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update .travis.yml.

v0.1
jajupmochi 5 years ago
parent
a7f911e74f
commit
076a150cf1
3 changed files with 279 additions and 53 deletions
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  1. +2
    -2
      .travis.yml
  2. +33
    -24
      gklearn/preimage/test_k_closest_graphs.py
  3. +244
    -27
      gklearn/preimage/xp_fit_method.py

+ 2
- 2
.travis.yml View File

@@ -4,7 +4,7 @@ python:
- '3.5'
- '3.6'
- '3.7'
- '3.8'
# - '3.8'
before_install:
- python --version
- pip install -U pip
@@ -15,7 +15,7 @@ before_install:

install:
- if [ $TRAVIS_PYTHON_VERSION == 3.8 ];
then pip install -r requirements.txt;
then pip install -r gklearn/tests/requirements.txt;
else pip install -r requirements.txt;
fi
- pip install wheel


+ 33
- 24
gklearn/preimage/test_k_closest_graphs.py View File

@@ -28,7 +28,7 @@ from gklearn.preimage.fitDistance import fit_GED_to_kernel_distance
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,
gkernel=None, dataset=None, init_ecc=None,
Gn=None, Kmatrix_median=None):
"""fit edit cost constants.
"""
@@ -50,26 +50,32 @@ def fit_edit_cost_constants(fit_method, edit_cost_name,
edit_cost_constants = random.sample(range(1, 10), 6)
print('edit cost constants used:', edit_cost_constants)
elif fit_method == 'expert': # expert
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]
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 = [3, 3, 1, 3, 3, 1]
edit_cost_constants = init_ecc
elif fit_method == 'k-graphs':
itr_max = 6
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
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 = [3, 3, 1, 3, 3, 1]
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',
@@ -81,12 +87,15 @@ def fit_edit_cost_constants(fit_method, edit_cost_name,
parallel=True)
elif fit_method == 'whole-dataset':
itr_max = 6
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]
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 = [3, 3, 1, 3, 3, 1]
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',
@@ -176,7 +185,7 @@ def compute_distances_to_true_median(Gn_median, fname_sm, fname_gm,
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,
dataset=None, edit_cost_name=None, init_ecc=None,
Kmatrix=None, parallel=True):
# dataset = dataset.lower()
@@ -210,7 +219,7 @@ def median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k, fit_metho
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,
gkernel=gkernel, dataset=dataset, init_ecc=init_ecc,
Gn=Gn, Kmatrix_median=Kmatrix_median)
time_fitting = time.time() - time0


+ 244
- 27
gklearn/preimage/xp_fit_method.py View File

@@ -25,7 +25,31 @@ def get_dataset(ds_name):
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)
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/'
@@ -95,6 +119,7 @@ def xp_fit_method_for_non_symbolic(parameters, save_results=True, initial_soluti
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
@@ -165,6 +190,10 @@ def xp_fit_method_for_non_symbolic(parameters, save_results=True, initial_soluti
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 = []
@@ -206,7 +235,7 @@ def xp_fit_method_for_non_symbolic(parameters, save_results=True, initial_soluti
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,
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]
@@ -292,7 +321,7 @@ def xp_fit_method_for_non_symbolic(parameters, save_results=True, initial_soluti
saveGXL(G_best_kernel, fn_pre_g_best_kernel + '.gxl', method='default')
# plot median graphs.
if ds_name == 'Letter-high':
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)
@@ -544,31 +573,221 @@ if __name__ == "__main__":
# Kmatrix=Kmatrix)
#### xp 5: Fingerprint, sspkernel, using LETTER2.
# #### xp 5: Fingerprint, sspkernel, using LETTER2.
# # 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
#
# # 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 = ['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}
# 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 = 'Fingerprint'
gkernel = 'structuralspkernel'
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)]
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'
# 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',
@@ -583,11 +802,7 @@ if __name__ == "__main__":
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
# 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 = ['k-graphs', 'expert', 'random', 'random', 'random']
for fit_method in fit_methods:
@@ -598,7 +813,9 @@ if __name__ == "__main__":
'edit_cost_name': 'LETTER2',
'ged_method': 'mIPFP',
'attr_distance': 'euclidean',
'fit_method': fit_method}
'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],


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