- #!/usr/bin/env python3
- # -*- coding: utf-8 -*-
- """
- Created on Tue Jan 8 17:48:06 2019
-
- @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
- import functools
- from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
-
- dslist = [
- {'name': 'ENZYMES', 'dataset': '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},
- # node symb/nsymb
- ]
-
- def run_ms(dataset, y, ds):
- from gklearn.kernels.randomWalkKernel import randomwalkkernel
- estimator = randomwalkkernel
- param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},
- {'alpha': np.logspace(-10, 10, num=41, base=10)}]
- ave_time = {}
- std_time = {}
- for compute_method in ['sylvester', 'conjugate', 'fp', 'spectral']:
- if compute_method == 'sylvester':
- param_grid_precomputed = {'compute_method': ['sylvester'],
- # 'weight': np.linspace(0.01, 0.10, 10)}
- 'weight': np.logspace(-1, -10, num=10, base=10)}
- elif compute_method == 'conjugate':
- mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
- param_grid_precomputed = {'compute_method': ['conjugate'],
- 'node_kernels':
- [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],
- 'edge_kernels':
- [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],
- 'weight': np.logspace(-1, -10, num=10, base=10)}
- elif compute_method == 'fp':
- mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
- param_grid_precomputed = {'compute_method': ['fp'],
- 'node_kernels':
- [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],
- 'edge_kernels':
- [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],
- 'weight': np.logspace(-3, -10, num=8, base=10)}
- elif compute_method == 'spectral':
- param_grid_precomputed = {'compute_method': ['spectral'],
- 'weight': np.logspace(-1, -10, num=10, base=10),
- 'sub_kernel': ['geo', 'exp']}
- _, 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))
- ave_time[compute_method] = average_gram_matrix_time
- std_time[compute_method] = std_gram_matrix_time
- print()
- return ave_time, std_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))
- degree_list = [np.mean(list(dict(g.degree()).values())) for g in Gn]
- idx_sorted = np.argsort(degree_list)
- degree_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_degree = []
- 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)]
- aved = np.mean(degree_list[len_1piece * piece:len_1piece * (piece + 1)])
- ave_degree.append(aved)
- 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 degrees are', ave_degree)
- print()
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