a python package for graph kernels.
-- All the kernels are tested on dataset Asyclic, which consists of 185 molecules (graphs).
-- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression.
-- For predition we randomly divide the data in train and test subset, where 90% of entire dataset is for training and rest for testing. 10 splits are performed. For each split, we first train on the train data, then evaluate the performance on the test set. We choose the optimal parameters for the test set and finally provide the corresponding performance. The final results correspond to the average of the performances on the test sets.
Kernels | RMSE(℃) | std(℃) | parameter | k_time |
---|---|---|---|---|
shortest path | 36.40 | 5.35 | - | - |
marginalized | 17.90 | 6.59 | p_quit = 0.1 | - |
path | 14.27 | 6.37 | - | - |
WL subtree | 9.00 | 6.37 | height = 1 | 0.85" |
In each line, paremeter is the one with which the kenrel achieves the best results.
In each line, k_time is the time spent on building the kernel matrix.
See detail results in results.md.