2gm = [] -# repeats_better_dis_k_gi2sm = [] -# repeats_better_dis_k_gi2gm = [] - - for i, (y, values) in enumerate(y_idx.items()): - print('\ny =', y) -# y = 'F' -# values = y_idx[y] -# values = values[0:10] - - k = len(values) - - sod_sm_list = [] - sod_gm_list = [] - dis_k_sm_list = [] - dis_k_gm_list = [] - dis_k_gi_min_list = [] - nb_sod_sm2gm = [0, 0, 0] - nb_dis_k_sm2gm = [0, 0, 0] - nb_dis_k_gi2sm = [0, 0, 0] - nb_dis_k_gi2gm = [0, 0, 0] - repeats_better_sod_sm2gm = [] - repeats_better_dis_k_sm2gm = [] - repeats_better_dis_k_gi2sm = [] - repeats_better_dis_k_gi2gm = [] - - for repeat in range(repeats): - print('\nrepeat =', repeat) - random.seed(rdn_seed_list[repeat]) - median_set_idx_idx = random.sample(range(0, len(values)), k) - median_set_idx = [values[idx] for idx in median_set_idx_idx] - print('median set: ', median_set_idx) - Gn_median = [Gn[g] for g in values] - - sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min \ - = median_on_k_closest_graphs(Gn_median, node_label, edge_label, - gkernel, k, fit_method=fit_method, graph_dir=ds['graph_dir'], - edit_costs=None, group_min=median_set_idx_idx, - dataset='Letter', cost=cost, parallel=False) - - # write result detail. - sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm)) - dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm)) - dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min)) - dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min)) - if save_results: - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow([ds_name, gkernel, fit_method, k, - y, repeat, - sod_sm, sod_gm, dis_k_sm, dis_k_gm, - dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm, - dis_k_gi2gm, median_set_idx]) - f_detail.close() - - # compute result summary. - sod_sm_list.append(sod_sm) - sod_gm_list.append(sod_gm) - dis_k_sm_list.append(dis_k_sm) - dis_k_gm_list.append(dis_k_gm) - dis_k_gi_min_list.append(dis_k_gi_min) - # # SOD SM -> GM - if sod_sm > sod_gm: - nb_sod_sm2gm[0] += 1 - repeats_better_sod_sm2gm.append(repeat) - elif sod_sm == sod_gm: - nb_sod_sm2gm[1] += 1 - elif sod_sm < sod_gm: - nb_sod_sm2gm[2] += 1 - # # dis_k SM -> GM - if dis_k_sm > dis_k_gm: - nb_dis_k_sm2gm[0] += 1 - repeats_better_dis_k_sm2gm.append(repeat) - elif dis_k_sm == dis_k_gm: - nb_dis_k_sm2gm[1] += 1 - elif dis_k_sm < dis_k_gm: - nb_dis_k_sm2gm[2] += 1 - # # dis_k gi -> SM - if dis_k_gi_min > dis_k_sm: - nb_dis_k_gi2sm[0] += 1 - repeats_better_dis_k_gi2sm.append(repeat) - elif dis_k_gi_min == dis_k_sm: - nb_dis_k_gi2sm[1] += 1 - elif dis_k_gi_min < dis_k_sm: - nb_dis_k_gi2sm[2] += 1 - # # dis_k gi -> GM - if dis_k_gi_min > dis_k_gm: - nb_dis_k_gi2gm[0] += 1 - repeats_better_dis_k_gi2gm.append(repeat) - elif dis_k_gi_min == dis_k_gm: - nb_dis_k_gi2gm[1] += 1 - elif dis_k_gi_min < dis_k_gm: - nb_dis_k_gi2gm[2] += 1 - - # save median graphs. - fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl' - fn_pre_sm_new = dir_output + 'medians/set_median.' + fit_method \ - + '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat) - copyfile(fname_sm, fn_pre_sm_new + '.gxl') - fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl' - fn_pre_gm_new = dir_output + 'medians/gen_median.' + fit_method \ - + '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat) - copyfile(fname_gm, fn_pre_gm_new + '.gxl') - G_best_kernel = Gn_median[idx_dis_k_gi_min].copy() - reform_attributes(G_best_kernel) - fn_pre_g_best_kernel = dir_output + 'medians/g_best_kernel.' + fit_method \ - + '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat) - saveGXL(G_best_kernel, fn_pre_g_best_kernel + '.gxl', method='gedlib-letter') - - # plot median graphs. - set_median = loadGXL(fn_pre_sm_new + '.gxl') - gen_median = loadGXL(fn_pre_gm_new + '.gxl') - draw_Letter_graph(set_median, fn_pre_sm_new) - draw_Letter_graph(gen_median, fn_pre_gm_new) - draw_Letter_graph(G_best_kernel, fn_pre_g_best_kernel) - - # write result summary for each letter. - sod_sm_mean_list.append(np.mean(sod_sm_list)) - sod_gm_mean_list.append(np.mean(sod_gm_list)) - dis_k_sm_mean_list.append(np.mean(dis_k_sm_list)) - dis_k_gm_mean_list.append(np.mean(dis_k_gm_list)) - dis_k_gi_min_mean_list.append(np.mean(dis_k_gi_min_list)) - sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean_list[-1] - sod_sm_mean_list[-1])) - dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_sm_mean_list[-1])) - dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean_list[-1] - dis_k_gi_min_mean_list[-1])) - dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_gi_min_mean_list[-1])) - if save_results: - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, y, - sod_sm_mean_list[-1], sod_gm_mean_list[-1], - dis_k_sm_mean_list[-1], dis_k_gm_mean_list[-1], - dis_k_gi_min_mean_list[-1], sod_sm2gm_mean, dis_k_sm2gm_mean, - dis_k_gi2sm_mean, dis_k_gi2gm_mean, nb_sod_sm2gm, - nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm, - repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm, - repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm]) - f_summary.close() - - - # write result summary for each letter. - sod_sm_mean = np.mean(sod_sm_mean_list) - sod_gm_mean = np.mean(sod_gm_mean_list) - dis_k_sm_mean = np.mean(dis_k_sm_mean_list) - dis_k_gm_mean = np.mean(dis_k_gm_mean_list) - dis_k_gi_min_mean = np.mean(dis_k_gi_min_list) - sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean)) - dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean)) - dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean)) - dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean)) - if save_results: - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, 'all', - sod_sm_mean, sod_gm_mean, dis_k_sm_mean, dis_k_gm_mean, - dis_k_gi_min_mean, sod_sm2gm_mean, dis_k_sm2gm_mean, - dis_k_gi2sm_mean, dis_k_gi2gm_mean]) - f_summary.close() - - print('\ncomplete.') - - -def xp_letter_h(): - ds = {'dataset': 'cpp_ext/data/collections/Letter.xml', - 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir']) - for G in Gn: - reform_attributes(G) -# ds = {'name': 'Letter-high', -# 'dataset': '../datasets/Letter-high/Letter-high_A.txt'} # node/edge symb -# Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:50] - gkernel = 'structuralspkernel' - node_label = None - edge_label = None - ds_name = 'letter-h' - dir_output = 'results/xp_letter_h/' - save_results = False - - repeats = 1 -# k_list = range(2, 11) - k_list = [150] - fit_method = 'k-graphs' - # get indices by classes. - y_idx = get_same_item_indices(y_all) - - if save_results: - # create result files. - fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'fit method', 'k', - 'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM', 'median set']) - f_detail.close() - fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'fit method', 'k', - 'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM', '# SOD SM -> GM', '# dis_k SM -> GM', - '# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM', - 'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM', - 'repeats better dis_k gi -> GM']) - f_summary.close() - - random.seed(1) - rdn_seed_list = random.sample(range(0, repeats * 100), repeats) - - for k in k_list: - print('\n--------- k =', k, '----------') - - sod_sm_mean_list = [] - sod_gm_mean_list = [] - dis_k_sm_mean_list = [] - dis_k_gm_mean_list = [] - dis_k_gi_min_mean_list = [] -# nb_sod_sm2gm = [0, 0, 0] -# nb_dis_k_sm2gm = [0, 0, 0] -# nb_dis_k_gi2sm = [0, 0, 0] -# nb_dis_k_gi2gm = [0, 0, 0] -# repeats_better_sod_sm2gm = [] -# repeats_better_dis_k_sm2gm = [] -# repeats_better_dis_k_gi2sm = [] -# repeats_better_dis_k_gi2gm = [] - - for i, (y, values) in enumerate(y_idx.items()): - print('\ny =', y) -# y = 'N' -# values = y_idx[y] -# values = values[0:10] - - k = len(values) - - sod_sm_list = [] - sod_gm_list = [] - dis_k_sm_list = [] - dis_k_gm_list = [] - dis_k_gi_min_list = [] - nb_sod_sm2gm = [0, 0, 0] - nb_dis_k_sm2gm = [0, 0, 0] - nb_dis_k_gi2sm = [0, 0, 0] - nb_dis_k_gi2gm = [0, 0, 0] - repeats_better_sod_sm2gm = [] - repeats_better_dis_k_sm2gm = [] - repeats_better_dis_k_gi2sm = [] - repeats_better_dis_k_gi2gm = [] - - for repeat in range(repeats): - print('\nrepeat =', repeat) - random.seed(rdn_seed_list[repeat]) - median_set_idx_idx = random.sample(range(0, len(values)), k) - median_set_idx = [values[idx] for idx in median_set_idx_idx] - print('median set: ', median_set_idx) - Gn_median = [Gn[g] for g in values] - - sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min \ - = median_on_k_closest_graphs(Gn_median, node_label, edge_label, - gkernel, k, fit_method=fit_method, graph_dir=ds['graph_dir'], - edit_costs=None, group_min=median_set_idx_idx, - dataset='Letter', parallel=False) - - # write result detail. - sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm)) - dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm)) - dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min)) - dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min)) - if save_results: - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow([ds_name, gkernel, fit_method, k, - y, repeat, - sod_sm, sod_gm, dis_k_sm, dis_k_gm, - dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm, - dis_k_gi2gm, median_set_idx]) - f_detail.close() - - # compute