- #!/usr/bin/env python3
- # -*- coding: utf-8 -*-
- """
- Created on Sun Dec 23 17:57:18 2018
-
- @author: ljia
- """
- import sys
- import numpy as np
- import networkx as nx
-
- sys.path.insert(0, "../../")
- from gklearn.utils.graphfiles import loadDataset
- from gklearn.utils.model_selection_precomputed import compute_gram_matrices
- from sklearn.model_selection import ParameterGrid
-
- from libs import *
- import multiprocessing
-
- dslist = [
- # {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',
- # 'task': 'regression'}, # node symb
- # {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',
- # 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt', },
- # # contains single node graph, node symb
- # {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds', }, # node/edge symb
- {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds', }, # unlabeled
- {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG.mat',
- 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, # node/edge symb
- # {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},
- # # node nsymb
- {'name': 'ENZYMES', 'dataset': '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},
- # node symb/nsymb
- ]
-
- def run_ms(dataset, y, ds):
- from gklearn.kernels.commonWalkKernel import commonwalkkernel
- estimator = commonwalkkernel
- param_grid_precomputed = [{'compute_method': ['geo'],
- 'weight': np.linspace(0.01, 0.15, 15)},
- # 'weight': np.logspace(-1, -10, num=10, base=10)},
- {'compute_method': ['exp'], 'weight': range(0, 15)}]
- param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},
- {'alpha': np.logspace(-10, 10, num=41, base=10)}]
-
- _, gram_matrix_time, _, _, _ = compute_gram_matrices(
- dataset, y, estimator, list(ParameterGrid(param_grid_precomputed)),
- '../../notebooks/results/' + estimator.__name__, ds['name'],
- n_jobs=multiprocessing.cpu_count(), verbose=False)
- average_gram_matrix_time = np.mean(gram_matrix_time)
- std_gram_matrix_time = np.std(gram_matrix_time, ddof=1)
- print('\n***** time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'
- .format(average_gram_matrix_time, std_gram_matrix_time))
- print()
- return average_gram_matrix_time, std_gram_matrix_time
-
-
- for ds in dslist:
- print()
- print(ds['name'])
- Gn, y_all = loadDataset(
- ds['dataset'], filename_y=(ds['dataset_y'] if 'dataset_y' in ds else None),
- extra_params=(ds['extra_params'] if 'extra_params' in ds else None))
- vn_list = [nx.number_of_nodes(g) for g in Gn]
- idx_sorted = np.argsort(vn_list)
- vn_list.sort()
- Gn = [Gn[idx] for idx in idx_sorted]
- y_all = [y_all[idx] for idx in idx_sorted]
- len_1piece = int(len(Gn) / 5)
- ave_time = []
- std_time = []
- ave_vnb = []
- for piece in range(0, 5):
- print('piece', str(piece), ':')
- Gn_p = Gn[len_1piece * piece:len_1piece * (piece + 1)]
- y_all_p = y_all[len_1piece * piece:len_1piece * (piece + 1)]
- avevn = np.mean(vn_list[len_1piece * piece:len_1piece * (piece + 1)])
- ave_vnb.append(avevn)
- avet, stdt = run_ms(Gn_p, y_all_p, ds)
- ave_time.append(avet)
- std_time.append(stdt)
-
- print('\n****** for dataset', ds['name'], ', the average time is \n', ave_time,
- '\nthe time std is \n', std_time)
- print('corresponding average vertex numbers are', ave_vnb)
- print()
|