result summary. - sod_sm_list.append(sod_sm) - sod_gm_list.append(sod_gm) - dis_k_sm_list.append(dis_k_sm) - dis_k_gm_list.append(dis_k_gm) - dis_k_gi_min_list.append(dis_k_gi_min) - # # SOD SM -> GM - if sod_sm > sod_gm: - nb_sod_sm2gm[0] += 1 - repeats_better_sod_sm2gm.append(repeat) - elif sod_sm == sod_gm: - nb_sod_sm2gm[1] += 1 - elif sod_sm < sod_gm: - nb_sod_sm2gm[2] += 1 - # # dis_k SM -> GM - if dis_k_sm > dis_k_gm: - nb_dis_k_sm2gm[0] += 1 - repeats_better_dis_k_sm2gm.append(repeat) - elif dis_k_sm == dis_k_gm: - nb_dis_k_sm2gm[1] += 1 - elif dis_k_sm < dis_k_gm: - nb_dis_k_sm2gm[2] += 1 - # # dis_k gi -> SM - if dis_k_gi_min > dis_k_sm: - nb_dis_k_gi2sm[0] += 1 - repeats_better_dis_k_gi2sm.append(repeat) - elif dis_k_gi_min == dis_k_sm: - nb_dis_k_gi2sm[1] += 1 - elif dis_k_gi_min < dis_k_sm: - nb_dis_k_gi2sm[2] += 1 - # # dis_k gi -> GM - if dis_k_gi_min > dis_k_gm: - nb_dis_k_gi2gm[0] += 1 - repeats_better_dis_k_gi2gm.append(repeat) - elif dis_k_gi_min == dis_k_gm: - nb_dis_k_gi2gm[1] += 1 - elif dis_k_gi_min < dis_k_gm: - nb_dis_k_gi2gm[2] += 1 - - # save median graphs. - fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl' - fn_pre_sm_new = dir_output + 'medians/set_median.' + fit_method \ - + '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat) - copyfile(fname_sm, fn_pre_sm_new + '.gxl') - fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl' - fn_pre_gm_new = dir_output + 'medians/gen_median.' + fit_method \ - + '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat) - copyfile(fname_gm, fn_pre_gm_new + '.gxl') - G_best_kernel = Gn_median[idx_dis_k_gi_min].copy() - reform_attributes(G_best_kernel) - fn_pre_g_best_kernel = dir_output + 'medians/g_best_kernel.' + fit_method \ - + '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat) - saveGXL(G_best_kernel, fn_pre_g_best_kernel + '.gxl', method='gedlib-letter') - - # plot median graphs. - set_median = loadGXL(fn_pre_sm_new + '.gxl') - gen_median = loadGXL(fn_pre_gm_new + '.gxl') - draw_Letter_graph(set_median, fn_pre_sm_new) - draw_Letter_graph(gen_median, fn_pre_gm_new) - draw_Letter_graph(G_best_kernel, fn_pre_g_best_kernel) - - # write result summary for each letter. - sod_sm_mean_list.append(np.mean(sod_sm_list)) - sod_gm_mean_list.append(np.mean(sod_gm_list)) - dis_k_sm_mean_list.append(np.mean(dis_k_sm_list)) - dis_k_gm_mean_list.append(np.mean(dis_k_gm_list)) - dis_k_gi_min_mean_list.append(np.mean(dis_k_gi_min_list)) - sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean_list[-1] - sod_sm_mean_list[-1])) - dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_sm_mean_list[-1])) - dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean_list[-1] - dis_k_gi_min_mean_list[-1])) - dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_gi_min_mean_list[-1])) - if save_results: - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, y, - sod_sm_mean_list[-1], sod_gm_mean_list[-1], - dis_k_sm_mean_list[-1], dis_k_gm_mean_list[-1], - dis_k_gi_min_mean_list[-1], sod_sm2gm_mean, dis_k_sm2gm_mean, - dis_k_gi2sm_mean, dis_k_gi2gm_mean, nb_sod_sm2gm, - nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm, - repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm, - repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm]) - f_summary.close() - - - # write result summary for each letter. - sod_sm_mean = np.mean(sod_sm_mean_list) - sod_gm_mean = np.mean(sod_gm_mean_list) - dis_k_sm_mean = np.mean(dis_k_sm_mean_list) - dis_k_gm_mean = np.mean(dis_k_gm_mean_list) - dis_k_gi_min_mean = np.mean(dis_k_gi_min_list) - sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean)) - dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean)) - dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean)) - dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean)) - if save_results: - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, 'all', - sod_sm_mean, sod_gm_mean, dis_k_sm_mean, dis_k_gm_mean, - dis_k_gi_min_mean, sod_sm2gm_mean, dis_k_sm2gm_mean, - dis_k_gi2sm_mean, dis_k_gi2gm_mean]) - f_summary.close() - - print('\ncomplete.') - - -#Dessin median courrant -def draw_Letter_graph(graph, file_prefix): - plt.figure() - pos = {} - for n in graph.nodes: - pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])]) - nx.draw_networkx(graph, pos) - plt.savefig(file_prefix + '.eps', format='eps', dpi=300) -# plt.show() - plt.clf() - - -if __name__ == "__main__": -# xp_letter_h() - xp_letter_h_LETTER2_cost() \ No newline at end of file diff --git a/gklearn/preimage/xp_monoterpenoides.py b/gklearn/preimage/xp_monoterpenoides.py deleted file mode 100644 index 2270471..0000000 --- a/gklearn/preimage/xp_monoterpenoides.py +++ /dev/null @@ -1,249 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Jan 16 11:03:11 2020 - -@author: ljia -""" - -import numpy as np -import random -import csv -from shutil import copyfile -import networkx as nx -import matplotlib.pyplot as plt - -from gklearn.utils.graphfiles import loadDataset, loadGXL, saveGXL -from gklearn.preimage.test_k_closest_graphs import median_on_k_closest_graphs, reform_attributes -from gklearn.preimage.utils import get_same_item_indices -from gklearn.preimage.find_best_k import getRelations - -def xp_monoterpenoides(): - import os - - ds = {'dataset': '../../datasets/monoterpenoides/dataset_10+.ds', - 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/'} # node/edge symb - Gn, y_all = loadDataset(ds['dataset']) -# ds = {'name': 'Letter-high', -# 'dataset': '../datasets/Letter-high/Letter-high_A.txt'} # node/edge symb -# Gn, y_all = loadDataset(ds['dataset']) -# Gn = Gn[0:50] - gkernel = 'treeletkernel' - node_label = 'atom' - edge_label = 'bond_type' - ds_name = 'monoterpenoides' - dir_output = 'results/xp_monoterpenoides/' - - repeats = 1 -# k_list = range(2, 11) - k_list = [0] - fit_method = 'k-graphs' - # get indices by classes. - y_idx = get_same_item_indices(y_all) - - # create result files. - fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'fit method', 'k', - 'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM', 'median set']) - f_detail.close() - fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv' - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'fit method', 'k', - 'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM', - 'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM', - 'dis_k gi -> GM', '# SOD SM -> GM', '# dis_k SM -> GM', - '# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM', - 'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM', - 'repeats better dis_k gi -> GM']) - f_summary.close() - - random.seed(1) - rdn_seed_list = random.sample(range(0, repeats * 100), repeats) - - for k in k_list: - print('\n--------- k =', k, '----------') - - sod_sm_mean_list = [] - sod_gm_mean_list = [] - dis_k_sm_mean_list = [] - dis_k_gm_mean_list = [] - dis_k_gi_min_mean_list = [] -# nb_sod_sm2gm = [0, 0, 0] -# nb_dis_k_sm2gm = [0, 0, 0] -# nb_dis_k_gi2sm = [0, 0, 0] -# nb_dis_k_gi2gm = [0, 0, 0] -# repeats_better_sod_sm2gm = [] -# repeats_better_dis_k_sm2gm = [] -# repeats_better_dis_k_gi2sm = [] -# repeats_better_dis_k_gi2gm = [] - - for i, (y, values) in enumerate(y_idx.items()): - print('\ny =', y) -# y = 'I' -# values = y_idx[y] - - k = len(values) -# k = kkk - - sod_sm_list = [] - sod_gm_list = [] - dis_k_sm_list = [] - dis_k_gm_list = [] - dis_k_gi_min_list = [] - nb_sod_sm2gm = [0, 0, 0] - nb_dis_k_sm2gm = [0, 0, 0] - nb_dis_k_gi2sm = [0, 0, 0] - nb_dis_k_gi2gm = [0, 0, 0] - repeats_better_sod_sm2gm = [] - repeats_better_dis_k_sm2gm = [] - repeats_better_dis_k_gi2sm = [] - repeats_better_dis_k_gi2gm = [] - - for repeat in range(repeats): - print('\nrepeat =', repeat) - random.seed(rdn_seed_list[repeat]) - median_set_idx_idx = random.sample(range(0, len(values)), k) - median_set_idx = [values[idx] for idx in median_set_idx_idx] - print('median set: ', median_set_idx) - Gn_median = [Gn[g] for g in values] - - sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min \ - = median_on_k_closest_graphs(Gn_median, node_label, edge_label, - gkernel, k, fit_method=fit_method, graph_dir=ds['graph_dir'], - edit_costs=None, group_min=median_set_idx_idx, - dataset=ds_name, parallel=False) - - # write result detail. - sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm)) - dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm)) - dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min)) - dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min)) - f_detail = open(dir_output + fn_output_detail, 'a') - csv.writer(f_detail).writerow([ds_name, gkernel, fit_method, k, - y, repeat, - sod_sm, sod_gm, dis_k_sm, dis_k_gm, - dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm, - dis_k_gi2gm, median_set_idx]) - f_detail.close() - - # compute result summary. - sod_sm_list.append(sod_sm) - sod_gm_list.append(sod_gm) - dis_k_sm_list.append(dis_k_sm) - dis_k_gm_list.append(dis_k_gm) - dis_k_gi_min_list.append(dis_k_gi_min) - # # SOD SM -> GM - if sod_sm > sod_gm: - nb_sod_sm2gm[0] += 1 - repeats_better_sod_sm2gm.append(repeat) - elif sod_sm == sod_gm: - nb_sod_sm2gm[1] += 1 - elif sod_sm < sod_gm: - nb_sod_sm2gm[2] += 1 - # # dis_k SM -> GM - if dis_k_sm > dis_k_gm: - nb_dis_k_sm2gm[0] += 1 - repeats_better_dis_k_sm2gm.append(repeat) - elif dis_k_sm == dis_k_gm: - nb_dis_k_sm2gm[1] += 1 - elif dis_k_sm < dis_k_gm: - nb_dis_k_sm2gm[2] += 1 - # # dis_k gi -> SM - if dis_k_gi_min > dis_k_sm: - nb_dis_k_gi2sm[0] += 1 - repeats_better_dis_k_gi2sm.append(repeat) - elif dis_k_gi_min == dis_k_sm: - nb_dis_k_gi2sm[1] += 1 - elif dis_k_gi_min < dis_k_sm: - nb_dis_k_gi2sm[2] += 1 - # # dis_k gi -> GM - if dis_k_gi_min > dis_k_gm: - nb_dis_k_gi2gm[0] += 1 - repeats_better_dis_k_gi2gm.append(repeat) - elif dis_k_gi_min == dis_k_gm: - nb_dis_k_gi2gm[1] += 1 - elif dis_k_gi_min < dis_k_gm: - nb_dis_k_gi2gm[2] += 1 - - # save median graphs. - fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl' - fn_pre_sm_new = dir_output + 'medians/set_median.' + fit_method \ - + '.k' + str(int(k)) + '.y' + str(int(y)) + '.repeat' + str(repeat) - copyfile(fname_sm, fn_pre_sm_new + '.gxl') - fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl' - fn_pre_gm_new = dir_output + 'medians/gen_median.' + fit_method \ - + '.k' + str(int(k)) + '.y' + str(int(y)) + '.repeat' + str(repeat) - copyfile(fname_gm, fn_pre_gm_new + '.gxl') - G_best_kernel = Gn_median[idx_dis_k_gi_min].copy() -# reform_attributes(G_best_kernel) - fn_pre_g_best_kernel = dir_output + 'medians/g_best_kernel.' + fit_method \ - + '.k' + str(int(k)) + '.y' + str(int(y)) + '.repeat' + str(repeat) - saveGXL(G_best_kernel, fn_pre_g_best_kernel + '.gxl', method='gedlib') - -# # plot median graphs. -# set_median = loadGXL(fn_pre_sm_new + '.gxl') -# gen_median = loadGXL(fn_pre_gm_new + '.gxl') -# draw_Letter_graph(set_median, fn_pre_sm_new) -# draw_Letter_graph(gen_median, fn_pre_gm_new) -# draw_Letter_graph(G_best_kernel, fn_pre_g_best_kernel) - - # write result summary for each letter. - sod_sm_mean_list.append(np.mean(sod_sm_list)) - sod_gm_mean_list.append(np.mean(sod_gm_list)) - dis_k_sm_mean_list.append(np.mean(dis_k_sm_list)) - dis_k_gm_mean_list.append(np.mean(dis_k_gm_list)) - dis_k_gi_min_mean_list.append(np.mean(dis_k_gi_min_list)) - sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean_list[-1] - sod_sm_mean_list[-1])) - dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_sm_mean_list[-1])) - dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean_list[-1] - dis_k_gi_min_mean_list[-1])) - dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_gi_min_mean_list[-1])) - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, y, - sod_sm_mean_list[-1], sod_gm_mean_list[-1], - dis_k_sm_mean_list[-1], dis_k_gm_mean_list[-1], - dis_k_gi_min_mean_list[-1], sod_sm2gm_mean, dis_k_sm2gm_mean, - dis_k_gi2sm_mean, dis_k_gi2gm_mean, nb_sod_sm2gm, - nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm, - repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm, - repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm]) - f_summary.close() - - - # write result summary for each letter. - sod_sm_mean = np.mean(sod_sm_mean_list) - sod_gm_mean = np.mean(sod_gm_mean_list) - dis_k_sm_mean = np.mean(dis_k_sm_mean_list) - dis_k_gm_mean = np.mean(dis_k_gm_mean_list) - dis_k_gi_min_mean = np.mean(dis_k_gi_min_list) - sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean)) - dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean)) - dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean)) - dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean)) - f_summary = open(dir_output + fn_output_summary, 'a') - csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, 'all', - sod_sm_mean, sod_gm_mean, dis_k_sm_mean, dis_k_gm_mean, - dis_k_gi_min_mean, sod_sm2gm_mean, dis_k_sm2gm_mean, - dis_k_gi2sm_mean, dis_k_gi2gm_mean]) - f_summary.close() - - - print('\ncomplete.') - - -#Dessin median courrant -def draw_Letter_graph(graph, file_prefix): - plt.figure() - pos = {} - for n in graph.nodes: - pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])]) - nx.draw_networkx(graph, pos) - plt.savefig(file_prefix + '.eps', format='eps', dpi=300) -# plt.show() - plt.clf() - - -if __name__ == "__main__": - xp_monoterpenoides() \ No newline at end of file diff --git a/gklearn/tests/test_graph_kernels.py b/gklearn/tests/test_graph_kernels.py new file mode 100644 index 0000000..bb442f3 --- /dev/null +++ b/gklearn/tests/test_graph_kernels.py @@ -0,0 +1,289 @@ +"""Tests of graph kernels. +""" + +import pytest +import multiprocessing + + +def chooseDataset(ds_name): + """Choose dataset according to name. + """ + from gklearn.utils import Dataset + + dataset = Dataset() + + # no node labels (and no edge labels). + if ds_name == 'Alkane': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=False) + irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} + dataset.remove_labels(**irrelevant_labels) + # node symbolic labels. + elif ds_name == 'Acyclic': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=False) + irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} + dataset.remove_labels(**irrelevant_labels) + # node non-symbolic labels. + elif ds_name == 'Letter-med': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=False) + # node symbolic and non-symbolic labels (and edge symbolic labels). + elif ds_name == 'AIDS': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=False) + # edge non-symbolic labels (no node labels). + elif ds_name == 'Fingerprint_edge': + dataset.load_predefined_dataset('Fingerprint') + dataset.trim_dataset(edge_required=True) + irrelevant_labels = {'edge_attrs': ['orient', 'angle']} + dataset.remove_labels(**irrelevant_labels) + # edge non-symbolic labels (and node non-symbolic labels). + elif ds_name == 'Fingerprint': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=True) + # edge symbolic and non-symbolic labels (and node symbolic and non-symbolic labels). + elif ds_name == 'Cuneiform': + dataset.load_predefined_dataset(ds_name) + dataset.trim_dataset(edge_required=True) + + dataset.cut_graphs(range(0, 3)) + + return dataset + + +# @pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +# @pytest.mark.parametrize('weight,compute_method', [(0.01, 'geo'), (1, 'exp')]) +# #@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +# def test_commonwalkkernel(ds_name, weight, compute_method): +# """Test common walk kernel. +# """ +# from gklearn.kernels.commonWalkKernel import commonwalkkernel + +# Gn, y = chooseDataset(ds_name) + +# try: +# Kmatrix, run_time, idx = commonwalkkernel(Gn, +# node_label='atom', +# edge_label='bond_type', +# weight=weight, +# compute_method=compute_method, +# # parallel=parallel, +# n_jobs=multiprocessing.cpu_count(), +# verbose=True) +# except Exception as exception: +# assert False, exception + + +# @pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +# @pytest.mark.parametrize('remove_totters', [True, False]) +# #@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +# def test_marginalizedkernel(ds_name, remove_totters): +# """Test marginalized kernel. +# """ +# from gklearn.kernels.marginalizedKernel import marginalizedkernel + +# Gn, y = chooseDataset(ds_name) + +# try: +# Kmatrix, run_time = marginalizedkernel(Gn, +# node_label='atom', +# edge_label='bond_type', +# p_quit=0.5, +# n_iteration=2, +# remove_totters=remove_totters, +# # parallel=parallel, +# n_jobs=multiprocessing.cpu_count(), +# verbose=True) +# except Exception as exception: +# assert False, exception + + +# @pytest.mark.parametrize( +# 'compute_method,ds_name,sub_kernel', +# [ +# # ('sylvester', 'Alkane', None), +# # ('conjugate', 'Alkane', None), +# # ('conjugate', 'AIDS', None), +# # ('fp', 'Alkane', None), +# # ('fp', 'AIDS', None), +# ('spectral', 'Alkane', 'exp'), +# ('spectral', 'Alkane', 'geo'), +# ] +# ) +# #@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +# def test_randomwalkkernel(ds_name, compute_method, sub_kernel): +# """Test random walk kernel kernel. +# """ +# from gklearn.kernels.randomWalkKernel import randomwalkkernel +# from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct +# import functools + +# Gn, y = chooseDataset(ds_name) + +# mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) +# sub_kernels = [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}] +# try: +# Kmatrix, run_time, idx = randomwalkkernel(Gn, +# compute_method=compute_method, +# weight=1e-3, +# p=None, +# q=None, +# edge_weight=None, +# node_kernels=sub_kernels, +# edge_kernels=sub_kernels, +# node_label='atom', +# edge_label='bond_type', +# sub_kernel=sub_kernel, +# # parallel=parallel, +# n_jobs=multiprocessing.cpu_count(), +# verbose=True) +# except Exception as exception: +# assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_ShortestPath(ds_name, parallel): + """Test shortest path kernel. + """ + from gklearn.kernels import ShortestPath + from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + import functools + + dataset = chooseDataset(ds_name) + + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + try: + graph_kernel = ShortestPath(node_labels=dataset.node_labels, + node_attrs=dataset.node_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + node_kernels=sub_kernels) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + + except Exception as exception: + assert False, exception + + +#@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint']) +@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint', 'Fingerprint_edge', 'Cuneiform']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_StructuralSP(ds_name, parallel): + """Test structural shortest path kernel. + """ + from gklearn.kernels import StructuralSP + from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct + import functools + + dataset = chooseDataset(ds_name) + + mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) + sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} + try: + graph_kernel = StructuralSP(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + node_attrs=dataset.node_attrs, + edge_attrs=dataset.edge_attrs, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + node_kernels=sub_kernels, + edge_kernels=sub_kernels) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +#@pytest.mark.parametrize('k_func', ['MinMax', 'tanimoto', None]) +@pytest.mark.parametrize('k_func', ['MinMax', 'tanimoto']) +@pytest.mark.parametrize('compute_method', ['trie', 'naive']) +def test_PathUpToH(ds_name, parallel, k_func, compute_method): + """Test path kernel up to length $h$. + """ + from gklearn.kernels import PathUpToH + + dataset = chooseDataset(ds_name) + + try: + graph_kernel = PathUpToH(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + depth=2, k_func=k_func, compute_method=compute_method) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_Treelet(ds_name, parallel): + """Test treelet kernel. + """ + from gklearn.kernels import Treelet + from gklearn.utils.kernels import polynomialkernel + import functools + + dataset = chooseDataset(ds_name) + + pkernel = functools.partial(polynomialkernel, d=2, c=1e5) + try: + graph_kernel = Treelet(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + sub_kernel=pkernel) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + except Exception as exception: + assert False, exception + + +@pytest.mark.parametrize('ds_name', ['Acyclic']) +#@pytest.mark.parametrize('base_kernel', ['subtree', 'sp', 'edge']) +@pytest.mark.parametrize('base_kernel', ['subtree']) +@pytest.mark.parametrize('parallel', ['imap_unordered', None]) +def test_WeisfeilerLehman(ds_name, parallel, base_kernel): + """Test Weisfeiler-Lehman kernel. + """ + from gklearn.kernels import WeisfeilerLehman + + dataset = chooseDataset(ds_name) + + try: + graph_kernel = WeisfeilerLehman(node_labels=dataset.node_labels, + edge_labels=dataset.edge_labels, + ds_infos=dataset.get_dataset_infos(keys=['directed']), + height=2, base_kernel=base_kernel) + gram_matrix, run_time = graph_kernel.compute(dataset.graphs, + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1], + parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True) + except Exception as exception: + assert False, exception + + +if __name__ == "__main__": +# test_spkernel('Alkane', 'imap_unordered') + test_StructuralSP('Fingerprint_edge', 'imap_unordered') \ No newline at end of file diff --git a/gklearn/utils/__init__.py b/gklearn/utils/__init__.py index 78832d3..79d0a39 100644 --- a/gklearn/utils/__init__.py +++ b/gklearn/utils/__init__.py @@ -20,3 +20,4 @@ from gklearn.utils.graph_files import load_dataset, save_dataset from gklearn.utils.timer import Timer from gklearn.utils.utils import get_graph_kernel_by_name from gklearn.utils.utils import compute_gram_matrices_by_class +from gklearn.utils.trie import Trie diff --git a/gklearn/utils/dataset.py b/gklearn/utils/dataset.py index e218596..abd7edd 100644 --- a/gklearn/utils/dataset.py +++ b/gklearn/utils/dataset.py @@ -70,6 +70,13 @@ class Dataset(object): if ds_name == 'Acyclic': ds_file = current_path + '../../datasets/Acyclic/dataset_bps.ds' self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'AIDS': + ds_file = current_path + '../../datasets/AIDS/AIDS_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Alkane': + ds_file = current_path + '../../datasets/Alkane/dataset.ds' + fn_targets = current_path + '../../datasets/Alkane/dataset_boiling_point_names.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file, filename_targets=fn_targets) elif ds_name == 'COIL-DEL': ds_file = current_path + '../../datasets/COIL-DEL/COIL-DEL_A.txt' self.__graphs, self.__targets, label_names = load_dataset(ds_file) @@ -79,6 +86,9 @@ class Dataset(object): elif ds_name == 'COLORS-3': ds_file = current_path + '../../datasets/COLORS-3/COLORS-3_A.txt' self.__graphs, self.__targets, label_names = load_dataset(ds_file) + elif ds_name == 'Cuneiform': + ds_file = current_path + '../../datasets/Cuneiform/Cuneiform_A.txt' + self.__graphs, self.__targets, label_names = load_dataset(ds_file) elif ds_name == 'Fingerprint': ds_file = current_path + '../../datasets/Fingerprint/Fingerprint_A.txt' self.__graphs, self.__targets, label_names = load_dataset(ds_file) @@ -89,10 +99,10 @@ class Dataset(object): ds_file = current_path + '../../datasets/Letter-high/Letter-high_A.txt' self.__graphs, self.__targets, label_names = load_dataset(ds_file) elif ds_name == 'Letter-low': # node non-symb - ds_file = current_path + '../../datasets/Letter-high/Letter-low_A.txt' + ds_file = current_path + '../../datasets/Letter-low/Letter-low_A.txt' self.__graphs, self.__targets, label_names = load_dataset(ds_file) elif ds_name == 'Letter-med': # node non-symb - ds_file = current_path + '../../datasets/Letter-high/Letter-med_A.txt' + ds_file = current_path + '../../datasets/Letter-med/Letter-med_A.txt' self.__graphs, self.__targets, label_names = load_dataset(ds_file) elif ds_name == 'MAO': ds_file = current_path + '../../datasets/MAO/dataset.ds' diff --git a/gklearn/utils/graph_files.py b/gklearn/utils/graph_files.py index 7594110..ce07a59 100644 --- a/gklearn/utils/graph_files.py +++ b/gklearn/utils/graph_files.py @@ -569,10 +569,10 @@ def load_tud(filename): elif 'fga' in locals(): content_targets = open(fga).read().splitlines() # targets (regression) targets = [int(i) for i in content_targets] - if class_label_map is not None: - targets = [class_label_map[t] for t in targets] else: raise Exception('Can not find targets file. Please make sure there is a "', ds_name, '_graph_labels.txt" or "', ds_name, '_graph_attributes.txt"', 'file in your dataset folder.') + if class_label_map is not None: + targets = [class_label_map[t] for t in targets] # create graphs and add nodes data = [nx.Graph(name=str(i)) for i in range(0, len(content_targets))] diff --git a/gklearn/utils/utils.py b/gklearn/utils/utils.py index 0dca111..17954f5 100644 --- a/gklearn/utils/utils.py +++ b/gklearn/utils/utils.py @@ -351,4 +351,77 @@ def compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, d if save_results: np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm_list=gram_matrix_unnorm_list, run_time_list=run_time_list) - print('\ncomplete.') \ No newline at end of file + print('\ncomplete.') + + +def find_paths(G, source_node, length): + """Find all paths with a certain length those start from a source node. + A recursive depth first search is applied. + + Parameters + ---------- + G : NetworkX graphs + The graph in which paths are searched. + source_node : integer + The number of the node from where all paths start. + length : integer + The length of paths. + + Return + ------ + path : list of list + List of paths retrieved, where each path is represented by a list of nodes. + """ + if length == 0: + return [[source_node]] + path = [[source_node] + path for neighbor in G[source_node] \ + for path in find_paths(G, neighbor, length - 1) if source_node not in path] + return path + + +def find_all_paths(G, length, is_directed): + """Find all paths with a certain length in a graph. A recursive depth first + search is applied. + + Parameters + ---------- + G : NetworkX graphs + The graph in which paths are searched. + length : integer + The length of paths. + + Return + ------ + path : list of list + List of paths retrieved, where each path is represented by a list of nodes. + """ + all_paths = [] + for node in G: + all_paths.extend(find_paths(G, node, length)) + + if not is_directed: + # For each path, two presentations are retrieved from its two extremities. + # Remove one of them. + all_paths_r = [path[::-1] for path in all_paths] + for idx, path in enumerate(all_paths[:-1]): + for path2 in all_paths_r[idx+1::]: + if path == path2: + all_paths[idx] = [] + break + all_paths = list(filter(lambda a: a != [], all_paths)) + + return all_paths + + +def get_mlti_dim_node_attrs(G, attr_names): + attributes = [] + for nd, attrs in G.nodes(data=True): + attributes.append(tuple(attrs[aname] for aname in attr_names)) + return attributes + + +def get_mlti_dim_edge_attrs(G, attr_names): + attributes = [] + for ed, attrs in G.edges(data=True): + attributes.append(tuple(attrs[aname] for aname in attr_names)) + return attributes \ No newline at end of file